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Measurement related to different machine vision functions is the base for developing of cyber-physical systems able to see and make decisions. These kinds of systems are emerging in all areas of our daily lives. They can be found in the medical area, in industry, in the agriculture, in all those interconnected cloud computing-based systems related to flying/terrestrial robotics, navigation, automated surgery, smart cities, smart health monitoring, etc. All of them are extremely dependent on the same: adequate coordinates measurement, properly selected data processing and data fusion algorithms, evaluation procedures for performance analysis of measurement within Machine Vision systems, processes and algorithms (both traditional and artificial intelligence), mathematical models for 3D-measurement purposes (measurement of displacements, surface profiles, deformations, data augmentation/interpolation, etc.), and distributed visual measurement systems, as well as distributed memory and sensory part. Cyber-physical systems can be implemented on almost any application, especially on those dotted by robots and automated guided devices (from aerospace applications to domestic cleaners). The success of the measurement process depends on the kind of sensors and their optoelectronics characteristics and intrinsic parameters, as well as their respective operating and processing. The correct approach selection for the application, the data acquisition and collection efficiency, the data processing algorithms, the hardware processors response time, and the intelligent auto adaptability to changing environments or conditions. Recently, the emergence of artificial intelligence algorithms and the internet of things have powerful development of such systems, highlighting the importance and the impact of the measurement accuracy related to machine vision performance. [ABSTRACT FROM AUTHOR]

Environmental working conditions have a great impact on the power demand and fuel consumption of vehicles, and the rational use of road terrain information plays an important role in the improvement of vehicle economy and energy sources durability. In this paper, a fuel cell hybrid electric bus (FCHEB) energy management strategy (EMS) based on deep deterministic policy gradient (DDPG) algorithm taking into account future terrain information is proposed based on cyber-physical system (CPS) to reduce the economic cost of FCHEB. First, this paper implements the information exchange between the vehicle system and the network layer through CPS to obtain the environmental working conditions of the vehicle operation. Second, future terrain information is introduced into the framework for the first time with hydrogen consumption, battery degradation and fuel cell durability constraints to rationally allocate the power of the FCHEB. The results show that the proposed strategy improves the power battery durability by 7.39% and reduces the total operating cost by 5.76% compared to the EMS that ignores the future terrain information. • An energy management strategy that considers future terrain information is proposed. • The proposed strategy achieves an effective combination with cyber physical system. • A trade-off between hydrogen consumption and energy sources durability is achieved. • The proposed strategy reduces the total cost by 7.81% compared to NTI EMS. [ABSTRACT FROM AUTHOR]

Digital transformation is a new trend that describes enterprise efforts in transitioning manual and likely outdated processes and activities to digital formats dominated by the extensive use of Industry 4.0 elements, including the pervasive use of cyber-physical systems to increase efficiency, reduce waste, and increase responsiveness. A new domain that intersects supply chain management and cybersecurity emerges as many processes as possible of the enterprise require the convergence and synchronizing of resources and information flows in data-driven environments to support planning and execution activities. Protecting the information becomes imperative as big data flows must be parsed and translated into actions requiring speed and accuracy. Machine learning and artificial intelligence have become critical in supporting extensive data collection and real-time processing to assist decision-makers in configuring scarce resources. In this paper, we present four different applications that investigate issues related to the broader maritime supply chain security domain affecting the planning, execution, and performance of complex systems while exploring novel frontiers in cyber research and education. This paper will focus on Machine Learning and AI applications on Unmanned Aerial Systems and Cryptography related to Cybersecurity in Maritimes and Shipbuilding Spheres. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper investigates the attack estimation and state reconstruction problem for linear cyber–physical systems with state delay and simultaneous sensor and actuator attacks. Reduced-order observer is designed for the augmented system to simultaneously estimate the actuator attack, sensor attack and the state of system. By adopting the double integral term into the Lyapunov–Krasovskii functional and decomposing the cross term, the delay-dependent results are obtained. The method proposed in this paper can accurately estimate the state, actuator and sensor attacks simultaneously without additional design. Compared with the previous method, the time delay information is fully utilized and the conservation is greatly reduced. Finally, the correctness of the results is verified by simulation. • Delay-dependent conditions are obtained for cyber–physical systems with state delay. • Both attack estimation and state reconstruction are considered. • Actuator attacks, sensor attacks and system state are estimated simultaneously. [ABSTRACT FROM AUTHOR]

This paper introduces a smart cyber physical multi-source energy system for electric vehicle applications. This system is realized in order to increase the autonomy of the vehicle as well as a good self-dispatch energy system. In this paper, three energy sources have modeled and integrated in the energy conversion chain of the vehicle. Moreover, the design of cyber energy dispatching system is proposed based on the genetic algorithm NSGA-II. Finally, the simulation and experimental results are demonstrated. [ABSTRACT FROM AUTHOR]

