**Technology Life-cycle Analysis** : Deep analytics evaluates and explores top technological innovations in terms of technology life cycle, technology trajectory, S-curve, technology diffusion and dominant design. Technology trajectory is the path that the solar technology takes through its time and life-cycle from the perspectives of rate of performance improvement, rate of diffusion or rate of adoption in the market. It is really interesting to analyze the impact of various factors on solar technology trajectory today. How to manage the evolution of this technological innovation? The nature of innovation may shift after a dominant design emerges. At present, the solar technology is waiting for the dominant design. The diffusion indicates how new solar technologies will spread through a population of potential adopters. It is controlled by the characteristics of innovation, economic environment and the adopters like innovators, early adopters, early majority, late majority and laggards.

At present, the technology of solar power is at growth phase of technology life-cycle [Figure 6.7]. It has come out from the emergence phase. It is interesting to understand how the life-cycle of solar technology is interacting with other technologies, systems, social impact, life-style and culture. The solar technology has been evolving from its parents such as other branches of electrical power system and electronics engineering; they are interacting with each other to form complex technological ecologies. The parents are adding their technological DNA which are the basic building blocks of new product development. A new development of solar technology must be nurtured properly since many technologies had perished at the emergence phase due to inherent constraints, uncertainties and risks. Next phase is growth; the solar technology has survived its early phases, it is adapting to various challenges of business model innovations and is forwarding to its intended environment with the emergence of competitors. It is basically the question of struggle for existence and survival for the fittest of the dominant design.

Initially, it may be difficult and costly to improve the performance of new solar technology; it may be costly for the adopters due to various uncertainties and risks. The performance is expected to improve with better understanding of the fundamental principles and system architecture. Gradually, this new technology may be adopted by large segments of the market due to reduced cost and risks. The rate of improvement of the new solar technology may be faster than the rate of market demand over time; the market share is expected to increase with high performance. The evolution of the solar technology is now passing through a phase of turbulence and uncertainty; various stakeholders of the supply chain are exploring different competing design options and a dominant design is expected to emerge in near future through the consensus and convergence of the system intelligence. Then, the producers will try to improve the efficiency and design of solar power systems based on stable benchmark of the industry. The dominant design must consider an optimal set of most advanced technological features which can meet the demand of the users, supply chain and design chain in the best possible way.
**Quantitative Analysis:** It is essential to exercise intelligent quantitative analysis through business analytics and technical analytics based on up-to-date technical and business data. Both types of analytics are closely associated. The scope of business analytics should be explored in terms of installed power generation capacity: Energy type, capacity (MW/GW), Period (year); PV market analysis based evolution of PV modules production : The basic parameters for this analysis may be energy sources (e.g. solar, wind, tidal, thermal, hydel, nuclear), market share, zone and growth rate; SWOT analysis; Period (year), zone, PV production; Top PV cell production companies, Period (year), MW; Type of PV cell, MW and PV Module comparison based on efficiency, and power rating.

The scope of technical analytics should be explored through advanced experimental set up and graphical data visualization techniques.

* Solar cell

* Cost vs. efficiency analysis : solar cell type (e.g. 1st, 2nd, 3rd generation), material (Si, III-V, II-VI, I, III, VI, Nano PV), cost, energy conversion efficiency;

* V-I characteristics : short circuit, open circuit, MPP at different irradiation and temperature

* P-V characteristics for equivalent series and parallel resistance

* Absolute error vs. voltage

* Company, device, size, efficiency (%), Power (MW);

* PV cell material, optimal absorption, band gap (minimum and maximum eV);

* Deposition time vs. evaporation rate

* Power electronic circuit performance analysis:

* DC-DC boost converter

* Performance analysis: input voltage, input current, input power, output voltage, output current, output power, duty ratio, efficiency (%), ripple (%)

* Transient performance analysis : time (S) vs. output voltage (V), time (S) vs. output current (A) under varying load and irradiation;

* Converter type (classical, proposed), output voltage, output current;

* Simulation testing analysis : input voltage, inductance, capacitance, load resistance, duty ratio, switching frequency;

* MPPT analysis : PV panel rating, rated voltage, rated current, rated power, open circuit voltage, short circuit voltage, short circuit current, MPPT;

* Duty ratio vs. output voltage graph;

* 3-D Radar plot of performance measurement: output voltage (V), output current (A) and efficiency (%);

* PV panel output analysis : voltage (V) vs. current (A), voltage (V) vs. power (W);

* Photovoltaic module integrated Inverter or micro-inverter

* Performance comparison of various types of micro-inverters : topology (series buffer, parallel buffer, flyback, boost, AC Link),
energy storage, input voltage (V), rated power(W), efficiency, reactive power, complexity (low, medium, high);

* Input voltage, output voltage, output power, line frequency;

* Switching frequency, buffer voltage, inductance, capacitance, transformer turns ration, MOSFET rating, resonant inductance and capacitance;

* Waveform analysis: current, voltage, line phase vs. current, line phase vs. phase shift, line voltage phase angle vs. impedance, line phase vs. efficiency; current waveform of flyback inverter under BCM and DCM control, input and output waveform, PV voltage vs. PV current, PV voltage vs. PV power, PV voltage and current under half and full load conditions, grid voltage and current under half and full load conditions;

* Comparative analysis on BCM and DCM in terms of switching frequency, power transfer, peak current sensing, loss analysis under high and low load, efficiency vs. output power

* Battery performance analysis in terms of battery capacity, nominal voltage, minimum battery voltage, maximum discharge current, maximum charging voltage, maximum charging current, steady state and transient analysis;

* Temperature profile of solar cooker (Time vs. Temperature)

**Shared vision** : Efficient change management is one of the most critical success factors in solar system implementation. It ensures that an organization and its workforce are ready, willing and able to embrace the new business processes and systems. The change management is a complex process. The change should occur at various levels: system, process, people and organization. Communication is the oil that ensures that everything works properly in solar system implementation. Management's honest determination to exert and maintain its right to decide within a policy of fairness and openness is essential for successful change management. An efficient leader creates understanding among his workforce through intelligent corporate communication.

**Communication protocol** : It is essential to communicate the scope of solar power to the common people, corporate sector, public policy makers, state and central governments, academic and research community through popular broadcast communication channels (e.g. TV, radio, social media, social networking sites, SMS, MMS, corporate web portals etc.) and adoption of rational and intelligent programmes, advertising and promotions. It is also possible to make innovative and creative documentary films on solar power evolution and broadcast the same to the academic and research community through school and college education systems and YouTube channels. It is rational to focus on introduction of courses on renewable energy and solar power in academic programmes of Electrical and Electronics Engineering (e.g. B.Tech, M.Tech, B.E.E, M.E., Ph.D.) and also business management (e.g. Technology Management, Strategic Management and Business Analytics for BBA, MBA, PGDM, PGDBM).
**Goal**: The strategic intelligence is associated with good governance, good wishes in public policy, industry analysis, efficient enterprise resource planning, supply chain management and marketing efforts (e.g. pricing, promotion, trust in communication, sales and distribution). Let us first consider *pricing strategy*. The diffusion of solar power requires a rational, discriminatory and fair pricing mechanism, incentive and subsidy policies. Industrial power play may be a serious threat against the adoption of solar energy. Existing power generation, transmission and distribution companies are comfortable with traditional thermal power technology. The coal, oil and gas industries are also not comfortable with the emerging trends of renewable sustainable energy. They do not want to save precious fossil fuels and oil. That is why, the old power generation and distribution firms are not serious in R&D and deployment of solar power system globally. Solar power is a real transformation initiative and the old firms are trying to resist against this change. Our society needs fundamental rethinking and radical redesign of energy policy. The pricing policy of solar power system (e.g. energy service charge, price of standalone solar power systems) should have necessary *competitive intelligence* to conquer the aforesaid threats.

Next, let us consider the *promotional strategy*. An intelligent marketing strategy is essential for proper technology management and enhancement of the awareness of new solar technology. The trade fairs and energy summits are expected to perform live product demonstration and interactive brainstorming sessions; audio and video display of solar power plants and standalone systems already in use in various places of the world; invite national and international technical experts and scientists from industry and academic institutions; invite energy policy makers, consultants, engineers, students, strategists, architects, construction and infrastructure project managers, entrepreneurs, traders, venture capitalists, banking & financial institutions (e.g. rural banks, national banks); invite Business Intelligence analysts to discuss possible growth roadmap and publish smart product catalogues and brochures. Event management plays an important promotional strategy; various workshops, conferences and seminars should encourage exchange of innovative ideas among the experts. There are other important initiatives such as training sessions for the workforce of construction and infrastructure firms, power generation, transmission and distribution companies and contractors; advertisements in popular business and industry magazines and daily newspapers; intelligent broadcast through TV, radio and corporate communication channels; pilot projects in villages, hilly areas, forests and deserts and active involvement of gram panchayat and municipal corporations in solar power system implementation programmes.

Another critical strategy is related to *production process and supply chain management of solar cells*. Sustainable photovoltaics need the production of next-generation smart materials, devices and manufacturing processes suitable for global needs, environment and resource availability, advanced manufacturing process and multi-scale modeling and reliability. Solar energy integration is a critical issue; it is essential to identify and assess key technical, economic, environmental, and policy barriers in the adoption of solar power globally. For correct road mapping and assessment, it is required to analyze market research, policy and technical data,
solar energy integration, storage and multi-scale (10 - 500 kW) concentrating solar power systems.

*Dominant design*: The technology of solar power is going through an evolution. The emerging technology is trying to achieve a *dominant design* which may have two major aspects: (1) solar power electronics resulting an intelligent circuit and (2) Nanotechnology based solar cell as stated above. Dominant design is a concept of technology management, identifies key technological features that become a de facto standard. The innovators must try to explore dominant design to win the market share. The dominant design may be a new technology, product or a set of key features as the outcome of a set of independent technological innovations. When a new technology emerges, different firms introduce a number of alternative designs based on incremental improvements. The dominant design enforces standardization, results economies of scale and competition starts based on cost, scale, product features and performance. Dominant design may not be better than other designs; simply incorporates a set of key features that emerge due to technological path-dependence and not necessarily stricts customer preferences. Dominant designs are expected to acquire more than 50% of the market share. The process of dominance passes through a few milestones. An innovator conducts R&D to create a new product or service or improve an existing design. The first working prototype of emerging technology may send a signal to competitors to review the feasibility of their research programs. The first commercial product is launched and directed at a small group of customers and force the competitors to review and speed up their research efforts. A clear front runner emerges from the early market. Finally, a particular technological trajectory achieves dominance.

*Technology trajectory* is the path that a technology takes through its time and life-cycle from the perspectives of rate of performance improvement, rate of diffusion or rate of adoption in the market. It is really interesting to analyze the impact of various factors and patterns of technology trajectories of solar power system today. How to manage evolution of technological innovation? The nature of innovation shifts markedly after the dominant design emerges. The pace of performance improvement utilizing a particular technological approach is expected to follow S-curve pattern. The evolution of innovation is determined by intersecting trajectories of performance demanded in the market vs. performance supplied by solar cell technology. Technology diffusion of solar power indicates how new technologies spread through a population of potential adopters. It is controlled by characteristics of innovation, characteristics of social environment and characteristics of the adopters such as innovators, early adopters, early majority, late majority and laggards. From the perspective of solar power, the basic objectives of technology management include several issues such as integrated strategic planning, forecasting, design, optimization, operation and control of products, processes and services to understand the dynamics of technology innovation, hype, priority, capability, maturity, adoption, diffusion, infusion, transfer, life-cycle, dominant design, spillover effects, blind spots and also the value of this emerging technology for our society. The basic objective of technology forecasting is to predict the future characteristics of solar power system.
Let us consider the issue of solar technology innovation, adoption and diffusion. We should analyze how over time an idea or product associated with solar power gains momentum and diffuses or spreads through a specific population or social system. Diffusion is the process by which innovations spread in our society over time, adoption is a decision of implementing innovations based on knowledge, and persuasion of individuals within a given system. The diffusion of innovation is the process by which new products are adopted by their intended audiences. It allows designers and marketers to examine why it is that some inferior products are successful when some superior products are not. Diffusion of innovation is responsible for the launch and spread of some of the most important advanced solar technologies in human society globally,

*Technology diffusion* is the process by which innovations of emerging solar technology is adopted by a population. The rate of diffusion depends on several factors such as nature and quality of innovation, how information about the innovation is communicated and characteristics of the population into which the technology is introduced. Why is technology diffusion important? Technology diffusion plays a major role globally today. We can increase the trade by removing the diffusion barriers since the countries achieve higher productivity by taking the technology from the diffusion process. What is meant by technology diffusion of solar technology? Technological diffusion is the process by which innovations (new products, new processes or new methods) spread within and across economies. What are the steps of diffusion? Diffusion occurs through a five step decision making process. It occurs through a series of communication channels over a period of time among the members of a similar social system. The five stages are awareness, interest, evaluation, trial, and adoption of solar technologies. The goal of this diffusion strategy is to spread the word about the innovation and encourage users to adopt it. These strategies may also be modified and used to target any other user group. The rate of diffusion is the speed with which the new idea spreads from one consumer to the next. Solar technologies have been evolving from various interesting applications; the rate of diffusion depends on the support of government's energy security policy globally.

Let us consider solar *technology spillover effects*. Technology spillover is ultimately a learning experience. A group of firms from emerging economies may enjoy unintentional technological benefits from R&D efforts of leading firms of solar technology from advanced economies (without any additional cost sharing). Successful technology spillovers depend on the absorptive capability of the receiving firms, technological gaps, interactions, information symmetry, strategic alliance, joint venture and knowledge flows between sending and receiving firms and also geographical and cultural proximity of a social process. Technology absorption is the acquisition, development, assimilation and utilization of technological knowledge and capability by a firm from an external source; it occurs between transferring and receiving entities. What factors influence the rate of adoption and diffusion of solar technology innovation? There are specific product and service attributes that affect the diffusion process and can influence consumer acceptance of new products and services; these factors are relative advantage, compatibility, complexity, trialability and observability. Transfer of technology is
the process of transferring or disseminating technology from one country that owns or holds it to another country through strategic alliance and joint ventures, Solar power system has been becoming more intelligent, advanced and efficient. Solar technology has a great positive impact on our life. New technology always changes our life very much and takes it to a new level. It is like the new ways of thinking or doing the normal things differently, better with less hassle and at a much affordable rate.

Another important issue is technology adoption of solar technology. Within the rate of adoption, there is a point at which an innovation reaches critical mass. The categories of adopters are innovators, early adopters, early majority, late majority, and laggards. Diffusion is a macro process concerned with the spread of a new product from its source to the consuming public. Solar technology diffusion is a measure of how widely technology is spread globally from the developed countries to the developing and undeveloped countries. Adoption is a micro process that focuses on the stages through which an individual consumer passes when deciding to accept or reject a new product. There may be various strategies of solar technology adoption : align technology and strategy; communicate for buy-in and engagement; perform a current systems analysis; develop training approach early and integrate technology deployment with change management and create an effective governance structure for solar technology innovation, adoption and diffusion.

# 7. STAFF-RESOURCES

## Staff-resources Analytics

do estimation, planning, capacity utilization, allocation and distribution of '5M' resources.

* Man : human capital management (scientists, business analysts, system analysts, project managers): talent acquisition, talent retention, training, reward and recognition;

* Machine (tools, electrical and electronic machines, computer hardware, software, internet);

* Material : Photonic cells;

* Method : process innovation for manufacturing of solar cells, PV panels and power electronic circuit;

* Money : (optimal fund allocation, project management, resource allocation, resource distribution)

The expert panel have explored the issue of optimal allocation and distribution of critical resources. It is essential to coordinate various R&D units of renewable energy and Powergrid Corporation, electrical, power electronics, photonics and power plant departments of engineering institutes, technology management department of management institutes and collaborative networks. The team of innovators must have creativity, shared vision, intellectual abilities, thinking style, knowledge, personality, motivation, commitment, confidence and group dynamics.
The intelligent and creative innovators should at this emerging technology in unconventional ways.

The world is enjoying today the evolution of internet and mobile communication technology which is the outcome of hard work, commitment and involvement of the great talents in electronics and telecommunication engineering. Similar type of involvement and commitment is essential from various forums of electrical and electronics engineering, photonics and renewable energy sectors globally. Organizational creativity should be fostered through intelligent human capital management, talent acquisition and retention strategy. The solar power system should be operated and maintained by a pool of intelligent, educated, efficient, productive, committed and motivated workforce. Active involvement, knowledge sharing and optimal human talent utilization is essential for innovation, adoption and diffusion of this emerging technology. The workforce should develop skills in erection, testing, commissioning, operation, maintenance and trading of solar power system. New business model requires the support of an effective human resource management system for talent acquisition and retention, skill development, training, career growth planning, incentive, reward and recognition.

# 8. SKILL-STYLE-SUPPORT

## Skill-style-support Analytics

* **Skill:** technical, system admin, management, legal, governance, surveillance, relationship management;

* **Style:** leadership, shared vision, goal setting, intelligent communication protocol, risk assessment and mitigation;

* **Support:** proactive, preventive and reactive support.

The expert panel are also exploring and analyzing *skill-style-support* for energy security. What should be the innovation model for effective diffusion of solar power technology? Is it possible to adopt *K-A-B-C-D-E-T-F model* (Ref.: session 1)? The innovators should develop different types of skills in technical (e.g. photonics, photovoltaic and renewable energy, power plant, power electronics, electrical and electronics), technology management and system administration such as research and development, maintenance support, knowledge management, system design and project management (e.g. erection, testing and commissioning). The system administrators must have multiple leadership skills such as smart thinking, communication, coordination and change management. The workforce should develop skills through effective knowledge management programmes. An effective knowledge management system supports creation, storage, sharing and application of knowledge in a transparent, collaborative and innovative way. The diffusion of solar power innovation demands the support of effective leadership style. It is really challenging for the project leaders to implement top technology innovations physically and practically. Top management can tackle the complexity of system implementation by developing an efficient team through group dynamics. It is essential to develop skills in development of solar power system through proper
coordination among design, supply and customer chain and R&D, production and
marketing functions. The basic objectives are to maximize fit with the needs of the
users, ensure quality assurance, minimize time to market, sales and distribution and
control product development cost. It may be an interesting initiative to make the
suppliers and the users involved in the development process.

The workforce involved in solar power trading should develop different types of
skills such as research and development, product design, sales, event management,
project management, erection, testing, commissioning and service maintenance. An
effective innovation model requires the support of a knowledge repository in the
form of a digital library which should extract good up-to-date data from top
journals and magazines on solar power electronics and solar energy.

What should be the ideal organization model for solar power trading? A traditional functionally centered organization model may be suitable for supporting end-to-end business processes. The business model of solar power requires the support of a functional structure enabled with advanced information and communication technology. The structure should have project, design, power generation, distribution, maintenance, revenue management, human resource management, supply chain management and finance cells. The business model requires a collaborative and cooperative work culture.

Solar technology management is not a trivial simple problem; it is associated with
multiple domains such as electrical engineering, power electronics, materials
science, structural engineering, physics, photonics, chemistry, nanotechnology and
business analytics. A collaborative platform is essential for effective interaction
among national and international technical experts and scientists from industry and
academic institutions; energy policy makers, consultants, engineers, technicians,
strategists, construction professionals, architects, infrastructure project managers,
entrepreneurs, traders, venture capitalists, banking and financial institutions,
industrial forums and trade fair committees and organizers. The social media, news
channels, radio and TV broadcast are expected to play a responsible role for general
awareness of the public and government and promotion of the technology globally.

The diffusion of solar power technology demands the support of great leadership
style; they are not only industry leaders but also political one. The style is basically
the quality of leadership; the leaders must have passion, motivation, commitment,
support, coordination, integration and excellent communication skill. The leaders
must be able to share a rational vision, mission and values of solar power system
among all the stakeholders honestly and appropriately in time. It is really
challenging for the great leaders to implement and build solar power system for
global energy security. They have to face and tackle threats from traditional
industries such as coal, oil and gas, thermal and nuclear power, selfish local bias,
power play and politics. They must understand the intelligence of business modeling
and system dynamics associated with solar energy, they must exercise fundamental
rethinking and radical redesign of global energy trading system. Top management
should be able to tackle the complexity of system implementation through a strong
project team, right mix of dedicated technical and business experts.

