bluuebunny/crossref_metadata_2025 huggingface dataset:
Connected Heterogenous Multi-Processing Architecture for Digitalization of Freight Railway Transport Applications
Authors: Losada Markos , Adin Iñigo , Perez Alejandro , Ramírez Roberto Carlos , Mendizabal Jaizki
Published: March 2022

The digitalisation of freight rail is an essential improvement to create modern functions that offer a cost-effective, attractive service and improved operational opportunities to operators. These modern functions need intelligence, detection, actuation and communications. For this, generally, it is possible to process raw data in the Edge and send meaningful data over a communication link. However, the power supply is not granted in a freight wagon and so low power strategies need to be adopted. This paper presents the implementation and testing of a wireless connected heterogeneous multiprocessing architecture. From the power consumption point of view, this system has been stressed by means of a generic FFT function to evaluate the different on-board computing devices that have been decided. From the communication point of view, the LPWAN LoRa technology has been tested and validated on robustness and coverage. Thanks to the heterogeneous nature of this architecture and its configurability, it allows us to propose the most suitable computing ressources, data analysis and communication strategy in terms of efficiency and performance for the functions that this wagon on board unit needs to host and support. With this approach, operation data are reported to the centralised freight driver assistant system.
Hierarchical Collaborated Fireworks Algorithm
Authors: Li Yifeng , Tan Ying
Published: March 2022

The fireworks algorithm (FWA) has achieved significant global optimization ability by organizing multiple simultaneous local searches. By dynamically decomposing the target problem and handling each one with a sub-population, it has presented distinct property and applicability compared with traditional evolutionary algorithms. In this paper, we extend the theoretical model of fireworks algorithm based on search space partition to obtain a hierarchical collaboration model. It maintains both multiple local fireworks for local exploitation and one global firework for overall population distribution control. The implemented hierarchical collaborated fireworks algorithm is able to combine the advantages of both classic evolutionary algorithms and fireworks algorithms. Several experiments are provided for in-depth analysis and discussion on the proposed algorithm. The effectiveness of proposed strategy is demonstrated on the benchmark test suite from CEC 2020. Experimental results validate that the hierarchical collaborated fireworks algorithm outperforms former fireworks algorithms significantly and achieves similar results compared with state-of-the-art evolutionary algorithms.
Improving Characteristics of LUT-Based Three-Block Mealy FSMs’ Circuits
Authors: Barkalov Alexander , Titarenko Larysa , Krzywicki Kazimierz , Saburova Svetlana
Published: March 2022

One of the very important problems connected with FPGA-based design is reducing the hardware amount in implemented circuits. In this paper, we discuss the implementation of Mealy finite state machines (FSMs) by circuits consisting of look-up tables (LUT). A method is proposed to reduce the LUT count of three-block circuits of Mealy FSMs. The method is based on finding a partition of set of internal states by classes of compatible states. To reduce the LUT count, we propose a special kind of state code, named complex state codes. The complex codes include two parts. The first part includes the binary codes of a state as the element of some partition class. The second part consists of the code of corresponding partition class. Using complex state codes allows obtaining FPGA-based FSM circuits with exactly four logic blocks. If some conditions hold, then any FSM function from the first and second blocks is implemented by a single LUT. The third level is represented as a network of multiplexers. These multiplexers generate either additional variable encoding collections of outputs or input memory functions. The fourth level generates FSM outputs. An example of synthesis and experimental results is shown and discussed. The experiments prove that the proposed approach allows reducing hardware compared to such methods as auto and one-hot of Vivado, JEDI. Further, the proposed approach produces circuits with fewer LUTs than for three-level Mealy FSMs based on joint use of several methods of structural decomposition. The experiments show that our approach allows reducing the LUT counts on average from 11 to 77 percent. As the complexity of an FSM increases, the gain from the application of the proposed method grows; the same is true for both the FSM performance and power consumption.
Beyond Perceptual Thresholds and Personal Preference: Towards Novel Research Questions and Methodologies of Quality of Experience Studies on Light Field Visualization
Authors: Kara Peter A. , Tamboli Roopak R. , Shafiee Edris , Martini Maria G. , Simon Aniko , Guindy Mary
Published: March 2022

With the upcoming emergence of the IEEE P3333.1.4 standard, the first ever standardization document on the subjective quality assessment of light field visualization, we are approaching a major milestone of light field Quality of Experience (QoE). The research efforts leading up to this point have answered the fundamental questions regarding perceptual thresholds and personal preference, and best practices regarding research methodologies have been formed. However, in order to introduce successful and efficient light field systems and services, research questions beyond perceptual thresholds and personal preference must be addressed, along with the associated methodologies. In this paper, we propose a comprehensive set of novel research questions and methodologies regarding light field QoE. Our work also provides a detailed discussion of related factors, particularly those that extend to test participants and thus may affect the results of subjective studies.
A Smart and Robust Automatic Inspection of Printed Labels Using an Image Hashing Technique
Authors: Khan Mehshan Ahmed , Ahmed Fawad , Khan Muhammad Danial , Ahmad Jawad , Kumar Harish , Pitropakis Nikolaos
Published: March 2022