The finite-horizon optimal secure tracking control (FHOSTC) problem for cyber–physical systems under actuator denial-of-service (DoS) attacks is addressed in this paper. A model-free method based on the Q-function is designed to achieve FHOSTC without the system model information. First, an augmented time-varying Riccati equation (TVRE) is derived by integrating the system with the reference system into a unified augmented system. Then, a lower bound on malicious DoS attacks probability that guarantees the solutions of the TVRE is provided. Third, a Q-function that changes over time (time-varying Q-function, TVQF) is devised. A TVQF-based method is then proposed to solve the TVRE without the need for the knowledge of the augmented system dynamics. The developed method works backward-in-time and uses the least-squares method. To validate the performance and features of the developed method, simulation studies are conducted in the end. • The augmented time-varying value function (TVRE) can be represented as the time-varying quadratic form is demonstrated. In conjunction with this finding, the time-varying Riccati equation for the finite-horizon optimal secure tracking control problem is derived. • The impact of the DoS attacks on the solution of the TVRE is demonstrated. The convergence of the TVRE is proved under certain assumptions. Furthermore, a lower bound on the probability of DoS attacks that satisfies these assumptions is derived. • A time-varying Q-function (TVQF) is designed. The equivalence between the approximation of the TVQF kernel matrix and the solution of the derived TVRE is proved. Additionally, a TVQF-based model-free optimal secure tracking control algorithm is developed. [ABSTRACT FROM AUTHOR]

Smart grids are two-way communications grids that converge Information Technology (IT) and Operational Technology (OT) to transfer energy-related information between different industry components within the grid. Smart grids have changed the energy sector by increasing sustainability, efficiency and integrating renewable energy sources. However, smart grids are vulnerable to IT-related attacks because they rely on Information and Communication Technology (ICT). By surveying relevant papers and evaluating accessible statistics, this study explores cybersecurity in smart grids by examining current communication protocols and standards. We carefully compile various datasets with general information about four of the most smart grid-related datasets. Our study and conclusions address the key components of a smart grid and offer information that can help create cybersecurity plans specifically for smart grids. This research contributes to the discourse on smart grid security, which is important for preserving the stability of contemporary energy systems. [ABSTRACT FROM AUTHOR]

In today's cyber–physical microgrid systems, the consensus-based secondary control is generally utilized to settle the voltage deviation and rough current allocation issues at the primary control level. However, time delays follow inevitably the introduction of sparse communication networks, and most existing works adopt passive tolerance approaches. To actively alleviate the unavoidable delay effect in microgrids' communication networks, a networked predictive control (NPC) strategy is proposed for an islanded DC microgrid subject to time delays in this paper. Firstly, the predictive approaches for both voltage and current are developed based on the cyber–physical microgrid model. Unlike the practice of passively tolerating time delays, the NPC strategy is proposed to actively compensate for the effect of communication delays by estimating real-time voltage and current values using the previously obtained prediction models. Moreover, to prove the generality of the developed method, the microgrid systems' stability can be derived from the Schur stability of the closed-loop system, thus the DC microgrid can achieve voltage regulation and proportional current sharing simultaneously. Finally, the performance of our method against the time delay effect is validated by extensive experiments on an islanded 48-V DC microgrid system, in terms of its feasibility, delay tolerance ability, and robustness to load changes and communication faults. Experimental results demonstrate the effectiveness and superiority of the NPC strategy. • To actively compensate delays, a networked predictive control strategy is proposed. • Unlike passive schemes, a proactive multi-step prediction mechanism is developed. • The NPC method is demonstrated to have certain universality and generalizability. [ABSTRACT FROM AUTHOR]

A cyber-physical system (CPS) is an interconnected physical system that can be monitored, controlled, and operated remotely. A systems-based analysis of CPS is critical for enhancing operational safety and security. Such analysis is particularly important in light of the evolving Fourth Industrial Revolution (Industry 4.0) which is largely driven by CPS, Internet of Things (IoT), algorithmic systems, automation, and smart manufacturing. As a result of industries' and societies' growing dependence on CPSs, their safe and secure applications need to be scrutinized to avoid any threats or harm to processes and human lives. In order to achieve this target, the current work analyzes the literature to investigate challenges and opportunities present in the CPSs. The analysis attempts to answer the following Research Questions (RQs): (RQ1) What are the prominent trends in the field of safety and security of CPSs? (RQ2) What are the opportunities in Industry 4.0 technologies related to safety? (RQ3) What characteristics of CPSs are likely to cause safety and security issues? (RQ4) What are the emerging safety and security issues introduced by CPSs? (RQ5) How were these issues addressed in the literature? CPSs can address some traditional safety issues due to characteristics like high efficiency and control. However, a CPS is challenged by heterogeneity, dependencies, complexity, unattended nature, increased machine intelligence, autonomous reconfiguration, and uncertainties. Safety and security challenges include opaque system failure, complex socio-technical system, human-machine interface, cyber-physical attacks, unsecured remote configuration, lack of standards, and resilience. The paper is structured under the following key headings: bibliometric results, opportunities from Industry 4.0 technologies, issues introduced by Industry 4.0 technologies, literature proposed solutions, and research challenges. Based on the assessment of about 900 relevant publications extracted from two databases (Web of Science (WoS) Core Collection and Engineering village (EV)), the documents were categorized into eight major divisions: (i) Safety Opportunities, (ii) Hazardous Characteristics, (iii) Fault Occurrence, (iv) Cyber-physical attacks, (v) Human factors, (vi) occupational Health and safety, (vii) standards, and (viii) Reliability and resiliency. Proactive strategies were classified under four options: identification, evaluation, prevention, and management. The paper also raises some research needs and unanswered questions that require further analysis to address how security and safety mechanisms can introduce more complexity in the system that can lead to new risks. The paper concludes by emphasizing digital process safety as part of the engineering curriculum to address the process industry's need for digital solutions and to make process safety learning a conscious choice. [ABSTRACT FROM AUTHOR]