9. CONCLUSION
The expert panel have explored the potential of solar power through deep analytics. It is clear from scope and SWOT analysis that solar power is a potential option of sustainable energy and business model innovation for the future as compared to other sources of energy. Solar power is at the growth phase of technology life-cycle. The technology is still not matured; there are several constraints such as efficiency of solar cell and cost of solar panels. It is possible to extend the scope of solar power to the battery charging of electric vehicles and drones (Ref. : session 7). It may be very useful and economical to adopt solar power driven water pumps in agriculture, warehouses, cold storages and other innovative applications (Ref.: Session 3). The ultimate success of any innovation effort no longer depends on a single element alone. It is important to understand the customers and competition and also to recognize and align the critical partners in the innovation ecosystem. It is essential to identify the blind spots of solar technology which are efficient solar cell, power electronics circuit and business model innovation.

## FURTHER READING

* R.H.Waterman, T.J.Peters and J.R. Phillips. Mckinsey & Company. Structure is not organization. Business Horizons. June'1980.

* M.W.Johnson, C.M.Chritensen and H.Kagermann. Reinventing your business model. Harvard Business Review. December'2008.

* M.Hammer. Process management and the future of six sigma. MIT Sloan Management Review, Vol. 43, No. 2, pp.26-32.2002.

* S.Chakraborty and S.K.Sharma. Enterprise Resource Planning: an integrated strategic framework. International Journal Management and Enterprise Development, Vol. 4, No. 5, 2007.

* R.Roy. Entrepreneurship. Oxford University Press. 2008.

* G.Boyle. Renewable Energy. 2nd Edition. Oxford University Press. 2004.

* Department of Energy, USA. Renewable energy: an overview. March'2001.

* F.Kreith and D.Y.Goswami (edited). Handbook of energy efficiency and renewable energy. CRC Press. 2007.

* B.K.Bose. Power Electronics and Motor Drives. Advances and trends. Elsevier. 2006.

* S.K.Mazumder. High-Frequency Inverters: From Photovoltaic, Wind and Fuel Cell Based Renewable and Alternative Energy DER/DG Systems to Energy-Storage Applications. University of Illinois, USA. 2010.

* X.Yang, Y.Song, G.Wang and W.Wang. A comprehensive review on the development of sustainable energy strategy and implementation in China. IEEE Transactions on Sustainable Energy, volume 1, no. 2, July'2010.

* H.M Upadahyaya, T.M.Razykov and A.N.Tiwari. Thin film PV Technology. In F.Kreith and D.Y. Goswami (Edited). Handbook of energy conservation and renewable energy. CRC Press, NY, USA. 2007.

* T.M.Razykov, B.Rech and A.N.Tiwari (Edited). Special issue on thin film PV. Solar Energy, N6. 2004.
* S.B.Kjaer, J.K.Pedersen and F.Blaabjerg. A review of single phase grid connected inverters for photovoltaic modules. IEEE Transactions Industrial Application. Volume 41, no. 5, pp. 1292-1306, Sep / Oct'2005.

* ABB online document : Distributed energy storage product presentation.
  2010.

* U.S. Department of Energy. Solar Energy Grid Integration System Energy Storage (SEGIS-ES). May'2008.

* S.J.Chiang, K.T.Chang and C.Y.Yen. Residential photovoltaic energy storage system. IEEE Transactions on Industrial Electronics. vol. 45, no. 3, pp 385-394, June'1998.

* R.W.De Doncker, C.Meyer, R.U.Lenke and F.Mura. Power electronics for future utility applications. IEEE 7th International conference Power Electronics Drive System. November'2007.

* F. Katiraei, R. Iravani, N. Hatziargyriou and A. Dimeas. Microgrids management. IEEE Power Energy Magazine, vol. 6, no. 3, pp. 54-65, May/Jun. 2008.

* M. Marinelli, F. Sossan, G. T. Costanzo and H. W. Bindner. Testing of a Predictive Control Strategy for Balancing Renewable Sources in a Microgrid. IEEE Transactions on Sustainable Energy.

* J.Charias. Maximum power solar converter. Microchip.

* R.Faranda and S.Leva. A comparative study of MPPT techniques for PV system. 7th WSEAS International Conference on Application of Electrical Engineering, Norway, July, 2008.

* T.Esram and P.L.Chapman. Comparison of photovoltaic array maximum powerpoint tracking techniques. IEEE Transactions on Energy Conversion. Vol. 22, No. 2, June 2007.

* J.Charais. Maximum power solar converter. Microchip Technology Incorporation. 2010.

* RIB, Italy. Solar amplifier. Code ACG9125.

* J.Falin and W.Li. A boost-topology battery charger powered from a solar panel. Texas Instruments. www.power.ti.com.

* http://www.MitsubishiElectric.com

* http://www.tatapowersolar.com

* http://www.msme.gov.in

* http://www.ediindia.org, www.nabard.org

* S. B. Kjaer, J. K. Pedersen, and F. Blaabjerg. A review of single-phase grid-connected inverters for photovoltaic modules. *IEEE Trans. Ind. Appl.*, vol. 41, no. 5, pp. 1292-1306, Sep./Oct. 2005.

* S. B. Kjaer, J. K. Pedersen, and F. Blaabjerg. Power inverter topologies for photovoltaic modules-a review. in *Proc. 37th IAS Annu. Ind. Appl. Conf. Meet. Rec.*, 2002, vol. 2, pp. 782-788.

* F. Blaabjerg, R. Teodorescu, M. Liserre and A. V. Timbus. Overview of control and grid synchronization for distributed power generation systems. *IEEE Trans. Ind. Electron.*, vol. 53, no. 5, pp. 1398-1409, Oct. 2006.
* B. J. Pierquet. *Designs for Ultra-High Efficiency Grid-Connected Power Conversion*. Ph.D. dissertation, Dept. Electric. Eng. Comput. Sci., Massachusetts Inst. Technol., Cambridge, 2011.

* P.Sanjeevikumar, E.Kabalci, A.Iqbal, H.Abu-Rub, O.Ojo. Control Strategy and Hardware Implementation for DC-DC Boost Power Conversion Based on Proportional-Integral Compensator for High Voltage Application. *Engineering Science and Technology: An International Journal (JESTECH)*, Dec.2014.

* P.Sanjeevikumar, A.Iqbal, H.Abu-Rub, M.Bishal. Implementation and control of extra high voltage dc-dc boost converter. *7th IET Intl. Conf. on Sustainable Energy and Intelligent System*, IET-SEISCON'13, Chennai, India. 2013.

* F.Blaabjerg, F.Iov, T.Kerekes, R.Teodorescu. Trends in power electronics and control of renewable energy systems. *14th Int. Power Electron. & Motion Control Conf.*, 2010.

* J. M. Guerrero, F. Blaabjerg, T. Zhelev, K. Hemmes, E. Monmasson, S. Jemei, M. P. Comech, R. Granadino, and J. I. Frau. Distributed generation: Toward a new energy paradigm. *IEEE Ind. Electron. Mag.*, Vol. 4, No. 1, pp. 52-64, Mar. 2010.

* R. Lasseter. Smart distribution: Coupled microgrids. *IEEE Proc., Vol. 99*, No. 6, pp. 1074-1082, Jun. 2011

* M. Barnes, J. Kondoh, H.Asano, J. Oyarzabal, G. Venkataramanan, R. Lasseter, N. Hatziargyriou, and T. Green. Real-world microgrids – an overview. *Proc. IEEE SoSE*, pp. 1-8, 2007.

* N. Hatziargyriou, H. Asano, R. Iravani and C. Marnay. Microgrids. *IEEE Power Energy Mag.*, Vol. 6, No. 4, pp. 78-94, Jul./Aug. 2007.

* F. Katiraei, R. Iravani, N. Hatziargyriou and A. Dimeas. Microgrids management. *IEEE Power and Energy Mag.*, Vol. 6, No. 3, pp. 54-65, 2008.

* A. Timbus, M. Liserre, R. Teodorescu, P. Rodriguez, and F. Blaabjerg. Evaluation of current controllers for distributed power generation systems. *IEEE Trans. Power Electron.*, Vol. 24, No. 3, pp. 654-664, 2009.

* M. Kazmierkowski, R. Krishnan, and F. Blaabjerg. Control in Power Electronics. London, U.K.: Academic, 2002.

* N. Pogaku, M. Prodanovic and T. Green. Modeling, analysis and testing of an inverter-based microgrid. *IEEE Trans. Power Electron.*, Vol. 22, No. 2, pp. 613-625, 2007.

* P. Kundur, Power system stability and control. New York: McGraw-Hill, 1994.

* J. Lopes, C. Moreira and A. Madureira. Defining control strategies for microgrids islanded operation. *IEEE Trans. Power Syst.*, Vol. 21, No. 2, pp. 916-924, May 2006.

* M. H. Nehrir, C. Wang, K. Strunz, H. Aki, R. Ramakumar, J. Bing, Z. Miao, and Z. Salameh. A Review of Hybrid Renewable/Alternative Energy Systems for Electric Power Generation: Configurations, Control, and Applications. *IEEE Trans. Sustain. Energy*, vol. 2, no. 4, pp. 392–403, Oct. 2011.
* K. Kurohane, T. Senjyu, A. Yona, N. Urasaki, T. Goya, and T.Funabashi. A hybrid smart AC/DC power system. *IEEE Trans. Smart Grid*, vol. 1, no. 2, pp. 199-204, 2010.

* K. T. Tan, P. L. So, Y. C. Chu, and M. Z. Q. Chen. Coordinated Control and Energy Management of Distributed Generation Inverters in a Microgrid. *IEEE Trans. Power Delivery*, vol. 28, no. 2, pp. 704-713, Apr. 2013.

* C. T. Rodríguez, D. V. de la Fuente, G. Garcerá, E. Figueres, and J. A. G. Moreno. Reconfigurable Control Scheme for a PV Microinverter Working in Both Grid-Connected and Island Modes. *IEEE Trans. Ind. Electron.*, vol. 60, no. 4, pp. 1582-1595, Apr. 2013.

* Sh. Jiang, W. Wang, H. Jin, and D. Xu. Power Management Strategy for Microgrid with Energy Storage System. in *Proc. 2011 IEEE IECON- 37th Annual Industrial Electronics Society Conf.*, pp. 1524-1529.

* International Standard for Testing Solar Cookers ASABE Standard S580
  Dr. Paul A. Funk, Avinashilingam University, Coimbatore, India, January
  1997.

* S. C. Mullick, T. C. Kandpal, and A. K. Saxena. Thermal test procedure for box-type solar cookers, *Solar Energy*, vol. 39, no. 4, pp. 353-360, 1987.

* S. K. Philip and H. N.Mistry. Solar cooker testing: a suggestion for change in BIS standards. *SESI Journal*, vol. 5, pp. 17-22,

1995.

* S. K. Philip, T.K.Chaudhuri and H. N.Mistry. Testing of solar box cookers, in *Proceedings of the 3rd International Conference on Solar Cookers Use and Technology*, Coimbatore, India, 1997.

* S. B. Joshi and A. R. Jani. Photovoltaic and Thermal Hybridized Solar Cooker. Hindawi Publishing Corporation ISRN Renewable Energy Volume 2013, Article ID 746189.

http://www.indiawaterportal.org accessed on 15.08.2018

* http://mnre.gov.in/ele-manager/UserFiles/Schemefor-Solar-Pumping-Programme-for-Irrigation-and-Drinking-Water-under-offgrid-and-Decentralised-Solar-applications.pdf accessed on 15.08.2018.

* Trombly, J. Technology solutions: Nano-PV set to accelerate solarenergy use. *Environ. Sci. Technol.*, 38, pp. 376-376A, 2004.

* Catchpole, K. R. Nanostructures in photovoltaics. *Phil. Trans. Math. Phys. Eng. Sci.*, 364, pp. 3493-3503, 2006.

* Tsakalakos, L.Nanostructures for photovoltaics. *Mater. Sci. Eng.*, 62(6), pp. 175-189, 2008.

* Green, M. Silicon photovoltaic modules: A brief history of the first 50 years. *Prog. Photovolt.*, 13, pp. 447-455, 2005.

* Jadhav, M. V., Todkar, A. S., Gambhire, V. R., and Sawant, S. Y. Nanotechnology for powerful solar energy. *Int. J. Adv. Biotechnol. Res.*, 2, pp. 208-212, 2011.

* Honsberg, C. B., Barnett, A. M., and Kirkpatrick, D. Nanostructured solar cells for high efficiency photovoltaics. in *Photovoltaic Energy Conversion, IEEE 4th World Conference*, Hawaii, USA, 2006.
* Nozik, A. J. Nanoscience and nanostructures for photovoltaics and solar fuels. *Nano Lett.*, 10(8), pp. 2735-2741, 2010.

* Green, M. A., Emery, K., Hishikawa, Y., and Warta, W. Solar cell efficiency tables (Version 34). *Prog. Photovolt. Res. Appl.*, 17, pp. 320-326, 2009.

* Goetzberger, A., Hebling, C., and Schock, H. Photovoltaic materials, history, status and outlook. *Mater. Sci. Eng. R Rep.*, 40, pp. 1-46, 2003.

* Banerjee, S., Misra, M., Mohapatra, S. K., Howard, C., Mohapatra, S. K., and Kamilla, S. K., Formation of chelating agent driven anodized TiO2 nanotubular membrane and its photovoltaic application. *Nanotechnology*, 21, pp. 145201, 2010.

* Jadhav, M. V., Todkar, A. S., Gambhire, V. R., and Sawant, S. Y. Nanotechnology for powerful solar energy. *Adv. Biotech. Res.*, 2(1), pp. 208-212, 2011.

* Energy and Environmental Science,

* Advanced Energy Materials,

* Progress in Photovoltaics: Research and Applications,

* Annual Review of Chemical and Bimolecular Engineering,

* Nano Energy, Renewable and Sustainable Energy Reviews,

* IEEE Transactions on Sustainable Energy,

* IEEE Transactions on Power Electronics,

* Polymer Reviews,

* Solar Energy Materials and Solar Cells,

* Solar Energy,

* Renewable Energy,

* Environmental Research Letters,

* IET Renewable Power Generation

* Journal of Photonics for Energy.

## QUIZ

* Explain the technology of solar power from the perspective of energy security. Justify it as a technology for humanity. What is the scope and emerging applications of this technology?

* What is the dominant design of solar power system?

* What are the basic elements of the system architecture?

* What do you mean by technology security? How to verify the security intelligence?

* What are the strategic moves of technology innovation, adoption and diffusion for solar power? What is the outcome of technology life-cycle and SWOT analysis?

* How to manage resources for this innovation project?

* What should be the talent management strategy? What are the skills, leadership style and support demanded by the technological innovation?

* How to manage technology innovation project efficiently? What should be the shared vision, common goals and communication protocols? How can you ensure a perfect fit among ‘7-S’ elements?
* Discuss the evolution of nanotechnology for solar cells.

* Design an intelligent power electronic circuit for solar power system.

## APPENDIX:

<figure><img src="image_72.png" /><figcaption>Figure 6.8 (a): AC coupled hybrid microgrid</figcaption></figure>

<figure><img src="image_73.png" /><figcaption>Figure 6.8 (b): DC coupled hybrid microgrid</figcaption></figure>

<figure><img src="image_74.png" /><figcaption>Figure 6.8 (c): AC-DC coupled hybrid microgrid</figcaption></figure>
<figure><img src="image_75.png" /><figcaption>Figure 6.9 : (a) single stage and (b,c) two-stage power conversion system for PV system</figcaption></figure>

<table><thead><tr><th>Comparison parameters</th><th>AC coupled hybrid microgrid</th><th>DC coupled hybrid microgrid</th><th>AC-DC coupled hybrid microgrid</th></tr></thead><tbody><tr><th>Topology</th><td>DGs and SEs are connected to AC bus through interfacing converters.</td><td>DGs and SEs are connected to the common DC bus, and an Interfacing Converter (IFC) is used to link the DC and AC buses.</td><td>DGs and SEs are connected to DC and AC buses, where these buses are linked by Interlinking Converter (ILC); most promising microgrid structures in the near future.</td></tr><tr><th>Complexity of structure</th><td>Simple, dominant structure, multiple port converters may be used.</td><td>Simple structure; multiple-port power converters may be used</td><td>Complex structure</td></tr><tr><th>Operation mode</th><td>Stand alone and grid connected; transition between two modes should be seamless and smooth.</td><td>Stand alone and grid connected</td><td>Stand alone and grid connected</td></tr><tr><th>Control strategy</th><td>Simple, the power control can be realized by current or voltage control.</td><td>Complexity: medium; central control scheme, uses optimization techniques to adjust set-points of each source.</td><td>Complex, Requires high coordination for voltage and power control between AC and DC subsystems; focus on DC and AC bus voltages and frequency control, power balance within DC and AC subsystems</td></tr><tr><th>Power management strategy</th><td>Grid connected operation mode: dispatched and undispatched output power; standalone: ac bus voltage and frequency control</td><td>Complexity: medium; Grid connected operation mode: dispatched and undispatched output power; standalone mode: ac and dc bus voltage control</td><td>Complexity: high; Grid connected operation mode: dispatched and undispatched output power; standalone mode: ac and dc bus voltage control</td></tr></tbody></table>
<table><caption>Table 6.1 : Comparison of power management strategies</caption><tr><td>Cost</td><td>May be cost effective</td><td>May be cost effective</td><td>May be slightly costly</td></tr><tr><td>Benefits</td><td>Cleanness, simple technologies, increasing demands for electrical energy and exhaustible nature of fossil fuel demands efficient solar microgrids.</td><td>Emerging new semiconductor devices use silicon carbide and gallium nitride for improved performance of power electronic switches.</td><td>Higher flexibility and reliability, improves overall efficiency and reduces the system cost with reduced number of power converters</td></tr></table>
SESSION 7: LOGISTICS SECURITY - ELECTRICAL & HYBRID VEHICLES, SMART BATTERIES, RAILTECH SECURITY & DRIVER ADVICE SYSTEM

Event : Technology for humanity and global security summit

Venue: Logistics security hall, Technology park: Sanada

Time Schedule : 10 a.m. – 12 p.m., 17.8.2020

Agents : Representatives of various global organizations, Technology management experts from science and technology forums, Logistics engineers, scientists, business development consultants of logistics industries.

Topic of discussion and key focus areas : Logistics security, Electrical vehicles, Hybrid vehicles, Smart batteries, Smart transformers, Battery charging mechanism, Solar power, Security and safety, Mobile communication, IoT, RailTech, Driver Advice System, Traffic control Centre, Real-time fault diagnostics, Fault tree analysis, FMEA, Time Failure Propagation Graph (TFPG), Control flow, Resource flow, Data flow

Keynote speakers : Prof. D. Jones, Dr. Simon Rodrigues, Dr. Andy Richardson, Prof. R. Prakash, Prof. B. Williams, Dr. S.Yokoo

1. SCOPE

Scope Analytics

Agents: system analysts, business analysts, agriculture scientists, engineers;

Stakeholders : Drivers, Traffic controller at TCC, passengers;

Moves : critical success factors analysis, requirements management;

Application domains : surface, rail, water, air transport;

Key technologies : mechanical, aeronautical, electrical, electronics, communication;

Civil infrastructure (roads, ports, airport, rail stations);

Security parameters: define a set of sustainable development goals for logistics security.

