Search query: Application of social factory in machine factories

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abstract: Nowadays, the critical dimensions (CD) of semiconductor manufacturing processes are gradually declining, and various materials, reactants, or machine equipment is becoming more and more difficult. Therefore, the green supply chain management (GSCM) of smart high tech factories has also become the target of competition for cutting edge companies, because of this management. The more mature the mechanism of high production speed, lower operating costs and the higher the quality. In addition, corporate social responsibility (CSR) governance has also become an important investment evaluation item for semiconductor factories in recent years. As long as internationally renowned manufacturers start CSR, it is a pity that there are already some manufacturers. The establishment of a supply chain management system also synchronously promotes CSR, but there are few manufacturers that integrate CSR and green supply chain management, let alone the application of an intelligent management platform (IMP) This study builds a GSM platform (GSMP) based on the aforementioned problems, and the cluster analysis of K means is used to optimize the management platform to optimize the overall performance of the management platform. Through the simulation results of this study, it is found that the performance of this management platform is far superior to the current mechanism of using human operation, whether it is the speed of emergency processing or the final management operation performance, which shows that this study industry contribution.
author_names: Hsiao-Chuan Wang

Kuo-Chi Chang

Kai-Chun Chu

Yuh-Chung Lin

Tsui-Lien Hsu

Fu-Hsiang Chang
title: Study of Green Supply Chain Management Platform Performance for Intelligent Hightech Factory Based on Advanced CSR Governance
venue: 2020 15th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT)
year: 2020

abstract: Nowadays, due to ever decreasing product life cycles and high external pressure to cut costs, the ramp up of production lines must be significantly shortened and simplified. This is only possible if a forecast of the impact of modification within an existing production environment is available, helping during decision making of production methods. This type of predictions will have a direct impact on the cost effective production process, maintaining concerns regarding the environmental and social impacts. These are the ideas behind the project Innovative Reuse of modular knowledge Based devices and technologies for Old, Renewed and New factories (ReBorn) The present paper describes the System Assessment Tool, a software application developed in ReBorn, which is used for assessing the sustainability of highly adaptive production systems through the use of Reliability, Life Cycle Cost and Life Cycle Assessment metrics, in the form of a equipment labeling scheme. This labeling scheme consists on classifying the dependability of industrial equipment, based on the collected metrics. To that intent, several simulation processes were performed, in order to assess the suitability of the labeling system, in comparison to the Quality metric that is highly preferable among the industrial partners. The simulation results show that a two layer equipment labeling system is the most suitable approach to be used for classification. Keywords Smart factories; Equipment label; Life cycle assessment; Re use; Production systems.
author_names: Susana Aguiar

Rui Pinto

Joao C P Reis

Gil Goncalves
title: A Life cycle Equipment Labeling System for Machine Classification in Smart Factories
year: 2017