Embedded systems in production equipment and Internet of Things (IoT) sensors on production lines are one of the elements that constitute an industrial cyber-physical system. In this paper, an in-depth study and analysis of the optimization of blasting parameters and prediction of vibration effects in open pit mines using deep neural network arithmetic are present. Based on the deep neural network research and analysis of the relationship between blasting parameters and rock fragmentation, a prediction model for blasting parameters and fragmentation for the East Open Pit Mine was established, and sensitivity analysis was performed on blasting parameters, and the unit consumption of explosives and the perforation rate were established. It was found that the average relative errors of both numerical simulation results and depth prediction results were no more than 10%, while the average relative errors of Sadowski's formula prediction results were more than 20%. The results show that the neural network optimized by a genetic algorithm and the numerical simulation has the highest accuracy in predicting the blasting result parameters. The research model and results obtained in this paper can be used as a reference guide for engineering practice. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a significant trend in the field of information technology and encourages the implementation of cyber-physical systems, smart connected products, and new business models. Many enterprises struggle to create business value from IoT technology because they have difficulties defining their organizational integration. Model-driven engineering (MDE) is considered an effective technique to address the complexity of IoT application development. Existing approaches focus on requirements from a technical perspective and exhibit a lack of integration with organizational and business model aspects. The paper proposes a modeling approach and a tool to support the development and configuration of IoT solutions in the example of air conditioning and cleanroom technologies (ACT) facilities. The main contributions of this paper are (a) an architecture for the IoT application, (b) a modeling language and tool support for IoT modeling, and (c) the expected practical benefits in the industrial case. [ABSTRACT FROM AUTHOR]

The digital deep integration of cyber space, physical space and social space facilitates the formation of Cyber–Physical–Social Systems (CPSS). Knowledge empowers CPSS to be capable of solving complex tasks and providing intelligent services. Knowledge representation, fusion, and reasoning are complementary in the knowledge graph and worthy of continuous exploration and in-depth research. Since the concept of knowledge representation learning has been proposed, various models have been developed and demonstrated their superior ability in knowledge fusion and reasoning. In this paper, we investigate the progress and function of representation learning models adopted in knowledge fusion and reasoning, and extract their commonalities and characteristics, which is a perspective that has never been developed before, to provide new ideas for scholars and realize the mutual promotion of knowledge fusion and reasoning. This paper comprehensively reviews classic methods and investigates advanced and emerging works, which are published in authoritative journals or conferences. Furthermore, to overcome the limitations of existing methods, we propose an integrated knowledge representation learning and application framework for CPSS and a series of tensor-based knowledge fusion and reasoning models. Finally, four future directions are discussed in this paper, we analyze the challenges faced by them and suggest promising solutions. • Extensive coverage of emerging advances about knowledge representation learning. • Targeted investigations and new perspectives about knowledge fusion and reasoning. • Tensor-enabled representation learning framework and promising models for CPSS. • Insightful prospects on representation learning-based knowledge reasoning and fusion. [ABSTRACT FROM AUTHOR]