★ Logistics security (travel, hospitalities, surface, water, rail, water, EVs and hybrid vehicles)

★ Responsible consumption and production (fuel, vehicles)

Requirements engineering for dominant design of future generation vehicles :

- Electrical & hybrid vehicles through smart, clean and solar micro grid

- Vehicle-to-Vehicle (V2V) communication

- Internet of Things (IoT), mobile communication

Objectives:

- safe driving trading-off cost vs. comforts of the passengers through real-time fault diagnostics

- maintain schedule of train service approximately as far as possible

- improve energy efficiency of driving as per standard operating procedures

- optimal capacity utilization of limited rail infrastructure

- optimize train movements within limits and regulatory compliance
* ensure security, safety and comforts at optimal cost

Constraints : Technological complexity, application integration, cost;

Prof. Jones and Dr. Yokoo have started this session through scope analytics. Sustainable transportation infrastructure demands widespread adoption of electric vehicles (EVs) and renewable energy sources. The use of EVs is growing due to increasing technological success of complex and reliable hybrid vehicles; technological diffusion of Li-ion batteries and increasing willingness of society, political world and the automobiles market due to increasing environmental air pollution, global warming and fuel consumption and high stress on the storage of fossil fuels. This session analyzes the technological innovation of electrical and hybrid vehicles through deep analytics. The dominant design includes smart batteries.

Prof. Jones has outlined the scope of the technological innovation on electrical vehicles which includes E-bus, E-truck, E-scooter, E-bike, E-bicycle, E-Taxi and E-private car. The scope can be extended to E-steamer/ launches / ships, electric trains (e.g. metro rail, mono rail, local trains) and small aircrafts (e.g. helicopters, hovercrafts, fighter planes, military aircrafts, HY4 plane free of carbon emission and driven by serial hybrid powertrain such as fuel cell and alternative energy sources like solar cell). It is interesting to develop power management system of the aircrafts which select appropriate energy sources as per the demand of aircraft and the propeller engine. Vehicles can be powered by internal combustion engine using gasoline, diesel or gas or electric drives. The critical success factors of vehicle manufacturing industry are sustainable, environment friendly design, improvements in power train systems, fuel processing and power conversion technologies.

Vehicles can be classified based on various types of synthetic fuels such as hydrogen, biodiesel, bioethanol, dimethylether, ammonia and electricity via electrical batteries : conventional gasoline vehicle (petrol or diesel, ICE), hybrid vehicle (gasoline fuel, electrical drive, rechargeable battery), electric vehicle (high capacity electrical battery, electrical drive), hydrogen fuel cell vehicle (high pressure hydrogen fuel tank, fuel cell, electrical drive), hydrogen internal combustion vehicle (high-pressure hydrogen fuel tank and ICE) and ammonia fueled vehicle (liquid ammonia fuel tank, hydrogen-fueled ICE). In case of electrical or hybrid vehicles, electricity may be produced from renewable energy sources or natural gas.

It is an interesting option to perform a comparative analysis among various type of vehicles based on a set evaluation parameters such as vehicle price, fuel cost, maintenance cost, economic attractiveness, driving range, energy and power features, safety, sustainability, fuel consumption, reduction in emission and environmental impact. The trading agents often try to trade-off miscellaneous critical factors such as vehicle performance, improved performance of fuel cell or batteries, cost, governmental subsidies and environmental pollution issues. It has been found that hybrid and electrical vehicles have many advantages than other types of vehicles in terms of high fuel price and environmental pollution, economics and environmental impact depends significantly on the source of electrical energy. If the electricity is generated from renewable energy sources, electric car is even better
than hybrid vehicles. Nickel metal hydride and Li-ion batteries have shown improved performance as compared to old lead-acid batteries.

Next critical issue is requirements engineering of vehicles : What should be the vision for the vehicles in future ? The requirements should be defined from several perspectives. It is essential to transform the design principles of the conventional vehicles. The traditional design is based on petrol and diesel engines for energy, internal combustion engine for power, manual control and independent standalone operation. The vision for the future vehicles may be based on electrical and hybrid system, light, clean, safe, fun and fashionable design, mobile communication, and Internet of Things (IoT). The design of the vehicles should promote mobile Internet and IoT enabled by electronic tags and sensors and seamless connection to IoT; the basic objectives are efficient traffic management and reduced travel time by collecting, processing and sharing big data. The electrical and hybrid vehicles should be integrated with smart power grid having clean renewable energy sources (e.g. solar, wind and hydroelectric power), dynamic pricing mechanism and optimal balance between supply and demand of electrical energy. The vehicles should be designed with real-time control capabilities, mobile connectivity and onboard intelligence for optimal utilization of road and parking space and traffic congestion control.

The automobiles market demands fundamental rethinking, radical redesign and reinvention of vehicles of the future. The enabling technologies should be developed in terms of dominant design and converged for proper diffusion of electrical and hybrid vehicles globally. The expected benefits of converging innovative solutions should be explored in terms of lower cost, sustainable economic growth and prosperity, enhanced freedom, mobility, safety, zero emissions, use of clean renewable energy to fight against air pollution, climate change and global warming, minimal traffic congestion, increased roadway throughput, fun, entertainment and autonomous driving.

Prof. Jones is also presting the scope of RailTech, an emerging technology in rail operation from the perspectives of intelligent management information systems. RailTech integrates intelligent Driver Advice System (DAS), traffic control centre (TCC) and real-time fault diagnostics (RTFD). The complexity of the technology has been analyzed through deep analytics. The basic building blocks of real-time fault diagnostics are graphical analytics (GA), fault tree analytics (FTA) and failure mode effects analytics (FMEA). The core components of the graphical analytics include time failure propagation graph (TFPG), control, resources and data flows analytics. What should be the top priority: high speed train or rail security and safety? The emerging technology should adopt a balanced approach.

RailTech integrates Driver Advice System (DAS), traffic control centre (TCC) and real-time fault diagnostics (RTFD) for rail operations. RailTech is analyzed based on literature reviews on rail safety, driver advice systems and real-time fault diagnostics from the perspectives of management information systems (MIS), extensive experience of rail travel of more than 100000 kilometers in last twenty five years and also training experience at Railways Corporation.

The ultimate goals of RailTech are to improve the performance of rail infrastructure and train services and minimize train delays which results decrease
in capacity utilization, punctuality, reliability and safety. Increased capacity of infrastructure also causes secondary delays and increase in route conflicts. It is essential to do analysis on various types of feedback and operational data for improving planning and control in rail operations and to monitor delays at stations using route conflicts, train and timetable data. Specifically, RailTech faces critical situations during natural disasters (e.g. flood, cyclone, storm, fog, mist and snowfall in winter). Deep analytics can find out chains of route conflicts, secondary delays, number of conflicts, time loss and delay jump.

RailTech safety and security is an emerging trend of top technological innovation today. Railway technology is associated with a complex, distributed and integrated information system. We must monitor the performance of railways infrastructure globally and analyze political and social trends, principal technological advancement of condition monitoring system, driver advice system and real-time fault diagnostics; system complexity, R&D challenges and future vision of railway security and safety.

Condition monitoring is an important issue of RailTech safety. It is a common practice to compare relative benefits and limitations of various modes of transportation such as surface, rail and air in terms of traffic congestion, environmental pollution, safety, reliability, consistency, cost and capacity utilization. Traditionally, the security and safety of RailTech system is analyzed in terms of frequency of accidents, fire hazards, delay, punctuality, reliability, consistency, probability of injuries or fatalities due to derailing and track faults. Gradually, the system has been getting equipped with electronics, communication and information technology which increases the scope of automated fault detection and diagnosis. It is possible to improve punctuality, profit and revenue in rail operations and increase operational availability and reliability of key railway infrastructure through effective coordination and integration among DAS, TCC and RTFD.

## 2. SYSTEM

### System Analytics

*Agents:* system analysts, business analysts, scientists, engineers;

*Moves*: requirements engineering, system design, prototype testing, production, erection, testing, commissioning;

RailTech System Intelligence

* advise a train driver the target arrival time along a specific route to satisfy the timetable and avoid conflicts with other trains;

* monitor the train's speed so that the advice is updated correctly and target time of the train is achieved;

* compute energy efficient speed/distance profile to achieve target time;

* advise a driver conflict free time target i.e. how far and fast a train can be driven safely within allocated and authorized movement hiding wider view of overall traffic management of rail network.

* *Computing schema*

* track train movement
* predict future train movement

* conflicts detection and resolution

* new target train timings to avoid conflicts

* speed profiles and choice of advice to the driver

* **Networking schema**

* 3G/4G/5G to DAS with built-in antenna

* 3G/4G/5G with external antenna

* 3G/4G/5G to a communication gateway onboard

* Data communication via SMS

* **Data schema**

* Explicit driving instructions : current speed target, time series analysis of train speed profile, advice to speed up or slow down;

* Temporal information : train running status (early / delay), optimized speed profile to realize time table;

* Decision support information : gradient profile, energy usage, position of other trains, conflict, traffic congestion;

* Data mining: monitor a set of performance metrics such as primary and secondary delays, capacity utilization, route conflicts, traffic congestion, signaling errors and real-time train scheduling and time table.

* **Application schema :**

* Provide advice to the train driver for running the train in a safe and efficient manner;

* Collect and recall from route knowledge the current and future speed targets such as infrastructure, regulation and operational factors;

* Select correct train speed to minimize delay and maximize safety;

* Monitor the speed of the train by collecting data from sensors, speedometer, noise and motion;

* Compare correct speed with train speed and compute speed gap;

* Speed control to minimize the difference between target required speed and actual train speed by changing the setting of the power or brake controller and considering train's response, gradients, curves and professional driving policy;

* Assist the driver to monitor the progress of a train continuously against a series of scheduled stops and understand if the train runs early or late between two timing points;

* Advice for any temporary speed restrictions within a route to give a perception of progress and recovery time to the driver;

Prof. B. Williams and Dr. Andy Richardson are presenting system analytics for electrical and hybrid vehicles and railways technology. BEVs are the simplest type of EV using electrical power from a single source of battery to power one or more electric motors. A single electric motor is connected to the front axle through a simple gearbox; a series of four hub motors may be attached to each wheel. The battery has many cells combined into modules and the modules are grouped into packs through series and parallel connections. For example, 100 Li-ion series
connected batteries with cell voltage of 3.6V can produce 360 V. The battery pack is the largest and most expensive component of the BEV; it is readily removable and swappable. Typically, EVs have higher initial costs, lower fuel costs, lower external costs, higher insurance costs, lower maintenance and repair costs. The cost of EV system depends on various types of elements such as size of key components (e.g. batteries, fuel cells, electric motors), desired performance, driving range, energy efficiency, cost of key materials for EV components (e.g. Li for batteries, platinum for fuel cells and carbon fiber for pressure vessels), life-cycle of key components, the impact of technological learning and economies of scale on manufacturing cost, energy use of EV, technology, design of the power train, drive cycle and weight; cost of energy (e.g. fuel production, distribution and delivery costs), insurance and maintenance cost.

There are various types of battery charging methods such as normal, induction, semi-fast and fast charging. Normal charging uses standard domestic socket, can be used anywhere but is limited in power. Semi-fast charging allows a higher charging power (up to 22 kW), suitable for most EVs. Fast charging needs a more expensive infrastructure. Inductive charging does not use plugs and cables and suitable for specific applications. The basic requirements of the technological innovation of EVs include availability of efficient electric energy storage devices or batteries and recharging infrastructure to transfer electric energy from the distribution grid to the battery.

EVs may also need intelligent communication protocol for vehicle identification and billing, charge cost optimization and grid load optimization. Typically, NCA (Li-Ni-Co), NCM (Li-Ni-Mn), LMO (Li-Mn spinel), LTO (Li-Titanate), Li ion Phosphate (LFP) technologies are used to make EV batteries. It is possible to do a comparative analysis of various types of batteries based on materials of anodes and cathodes, safety, performance,, cost, life-span, specific energy and specific power. The value chain associated with electrical vehicles has various processes such as component production, cell production, module production, pack assembly, vehicle integration, use, reuse and recycling.

## Value chain analysis of EV batteries

* Component production : cathode, anode, binder, electrolytes, separator, active materials

* Cell production : single cell assembly

* Module production: cells into larger modules

* Pack assembly: install modules together with power charging & temp.

* Vehicle integration: Integrate battery pack into vehicles battery car interfaces, plugs, mounts

* Use : Use during battery life -time

* Reuse & recycle: Cleaning and deconstruction

**Smart Batteries:** Smart batteries are essential part of the dominant design of electrical and hybrid vehicles; Smart batteries should have higher energy density and tolerance to higher temperatures; should avoid the use of dangerous toxic materials, should be nonflammable and safe to use and should withstand higher voltage. Smart batteries are expected to be simple in design, cheaper and lighter in
weight as compared to present batteries; won't need liquid cooling; the batteries should be long lasting, fire-proof and should permit faster charging. Existing battery technology for EVs is very expensive and has limitations in terms of poor system performance at low temperature, impact of pressure, breakage due to mechanical stress and risks of dendrites.

EVs can be classified into non-hybrid vehicles (ICE) drive), hybrid electric vehicle (micro, mild, full, plug-in), extended range EV (EREV), BEV and fuel cell electric vehicle (FCEV). The hybrid solution obtains reduction of consumption and emissions; heat engines are operated more efficiently; electric power accumulators and electric motors allow energy recovery during braking and its use for traction purposes. In case of series hybrid vehicles, the electric motor supplies power to the wheels. In case of parallel hybrid vehicles, both the heat engine and the electric motor supply power to the wheels. In case of series-parallel hybrid vehicles, the heat engine can drive the wheels directly. It is an interesting agenda to do a multidimensional comparative analysis on various types of batteries (e.g. Li-Ni-Co-Al, Li-Ni-Mn-Co, Li-Mn, Li-Mn-P, Li-Ti) based on six dimensions – life span, cost, specific energy, specific power, safety and performance.

## Electrical Vehicle's Battery Charging Mechanism

Let us analyze the battery charging mechanism of electrical and hybrid vehicles. The basic objective is to develop an efficient market mechanism or market interface where the trading agents i.e. the service consumers of electrical vehicles and the service providers of battery charging stations act rationally and negotiate a flexible and intelligent service contract on the replenishment of charged batteries based on approximate estimation of their preferences in EV charging scenario. The market mechanism is expected to maximize the utilities of the trading agents through efficient resource allocation subject to the constraints of time and cost and limited supply of electrical energy. Traditional auction mechanism may not be an interesting option in this business model.

**Agents**: Service consumer (B), Service provider (S);

**Input**: Demand plan of B, Supply plan of S;

**System**: Electrical / hybrid vehicles, batteries, battery charging stations,

**Objectives**: minimize mismatch between demand and supply plans of charged batteries;

**Constraints**: time, cost, technology;

**Strategic moves**: Select optimal resource allocation heuristics - FCFS (First Come First Served), Most Requests First (MRF), Longest Waits First (LWF), Selective resource allocation for emergency load (linear / proportional allocation);

**Protocol**: The agents settle single or multiple intelligent service contracts. Collaborative planning, forecasting and replenishment (CPFR), Swing option, Push pull, Group buying;

**Payment function**: verify business intelligence of service contracts in terms of (pay per use, payment mode, payment terms);
*Security intelligence:* verify security intelligence in terms of rationality, fairness, correctness, transparency and accountability; B and S preserve privacy of SC contract as per revelation principle;

*Output:* Battery charging service contract.

Collaborative planning, forecasting and replenishment (CPFR) is a strategic tool for comprehensive value chain management. This is an initiative among all the stakeholders of the supply chain and service chain in order to improve their relationship through jointly managed planning, process and shared information. The ultimate goal is to improve the position of the battery charging service provider in the competitive market and the optimization of its own value chain in terms of optimal inventory, improved sales, higher precision of forecast, reduced cost and improved reaction time to customer demands. The interplay between trust and technology encourages the commitment of collaboration among the trading agents. Let us consider a specific scenario of multi-party negotiation in battery charging of electrical vehicles. *Swing option* is a specific type of supply contract in trading of stochastic demand of a resource such as charged battery for electrical / hybrid vehicles. It gives the owner of the swing option the right to change the required delivery of a resource through short time notice. It gives the owner of the swing option multiple exercise rights at many different time horizons with exercise amounts on a continuous scale. A typical swing option is defined by a set of characteristics and constraints. There are predefined exercise times $t_i$, $i \in [1, 2, ..., n]$, $1 \le t_1 \le t_2 \le \dots \le t_n \le T$ at which a fixed number of $d_0$ units of a resource may be obtained. With a notice of specific short period, the owner of the option may use swing right to receive more (up-swing) or less (down-swing) than $d_0$ at any of $n$ moments. The scheme permits swing only at g out of possible $n$ time moments where $g \le n$ is swing number constraint. A freeze time constraint forbids swings within short interval of the moments. The local constraints up-swing [$\alpha$] and down-swing limits [$\beta$] define how much the requested demand $d_i$ at time $t_i$ may differ from $d_0$. There are two global constraints which restrict the total requested volume D within the contract period by maximum total demand ($\gamma$) and minimum total demand ($\lambda$). The option holder must pay penalty determined by a function for violating local or global constraints. In this contract, the primary negotiation issue may be a discriminatory pricing plan which depends on the negotiation of a set of secondary issues such as up-swing limit, down-swing limit, maximum total demand, minimum total demand, penalty function and number of swings for a specific period.

Dr. Richardson is discussing various critical issues of railtech system. The system should be designed to satisfy multiple objectives subject to various constraints. The system should have clearly defined objectives, decision making procedures and quantitative measures of performance. The state of the system at a specific time is a set of properties of the system. RailTech system is a complex grouping of interlinked components; it can be decomposed into a set of interacting schema such as computing, networking, data, application and security schema. Each schema can be outlined as stated above. DAS is basically an information system. The basic building blocks of the computing schema are a set of algorithms which compute critical system parameters. The networking schema is the communication system. The data
schema processes data collected by the sensors and various measuring instruments.
The application schema defines the basic features of driver advice system. The
complexity of the system should be analyzed in terms of number of interacting
elements, number of relationships among the elements, number of objectives and
number of ways the system interacts with internal and external environment.

3. STRUCTURE

Structure Analytics

RailTech System Architecture

* Driver advice system

- Option 1 : DAS at TCC (Traffic Control Centre) /* refer figure 7.1*/

* Option 2 : DAS Task shared between TCC and train /* refer figure 7.2*/

- Option 3 : DAS tasks mainly onboard /* refer figure 7.3*/

* Railway system

- track side infrastructure

- vehicle or rolling stock

* Railway infrastructure assets

* mechanical actuators (e.g. point machines, level crossing barriers, train stops, mechanical signals);

* power supply equipment (e.g. substations, rectifiers, transformers);

* track (e.g. rail, pads, sleepers, ballast, substructure);

* signaling and telecoms (e.g. track circuits, interlockings, axle counters);

* Railway rolling stock

* bogie : wheels, axles, suspension components;

* traction and braking systems: motors, power electronics system, transformers, brakes;

* auxiliary subsystems : doors; air conditioning system;

* train communication bus (integrating the subsystems to a central controller);

Dr. Williams is focusing on the structure of emerging technologies related to
logistics security. The new automotive DNA is expected to be developed through
proper integration of electrical drives and connected vehicle technologies. The
design principles are based on electrical drives with electrical motors for power,
renewable solar energy for battery charging and electronic systems for control.
Hybrid EVs may use batteries and electric motors to improve the efficiency of
mechanically driven vehicles. Smart vehicles are expected to be lighter, more
conducive to electrical drives and renewable sources of energy to avoid air pollution.
The computing schema should be able to estimate price and incentives for
regulating demand and supply of electrical energy. Smart vehicles should be able to
communicate wirelessly with each other and with roadway infrastructure and
activities through global positioning system (GPS) technology and digital maps.
Intelligent vehicle-to-vehicle (V2V) communication protocols should be able to avoid
collision and crashes.
<table><caption>Table 7.1 : Structural analysis of electrical and hybrid vehicles</caption><thead><tr><td>SL No.</td><td>Structural components</td><td>Technology schema</td></tr></thead><tbody><tr><td>1.</td><td>Mechanical system</td><td>Mechanical components, transmission, brakes</td></tr><tr><td>2.</td><td>Electrical & Electronics system</td><td>Power supply, battery charger, batteries, inverter, motor</td></tr><tr><td>3.</td><td>Battery charging station</td><td>Normal, induction, fast and semi-fast charging</td></tr><tr><td>4.</td><td>Information & communication technology</td><td>Digital transformation : Computing, networking, data, application and security schema, intelligent communication protocol</td></tr></tbody></table>

The topology of smart vehicles has been analyzed in terms of a set of sub-systems or modules, connectivity, type of connections, layers, interfaces between layers and organization of layers. The structure of EVs has three major components: mechanical system (e.g. brakes, wheel), electrical and electronics system (e.g. battery charging) and information & communication systems (e.g. driver advice system) [Table 8.1]. The sensors collect data from various systems and provide the inputs to DAS. DAS analyzes the sensed data and shows the output (e.g. alerts, advice) to the driver. Figure 8.5 shows typical structure of a hybrid EV.

