Integration Of Cyber-physical Systems Research Articles

Evaluating the Preparedness of Freight Logistics Firms for Cyber-Physical Systems Integration: A SAP-LAP Methodology for Sustainable Development

Evaluating the Preparedness of Freight Logistics Firms for Cyber-Physical Systems Integration: A SAP-LAP Methodology for Sustainable Development

Development of Smart Factory 4.0 using cyber-physical system-based workshop in Rolling Stock Industry to Support Industry 4.0

Driven by the concept of mass customization, manufacturing enterprises are now required to transform their traditional workshops into smart workshops. The integration of cyber-physical systems (CPS), which serves as the core of Industry 4.0, is crucial in achieving smart manufacturing. By implementing CPS, the conventional workshop can transition into a new paradigm characterized by intelligence and flexibility. However, the implementation of CPS in workshops is a complex undertaking that is still in its early stages. Therefore, this research paper aims to provide a comprehensive perspective on CPS-based workshops, with the intention of facilitating their implementation in the industry. Initially, the paper identifies seven key features of CPS-based workshops, namely self-sensing, self-awareness, self-assessment, self-optimization, self-adjustment, self-configuration, and self-control. Subsequently, the paper proposes the architectural framework of CPS-based workshops from a technical standpoint. Furthermore, a conceptual model of CPS-based workshops is developed, which highlights the three fundamental elements and closed-loop mechanism of such workshops. Finally, a case study of a Rolling stock manufacturing company in Indonesia is presented to demonstrate the feasibility of implementing CPS-based workshops in the industry.

Unsupervised deep domain adaptation algorithm for video based human activity recognition via recurrent neural networks

The integration of cyber-physical systems and artificial intelligent human activity recognition (HAR) applications enables intelligent interactions within a physical environment. In real-world HAR applications, the domain shift between training (source) and testing (target) images captured in different scenarios leads to low classification accuracy. Existing unsupervised domain adaptation (UDA) methods often require some labeled target samples for model adaptation, which limits their practicality. This study proposes a novel unsupervised deep domain adaptation algorithm (UDDAA) for HAR using recurrent neural networks. UDDAA introduces a maximum mean class discrepancy (MMCD) metric, accounting for both inter-class and intra-class differences within each domain. MMCD extends the maximum mean discrepancy to measure the class-level distribution discrepancy across source and target domains, aligning these distributions to enhance domain adaptation performance. Without relying on labeled target data, UDDAA predicts pseudo-labels for the unlabeled target dataset, combining these with labeled source data to train the model on domain-invariant representations. This approach makes UDDAA highly practical for scenarios where labeled target data is difficult or expensive to obtain, enabling human-computer interaction (HCI) systems to function effectively across varied environments and user behaviors. Extensive experiments on benchmark datasets demonstrate UDDAA's superior classification accuracy over existing baselines. Notably, UDDAA achieved 92% and 99% accuracy for University of Central Florida database (UCF) to Human Motion Database (HMDB) and HMDB to UCF transfers, respectively. Additionally, on personal recorded videos with complex backgrounds, it achieved high classification accuracies of 95% for basketball and 90% for football activities, underscoring its generalization ability, robustness, and effectiveness.

Implementation of Industry 4.0 and Digital Manufacturing Practices in Indian Manufacturing Industries Using Discrete Event Simulation (DES)