This work is focused on the development of a smart and automatic inspection system for printed labels. This is a challenging problem to solve since the collected labels are typically subjected to a variety of geometric and non-geometric distortions. Even though these distortions do not affect the content of a label, they have a substantial impact on the pixel value of the label image. Second, the faulty area may be extremely small as compared to the overall size of the labelling system. A further necessity is the ability to locate and isolate faults. To overcome this issue, a robust image hashing approach for the detection of erroneous labels has been developed. Image hashing techniques are generally used in image authentication, social event detection and image copy detection. Most of the image hashing methods are computationally extensive and also misjudge the images processed through the geometric transformation. In this paper, we present a novel idea to detect the faults in labels by incorporating image hashing along with the traditional computer vision algorithms to reduce the processing time. It is possible to apply Speeded Up Robust Features (SURF) to acquire alignment parameters so that the scheme is resistant to geometric and other distortions. The statistical mean is employed to generate the hash value. Even though this feature is quite simple, it has been found to be extremely effective in terms of computing complexity and the precision with which faults are detected, as proven by the experimental findings. Experimental results show that the proposed technique achieved an accuracy of 90.12%.
Learning Control for Flexible Manipulators with Varying Loads: A Composite Method with Robust Adaptive Dynamic Programming and Robust Sliding Mode Control
Authors: Xu Yiming , Wang Xiao , Wang Li , Wang Kai , Ma Lei
Published: March 2022

This paper focuses on the learning-based motion control for flexible manipulators with varying loads via the singularly perturbed technique. Considering the two-timescale feature of the flexible manipulator, system dynamics are decomposed into fast and slow subsystems, and corresponding sub-controllers are designed with robust adaptive dynamic programming (RADP) and robust sliding mode control (RSMC) methods, respectively. In the proposed composite control framework, an RADP-based sub-controller is developed to realize the trajectory tracking and alleviate the parametric uncertainty utilizing rotating angles in the slow timescale, while an RSMC sub-controller is introduced to improve the vibration suppression in the fast timescale. Finally, the stability of the closed-loop system is guaranteed, and simulations are carried out to show the effectiveness of the proposed control algorithm.
Cheyne-Stokes Respiration Perception via Machine Learning Algorithms
Authors: Yuan Chang , Khan Muhammad Bilal , Yang Xiaodong , Shah Fiaz Hussain , Abbasi Qammer Hussain
Published: March 2022

With the development of science and technology, transparent, non-invasive general computing is gradually applied to disease diagnosis and medical detection. Universal software radio peripherals (USRP) enable non-contact awareness based on radio frequency signals. Cheyne-Stokes respiration has been reported as a common symptom in patients with heart failure. Compared with the disadvantages of traditional detection equipment, a microwave sensing method based on channel state information (CSI) is proposed to qualitatively detect the normal breathing and Cheyne-Stokes breathing of patients with heart failure in a non-contact manner. Firstly, USRP is used to collect subjects’ respiratory signals in real time. Then the CSI waveform is filtered, smoothed and normalized, and the relevant features are defined and extracted from the signal. Finally, the machine learning classification algorithm is used to establish a recognition model to detect the Cheyne-Stokes respiration of patients with heart failure. The results show that the system accuracy of support vector machine (SVM) is 97%, which can assist medical workers to identify Cheyne-Stokes respiration symptoms of patients with heart failure.
A Two-Stage Approach to Locate and Size PV Sources in Distribution Networks for Annual Grid Operative Costs Minimization
Authors: Montoya Oscar Danilo , Rivas-Trujillo Edwin , Hernández Jesus C.
Published: March 2022

This paper contributes with a new two-stage optimization methodology to solve the problem of the optimal placement and sizing of solar photovoltaic (PV) generation units in medium-voltage distribution networks. The optimization problem is formulated with a mixed-integer nonlinear programming (MINLP) model, where it combines binary variables regarding the nodes where the PV generators will be located and continuous variables associated with the power flow solution. To solve the MINLP model a decoupled methodology is used where the binary problem is firstly solved with mixed-integer quadratic approximation; and once the nodes where the PV sources will be located are known, the dimensioning problem of the PV generators is secondly solved through an interior point method applied to the classical multi-period power flow formulation. Numerical results in the IEEE 33-bus and IEEE 85-bus systems demonstrate that the proposed approach improves the current literature results reached with combinatorial methods such as the Chu and Beasley genetic algorithm, the vortex search algorithm, the Newton-metaheuristic algorithm as well as the exact solution of the MINLP model with the GAMS software and the BONMIN solver. All the numerical simulations are implemented in the MATLAB programming environment and the convex equivalent models are solved with the CVX tool.
Cyber Secure Framework for Smart Agriculture: Robust and Tamper-Resistant Authentication Scheme for IoT Devices
Authors: Alyahya Saleh , Khan Waseem Ullah , Ahmed Salman , Marwat Safdar Nawaz Khan , Habib Shabana
Published: March 2022