It is essential to adopt a new automotive DNA which can transform the design principles of smart vehicles. The structure of traditional vehicles is mechanically driven and powered by ICE, energized by petrol and diesel, mechanical control system and operated in standalone mode. The new structure is expected to be more flexible, automated and simple to use. Another important part of the structure is mobile Internet; it is the backbone of communication schema which should enable the vehicles to share real-time and location-specific big data for optimal traffic management, minimal and more predictable travel time and no distraction of driving. The vehicles should be integrated with IoT and may be considered as nodes in mobile networks. The structure is expected to be less expensive to own and operate and should have light weight and less space. The integration of a set of transformative ideas should optimize the benefits, positive impacts and side effects in terms of design direction, energy, environment, safety, congestion and access inequality. These enabling technologies have been getting matured and converged gradually for practically feasible and large scale production. Common components of hybrid vehicle are power supply, normal, onboard and quick charging system, drive battery, inverter, motor, transmission and driving, regenerative braking and electricity generation system.

**Smart Batteries:** Smart batteries are an essential part of electrical and hybrid vehicles; let us explore and analyze the scope, system, security and strategy of smart batteries for EVs. Solid State Batteries (SSB) may be the future of EVs. Alternatively, EVs can use batteries having Li-ion, Ni-metal hydride (NiMH) and electric double layers ultra capacitors led by Li-ions. SSB is an emerging technology
that uses solid electrodes and solid electrolyte such as ceramics (oxides, sulfides and
phosphates), glass and solid polymers. Solid state batteries are safer with higher
energy densities but at higher cost. Li-ion batteries use liquid or polymer
electrolytes. SSB is generally used in pacemakers, RFID and wearable devices.
Presently, the technology of SSB is being developed at Tesla, Toyota, Dyson (Sakti
3), Caterpillar (Fisker), Swiss Fraunhofer institute, Cambridge University; the
technology has been diffusing globally. Toyota has planned to use solid state
batteries in EVs by 2020. The challenge is to explore a solid conductive material fit
for large batteries. Solid Power and Volkswagen are also investing to build high
capacity manufacturing plants of SSB.

The next interesting issue is structure of RailTech system. The topology of
technology should be analyzed in terms of nodes, connectivity, type of connections,
layers, interfaces between layers and organization of layers. RailTech structure has
three major components: trackside infrastructure, rolling stock or vehicle and
driver advice system. Dr. Williams and Dr. Yokoo are giving the basic overview of
rolling stock, infrastructure and the outcome of SWOT analysis of various types of
DAS architectures. The sensors collect data from the track and rolling stock and
provide the inputs to DAS. DAS analyzes the sensed data and shows the output (e.g.
alerts, advice, recommendations) to the driver and traffic control centre (TCC).

SWOT Analysis on various DAS architectures : It is essential to analyze strength,
weakness, opportunities and threats of RailTech innovation. There are
opportunities of growth of the technology in various types of train services such as
metro rail, local train and long distance train. But, the technological innovation may
face various types of threats such as complexity, integration and digital
transformation through sensors, information and communication technologies. It is
hard to integrate the mechanical and information systems physically. Let us analyze
three different types of system architectures in figures 7.1, 7.2 and 7.3. We have
extended these architectures by incorporating the concept of real-time fault
diagnostics. In each system architecture, the basic building blocks of RTFD are
three analytics i.e. graph analytics (GA), fault tree analytics (FTA) and failure mode
effects analytics (FMEA); the output of these analytics is fed to DAS. The core
components of GA include control, resources and data flow analytics. In figure 7.1,
DAS intelligence is deployed at TCC and the onboard system only displays advice
information to the driver. The system can be implemented using existing driver
interface without any additional system in the train. But, the scope of data displayed
and dynamic update of compensating feedback to the driver is limited. This is an
interesting practical and economic solution for possible DAS rollout in future.
<figure><img src="image_76.png" /><figcaption>Figure 7.1 : DAS tasks done at Traffic Control Centre (TCC)</figcaption></figure>

In figure 7.2, DAS intelligence is shared between TCC and train. TCC computes various critical parameters like previous architecture. DAS definitions and displays are done onboard. The solution optimizes the exchange of information between TCC and onboard in real-time. But, the advice can be poor if the real characteristics of the train system differ significantly from the characteristics known by TCC. The computation should be done correctly where it is easier to access the required data. In figure 7.3, DAS tasks are mainly onboard. DAS intelligence is implemented in the train. TCC computes a set of functions as mentioned previously. It can adjust speed and optimizes energy to drive the train based on advanced algorithms. The cost of communication between TCC and the train is reduced. But, there is risk of consistency of local optimization.

<figure><img src="image_77.png" /><figcaption>Figure 7.2: DAS tasks shared between the train and traffic control centre (TCC)</figcaption></figure>
<figure><img src="image_78.png" /><figcaption>Figure 7.3 : DAS tasks mainly onboard in the train</figcaption></figure>

**Computing schema** : This schema performs data processing task with the support of algorithmic intelligence. There may be various options for computing schema such as data processing only at TCC, data processing in a standalone system, data processing integrated with DMI, data processing integrated with third party applications (e.g. communication, position services), data processing integrated with train management system. In case of option 1, the processing takes place at the TCC and DAS only receives advice and displays it to the driver. It involves installation of minimum equipment on the train. Onboard stand-alone system requires a dedicated processing unit at a suitable location of the train. This option requires dedicated space and connections but can be installed through minimum interaction with cab and other onboard systems. Another option is to integrate data processing unit with driver machine interface. It reduces the number of modules to be installed but the maintenance task may be difficult. Another option is to integrate data processing unit with other applications (e.g. positioning, communication) through shared services. The computing schema performs various types of tasks such as tracking train movement, prediction of future train movement, conflicts detection and resolution, finding new target train timings to avoid conflicts and estimation of speed profiles and choice of advice to the driver.

**Networking or communication schema** : This schema controls exchange of data between traffic control centre, trackside and train through various types of communication options such as 3G/GPRS/4G data communication network, with or without integral antenna, 3G/GPRS/4G data to a communications gateway on the train, GSM-R text messaging and ERTMS/ETCS data communications. Another interesting issue communication through SMS between TCC and DAS. In case of 3G/GPRS/4G to DAS with built-in antenna, data communications to moving trains is provided via public telecom providers through GPRS, 3G and 4G by mounting data terminal within DAS equipment on the train with an integral aerial. No external connection is required to DAS but there may be poor communication coverage in several zones and electromagnetic compatibility problems (EMC). In
case of 3G/GPRS/4G to DAS with external antenna, DAS with integral data terminal is permanently installed on a train. The only external connection is the antenna. The schema of 3G/GPRS/4G to a communications gateway onboard requires the existence of a shared communications server on each train integrated with a shared positioning system. It is a costly option though it avoids installation of antenna over train roof. These schemes can use dedicated railway GSM-R network and public networks. It is an open agenda whether it is possible to adopt smartphones using 5G / 6G / 7G / 8G wireless communication schema with high speed of data exchange and also Internet of Thing (IoT) for sophisticated driver advice system in future.

**Application schema:** A simple DAS may provide predefined timetable information and other generic advice on paper or on a screen. It may be stand alone onboard applications with timetable information and other critical data loaded into the system, independently of the traffic management system. A complex DAS may provide the driver with dynamic advice on how to drive the train as per predefined timetable. The onboard advisory component receives pre-planned timetable, route and train related information from existing traffic management system and computes advice dynamically as per the behaviour of the driver. A sophisticated DAS may provide intelligent advice to the driver of a train based on real time traffic plan. The basic objective of the system is to optimize traffic flow for the railway network by dynamically adjusting the timetable to avoid conflicts. The onboard advisory component is linked to a real-time traffic management system that can calculate new train timings to avoid conflicts and communicate them to the driver.
One of the key components of DAS is *Driver Machine Interface (DMI)*. The driver of the running train should be able to see it regularly; it should be positioned in his field of vision on or around the cab desk. There may be various options of DMI such as portable device on the cab desk, dedicated DAS-DMI permanently fitted, integrated with ERTMS/ETCS DMI, integrated with TMS DMI, integrated with GSM-R DMI. DMI system may be implemented in a portable device carried by the driver or it may be linked with other parts through wired or wireless link. It incurs minimum hardware cost and it is possible to replace faulty unit easily. But, the portable device may be vulnerable to mismanagement, loss or damage. Another possible option is to permanently modify the dashboard to accept a dedicated display screen for DAS. The screen size and information display can be optimized for the application but there are space constraints. Another important component of DAS is *train positioning system* to determine the location of a train in real-time so that DAS can provide correct speed advice. There may be various options of train positioning system such as dedicated GNSS positioning system with or without integrated antenna, shared GNSS positioning system on the train, ERTMS/ETCS positioning system and train positioning with information available from TCC. GNSS with built-in antenna requires a dedicated GNSS positioning system with the support of satellite communication. Alternatively, GNSS can be connected with external antenna on the roof of the train.
*Data schema*: DAS can provide various types of information to the driver of a train such as explicit driving instructions (e.g. current speed target, time series analysis of train speed profile, advice to speed up or slow down), temporal information (e.g. : train running status, time delay optimized speed profile to realize time table), decision support information (e.g. gradient profile, energy usage, position of other trains, conflict, traffic congestion) and a set of monitored performance metrics (e.g. primary and secondary delays, capacity utilization, route conflicts, traffic congestion, signaling errors and real-time train scheduling and time table). One of the critical features of data schema is driver integration through human machine integration: alternative context of advice in terms of ‘follow-me’ or ‘trade-off’ method (what information is important to the driver) and alternative forms of advice (how the information is transformed to advice). DAS could guide various parameters such as distance or time interval updated advice, event updated advice, updated speed profile advice and contextual advice without target setting. There may be various forms of advice such as suggested speed based on difference in current speed and target speed, current target speed and duration and target; time keeping based on difference between the actual time at a particular point in the route and the target time, current target departure and arrival time, action on controls (e.g. brake pressure) and energy savings based on speed and time.

## 4. SECURITY

### Security Analytics

* Call real time fault diagnostics for infrastructure and rolling stock monitoring through pattern recognition using artificial neural networks and knowledge based expert systems for fault detection and diagnosis.

* Graph Analytics

* Fault Tree Analytics

* FMEA Analytics

* Data logging and event recording analytics

* Event recording and data analytics

* Online health monitoring analytics

* Call threat analytics

* assess risks of single or multiple threats on RailTech; analyze performance, sensitivity, trends, exception and alerts.

* what is corrupted or compromised: agents, communication schema, data schema, application schema, computing schema and RailTech System?

* time: what occurred? what is occuring? what will occur? assess probability of occurrence and impact.

* insights: how and why did it occur? do cause-effect analysis.

* recommend: what is the next best action?

* predict: what is the best or worst that can happen?

* Verify security intelligence of RailTech / EVs system at levels L1, L2, L3, L4 and L5;

Level1 [L1 - access control]:
* authentication, authorization, correct identification, privacy, audit; confidentiality, data integrity, non-repudiation;

* private view of data through role based access control

* assess the risk of privacy attack;

Level2 [L2 - computing and communication schema]: fairness, correctness, transparency, accountability, trust, commitment, rationality;

Level3 [L3 - system performance] : robustness, consistency, liveness, reliability, resiliency, deadlock freeness, lack of synchronization, safety and reachability;

Level4 [L4 - malicious attacks] : detect the occurrence of any malicious attack on the RailTech system.

* rail network delay due to coremelt or network traffic congestion

* rushing attack

* sybil attack

* false data injection attack

* other attacks: data integrity attack, node deletion, flaws in workflows, poor QoS, information leakage, physical attacks on the drivers by terrorists or malicious agents.

Level5 [L5 - Business intelligence]: audit flaws in payment function computation.

Dr. Simon Rodrigues and Prof. R. Prakash are discussing on the security intelligence of emerging logistics technologies. It is essential to design electrical / hybrid vehicles in terms of security at various levels – L₁, L₂, L₃, L₄ and L₅. Level L₁ verifies system performance in terms of safety, reliability, consistency, stability, robustness and reach. The other critical design issues are also associated with resiliency, reachability, deadlock-freeness, synchronization and interactive intelligent communication among electrical / hybrid vehicles. Solid-state battery technology has higher energy density and tolerance to higher temperatures; may avoid the use of dangerous toxic materials, nonflammable and safer; can withstand higher voltage and longer life-cycle and support faster recharging rate. The safety of electrical / hybrid vehicles depends on access control at level L₂ in terms of authentication, authorization of the drivers, correct identification of the system components and audit of quality control issues. The security schema is also designed and verified at level L₃ in terms of fairness, correctness, transparency, accountability, trust and commitment. The safety of the electrical / hybrid vehicles may be threatened at level L₄ through the corruption of various stakeholders such as car manufacturers, drivers, passengers and battery charging station supervisors. The design of the vehicles is expected to assess and mitigate the risks of various types of attacks at level L₅ such as false data injection, sybil, shilling and traffic congestion.

Vehicle to Vehicle (V2V) Communication for Anti-collision System : The scope of emerging communication technology has been explored for intelligent V2V communication against the threats of accidents and collisions of vehicles on roads. The wireless technology is going through an evolution of a set of generations (1G→2G→3G→4G→5G→G→7G). One of the most interesting applications of this emerging technology is Vehicle-to-Vehicle (V2V) communication, Most cars are
expected to have a 4G or 5G cellular connection for V2V communication and the
security of the drivers and passengers. 5G Automotive Association have been
promoting C-V2X communication technology. It provides for communication
between vehicles and communication between vehicles and infrastructures, leading
to increase in autonomous self-driving cars and IOT (Internet of Things). The speed
of 5G technology in upcoming self-driving cars may be vital in helping the
capabilities of autonomous cars realize their full potential (Llanasas, 2019). Current
4G network doesn't possess the required speed needed to provide self-driving
vehicles that could prevent catastrophic accidents or collision (Llanasas, 2019. 5G is
expected to be the basic building block of anti collision system of next generation
vehicles.

5G is the fifth generation wireless technology for digital cellular networks with wide deployment in 2019. The frequency spectrum of 5G is classified as millimeter waves, mid band and low band. Low band uses a similar frequency range as 4G. 5G millimeter wave is the fastest having actual speeds 1–2 Gbit/s down. Frequencies are above 24 GHz reaching up to 72 GHz, above lower boundary of extremely high frequency band. 5G mid-band is the most widely deployed in over 20 networks; speed in a 100 MHz wide band is 100–400 Mbit/s down. 5G low-band offers similar capacity to advanced 4G; latencies between 25 -35 milliseconds. 5G networks are digital cellular networks in which the covered service area is divided into cells. Analog signals (e.g. sounds, images) are digitized by an analog-to-digital converter and transmitted as a stream of bits. All 5G wireless devices in a cell communicate by radio waves with a local antenna array and low power automated transceiver in the cell. The local antennas are connected with telephone network and Internet through a high bandwidth optical fiber or wireless backhaul connection. A mobile device crossing from one cell to another is automatically handed off seamlessly to the new cell.

6G (sixth generation) is the successor to 5G cellular technology; 6G networks are expected to use higher frequencies, higher capacity and much lower latency than 5G networks. 6G is a wireless technology that is beyond 5G. China has officially launched R&D works for 6G mobile networks. It would be about a decade before 6G comes along, NTTDoCoMo has presented the evolution of wireless technology from 3G in 2000s, 4G in 2010, 5G in 2020 and it is reasonable to expect 6G in 2030. It is not exactly known how fast 6G will be yet. It may be governed by the standards of International Telecommunication Union (ITU). If everything connects together using 5G, 6G with higher data speeds and lower latency makes instant device-to-device connection possible in various application such as autonomous cars, drones and smart cities, integration of our brains with computers and greatly improved touch control systems. 7G is the next generation communication technology. It is being adopted in Norway, China, Japan and other developed countries of the world. In Norway, Internet speed is fastest. Utilizing superior design and technology, 7G Network is expected to deliver millions of calls reliably every day,

The air interface defined by 3GPP for 5G is known as New Radio (NR), and the
specification is subdivided into two frequency bands, FR1 (below 6 GHz) and FR2
(mmWave) each with different capabilities. The next issue is frequency range 1 (< 6
GHz ); maximum channel bandwidth defined for FR1 is 100 MHz, the most widely
band is around 3.5 GHz. For frequency range 2 (> 24 GHz), minimum channel bandwidth defined for FR2 is 50 MHz and the maximum is 400 MHz. From the perspective of performance analysis; 5G speeds are expected to range from ~50Mbit/s to over 2Gbit/s even 100Gbit/s, 100x faster than 4G. The fastest 5G, known as mmWave, delivers speeds of up to and over 2Gbit/s. The latency of 5G is 8–12 milliseconds. It is governed by International Telecommunication Union's IMT-2020 standards.

Next, let us consider the deployment of 5G, Nine companies sell 5G radio hardware and 5G systems for carriers- Altiostar, Cisco Systems, Datang Telecom, Ericsson, Huawei, Nokia, Qualcomm, Samsung and ZTE. Large quantities of new radio spectrum (5G NR frequency bands) have been allocated to 5G. 5G devices include Samsung Galaxy S10 5G. The technology is expected to be available in Australia, Argentina, Bulgaria, Canada, China, Finland, Germany, India, Monaco, Netherlands, New Zealand, Norway, Pakistan, Philippines, Romania, Russian Federation, San Marino, South Africa, South Korea, Taiwan, Thailand, Uruguay, Vietnam, Qatar, Mexico, USA, Sweden and Panama.

ITU-R has defined three main uses for 5G as faster and reliable connection - Enhanced Mobile Broadband (eMBB), Ultra Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC). Only eMBB is deployed in 2019; URLLC and mMTC are several years away in most locations. eMBB uses 5G as a progression from 4G with faster connections, higher throughput, and more capacity; mMTC is expected to connect a large number of low power, low cost devices, which have high scalability and increased battery lifetime, in a wide area. 5G technology may connect some of 50 billion connected IoT devices. So far, we have discussed the strength of the emerging wireless technologies. But, there are various constraints such as interference, security, surveillance and health concerns. The spectrum used by remote sensing, weather and Earth observation satellites will potentially be significant without effective controls. The technology has health concerns; the radiation could have adverse health effects. There are concerns of data security and privacy, surveillance concerns, threats of potential espionage of foreign users by 5G equipment vendors.

**Internet of Things (IoT)**: Internet of Things is the integration of several technologies and communications solutions such as identification and tracking technologies, wired and wireless sensor and actuator networks, enhanced communication protocols shared with next generation Internet and distributed intelligence for smart objects fitted with EVs / HVs. IoT can be effectively used for assisted driving, transportation and logistics domain. Advanced cars, trains, buses, bicycles, roads and transported goods are becoming equipped with sensors, RFID tags, actuators and processing power. These objects can send important information to traffic control sites for better transportation planning, effective route optimization for energy saving, tracking of delivery time and delay, faults and monitoring in the supply chain and transportation network. These objects can provide important information to the driver and/or passengers of a car to allow better navigation, safety, collision avoidance systems and monitoring of transportation of hazardous materials.
**Real-Time Fault Diagnostics (RTFD)**: The fourth element of deep analytics is security. Please refer to five test cases as discussed in section 9. We have analyzed those cases and outline the aforesaid security schema comprehensively. It is essential to monitor and detect faults of a complex real-time system; assess the chance of various types of faults and explore efficient and reliable fault detection and isolation methods based on artificial intelligence [30]. The basic building blocks of real-time fault diagnostics are soft computing and AI methods such as knowledge based expert system, model based system, if-then rule based system, artificial neural network (ANN), fuzzy logic, genetic algorithm (GA), decision tree and Bayesian network. It is possible to monitor real-time system, faults detection, diagnosis and correction at process and functional levels through a set of quantitative and qualitative performance metrics.