Industry 4.0 and digital manufacturing have emerged as significant areas of research in recent years. According to several reputed research papers, Industry 4.0 is a new industrial revolution that involves the integration of cutting-edge technologies such as the Simulation (Discrete Event Simulation), Internet of Things (IoT), artificial intelligence (AI), and big data analytics into the manufacturing process. Smart manufacturing, on the other hand, is the implementation of these technologies in the manufacturing sector to increase efficiency, reduce waste, and improve product quality. The research suggests that Industry 4.0 and smart manufacturing have the potential to revolutionize the manufacturing industry by enabling companies to achieve higher levels of automation, productivity, and agility. The Indian manufacturing industry has witnessed substantial growth in recent years, becoming a significant contributor to the country's economic development. To maintain its competitive edge in the global market and meet the increasing demand for innovative and cost-effective products, the industry has been actively adopting digital manufacturing practices. In this context, Industry 4.0, characterized by the integration of cyber-physical systems, Simulation, the Internet of Things, and data analytics, offers new opportunities for enhancing efficiency and productivity. This research paper presents a comprehensive study on the successful implementation of digital manufacturing practices in the Indian automobile industry through the use of simulation approaches, in alignment with Industry 4.0 principles. The study aims to identify the key challenges faced by the industry and proposes viable solutions to harness the full potential of digitalization. The paper delves into the critical aspects of simulation techniques, such as discrete event simulation and virtual manufacturing, and their role in optimizing manufacturing processes, reducing lead times, and enhancing product quality. The results of this study indicate that the integration of digital manufacturing practices with Industry 4.0 principles has resulted in significant improvements across the entire value chain of the Indian automobile industry. Reduced production costs, increased production flexibility, and enhanced product quality have been observed as the key outcomes of this strategic alignment.

Integration of Islamic Law Regulation and Positive Law in Addressing Business Ethics Challenges in The Era of Industrial Revolution 4.0 and Society 5.0

The era of the Industrial Revolution 4.0 and Society 5.0 has brought significant changes in various aspects of life, including in the business world. The rapid development of technology and the integration of cyber-physical systems have changed the way businesses are conducted, giving rise to new and complex business ethics challenges. The normative juridical research method is employed in this study, which focuses on positive legal regulations related to legislation. Two approaches, the Statute Approach and the Conceptual Approach, are utilized. Primary and secondary legal sources are employed to understand and apply the law. Integrating Islamic Law and Positive Law regulations refers to the effort to combine their principles harmoniously. This integration involves identifying similarities and differences, considering contemporary aspects such as technology and globalization. Ethical challenges in business during the Fourth Industrial Revolution and Society 5.0 era include ensuring justice, sustainability, and humanity in business practices. Islamic and Positive Law regulations, such as usury and consumer protection prohibition, help guide ethical business behavior. Integrating Islamic and Positive Law regulations can address business ethics challenges. Collaboration among the government, society, and Islamic organizations is necessary to strengthen regulatory implementation. Education on Islamic values-based business ethics also needs to be reinforced to cultivate responsible business leaders for the future.

Cyber-Physical Systems as a New Frontier for Economic Security: The Impact of Advanced Data Capturing and Networking Technology on National Economies

This study investigates the burgeoning interface between cyber-physical systems (CPS) and economic security, a vital yet underexplored domain. With the advent of sophisticated data capturing and networking technology, the paradigm within which national economies operate is shifting. This research delineates the ramifications of such technologies on economic stability, employment trends, and national gross domestic product (GDP), alongside the emergent cybersecurity implications. The methodology encompasses a cross-sectional analysis of various economies that have adopted CPS at differing scales and capacities. Through quantitative data, the study reveals trends and patterns in economic performance metrics post-adoption of advanced data capturing and networking infrastructures. Complementarily, qualitative assessments from expert interviews offer insights into the broader economic and security implications. Preliminary findings suggest that the integration of CPS correlates with a multifaceted impact on economic security, including the creation of new industries and the obsolescence of traditional sectors. A notable rise in efficiency and productivity across industries utilizing CPS is counterbalanced by the challenges of increased cybersecurity threats and job market disruptions. The study contributes to the discourse on economic security by framing CPS adoption not just as a technological upgrade but as a critical economic event with widespread and significant consequences.

Improving Manufacturing Efficiency with Industry 4.0 Technologies: A Comparative Analysis of Smart Factories

The advent of Industry 4.0 technologies has ushered in a new era of manufacturing marked by the integration of cyber-physical systems, data-driven decision-making, and automation. This study presents a comprehensive comparative analysis of the impact of Industry 4.0 technologies on manufacturing efficiency through a focus on smart factories. The research examines how these technologies, such as the Internet of Things (IoT), artificial intelligence, and advanced robotics, contribute to operational improvements across diverse manufacturing environments. The methodology involves a comparative case study approach, analyzing multiple smart factories from various industries to identify common trends and distinct challenges. Key performance indicators (KPIs) related to efficiency, productivity, and resource utilization are assessed, providing a nuanced understanding of the transformative effects of Industry 4.0. The findings reveal that the implementation of smart technologies leads to significant improvements in production processes, reduced downtime, and enhanced product quality. Furthermore, the study explores the challenges encountered during the adoption of Industry 4.0, including cybersecurity concerns, workforce upskilling, and the integration of legacy systems. Insights gained from successful implementations serve as a guide for manufacturers seeking to optimize their operations through the adoption of Industry 4.0 technologies. The research contributes to the existing literature by offering a comparative perspective on smart factories, shedding light on best practices, and facilitating a deeper understanding of the factors influencing successful Industry 4.0 adoption.

