Cyber-Physical Systems Technologies Research Articles

Application of Cyber-Physical System Technology on Material Color Discrimination

With the innovative advance in science and technology, manufacturing production methods have made considerable progress. However, before the production process is actually implemented, it is important to examine whether the design can meet the actual need. By applying cyber-physical system technology to test the production process, the development problems of the actual construction can be avoided. Based on the existing components, this study incorporated the cyber-physical system via innovative integration. In addition to the human–machine interface, this was employed as the operating spindle to integrate the material color identification system of the physical organization. This study also adopted the automated virtual factory constructed by the simulation software of Factory IO with an aim to achieve the technical application.

Guest Editorial Special Edition of the IEEE-CJECE

Welcome to a Special Issue of the IEEE Canadian Journal of Electrical and Computer Engineering (IEEE-CJECE), which presents articles in the research areas of the Journal’s Former Area Editor, Dr. Sameh Sourour, in his memorial. Sameh Sorour was with the School of Computing at Queen’s University, Kingston, ON, Canada, where he was leading research in mobile edge computing, edge learning and autonomous vehicles with funding from federal, provincial and industry sources. He received his B.Sc. and M.Sc. degrees from Alexandria University, Alexandria, Egypt, in 2002 and 2006, respectively, and the Ph.D. from the University of Toronto, Toronto, ON, Canada, in 2011. His Ph.D. thesis was nominated for the Governor General’s Gold Medal Award. After his graduation, he held a MITACS industrial postdoctoral fellowship with Siradel Canada and the University of Toronto. Prior to moving to Queen’s University in 2019, he held another postdoctoral fellowship at the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, a Lecturer position at the King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, and an Assistant Professor position at the University of Idaho, Moscow, ID, USA. During his Ph.D. degree and postdoctoral fellowships, he led several research projects with industrial partners and government agencies, such as LG Korea, the European Space Agency, the Canadian National Institute for the Blind (CNIB), and Siradel France. Dr. Sorour was a Senior Member of the IEEE and an Editor for IEEE Communications Letters. Also, he was an Area Editor in the IEEE-CJECE. His research and educational interests lied in the broad areas of advanced computing, learning, and networking technologies for cyber-physical and autonomous systems. The Guest Editors of this issue are 1) Prof. Hossam Hassanein, Director of School of Computing, Queen’s University, where Dr. Sorour held his last academic title; 2) Prof. Shahrokh Valaee, the Ph.D. advisor of Dr. Sourour at the University of Toronto.

Intelligent Computing in Electrical Utility Industry 4.0: Concept, Key Technologies, Applications and Future Directions

Industry 4.0 (I-4.0) is referred to as ‘fourth industrial revolution’ towards incorporation of artificial intelligence and digitalization of industrial systems. It is meticulously associated with the development and advancement of evolving technologies such as: Internet of Things, Cyber-Physical System, Information and Communications Technology, Enterprise Architecture, and Enterprise Integration. Power systems of today face several challenges that need to be addressed and application of these technologies can make the modern power systems become more effective, reliable, secure, and cost-effective. Therefore, a widespread analysis of I- 4.0 is performed in this paper and a summary of the outcomes, future scope, and real-world application of I- 4.0 on the electrical utility industry (EUI) is reported by reviewing the existing literature. This report will be helpful to the investigators interested in the area of I- 4.0 and for application in EUI.

Multiparameter Optimization Framework of Cyberphysical Systems: A Case Study on Energy Saving of the Automotive Engine

Multiparameter optimization of complex electromechanical systems in a physical space is a challenging task. CPS (Cyberphysical system) technology can speed up the solution of the problem based on data interaction and collaborative optimization of physical space and cyberspace. This paper proposed a general multiparameter optimization framework by combining physical process simulation and clustering genetic algorithm for the CPS application. The utility of this approach is demonstrated in the instance of automobile engine energy-saving in this paper. A 1.8-L turbocharged GDI (gasoline direct injection) engine model was established and calibrated according to the test data and physical entity. A joint simulation program combining CGA (Clustering Genetic Algorithm) with the GDI engine simulation model was set up for the engine multiparameter optimization and performance prediction in cyberspace; then, the influential mechanism of multiple factors on engine energy-saving optimization was analyzed at 2000 RPM (Revolutions Per Minute) working condition. A multiparameter optimization with clustering genetic algorithm was introduced for multiparameter optimization among physical and digital data. The trade-off between fuel efficiency, dynamic performance, and knock risk was discussed. The results demonstrated the effectiveness of the proposed method and that it can contribute to develop a novel automotive engine control strategy in the future.