Internet of Things (IoT) as refers to a network of devices that have the ability to connect, collect and exchange data with other devices over the Internet. IoT is a revolutionary technology that have tremendous applications in numerous fields of engineering and sciences such as logistics, healthcare, traffic, oil and gas industries and agriculture. In agriculture field, the farmer still used conventional agriculture methods resulting in low crop and fruit yields. The integration of IoT in conventional agriculture methods has led to significant developments in agriculture field. Different sensors and IoT devices are providing services to automate agriculture precision and to monitor crop conditions. These IoT devices are deployed in agriculture environment to increase yields production by making smart farming decisions and to collect data regarding crops temperature, humidity and irrigation systems. However, the integration of IoT and smart communication technologies in agriculture environment introduces cyber security attacks and vulnerabilities. Such cyber attacks have the capability to adversely affect the countries’ economies that are heavily reliant on agriculture. On the other hand, these IoT devices are resource constrained having limited memory and power capabilities and cannot be secured using conventional cyber security protocols. Therefore, designing robust and efficient secure framework for smart agriculture are required. In this paper, a Cyber Secured Framework for Smart Agriculture (CSFSA) is proposed. The proposed CSFSA presents a robust and tamper resistant authentication scheme for IoT devices using Constrained Application Protocol (CoAP) to ensure the data integrity and authenticity. The proposed CSFSA is demonstrated in Contiki NG simulation tool and greatly reduces packet size, communication overhead and power consumption. The performance of proposed CSFSA is computationally efficient and is resilient against various cyber security attacks i.e., replay attacks, Denial of Service (DoS) attacks, resource exhaustion.
A Model for the Definition, Prioritization and Optimization of Indicators
Authors: Almeida Frederico Viana , Canedo Edna Dias , de Oliveira Albuquerque Robson , de Deus Flávio Elias Gomes , Sandoval Orozco Ana Lucila , García Villalba Luis Javier
Published: March 2022

Context: The definition and prioritization of indicators is now a common reality and an integral part of the evolution of the strategic, technical and business processes of any organization, whether public or private. Purpose: This paper proposes a new model regarding the definition and prioritization of indicators. Herein, we also investigate the definition and prioritization models currently adopted by academia and industry, and analyze the context of the proposed strategies against the traditional view of indicator definition currently adopted. In addition, we conducted a survey with organizations that had well-defined indicator management processes, seeking to identify customer expectations with a new indicator management model proposed by this work. Method: To gather evidence, we defined a methodology that relates the literature review and an exploratory case study with the application of an experiment. Driven by a set of research questions, this methodology comprised four main phases: planning, literature review, experiment execution and documentation of results. The method used is supported by some techniques, such as design thinking, design sprint and the Cynefin framework. Results: The analysis of the results was carried out in two different ways: Through the verification of the achievement of specific objectives and through a questionnaire applied to assess the degrees of perception of all employees who participated in the work. Regarding the specific objectives, it is clear that most of the objectives were achieved. Regarding the applied questionnaire, it is clear that, although the collaborators did not have adequate knowledge regarding the conceptual and practical aspects of some approaches used in the proposed model, there was a general perception that the model, in fact, supported top management for decision making. For professionals, the proposed model has a restricted scope; that is, it does not serve all types of organizations. Conclusion: The model proposed in this work proved to be effective, considering that the indicators were defined, prioritized and optimized, with a focus on the user experience. As future work, we intend to expand the scope of the model’s performance, evaluating business indicators alongside IT indicators.
An Experimental Analysis of Various Machine Learning Algorithms for Hand Gesture Recognition
Authors: Bhushan Shashi , Alshehri Mohammed , Keshta Ismail , Chakraverti Ashish Kumar , Rajpurohit Jitendra , Abugabah Ahed
Published: March 2022

Nowadays, hand gestures have become a booming area for researchers to work on. In communication, hand gestures play an important role so that humans can communicate through this. So, for accurate communication, it is necessary to capture the real meaning behind any hand gesture so that an appropriate response can be sent back. The correct prediction of gestures is a priority for meaningful communication, which will also enhance human–computer interactions. So, there are several techniques, classifiers, and methods available to improve this gesture recognition. In this research, analysis was conducted on some of the most popular classification techniques such as Naïve Bayes, K-Nearest Neighbor (KNN), random forest, XGBoost, Support vector classifier (SVC), logistic regression, Stochastic Gradient Descent Classifier (SGDC), and Convolution Neural Networks (CNN). By performing an analysis and comparative study on classifiers for gesture recognition, we found that the sign language MNIST dataset and random forest outperform traditional machine-learning classifiers, such as SVC, SGDC, KNN, Naïve Bayes, XG Boost, and logistic regression, predicting more accurate results. Still, the best results were obtained by the CNN algorithm.
The Experimental Evaluation of Energy Efficiency and Carbonic Emission Rates for All Stable Loads of Larger-Scale (+600 MW) Coal-Fired Power Generation Units in Vietnam
Authors: Hoang Anh T. , Nguyen Tuyen V. , Nguyen Bao T.
Published: March 2022

