Advanced persistent threats (APTs) poses significant challenges to security of industrial control systems (ICSs). Existing security measures of ICSs are based on insufficient and isolated security requirements (SRs), which are elicited for individual devices and often overlook APT attacks with complex strategies. To solve this problem, we propose an elicitation method for SRs that incorporates Secure Tropos method and threat modeling to APTs. The Secure Tropos can model ICS components and interactions. Notably, we adopt MITRE ATT&CK for ICS to design APT attacks. To verify the effectiveness of the proposed method, we used it in an ICS for an industrial fluid catalytic cracking process to elicit 109 common SRs and 103 specific SRs. In addition, we conduct performance evaluations with existing elicitation methods based on two standards: 1) IEC 62443 and 2) NIST SP 800-82. The results demonstrate that the proposed method achieves a good balance between the effectiveness and cross-platform capability.

ABSTRACT In recent years, with the significant increase in the volume of three‐dimensional medical image data, three‐dimensional medical models have emerged. However, existing methods often require a large number of model parameters to deal with complex medical datasets, leading to high model complexity and significant consumption of computational resources. In order to address these issues, this paper proposes a 3D Lightweight Volume Convolutional Neural Network (3D LVCN), aiming to achieve efficient and accurate volume segmentation. This network architecture combines the design principles of convolutional neural network modules and hierarchical transformers, using large convolutional kernels as the basic framework for feature extraction, while introducing 1 × 1 × 1 convolutional kernels for deep convolution. This improvement not only enhances the computational efficiency of the model but also improves its generalization ability. The pro‐posed model is tested on three challenging public datasets, namely spleen, liver, and lung, from the medical segmentation decathlon. Experimental results show that the proposed model performance has in‐creased from 0.8315 to 0.8673, with a reduction in parameters of approximately 5%. This indicates that compared to currently advanced model structures, our proposed model architecture exhibits significant advantages in segmentation performance.

Localization of anomalies triggered by cyberattacks in industrial cyber-physical systems (ICPSs) is essential for implementing effective security measures due to the potential threats posed by such attacks. Identifying anomalies involves detecting deviations from normal behavior. This paper presents a digital twin-driven multi-task network (DTMTN) for anomaly localization in ICPSs. The DTMTN approach leverages the intrinsic characteristics of the ICPS, modeled within the digital twin, to predict system states. However, as constructing an accurate digital twin model is often impractical, the approach incorporates a multi-task network to enhance prediction reliability. This network serves two primary functions: it compensates for prediction inaccuracies and reconstructs deviations, thereby improving overall prediction accuracy. When an anomalous state occurs, DTMTN predicts normal system behavior to reconstruct the anomaly, allowing the original normal state to be inferred. The resulting prediction deviations are then analyzed to accurately pinpoint the location of the anomaly. Experiments conducted on a simulation testbed of a fluid catalytic cracking fractionation unit validate the effectiveness of the proposed approach.

Advanced driver assistance system (ADAS) is effectively promoting the vehicular automation level and it is critical to ensure its functional safety. While existing analysis mainly focuses on individual applications of ADAS, safety violations in the overall system can be found by extensive road tests, which are not only costly in terms of time and money but also lack a formal safety guarantee. This is because tests may not cover all driving scenarios, especially the ones that involve discrete mode switching. In this paper, we focus on the longitudinal vehicle motion and provide a pipeline to perform safety verification for all the related ADAS applications. To that end, we specify safety constraints and boundaries for a vehicle's longitudinal cruising and collision avoidance and validate a longitudinal dynamic model against the high-fidelity simulation software CarSim. Then we define hybrid automata to describe the closed-loop system composed of the vehicle dynamics and the ADAS. Finally, by computing the reachable sets of the hybrid automata and comparing them with the specified safety boundaries, the ADAS is verified. Numerical experiments demonstrate the efficacy of the proposed approach.