A novel approach detection for IIoT attacks via artificial intelligence

The Industrial Internet of Things (IIoT) is a paradigm that enables the integration of cyber-physical systems in critical infrastructures, such as power grids, water distribution networks, and transportation systems. IIoT devices, such as sensors, actuators, and controllers, can provide various benefits, such as performance optimization, efficiency improvement, and remote management. However, these devices also pose new security risks and challenges, as they can be targeted by malicious actors to disrupt the normal operation of the infrastructures they are connected to or to cause physical damage or harm. Therefore, it is essential to develop effective and intelligent solutions to detect and prevent attacks on IIoT devices and to ensure the security and resilience of critical infrastructures. In this paper, we present a comprehensive analysis of the types and impacts of attacks on IIoT devices based on a literature review and a data analysis of real-world incidents. We classify the attacks into four categories: denial-of-service, data manipulation, device hijacking, and physical tampering. We also discuss the potential consequences of these attacks on the safety, reliability, and availability of critical infrastructures. We then propose an expert system that can detect and prevent attacks on IIoT devices using artificial intelligence techniques, such as rule-based reasoning, anomaly detection, and reinforcement learning. We describe the architecture and implementation of our system, which consists of three main components: a data collector, a data analyzer, and a data actuator. We also present a table that summarizes the main features and capabilities of our system compared to existing solutions. We evaluate the performance and effectiveness of our system on a testbed consisting of programmable logic controllers (PLCs) and IIoT protocols, such as Modbus and MQTT. We simulate various attacks on IIoT devices and measure the accuracy, latency, and overhead of our system. Our results show that our system can successfully detect and mitigate different types of attacks on IIoT devices with high accuracy and low latency and overhead. We also demonstrate that our system can enhance the security and resilience of critical infrastructures by preventing or minimizing the impacts of attacks on IIoT devices.

Explainable Artificial Intelligence into Cyber-Physical System Architecture of Smart Cities: Technologies, Challenges, and Opportunities

The demand for smart cities stems from the increasing urbanization, limitations on resources, and the aspiration to establish metropolitan areas that are more habitable, adaptable, and interconnected. The integration of Cyber physical Systems (CPS) into smart cities has proven advantageous across diverse areas, such as healthcare, transportation, and manufacturing but as the intricacy of CPS Architecture escalates, the cognitive processes involved in decision-making get progressively convoluted, hence posing a challenge in comprehending and elucidating the system's conclusions in Smart Cities. To enhance the comprehensibility of judgements, it is imperative to combine the CPS architecture with XAI. To visualize the benefits of this integration, the authors carried out a comprehensive analysis utilizing the Scopus database. Diverse search queries were formulated and executed to acquire pertinent articles. Following the search, a total of 24 papers pertaining to the integration of Explainable Artificial Intelligence (XAI) and Cyber-Physical Systems (CPS) were chosen for analysis. A comprehensive evaluation of these publications and a meta-analysis of the results received from the Scopus database allowed for a deep grasp of the topic. The importance of XAI and CPS in Smart Cities rests in their ability to enhance decision-making, promote transparency, and increase efficiency. This study examines the factors that come before and the outcomes that result from combining XAI and CPS, encompassing the technologies involved, the difficulties faced, and the potential advantages. Furthermore, this study highlights the urgent necessity to incorporate XAI with CPS, as evidenced by the research findings published in the Scopus database.