abstract: The possibility of having the Internet accessibility everywhere and every time and the progressive reduction of the dimensions of hardware equipment, in addition to the advent of novel ICT paradigms realizing the Internet of Things and Smart City visions, made available the design and realization of an innovative type of applications where data play a key role, promising to achieve a non negligible impact on the society and business processes. The right management, control, and distribution of data are extremely critical process in our society, so that it is always referred as information society. This is boosted by the proliferation of the social networks within our lives, and the upcoming of the Internet of Things within our homes and factories. Having such a vast volume of data to be processed in order to take decisions and/or infer knowledge poses a number of challenges. The issue of properly storing such data leads to the advent of different approaches than the traditional relational databases, such as the NoSQL products. The issue of supporting the scalability of the overall communication infrastructure and to balance the load of the consequent processing cause the shift from proprietary and centralized computing solutions to more distributed and multi tenant approaches, such as the cloud computing and its variants. However, the processing is still centralized in the core of the infrastructures, and we only see a change on how such a core is concretely realized. Decentralizing the processing by distributing in along all the layers of the infrastructure is extremely needed, so as to handle the data scale and also the demand for timely responses. To let this to happen, novel processing approaches and schemes are emerging. We are witnessing an interplay among soft computing and machine learning for big data mining and processing.1 A non marginal aspect is also on how to extract what is hidden in such a large amount of information, which is mostly non structured or suitable for all the possible applications using them. It is extremely important to learn in an unsupervised manner from these data so as to extract the knowledge that they contain. A lot of historical data is currently available and storing all of them will surely reach the storage limits of the system. It is important to infer knowledge and keep only it instead of all the data. Last, policy makers need supports when taking decisions and such data can be of pivotal importance for them. In smart cities and factories certain decisions may be needed to be taken in an automatic manner so as to fast react to certain sudden and potentially critical events. It is extremely crucial understand how artificial intelligence can be used in such contexts and their possible limits. The aim of this special issue was to bring together experiences between soft computing and machine learning to the key issue of data mining to promote a convergence and cross fertilization among them. To this Special Issue, 14 articles, as they present the most interesting research studies within the subject matter of this special issue. The paper "Personalized content recommendation scheme based on trust in online social networks" by Jaesoo Yoo et al2 presents a solution for content recommendation leveraging on trust estimations obtained from data extracted by online social network services. The behavior of a given person over the social network and the relationship with other users are collected and used to infer his/her trustworthiness by aggregating its experience and reputations. The proposed approach has been implemented and assessed, showing a reduced error than the main related works within the current literature. The paper "Deep Learning for EEG Data Analytics: A Survey" by Jason J. Jung et al3 analyses the current literature semi supervised learning using EEG data analytics and points our pros and cons of these solutions to analyze EEG data. Such a study presents what are the main applications of EEG data analysis and provides concrete examples. The study ends by highlighting the future possible research directions in the context of analyzing EEG data, mainly by leveraging the recent findings deep learning, and possible additional applications. The paper "Extraction of abstracted sensory data to reduce the execution time of context aware services in wearable computing environments" by Jongsun Choi et al4 presents a reducing method for the execution time of context aware services. The proposed method gives a better performance without searching every single keyword element among the triplet sets. Also, they suggested method to the context aware workflow middleware and demonstrated that the proposed method improves the processing time by at least 30% compared with the linear search by enhancing the comparison module in the middleware. The paper "Opcode sequence analysis of Android malware by a convolutional neural network" by Wenbo Shi et al5 discusses a new malware detection system for mobile devices running the Android Operative System. The proposed scheme uses an optimized deep convolutional neural network to infer knowledge from opcode sequences. The learning solution has been used in a supervised manner by training it multiple times using as input the raw opcode sequences obtained from a selected decompiled known Android malware. This allows to have a proper learning of the
author_names: Chang Choi

Florin Pop

Jun Huang
title: Novel data mining paradigms based on soft computing and machine learning in the current and upcoming information society revolution
venue: Concurr. Comput. Pract. Exp.
year: 2020