Electric bus transport, a popular mode of public transportation, offers punctual, safe, and comfortable services to passengers through the efficient and effective use of designated road space. The performance of electric bus transport systems depends largely on the design of proper locations of bus stops, with the consideration of passenger demands, waiting time, and traveling time. Optimal electric bus route planning can attract an increasing number of passengers and increase public transit services. Aiming to provide guidance for the electric bus route planning of developing cities, this study proposed an intelligent route planning method to minimize the waiting time and traveling time of passengers, in order to achieve the best comfortable level. In addition, a self‐learning anomaly detection method based on reinforcement learning (RL) was proposed to eliminate abnormal data caused by traffic accidents or emergencies. With a large spatiotemporal dataset collected over 3 years from a real electric bus project in Yantai, China, we developed a prototype system and conducted extensive experiments to evaluate the proposed intelligent route planning method. The results showed that the proposed method can reduce the passengers’ waiting time and attract more passengers traveling by electric bus. In addition, the proposed method has achieved optimal route planning recommendation (RPR) subject to 1,872,391 passenger demands on electric bus services; more than 86% of them were accurately predicted, and more than 97% were satisfied with recommendation results.

Due to the lack of effective attack detection measures, cyberattacks may cause strong damage to industrial cyber–physical systems (CPSs). The embedding of attack categories learned by the existing attack detection methods is highly coupled to each other with fuzzy boundaries and overlapped neighborhood, leading to weak robustness and high false positive rates. To address these issues, in this article, we propose a few-shot attack detection method based on decoupled prototype learning (DPL-FSAD), aiming to enhance the detection accuracy and generalization capabilities for malicious attacks in CPS. Specifically, we first introduce feature contrastive learning to extract differentiated features from highly similar samples, achieving compact intraclass and sparse interclass feature embedding space. To solve the problem of fuzzy boundaries of different attack categories, prototype contrastive learning is then employed to reduce the coupling degree among prototypes and enhance their discriminability. A regularization term is exploited to mitigate the overfitting problem by reducing the gap between the feature embedding and prototypes. Furthermore, an orthogonal constraint is employed to separate prototypes of different attack types, generating a decoupled prototype embedding space. The experimental results on three public cyberattack datasets show that, compared with the suboptimal model a few-shot learning model with Siamese convolutional neural network (FSL-SCNN), the proposed DPL-FSAD can improve the precision by 5.53%,F1-score by 3.3%, and reduce the false positive rate by 2.37% in average, which proves that the space decoupled prototype learning is effective for improving the generalization and robustness of industrial CPS attack detection in few-shot scenario.

Cyber-physical systems (CPSs) seamlessly integrate communication, computing, and control, thus exhibiting tight coupling of their cyber space with the physical world and human intervention. Forming the basis of future smart services, they play an important role in the era of Industry 4.0. However, CPSs also suffer from increasing cyber attacks due to their connections to the Internet. This article investigates resilient control for a class of CPSs subject to actuator attacks, which intentionally manipulate control commands from controllers to actuators. In our study, the supertwisting sliding-mode algorithm is adopted to construct a finite-time converging extended state observer (ESO) for estimating the state and uncertainty of the system in the presence of actuator attacks. Then, for the attacked system, a finite-time converging resilient controller is designed based on the proposed ESO. It integrates global fast terminal sliding-mode and prescribed performance control. Finally, an industrial CPS, permanent magnet synchronous motor control system, is investigated to demonstrate the effectiveness of the composite resilient control strategy presented in this article.

Advanced cyber-physical power systems (CPPS) has been put forward by the strong integration of energy networks and communication networks. While CPPS brings a promising solution with high efficiency, strong flexibility, great scalability, and improved reliability, it inevitably poses some security challenges. In order to address these challenges, it is essential to accurately describe the attack behavior and system security situation. In this article, a dynamic risk propagation evaluation approach is proposed for accurately predicting attacks and quantitatively analyzing system risk. It is equipped with a partitioned cellular automata model to deal with spatial heterogeneity in the partitioned system. The intentions of targeted attack are also considered for predicting attacks. Then, the cyber-to-physical risk is quantitatively identified from multiple dimensions. Finally, the verification of attack intention is designed to dynamically update and adjust the predicted result. The presented approach is demonstrated through a case study on a CPPS.

