scholarly journals Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 763 ◽  
Author(s):  
Javier Villalba-Díez ◽  
Martin Molina ◽  
Joaquín Ordieres-Meré ◽  
Shengjing Sun ◽  
Daniel Schmidt ◽  
...  
Keyword(s):  
Deep Learning ◽  
Complex Networks ◽  
Industry 4.0 ◽  
Lean Management ◽  
Organizational Growth ◽  
Regular Structures ◽  
New Business ◽  
Patterns Of Behavior ◽  
Value Streams ◽  
Physical Elements

In the near future, value streams associated with Industry 4.0 will be formed by interconnected cyber–physical elements forming complex networks that generate huge amounts of data in real time. The success or failure of industry leaders interested in the continuous improvement of lean management systems in this context is determined by their ability to recognize behavioral patterns in these big data structured within non-Euclidean domains, such as these dynamic sociotechnical complex networks. We assume that artificial intelligence in general and deep learning in particular may be able to help find useful patterns of behavior in 4.0 industrial environments in the lean management of cyber–physical systems. However, although these technologies have meant a paradigm shift in the resolution of complex problems in the past, the traditional methods of deep learning, focused on image or video analysis, both with regular structures, are not able to help in this specific field. This is why this work focuses on proposing geometric deep lean learning, a mathematical methodology that describes deep-lean-learning operations such as convolution and pooling on cyber–physical Industry 4.0 graphs. Geometric deep lean learning is expected to positively support sustainable organizational growth because customers and suppliers ought to be able to reach new levels of transparency and traceability on the quality and efficiency of processes that generate new business for both, hence generating new products, services, and cooperation opportunities in a cyber–physical environment.

scholarly journals Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2841 ◽  
Author(s):  
Javier Villalba-Diez ◽  
Xiaochen Zheng ◽  
Daniel Schmidt ◽  
Martin Molina
Keyword(s):  
Deep Learning ◽  
Problem Solving ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Behavioral Pattern ◽  
Management Problem ◽  
Behavioral Patterns ◽  
Lean Management ◽  
Capture Process

Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, to discover relevant neurological characteristics of problem-solving behavioral patterns, and second, to conduct a characterization of two problem-solving behavioral patterns with the aid of deep-learning architectures. This is done by combining electroencephalographic non-invasive sensors that capture process owners’ brain activity signals and a deep-learning soft sensor that performs an accurate characterization of such signals with an accuracy rate of over 99% in the presented case-study dataset. As a result, the deep-learning characterization of lean management (LM) problem-solving behavioral patterns is expected to help Industry 4.0 leaders in their choice of adequate manufacturing systems and their related problem-solving methods in their future pursuit of strategic organizational goals.

scholarly journals Lean 4.0 - A conceptual conjunction of lean management and Industry 4.0

Procedia CIRP ◽  
2018 ◽  
Vol 72 ◽  
pp. 622-628 ◽  
Author(s):  
A. Mayr ◽  
M. Weigelt ◽  
A. Kühl ◽  
S. Grimm ◽  
A. Erll ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Lean Management

ProBot – A Procedure Chatbot for Digital Procedural Adherence

2020 ◽  
Vol 64 (1) ◽  
pp. 224-228
Author(s):  
Nilesh Ade ◽  
Noor Quddus ◽  
Trent Parker ◽  
S.Camille Peres
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Natural Language ◽  
Industry 4.0 ◽  
Natural Language Generation ◽  
Conversational Agent ◽  
Safe Distance ◽  
Process Industries ◽  
Language Generation ◽  
Plastic Pellets

One of the major implications of Industry 4.0 will be the application of digital procedures in process industries. Digital procedures are procedures that are accessed through a smart gadget such as a tablet or a phone. However, like paper-based procedures their usability is limited by their access. The issue of accessibility is magnified in tasks such as loading a hopper car with plastic pellets wherein the operators typically place the procedure at a safe distance from the worksite. This drawback can be tackled in the case of digital procedures using artificial intelligence-based voice enabled conversational agent (chatbot). As a part of this study, we have developed a chatbot for assisting digital procedure adherence. The chatbot is trained using the possible set of queries from the operator and text from the digital procedures through deep learning and provides responses using natural language generation. The testing of the chatbot is performed using a simulated conversation with an operator performing the task of loading a hopper car.

Datenmacht

2021 ◽  
Author(s):  
Simon Deuring
Keyword(s):  
Case Studies ◽  
Legal System ◽  
Industry 4.0 ◽  
Technological Development ◽  
Free Market ◽  
Balance Of Power ◽  
Cost Reductions ◽  
New Business ◽  
The Law

Data shifts the balance of power in the economy dramatically. However, digitisation also offers a multitude of opportunities: the development of new business areas, cost reductions and personalised offers. The increasing speed of technological development forces the legal system to tread on thin ice. Is the key in a regulated or free market? The book shows risks and opportunities of both options, as well as the strengths and weaknesses in European and national law. By using the latest case studies and entering new areas of the law, the book explores the question of how the Industry 4.0 should be designed.

scholarly journals Precision Measurement for Industry 4.0 Standards towards Solid Waste Classification through Enhanced Imaging Sensors and Deep Learning Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Leow Wei Qin ◽  
Muneer Ahmad ◽  
Ihsan Ali ◽  
Rafia Mumtaz ◽  
Syed Mohammad Hassan Zaidi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Solid Waste ◽  
Precision Measurement ◽  
Mobile Applications ◽  
Industry 4.0 ◽  
Learning Model ◽  
Edge Computing ◽  
Support Vector ◽  
Waste Classification ◽  
Deep Learning Model