Performance guarantees and tests of thermal power plants are usually carried out at 100% rate output capacity. However, fossil-fired power plants have decreased full load hours, affecting energy efficiency, and are subjected to frequent load changes caused by variable renewable electricity and potential grid stability. Therefore, this study is conducted to calculate and draw the characteristic curves for all stable loads of coal-fired power units including the 60%, 75%, and 100% rate output. The study focuses on the corrected plant net heat rates—gross unit outputs, net standard coal consumption rates—throttle steam pressures, and corrected plant net efficiencies—carbonic emission rates. In addition, the experimental investigation for energy efficiency and carbonic emission of the latest larger-scale (+600 MW) coal-fired power generation units in Vietnam are also implemented using a performance guarantee calculation software called “PG_Cal” version 0.0, which is based on a mass and energy balance method by MATLAB programing language. From the results of this study, it is suggested that the performance guarantees and tests of new coal-fired units should be carried out at different stable loads, including minimum load. Vietnam should apply the ultra-supercritical technology for new units in order to increase their efficiency and decrease carbon dioxide emissions.
Experimental Analysis of Flow Boiling in Horizontal Annulus—The Effect of Heat Flux on Bubble Size Distributions
Authors: Zajec Boštjan , Cizelj Leon , Končar Boštjan
Published: March 2022

Subcooled flow boiling was experimentally investigated in a horizontal annulus with a temperature-controlled boiling surface and transparent outer pipe facilitating visualization. Boiling occurs on a copper tube with a diameter of 12 mm in an annulus with a 2 mm gap. Refrigerant R245fa is used as a working fluid. The focus of this study is to explore the effect of heat flux variation on the boiling flow patterns at approximately constant inlet flow conditions of the working fluid (fixed mass flux and inlet fluid temperature). Subcooled flow boiling is recorded by a high-speed camera, images are analyzed by a neural network to determine the bubble size distributions and their variation with the heat flux. The experimental setup being a part of the laboratory THELMA (Thermal Hydraulics experimental Laboratory for Multiphase Applications) at the Reactor Engineering Division of Jožef Stefan Institute, analysis methods and measurement results are presented and discussed.
Experiment and Numerical Analysis of Thermal Performance of a Billboard External Receiver
Authors: Fang Jiabin , Qaisrani Mumtaz A. , Tu Nan , Wei Jinjia , Wan Zhenjie , Jin Yabin , Khalid Muhammad , Ahmed Naveed
Published: March 2022

The receiver serves as a critical component in tower-type concentrated solar power plants. Responsible for light-heat conversion, the efficiency of the receiver significantly affects the overall performance of the power plant. In the current study, the thermal performance of external receivers was investigated. An experiment was set up similarly using the solar simulator to experimentally investigate the heat losses of a billboard receiver. A billboard-type external receiver was designed, fabricated, and experimented with. A solar simulator having seven xenon lamps characteristics similar to the sunlight spectrum was used to obtain heat flux at the surface of the receiver. Convection losses in the head-on wind direction were evaluated, along with the radiation losses. The thermal efficiency of the billboard receiver calculated experimentally was around 83.9%. Numerical simulations were also carried out to compare the results against the experimental data. A variation of ±5% observed between both results validate the model proposed in the current study.
Pro-Ecological Activities and Shaping the Competitive Advantage of Small and Medium-Sized Enterprises in the Aspect of Sustainable Energy Management
Authors: Klemke-Pitek Monika , Majchrzak Magdalena
Published: March 2022

Small and medium-sized enterprises around the world play a key role in building economic growth and maintaining environmental sustainability. This strategic role in the economy depends on the possessed competitive advantage, which will increasingly depend on the ecological behavior of SMEs. Therefore, it is justified to undertake research the main goal of which is to identify the pro-ecological activities of SMEs conducive to achieving a competitive advantage. The original empirical research was conducted in 2021 on a sample of 452 small and medium-sized enterprises in Poland. The research was based on a questionnaire. The research allowed for the assessment of sustainable energy management by assessing the awareness of entrepreneurs, assessing the pro-ecological activities undertaken, and assessing the impact of pro-ecological activities on shaping the competitive advantages of enterprises in 2015–2020. The surveyed entrepreneurs considered the most important components of the company’s competitive advantage and sustainable energy management to be those investments aimed at achieving a high input–result ratio in a short time. In order to review the pro-ecological activities in Poland against the background of international data, other research results in this area are presented. The cited data confirm the results of the conducted extensive survey research. In the case of many countries and SMEs, environmental awareness is relatively low. Where it occurs, it is not translated into real activities in the field of sustainable energy management in the absence of economic efficiency.
European Energy Regulatory, Socioeconomic, and Organizational Aspects: An Analysis of Barriers Related to Data-Driven Services across Electricity Sectors
Authors: Psara Kyriaki , Papadimitriou Christina , Efstratiadi Marily , Tsakanikas Sotiris , Papadopoulos Panos , Tobin Paul
Published: March 2022