Cyber attacks threaten the security of cyber-physical systems (CPSs) seriously. Resilient control has been studied to defend cyber attacks. However, existing resilient control schemes have not considered system structure changes caused by actuator attacks. Such structure changes are more destructive and harmful than the actuator attack scenarios investigated in the literature, demanding new resilient control strategies. They will be addressed in this article in cloud computing environments, which are increasingly deployed in large-scale CPSs. More specifically, a resilient control scheme is designed which consists of two controllers: a local resilient controller and cloud-based resilient controller. The local resilient controller withstands actuator attacks that simply tampers the actuator output to a large extent. The cloud-based resilient controller aims to resist the actuator attacks that destroy the system structure. Simulations are conducted on a permanent synchronous motor control system to demonstrate the proposed resilient control scheme.

5G communication network has become a major pillar in the evolution of interconnected industrial systems. However, the introduction of 5G network may lead to unknown risks in the systems. To reveal the impact of network threats on 5G-based industrial systems, a 5G network security analysis approach combining formal modeling and attack penetration is proposed. Firstly, the 5G network models based on topology and transmission events are established to cope with diverse and hidden attack routes and behaviors. Then, the attack module is integrated into the network model. With attack penetration to the models, potential vulnerabilities are exploited and quantified based on the hierarchical-topology model, and network reliability is evaluated based on the transmission-event model. The simulation results identify and quantify network vulnerabilities under various attacks, including access authentication failure, destruction of data integrity, illegal control of Network Functions (NFs), and malicious consumption of shared slicing resources. Meanwhile, a more unpredictable outcome is that there is a threshold of access probability,$\alpha $, to measure the impacts of attacks against the bearer network and core network on reliability. Finally, a practical case about the impact of network security on a 5G-based coupled-tank system is discussed, which further proves the feasibility of our approach.

TOR (The Onion Router) network is a widely used open source anonymous communication tool, the abuse of TOR makes it difficult to monitor the proliferation of online crimes such as to access criminal websites. Most existing approches for TOR network de-anonymization heavily rely on manually extracted features resulting in time consuming and poor performance. To tackle the shortcomings, this paper proposes a neural representation learning approach to recognize website fingerprint based on classification algorithm. We constructed a new website fingerprinting attack model based on convolutional neural network (CNN) with dilation and causal convolution, which can improve the perception field of CNN as well as capture the sequential characteristic of input data. Experiments on three mainstream public datasets show that the proposed model is robust and effective for the website fingerprint classification and improves the accuracy by 12.21% compared with the state-of-the-art methods.

Resilient control of cyber-physical systems (CPSs) against actuator and/or sensor attacks has been extensively researched. However, the existing research considers actuator attacks and sensor attacks separately and also designs resilient controllers based on complex nonlinear system models caused by unknown actuator and sensor attacks. This increases the difficulty in the analysis, computation, and control of CPSs under attacks. To address this issue, this article introduces an idea to deal with both actuator attacks and sensor attacks together with feedback linearization control. This simplifies the mathematical modeling of attacked CPSs, thus reducing the difficulty of resilient controller design. Then, from the simplified modeling, a composite controller is designed to enhance system resilience. It ensures the dynamic and steady-state performance of CPSs under attacks. Simulation studies are undertaken to demonstrate the effectiveness of the proposed method.

With the rapid development of functional requirements in the emerging Industry 4.0 era, modern industrial control systems (ICSs) are no longer isolated islands, making them more vulnerable to various cyberattack threats. Cyberattacks on ICSs may have disruptive consequences, such as significant social and economic losses. To proactively address the security issue of ICSs, this article presents a unified architectural approach from the perspectives of cyberthreats on ICSs, security-related ICS technologies, and methods for ICSs. It incorporates secure networks, secure control systems, secure physical processes, and their interactions seamlessly into a unified framework. To increase the resistance of ICSs against intrusions, the network security in our architectural approach is to secure the data in motion through the integration of secure network architecture, secure industrial network protocols, and secure end-to-end communications. The protection of control systems in our architectural approach is risk-based and hierarchical and encompasses prevention- and tolerance-centric defenses. It provides a layer-by-layer defense so that an acceptable level of cybersecurity risk is achieved and maintained. Aiming to maintain the stable operation of physical ICS processes, the secure control in our architectural approach implements a security process against process-aware attacks through a resilient safety control scheme. The global and systematic architectural approach presented in this article for the ICS cybersecurity will help facilitate the design and implementation of cyberattack-resilient ICSs in the networked world. For further development of ICS security technologies, emerging challenges are identified and discussed to motivate future research efforts.