Formal Verification of Multitask Hybrid Systems by the OTS/CafeOBJ Method

Hybrid systems combine both continuous and discrete behaviors, which occur frequently in safety-critical applications in various domains including Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) applications such as health care, transportation, and robotics. For safe and reliable information society with IoT and CPS technologies, it is important to establish a way to specify and verify hybrid systems formally. Formal descriptions of hybrid systems may help us to verify desired properties of a given system formally with computer supports. We propose a way to describe a formal specification of a given multitask hybrid system as an observational transition system (OTS) in CafeOBJ algebraic specification language. OTSs are models where systems behaviors are described through observations. CafeOBJ supports specification execution based on a rewrite theory. We verify that OTS/CafeOBJ specifications of hybrid systems satisfy desired property by the proof score method based on equational reasoning implemented in CafeOBJ interpreter. In this paper, we specify a signal control system with an arbitrary number of vehicles by our proposed method, and verify the system satisfies a safety property by the proof score method.

Cyber-Physical Systems as an Enabler of Circular Economy to Achieve Sustainable Development Goals: A Comprehensive Review

Industrialization has brought wealth, prosperity, and abundance to many nations. However, it has had many drawbacks on people’s health and the environment. Several paradigms have been proposed and implemented in an effort to suppress and reverse the adverse impacts of human activities and industrialization. A popular approach that serves as a solution to many of the negative implications of industry today is the circular economy (CE). With today’s top-notch technologies, the transition from the conventional linear economy to CE has been made easier. For example, the implementation of cyber-physical systems (CPSs), an Industry 4.0 (I4.0) tool, within a CE can substantially increase efficiency and reduce waste. This work presents a thorough review on the applications of cyber-physical systems within each of the CE stages, the contribution of different CPS technologies to the sustainable development goals (SDGs), and the current state of the CE assessment tools. The contribution of different CPS tools to each CE stage is demonstrated through several practical examples. In addition, this work reveals how the different CPS technologies applications contribute to the attainment of different SDGs set by the United Nations. It is also deduced from the review of the current state of the CE assessment tools that there is a need for a standardized comprehensive CE measuring or rating system to allow businesses on the micro, meso, and macro levels of the economy improve their circulatory.

Global Challenges of Digital Transformation of Markets: Collaboration and Digital Assets

Cyber-physical system (CPS) and digital twin (DT) technologies are the key enablers of smart manufacturing. The main idea of CPS is to build bi-directional interaction channels between the physical and cyber worlds. The research gap is ontological consideration of the concept of the digital object (DO) as a representation of a physical object (PO) in the digital space/world. The objective of this study is an ontological analysis of the digital object (DO). This object is fairly well-understood from a technical point of view; although there are many options for its definition, its basic composition and functionality are defined clearly, but currently in the economic science DO has not yet been enough considered. The DO, which first appeared as a digital twin has not been properly explored by economic science. Authors attempt to determine whether all the properties and characteristics of the DO are described by modern economic language or whether there is a need to introduce new concepts and categories to describe such objects. The ontological analysis of the DO within the existing conceptual framework of economic science is presented. The result of the research is comprehensive study of DO which allows the consideration of the additional benefits that economic actors can gain from using the DO. We propose to analyze the DO in terms of such economic categories as goods; innovation process; the system of division of labor; the role of market participants in the creation and use of the DO; intellectual property; etc.