To address the limitations of 5 G, 6 G wireless networks are envisaged to provide sub-millisecond latency, ultra-high connection density, extremely high data rates, better coverage, reliability, availability, etc., for cloud computing, Internet-of-Everything, and cyber-physical systems. However, avoiding several trust-related issues in the design of the wireless networks may delay the goal. In recent years, Blockchain has become a promising technology that can transform various fields by providing innovative and profitable solutions. Blockchain's intrinsic features, such as immutability, decentralization, anonymity, and transparency, can corroborate trust among isolated networks to secure access control, authentication, better integrity, secrecy, and effectual resource sharing. This paper paints a holistic picture of 6 G wireless networks and Blockchain, including architecture, Blockchain-assisted 6 G services, deployment, 6 G-assisted Blockchain service models, consensus mechanisms for Blockchain-as-a-Service, 6 G-assisted mining and consensus, and others. It also sheds light on the integration possibilities of Blockchain and 6 G, its potential benefits, and the effects of consensus mechanisms on 6 G concerning security, scalability, and energy consumption, to name a few. The existing 6 G projects and applications have also been summarized, along with the discussion on research and projects to standardize 6 G. Various research directions for Blockchain, 6 G, and their integration have also been explored and reported. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0, achieving both energy efficiency and robust security in Cyber-Physical Systems (CPSs) presents a significant challenge because of the resource requirements and complexity of these systems. This paper presents a novel method to integrate energy efficiency and robust security measures in CPSs. We propose the integration of anomaly detection techniques into the CPSs, to facilitate self-adaptation to changing conditions and threats, thereby enhancing system flexibility and reliability while also optimizing energy consumption. Our approach enhances the flexibility and reliability of CPSs by integrating Deep Reinforcement Learning (DRL) into the MAPE-K (Monitor-Analyze-Plan-Execute with Knowledge) control loop. This integration not only streamlines anomaly detection but also optimizes energy consumption, ensuring efficient and effective management of critical system functions. The outcome is a marked improvement in the adaptive decision-making capabilities of CPSs, leading to heightened security and better energy efficiency across various sectors and applications. This study significantly advances sustainable industrial practices within the Industry 4.0 paradigm, emphasizing the development of CPSs that excel in both energy efficiency and robust security. [ABSTRACT FROM AUTHOR]

Abstract Cyber security has been a growing concern for large organizations, especially financial and governmental institutions, as security breaches in the systems they depend on have repeatedly resulted in losses of billions of dollars per year, and this cost is on the rise. A primary reason for these breaches is that the systems in question are cyber-physical - a mix of people, processes, technology, and infrastructure. However, existing security solutions for cyber-physical systems are likely to become obsolete; even though security agents issue new sets of vulnerability indicators and patches to address security breaches, these vulnerability indicators change over time, which is a never-ending process. To tackle this problem, a multi-realm security attack event monitoring framework was proposed to monitor, model, and analyse security events in social(business process), cyber, and physical infrastructure components of cyber-physical systems. In this paper, we evaluate this security attack event monitoring framework. The evaluation was performed by using a large-scale case study on a medical emergency response system. [ABSTRACT FROM AUTHOR]

• This comparative analysis provides valuable insights into the selection and utilization of network and power simulators, offering researchers a guide to effectively enhance Smart Grid (SG) reliability through integrated modelling and simulation methodologies. • The comparison of power simulators for SG applications reveals a diverse range of options with varying strengths and limitations. Each simulator offers specific capabilities, requiring careful consideration based on research requirements. • Network simulators offer significant benefits for network research for SG applications, providing cost-effective environments for controlled experiments. However, there's a need for critical evaluation to address limitations and enhance realism. • Emphasizes the significance of co-simulation. Co-simulation platforms efficiently optimize SG reliability by enabling integrated analysis of power system dynamics and communication network behaviour. • Provides a detailed review of co-simulation platforms built in the past and their capabilities and limitations. • Provides guidance on selecting co-simulation platforms based on research requirements, including detail level, real-time capabilities, and ease of integration. • Suggests future directions for SG research, including integrating machine learning, leveraging virtualization and cloud technologies, and fostering collaboration with industry. In order to provide effective, safe, and dependable grid operation, the use of information and communication technologies (ICT) for real-time monitoring and control of the Smart Grid (SG) has grown in recent years. With such technologies, our system evolved into a complex cyber-physical system (CPS) in which communications networks play an important role in the dynamic performance of SG. Therefore, it is imperative to perform integrated modeling and simulation (IMS) of control and communication systems to evaluate the SG CPS for new control strategies or select appropriate communication technologies for specific applications before implementing them on a larger scale. This paper provides a state-of-the-art literature review about co-simulation, one of the key methods to perform an IMS. The paper starts with a brief review of the SG communication infrastructure, communication, and SG simulators used for co-simulations to give the readers an understanding of the requirements and issues for modeling and simulating SG communication systems. Then, detailed literature about the co-simulation platform covers the individual simulators used, the key features, test case advantages, and the limitations of different co-simulation platforms. In the discussion section, comparative analyses of co-simulation platforms are provided. The review summarizes critical challenges and opportunities in co-simulation, emphasizing the potential of IMS for advancing SG design and operation. [ABSTRACT FROM AUTHOR]

The rapid development of cyber-physical power system has highlighted the pressing issue of cyber attacks (CAs) faced by large-scale renewable energy power systems (LREPS). The injection of false data attacks on critical electrical equipment by attackers can lead to abnormal system frequencies, triggering cascade failures and causing widespread blackouts. This paper introduces a tri-level defense model specifically crafted for LREPS, comprising an upper, middle, and lower level (defender-attacker-operator). By promoting collaborative decision-making among these agents, the model aims to mitigate the rate of change of frequency (RoCoF) and effectively withstand CAs. The upper-level employs an economic-inertia optimization strategy, scheduling generator units to maximize system inertia while minimizing generation costs. By leveraging the strong duality theorem, the tri-level model is decomposed into a bi-level model consisting of a master problem and a subproblem, solved using the Benders decomposition method. Furthermore, this paper also considers augmenting the virtual inertia of renewable energy units when defense resources are insufficient, ensuring RoCoF remains within limits after CAs. The effectiveness and superiority of the proposed model and strategy are validated through case studies based on the improved IEEE 118-bus system. The results demonstrate that the model can reduce load shedding caused by the worst-case CAs, enhance system inertia, lower RoCoF , and improve the stability of LREPS. • Propose a defense model against frequency disturbances caused by cyber attacks. • Minimize load shedding under worst-case attack. • Enhance system inertia through economic-inertia optimization. • Model significantly reduces rate of change of frequency. [ABSTRACT FROM AUTHOR]