Multimicrogrid (MMG) systems play an increasingly important role in the smart grid. They come with various potential cyberattacks, which may cause power supply interruption or even human casualties. Therefore, decision-making for timely mitigation of cyberattack risks is highly desirable in the security protection of power systems. However, there is a lack of effective decentralized decision-making strategies that are able to deal with MMG scenarios through distributed consensus. To address this issue, a decentralized consensus decision-making (DCDM) approach is proposed in this article for the security of MMG systems. It achieves decentralized consensus without the need of a trusted authority or central server, making it distinct from existing consensus methods. Meanwhile, it guarantees the consistency and nonrepudiability of consensus results, which are stored on the blockchain in sequence. In each of the distributed agents, the approach consists of a fuzzy static Bayesian game model (FSB-GM) to determine the optimal security strategy and a hybrid consensus algorithm to achieve consensus. The FSB-GM considers the fuzzy preferences of different types of attackers and defenders. The hybrid consensus algorithm is implemented by the fusion improvement of two consensus mechanisms in the blockchain. The effectiveness of the presented approach is demonstrated through a case study on an MMG system.

Neighborhood area networks (NANs) are essential communication infrastructure in smart grid. They support communications for various applications including time-critical ones. A typical NAN communication scenario is to send commands from a control center simultaneously to a large number of nodes, demanding low-latency broadcast communications. This is challenging due to the limited bandwidth and large number of nodes in wireless NANs. While some broadcast schemes, e.g., opportunistic flooding, have been developed for general wireless sensor networks, they are not optimized for smart grid NANs with unique characteristics and low-latency requirements for time-critical applications. Therefore, a constrained broadcast scheme with minimized latency (CBS-ML) is presented in this paper for low-latency NAN communications. To avoid traffic congestion, it constrains the broadcast to a small number of core nodes. Theoretical developments are presented to show how to select core nodes based on network topology and link reliability. Simulations are conducted to demonstrate the proposed CBS-ML.

Industrial control systems (ICSs) in networked environments face severe cyber-security risks and challenges. A timely response to cyber-attacks is of paramount importance for mitigating risks. However, the security policy developed for an ICS may be conflicting with the ICS's safety policy, on which much attention has been paid for a long time in industrial control. An inappropriate enforcement of the security policy may deteriorate the ICS performance or even result in severe unexpected consequences. To tackle this problem, a risk-based security task scheduling approach is presented for ICSs with consideration of the safety policy. It ensures a timely response to cyber-attacks without compromising safety. More specifically, the approach reconciles security tasks and safety tasks according to a designed resolution policy, so as to acquire contradiction-free security and safety (S&S) tasks. Then, a real-time risk assessment method is developed to characterize the subtle change of the system risk with the implementation of the reconciled S&S tasks. After that, a task scheduling method is designed with the risk as the optimization objective, i.e., it searches the optimal task scheduling scheme by minimizing the risk posture. The resulting scheduling scheme ensures the smooth implementation of the S&S policy, which reflects the optimal recovery process against the risk. Finally, case studies on a hardware-in-the-loop testbed are conducted to demonstrate the effectiveness of the proposed approach.

Decision-making is a key component of industrial control system (ICS) security. However, due to the stability and real-time requirements of ICSs, current decision-making approaches typically designed for IT systems are not entirely suitable for ICSs. In this paper, with consideration of the characteristics of ICSs, a risk-based multistep dynamic decision-making approach for protecting ICSs is proposed. Following this proposal, multiple models, including multilayer Bayesian network, process model, and attack-defense strategy model are built first. On this basis, a state controller is designed to ensure the safe degradation/upgradation of ICSs. Finally, a game theory-based optimal defense strategy generation approach is presented. To verify the effectiveness of the proposed approach, a simulation on a simplified chemical reactor control system is conducted in MATLAB. The simulation results clearly demonstrate that the proposed dynamic decision-making approach has the ability to generate the optimal defense strategy to minimize system loss.