Security and reliability of N-version cloud-based task solvers with individual version cancellation under data theft attacks

As one of the enabling technologies for cyber-physical systems and Internet of Things systems, the cloud computing provides cost-effective resources in an on-demand manner. This merit lends the cloud to running critical services that need redundancy to achieve high reliability. This paper models a cloud service using the N-version programming (NVP) redundancy technique that creates and runs multiple task solver versions (TSVs) in parallel to perform a requested service and decides the output using the threshold voting. A malicious attacker may get an unauthorized access to a user's data when the user's and attacker's virtual machines co-reside in the same cloud server. To reduce the chance of the co-residence attack success and users’ expense, an individual TSV cancellation policy is implemented, which removes a TSV's virtual machine from its host server immediately once this TSV completes the task execution. A probabilistic method is proposed to evaluate the task reliability and data security under the considered cloud service model. Constrained optimization problems are further formulated and solved, which find the optimal number of TSVs maximizing the task reliability subject to providing a desired level of data security. Examples are presented to demonstrate interactions and impacts of different parameters on the task reliability and data security, as well as on the optimization solutions.

Hash-MAC-DSDV: Mutual Authentication for Intelligent IoT-Based Cyber-Physical Systems.

Cyber-Physical Systems (CPS) connected in the form of Internet of Things (IoT) are vulnerable to various security threats, due to the infrastructure-less deployment of IoT devices. Device-to-Device (D2D) authentication of these networks ensures the integrity, authenticity, and confidentiality of information in the deployed area. The literature suggests different approaches to address security issues in CPS technologies. However, they are mostly based on centralized techniques or specific system deployments with higher cost of computation and communication. It is therefore necessary to develop an effective scheme that can resolve the security problems in CPS technologies of IoT devices. In this paper, a lightweight Hash-MAC-DSDV (Hash Media Access Control Destination Sequence Distance Vector) routing scheme is proposed to resolve authentication issues in CPS technologies, connected in the form of IoT networks. For this purpose, a CPS of IoT devices (multi-WSNs) is developed from the local-chain and public chain, respectively. The proposed scheme ensures D2D authentication by the Hash-MAC-DSDV mutual scheme, where the MAC addresses of individual devices are registered in the first phase and advertised in the network in the second phase. The proposed scheme allows legitimate devices to modify their routing table and unicast the one-way hash authentication mechanism to transfer their captured data from source towards the destination. Our evaluation results demonstrate that Hash-MAC-DSDV outweighs the existing schemes in terms of attack detection, energy consumption and communication metrics.

Towards asynchronous federated learning based threat detection: A DC-Adam approach

The increasing popularity and widespread use of Internet of Things (IoT) and Cyber-Physical Systems (CPS) technologies have produced a significant need for the integration of cloud and edge computing with distributed detection solutions to handle the growing volume of distributed security threats. While deep learning-based approaches have been used to detect anomalous behaviors in complex data patterns, the heterogeneity in IoT networks still poses paramount challenges to update synchronization across learning nodes in distributed training. Particularly, the non-independent and identically distributed (non-IID) data patterns over remote nodes significantly affect the performance of model training provisioned on cloud and edge computing servers, and most existing works have assumed a homogeneous setting. The heterogeneity brings the gradient delay problem, causing the gradient inconsistency in the barrier-free asynchronous mode. In this paper, we propose a Delay Compensated Adam (DC-Adam) approach, an asynchronous federated learning-based detection approach, for IoT devices with limited resources. To overcome the notorious gradient delay problem, we develop a Taylor Expansion-based scheme to compensate for the inconsistency caused by asynchronous communication. Moreover, a pre-shared data training strategy for non-IID data is developed to avoid the convergence divergence under the non-IID patterns. After the collaborative model training procedure, we append an additional local training process at each client to fit respective patterns. Via a combination of theoretical analysis of convergence and practical experimental results, we validate the efficacy of our proposed approach compared to the other state-of-the-art approaches. Compared with benchmark approaches, we demonstrate that our proposed method can converge stably, and that it outperforms the barrier-free asynchronous federated learning by 12.8% (accuracy), 14% (precision). 8.71% (recall), and 11.16% (F1 score) on average.

