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Applied Sciences
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Advanced Cyber Physical Systems for Manufacturing and Energy Industries
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Advanced Cyber Physical Systems for Manufacturing and Energy Industries

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 October 2019) | Viewed by 17806

Special Issue Editors

Prof. Dr. Amy J.C. Trappey

E-Mail Website
Guest Editor
Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 300044, Taiwan
Interests: knowledge engineering; intelligent system design; intellectual property (IP, patents, trademarks) analysis; engineering (tangible and intangible) asset management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. John P.T. Mo

E-Mail Website
Guest Editor
School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
Interests: robotics; product development; mechatronics; mobile robotics; system modeling; automation; machining; advanced control theory
Special Issues, Collections and Topics in MDPI journals
Dr. Josip Stjepandic

E-Mail Website
Guest Editor
Director of 3D Business Unit, PROSTEP, Germany
Interests: systems engineering; modular design; digital factory

Special Issue Information

Applied Sciences calls for research paper submissions for considering in a Special Issue publication featuring on advanced cyber physical systems (CPS) as key enabling technologies applied in the context of advanced manufacturing and energy industries. Unpublished original contributions from prospective authors are invited for consideration by the special issue, subject to blind reviews, with main focus on innovative CPS enabling methodologies (e.g., systems architectures and designs, modeling, algorithms, performance evaluation), real world CPS applications particularly for the manufacturing and energy sectors, and impact assessments in the context of Industry 4.0. Comprehensive case studies and in-depth literature reviews and patent informatics papers are welcome.

Industry 4.0 is the next incremental advancement in manufacturing and energy services that enables objects with micro intelligence using underlying and integrated technologies such as CPS, Internet of Things (IoT), cloud computing, and big data analytics. CPS is a transformative technology for upgrading, interconnecting, and managing inter-operable and networked physical devices with intelligent computational capabilities. CPS is a broad area of system science and engineering, which supports applications across industries (in productions, supply chain integration, logistic management, and other service sectors) and is viewed as a key enabler with the availability and affordability of sensors, data acquisition and digitization, computer networks, and computational power. CPS embedding in manufacturing is expected to grow at an exponential pace. The dynamic pace of CPS technology evolution creates new identification and implementation challenges for Industry 4.0. This special issue seeks to explore the areas related to these challenges.

Topics of the special issue interests and focuses include, but not limited to 

  1. The principles of CPS for manufacturing and energy industries and industry 4.0 in general.
  • Vertical/horizontal integration and M2M Communication
  • Industrial interoperability and compatibility
  • Information and intelligence capturing, regulation and transfers
  • Intelligent decision making and decision supports
  • Decentralized control of production processes
  • Seamless digital engineering and digital twins
  • Cyber-physical production system
  • Architecture models
  • IT governance, data security and access
  1. CPS application and implementation areas
  • Lifecycle value chain management
  • Production line optimization and productivity enhancement
  • Information quality, reliability and security
  • Mass customization for products and services
  • Immersive technologies: coupling of real and virtual worlds for production management
  • Self-organizing/self-learning production processes with flexibility
  • Digital Transformation
  1. Big Data in CPS
  • Machine Learning methods for big data analytics
  • Modeling and representation methods for big data visualization
  • Data provenance, cleaning, filtering and governance
  • Distributed data processing (e.g. fog computing)
  • Intelligent gateway architecture for communication, control and data management
  1. Service-Oriented CPS
  • Cloud Services
  • Service Platforms for CPS
  • Quality of Services modeling for CPS
  • Smart process and workflow management for Service-Oriented CPS
  • Smart contracts and services
  • Service Models for Edge Computing
  • Service-Oriented data modeling
  • Deployable and re-configurable services for CPS

Prof. Amy J.C. Trappey
Prof. John Mo
Dr. Josip Stjepandic
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