Achievement of precision measurement is highly desired in a current industrial revolution where a significant increase in living standards increased municipal solid waste. The current industry 4.0 standards require accurate and efficient edge computing sensors towards solid waste classification. Thus, if waste is not managed properly, it would bring about an adverse impact on health, the economy, and the global environment. All stakeholders need to realize their roles and responsibilities for solid waste generation and recycling. To ensure recycling can be successful, the waste should be correctly and efficiently separated. The performance of edge computing devices is directly proportional to computational complexity in the context of nonorganic waste classification. Existing research on waste classification was done using CNN architecture, e.g., AlexNet, which contains about 62,378,344 parameters, and over 729 million floating operations (FLOPs) are required to classify a single image. As a result, it is too heavy and not suitable for computing applications that require inexpensive computational complexities. This research proposes an enhanced lightweight deep learning model for solid waste classification developed using MobileNetV2, efficient for lightweight applications including edge computing devices and other mobile applications. The proposed model outperforms the existing similar models achieving an accuracy of 82.48% and 83.46% with Softmax and support vector machine (SVM) classifiers, respectively. Although MobileNetV2 may provide a lower accuracy if compared to CNN architecture which is larger and heavier, the accuracy is still comparable, and it is more practical for edge computing devices and mobile applications.

Economic and legal regulation of economic activity in the formation of Industry 4.0

2021 ◽  
pp. 105-110
Author(s):  
Nataliya Vnukova
Keyword(s):  
Economic Activity ◽  
Industry 4.0 ◽  
Business Models ◽  
Legal Regulation ◽  
Digital Economy ◽  
Main Body ◽  
National Academy Of Sciences ◽  
Industrial Economics ◽  
New Business ◽  
Develop Technology

Problem setting. The development of the digital economy, taking into account international experience, provides a comprehensive analysis of the regulation of economic activity in relation to the application of Industry 4.0 technologies. Conditions for the development of the digital economy and society provide a thorough analysis of economic and legal regulation of economic activity on the use of innovations in Industry 4.0. Actualization of processes is enhanced by the practical actions of various stakeholders, which emphasizes the need for economic and legal support of this process. Therefore, there is a need to develop recommendations for identifying changes in the economic and legal regulation of the actions of economic entities to develop technologies in Industry 4.0. Analysis of resent researches and publications. OECD conducted a study on the development of digital economy and new business models (2014), Polish scientists R. Pukala, M. Ratajczak, Wozniak Jechorek B. consider the problems of communication in the context of digitalization and startups, recommendations for enterprise development in Industry 4.0 on the basis of their intellectualization provided by researchers of the Institute of Industrial Economics of the National Academy of Sciences of Ukraine N. Bryukhovetskaya and O. Chorna. Plakitkin by Yu. and L. consider programs of Industry 4.0 and digital economy. Target of research. Development of theoretical provisions and practical recommendations for determining changes in the economic and legal regulation of the actions of economic entities to develop technology 4.0 Industry. Article’s main body. The current changes in the regulation of economic activity that occur during the development of Industry 4.0 are considered. An innovative approach to the use of modern search engine Google Trends to determine the interest in the digital economy in the world, the results of a survey to understand the concept of Industry 4.0 and determine the potential level of interest of businesses in Ukraine to invest in the industry 4.0 Conclusions and prospects for the development. To regulate economic activity in the context of the development of Industry 4.0 requires developments in the field of law, the results of the survey indicate the need for further analytical and organizational activities to increase the interest of different categories of businesses in the development of Industry 4.0.

The Implementation of Industry 4.0 by Using Industrial and Service Robots in the Production Processes

2020 ◽  
pp. 1-30 ◽  
Author(s):  
Isak Karabegović ◽  
Edina Karabegović ◽  
Mehmed Mahmic ◽  
Ermin Husak
Keyword(s):  
Radio Frequency Identification ◽  
Industry 4.0 ◽  
Production Systems ◽  
Service Robots ◽  
Production Processes ◽  
New Business ◽  
3D Printers ◽  
Frequency Identification ◽  
Smart Factories ◽  
The Internet Of Things

From the very knowledge of Industry 4.0, its implementation is carried out in all segments of society, but we still do not fully understand the breadth and speed of its implementation. We are currently witnessing major changes in all industries, so new business methods are emerging. There is a transformation of production systems, a new form of consumption, delivery, and transportation, all thanks to the implementation of new technological discoveries that cover robotics and automation, the internet of things (IoT), 3D printers, smart sensors, radio frequency identification (RFID), etc. Robotic technology is one of the most important technologies in Industry 4.0, so that the robot application in the automation of production processes with the support of information technology brings us to smart automation (i.e., smart factories). The changes are so deep that, from the perspective of human history, there has never been a time of greater promise or potential danger.

Industry 4.0 and Sustainability

2020 ◽  
pp. 67-84
Author(s):  
Sibel Yildiz Çankaya ◽  
Bülent Sezen
Keyword(s):  
Sustainable Development ◽  
Global Warming ◽  
Natural Resources ◽  
Industry 4.0 ◽  
Business Models ◽  
Industrial Revolution ◽  
New Business ◽  
The Impact ◽  
Industrial Revolutions ◽  
New Business Models

Modern industry developed over several centuries and three industrial revolutions. Today, we experience the fourth era of the industrial revolution, Industry 4.0. The advance of industrialization brought along many problems, including environmental pollution, global warming, and depletion of natural resources. As a result, the concept of sustainability began to gain importance. Sustainability can be achieved through a balance between economic, social, and environmental processes. In order to establish such balance, businesses need new business models or insights. At this point, Industry 4.0 can be regarded as a new business mindset that will help businesses and communities move towards sustainable development. The technologies used by Industry 4.0 bear a strong promise to solve these problems, after all. Even though Industry 4.0 attracts a lot of attention lately, few works are available on its impact on sustainability. This chapter examines the impact of Industry 4.0 on sustainability.

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