Data-driven services offer a major shift away from traditional monitoring and control approaches that have been applied exclusively over the transmission and distribution networks. These services assist the electricity value chain stakeholders to enhance their data reach and improve their internal intelligence on electricity-related optimization functions. However, the penetration of data-driven services within the energy sector poses challenges across the regulatory, socioeconomic, and organizational (RSEO) domains that are specific to such business models. The present review examines the existence and importance of various obstacles across these domains regarding innovative energy services, new business models, data exchanges, and other actors’ synergies across the electricity data value chain. This research is centered around the European landscape, with a particular focus on the five demonstration countries (Greece, Spain, Austria, Finland, and Croatia) of the SYNERGY consortium. A state-of-the-art analysis on the regulatory, socioeconomic, and organizational aspects related to innovative energy services (IESs) revealed a plethora of such potential obstacles that could affect, in various degrees, the realization of such services, both at a prototyping and a market replication level. More specifically, 13 barriers were identified in the regulatory domain, 19 barriers were identified in the socioeconomic domain, and 16 barriers were identified in the organizational domain. Then, a comprehensive, survey-based data gathering exercise was designed, formulated, and conducted at a national level as well as at a stakeholder type level. To ensure that our analysis encompassed business-wide perspectives and was validated from the whole electricity data value chain, we utilized a trilevel analysis (i.e., partner, stakeholder type, demo country) to formulate qualitative interviews with business experts from each stakeholder type (namely TSOs, DSOs, aggregators/ESCOs, facility managers/urban planners, and RES Operators). By combining the quantitative data with the qualitative interviews, further recommendations on identifying and facilitating ways to overcome the identified barriers are provided. For the regulatory domain, it is recommended to treat nationally missing regulations by conforming to the provisions of the relevant EU directives, as well as to provide a flexibility-related regulation. For the socioeconomic domain, recommendations were made to increase consumer awareness and thus alleviate the three more impactful barriers identified in this domain. All organizational barriers can be alleviated by taking complex big-data-related issues away from the hands of the organizations and offering them data-as-a-service mechanisms that safeguard data confidentiality and increase data quality.
Investigating the Efficiencies of Fusion Algorithms for Accurate Equipment Monitoring and Prognostics
Authors: Akpudo Ugochukwu Ejike , Hur Jang-Wook
Published: March 2022

Recent findings suggest the need for optimal condition monitoring due to increasing counter-productive issues ranging from threats to life, malware, and hardware failures. Several prognostic schemes have been reported across many disciplines; however, the issues of sensor data discrepancy emanating from varying loading and operating conditions of cyber-physical system (CPS) components still remain a challenging factor. Nonetheless, a significant part of these prognostic schemes comprises a sensor/feature fusion module for comprehensive health indicator (HI) construction. This study investigates the prowess of unsupervised fusion algorithms for constructing optimal HI construction on two publicly available datasets—a simulated turbofan engine degradation experiment and an actual production plant condition monitoring dataset. The fusion efficiencies of the algorithms were evaluated using standard metrics for prognostic parameter assessments. The results show that the autoencoder is more reliable for real-life applications, including cases with uniform degradation patterns and the more complex scenarios with irregular degradation paths in the sensor measurements/features, and is expected to direct continued research for improved multi-sensor-based prognostics and health management of industrial equipment.
Selected Mathematical Optimization Methods for Solving Problems of Engineering Practice
Authors: Vagaská Alena , Gombár Miroslav , Straka Ľuboslav
Published: March 2022

Engineering optimization is the subject of interest for many scientific research teams on a global scale; it is a part of today’s mathematical modelling and control of processes and systems. The attention in this article is focused on optimization modelling of technological processes of surface treatment. To date, a multitude of articles are devoted to the applications of mathematical optimization methods to control technological processes, but the situation is different for surface treatment processes, especially for anodizing. We perceive their lack more, so this state has stimulated our interest, and the article contributes to filling the gap in scientific research in this area. The article deals with the application of non-linear programming (NLP) methods to optimise the process of anodic oxidation of aluminium using MATLAB toolboxes. The implementation of optimization methods is illustrated by solving a specific problem from engineering practice. The novelty of this article lies in the selection of effective approaches to the statement of optimal process conditions for anodizing. To solve this complex problem, a solving strategy based on the design of experiments approach (for five factors), exploratory data analysis, confirmatory analysis, and optimization modelling is proposed. The original results have been obtained through the experiment (performed by using the DOE approach), statistical analysis, and optimization procedure. The main contribution of this study is the developed mathematical-statistical computational (MSC) model predicting the thickness of the resulting aluminium anodic oxide layer (AOL). Based on the MSC model, the main goal has been achieved—the statement of optimal values of factors acting during the anodizing process to achieve the thickness of the protective layer required by clients, namely, for 5, 7, 10, and 15 [μm].
Application of Far-Gather Seismic Attributes in Suppressing the Interference of Coal Beds in Reservoir Prediction
Authors: Mao Yunxin , Yan Chunjing , Zhang Ruoyu , Li Yangsen , Lou Min , Dou Luxing , Zhou Xinrui , Wang Xixin
Published: March 2022