Digital Twin and Cyber-Physical System Integration in Commercial Vehicles: Latest Concepts, Challenges and Opportunities

Digital Twin and Cyber-Physical System Integration in Commercial Vehicles: Latest Concepts, Challenges and Opportunities

Safety and Security Risk Assessments in Cyber Physical Space

As technological advancements continue to bridge the gap between the digital and physical worlds, the integration of Cyber-Physical Systems (CPS) has become pervasive in critical infrastructure, industrial processes, and daily life. This research paper explores the multifaceted landscape of Cyber-Physical Systems Security, addressing the intricate challenges and emerging threats posed by the convergence of the digital and physical realms. The study investigates the vulnerabilities inherent in CPS, encompassing critical infrastructure systems, industrial control systems, and interconnected smart devices. Additionally, the paper examines contemporary security measures, risk mitigation strategies, and the role of advanced technologies in fortifying Cyber-Physical Systems against malicious cyber threats. Through a comprehensive analysis of existing frameworks and real-world case studies, this research aims to contribute valuable insights to the ongoing discourse on safeguarding the integrity, confidentiality, and availability of Cyber-Physical Systems in our interconnected and technology-driven society. The findings of this research are anticipated to inform cybersecurity practitioners, policymakers, and researchers alike, fostering a more resilient and secure foundation for the future of Cyber-Physical Systems.

Facilitating the role of quantity surveyors through integration of cyber-physical systems

Purpose Cyber-physical systems (CPS) enable the synergistic integration of virtual models with the physical environment. This integration is gaining recognition for its potential to enhance construction project information management, thereby contributing to improved cost management in construction. Similarly, quantity surveyor (QS) plays a key role in construction projects by estimating and monitoring construction costs. Consequently, this research aims to explore redefining the role of QSs by integrating CPS. Design/methodology/approach The research adopted an interpretivism stance to collect and analyse data. Two rounds of 21 and 19 semi-structured interviews were conducted, with experts selected through heterogeneous purposive sampling. Code-based content analysis was used to analyse the data using NVivo12. MS Visio data visualisation tool was used to present the findings. Findings It is empirically proven the potential of CPS to facilitate nine key roles of QSs in all stages of the RIBA plan of work through the identification of 15 CPS applications and technologies. Nine key roles of QSs that CPS can facilitate were identified as preliminary estimation, measurement and quantification, contract administration, preparation of BOQ, interim valuation and payments, tender and contract documentation, cost planning, cost control and procurement advice. The study explored how adopting CPS technologies can transform traditional quantity surveying practices and enhance their value within the construction industry. Originality/value The findings add to the body of knowledge by redefining the role of QS through the integration of CPS for the first time and then by highlighting the usages of CPS in the construction industry rather than limiting it to a specific sector of the construction industry, as previous studies have done. This research uncovers several other research arenas on CPS as being the very first research to evaluate CPS to facilitate key roles of QSs. The findings can enhance the awareness and the practical implementation of CPS by intervening to form more partnerships among application developers and industry leaders.

Real-time Cyber-Physical Security Solution Leveraging an Integrated Learning-Based Approach

Cyber-Physical Systems (CPS) has emerged as a paradigm that connects cyber and physical worlds, which provides unprecedented opportunities to realize intelligent applications such as smart home, smart cities, and smart manufacturing. However, CPS faces a great number of information security challenges (e.g., attacks) due to the integration of CPS as well as the human behaviors and interactions. Therefore, accurate and real-time attack detection and identification are essential to ensure information security and reliability of CPS. In this paper, we propose a novel integrated learning method that accurately detects an attack of a CPS system and then identifies the attack type in real time. Specifically, we consider a One-Class Support Vector Machine (OCSVM) model that only relies on the data from the normal state for training to achieve a real-time and effective detection of a CPS system state (i.e., normal or under-attack). If the system is detected to be under-attack, we then develop a Pairwise Self-supervised Long Short-Term Memory (PSLSTM) approach to identify the attack type, which aims to accurately distinguish the known attack types and discover unknown new attacks. Lastly, experimental results show the proposed method achieves promising performances compared with conventional and state-of-the-art learning-based benchmarks.