A Proactive Manufacturing Resources Assignment Method Based on Production Performance Prediction for the Smart Factory

With the wide application of advanced industrial Internet of Things (IIoT) and cyber physical system (CPS) technologies, the manufacturing resources assignment method is transformed from manual and passive mode to intelligent and active mode. However, due to the lack of real-time analysis and accurate prediction of production performance, the production adjustment demands are often released after production exceptions happen, and production decisions are often made based on historical production information, which may lead to the problem of production interruption or performance reduction. To address this issue, a proactive manufacturing resources assignment (PMRA) method based on production performance prediction for the smart factory is proposed. First, the advanced IIoT and CPS technologies are applied to create a cloud-edge cooperation environment for a smart factory, where the resources are made smart with distributed control capacity, and cloud center and edge resources can collaborate dynamically. Second, a real-time colored Petri net enabled key production performance indicators analysis and prediction method are proposed to extract real-time production information and predict future production status accurately. Then, the PMRA method is presented to assign the resources before production exceptions happen. Finally, a case study from a typical manufacturer for computer numerical control machine tools in North China is used to validate the proposed method and results show that the proposed PMRA method can largely reduce the total tardiness and the total energy consumption.

Quality factors and performance outcome of cloud-based marketing system

PurposeThis study aims to propose an integrated model based on the DeLone and McLean Information System Success Model (DMISS) to examine whether quality factors (system, service and information) can affect user satisfaction and performance of cloud-based marketing systems.Design/methodology/approachRecently, technologies change quickly, innovation becomes a vital base of productivity and sustainable growth of businesses is widely accepted. Cyber-physical system technologies help industries change production and marketing cycles according to customers’ needs in real-time. In addition, processing information through cloud service helps companies meet customer needs. The advantages of cloud technology also make it easier for companies to quickly collect the latest data from various sources, making it more effective in decision-making. This research recommends cloud-based marketing to help companies maximize their revenue by providing useful information and better quality for business development. The data were gathered from China automotive companies’ customers. A total of 220 questionnaires were distributed, and 165 (82.5%) usable questionnaires were analyzed using structural equation modeling.FindingsThis study verified that costumers’ perceived information quality, system quality and service quality positively caused the user satisfaction in the cloud-based marketing system.Practical implicationsThis paper presents beneficial advice for improving cloud-based marketing systems. Besides, the topic is relevant to cloud-based marketing systems’ success. A better understanding of the impact of intention to use and user satisfaction on cloud-based marketing systems could significantly enhance companies’ success. This paper’s theoretical and practical contributions are expressed to guide organizations and policymakers in increasing cloud-based marketing systems acceptance.Originality/valueThis study empirically tests the relationship of quality factors and performance outcome of cloud-based marketing system through a model based on DeLone and McLean theory. This study bridges the research gap by identifying the factors that drive the adoption of cloud-based services in marketing and the impact of user satisfaction and intention to use on the cloud-based marketing system performance in the case of china companies.

Integrated Scheduling of Real-Time and Interactive Tasks for Configurable Industrial Systems

With the recent advances in Internet of Things and cyber-physical systems technologies, smart industrial systems support configurable processes consisting of human interactions as well as hard real-time functions. This implies that irregularly arriving interactive tasks and traditional hard real-time tasks coexist. As the characteristics of the tasks are heterogeneous, it is not an easy matter to schedule them all at once. To cope with this situation, this article presents a new task scheduling policy that uses the notion of “virtual real-time task” and two-phase scheduling. As hard real-time tasks must keep their deadlines, we perform offline scheduling based on genetic algorithms beforehand. This determines the processor's voltage level and memory location of each task and also reserves the virtual real-time tasks for interactive tasks. When interactive tasks arrive during the execution, online scheduling is performed on the time slot of the virtual real-time tasks. As interactive workloads evolve over time, we monitor them and periodically update the offline scheduling. Experimental results show that the proposed policy reduces the energy consumption by 66.8% on average without deadline misses and also supports the waiting time of less than 3 (s) for interactive tasks.