The sandstone reservoir of the Pinghu Formation in the Xihu Depression, East China Sea is characterized by great depth, small thickness, radical facies change and a widespread coal bed. It is difficult to describe the reservoir accurately using conventional reservoir prediction methods. In order to analyze the influence of coal-bearing strata on the prediction of the mid-low thickness sandstone reservoir, the seismic response of different sandstone–coal stratigraphic assemblages was simulated by seismic forward modeling. The modeling result indicates that the post-stack seismic response is dominated by coal bed, whereas the response of sandstone can hardly be recognized. In contrast, the difference between the pre-stack AVO (amplitude versus offset) response characteristics of coal seams and gas-bearing sandstones has been clarified based on the statistics pertaining to AVO characteristics of drilled wells. Therefore, we propose a method to reduce the interference of coal beds in sandstone reservoir prediction using far-gather seismic information. This method has significantly improved the accuracy of reservoir prediction and sand description in sand–coal coupled environments and has been applied successfully in the exploration of coal-rich strata in the Pingbei slope belt, Xihu Depression.
Effects of Colloid Milling and Hot-Water Pretreatment on Physical Properties and Enzymatic Digestibility of Oak Wood
Authors: Kim Tae Hoon , Park Seung Hyeon , Le Tin Diep Trung , Kim Tae Hyun , Oh Kyeong Keun
Published: March 2022

A two-step process using colloid milling (CM) and hot water (HW) treatment was evaluated for its ability to improve xylose recovery and the enzymatic digestibility of oak wood. In the first step, CM pretreatment was applied at a milling (feeding) speed of 100 mL/min with four different milling times (3, 6, 9, and 12 h), and the enzymatic digestibility and physical properties of each substrate were measured. In the second-step, the HW pretreatment was applied to enhance the enzymatic digestibility and xylan recovery at various reaction severities (Log R0) from 2.07 to 4.43 using 12 h colloid-milled (CM-treated) oak wood. Compared with untreated oak wood, CM not only significantly disrupted the structure of oak wood but also increased its Brunauer–Emmett–Teller surface area (42-fold) and pore volume (28-fold). The crystallinity of two-step-treated oak wood was decreased to 34.8, while the enzymatic digestibility of 12 h CM-treated oak wood was increased to 58.1% at enzyme loading of 30 filter paper units (FPU)/g glucan for 96 h. After HW treatment of CM-treated oak wood at Log R0 = 3.83, 80.7% of xylan recovery yield and 91.1% of enzymatic digestibility (with 15 FPU/g glucan at 96 h) was obtained, which was 84.2% higher than the enzymatic digestibility of untreated oak wood (6.9%).
Optimal Scheduling of Battery Energy Storage Systems and Demand Response for Distribution Systems with High Penetration of Renewable Energy Sources
Authors: Zhang Xuehan , Son Yongju , Choi Sungyun
Published: March 2022

The penetration of renewable energy sources (RESs) is increasing in modern power systems. However, the uncertainties of RESs pose challenges to distribution system operations, such as RES curtailment. Demand response (DR) and battery energy storage systems (BESSs) are flexible countermeasures for distribution-system operators. In this context, this study proposes an optimization model that considers DR and BESSs and develops a simulation analysis platform representing a medium-sized distribution system with high penetration of RESs. First, BESSs and DR were employed to minimize the total expenses of the distribution system operation, where the BESS model excluding binary state variables was adopted. Second, a simulation platform based on a modified IEEE 123 bus system was developed via MATLAB/Simulink for day-ahead scheduling analysis of the distribution system with a high penetration of RESs. The simulation results indicate the positive effects of DR implementation, BESS deployment, and permission for electricity sales to the upper utility on decreasing RES curtailment and distribution system operation costs. Noticeably, the RES curtailments became zero with the permission of bidirectional power flow. In addition, the adopted BESS model excluding binary variables was also validated. Finally, the effectiveness of the developed simulation analysis platform for day-ahead scheduling was demonstrated.
An Improved Control Strategy of PMSM Drive System with Integrated Bidirectional DC/DC
Authors: Liu Dongliang , Guo Xinhua , Lei Youjian , Wang Rongkun , Chen Ruipei , Chen Fenyu , Li Zhongshen
Published: March 2022