abstract: Machine learning (ML) is nowadays embedded in several computing devices, consumer electronics, and cyber physical systems. Smart sensors are deployed everywhere, in applications such as wearables and perceptual computing devices, and intelligent algorithms power our connected world. These devices collect and aggregate volumes of data, and in doing so, they augment our society in multiple ways; from healthcare, to social networks, to consumer electronics, and many more. To process these immense volumes of data, ML is emerging as the de facto analysis tool that powers several aspects of our Big Data society. Applications spanning from infrastructure (smart cities, intelligent transportation systems, smart grids, and to name a few) to social networks and content delivery, to e commerce and smart factories, and emerging concepts such as self driving cars and autonomous robots, are powered by ML technologies. These emerging systems require real time inference and decision support; such scenarios, therefore, may use customized hardware accelerators, are typically bound by limited resources, and are restricted to limited connectivity and bandwidth. Thus, near sensor computation and near sensor intelligence have started emerging as necessities to continue supporting the paradigm shift of our connected world. The need for real time intelligent data analytics (especially in the era of Big Data) for decision support near the data acquisition points emphasizes the need for revolutionizing the way we design, build, test, and verify processors, accelerators, and systems that facilitate ML (and deep learning, in particular) implemented in resource constrained environments for use at the edge and the fog. As such, traditional von Neumann architectures are no longer sufficient and suitable, primarily because of limitations in both performance and energy efficiency caused especially by large amounts of data movement. Furthermore, due to the connected nature of such systems, security and reliability are also critically important. Robustness, therefore, in the form of reliability and operational capability in the presence of faults, whether malicious or accidental, is a critical need for such systems. Moreover, the operating nature of these systems relies on input data that is characterized by the four "V's" velocity (speed of data generation) variability (variable forms and types) veracity (unreliable and unpredictable) and volume (i.e. large amounts of data) Thus, the robustness of such systems needs to consider this issue as well. Furthermore, robustness in terms of security, and in terms of reliability to hardware and software faults, in particular, besides their importance when it comes to safety critical applications, is also a positive factor in building trustworthiness toward these disrupting technologies from our society. To achieve this envisioned robustness, we need to refocus on problems such as design, verification, architecture, scheduling and allocation policies, optimization, and many more, for determining the most efficient, secure, and reliable way of implementing these novel applications within a robust, resource constrained system, which may or may not be connected. This special issue, therefore, addresses a key aspect of fog and edge based ML algorithms; robustness (as defined above) under resource constraint scenarios. The special issue presents emerging works in how we design robust systems, both in terms of reliability as well as fault tolerance and security, while operating with a limited number of resources, and possibly in the presence of harsh environments that may eliminate connectivity and pollute the input data.
author_names: Theocharis Theocharides

Muhammad Akmal Shafique

Jungwook Choi

Onur Mutlu
title: Guest Editorial: Robust Resource Constrained Systems for Machine Learning
venue: IEEE Des. Test
year: 2020

abstract: Abstract The factories of the future will make use of actuators, sensors and cyber physical systems (CPS) to provide an environment in which human beings, machines, and resources will communicate as in a social network. In such a network, communication between various "objects" relay the current state of the physical world. Business decisions are made using the information and it is therefore critical that this information is accurate and in real time. Information flow is a key enabler of such future factories. Industrial engineers, as designers and improvement agents of such factories of the future, will need to develop better skills in various aspects of data analytics and information communication technologies. This paper describes the development and implementation of a low cost RFID track and trace system (by students) for application in a Learning Factory for teaching undergraduate industrial engineering students key concepts related to Industry 4.0 and "smart factories" The benefit of this system is not only a demonstrator to be used in the Learning Factory, but also can be used to teach students in a "learning by doing" fashion critical skills related to real time tracking in a manufacturing environment. The system also demonstrates potential low cost implementation of such technologies in SME's.
author_names: Louis Louw

Mark Walker
title: Design and implementation of a low cost RFID track and trace system in a learning factory
year: 2018

abstract: The services and applications of the internet of things (IoT) support various areas such as smart home, smart car, smart factory and so on. Previously, different platforms were used for each application and service. Recently, several groups of companies have been trying to integrate and standardise the IoT platforms and two major platforms among them are oneM2M and the Open Interconnect Consortium (OIC) oneM2M and OIC standards enable connected devices to communicate each other regardless of their manufacturers and operating systems. Also, there are attempts to interwork two open platforms. However, security interoperability has been less focused in the interworking. In this paper, we present the comparison of security related methods between oneM2M and OIC standard specifications for the security interoperability. We compare the basic concepts and terminology of two standards by clarifying the same spelled basic terms with different meanings and different terms having similar concepts. Moreover, we analyse security related methods in OIC and oneM2M in a comparative manner. By comparative analysis, we help users understand the interworking and interoperability between oneM2M and OIC standard specifications.
author_names: Dong In Kim