Faults may occur due to various reasons such as failure of hardware components (e.g. sensors, triggers), environmental factors (e.g. noise) and flaws in software (e.g. algorithms, logic, coding, protocols, invalid and incorrect inputs and feedback, knowledge base, inference mechanism and knowledge representation). There are other constraints such as inconsistency in knowledge base and flaws in knowledge engineering, learning errors and data visualization. Traditionally, track geometry is monitored using accelerometers and camera; cracks in tracks are detected through non-destructive test; measurements are acquired through various sensors of specialist trains and data is analyzed on the train. If the value is above predetermined threshold, then it requires the effort of the experts to identify and diagnose the faults.

Real-time fault diagnostics are basically condition monitoring systems having three core components: (a) data logging and event recording system, (b) event recording and data analytics and (c) online health monitoring system. The first component can detect faults (e.g. change in logic and operation time), can give hard evidence of accidents and other abnormal conditions caused by the malfunctioning of RailTech system and also can record the performance and status of critical equipments and operations in the form of digital data. On-Train Maintenance Recorder (OTMR) is equivalent to a black box and may use GPS and other sophisticated systems for real time data communication.

The second component may have intelligent data analytics (e.g. statistical analysis, sequence mining) and remote access to critical data. The second component may record door opening time, traction current and energy usage. Track based monitoring is useful for the verification of the performance of vehicles such as monitoring of wheel with strain gauge or fibre optic sensors and the monitoring of hot axles boxes.

The third component is basically knowledge based expert system which can collect digital and analog data from various critical equipments, analyze sensed data, compare with an inbuilt database of healthy and simulated faulty operational modes, flag alarms and recommend diagnosis to the drivers and maintenance workforce. Automated system verification is essential for scalability and robustness in fault diagnosis. The basic building blocks of RTFD are following three analytics the output of the same is fed to DAS.
Call Graphical Analytics; Verify robustness and consistency of RailTech system performance;

* Time: Sense failure propagation in dynamic systems through [TFPG = F, D, E, M, ET, EM, DC, DS]; [TFPG]

* TFPG transition system: S = (X,I,T); X= state variable, I = initial state, T = state transition;

* F: failure nodes;

* D: discrepancy nodes;

* E: edges connecting all nodes;

* M: system modes;

* ET: E, a map that associates every edge with minimum and maximum time for the failure of propagation;

* EM: E, a map that associates every edge with a set of modes either ON or OFF;

* DC: D, a map defining the class of each discrepancy either AND or OR;

* DS: D, a map defining monitoring status of discrepancy as either M (when discrepancy is monitored by alarm) or U (when the discrepancy is not monitored).

* Control flow [Control Flow Analytics (CFA)]

* Resources flow [Resources Flow Analytics (RFA)]

* Data flow [Data Flow Analytics (DFA)]

Detect, isolate and correct problems early and re-allocate resources as per demand.
Output:

* Timed Failure Propagation Graph (TFPG)

* System performance scorecard

* Data visualization: error trace / error trace / fault propagation path

The graphical analytics (GA) analyze RailTech System performance from the perspectives of four dimensions: time, control flow, resources flow and data flow. TFPG considers only time failure but ignores the other important aspects of vehicle diagnostics such as control, resources and data flows. RTFD is expected to give a comprehensive view of system failure not only in terms of time delay but also flaws in control, data and resource flows. It is an interesting research agenda whether a single graph can show the propagation of different types of failures in system performance from the perspectives of time delay and flaws in resource, data and control flows simultaneously and comprehensively.

Time Failure Propagation Graph (TFPG): Timed Failure Propagation Graph is a rich formalism and is used to model the occurrence and propagation of failure and their direct and indirect effects, Boolean combinations of faults, time delays, events and state transitions in the design of real-time fault diagnostics of a complex, autonomous and dynamic system. There are several important issues: no critical
state of a system should be ignored; no spurious and nonexistent state should be considered for analysis; it is essential to perform fault detection, isolation and recovery, sense and compute time delays between events correctly and validate completeness and tightness of the graph through intelligent model checking algorithms. TFPG can model and reason the issues of spurious and missing alarms, intermittent faults and reduction of a large scale vehicle diagnostics system. It is an interesting open research agenda whether TFPG can verify performance of RailTech system in terms of reliability, consistency, liveness, safety, robustness, stability, deadlock freeness and reachability comprehensively.

Timed Failure Propagation Graph is a directed graph model (Figure 7.4); the structure has a set of nodes (V) and edges (E). The nodes represent failure modes (F) i.e. root events of the propagations of failure and discrepancies (D) i.e. possible deviations from nominal behavior caused by failure modes. Failure modes do not have any incoming edges; discrepancies must have at least one incoming edge and must be reachable from a failure mode node. Circular paths are possible. Edges (E) model the dependency between the nodes ($V = F \cup D$) and capture propagation of time delays; the edges can be activated or deactivated based on a set of operation modes (M). ET is a map that associates every edge with minimum and maximum propagation time ($t_{min}, t_{max}$) of a failure. EM is a map that associates every edge with a set of modes in M. DC: D is a map defining the type of each discrepancy either AND or OR.

<figure><img src="image_79.png" /><figcaption>Figure 7.4: Time Failure Propagation Graph</figcaption></figure>

Is there any other type discrepancy apart from AND or OR such as NAND, NOR, NOT, XOR and loop? Section 5.1.1.2 gives an overview of various types of control flow patterns in RailTech system. DS: D is a map defining monitoring status of discrepancy as either M when (discrepancy is monitored by alarm) or U (when the discrepancy is not monitored). A TFPG transition system is represented by $S = (X,I,T)$ where X is a set of state variables; $I(X)$ is initial state, X' is next state of X and T represents transition relation. A state s of S is an assignment to the state
variable of X. A trace of s is an sequence $\lambda := s_0,s_1,...s_n$ such that $s_0 = I(X)$ and
$(s_i,s_{i+1}) = T(X,X')$.

Control Flow Analytics [CFA]

System: RailTech system (Mechanical, Electrical, Electronics, Information system : Driver Advice System [DAS], Communication system);

Assess risks: Verify correctness, reliability, consistency, liveness, deadlock freeness, synchronization and reachability in control flows.

* Basic control flows : Sequential, Parallel split, Synchronization, Exclusive choice, Simple merge;

* Special branching and synchronization control flows: Multi choice, Synchronizing merge, Multi merge, Discriminator, m-out-of-n join;

* Structural control flows : Loop, Implicit or explicit termination, Multiple instances;

* State based control flows: Deferred choices, Interleaved parallel routing, Milestone, Cancel;

Mitigate risks.

* Replace faulty components through proactive and reactive maintenance;

* Rectify circuit connectivity;

* Correct Boolean logic in electrical and electronic circuit: AND / NAND/ Exclusive OR/ NOR/ XOR/ XNOR/ NOT;

Resource Flow Analytics [RFA]

System: RailTech system (Mechanical, Electrical, Electronics, Information and Communication system), smart coaches;

Assess risks: Verify correctness, and fairness of resource management;

* Resource planning: sense demand supply mismatch.

* Resource allocation : automatic / semi-automatic execution, direct allocation, role based allocation, deferred allocation, authorization in allocation, separation of duties, case / exception handling, capability based allocation, history based allocation, organizational allocation, resource initiated allocation

* Resource distribution : Single / multiple resources distribution, early / late distribution, priority queue, autonomous resource allocation;

* Resource mobilization

* Resource sharing

* Resource delegation, escalation, deallocation, suspension, resumption, skip, redo, stateful / stateless reallocation, simultaneous / chained execution;

Mitigate risks:

* Optimal resource allocation (e.g. avoid shortage or surplus of resources such as fuel, safety stock)

* Sensor based on board condition monitoring system
* Monitor health of critical system components (e.g. wheels) through sensors, CCTVs, smoke detectors, Fire extinguishers, IoT and integrated information system.

* Real-time fault detection: Detect defects and major causes for derailments and delays and deterioration in tracks, running trains and other rail infrastructure.

* Trade-off cost vs. comforts / luxury;

## Data Flow Analytics [DFA]

**System**: RailTech system (Information and Communication schema of DAS)

**Data elements:** Single / multiple instance DAS operating environment data, Atomic and block task data, Boolean and numeric data, time series data;

Assess risks. Verify correctness, reliability and consistency of data schema.

* Data visibility

* Data interaction among various elements of DAS

* Data transfer between various components of networking, computing and application schema of DAS

* Data transformation

* Data based routing of various control flow patterns associated with DAS

* Data analysis / mining

* ETL (Extract, Transform, Load) mechanism

* Data cleaning and selection

* Knowledge discovery from data

* Data visualization

## Mitigate risks:

* Sense flaws in data flow, streamline data management.

* Detect noise, missing, imprecise sensor data; clean data before analysis.

## Fault Tree Analytics [FTA]

Call fault tree analytics;

**Objectives**: identification and analysis of conditions and factors that cause the occurrence of faults;

**System verification mechanism**: verify reliability and safety of RailTech system performance through top-bottom approach;

* Define fault tree structure based on system data, scope and applications related to RailTech System;

* Fault tree development, construction and evaluation;

* Events

* Basic event /* failure of a component */

* External event /* normally expected to occur */

* Undeveloped event /* insufficient data */

* Conditioning event /* conditions that control logic gates */

* Logic gates

* AND gate /* o/p will occur only if all independent i/ps occur */
* Priority AND gate /* o/p occurs if the i/ps occur in a specific sequence specified by a conditioning event */

* OR gate /* the o/p will occur if any i/p occurs */

* Exclusive OR gate /* o/p will occur if exactly one i/p occurs */

* Inhibit gate /* o/p will occur if i/p occurs under an enabling condition specified by a conditioning event*/

* Compute failure rate from fault tree analysis;

Output: FTA provides following output to the driver through automated data visualization tool.

* report

* error trail / error trace / fault propagation path

<figure><img src="image_80.png" /><figcaption>Figure 7.5: Fault tree</figcaption></figure>

FTA is generally used as a diagnostic tool to understand the logic leading to a faulty state; does root cause and pareto analysis; evaluates, monitors and controls safety, consistency and reliability of a complex system. FTA is a deductive reasoning that analyses an undesired top level event and identifies fault configurations i.e. minimal cut sets; a cut set is a set of faults that represents a necessary, but not sufficient, condition that may cause an undesired state of a system. Event X occurs only if both events Y and Z occur. Event Y occurs if either event P or Q occurs. The standards, frequently used symbols, conventions, events and logic gates for fault tree diagrams can be found in .

## Failure Mode Effect Analytics [FMEA]

Call Failure Mode Effect Analytics (FMEA);

Objectives: identification and analysis of conditions and factors that cause the occurrence of faults;

Verify reliability and safety of RailTech system performance through bottom-up approach;

Output:

* FMEA Table: it highlights causality relationship between faults and undesired states or properties.

* Data visualization -error trail / error trace / fault propagation path;
Let us exercise a comparative analysis on TFPG, FTA and FMEA. FTA is a deductive and top-down method which analyzes the effects of a fault and events on a complex system. FMEA is an inductive and bottom-up method which analyzes the effects of a single component on a system. FTA shows how a system is protected to single or multiple faults. It is not good at finding all possible faults of a system. FMEA is good at finding all possible faults exhaustively and their local effects. It is not good at finding multiple failures and their effects at a system level. FTA considers external events but FMEA does not consider it. TFPG allows fine grained and precise analyses that FMEA cannot do. FTA explores only subsets of propagation paths in response to specific events, TFPG presents a more comprehensive and integrated view of failure in a complex and dynamic system.

## Knowledge based Expert System

Abnormal operating conditions and faults may cost rail system significantly but may be prevented through intelligent prediction and control mechanism of knowledge based expert system. An expert system is expected to monitor, detect and diagnose abnormal conditions of rail system and provides safeguards against unexpected process conditions. It is possible to model faults and instability in a complex rail system through expert system which is basically a knowledge based real-time fault diagnostics. The expert system uses the valuable knowledge from the experts, operators and real-time data from various sensors, measuring instruments and various electrical and electronic devices connected to the rail system. Soft computing algorithms (e.g. fuzzy logic and ANN) may be used for mining acquired real-time data and knowledge discovery from data.

<figure><img src="image_81.png" /><figcaption>Figure 7.6 : Knowledge based expert system for real-time fault diagonistics</figcaption></figure>

There are two methods of fault diagnosis : model based approach and knowledge based approach. Model based approach adopts quantitative models to estimate states and parameters of a system. But, it is almost impossible to obtain a model that exactly matches the process behavior of rail system in practice. The mismatch between the behavior of the model and rail system may lead to large error signals.
Abnormal operation may cause false alarms unless appropriate thresholds are used. It may be impossible to model non-linear systems by analytical equations. These negative aspects demand the support of alternative knowledge based methods in fault diagnosis such as ANN, fuzzy logic and CBR. Knowledge based fault diagnosis is done based on the evaluation of real-time data according to a set of rules which human experts (e.g. train drivers, engineers, support staff) have learnt from past experience. The knowledge may be input and output process variables, patterns of abnormal process conditions, fault symptoms, Linear approximation of nonlinear system may result significant errors. The combined approach of knowledge based fault diagnosis with real-time process variables improves the reliability, consistency and efficiency of the system. Knowledge based fault diagnosis has three basic steps [Figure 7.6]: (a) acquire real time process data from sensors and intelligent mechanical, electrical and electronic devices; the sensed variables indicate problem of safety and stability of rail system; (b) make inferences or diagnosis based on acquired process data; (c) take actions based on inferences such as raising alarms, informing drivers and automated on/off operation of connected equipments for resiliency of the system.

Rail system demands continuous monitoring through an adaptive security schema. Advanced analytics is the basic building block of next generation security protection which should be able to manage an enormous volume, velocity and variety of data through AI and machine learning techniques. User Entity Behavior Analytics detect anomalous patterns by comparing with the normal profile and the activities of the users and trigger alarms by sensing single or multiple attacks on rail system. Dynamic data protection is an effective way to move towards adaptive security architecture. DDP surfaces anomalies and adjusts individualized data security controls proactively in near real-time to protect critical data of rail system. Adaptive Security with dynamic data protection is expected to offer many benefits over traditional security schema. Adaptive security is a critical feature of a technology that monitors rail network in real time to detect any anomalies, vulnerabilities or malicious traffic congestion. If a threat is detected, the technology should be able to mitigate the risks through a set of preventive, detective, retrospective and predictive capabilities and measures. Adaptive security analyzes the behaviors and events of rail system to protect against and adapt to specific threats before the occurrence of known or unknown types of malicious attacks.

# 5. STRATEGY

## Strategy Analytics

**Agents**: System analysts, business analysts, scientist, engineers, technology management consultants;

**Strategic moves**: Focus on emerging logistics technologies.

* Call deep analytics ‘7-S’ model; explore how to ensure a perfect fit among 7-S elements – scope, system, structure, security, strategy, staff-resources, skill-style-support;

* Define a set of security goals and emerging technologies accordingly.
★ Do SWOT analysis: strength, weakness, opportunities and threats of existing technologies as compared to emerging technologies.

- Fair and rational business model innovation.

- Who are the consumers?

- What should be the offering of products and services?

- What do the consumers value?

- What is the rational revenue stream ?

- How to deliver values to the consumers at rational cost?

★ Do technology life-cycle analysis on ‘S’ curve : presently at emergence phase of ‘S’ curve.

★ Explore technology innovation-adoption-diffusion strategy.

- Real-time fault diagnostics using fault tree analytics, FMEA and TFPG.

- Automated verification of security intelligence at multiple levels using deep analytics

- Distribution of intelligence

- Processing unit integration

- Integration of driver's interfaces, train positioning and communication schema

- Exchange of information between track and train

- SWOT and TLC analysis

★ Explore innovation model and knowledge management system for creation, storage, sharing and application of knowledge.

★ Adopt ‘4E’ approach for innovation projects on logistics technologies : envision, explore, exercise and extend.

Prof. Prakash is analyzing the strategic moves for the innovation, adoption and diffusion of emerging logistics technologies. This element should be analyzed from different perspectives such as R&D policy, learning curve, SWOT analysis, technology life-cycle analysis and knowledge management strategy. An intelligent R&D policy should be defined in terms of shared vision, goal, strategic alliance, collaborative, collective and business intelligence. Top technological innovation is closely associated with various strategies of organization learning and knowledge management, more specifically creation, storage, transfer and intelligent application of knowledge. It is essential to analyze strength, weakness, opportunities, threats, technological trajectories, technology diffusion and dominant design of the technology of electrical and hybrid vehicles through logical and analytical reasoning.

The technological innovation is closely associated with R&D policy and organizational learning strategies in new product development and process innovation. There are various strategies of learning such as learning by doing and learning before doing. Learning by doing is effective in those technologies which demand low level of theoretical and practical knowledge. On the other side, learning before doing is possible through various methods such as prototype testing, computer simulations, pilot production run and laboratory experiments. It is effective for the innovation of EVs where deep practical and theoretical knowledge
can be achieved through laboratory experiments that model future commercial production experience.

Let us explore the role of deep analytics on technological innovation of EVs. It is interesting to analyze the impact of different learning strategies and timing of technology transfer on product development performance, process re-engineering and R&D cost of this technological innovation. It is important to compare the effectiveness of various types of learning strategies in terms of cost, quality and time. It is also critical to analyze the relationship between process innovation and learning curve in terms of dynamic cost reduction and improvements in yield. It is essential to identify the critical success factors (e.g. resource allocation, ERP and SCM strategies) that influence the rate of learning and superior performance.

It is rational to evaluate strength, weakness, opportunities and threats of the technological innovation on electrical and hybrid vehicles. There may be major and minor strengths and weaknesses. Strength indicates positive aspects, benefits and advantages of EVs. Weakness indicates negative aspects, limitations and disadvantages of the technology. Opportunities indicate the areas of growth of the market of EVs and industries from the perspective of profit. Threats are the risks or challenges posed by an unfavorable trend causing deterioration of profit or revenue and losses.

Let us first do SWOT analysis on EV technology and explain the strength of EVs. Wide spread adoption of electrical and hybrid vehicles can limit the environmental pollution of conventional fuel based transportation and can reduce dependence on oil (e.g. petrol and diesel). Intelligent supply chain contracts and switching stations may increase driving. The adoption of electrical vehicles is safe, reliable and consistent. Rational policy intervention is expected to consider battery purchase subsidies and R&D for advancement of battery techniques in terms of safety, system performance, cost, life-span, specific energy and specific power. Sustainable green transportation is a critical research agenda of government, environmentalists, industry and academics; green fuel (electrical, biofuel, hydrogen, natural gas) can limit environmental pollution and oil dependence.

Next, let us talk about the weakness of EV technology. The transition from conventional gasoline vehicles to EVs poses several challenges. Increase in adoption of EVs may create unprecedented strains on the existing power generation, transmission and distribution infrastructure. Renewable energy is typically intermittent which may result a potential mismatch between supply and demand. There are other constraints such as range anxiety and high battery cost that may limit consumer adoption. It is rational to adopt novel switching station based solution. Electrical and hybrid vehicles can use standardized batteries that when depleted can be switched for fully charged batteries at switching station. The consumers can pay for miles driven and don't pay for upfront battery purchase. In case of range anxiety, an EV may have insufficient range to reach its destination. It demands technological advancement and high energy storage capacity of batteries.