With the development of power electronic technology, adding bidirectional DC/DC to the DC side of the motor controller has become an effective way to broaden the speed of motors. However, the motor drive system integrated with bidirectional DC/DC increases the switching loss and current harmonics of the inverter. Therefore, in this paper, a method based on fixed space vector pulse width modulation (SVPWM) modulation index and voltage instruction compensation are proposed, In this method, by considering the back electromotive force of the motor and the security margin of the weak magnetic region, the output voltage command amplitude of the current loop and the limit value of the inverter output is judged, and the optimal inverter input voltage is obtained, which effectively reduces the switching loss of the inverter, broadens the high efficient area of motor operation, and improves the efficiency of the controller. At the same time, the standardized benchmark composed of motor parameters is used to per-nit the permanent magnet synchronous motor (PMSM) mathematical model, which is helpful for the derivation of the formula and dramatically simplifies the calculation amount of the microcontroller. Moreover, the simulation and experimental results prove the effectiveness and feasibility of the algorithm.
The Effect of Hydrogen Peroxide on NH3/O2 Counterflow Diffusion Flames
Authors: Yang Wenkai , Al Khateeb Ashraf N. , Kyritsis Dimitrios C.
Published: March 2022

The impact of adding H2O2 in the fuel stream on the structure of non-premixed opposed-flow NH3/O2 flames was investigated numerically using a verified computational tool and validated mechanism. The results illustrate the dual role of the added H2O2 within the fuel jet. A small amount of H2O2 within the NH3 stream acted as a fuel additive that enhanced the reaction rate via reducing the kinetic time scale. However, a novel flame structure appeared when the H2O2 mole fraction within the fuel stream increased to χH2O2 > 16%. Unlike the pure NH3/O2 flame, a premixed reaction zone was discerned on the fuel side, in which H2O2 reacts with NH3 and played the role of an oxidizer. Then, the remaining NH3 that survived premixed combustion continues reacting with O2 and forms a non-premixed flame. As a result of this structure, it was shown that the well-established conclusion of “near-equilibrium” non-premixed flame analysis in which the strain on the flame is determined by the momentum fluxes of the counter-flowing streams does not hold for the flames that were studied in this paper. It was also shown that when H2O2 acted as an oxidizer, it produced substantial amounts of HO2, which allowed for low-temperature formation of NO2 through the reaction of NO with HO2.
Waveguide Concentrator Photovoltaic with Spectral Splitting for Dual Land Use
Authors: Vu Hoang , Tien Tran Quoc , Park Jongbin , Cho Meeryoung , Vu Ngoc Hai , Shin Seoyong
Published: March 2022

This research presents a highly transparent concentrator photovoltaic system with solar spectral splitting for dual land use applications. The system includes a freeform lens array and a planar waveguide. Sunlight is first concentrated by the lens array and then reaches a flat waveguide. The dichroic mirror with coated prisms is located at each focused area at the bottom of a planar waveguide to split the sunlight spectrum into two spectral bands. The red and blue light, in which photosynthesis occurs at its maximum, passes through the dichroic mirror and is used for agriculture. The remaining spectrums are reflected at the dichroic mirror with coated prisms and collected by the long solar cell attached at one end of the planar waveguide by total internal reflection. Meanwhile, most of the diffused sunlight is transmitted through the system to the ground for agriculture. The system was designed using the commercial optic simulation software LightTools™ (Synopsys Inc., Mountain View, CA, USA). The results show that the proposed system with 200× concentration can achieve optical efficiency above 82.1% for the transmission of blue and red light, 94.5% for diffused sunlight, which is used for agricultural, and 81.5% optical efficiency for planar waveguides used for power generation. This system is suitable for both high Direct Normal Irradiance (DNI) and low DNI areas to provide light for agriculture and electricity generation at the same time on the same land with high efficiency.
Advancement of the Monitoring System for Arch Support Geometry and Loads
Authors: Woszczyński Mariusz , Rogala-Rojek Joanna , Stankiewicz Krzysztof
Published: March 2022

As part of the RFCS project, which aimed to improve transport safety in mines, ITG KOMAG proposed a system for monitoring loads and geometric of arch support. The system’s function is to control safety, mainly during suspended monorail runs. This paper presents a hardware model and a measurement method based on the use of vibrating wire strain gauges and draw-wire sensors. The challenge was to properly adapt the vibrating wire strain gauge operation to the requirements of the ATEX directive on the safe use of electrical equipment in underground mines. The signal transducer algorithm and potential mounting locations for the proposed sensors were discussed. The results of tests carried out using the ŁP arc support are presented, reflecting the actual behavior of the casing during loading in accordance with the test methodology proposed by the Central Mining Institute. In order to compare the results with another measurement method, film strain gauges were additionally applied. The results confirm the usefulness of the proposed method for testing in real conditions. The speed and simplicity of installation of vibrating wire strain gauges provides an advantage over the use of film strain gauges, which are very difficult to install in underground conditions.
BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones
Authors: Bou-Rabee Mohammed A. , Naz Muhammad Yasin , Albalaa Imad ED. , Sulaiman Shaharin Anwar
Published: March 2022