• The model of the cyber-physical-transportation network is established. • A two-stage model is applied to enhance the resilience for power system. • The accurate model of restoration time of faulty components is presented. • The load recovery model of cyber-physical is established. In this paper, a two-stage collaborative stochastic optimization method for pre-disaster emergency repair station planning and post-disaster recovery strategy of cyber-physical-transportation networks is proposed in this paper to enhance the resilience under extreme scenarios. In the first stage, based on the principle of hierarchical portioning, the responsible area for the emergency repair station is divided by the community theory, and the emergency repair station planning model is developed. In the second stage, the time sequence of repairing the faulty components is first established based on the graph theory. Then, based on the spatial distribution of adjacent time-series faulty components on the transportation network, a faulty component restoration time model is established to accurately reflect the faulty restoration time. On this basis, the functional coupling effects of economic dispatch control and substation automation system failures on load recovery are analyzed to establish a load recovery model. The case studies of the IEEE RTS-79 test system demonstrate that, compared with traditional independent recovery model, the model proposed in this paper performs better in enhancing resilience under extreme scenarios. [ABSTRACT FROM AUTHOR]

The advent of Industry 4.0 has revolutionized the manufacturing industry by incorporating interconnectivity, decentralized decision-making, resource optimization, and automation. This transformation has been made possible through the integration of advanced technologies such as Artificial Intelligence, Cyber-Physical Systems, the Internet of Things, Big Data Analytics, and Blockchain Technology. However, while Industry 4.0 has primarily focused on production efficiency and real-time transparency, the need for the transition toward a holistically sustainable environment has become increasingly urgent. As a result, the term Industry 5.0 was devised, merging the concept of Industry 4.0 with sustainability, resilience, and human-centric goals. This paper explores the potential areas of influence these technologies have on the environmental dimension of sustainability. The findings indicate that various Industry 4.0 technologies can contribute to environmental sustainability in manufacturing. These technologies offer opportunities to optimize supply chain performance, improve product traceability, reduce waste, enhance resource management, and minimize overall environmental impact. By understanding the implications of Industry 4.0 for environmental sustainability, researchers, policymakers, and industry practitioners can collaborate to develop strategies that integrate advanced technologies and sustainable practices in the manufacturing sector. The research provides insights into the potential benefits of these technologies for environmental sustainability and calls for further investigation to fully explore their environmental dimensions. [ABSTRACT FROM AUTHOR]

Recent social, healthcare, and geopolitical events have caused major disruptions to global supply chains, leading to significant negative consequences on the availability and cost of raw materials for various industries. The injection molding sector has been significantly impacted, with supply chain disruptions resulting in reduced availability of plastic granules and overall increases in raw material costs. To overcome these challenges, plastic component manufacturers need to optimize their material validation process to make the most efficient use of available materials. This article proposes a fully automatic solution for optimizing plastic component production in the context of disrupted supply chains, leveraging a Cyber-Physical Production Systems (CPPS) composed by a Cobot, an automatic guided vehicle, an injection molding machine, and a test bench for plastic components. The paper details the system architecture, implementation, and experimental results, demonstrating the effectiveness of the proposed approach. The results show that the CPS-enabled manufacturing system can successfully adapt to changes in material availability, increase production efficiency, and reduce waste. The proposed approach offers a promising solution for the challenges posed by disrupted supply chains in various manufacturing contexts and with different granulates and products. [ABSTRACT FROM AUTHOR]

Artificial Intelligent Robotic (AI Robotic) is the formal term for an 'intelligent induced' robot that can operate with vision (eye) to detect a product defect, or a control operating system to assist robot to differentiate good and reject product during production. A number of cutting-edge manufacturing automation technologies have been developed, including automatic inspection, autonomous robots, additive manufacturing, ubiquitous manufacturing, cloud manufacturing, and cyber-physical systems. Intelligent and automation technologies for worker assistance have been considered feasible. As knowledge-driven industries develop in terms of artificial intelligence and robotic automation, and to upgrade the existing and next generation of workforce, higher and further education is needed to advance a better understanding of robotic automation in manufacturers who face endless challenges such as labor shortages, high turnover labor force, product innovation, quality matters, and many others. This paper provides a general view of food supply chain challenges when faced with labor issue, environmental disaster and production problems. AI Robotic application would be a good source of problem solving in food manufacturing, food processing and food handling process. [ABSTRACT FROM AUTHOR]