Industrial control systems (ICSs) are facing more and more cybersecurity issues, leading to increasingly severe risks in critical infrastructure. To mitigate risks, developing an appropriate security strategy is of paramount importance. However, existing efforts on decision making in ICSs inherit some limitations, such as the lack of consideration of the strategy for securing both cyber and physical domains and a tradeoff between security and system requirements. To overcome these limitations, a decision-making approach is presented in this paper for intrusion response in ICSs. Aiming to determine the optimal security strategy against attacks promptly, it tries to secure the most “dangerous” attack paths and respond to functional failures. In this approach, measures that cover both cyber and physical domains are designed with in-depth analysis of attack propagation. They ensure the completeness of candidate security strategy space. A number of Pareto optimal solutions are determined from the strategy space through multiobjective optimization. The objective is to maximize the objective vector composed of security benefit, system benefit, and state benefit. Then, these solutions are prioritized by using a distance-based evaluation method, which pursues the optimal protection ability by making the objective vector of the selected strategy closest to the ideal one. The effectiveness of the proposed approach is demonstrated with a case study on a simulated process control system.

Multimicrogrid (MMG) systems have the potential to play an increasingly important role in the transformation of existing power grid to smart grid. However, the open and distributed connectivity of MMGs exposes the systems into various cyber-attacks, which may cause serious failures or physical damages, such as power supply interruption and human casualties. Therefore, ensuring the security of MMGs is of paramount importance. To address this issue, a new collaborative intrusion detection (CID) approach using blockchain is proposed in this paper for MMG systems in smart grid. Due to the consensus mechanism of blockchain, the approach is designed without the need of a trusted authority or central server while improving the accuracy of intrusion detection in a collaborative way. It is equipped with a proposal generation method that combines periodic and trigger patterns to generate the detection target of CID, i.e., a proposal. From the generated proposals together with the correlation model of MMGs, a CID is achieved by using the consensus mechanism. The final detection results of CID are stored on blockchain in sequence. The use of an incentive mechanism motivates a single microgrid to participate in consensus. The effectiveness of the presented approach is demonstrated through a case study on an MMG system.

Modern power grid is increasingly integrated with battery energy storage systems (BESSs). This paper deals with the problem of state-of-charge (SoC) balance control for multiple distributed BESSs in smart grid. The BESSs are expected to work cooperatively to not only fulfil the overall power requirement but also meet the constraints of the same relative SoC variation rate. To achieve this objective, a distributed SoC balance control approach is presented with event-triggered signal transmissions. It is designed with the dynamic average consensus (DAC) mechanism for parameter estimations. The DAC enables distributed control of each BESS through communicating with its neighboring BESSs. Different from traditional periodic signal transmission, the event-triggered signal transmission embedded in our approach allows each BESS to transmit signal to its neighboring BESSs only when needed, thus reducing the communication traffic. Theoretical lower bounds are established for consecutive interevent intervals such that the Zeno behavior is excluded. Case studies are conducted to demonstrate the effectiveness of the presented approach.

With the evolution of information, communications, and technologies, modern industrial control systems (ICSs) face more and more cybersecurity issues. This leads to increasingly severe risks in critical infrastructure and assets. Therefore, risk analysis becomes a significant yet not well investigated topic for prevention of cyberattack risks in ICSs. To tackle this problem, a dynamic impact assessment approach is presented in this paper for risk analysis in ICSs. The approach predicts the trend of impact of cybersecurity dynamically from full recognition of asset knowledge. More specifically, an asset is abstracted with properties of construction, function, performance, location, and business. From the function and performance properties of the asset, object-oriented asset models incorporating with the mechanism of common cyberattacks are established at both component and system levels. Characterizing the evolution of behaviors for single asset and system, the models are used to analyze the impact propagation of cyberattacks. Then, from various possible impact consequences, the overall impact is quantified based on the location and business properties of the asset. A special application of the approach is to rank critical system parameters and prioritize key assets according to impact assessment. The effectiveness of the presented approach is demonstrated through simulation studies for a chemical control system.

With the increasing deployment of data network technologies in industrial control systems (ICSs), cybersecurity becomes a challenging problem in ICSs. Dynamic cybersecurity risk assessment plays a vital role in ICS cybersecurity protection. However, it is difficult to build a risk propagation model for ICSs due to the lack of sufficient historical data. In this paper, a fuzzy probability Bayesian network (FPBN) approach is presented for dynamic risk assessment. First, an FPBN is established for analysis and prediction of the propagation of cybersecurity risks. To overcome the difficulty of limited historical data, the crisp probabilities used in standard Bayesian networks are replaced in our approach by fuzzy probabilities. Then, an approximate dynamic inference algorithm is developed for dynamic assessment of ICS cybersecurity risk. It is embedded with a noise evidence filter in order to reduce the impact from noise evidence caused by system faults. Experiments are conducted on a simplified chemical reactor control system to demonstrate the effectiveness of the presented approach.