Next, let us explore the opportunities and threats of EV technology. Transportation sector is a significant contribution to environmental pollution. Geopolitical uncertainties often result increased vulnerabilities of oil based transportation infrastructure. The adoption of electrical vehicles is expected to result the growth of
electrical drives, hybrid vehicles, renewable energy (e.g. solar microgrid, wind, tidal), power plants, standardized batteries and electrical battery charging stations. This business model requires an efficient payment function and market clearing mechanism. It is not rational to buy batteries; rather it should be replenished at battery charging stations. EVs require adequate supply of electrical energy; thermal power may not be an interesting option from the perspectives of environmental pollution.

**SWOT analysis on Smart Batteries** : Let us exercise SWOT analysis on Li-ion and solid state batteries. Electrical vehicles may be dearer to buy but cheaper to run. But there are issues of range and rate of efficient battery charging mechanism. Electrical batteries may be the game changer in the innovation of electrical and hybrid vehicles technology. Solid state batteries replace the wet electrolyte of lithium ion batteries with a solid electrolyte. Current lithium-ion batteries are flammable and produce heat and have short life span; constant charging and discharging slowly erodes the performance of the battery. Smart batteries are expected to be simple in design, cheaper and lighter in weight as compared to present Li-ion batteries; won't need liquid cooling; the smart batteries should be long lasting, fire-proof and should permit faster charging.

Let us discuss the limitations of existing battery technologies of EVs. SSBs are generally very expensive; those batteries have other limitations such as poor system performance at low temperature, impact of pressure, breakage due to mechanical stress and risks of dendrites. Li metal dendrites from the anode piercing through the separator and growing towards the cathode in the form of crystal like structure. Generally, solid Li anodes in SSBs replace graphite anodes in Li-ion batteries for higher energy densities, safety, and faster recharging time. Solid Li anode experiences the formation of Li dendrites due to the reactivity of the metal. Li dendrites penetrate the separator between the anode and cathode to prevent short circuits. The penetration of Li dendrites into the separator may cause short circuit, overheating, fire or explosion from thermal runaway propagation and reduction of columbic.

There are also financial barriers of existing battery manufacturing plant; they have to invest significantly on solid state batteries. There is a huge difference between a technology that works on a small scale and one that is ready for mass market production. Cars charge as they drive; EVs demand the support of smart batteries which should be cheaper, smaller in size, light weight, non-flammable, increased life cycle (say 2-10 years), higher capacity and fit for faster charging and long range. The industry is looking for a sustainable, affordable and widespread energy conversion system. Is it possible to have a battery with two times the density of current batteries at 1/5 of the cost?

**Technological life-cycle analysis** : Deep analytics can evaluate and explore the technological innovation of EVs in terms of technology life-cycle, technology trajectory, S-curve, technology diffusion and dominant design. No element in this universe exists eternally. Similarly, the technology of EVs has emerged and is now growing to some level of maturity. It is essential to evaluate the status of each
technological innovation through TLC analysis. Some technologies may have relatively long technology life-cycle; others never reach a maturity stage. Emergence of new technologies follows a complex nonlinear process. It is hard to understand how the technology life-cycle interacts with other technologies, systems, cultures, enterprise activities and impacts on society. All technologies evolve from their parents at birth or emergence phase; they interact with each other to form complex technological ecologies. The parents add their technological DNA which interacts to form the new development. A new technological development must be nurtured; many technologies perish before they are embedded in their environments. Next phase is growth; if a technology survives its early phases, it adapts and forwards to its intended environment with the emergence of competitors. This is a question of struggle for existence and survival for the fittest. Next phase is a stable maturity state with a set of incremental changes. At some point, all technologies reach a point of unstable maturity i.e. a strategic inflection point. The final stage is decline and phase out or expire; existing technologies of oil fuelled vehicles will eventually decline and are phased out or expire at a substantial cost.

Let us consider the analysis of the performance of a new technology vs. effort; it is basically an S-curve. Initially, it is difficult and costly to improve the performance of the new technology of EVs. The performance is expected to improve with better understanding of the fundamental principles and system architecture. Next, let us analyze the adoption of the technology over time which is also an S curve. Initially, the new technology of electrical and hybrid vehicles may be costly for the adopters due to various uncertainties and risks. Gradually, this new technology is expected to be adopted by large segments of the market due to reduced cost and risks.

The rate of improvement of the new technology may be faster than the rate of market demand over time; the market share increases with high performance. Technological change follows a cyclical pattern. The evolution of the new technology of EVs is passing through a phase of turbulence and uncertainty; various stakeholders of the supply chain are exploring different competing design options of the new technology and a dominant design is expected to emerge along with a consensus and convergence of structure. Then, the producers will try to improve the efficiency and design of the EVs based on stable benchmark of the industry. The dominant design of EVs must consider an optimal set of most advanced technological features such as smart batteries, solar power enabled battery charging mechanism and V2V communication, which meet the demand of the customer, supply and design chain in the best possible way.

*Technology trajectory* is the path that the technology of EVs takes through its time and life-cycle from the perspectives of rate of performance improvement, rate of diffusion or rate of adoption in the market. It is really interesting to analyze the impact of various factors and patterns of technology trajectories of this innovation today. How to manage the evolution of this technological innovation? The nature of innovation shifts markedly after a dominant design emerges. The pace of performance improvement utilizing a particular technological approach is expected to follow an S-curve pattern. The evolution of innovation is determined by intersecting trajectories of performance demanded in the market vs. performance supplied by technologies. Technology diffusion indicates how new technologies
spread through a population of potential adopters. It is controlled by characteristics of innovation, characteristics of social environment and characteristics of the adopters such as innovators, early adopters, early majority, late majority and laggards. What should be the innovation model for effective innovation, adoption and diffusion of emerging technology of EVs / HVs? It is rational to adopt K-A-B-C-D-E-T-F model.

It is expected that RailTech will go through emergence, diffusion, development and maturity phases in this decade. At present, the technology is at growth phase of TLC. It is not a trivial task to evaluate and explore technology life-cycle in terms of S-curve, trajectory, diffusion strategy and dominant design of RailTech. It is hard to understand how RailTech interacts with other technologies, systems, cultures, enterprise activities and impacts on society. What should be the innovation model for effective innovation, adoption and diffusion of emerging technology of Railtech security? It is an interesting option to adopt K-A-B-C-D-E-T-F model. Initially, it may be difficult and costly to improve the performance of the RailTech; the performance is expected to improve with better understanding of the fundamental principles and system architecture. The evolution of this technology passes through a phase of turbulence and uncertainty; various stakeholders associated with the system may explore different competing design options of the new technology and a dominant design will emerge through consensus and convergence of structure.

# 6. STAFF-RESOURCES

## Staff-resources analytics

* **Innovators** : R&D units of automobile manufacturing companies; technology management departments of academic institutes, collaborative networks;

* Organizational creativity, organization structure, cooperative work culture, human capital management, talent management, knowledge management;

* do estimation, planning, capacity utilization, allocation and distribution of '5M' resources.

<figure><img src="image_82.png" /></figure>

* **Man** : human capital management (scientists, business analysts, system analysts, project managers, engineers): talent acquisition, talent retention, training, reward and recognition;

<figure><img src="image_83.png" /></figure>

* **Machine** (mechanical, electrical, electronics);

<figure><img src="image_84.png" /></figure>

* **Material** (steel, paint);

<figure><img src="image_85.png" /></figure>

* **Method** : process innovation;

<figure><img src="image_86.png" /></figure>

* **Money** : (optimal fund allocation, project management, resource allocation, resource distribution).

Dr. Rodrigues and Prof. Parakash are presenting on 'staff-resources' for logistics security. The sources of EVs innovation may be R&D units of automobiles manufacturing companies, mechanical, electrical and electronics departments of engineering institutes, technology management of management institutes and collaborative networks. Creativity is the underlying process for EV technology innovation which can promote new ideas through shared vision, intellectual
abilities, thinking style, knowledge, personality, motivation, commitment, confidence and group dynamics. It demands the motivation and commitment of the creative people to look at the problems in unconventional ways. Organizational creativity is closely associated with human capital management, talent acquisition and retention policy, complex and tacit knowledge management strategy, organization structure, corporate culture, routine and incentive policy.

Prof. Prakash is outlining sixth element of deep analytics i.e. staff-resources in terms of 5M – man, machine, material, method and money. ‘Man’ analyzes various aspects of human capital management of this technological innovation such as talent acquisition and retention strategy, training, payment function, compensation, reward, incentive and performance evaluation. ‘Machine’ analyzes the basic aspects of tools and automated / semi-automated / manual machines; ‘material’ analyzes planning of raw materials, equipments, semi-finished and finished goods. ‘Method’ explores various aspects of process innovation, intelligent mechanism and procedure. Finally, ‘money’ highlights optimal fund allocation for R&D, rational investment analytics, intelligent project analytics and portfolio rationalization.

It is crucial to analyze the dynamics of technological innovation in terms of sources of innovation and roles of individuals, firms, organizations, government and collaborative networks; various resources required for effective technological evolution and diffusion such as 5M i.e. man, machine, material, method and money; dominant design factors, effects of timing and mode of entry. This innovation demands the commitment of creative people. Individual inventors may contribute through their inventive and entrepreneurial traits, skills and knowledge in multiple domains and highly curious argumentative mindset. Some users or customers may innovate based on their own needs. Many firms have set up excellent R&D lab and also collaborative networks with customers, suppliers, academic institutes, competitors, government laboratories and nonprofit organizations. Many universities have defined mission and vision of research on EVs and are contributing through publication of research papers. The Governments of many developed and developing countries are also playing active roles in R&D either directly or indirectly or through collaboration networks and start-ups (e.g. science parks and incubators).

A complex technological innovation like EVs often needs collaborative intelligence to manage the gap between demand and supply of a specific set of capabilities, skills and resources. It is possible to control cost, speed and competencies of the technological innovation on EVs through efficient sharing mechanisms. It is rational to share the cost and risks of this new innovation through creation, storage, transfer and application of knowledge among the partners of the innovation ecosystem. There are different modes of collaboration such as strategic alliance, joint ventures, technology licensing, outsourcing and collective research organizations. Collaborative networks are other sources of innovation. Collaboration is facilitated by geographical proximity, regional technology clusters and technology spillovers. Technological spillover results from the spread of knowledge across organizational or regional boundaries; it occurs when the benefits from R&D activities of a firm spill over to other firms. But, it may be hard to control the development of product and process innovation protecting IP of
proprietary technologies. The critical success factors of collaborative networks may be right selection of innovation partners having strategic and resource fit, transparent and flexible monitoring and governance process so that the innovation partners understand their rights and obligations.

The technological innovation of EVs demands the motivation and commitment of creative people. It is not a trivial problem; need useful and novel support of creative, skilled, experienced and knowledgeable talent. Creative talent can look at the problems in unconventional ways; can generate new ideas and articulate shared vision through their intellectual abilities, knowledge, novel thinking style, personality, motivation, confidence, commitment and group dynamics. The impact of knowledge on creativity is double-edged. Lack of knowledge is a major constraint to the original contribution in a technological innovation. A creative person is expected to have confidence in own capabilities, tolerance for ambiguity, interest in solving problems and willingness to overcome obstacles by taking reasonable risks. A cooperative and collaborative environment must recognize and reward creative talent in time.

The sources of RailTech innovation may be R&D units of Railways Corporation, MIS and IT units of engineering and management institutes and collaborative networks. Creativity is the underlying process for RailTech innovation which can promote new ideas through shared vision, intellectual abilities, thinking style, knowledge, personality, motivation, commitment, confidence and group dynamics. It demands the motivation and commitment of the creative people to look at the problems in unconventional ways. Organizational creativity is closely associated with human capital management, talent acquisition and retention policy, complex and tacit knowledge management strategy, organization structure, corporate culture, routine and incentive policy.

## 7. SKILL-STYLE-SUPPORT

### *Skill-style-support Analytics*

* New skills developments for EV drivers, battery charging stations and traffic controller

* Data visualization and data analysis (Information and communication technology,

* Battery charging

* Coordination and integration in traffic control;

* knowledge of operation and best practices of vehicle manufacturing, robotics, technical, system administration, management, governance, supply chain management;

* New skills developments for train drivers and traffic controller

* Data visualization and data analysis (e.g. fault tree, FMEA, TFPG)

* Information and communication technology,

* Coordination and integration in traffic control;

* Style: Project management by objectives, shared vision and goal setting; leadership, shared vision, goal setting, intelligent communication, risk assessment and mitigation, innovation project management; energy efficient
driving style, operation management by objectives, shared vision and goal setting

* Support: proactive, reactive, preventive and breakdown maintenance of EVs system (e.g. mechanical, electrical, electronics and IT schema); proactive, reactive, preventive and breakdown maintenance of RailTech system (e.g. mechanical, electrical, electronics and IT schema;

* Audit gaps in skills, style and support through rational talent acquisition, retention and training strategies.

Finally, Dr. Prakash is discussing on 'skill-style-support' for logistics security. The workforce involved in EVs innovation are expected to develop different types of skills in technical (e.g. battery charging, solar power, hybrid vehicles), management and system administration domains such as research and development, maintenance support, knowledge management, system design, process innovation and project management. The system administrators should have leadership skill in smart thinking, communication, coordination and change management. The workforce can develop skills through effective knowledge management programmes. An effective knowledge management system supports creation, storage, sharing and application of knowledge in a transparent, collaborative and innovative way. The diffusion of EVs innovation demands the support of effective leadership style. It is really challenging for the project leaders to implement top technology innovations physically and practically. Top management should be able to tackle the complexity of system implementation with the support of efficient teams.

It is essential to develop multiple skills in new EVs system development through proper coordination among design and supply chains. The basic objectives are to maximize fit with the needs of the drivers, ensure quality assurance and control R&D cost. It is an interesting option to get the suppliers and the drivers in the development process. It is really challenging to develop a complex EV system through a sound knowledge base and problem solving capability.

What should be the right organization model for this technological innovation? A traditional functionally centered organization model may not be suitable for supporting end-to-end business processes. Such process management is more than a way to improve the performance of individual processes; it is a way to operate and manage a business. An enterprise that has institutionalized process management and aligned management systems to support is a process enterprise. It is centered on its customers, managed around its processes and is aligned around a common, customer oriented goal. The business models of EVs require the support of a process enterprise structure enabled with advanced information and communication technology. The structure should have project, design, production, supply chain management maintenance, human resource management, sales & marketing and finance cells. The structure should be governed by an executive committee comprising of CEO and directors. The process managers should be able to identify core processes in the value chain; communicate throughout the organization about these critical processes; create and deploy measures regarding end-to-end process performance and define process owners with end-to-end authority for process design, resource procurement, process monitoring for redesign and improvement.
The structure of process enterprise requires a collaborative and cooperative work culture. Top innovations need proactive, reactive and preventive support for proper technology management.

What should be the innovation model for effective diffusion of Railtech security? Is it possible to adopt K-A-B-C-D-E-T-F model? The workforce involved in RailTech innovation are expected to develop different types of skills in technical (e.g. MIS, information and communication technology), management and system administration domains such as research and development, maintenance support, knowledge management, system design, process innovation and project management. The system administrators should have leadership skill in smart thinking, communication, coordination and change management. The workforce can develop skills through effective knowledge management programmes. An effective knowledge management system supports creation, storage, sharing and application of knowledge in a transparent, collaborative and innovative way. The diffusion of RailTech innovation demands the support of effective leadership style. It is really challenging for the project leaders to implement top technology innovations physically and practically. Top management should be able to tackle the complexity of system implementation with the support of efficient teams.

It is essential to develop multiple skills in new RailTech system development through proper coordination among design and supply chains. The basic objectives are to maximize fit with the needs of the drivers, ensure quality assurance and control R&D cost. It is an interesting option to get the suppliers and the train drivers in the development process. It is really challenging to develop a complex RailTech system through a sound knowledge base and problem solving capability.

## 8. CONCLUSION

What must be done to realize the future vision of EVs? It is essential to manage the fit among the design of EVs, energy and communication infrastructure and corporate governance; form coalition of the stakeholders; explore intelligent mechanisms and build a broad consensus around a common vision. There are critical issues such as purchasing dynamics and shared mobility. The preference of young generation has been changing. It is also essential to define a rational Govt. policy for the innovation, adoption and diffusion of EVs such as development of battery charging infrastructure, rationalization of profit margin of the manufacturers and dealers of EVs and reduction of tax. It is also necessary to stimulate the political support and consumer demand through SWOT analysis, what is at stake and what is possible, threats analysis and risk mitigation strategies. It may be interesting to launch a set of pilot projects at sufficient scale and investment to enable the integration of the key innovative solutions of electrical and hybrid vehicles. The aforesaid problem is an extremely complex issue in the world since we are not rich and blessed with adequate supply of natural resources in oil and gas sector; the situation demands proper coordination and integration among seven 'S' factors of deep analytics.

How to manage evolution of technological innovation in RailTech security and safety, specifically for long distance trains? The nature of innovation is expected to
shift after the emergence of a dominant design. The pace of improvement of RailTech performance should be monitored properly. The evolution of this innovation is influenced by intersecting trajectories of performance as demanded by the drivers and the performance supplied by RailTech system. The diffusion of innovation depends on how the new technology will spread through a population of potential adopters globally. The present work considers only a specific area of RailTech with focus on DAS and real-time fault diagnostics from the perspectives of MIS. It is really an interesting and potential research agenda. It is possible to extend this study in various ways. Is it rational to imagine a train as a block chain? There are other various issues of RailTech safety and security such as consistency in train length, fire hazards, food poisoning, health & hygiene issues, anti-collision devices, modernization of rail signaling system, smart coaches, smart grid, black box, use of CCTV/Wi-Fi/Webcam, unmanned level crossing, passengers crossing railway tracks while talking on mobile phones, derailment of trains due to cracks in tracks or poor maintenance, rushing attacks, non-availability of overbridges, footbridges and skywalks and miscellaneous flaws in mechanical, electrical and civil infrastructure. Is it possible to extend this study to the decision making problems of external and home affairs ministries in corporate governance such as human traffic and refugees control in a country? An intelligent rail budget is expected to address all these issues rationally.

## FURTHER READING

* U.Eberle and R.V. Helmolt. 2010. Sustainable transportation based on electrical vehicle concepts: a brief review. Energy & Environmental Science, 3, 680-699.

* K. Girotra and S. Netessine. 2011. The electrical vehicle renaissance: Better place Inc. Teaching case. INSEAD.

* BCG report 2010. Batteries for electric cars. Challenges, opportunities and the outlook to 2020.

* V. Robu, E.H. Gerding, S. Stein, D.C. Parkes, A. Rogers & N.R. Jennings. 2013. An online mechanism for multi-unit demand and its application to plug-in hybrid electric vehicle charging. Journal of Artificial Intelligence Research, 48, 175-230.

* H.Stromberg, P.Andersson, S. Almgren, J. Ericsson, M. Karlsson & A. Nabo. 2011. Driver interfaces for electric vehicles. In Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI '11, pp. 177-184, New York, NY, USA. ACM.

* C. Ahn, C.Li and H.Peng. 2011. Optimal decentralized charging control algorithm for electrified vehicles connected to smart grid. Journal of Power Sources, 196 (23), 10369 - 10379.

* K. Clement-Nyns, E. Haesen and J. Driesen. 2010. The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Transactions on Power Systems, 25 (1), 371-380.
* C.M. Flath, J.P. Ilg, S. Gottwalt, H. Schmeck and C. Weinhardt. 2014. Improving electric vehicle charging coordination through area pricing. Transportation Science, 48 (4), 619-634.

* K. Hayakawa, E. Gerding, S. Stein and T. Shiga. 2015. Online mechanisms for charging electric vehicles in settings with varying marginal electricity costs. In 24th International Joint Conference on Artificial Intelligence (IJCAI), pp. 2610-2616.

* M. Granovskii, I. Dincer, M.A. Rosen, J. Power Sources 159. 2006. 1186.

* C. Zamfirescu, I. Dincer, J. Power Sources 185. 2008. 459.

* C. Zamfirescu, I. Dincer, Fuel Process. Technology. 90. 2009. 729.

* C. Handley, N. Brandon, R. Vorst, J. Power Sources 106. 2002. 344.