The forthcoming industrial requirements to ensure increasingly flexible and agile production processes will require an ever-increasing ability to recognise, predict and optimise the state of processes with respect to the planned state. Advanced tools such as Digital Twins (DT) and Cyber Physical Systems (CPS) can help manage and address these problems. This paper presents some results of the authors' research on the adoption of a basic methodology for the design and implementation of Digital Twins aimed at manufacturing applications and subsequent integration into a Cyber Physical Production Systems (CPPS). Reference publications were selected based on the level of detail of DT and CPS architectures. The aim is also to combine methodological development with industrial tools and requirements. The architecture is potentially applicable to different production scenarios. Finally, the first results of application on a scale production line are shown. [ABSTRACT FROM AUTHOR]

The product environmental footprint (PEF) is one of two life cycle assessment methods from the European Commission. With their published recommendation on environmental footprint methods, the European Commission provides a framework to assess the PEF for every product in a company. Since there is a high probability that the PEF will be mandatory for companies shortly, it is crucial that this recommendation guides companies and mainly technical employees through all phases of the PEF and enables them to execute a PEF study correctly. Therefore, this paper aims to analyze the process of calculating a PEF for a product critically. A PEF study is conducted on a smart luminaire with the software program OpenLCA. The use case concludes that many aspects of the PEF still need to be clarified. Especially the calculation methods behind every impact category need to be more transparent. Further, a comparison of the use case with a provided tutorial from OpenLCA is made. The comparison shows that no information is available on how to model the end-of-life and the use stages, which are mandatory in a PEF study. [ABSTRACT FROM AUTHOR]

Intent on the smart vehicles of a platoon to follow their correct routes, they must exchange their state information with each other based on a communication graph. Meanwhile, malicious factors like Denial-of-Service (DoS) attacks, one of the major types of cyber-attacks, can affect vehicles and divert them from their correct routes. Also, spreading the erroneous data induced by a DoS attack from the attacked agent gradually destabilizes the platoon. Therefore, preserving all vehicles' safety is a vital issue for the platoon. In this paper, in the first step, detection and measurement of the DoS attack modeled by a time-varying delay are conducted by exploiting two incremental counters through a DoS detection and measurement algorithm. Also, supplementary to this, a novel vehicular resilient control strategy based on switching systems is proposed to retrieve the attacked agent to the leader–follower consensus. In the end, the capability of the proposed algorithms will be indicated by presenting an illustrative case study. • The detection and measurement of DoS attacks are fulfilled. • The attacked agents of the platoon are isolated to prevent the spread of wrong data. • A time-varying delay models the DoS attack. • The attacked agent is retrieved and returned to leader–follower consensus. [ABSTRACT FROM AUTHOR]

In this paper, we study a class of discrete-time cyber-physical systems (CPSs) with transmission delays and sparse malicious attacks on both input and output transmission channels, and present design methods for secure observer and controller. First, the states and sparse malicious attacks of the original system are reorganized into the states of the new system obtained by an improved augmentation method (rather than a singular augmentation method). Then, based on the new system, the states and attacks can be reconstructed simultaneously by the designed Luenberger-like observer. Furthermore, a novel dynamic output feedback (DOF) controller is proposed using the estimated values of the attacks, and a cone complementarity linearization (CCL) algorithm is designed to obtain the controller gains. The results show that the closed-loop system is stable in the sense of Lyapunov that both the estimation errors and the original system states will converge to a small neighborhood of the origin. In addition, the correlation results without delay are given. Finally, two examples are given to illustrate the effectiveness and superiority of the proposed method. • In addition to the sparse attacks that occur in both input and output channels, this paper also considers the transmission delays in both channels. • This paper introduces a multi-delay dynamic output feedback controller that transforms the inherently unstable original system into one that ultimately achieves Lyapunov stability. • In the case without transmission delay, compared with the results in previous researches, the proposed method of designing the observer and controller separately can reduce the difficulty of solving observer gains and controller gains, and enhance the feasibility. [ABSTRACT FROM AUTHOR]

This paper deals with learning stability of partially observed switched linear systems under arbitrary switching. Such systems are widely used to describe cyber–physical systems which arise by combining physical systems with digital components. In many real-world applications, the internal states cannot be observed directly. It is thus more realistic to conduct system analysis using the outputs of the system. Stability is one of the most frequent requirement for safety and robustness of cyber–physical systems. Existing methods for analyzing stability of switched linear systems often require the knowledge of the parameters and/or all the states of the underlying system. In this paper, we propose an algorithm for deciding stability of switched linear systems under arbitrary switching based purely on observed output data. The proposed algorithm essentially relies on an output-based Lyapunov stability framework and returns an estimate of the joint spectral radius (JSR). We also prove a probably approximately correct error bound on the quality of the estimate of the JSR from the perspective of statistical learning theory. [ABSTRACT FROM AUTHOR]