Visual sensor networks (VSNs) are highly vulnerable to attacks due to their open deployment in possibly unattended environments. To improve the network security of VSNs, an intrusion detection system (IDS) is an effective countermeasure. However, as visual sensors can produce big and dynamic video data, it is a tough task to rapidly and effectively detect attacks in VSNs. Moreover, attack samples in VSNs are generally too rare for IDSs to fully understand the behaviors of attacks. Facing these difficulties, in this paper, we propose an efficient intrusion detection approach for VSNs, which is based on traffic pattern learning. In the proposed approach, a traffic model is developed to describe the dynamic characteristics of network traffic in VSNs. Based on this model, the optimal feature set for traffic pattern learning can be extracted. Then a hierarchical self-organizing map (HSOM) is employed to learn traffic patterns and detect intrusions. Furthermore, an active learning strategy is devised to accelerate the training process of the HSOM and better learn the patterns of attacks. Experimental results show that the proposed approach has high detection accuracy and good real-time performance.

Intrusion response is a critical part of security protection. Compared with IT systems, industrial automation systems (IASs) have greater timeliness and availability demands. Real-time security policy enforcement of intrusion response is a challenge facing intrusion response for IASs. Inappropriate enforcement of the security policy can influence normal operation of the control system, and the loss caused by this security policy may even exceed that caused by cyberattacks. However, existing research about intrusion response focuses on security policy decisions and ignores security policy execution. This paper proposes a general, real-time control approach based on table-driven scheduling of intrusion response in IASs to address the problem of security policy execution. Security policy consists of a security service group, with each type of security service supported by a realization task set. Realization tasks from several task sets can be combined to form a response task set. In the proposed approach, first, a response task set is generated by a nondominated sorting genetic algorithm (GA) II with joint consideration of security performance and cost. Then, the system is reconfigured through an integrated scheduling scheme where system tasks and response tasks are mapped and scheduled together based on a GA. Furthermore, results from both numerical simulations and a real-application simulation show that the proposed method can implement the security policy in time with little effect on the system.

Currently, an increasing number of information/communication technologies are adopted into the industrial control systems (ICSs). While these IT technologies offer high flexibility, interoperability, and convenient administration of ICSs, they also introduce cybersecurity risks. Dynamic cybersecurity risk assessment is a key foundational component of security protection. However, due to the characteristics of ICSs, the risk assessment for IT systems is not completely applicable for ICSs. In this paper, through the consideration of the characteristics of ICSs, a targeted multilevel Bayesian network containing attack, function, and incident models is proposed. Following this proposal, a novel multimodel-based hazardous incident prediction approach is designed. On this basis, a dynamic cybersecurity risk assessment approach, which has the ability to assess the risk caused by unknown attacks, is also devised. Furthermore, to improve the accuracy of the risk assessment, which may be reduced by the redundant accumulation of overlaps amongst different consequences, a unified consequence quantification method is presented. Finally, to verify the effectiveness of the proposed approach, a simulation of a simplified chemical reactor control system is conducted in MATLAB. The simulation results can clearly demonstrate that the proposed approach has the ability to dynamically calculate the cybersecurity risk of ICSs in a timely manner. Additionally, the result of a different comparative simulation shows that our approach has the ability to assess the risk caused by unknown attacks.