Recent research on solar irradiance forecasting has attracted considerable attention, as governments worldwide are displaying a keenness to harness green energy. The goal of this study is to build forecasting methods using deep learning (DL) approach to estimate daily solar irradiance in three sites in Kuwait over 12 years (2008–2020). Solar irradiance data are used to extract and understand the symmetrical hidden data pattern and correlations, which are then used to predict future solar irradiance. A DL model based on the attention mechanism applied to bidirectional long short-term memory (BiLSTM) is developed for accurate solar irradiation forecasting. The proposed model is designed for two different conditions (sunny and cloudy days) to ensure greater accuracy in different weather scenarios. Simulation results are presented which depict that the attention based BiLSTM model outperforms the other deep learning networks in the prediction analysis of solar irradiance. The attention based BiLSTM model was able to predict variations in solar irradiance over short intervals in continental climate zones (Kuwait) more efficiently with an RMSE of 4.24 and 20.95 for sunny and cloudy days, respectively.
An Extensive Review and Comparison of Modern Biomass Torrefaction Reactors vs. Biomass Pyrolysis—Part 1
Authors: Piersa Piotr , Unyay Hilal , Szufa Szymon , Lewandowska Wiktoria , Modrzewski Remigiusz , Ślężak Radosław , Ledakowicz Stanisław
Published: March 2022

Major efforts are currently being made in the research community to address the challenges of greenhouse gas emissions from fossil fuel combustion by using lignocellulosic biomass, agricultural waste, and forest residues as cleaner energy sources. However, its poor qualities, such as low energy density, high moisture content, irregular shape and size, and heterogeneity, make it impossible to utilize in its natural state. Torrefaction, a simple heat treatment method, is used frequently with natural bioresources to improve their thermal characteristics so that they may be used as energy sources in domestic power plants. The quality of the resulting torrefied solids (biochar) is determined by the heat condition settings in the absence of oxygen, and it may be enhanced by carefully selecting and altering the processing parameters. The comprehensive overview presented here should serve as a useful toolkit for farmers, combined heat and power plants, pulp and paper installations, and other industrial plants that use biomass as a substrate for biofuel production. This research focuses on torrefaction product properties, reaction mechanisms, a variety of technologies, and torrefaction reactors. It is impossible to determine which torrefaction technology is superior as each reactor has unique properties. However, some suggestions and recommendations regarding the use of torrefaction reactors are given.
The Use of Equipment for the Study of Phase Changes to Determine the Conditions of Precipitation of Inorganic Sediments in Geothermal Waters
Authors: Kuśnierczyk Jerzy , Szuflita Sławomir , Wojnicki Mirosław , Warnecki Marcin , Kremieniewski Marcin
Published: March 2022

In Poland, there are low-temperature geothermal reservoirs that can be used for various purposes in many regions of the country. Low-temperature deposits of geothermal waters are common and occur much more frequently than high-temperature deposits. They contain water with temperatures lower than 150 °C. Their temperature normally ranges from 20 to 90 °C. Achieving a state of equilibrium depends on many factors, including the kinetics of reactions between the individual components of the system, temperature, reactivity of reservoir rock, concentration of chemical components in the water and the time the water remains in contact with the rock. Therefore, this article presents the possibility of checking the conditions of precipitation of inorganic sediments in geothermal waters with the use of PVT equipment. Tests were carried out with the use of geothermal waters under given dynamic conditions (pressure, temperature and flow). This paper confirms the suitability of using the equipment for PVT (device for the study of phase changes) testing in order to determine the conditions for the precipitation of inorganic sediments in geothermal projects. Tests on the precipitation of solid sediments in geothermal waters were carried out. A result of the research is the adaptation of the equipment for PVT testing in order to determine the conditions for the precipitation of inorganic deposits in geothermal waters. As a result, different capillary blocking times were obtained depending on the measurement conditions (e.g., for P = 40 bar, T = 120 °C and q = 1 cm3/min, the blocking start time was 10.8 min). The authors found that solid sludge inhibitors, as well as other chemicals used, including paraffin inhibitors, must be periodically adjusted to prevent precipitation in geothermal waters.
Reactive Power Optimization Model for Distribution Networks Based on the Second-Order Cone and Interval Optimization
Authors: Yang Minsheng , Li Jianqi , Du Rui , Li Jianying , Sun Jian , Yuan Xiaofang , Xu Jiazhu , Huang Shifu
Published: March 2022

Traditional reactive power optimization mainly considers the constraints of active management elements and ignores the randomness and volatility of distributed energy sources, which cannot meet the actual demand. Therefore, this paper establishes a reactive power optimization model for active distribution networks, which is solved by a second-order cone relaxation method and interval optimization theory. On the one hand, the second-order cone relaxation technique transforms the non-convex optimal dynamic problem into a convex optimization model to improve the solving efficiency. On the other hand, the interval optimization strategy can solve the source–load uncertainty problem in the distribution network and obtain the interval solution of the optimization problem. Specially, we use confidence interval estimation to shorten the interval range, thereby improving the accuracy of the interval solution. The model takes the minimum economy as the objective function and considers a variety of active management elements. Finally, the modified IEEE 33 node arithmetic example verifies the feasibility and superiority of the interval optimization algorithm.