Hyun-Gu Lee

Alavalapati Goutham Reddy

Jaehwan John Lee

Ji Sun Shin
title: Comparison of security related methods in Open Interconnect Consortium and one machine to machine for security interoperability
year: 2019

abstract: The production processes of dry method and wet method for producing machine made sand are introduced. The feasibility of machine made sand as a kind of mixture to produce the pile concrete are tested and studied, and the mix proportion of the concrete is optimized. In addition, combining with the factory production status, the application situation of machine made sand in the PHC pile engineering are discussed, and it points out that the using the machine made sand to produce PHC piles has good economic and social benefits.
author_names: Xu Jian-zha
title: Quality Discussion of Machine made Sand and Its Application for PHC Piles
year: 2014

abstract: Since the 1970's, we have been developing technologies in industrial machine vision including intelligent character recognition to produce automated machines for factories, banks, and post offices. In my talk, I will first introduce a brief history of the industrial machine vision and the intelligent character recognition technologies in applied fields. Then I will discuss the strategies and developments in the fields. I will end my talk by touching on my personal experience as a researcher. As time has passed, our major research objective has changed from factory automation to office automation and from office automation to social/security automation. Consequently, the demand for machines that are capable of dealing with more complex and difficult automation tasks has grown. To meet these demands, a machine often requires multiple recognition procedures. This normally leads to the final recognition rate worsening as the number of procedures increases. Therefore, we propose a multiple hypothesis strategy and an information integration strategy to improve the final recognition rate so that it can meet the machine's specification. Then, it will be shown that the rejection ability of the recognition procedures has an important role in using these strategies effectively. The usefulness of these strategies has been proved through the successful development of mail sorting machines, document readers, and intelligent automated teller machines. Those developments are also described in detail in my talk. Finally, I would like to touch on my experiences as an industrial researcher, which can be summed up by the phrases "practicality first, novelty second," "development first, research second," and "non vision first, vision second."
author_names: Hiroshi Sako
title: Recognition Strategies in Machine Vision Applications
venue: International Machine Vision and Image Processing Conference (IMVIP 2007)
year: 2007

abstract: With increasing automation, the work in manufacturing industry is getting knowledge intensive: workers need to solve complex problems to keep systems running. The knowledge needed in these situations often cannot be found in ready made manuals but it is tacit knowledge possessed by co workers. This paper presents a user study of a knowledge sharing platform. The platform integrates production information and social media features, thus connecting discussions to specific machine states or error situations. The platform was evaluated with factory workers in a workshop. The results show that integration of production information and social media features such as messaging is valuable, as it proactively provides practical solutions to the problem at hand. The results also show that the use of social media features in industrial environment requires changes in organizational policies, processes and culture. The findings of this study can be considered when developing social media applications for industrial use.
author_names: Susanna Aromaa

Maria Tsourma

Stylianos Zikos

Eija Kaasinen

Mariia Kreposna

Anastasios Drosou

Dimitrios Tzovaras
title: User Experience of a Social Media Based Knowledge Sharing System in Industry Work
venue: IHIET
year: 2019

abstract: Factories of the Future will be distinguished by intelligent machines, automation, human factors integration and knowledge management. Modelling and simulation is recognised as a key enabling technology essential to economic, social and environmental sustainability of future manufacturing systems. This talk will explore the history, recent achievements and directions in modelling and simulation for 21st century factories and supply chains. A systems science approach is employed, from stakeholder engagement through participative modelling to self tuning and self assembling simulations. Our contributions lower the cost of the application of modelling and simulation to manufacturing processes, enabling real time planning, dynamic risk analysis, dashboards and 3D visualisation. This realisation of the virtual factory integrates human factors and decisions into the core technology platform. The implications to future manufacturing enterprises are explored through a series of case studies from aerospace, mining and small and medium manufacturing enterprises.
author_names: Saeid Nahavandi
title: Factories of the future a system of systems engineering perspective
year: 2013