Web-based systems have enjoyed tremendous growth in both theory and applications. They are highly visible and influential realizations of user-oriented technology supporting numerous human pursuits realized across the e-service. In this paper, we focus on web-based e-service systems for the permanent magnet synchronous motor (PMSM) remote control. These systems can provide web services for updating factors and the fuzzy law of Takagi-Sugeno fuzzy, when the PMSM devices are required. This paper designs the controller of the PMSM with uncertain inertia and friction factors working under load noise in Web-based e-service systems. This controller is based on rotor field-oriented control (RFOC) structures, internal model control (IMC), and improved direct adaptive fuzzy (IDAF). In order to enhance the transient quality for the case of uncertain inertia and friction factors, we use the IDAF algorithm for the outer loop (speed loop). The IDAF is designed based on the direct adaptive fuzzy algorithm combined with the G-Fuzzy system for adjusting online updating adaption factors. The essence of IDAF is a self-learning and self-adaption system with enhanced adaptive ability through the G-Fuzzy system. For the inner loop (current loop), an improved IMC (IIMC) structure is proposed to reduce the effect of load noise. The IIMC combines the tradition IMC and a speed feedback loop to enhance the antiload noise ability of the system. The difference between our control structure and the traditional control structure is that the system could automatically realize antiload noise in the inner loop before adjusting the speed in the outer loop. This will create really high performances for PMSM control systems. We also demonstrate the effect of this control algorithm on PMSM-RFOC system control. The extensive simulation results demonstrate that the current response satisfies the condition of ability and settling time. Especially, the antiload noise ability and transient quality of the system are controlled independently. Thus, it is a solid foundation upon which to develop a high-quality PMSM electric drive in the e-service.

Transient faults are a dominant kind of threat to system safety in networked control systems (NCSs) due to their high occurrence rate and wide variety. However, they are hardly detected accurately in NCSs because of their unpredictable nature and short duration. Hence, fault propagation analysis (FPA) has become a bottleneck issue for fault-tolerant control in NCSs, which is used to analyze the fault effects and identify the approximate zone where transient fault occurred. In this paper, an innovative ontology-based FPA approach (ontologyFPA) is proposed to analyze transient fault propagation effects in NCSs. From the view of object-centered ontology, function, behavior, and structure models are built to reflect system abstraction hierarchies, and fault propagation effects and traces are identified from behaviors to functions through the mapping relationships of abstraction models. From the view of system-centered ontology, information-based workflows are employed to represent system independence in which fault propagation is investigated by excavating different effect traces among serial tasks in control loops. To illustrate the processes of propagation analysis, the application of ontologyFPA in a steam generator water level control system is presented. Finally, based on a unified simulation platform described by the architecture analysis and design language (AADL), two types of faults are injected to inspect the fault propagation processes between abstraction hierarchies, while another type is injected to investigate the processes in workflows. The results demonstrate that the proposed approach is effective in terms of identifying transient fault propagation effects and traces.

Industrial process automation is undergoing an increased use of information communication technologies due to high flexibility interoperability and easy administration. But it also induces new security risks to existing and future systems. Intrusion detection is a key technology for security protection. However, traditional intrusion detection systems for the IT domain are not entirely suitable for industrial process automation. In this paper, multiple models are constructed by comprehensively analyzing the multidomain knowledge of field control layers in industrial process automation, with consideration of two aspects: physics and information. And then, a novel multimodel-based anomaly intrusion detection system with embedded intelligence and resilient coordination for the field control system in industrial process automation is designed. In the system, an anomaly detection based on multimodel is proposed, and the corresponding intelligent detection algorithms are designed. Furthermore, to overcome the disadvantages of anomaly detection, a classifier based on an intelligent hidden Markov model, is designed to differentiate the actual attacks from faults. Finally, based on a combination simulation platform using optimized performance network engineering tool, the detection accuracy and the real-time performance of the proposed intrusion detection system are analyzed in detail. Experimental results clearly demonstrate that the proposed system has good performance in terms of high precision and good real-time capability.

Space vector PWM inverter has been emerged as a promising technique to design a powerful supply for AC motor control applications in modern motor drive systems. In this paper, we implement a hardware model of three-phase voltage source inverter based on space vector PWM algorithm using 16-bit Digital Signal Controller dsPIC30F4011. The experimental results are carefully investigated to demonstrate that our proposed approach gains a high performance.

In networked control systems (NCS), the performance degradation introduced by the heterogeneous and dynamic environment has intensified the need for reconfigurable protocol stacks (RPS). In this paper, an IEC61499-based method is proposed for the model-driven development of RPS. The method is enabled by defining a novel RPS function block (FB), which unifies the communication behavior and interface of nodes in NCS. Beyond existing communication FBs in IEC61499, the parameter reconfiguration of routing and scheduling table in RPS FB is highlighted as the core of communication layer function to adapt environment and system variations. Furthermore, the method allows for the code reconfiguration on Java algorithms in RPS FB under different application requirements. Through porting the Java virtual machine on different platforms, the code reconfiguration is implemented by reloading the .class file for a specified protocol FB. A case study on the embedded platform, such as DSP/BIOS and ARM/Linux, is conducted to demonstrate the effectiveness and feasibility of the proposed reconfiguration method for maintaining stable and predictable behavior in NCS.