The digitization of the water sector has led to the emergence of Smart Water Metering Networks (SWMNs), which enable automated and continuous water consumption measurement. However, challenges persist in efficiently managing and transmitting the vast amount of data generated by these networks. To address this, researchers have proposed anomaly detection techniques to identify and detect anomalies within SWMNs. In the realm of anomaly detection, a substantial body of research has emerged. However, there is a notable gap in the literature on a comprehensive synthesis of Machine Learning (ML) applications for anomaly detection in SWMNs. To bridge this knowledge gap, this study evaluated thirty-two research papers written between 2016 and 2023, focusing on ML applications for anomaly detection in smart water systems, smart water grids, water distribution networks, and water networks. From an initial pool of 725 research papers, those directly related to ML-based anomaly detection techniques were selected and analysed using both quantitative and qualitative data analysis. The study revealed that ML techniques such as k-Nearest Neighbors (kNN), Autoencoders, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Random Forest, Decision Trees, and Support Vector Machines (SVM) have made significant contributions to anomaly detection within these networks. Notably, researchers frequently employed multiple ML techniques to enhance accuracy. Performance metrics analysis demonstrated that F1 score, precision, accuracy, and recall were commonly used to assess the quality of ML anomaly detection techniques. This review aims to provide researchers with recommendations for selecting suitable ML anomaly detection techniques in SWMNs. The findings underscore the promising nature of ML applications for anomaly detection in SWMNs and highlight the need for further research in this area. • Water networks use Cyber Physical Systems, leveraging adaptability for benefits. • Machine Learning (ML) detects diverse anomalies in Water Networks effectively. • Data availability crucial for robust ML anomaly detection in Water Networks. • ML techniques enable real-time anomaly detection, identifying Water Network issues. • Research on ML anomaly detection in SWMNs lacks systematic literature review. [ABSTRACT FROM AUTHOR]

This paper addresses the critical task of hacker identification within the cyber traceability system. While the latest hacker group identification method based on the heterogeneous graph attention network (HGHAN) holds promise in discovering hacker groups, its potential is hindered by the underutilization of node information and poor training efficiency. Particularly, attribute information dilution during node feature extraction and lengthy training for node embedding vector reassignment when new nodes are added have been observed. To rectify these shortcomings, the paper presents an improved model for hacker group identification. This novel approach leverages dynamic heterogeneous graph node updating to significantly boost efficiency without compromising the original model's classification accuracy. The key aspects of the method involve pre-learning for node attribute training vectors, LSTM (Long Short-Term Memory) and attention mechanisms for node feature vector refinement, and introducing a sparse matrix and dynamic node update scheme. The experimental results demonstrate marked improvements in training efficiency and graph update processes while maintaining classification accuracy. This advancement signifies the improved HGHAN model's capacity to adeptly navigate real-world network dynamics, assisting researchers in pinpointing malicious attackers amid cyber incidents. [Display omitted] • Implement heterogeneous graph node embedding vector updates in dynamic scenarios to replace retraining. • Adopt pre-training in the node feature extraction process to enhance feature utilization. • Introduce a triplet sparse matrix in the node embedding process to save space and improve efficiency. • The experiments show that the proposed model can greatly reduce the time consumption without affecting the identification effect of the basic model. [ABSTRACT FROM AUTHOR]

The academic community has extensively studied fault management in dynamical and cyber-physical systems, leading to the development of various model-based and data-driven/learning-enabled methods. Although these advanced designs show promise for improving conventional practices in aircraft systems, there is a noticeable disparity between academic methodologies and the specific needs of the aviation industry. The paper begins with an examination of the current practices within the aviation industry alongside the academic state of the art. It highlights commonly overlooked issues that hinder the transition from laboratory development to practical flight applications. Looking ahead, the paper anticipates evolving needs driven by the transition towards greater autonomy and intelligence within connected and distributed cyber-physical flight environments. This includes the emerging trend towards the introduction of Single Pilot Operations (SPO). The paper presents an outline of a combined model-based/data-driven vision, under human oversight, to navigate this complex transition. [ABSTRACT FROM AUTHOR]

The evolution of power systems into Cyber Physical Power Systems (CPPS), characterized by the integration of information and communication technologies with traditional electrical infrastructure, has resulted in new opportunities and challenges. This paper presents a comprehensive and systematic review of advancements in CPPS. In response to the unique attributes of CPPS, such as device vulnerabilities and data falsification, this review critically examines the state-of-the-art literature. The primary contributions of this paper include the systematic organization of existing research and an in-depth analysis of significant milestones in the field of CPPS, leading to the development of a taxonomy for categorizing various drivers. Furthermore, this paper elucidates current strategies for defining clear terms, models, methodologies, and attributes essential to cybersecurity policies within CPPS. The incorporation of relevant standards employed in the CPPS domain is also a paramount aspect of this review. In addition to these contributions, the paper identifies areas warranting further research and highlights emerging trends within CPPS, aiming to offer valuable insights to industry practitioners and to catalyze future research endeavors. [ABSTRACT FROM AUTHOR]