RETRACTED: Artificial intelligence student management based on embedded system

Artificial intelligence embedded developers, see to provide an innovative solution as possible. Artificial Intelligence (AI) has seen the development of the Internet's new things, an essential technology for cyber-physical systems, such as robots and autonomous vehicles. Embedded AI is a broad term for using deep learning and software platforms to improve the employee's day-to-day aspects. Sustainable development of society and the economy are promoting science and technology. Artificial intelligence is a topic of hot critical advances in human science and technology. Continuous advancements in artificial intelligence are not only human beings; many of the new for a variety of new future services have opened an unprecedented experience. This initiative can be defined on information known as (recorded), which are generated internally. Information embedded in the processed records obtained the knowledge to help the organization make timely and sufficient information. The student information management system of the new type based on fingerprint recognition is designed to achieve the recognition and management of student information. To ensure the security of data transmission, and suggests an improved data encryption method.

Computer graphics and visual computing use cases for Industry 4.0 and Operator 4.0

In the context of smart manufacturing, the concept of Visual Computing is a key enabling technology for Industry 4.0. Visual Computing and Physically-based simulation enables the implementation of interactive, visual and (in most cases) non-disruptive solutions within the context of (a) the production process (off-line or in-line) the shop floor, (b) the interaction with customers and clients in the whole value chain, and/or (c) the product-service schemas. Technologies such as Industrial Big Data, Human-Robot Collaboration, IIOT & 5G, Cybersecurity, Cyber-Physical Systems, Semantic Technologies, Visual Computing and other related technologies have a strong impact in the new ways smart manufacturing is reconfiguring the production. This article presents a theoretical framework for the incorporation of Visual Computing and Physically-based Simulation technologies to Industry 4.0 and Operator 4.0 scenarios. This framework has been already implemented and supports different smart manufacturing scenarios, already running in many SME. Three practical study case scenarios (already deployed into SMEs) are presented and discussed.

Vibration signal diagnosis and analysis of rotating machine by utilizing cloud computing

Abstract Vibration signal diagnosis and analysis plays an important role in the industrial machinery since it enhances the machinery performance under supervision. The information regarding the future condition is given by vibration diagnosis techniques which is growing interest for the scientific and industrial communities. Information for failure diagnostic and prediction are provided by the motor vibration through signal processing. The development of mechanical systems fault prognosis and in the last decades, research is done at a very rapid rate. The examination of vibration signal monitoring is done in this paper with the aid of Cyber-Physical Systems (CPS) and Cloud Technology (CT). The machines maintenance strategies are implemented by using the data collected from machines which are based on the fault prognosis. The cloud computing platform is presented in this paper which is having three layers and the unlabelled data is received to generate an interpreted online decision. Feature extraction of the vibration signal is obtained in terms of range, mean value, root mean square value, and standard deviation and crest values. The performance of the model is evaluated by utilizing the classical statistical metrics such as RMSE Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) of the vibration signal. It is obtained that the proposed technique is 25% and 90% better than the Adaptive Neurofuzzy Inference System and the Single Modeling System respectively in terms of RMSE. The performance in terms of MAPE, then the proposed technique outperforms the existing Adaptive Neurofuzzy Inference System and the Single Modeling System by 8 % and 60% respectively. The presented technique is better than the existing Adaptive Neurofuzzy Inference System and the Single Modeling techniques by average of 15% and 30 % respectively.

CPS-based manufacturing workcell for the production of hybrid medical devices

Cyber-physical system (CPS) can increase efficiency and lower costs in manufacturing operations. CPS and related technologies can help medical device manufacturers to meet the rising demands for quality and affordable healthcare products and services. In this paper, a novel architecture for CPS-based manufacturing workcell systems was proposed. Its contribution is to provide quality inspection and operator guidance in manual assembly systems, specifically for the production of hybrid medical devices. The proposed system involves the integration of enabling technologies including Machine Vision (MV), Internet-of-things (IoT) and Augmented Reality. The group illustrated and evaluated the implementation of two CPS prototypes based on the proposed architecture on the fabrication of a specific hybrid medical device, verifying their effectiveness as guidance tools for operators. The two prototypes, CPS-1 and CPS-2 differ in their MV subsystems by having non-deep learning and deep learning-based image processing methods respectively. Experimental results illustrate that, when exposed to various changing conditions, the Regional Convolutional Neural Network (R-CNN) of CPS-2, on average, has outperformed the non-deep learning image processing method of CPS-1 by 42.3% and 8.5%, in terms of accuracy and precision respectively. Hence, this presents evidence on the usefulness of R-CNN-based MV system to increase the flexibility and adaptability of the CPS in terms of object monitoring and reporting.