This paper aims to guarantee high-precision tracking of the desired optical path difference velocity for a Fourier transform spectrometer (FTS) in a space exploration system with time-varying parameters and nonlinear dynamics. A novel characteristic model-based adaptive discrete-time sliding mode control (ADSMC) scheme is proposed. The design of the ADSMC includes characteristic modeling, characteristic model-based discrete-time sliding mode control, and the estimator of the uncertain coefficients. The stability analysis of the ADSMC is also given in this paper. Simulation and experimental results demonstrate that the proposed characteristic model-based ADSMC can achieve high-precision control over a Michelson interferometer-based FTS. The significant advantages of the proposed ADSMC are its robustness and better control performance over the external disturbance and internal parameter uncertainty of the system.

In NCS (networked control systems), the control performance depends on not only the control algorithm but also the communication protocol stack. This paper presents a framework for the protocol reconfiguration in NCSs. Task queuing, network transmission policy and communication link control are emphasized as three key protocol functionalities related to the system performance. Based on it, data and event exchange models for layers of protocol stack are developed according to the standard IEC61499, which allows for the dynamic reconfiguration management of distributed resources as well as provides a unified real time communication service for control tasks. Finally, experiments are conducted to illustrate the application and efficiency of the proposed reconfigurable protocol stack.

Nowadays, due to the increasing user requirements of efficient and personalized services, a perfect travel plan is urgently needed. However, at present it is hard for people to make a personalized traveling plan. Most of them follow other people's general travel trajectory. So only after finishing their travel, do they know which scene is their favorite, which is not, and what is the perfect order of visits. In this research we propose a novel spatio-temporal sequence (STS) searching, which mainly includes two steps.

The Industrial Ethernet is promising for the implementation of a Cloud Computing based control system. However, numerous standard organizations and vendors have developed various Industrial Ethernets to satisfy the real-time requirements of field devices. This paper presents a real-time reconfigurable protocol stack to cope with this challenge, by introducing the architecture with a core of dynamic routing and autonomic local scheduling. It is based on the deterministic and stochastic Petri-Nets (DSPN) method to illustrate the performance of producer/consumer based application model, CSMA/CD based node accessing activities, and TDMA based resource allocation for real time and non-real time traffic. Furthermore, the predicted time distribution for evaluating the stability of a control system can be obtained from the proposed DSPN model. It is shown that the DSPN modeling yields good verification analysis and performance prediction results through a real experimentation.

Abstract Multi‐granularity of time and time zone difference are two aspects of a time management model (TMM) for distributed workflow management system (DWfS). There are some recent literatures concerning each of them. However, current researches on DWfS have not put them together but have built up an integrated model. As business and scientific collaboration processes across continents become more and more frequent, it is increasingly important to build an integrated model to take them as a whole to make the time transformation and temporal verification in collaboration processes easy and efficient. Aiming at solving this problem, an integrated TMM, DWfS‐TMM, is proposed in this paper. The DWfS‐TMM model consists of a set of general time ontology and a set of general rules for unified transformation between different time zones and different time granularity units. The representation of build‐time and run‐time temporal constraints in workflow processes and the temporal consistency checking method in this model is investigated. Finally, a real case study is investigated and it is shown from this case study that the presented model is useful and practicable. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract The time model for business cooperation across organizations and in business service grid has a special and important requirement, i.e. workday calendar model (WdCM). Workflow plays an important role in modelling business processes across multi‐enterprises or in service grid environments. However, current research in workflow time models does not pay sufficient attention to the differences in workday calendar between individual organizations. This paper aims to solve this problem by clarifying some basic concepts involved in the workday calendar models and presenting an XML‐based framework for workday calendar expressions, i.e. WdCM. Based on this WdCM, temporal constraints and the time optimization issue of workflow processes are investigated. Finally, a case study is analysed to demonstrate the feasibility of our workday calendar model. Copyright © 2007 John Wiley & Sons, Ltd.