Task Allocation in Human-Robot Collaboration: A Simulation-based approach to optimize Operator's Productivity and Ergonomics

This paper delves into the transformative realm of Industry 4.0, emphasizing the pivotal role of Human-Robot Collaboration (HRC) in redefining manufacturing landscapes. Central to this revolution is the integration of cyber-physical systems, automation, and the Internet of Things (IoT), culminating in the advent of smart manufacturing. We explore how HRC fosters a synergistic work environment, blurring the traditional boundaries between human workers and robotic counterparts. This research emphasizes the importance of intelligent task allocation in optimizing operational efficiency while safeguarding the ergonomic well-being of human operators. A simulation-based approach is presented, focusing on task allocation within HRC contexts to enhance both human operators' productivity and fatigue levels. The study provides an analysis of fatigue dynamics and its repercussions on industrial settings. The paper outlines various collaborative scenarios, including sole human operation, robot support, a Flexible Workstation (F-WS) support, and combined robot-FWS support, evaluated through a case study of an assembly station. The results underscore the tangible benefits of integrating robotic and F-WS support, demonstrating a balanced improvement in processing speed, operator efficiency, and a significant reduction in fatigue accumulation. In conclusion, this paper advocates for a balanced, human-centric approach to Industry 4.0, where the integration of HRC and ergonomic considerations pave the way for a sustainable, efficient, and health-preserving industrial future.

Integration of Cyber-Physical Systems, Digital Twins and 3D Printing in Advanced Manufacturing: A Synergistic Approach

Integration of Cyber-Physical Systems, Digital Twins and 3D Printing in Advanced Manufacturing: A Synergistic Approach

Modeling barriers to adoption of digitization in supply chains using FTOPSIS and its impact on sustainability TBL

PurposeThis article aims at - exploring and prioritizing the barriers to adoption of digitization in supply chains (SCs), categorizing them into sustainability triple bottom line (STBL) based upon their direct impact and suggesting digital technologies to address each barrier.Design/methodology/approachA five-phase methodology is used which consists of an exploration of 44 barriers to the adoption of digitization in SCs, analysis of 44 barriers for mean, standard deviation and Cronbach alpha based on questionnaire-based feedback of 25 experts, extraction of 10 most significant barriers through 05 experts, followed by categorization of the barriers into STBL referring to their direct impact on STBL, prioritization of ten barriers using Fuzzy Technique for Order Performance by Similarity to Ideal Solution and recommendation of digital technologies to address each barrier.FindingsWhile all the barriers considered in this study significantly impede the adoption of digitization in SCs, lack of top management commitment (B1) is found to be most crucial while lack of culture toward use of information and communication technology required for digitization (B3) has minimum impact. Large investment in digital infrastructure (B6), difficulty in integration of cyber physical systems (CPSs) on varied platforms (B8) and lack of experts having knowledge of digital technologies (B2) are equally important barriers requiring more attention while adopting digitization in SCs.Research limitations/implicationsThis study is mainly based on feedback from 25 seasoned experts; a wider cross section of experts will give more insight.Practical implicationsThe outcomes are very significant for organizations looking to adopt digitization in their SCs. Simultaneous consideration to all the barriers becomes impractical hence prioritization of same will be useful for the SC managers to benchmark their preparedness and decide strategies for the adoption of digitization with due consideration toward the impact of barriers on STBL. The digital technologies recommended will further aid in planning the digital strategies to address each barrier.Originality/valueA unique approach to explore, analyze, prioritize and categorize the barriers to adoption of digitization in SCs is used to provide a deeper understanding of factors deterring the same. It implies that a supportive top management along with systematic allocation of finances plays a crucial role. The importance of availability of digital experts for integrating CPSs on a single platform is also highlighted. The digital technologies recommended will further assist the organizations toward adoption of digitization in SCs with due consideration to STBL.

Industry 4.0 and Technological Evolution in CNC Machines

This work, entirely based on a documental research strategy, consists of an analysis of the technological evolution of the machining processes with Computer Numerical Control machines regarding the new concept of Industry 4.0. This concept involves integration of cyber-physical systems with the manufacturing processes, the use of the Internet of Things in industrial processes and the development of complex virtual systems. An attempt was made to understand how current processes can be adapted or changed to the concept under study when analyzing the evolution of machining tools for CNC machines. A thorough study was done to adopt the distinguishing features of the Industry 4.0 concept, applying it to machining processes in CNC machines. The use of virtual technology in machining tools is still a subject in development, and it was one of the main focus of this work. The main factors that can influence directly or indirectly the production processes of a factory with CNC machines were analyzed. The types of machining processes for CNC machines and the types of machining tools developed with virtual technology possibilities were studied and explored. Following the analysis, suggestions for future applications and for improvements in production structures are proposed.