* P. Van den Bossche. 2003. The Electric Vehicle: Raising the Standards. Ph.D. Thesis, Vrije Universiteit Brussel.

* D.L.Burns, B. McCormick and C.E. Borroni-Bird. Vehicle of Change. 2002. Scientific American 287: 64–73.

* L. Burns. 1993. Busy Bodies: Why Our Time-Obsessed Society Keeps Us Running in Place. New York: Norton.

* W.J. Mitchell. 1994. City of Bits: Space, Place, and the Infobahn. Cambridge, Mass.: MIT Press.

* W.J.Mitchell. 1999. E-topia. Cambridge, Mass.: MIT Press.

* W.J.Mitchell. 2004. Me++: The Cyborg Self and the Networked City. Cambridge, Mass.: MIT Press.

* R.L.Ackoff, J. Magidson and J.A. Herbert. 2006. Idealized Design: How to Dissolve Tomorrow's Crisis Today. Upper Saddle River, N.J.: Wharton School Publishing.

* R. Adams and T. Brewer. 2004. A Plan for 21st Century Land Transport. International Journal of Vehicle Design 35 (1/2): 137–150.

* J.H.Ausubel, C. Marchetti and P. Meyer. 1998. Toward Green Mobility: The Evolution of Transport. "European Review 6 (2) (1998): 137–156.

* G. Boyle, ed. 2004. Renewable Energy. Oxford University Press.

* G. Boyle, ed. 2007. Renewable Energy and the Grid: The Challenge of Variability. London: Earthscan Publications.

* G. Boyle, B. Everett, and J. Ramage. 2003. Energy Systems and Sustainability. Oxford University Press.

* R. Brandon. 2002. Auto Mobile: How the Car Changed Life. London: Macmillan.

* C.M.Christenson. 1997. The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail. Cambridge, Mass.: Harvard Business School Press.

* P. Droege. 2006. The Renewable City: A Comprehensive Guide to an Urban Revolution. Chichester: Wiley.

* P.Droege. 2008. Urban Energy Transition: From Fossil Fuels to Renewable Power. Oxford: Elsevier Science.

* S. Grava. 2003. Urban Transportation Systems. New York: McGraw-Hill.
* S.Henley. 2007. The Architecture of Parking. New York: Thames and Hudson.

* I.M. Hoffert, K. Caldeira, G. Benford, D. R. Criswell, C. Green, H. Herzog, A. K. Jain, H. S. Kheshgi, K. S. Lackner, J.S. Lewis, H. D. Lightfoot, W. Manheimer, J. C. Mankins, M. E. Mauel, L. J. Perkins, M. E. Schlesinger, T. Volk and T. M. L. Wigley. 2002. Advanced Technology Paths to Global Climate Stability: Energy for a Greenhouse Planet. Science 298 (5595): 981–987.

* D.A. Kirsch. 2000. The Electric Vehicle and the Burden of History. New B runswick, N.J.: Rutgers University Press.

* Ladd, Brian. 2008. Autophobia: Love and Hate in the Automobile Age. Chicago: University of Chicago Press.

* D. Mohan. 2008. Mythologies, Metros, and Future Urban Transport. TRIPP Report 08-01. New Delhi: Transportation Research and Injury Prevention Program.

* G. Mom. 2004. The Electric Vehicle: Technology and Expectations in the Automobile Age. Baltimore: Johns Hopkins University Press.

* R.A.Popa, H. Balakrishnan and A. J. Blumberg. 2009. VPriv: Protecting Privacy in Location-Based Vehicle Services. 18th USENIX Security Symposium. Montreal, August.

* M. Safdie and W. Kohn. 1998. The City After the Automobile: An Architect's Vision. Boulder, Colo.: Westview Press.

* R. Strzelecki and B. Grzegorz eds. 2008. Power Electronics in Smart Electrical Energy Networks. London: Springer.

* J. Weinert, M.Chaktan and C. Cherry. 2007. The Transition to Electric Bikes in China and Key Reasons for Rapid Growth " Transportation 34: 301 – 318.

* M.A. Weiss, J. B. Heywood, E.M.Drake, A. Schafer and F.F. AuYeung. 2000. On the Road in 2020: A Life-Cycle Analysis of New Automobile Technologies. Energy Laboratory Report MIT EL 00-003. Energy Laboratory, MIT. October.

* D. Giusto, A. Iera, G. Morabito, L. Atzori (Eds.), The Internet of Things, Springer, 2010.

* T. Albrecht. 2005. Energy-efficient train control in suburban railways: experiences gained from onboard tests of a driver assistance system. In: 1st International Seminar on Railway Operations Modelling and Analysis, Delft, Netherlands.

* T. Albrecht. 2009. The influence of anticipating train driving on the dispatching process in railway conflict situations. Netw. Spat. Econ. 9 (1), 85.

* T. Albrecht. 2013. Human factor challenges in the development of a driver advisory system for regional passenger trains. In: Rail Human Factors: Supporting Reliability, Safety and Cost Reduction, pp. 129–138.

* S.Dekker. 2007. Conflict detection and human-machine coordination. In: Hindsight, 4, January 2007. EUROCONTROL Directorate of ATM
Programme – Security, Safety and Human Factors Business Division (DAP/SSH), pp 7–9.

* DeltaRail Group. 2008. Advisory speeds for drivers for energy management and regulation. In: RSSB Interim Report Milestone 2, Issue 01 and 02.

* DeltaRail Group. 2009. Driver advisory information for energy management and regulation. In: Stage 1 Report, RSSB Research Programme, T724.

* W. Hamilton and T. Clarke. 2005. Driver performance modelling and its practical application to railway safety. Appl. Ergon. 36 (6), 661–670.

* P.G.Howlett, I.P. Milroy and P.J. Pudney. 1994. Energy-efficient train control. Control Eng. Pract. 2 (2), 193–200.

* R. Liu AND I.M. Golovitcher. 2003. Energy-efficient operation of rail vehicles. Transp.Res. A 37, 917–932.

* P. Lukaszewicz. 2008. Methods for energy efficient driving – algorithms. RailEnergy, Document ID NRG-KTH-D-2.3-005.

* I. Mitchell. 2009. The sustainable railway: use of advisory systems for energy savings. IRSE News, No.151, pp. 2–7.

* Rail Safety and Standards Board, 2008. The Rule Book. GE/RT8000, Issue 7.

* Rail Safety & Standards Board, 2008. Good Practice Guide To Train Driver Training. RS/221, Issue 1.

* Rail Safety and Standards Board, 2008. Train Movement – Staff Suitability and Fitness Requirements, GO/RT3451, Issue 01.

* S. Tschirner, S., A.W.Andersson and B. Sandblad. 2013. Designing train driver advisory systems for situation awareness. In: Rail Human Factors: Supporting Reliability, Safety and Cost Reduction. pp.150–159.

* J. Wardale. 2008. Advice on energy efficient driving. Presentation to drivers, CrossCountry Rail.

* C.Conte and A. Schöbel. 2007. Identifying dependencies among delays. In: Proceedings of 2nd International Seminar on Railway Operations Research (RailHannover 2007), Hannover.

* W.Daamen, T. Houben, R.M.P. Goverde, I.A. Hansen, V.A. Weeda. 2006. Monitoring system for reliability of rail transport chains. In: Proceedings of the 7th World Congress on Railway Research (WCRR 2006), Montreal.

* S. De Fabris, G.Longo and G. Medeossi. 2008. Automated analysis of train event recorder data to improve micro-simulation models. In: J. Allan, E. Arias, C.A. Brebbia, C. Goodman, A.F. Rumsey, G. Sciutto, G. and A. Tomii (Eds.), Computers in Railways XI. WIT Press, Southampton, 575–583.

* A. Exer. 1995. Rail traffic management. In: Bailey, C. (Ed.), European Railway Signalling. IRSE, A&C Black, London, 311–342.

* H. Flier, R. Gelashvili, R., T. Graffagnino and M. Nunkesser. 2009. Mining railway delay dependencies in large-scale real-world delay data. In: Ahuja, R.K., Möhring, R.H., Zaroliagis, C.D. (Eds.), Robust and Online Large-Scale Optimization: Models and Techniques for Transportation Systems, LNCS 5868. Springer, Berlin, 354–368.

* R.M.P. Goverde. 2005. Punctuality of railway operations and timetable stability analysis. PhD thesis, Delft University of Technology.
* R.M.P.Goverde. 2011. A delay propagation algorithm for large-scale railway traffic networks. Transportation Research, Part C 18 (3), 269-287.

* R.M.P.Goverde, W. Daamen, I.A. Hansen, I.A. 2008. Automatic identification of route conflict occurrences and their consequences. In: Allan, J., Arias, E., Brebbia, C.A., Goodman, C., Rumsey, A.F., Sciutto, G., Tomii, A. (Eds.), Computers in Railways XI. WIT Press, Southampton, pp. 473-482.

* P.Konstantinos, P. Tzieropoulos and D. Emery. 2014. Railway driver advice systems: Evaluation of methods, tools and systems. Journal of Rail Transport Planning & Management.

* M.Ullius. 2004. Verwendung von Eisenbahnbetriebsdaten für die Schwachstellenund Risikoanalyse zur Verbesserung der Angebots- und Betriebsqualität. PhD thesis, Swiss Federal Institute of Technology, Zurich.

* N.Van Oort. 2011. Service reliability and urban public transport design. PhD thesis, Delft University of Technology.

* Fault Tree Analysis, CEI/IEC 61025:2006.

* S. Padalkar, J. Sztipanovits, G. Karsai, N. Miyasaka and K. C. Okuda. 1991. Real-time fault diagnostics. IEEE Expert, vol. 6, no. 3, pp. 75-85.

* A.Misra, J.Sztipanovits, A. Underbrink, R. Carnes and B. Purves. 1992. Diagnosability of dynamical systems. In 3rd International Workshop on Principles of Diagnosis.

* A.Misra. 1994. Senior-based diagnosis of dynamical systems. Ph.D. Dissertation, Vanderbilt University.

* S.C.Ofsthun and S. Abdelwahed. 2007. Practical applications of timed failure propagation graphs for vehicle diagnosis. In Autotestcon, 2007 IEEE, 250-259.

* S. Abdelwahed, G. Karsai and G. Biswas. 2006. Notions of Diagnosability for Timed Failure Propagation Graphs. In IEEE Systems Readiness Technology Conference, AUTOTESTCON'06, CA.

* R.Alur, and T.A. Henzinger. 1993. Real-time logics: complexity and expressiveness. Information and Computation 104(1):35-77.

* B.Bittner, M.Bozzano, A. Cimatti, M.Gario and A.Griggio. 2014. Towards pareto-optimal parameter synthesis for monotonic cost functions. In Proceedings of the 14th Conference on Formal Methods in Computer-Aided Design, 23-30.

* B. Bittner, M. Bozzano, R.Cavada, A. Cimatti, M.Gario, A. Griggio, C. Mattarei, A. Micheli, and G. Zampedri. 2015. The xSAP safety analysis platform. arXiv preprint arXiv:1504.07513.

* M. Bozzano, A. Cimatti, A. Pires, D. Jones, G. Kimberly, T. Petri, R. Robinson, and S. Tonetta. 2015. Formal Design and Safety Analysis of AIR6110 Wheel Brake System. In Proc. CAV 2015, 518-535.

* M. Bozzano, A. Cimatti, M. Gario and A. Micheli. 2015. Smt-based validation of timed failure propagation graphs. In 29th AAAI Conference on Artificial Intelligence.
* M. Bozzano, A. Cimatti and f. Tapparo. 2007. Symbolic fault tree analysis for reactive systems. In *Automated Technology for Verification and Analysis*, Springer, 162–176.

* R. Cavada, A. Cimatti, M. Dorigatti, A. Griggio, A. Mariotti, A. Micheli, S. Mover, M. Roveri and S. Tonetta. 2014. The nuxmv symbolic model checker. In *Computer Aided Verification*.334–342. Springer.

* C. Roberts and R.M.Goodall. 2009. Strategies and techniques for safety and performance monitoring on railways. Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Spain.

## Quiz

* Justify EVs & HVs as a technology for humanity. What is the scope of this technology?

* What is the dominant design of the technology related to EVs / HVs?

* What are the basic elements of the system architecture of EVs / HVs?

* What do you mean by technology security of EVs / HVs? How can You verify the security intelligence?

* What are the strategic moves of technology innovation, adoption and diffusion for EVs & HVs'? What is the outcome of technology life-cycle analysis and SWOT analysis of conventional and electrical vehicles?

* How to manage resources for innovation project of EVs / HVs?

* What should be the talent management strategy? What are the skills, leadership style and support demanded by the technological innovation?

* How do You manage technology innovation project efficiently for EVs/ HVs? What should be the shared vision, common goals and communication protocols? How can you ensure a perfect fit among '7-S' elements?

* Design smart battery for EVs / HVs.

* Evaluate the scope of V2V communication and IoT technology for the security of future generation vehicles.

* Explain the technology of Railtech Security and Safety? Justify it as a technology for humanity. What is the scope of this technology?

* What is the dominant design of rail DAS?

* What are the basic elements of the system architecture rail DAS?

* How should you manage resources for railtech innovation project?

* What should be the talent management strategy? What are the skills, leadership style and support demanded by railtech innovation?

* How should You manage railtech innovation project efficiently? What should be the shared vision, common goals and communication protocols? How can you ensure a perfect fit among '7-S' elements?

* Design an optimal DAS architecture through SWOT analysis.

* Design Real-time Fault Diagnostics which can be integrated with DAS.

* What are the strategic moves of technology innovation, adoption and diffusion for Railtech Security and Safety”? What is the outcome of
technology life-cycle analysis? What do you mean by railtech security? How
do You verify the security intelligence? Please refer to following cases.

* Please study following 5 cases and suggest suitable technological solutions.

Test case 1 (Fire in metro rail) : It was an incident of fire in metro rail of the metropolitan city Sidben.

19:02 : Trip of electrical connection near Masco station, suffocating black smoke was spreading rapidly inside the tunnel of metro rail.

19:03: The information was given to the fire brigade and police. The helpless passengers were trapped inside the locked and fully packed metro rail compartments; they were traumatized; it was a horrific experience; the time lasted for 55 minutes; the passengers tried to escape from the locked compartments by breaking glass windows but could not be successful; some passengers were injured in stampede; they called metro rail helpline, the public address system did not work; a passenger broke her leg; the metro rail boards and the state governments were involved in blame game; the metro rail board set up a committee. The metro rail wrecks are in dangerous conditions; there was no proper maintenance; anything from the rail compartment falling on the rail track might cause fire.

19:05: The metro rail employees started extinguishing fire by spraying water.

19:14 : The electrical supply was disconnected in third rail.

19:20 : The emergency rescue operation started.

19:55 : The rescue operation ended.

Deep analytics raised a set of important issues on the aforesaid accident in metro rail:

- Was the disaster management team ready to control the situation? What was the composition of disaster management team: NDRF, NSG and the employees of metro rail?

- What was the response time in such type of disaster? What are the devices and equipments available: jacket, gas mask, oxygen cylinders or anything else?

- What were the fire protection strategies? Can the employees of rail stations tackle small fire incidents? Can the fire brigade tackle major fire accidents? Are there water pipes in the tunnel for extinguishing fire?

- Is it possible to save the passengers from various acts of terrorism through the efforts of the police, military and NSG?

- Did the public address system work correctly in time?

- Was the logic of emergency door opening correct: was it a fully automated or manual system? Did the doors open only at station platforms?

- Was the maintenance problem considered as the root cause of the accident?

- Was there high risk of accidents in the metro rail? Who should be punished for the irresponsibilities? What was the accountability of higher authorities? Who took the responsibilities of any damage of the passengers?

- Why did not the driver drive the train towards next station from the spot of accident? Why did the train stop in the tunnel?

- Why did the emergency brake work? Was it due to the disconnection of power supply in train protection circuit?
* Was the driver able to contact the control room?

* Did the passengers get down before the disconnection of power supply in the third rail?

* Where were the train driver and guard during the time of accident?

* Was there any metro rail helpline number; how to contact the control room?

The possible risk mitigation strategies are expected to include several critical measures such as fire protection system, smoke detectors and CCTVs in each compartment, regulatory compliance on smoking inside running train, ban on carrying of hazardous materials (e.g. bombs, fireworks, explosives, kerosene, petrol and diesel) by the passengers, fire protection from the gas oven at pantry cars, use of induction cookers instead of gas ovens, announcements and alerts in time through public address system, automated air conditioning system in the tunnel, automated control of the velocity and direction of airflow to resist spreading of smoke and fire, automated airflow control system in the compartment after sensing smoke, footpath inside the tunnel, disconnection of power supply in third rail, fire caused by antisocialists and terrorists such as explosion or nerve gas attack and readiness of fire brigade and disaster management workforce. There are other problems of sensors enabled automatic closing of the doors of metro trains. A door may remain open during running condition. On the contrary, a door may be closed during stoppage at a station. Such types of problems occur due to the malfunctioning of limit switches.

**Test case 2 (Fog and mist in winter)**: It was winter in January'2018. Smith was pursuing his Doctoral programme at a management institute. He went to Nelhi to present his paper at an International Conference. He was accompanied with his mother (50 years), wife (35 years) and son (3 years). It was a very cold night. Smith was returning to Cowrah from Nelhi by a superfast mail. The train started at 1 a.m. with a delay of 4 hours. The train arrived near Panpur and stopped. The reason was poor visibility to the train driver due to dense fog and mist. The temperature was about 2 degree centigrade. The train could not start for about 4 hours till the sunrise in the morning. It was a horrible experience to Smith and his family members. They were in non-AC compartment. The kids inside the train compartment were crying and coughing in the severe cold weather. All the passengers and the driver were helpless at that night.

**Test case 3 (Breakdown during journey)**: Smith was returning from Chennai to Cowrah by a superfast express train. The train started at 23-45. At midnight, the train suddenly halted with heavy jerk. The passengers became frightened. The driver discovered that the gas pipe was broken. He replaced the gas pipe through shearing and welding operations in the darkness. The train was delayed by 1 hour. Was it possible to predict the poor health of the gas pipe in time?

**Test case 4 (Act of terrorism)**: There were rail accidents with many casualties in country X due to various acts of terrorism. One case, the fishplates were taken out by the terrorists. There were five cases where the railway tracks were blown out with highly dangerous explosive bombs by the malicious agents. There were some
horrific incidents of robbery and loot by the dacoits at night. There were also many incidents of theft of shoes and belongings of the passengers. Can automated check-in system at various rail stations ensure security and safety of the passengers? Do the passengers need protection by the armed security force and watchdogs during journey by rail?

*Test case 5 (Healthcare problem during rail journey):* There were frequent cases of allergic disorders, itching, skin rash, heart burning, stomach upset, loose motion and food poisoning of the train passengers during long distance rail journey in summer and also cold and cough, breathing problems and fever in winter. There are high risks of attacks of infectious diseases (e.g. conjunctivitis, eye infection, skin rashes etc) from other travelers during rail travel. Should the passengers carry emergency medicines (e.g. anti-allergic tablets, fever, cold and cough, digestion problem, stomach upset) and pure drinking water as proactive and reactive approaches of healthcare? Should they avoid spicy, oily, rich and fast food during travel? Railway catering services must take care of such issues. The travelers should clean their seats and berths with disinfection germ protection cleaning agents proactively. During epidemic and pandemic disaster, railways authorities should arrange quarantined compartments of each long distance train for contact isolation of the travelers and passengers. They should also broadcast the aforesaid precautionary measures to the travelers and passengers through intelligent broadcast communication channels.
SESSION 8: TECHNOLOGIES for HUMANITY -
FINANCIAL SECURITY & PROOF OF WORKS

Abstract:

Event : Technology for humanity and global security summit

Venue: Financial security hall, Technology park : Sanada

Time Schedule : 2 p.m. – 5 p.m. , 17.8.2020

Agents : Representatives of various global organizations (Global nations, global financial organization, global economic forum), Technology management experts from science and technology forums, financial engineers, scientists, representatives and ministers from the departments of finance of developed, developing and underdeveloped countries, CEOs of financial corporations, business development consultants, representatives from NGOs.