The central claim of the paper is that the development and control of Cyber-Physical Production Systems (CPPS) requires a systematic approach to handle and include explicit ethical considerations. Since the contribution of artificial intelligence (AI) technologies, and of agent-based models in particular, was instrumental in the evolution of CPPSs, approaches of ethical AI should be endorsed in CPPS development by design. The paper discusses recent advances for ethical AI and suggests a pathway from ethical norms towards standards. As it is argued, taking the responsible AI approach is promising when tackling the main ethic-related challenges of Cyber-Physical Production Systems. We expose a number of dilemmas to be resolved so that AI systems incorporated in CPPS cause no damages either in humans, equipment or in the environment and increase the trust in the users of current and future AI technologies. [ABSTRACT FROM AUTHOR]

The paper investigates the secure control problems for cyber–physical systems (CPSs) when the transmission channels suffer from Denial-of-Service (DoS) attacks based on switching observer and unknown input reconstruction (UIR). Firstly, an augmented system whose system state consists of the original system state and the measurement noises is set up, and the preconditions for the original system and augmented system are discussed in detail. Secondly, a full-order observer is constructed to generate the estimations of the augmented system state. Besides, based on the state estimation, an algebraic UIR method is developed and the UIR decouples the control input signal successfully. Thirdly, under the situation that some transmission channels suffer from DoS attacks, an observer-based secure controller is designed based on state estimation feedback and UIR feedback in view of a switching system. The stability of the switching system is analyzed as well. Finally, to verify the effectiveness of the proposed protocols, two simulation examples and the comparison with existing methods are given. • A state observer and UIR are purposed for CPSs under period communication mechanism. • The observers and controller have better performance compared to some existing works. • The secure protocols show good ability in handling DoS attacks with unknown inputs. [ABSTRACT FROM AUTHOR]

The availability of industrial network topologies is limited, which presents a significant challenge for researchers in this field. Network generators are essential tools for generating realistic simulation outcomes in network research. Thus, it is crucial to use realistic topologies that can accurately represent the characteristics of the target network. The primary objective of this study is to address the existing research gap by developing an industrial network topology generator for novel topology datasets specific to industrial networks and the industrial Internet of Things (IIoT). In this paper, we describe the development of the graph theoretic ind ustrial topology gen erator (GENIND), a topology generator that can produce realistic IIoT and industrial network topologies. The proposed GENIND network topology generator is validated through comparisons with real-world industrial network topologies. The evaluation framework assesses the performance of the proposed GENIND system using three main measures: the overall network structure, path-related factors, and resilience topological structure features. The results of the evaluation demonstrate a high degree of similarity between the graphs generated by the GENIND system and the real-world industrial networks used for comparison. This finding suggests that the generated graphs can be utilized as reliable tools to predict the behavior of industrial networks under various conditions. [ABSTRACT FROM AUTHOR]

Cyber–physical systems (CPSs) are complex systems that involve technologies such as control, communication, and computing. Nowadays, CPSs have a wide range of applications in smart cities, smart grids, smart manufacturing and intelligent transportation. However, with integration of industrial control systems with modern communication technologies, CPSs would be inevitably exposed to increasing security threats, which could lead to severe degradation of the system performance and even destruction of CPSs. This paper presents a survey on recent advances on security issues of industrial cyber–physical systems (ICPSs). We specifically discuss two typical kinds of attacks, i.e., Denial-of-Service (DoS) attack and Deception attack, and present recent results in terms of attack detection, estimation, and control of ICPSs. Classifications of current studies are analyzed and summarized based on different system modeling and analysis methods. In addition, advantages and disadvantage of various methodologies are also discussed. Finally, the paper concludes with some potential future research directions on secure ICPSs. • A more comprehensive survey on attack detection, secure estimation, and control of ICPSs from the control science perspective. • Classifications of current studies are analyzed and summarized based on different system modeling and analysis methods. • Advantages and disadvantage of various methodologies are discussed. [ABSTRACT FROM AUTHOR]

This paper introduces a cyber-physical system for building automation and control that is developed based on a distributed model predictive control. The implemented distributed method significantly reduces computation overhead with respect to the centralized methods. However, continuous data transfer between subsystems, which are often far from each other, is required when using this method. Information transmission between subsystems is very often subject to the limitations of transmission bandwidth and/or short communication range resulting in significant communication overhead. This causes significant time latency between making measurements and applying control commands, which adversely affects the control performance. Therefore, the distributed method used in this paper implements a two-level communication architecture to reduce the communication overhead. In order to avoid collision in communication inside neighborhood using this method, the TDMA-OFDMA scheme is used for wireless communication between distributed devices. Under these assumptions, the communication overhead is calculated. Then, a novel algorithm for finding the size of neighborhoods resulting in the lowest time latency between making measurements and applying control commands is presented for a typical office building. Finally, the satisfactory performance of the proposed cyber-physical system for the temperature control of a typical office building in the presence of disturbance and model inaccuracy is illustrated using computer simulations. [ABSTRACT FROM AUTHOR]