26 pages, 4327 KiB  
Article
Developing a Quick Response Product Configuration System under Industry 4.0 Based on Customer Requirement Modelling and Optimization Method
by Ching-Hung Lee, Chun-Hsien Chen, Chenyu Lin, Fan Li and Xuejiao Zhao
Appl. Sci. 2019, 9(23), 5004; https://doi.org/10.3390/app9235004 - 20 Nov 2019
Cited by 42 | Viewed by 4197
Abstract
In the Industry 4.0 environment, the new manufacturing transformation of mass customization for high-complexity and low-volume production is moving forward. Based on cyber-physical system (CPS) and Internet of things (IoT) technology, the flexible transformation of the manufacturing process to suit diverse customer manufacturing [...] Read more.
In the Industry 4.0 environment, the new manufacturing transformation of mass customization for high-complexity and low-volume production is moving forward. Based on cyber-physical system (CPS) and Internet of things (IoT) technology, the flexible transformation of the manufacturing process to suit diverse customer manufacturing requirements is very possible, with the potential to provide digital “make-to-order” (MTO) services with a quick response time. To achieve this potential, a product configuration system, which translates the voice of customers to technical specifications, is needed. The purpose of this study is to propose a methodology for developing a quick-response product configuration system to enhance the communication between the customer and the manufacturer. The aim is to find an approach to receive requests from customers as inputs and generate a product configuration as outputs that maximizes customer satisfaction. In this approach, engineering characteristics (ECs) are defined, and selection pools are initially constructed. Then, quality function deployment (QFD) is modified and integrated with the Kano model to qualitatively and quantitatively analyze the relationship between customer requirements (CRs) and customer satisfaction (CS). Next, a mathematical programming model is applied to maximize the overall customer satisfaction level and recommend an optimal product configuration. Finally, sensitivity analysis is conducted to suggest revisions for customers and determine the final customized product specification. A case study and an OrderAssistant system are implemented to demonstrate the procedure and effectiveness of the proposed quick response product configuration system. Full article
(This article belongs to the Special Issue Advanced Cyber Physical Systems for Manufacturing and Energy Industries)
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15 pages, 1676 KiB  
Article
Decision-Making Method for Estimating Malware Risk Index
by Dohoon Kim
Appl. Sci. 2019, 9(22), 4943; https://doi.org/10.3390/app9224943 - 17 Nov 2019
Cited by 3 | Viewed by 2915
Abstract
Most recent cyberattacks have employed new and diverse malware. Various static and dynamic analysis methods are being introduced to detect and defend against these attacks. The malware that is detected by these methods includes advanced present threat (APT) attacks, which allow additional intervention [...] Read more.
Most recent cyberattacks have employed new and diverse malware. Various static and dynamic analysis methods are being introduced to detect and defend against these attacks. The malware that is detected by these methods includes advanced present threat (APT) attacks, which allow additional intervention by attackers. Such malware presents a variety of threats (DNS, C&C, Malicious IP, etc.) This threat information used to defend against variants of malicious attacks. However, the intelligence that is detected in this manner is used in the blocking policies of information-security systems. Consequently, it is difficult for staff who perform Computer Emergence Response Team security control to determine the extent to which cyberattacks such as malware are a potential threat. Additionally, it is difficult to use this intelligence to establish long-term defense strategies for specific APT attacks or implement intelligent internal security systems. Therefore, a decision-making model that identifies threat sources and malicious activities (MAs) that occur during the static and dynamic analysis of various types of collected malware and performs machine learning based on a quantitative analysis of these threat sources and activities is proposed herein. This model estimates malware risk indices (MRIs) in detail using an analytic hierarchy process to analyze malware and the probabilities of MAs. The analysis results were significant, as the consistency index of the estimated MRI values for 51300 types of malware, which were collected during a specific control period, was maintained at <0.051. Full article
(This article belongs to the Special Issue Advanced Cyber Physical Systems for Manufacturing and Energy Industries)
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25 pages, 3706 KiB  
Article
A Machine Learning Approach for Solar Power Technology Review and Patent Evolution Analysis
by Amy J.C. Trappey, Paul P.J. Chen, Charles V. Trappey and Lin Ma
Appl. Sci. 2019, 9(7), 1478; https://doi.org/10.3390/app9071478 - 9 Apr 2019
Cited by 30 | Viewed by 10312
Abstract
Solar power systems and their related technologies have developed into a globally utilized green energy source. Given the relatively high installation costs, low conversion rates and battery capacity issues, solar energy is still not a widely applied energy source when compared to traditional [...] Read more.
Solar power systems and their related technologies have developed into a globally utilized green energy source. Given the relatively high installation costs, low conversion rates and battery capacity issues, solar energy is still not a widely applied energy source when compared to traditional energy sources. Despite the challenges, there are many innovative studies of new materials and new methods for improving solar energy transformation efficiency to improve the competitiveness of solar energy in the marketplace. This research searches for promising solar power technologies by text mining 2280 global patents and 5610 literature papers of the past decade (January 2008 to June 2018). First, a solar power knowledge ontology schema (or a key term relationship map) is constructed from the comprehensive literature and patent review. Non-supervised machine learning techniques for clustering patents and literature combined with the Latent Dirichlet Allocation (LDA) topic modeling algorithm identify sub-technology clusters and their main topics. A word-embedding algorithm is applied to identify the patent documents of the specified technologies. Cross-validation of the results is used to model the technology progress with a patent evolution map. Initial analysis show that many patents focus on solar hydropower storage systems, transferring light generated power to waterpower gravity systems. Batteries are also used but have several limitations. The objectives of this research are to review solar technology development progress and describe the innovation path that has evolved for the solar power domain. By adopting unsupervised learning approaches for literature and patent mining, this research develops a novel technology e-discovery methodology and presents the detailed reviews and analyses of the solar power technology using the proposed e-discovery workflow. The insights of global solar technology development, based on both comprehensive literature and patent reviews and cross-analyses, helps energy companies select advanced technologies related to their key technical R&D strengths and business interests. The structured solar-related technology mining can be extended to the analysis of other forms of renewable energy development. Full article
(This article belongs to the Special Issue Advanced Cyber Physical Systems for Manufacturing and Energy Industries)
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