Topic of discussion and key focus areas: financial security, fault attacks, proof of works, block chain, mobile commerce.

Keynote speakers: Prof. S. Ramaswamy, Prof. Daniel Parker, Prof. G. Nissim, Prof. Harry Goldwasser, Prof. M. Sandholm, Dr. Richardson, Dr. Nakamoto, Dr. Shi, Dr. Sarapova

1. SCOPE

Scope Analytics

Agents: Scientists, system analysts, financial analysts;

Moves : Critical success factors analysis, Requirements management;

Security parameters: Define a set of sustainable development goals for financial security.

• Banking

* Financial services (postal, mutual fund, bond)

* Tax

* Insurance (general, life, health)

* Retirement planning

* Stock and derivatives trading

Dr. Parker is analyzing financial security from the perspectives of technology, business and government. The emerging technology is analyzed through '7-S' model : Scope, System, Structure, Security, Strategy, Staff-resources and Skill-Style-Support.. The technology is applicable to various interesting areas such as anti-money laundering, insurance, stock trading, portfolio management, crypto currencies, banking and financial services. The system for the technology has been analyzed in terms of business analytics, big data analytics, data visualization and performance scorecard. Structure is focused on computing, data, networking and application schema of financial system. Security explores various aspects of fairness, correctness, authentication, authorization, correct identification, privacy and audit of financial data. The strategy of innovation, adoption and diffusion debates whether the technology is an evolution or
revolution through SWOT analysis and technology life-cycle analysis. The technology is at emergence phase of S-Curve. It has been evolving with advancement of digital technologies such as web, cloud computing, internet, mobile communication, tablets, laptops and desktop computers and intelligent software (e.g. analytics, data visualization). On the other side, the technology is facing several challenges today in terms of regulatory compliance and creation of new job opportunities. The technology demands the availability of skilled resources such as business analysts, big data analysts, digital system administrators and support staff.

## 2. SYSTEM

### System Analytics

**Agents**: system analysts, business analysts, scientists;

**Moves**: requirements engineering, system design, coding, prototype testing, installation, testing, commissioning

**Emerging technologies**: Innovate a set of emerging digital technologies to ensure financial security such as anti-money laundering, fraud detection, proof of works, secure multi-party computation and financial analytics;

Prof. G. Nissim and Prof. Goldwasser are outlining the system associated with the technology of financial security. Financial analytics is an intelligent, complex, hybrid, multi-phased and multi-dimensional data analysis system. The basic steps of computation are data sourcing, data filtering / preprocessing, data ensembling, data analysis and knowledge discovery from data. The authorized data analysts select an optimal set of input variables, features and dimensions (e.g. scope, system, structure, security, strategy, staff-resources, skill-style-support) correctly being free from malicious attacks (e.g. false data injection, shilling); input data is sourced through authenticated channels accordingly. The sourced data is filtered, preprocessed (e.g. bagging, boosting, cross validation) and ensembled. It is rational to adopt an optimal mix of quantitative (e.g. regression, prediction, sequence, association, classification and clustering algorithms) and qualitative methods for multi-dimensional analysis. The analysts define intelligent training and testing strategies in terms of selection of correct soft computing tools, network architecture – no. of layers and nodes; training algorithm, learning rate, no. of training rounds, cross validation and stopping criteria. The hidden knowledge is discovered from data in terms of business intelligence. The analysts audit fairness and correctness of computation and also reliability, consistency, rationality, transparency and accountability of the analytics.

Financial analytics can process precisely targeted, complex and fast queries on large data sets of real-time and near real-time systems. Business analytics follows a systematic, streamlined and structured process that can extract, organize and analyze large amounts of data in a form being acceptable, useful and beneficial for an entity. It is basically a specific type of distributed computing across a number of server or nodes to speed up the analysis process. Generally, shallow analytics use the concept of means, standard deviation, variance, probability, proportions, pie charts,
bar charts and tabs to analyze small data set. Business analytics analyze large data sets based on the concepts of data visualization, descriptive and prescriptive statistics, predictive modeling, machine learning, multilevel modeling, data reduction, multivariate analysis, regression analysis, logistic regression analysis, text analysis and data wrangling. Deep analytics is often coupled with business intelligence applications which perform query based search on large data, analyze, extract information from data sets hosted on a complex and distributed architecture and convert that information into specialized data visualization outcome such as reports, charts and graphs. Big data refers to large data being generated continuously in the form of unstructured, semi-structured and structured data produced by social network to scientific computing applications. The dataset may range from a few hundred gigabytes to terabytes beyond the capacity of existing data management tools that can capture, store, manage and analyze. Big data is characterized by volume, velocity, variety, variability, complexity and low value density. Capital market firms use big data technologies to mitigate risks for fraud mitigation, ondemand enterprise management, regulation, trading analysis and data tagging.

## 3. STRUCTURE

### *Structure Analytics*

*Agents:* system analysts, business analysts;

*Moves:* Design and configure

* Organization structure

* Technology forums

* National level: Government (E-governance model), NGOs, research organizations;

* International level: strategic alliance among global organizations (nations, heath, child, peace);

* System architecture (topology, modules, nodes, connectivity, layers);

* Emerging technologies: Innovate a set of emerging technologies as per the goals of financial security;

* Level 1: digital technology, information technology, computer science, electrical, electronics, telecommunication, civil;

* Level 2: Identify fundamental building blocks of information technology:

* computing

* data (data warehouses, big data analytics, performance scorecard)

* networking (web connectivity)

* security schema

* application schema

Prof. Sandholm is analyzing the structure i.e. the backbone of a system associated with a specific technological innovation on financial security. What are the basic elements of the system architecture associated with FINTECH? It has five critical
viewpoints: computing, data, networking, application and security schema. The topology of the system architecture may be analyzed in terms of nodes, connectivity, type of connections, layers, interfaces between layers and organization of layers.

## 4. SECURITY

### Security Analytics

Verification mechanism: audit security intelligence through intelligent surveillance technologies.

* **multi-party corruption:** do surveillance through security council of global organization, police, army, detectives, journalists;

* **access control:** verify authentication, authorization, correct identification, privacy, audit confidentiality, data integrity and non-repudiation;

* **financial security policy:** verify rationality, fairness, correctness, transparency, accountability, trust and commitment;

* **system performance:** verify reliability, consistency, scalability, resiliency, liveness, deadlock freeness, reachability, synchronization, safety;

* **malicious attacks:** verify the risk of Sybil, false data injection, shilling: push and pull, denial of service (DoS), fault injection attack;

* **web security:** session hijack, phishing, hacking, cross site request forgery, cross site script, broken authentication, improper error handling;

call threat analytics and assess risks of emerging financial technologies:

* what is corrupted or compromised (agents, computing schema, communication schema, data schema, application schema)? detect type of threat.

* time: what occurred? what is occuring? what will occur? assess probability of occurrence and impact.

* insights: how and why did it occur? do cause-effect analysis on performance, sensitivity, trends, exception and alerts.

* recommend: what is the next best action?

* predict: what is the best or worst that can happen?

**Output:** security intelligence

Prof. Ramaswamy and Dr. Nakamoto are analyzing the security of emerging banking, postal and financial services. The security intelligence of financial system is a multi-dimensional parameter which should be verified at various levels. The regulatory clauses should be defined and audited by a group of authorized agents correctly and rationally. It is crucial to verify and evaluate various rules and regulatory clauses for financial security in terms of fairness, correctness, rationality, transparency, accountability, commitment and trust. It is essential to evaluate the performance of the system in terms of reliability, consistency, and stability. The performance of the system is expected to be consistent and reliable. Liveness ensures that under certain conditions an event will ultimately occur. Deadlock freeness indicates that the system should never be in a state in which no progress is possible. The system should be protected from various types of internal and external
malicious attacks such as false data injection, Sybil, shilling and denial of service (DoS) attack. The auditors must assess the threats of such types of malicious attacks by adversaries. It is also important to assess the risk of multi-party corruptions on the financial security technologies in terms of agents, policy, procedure and protocol. An efficient knowledge based system is expected to monitor the gaps and violations in regulatory compliance in real-time and diagnose any fault just like supervisory control and data acquisition system. Dr. Ramaswamy is analyzing the following case during today's age of technology transition.

Case: Fault attacks and proof of works in banking, postal and financial services

“

Feedback - Technical problems and operational issues at your post offices and banks at Talkia, Cowrah 811106

To: Mrs. Darithi Thatcher, Minister of Finance, Govt. of Vindia,

CC: Mr. Tony Lucas, The Manager, State bank of Vindia, Talkia; Mr. Rineet Sandey, Director General, Post, Govt. of Vindia, Ms. Smita Kugar, Member, Technology; Dr. Charles Lamb, Member Operation, Mr. Madpita Rosui, Chairperson, Postal Service Board

The Postmaster, Post Office – Talkia, Cowrah – 811106;

Respected Sir / Madam,

We, the residents of Talkia have been experiencing various type of quality of service and operational problems such as delay in service and long queues due to technical errors and malfunctioning of Internet connectivity, printer, monitor and other hardware devices at local banks and post offices in Talkia, Vindia. There is shortage of postcards and envelops at post offices. We are getting misleading SMS message from your post offices with incorrect account balances, sometimes we are not getting important SMS messages related to high valued transactions; there are cases of messages drop, denial of service and fault attacks. This is basically the problem of your information and communication system.

We would also like to elaborate and clarify the problems related to your operations, information and communication system in terms of workplace safety, fairness, correctness, transparency, Sybil attack and accountability through following illustrations.

a) Leakage of water from the roof of your post office damaging important documents and systems, particularly during rain

b) During printing of savings passbook, the last digit of new MIS (Monthly Income Savings) account number is not getting printed due to the problem of your information system. The printing is not transparent and is very hedgy; there is also alignment problem. It is basically the fault related to your proof of works.

c) The color of new MIS passbook's front and back cover is not matching with your logo (red and yellow); it is now (red + light pink); the name of 'printed by' of the passbook at last page is also missing.

d) Nominee field is blank in the passbooks though the information was given during opening of accounts by the applicants.

Request your intervention for necessary corrective actions on immediate basis for our relief.
Regards.
L.R. Bukla, MLA, Talkia

“

Financial systems may face various types of threats from both external and internal environments but it should be vigilant and protected through a set of security policies. An emerging financial technology demands the support of security architecture so that the associated system can continuously assess and mitigate risks intelligently. It is required to verify the efficiency of access control of financial system in terms of authentication, authorization, correct identification, privacy, audit, confidentiality, non-repudiation and data integrity. For any secure service, the system should ask the identity and authentication of one or more agents involved in a transaction. The agents of the same trust zone may skip authentication but it is essential for all sensitive communication across different trust boundaries. After the identification and authentication, the system should address the issue of authorization. The system should be configured in such a way that an unauthorized agent cannot perform any task out of scope. The system should ask the credentials of the requester; validate the credentials and authorize the agents to perform a specific task as per agreed protocol. Each agent should be assigned an explicit set of access rights according to role. Privacy is another important issue; an agent can view only the information according to authorized access rights. A protocol preserves privacy if no agent learns anything more than its output; the only information that should be disclosed about other agent's inputs is what can be derived from the output itself. The agents must commit the confidentiality of data exchange associated with private communication.

”

## 5. STRATEGY

### *Strategy Analytics*

Agents: System analysts, business analysts, technology management consultants;

Strategic moves :

* Call deep analytics ‘7-S’ model; explore how to ensure a perfect fit among 7-S elements : scope, system, structure, security, strategy, staff-resources, skill-style-support;

* Define a set of financial security goals and emerging technologies accordingly.

* Do SWOT analysis: strength, weakness, opportunities and threats of financial security technologies.

* Fair and rational business model innovation

* Who are the customers?

* What should be the offering of products and services?

* What do the customers value?

* What is the rational revenue stream?

* How to deliver values to the customers at rational cost?
* Do technology life-cycle analysis on 'S' curve : presently at emergence phase of 'S' curve.

* Explore technology innovation-adoption-diffusion strategy.

* Explore innovation model and knowledge management system for creation, storage, sharing and application of knowledge.

* Adopt '4E' approach for the implementation of financial security globally : Envision, Explore, Exercise and Extend.

Dr. Ramaswamy is analyzing the strategy for the innovation, adoption and diffusion of financial security technologies. This element can be analyzed from different dimensions such as R&D policy, learning curve, SWOT analysis, technology life-cycle analysis and knowledge management strategy. It is essential to analyze strength, weakness, opportunities, threats, technological trajectories, technology diffusion and dominant design of top innovations related to financial security today. In the first decade of the 21 Century, financial security technologies were applicable to banking and trading services. Today, the scope of the same has been getting explored in miscellaneous emerging financial services such as financial literacy and education, retail banking, investment to crypto-currencies. In 2008, global investment into Fintech sector was around $900 million and was $27 billion in 2017. Fintech is a flourishing market due to various factors :

(a) Internet and mobile revolution : smartphone penetration surge from 53% in 2014 to 64% in 2018; the number of internet users has been rising from 481 million in December'2017 to 500 million in June'2018; the number of cheap and affordable smartphone users have been rising from 199 million in 2015 to 378 million in 2018.

(b) almost 40% of the population do not have bank account and over 80% of money transactions are made in cash.

(c) Fintech funding was 21.47% of total startup funding rounds across every market during the first quarter of 2018 and raised over $251 million of funding. Fintech is a startup trend is upsetting the structured format of traditional financial companies such as banks, mobile payments, money transfers and asset management. The worth of Fintech market is expected to double in 2020.

SWOT Analysis : It is rational to evaluate strength, weakness, opportunities and threats of this innovation. There may be major and minor strengths and weaknesses. Strength indicates positive aspects, benefits and advantages of a strategic option. Weakness indicates negative aspects, limitations and disadvantages of that option. Opportunities indicate the areas of growth of market and industries from the perspective of profit. Threats are the risks or challenges posed by an unfavorable trend causing deterioration of profit or revenue and losses. Financial technologies touche technology, business, and government. Financial technologies have been evolving over the past several decades, there are various types of technological challenges such as risk of cyber attacks, money laundering, information security and privacy and trust in digital currency. Government should play the role of regulator protecting markets and consumers and promoting the innovation, adoption and diffusion of financial technologies globally.
<figure><img src="image_87.png" /><figcaption>Figure 8.1 : SWOT Analysis</figcaption></figure>

<figure><img src="image_88.png" /><figcaption>Figure 8.2 : Technology life-cycle analysis of Financial security technologies</figcaption></figure>

Financial security technology life-cycle analysis: Deep analytics evaluate and explores top technological innovations in terms of technology life-cycle, technology trajectory, S-curve, technology diffusion and dominant design. No element in this universe exists eternally. Similarly, each technology emerges, grows to some level of maturity and then declines and eventually expires, At present, most of financial security innovations exists at emerging or birth phase of S-curve as per perception; some are growing at fast pace.

## 6. STAFF-RESOURCES

### Staff-resources Analytics

do estimation, planning, capacity utilization, allocation and distribution of '5M' resources.

* Man (human capital management [scientists, business analysts, system analysts, project managers], talent acquisition, talent retention, training, reward and recognition);

* Machine (computer hardware, software, internet);

* Material (Financial data);

* Method (process innovation);

* Money (optimal fund allocation, project management, resource allocation, resource distribution).
Dr. Richardson is analyzing the need of staff-resources in terms of 5M (man, machine, material, method and money) for the innovation of financial technologies. In this connection, human capital should be considered as a strategic asset and a sustainable resource of technological innovation. Talent management demands the skills of human resources for the innovation of financial security technologies. ‘Man’ explores various aspects of human capital management of technological innovations such as talent acquisition and retention strategy, training and performance evaluation. ‘Machine’ indicates the basic need of computer hardware, software and internet. ‘Machine’ analyzes various aspects of digital technologies for banking, insurance and financial services such as hardware, software and networking schema. ‘Material’ analyzes planning of essential resources such as mobile phones, tablets, desktops and laptops to be used for financial operation. ‘Method’ explores various aspects of process innovation, intelligent mechanism and procedure associated with financial corporations. Finally, ‘money’ highlights optimal fund allocation for innovation, adoption and diffusion of financial technologies.

It is crucial to analyze the dynamics of this technological innovation in terms of sources of innovation and roles of individuals and organizations, government and collaborative networks; various types of resources are required for effective technological evolution and diffusion. Innovation demands the commitment of creative people. Creativity is the underlying process for technological innovation which promotes new ideas through intellectual abilities, thinking style, knowledge, personality, motivation and commitment. Innovation demands the motivation and commitment of creative people. The innovation needs useful and novel support of creative, skilled, experienced and knowledgeable talent. Creative talent can look at the problems in unconventional ways; can generate new ideas and articulate shared vision through their intellectual abilities, knowledge, novel thinking style, personality, motivation, confidence, commitment and group dynamics. A cooperative and collaborative environment must recognize and reward creative talent in time. Organizational creativity is associated with several critical factors such as human capital management, talent acquisition and retention policy, complex and tacit knowledge management strategy and organization structure.

# 7. SKILL-STYLE-SUPPORT

## Skill-style-support Analytics

* **Skill:** technical, financial management, digital technology, information system, legal, corporate governance;

* **Style:** leadership, shared vision, goal setting, intelligent communication protocol, risk assessment and mitigation;

* **Support:** proactive, preventive and reactive support.

Dr. Shi and Dr. Sarapova are exploring skill-style-support necessary for the innovation of financial security technologies. The workforce involved in innovation are expected to develop different types of skills in banking, financial services, regulatory compliance and digital governance. They should be creative in
development of financial security technologies. The diffusion of new technological
innovation depends on the skills and capabilities of the human talent. The workforce
are expected to develop different types of skills in financial analysis, business
analytics, big data analytics, digital technologies (e.g. information and
communication technologies, web technology, computer science), system
administration and system maintenance. The workforce should develop skills
through effective knowledge management programmes. An effective knowledge
management system supports creation, storage, sharing and application of
knowledge in a transparent, collaborative and innovative way.

The system administrators must have leadership skills in smart thinking, communication, coordination and change management. The diffusion of financial security demands the support of smart leadership style; The style is basically the quality of leadership; great leaders must have passion, motivation, commitment, support, coordination, integration and excellent communication skill. What should be the innovation model for effective diffusion of financial security technologies? Is it possible to adopt K-A-B-C-D-E-T-F model? The diffusion of innovation needs the support of great leadership style. They are expected to have leadership skills in smart thinking, communication, coordination and change management. Top management must tackle the complexity of system implementation by developing a dedicated project team, a right mix of committed resources and talents like technical and business experts.

FURTHER READING

* P.Harker. 2017. Fintech: revolution or evolution. April, USA.

* N. S. Sachchidanand Singh. Big data analytics. International Conference on Communication, Information & Computing Technology (ICCICT), pp. 1-4, 2012.

* R. Verma and S. R. Mani, Use of big data technologies in capital markets. April, 2014.

* M. Peat. Big data in finance. In Finance: The Magazine for Finsia Members, vol. 127, no. 1, pp. 34–36, 2013.

* H. Markowitz, Portfolio selection. The Journal of Finance, vol. 7, no. 1, pp. 77–91, 1952.

* H.-H. Chen. Stock selection using data envelopment analysis. Industrial Management and Data Systems, vol. 108, no. 9, pp. 1255–1268, 2008.

* N. Koochakzadeh, A heuristic stock portfolio optimization approach based on data mining techniques. PhD thesis, Department of Computer Science, University of Calgary, March 2013.

* T. Ware. Adaptive statistical evaluation tools for equity ranking models. Canadian Industrial Problem Solving Workshops (Calgary, Canada, May 15-19, 2005),2005.

* V.Sharma, B.Pandey and V.Kumar. Importance of big data in financial fraud detection. International Journal Automation and Logistics. Volume 2, No. 4, 2016.

* Chintalapati, S.S. and Jyotsna, G. 2013. Application of data mining techniques for financial accounting fraud detection scheme. International