EPEC 4.0: an Industry 4.0-supported lean production control concept for the semi-process industry

The combination of lean production and Industry 4.0 exhibits excellent potential in advancing operations management and, especially, production control. This improvement potential is applicable to the semi-process industry (S-PI), as higher product variety and higher demand variability require greater flexibility from production systems. This flexibility is difficult to achieve in S-PI environments, in which it is imperative to cope with the long setup times and stability needed for the high utilization requirements. Heijunka addresses this stability-flexibility paradox but was designed for short setup times and is thus considered not directly applicable. In this article, we use design science research to adapt Heijunka to the S-PI and combine it with cyber-physical systems technologies. The developed production control concept, every product every cycle (EPEC) 4.0, aims to schedule the production system as efficiently as possible, providing the necessary flexibility and minimum schedule perturbation. Furthermore, we present the findings from a validation case study within the automotive supply industry.

Log-flock: A blockchain-enabled platform for digital asset valuation and risk assessment in E-commerce logistics financing

E-commerce retail sales have seen significant growth in the past decade with its proliferation into the consumer markets at unprecedented rates. This has led to the rise of logistics companies (LC) and the advent of Logistics financing as a tool used by them to attain business expansion. The current climate in e-commerce that is characterized by an upswing in sales requires the LCs to equip themselves with sufficient capital to elevate or modernize their operational capabilities. But, these small and medium-sized enterprises tend to overlook their business expansion opportunities due to the unfavorable financing environment created due to their: (i) limited tangible assets (ii) lack of clarity on operating capability, and (iii) lack of reliable information-sharing mechanism. To address these problems, this study aims to utilize the digital assets of the LC to aid logistics financing. This study introduces a logistics financing platform (Log-Flock), that leverages the abilities of the Internet of Things (IoT), cyber-physical system (CPS), and blockchain technologies to support the utilization of digital assets of the LC for logistics financing. The IoT and CPS are combined to support the generation of the digital assets whereas the core constructs of blockchain technology, such as the tokens and smart contracts, are used to (i) introduce incentivized rewarding mechanisms to motivate the engagement of users and, (ii) to perform the digital asset valuation and risk assessment in logistics financing. Some of the expected benefits from the study include: (i) This novel platform if implemented, would significantly reduce the time for financing. (ii) It communicates the operating capability of the LCs in the form of credit rating which is easily comprehensible for the financing institutions.

Big data driven vehicle battery management method: A novel cyber-physical system perspective

The establishment of an accurate battery model is of great significance to improve the reliability of electric vehicles (EVs). However, the battery is a complex electrochemical system with hardly observable and simulatable internal chemical reactions, and it is challenging to estimate the state of battery accurately. This paper proposes a novel flexible and reliable battery management method based on the battery big data platform and Cyber-Physical System (CPS) technology. First of all, to integrate the battery big data resources in the cloud, a Cyber-physical battery management framework is defined and served as the basic data platform for battery modeling issues. And to improve the quality of the collected battery data in the database, this work reports the first attempt to develop an adaptive data cleaning method for the cloud battery management issue. Furthermore, a deep learning algorithm-based feature extraction model, as well as a feature-oriented battery modeling method, is developed to mitigate the under-fitting problem and improve the accuracy of the cloud-based battery model. The actual operation data of electric buses is used to validate the proposed methodologies. The maximum data restoring error can be limited within 1.3% in the experiments, which indicates that the proposed data cleaning method is able to improve the cloud battery data quality effectively. Meanwhile, the maximum SoC estimation error in the proposed feature-oriented battery modeling method is within 2.47%, which highlights the effectiveness of the proposed method.