Autonomous Vehicles and the Linkages to Smart Mobility: Impact and Implications on Digital Globalisation

Abstract Human civilisation is undergoing a transformation, perceived as the institutionalisation of digital globalisation. The 20th century witnessed greater strides in the form of the internet revolution, automobile technologies and smart mobility, related transformation, dissemination and integration of related technologies as well, leading to the emergence of digital globalisation. The integration of cyber-physical systems has accelerated the rate of urbanisation, and the currently prevailing enhanced standards of living have increased the number of automobiles in cities that have been integrated into the digitally globalised ecosystems, leading to the emergence of autonomous vehicles (AVs). The present study analyses the emerging threats and vulnerabilities of AVs and smart mobility, which have created more security concerns for AVs across nation-states, leading as well to the emergence of digital globalisation. The article theorises the aspects of human security linkages and utilises a capacity matrix (qualitative approach) to examine the development, institutionalisation and integration of AVs into digital globalisation and the related challenges emerging along with it.

Data-driven Evaluation Method for Cyber-Physical System Reliability of Integrated Energy System

Background:: In order to achieve the social goal of low carbon and high efficiency, integrated energy system (IES) with deep integration of cyber-physical systems (CPS) are being developed rapidly. The reliability evaluation is an important basis for IES planning and operation. Considering the deep coupling relationship between various systems, energy networks, and cyber networks, and the impact of multiple uncertainties on the integrated energy system, the traditional single index is difficult to quantify the reliability of the system, and the current evaluation methods rely on mechanism analysis and accurate modeling. It is difficult to deal with the coupling relationship between integrated energy systems, energy networks and information networks. So, the quantitative evaluation of the reliability level index of integrated energy systems under the cyber physical system is established, and a data-driven evaluation method is proposed. Methods:: In this article, the energy entropy and information entropy theory are combined to establish the reliability evaluation index of integrated energy system integration of cyber-physical systems, and a data-driven evaluation method based on random matrix theory (RMT) is proposed. Results:: A reliability evaluation model is established through RMT, and the single-ring law is used to analyze the statistical properties of the matrix model to identify the abnormal state of the system. Then the reliability of cyber and physical layers are evaluated based on energy entropy and information entropy, respectively. In addition, sensitivity analyses on the impact of node interruption probability, network topology, access load rate, and other factors are carried out. Conclusion:: The examples of IES are given to verify the feasibility and effectiveness of the proposed method.

Cyber-physical systems for facilities management: a Delphi study on the propelling measures

PurposeThrough its advanced computational capabilities, cyber–physical systems (CPS) proffer solutions to some of the cultural challenges plaguing the effective delivery of facilities management (FM) mandates. This study aims to explore the drivers for the uptake of CPS for FM functions using a qualitative approach – the Delphi technique.Design/methodology/approachUsing the Delphi technique, the study selected experts through a well-defined process entailing a pre-determined set of criteria. The experts gave their opinions in two iterations which were subjected to statistical analyses such as the measure of central tendency and interquartile deviation in ascertaining consensus among the experts and the Mann–Whitney U test in establishing if there is a difference in the opinions given by the experts.FindingsThe study’s findings show that six of the identified drivers of the uptake of CPS for FM were attributed to be of very high significance, while 12 were of high significance. Furthermore, it was revealed that there is no significant statistical difference in the opinions given by experts in professional practice and academia.Practical implicationsThe study’s outcome provides the requisite insight into the propelling measures for the uptake of CPS for FM by organisations and, by extension, aiding digital transformation for effective FM delivery.Originality/valueTo the best of the authors’ knowledge, evidence from the literature suggests that no study has showcased the drivers of the incorporation of CPS for FM. Hence, this study fills this gap in knowledge by unravelling the significant propelling measures of the integration of CPS for FM functions.