The main problem addressed in this paper is the controller design under composite switching in large-scale interconnected systems. Composite switching systems are widely present in large-scale cyber-physical systems, such as microgrids and industrial process control systems, motivating the need for scalable and efficient control methods. To this end, this paper develops an integrated model for a large-scale system formed by interconnecting individual subsystems, each of which is a composite switched system, and investigates the scalable control problem for such an interconnected composite switched system (ICSS), a unified switching signal governs all subsystems, where the signal is generated through a logical function driven by random variable inputs. First, the semi-tensor product (STP) technique, combined with the dimension expansion method, is used to compress the composite switching signal, treating it as part of the state and cascading it with the states of each subsystem. This results in a new system state and an expanded interconnected system model described in the form of a linear time-invariant (LTI) system. The key contributions of this paper include the establishment of an integrated model that captures the interconnection and composite switching behavior of the large-scale system, as well as the development of a scalable distributed state feedback control algorithm that leverages this unified model. Based on this, the LTI model of the interconnected large system under distributed state feedback control is provided. Next, the necessary and sufficient conditions for the mean-square stability of this large system are given, and the scalability and design method of the distributed state feedback strategy are implemented based on a recursive algorithm. Finally, quantitative simulation results based on a DC microgrid example demonstrate that system state trajectories decay to zero under allowable composite switching, confirming the theoretical mean-square stability and demonstrating the practical feasibility of the control framework.

This paper investigates the voltage regulation problems in DC microgrids (DC-MGs) with nonlinear ZIP loads under malicious cyber-physical attacks, proposing an adaptive decentralized fully-actuated two-step control strategy rooted in the fully actuated system (FAS) approach. At first, a unified fully actuated distributed generation unit (FA-DGU) model is developed, integrating nonlinear load dynamics, renewable energy intermittency, attack-induced disturbances, and power line coupling into a lumped uncertainty framework. Then, a decentralized controller is designed using Radial Basis Function neural networks (RBFNNs) to adaptively approximate unknown ZIP load characteristics and attack-generated perturbations, eliminating dependence on communication networks while enabling plug-and-play (PnP) operations through purely local measurements. Following this, Lyapunov stability analysis rigorously proves the asymptotic convergence of voltage regulation errors at the point-of-common-coupling (PCC) under heterogeneous uncertainties. Finally, various uncertain scenarios in DC-MGs are simulated using MATLAB/Simulink, thereby verifying the feasibility and effectiveness of the proposed control method.

Low frequency (LF) magnetoelectric (ME) antennas based on the acoustic wave drive principle provide the broad potential application prospects in Internet of underground things (IoUT) with the tight energy budget and high-loss medium. However, the frequency reconfigurable magnetoelectric antennas with ultra-low energy consumption have not received the deserved attention, which can enhance the adaptability in different IoUT scenarios. In this study, a nonvolatile frequency reconfigurable ME antenna based on the Metglas/[Pb(Mg1/3Nb2/3)O3](1-x)-[PbTiO3]x/Metglas/Pb(Zr,Ti)O3/Metglas/[Pb(Mg1/3Nb2/3)O3](1-x)-[PbTiO3]x/Metglas laminate is proposed for the first time. By applying specific depolarized electric field pulses to PMN-PT, the varied nonvolatile polarization and piezo-stress change its own permittivity and neighboring Metglas’ permeability, respectively. Thus, the multi-mode reactance of ME antenna can be modulated nonvolatilely, resulting in the operating frequency of antenna tuned from 52.16 kHz to 73.57 kHz. The proposed antenna realizes the frequency reconfiguration more conveniently and energy-efficiently than the current tuning methods of ME heterojunction. Specifically, the reconfiguration energy consumption is reduced by 3 orders of magnitude compared to the varactor-based tuning method requiring bias voltages. Meanwhile the transmission energy is reduced by 3 orders compared to the LoRa antenna by leveraging the low underground attenuation property of LF magnetic field. Moreover, the antenna's frequency reconfiguration capability ensures the high transmission rates and SNR in underground scenarios with different losses, reducing the energy consumption by 90.11% compared to the conventional ME antenna. Correspondingly the proposed antenna provides an environment aware and energy efficient method for the underground wireless sensing application with varying mediums and tight energy budget.

As data sharing is essential in applications such as distributed state estimation of cyber-physical power systems (CPPSs), the issue of data privacy leakage among individual regional system operators is increasingly concerned. To solve the issue, this paper proposes a new distributed security state estimation (DSSE) method based on homomorphic encryption for privacy-preserving consensus. First, considering that regional measurement data managed by individual regional system operators could be attacked by false data injection attacks (FDIAs), a new active attack detection method based on the statistical characteristics of the watermarking signal before and after FDIAs is proposed to improve detection proactivity and accuracy, and it is found that the detection accuracy is positively correlated with the watermarking intensity when the signal to interference plus noise ratio (SINR) is greater than 10db. Second, considering that each regional system operator needs to exchang intermediate data under the premise of protecting data privacy to guarantee the consistency process of distributed state estimation, a homomorphic encryption (HE)-based privacy-preserving consensus method is proposed, where a hash function-based dual verification mechanism is presented to prevent ciphertext data from being tampered by FDIAs. Third, according to the detection results and data compensation mechanism, a local secure state estimation model is proposed, and it is proved that the upper and lower bounds of the reconstructed estimation error covariance are not only related to system parameters and external noise but also negatively related to the compensation error. Furthermore, according to a Lyapunov function including privacy-preserving consensus, sufficient condition for consistency is proven, which depends on Laplacian matrix of the system and the iteration step size. Finally, experimental results demonstrate the feasibility and effectiveness of the proposed dynamic watermarking-based active attack detector and distributed secure state estimation method for CPPSs. Moreover, the computational overhead of incorporating advanced IND-CPA countermeasures (i.e., ciphertext re-randomization and branchless arithmetic) is quantified, which confirms the feasibility of practical deployment.

High-dimensional feature spaces in Scientific and Technical Service Resources (STSR) classification present significant challenges, including increased computational costs and diminished accuracy. Identifying an optimal subset of features from raw text vectors is thus critical for effective data classification . This paper introduces a novel metaheuristic algorithm called Binary Banyan Tree Growth Optimization (BBTGO), specifically designed for high-dimensional feature selection (FS). Inspired by the unique growth patterns of the banyan tree , BBTGO leverages a combination of innovative Boolean vectors, including rooting, multi-trunk, and adjustment operator, along with a perturbation phase to enhance the search efficiency and reduce feature dimensionality. These operators enhance the search for promising regions and reduce features by utilizing the optimal solutions clustered within subgroups. Furthermore, BBTGO incorporates a dynamic adjustment mechanism that periodically activates different growth operators to meet the search demands of high-dimensional space. We rigorously evaluate the exploration and exploitation capabilities of BBTGO through comprehensive statistical analyses of various performance metrics. The proposed method demonstrates superior results on 12 high-dimensional benchmark datasets and is successfully applied to feature selection in STSR text classification tasks . Experimental results show that BBTGO significantly outperforms existing methods in terms of classification accuracy , selected features, convergence speed, and processing time. These results underscore the potential of BBTGO as a robust and versatile solution for high-dimensional FS, with broad applicability to real-world classification challenges.

This article investigates the observer-based adaptive decentralized control problem for a class of uncertain interconnected nonlinear fully actuated systems (FAS), considering nonsmooth actuator dynamics including actuator failures and unknown control gains. Based on the dynamic gain scaling technique, a dynamic state observer is constructed. By utilizing the high-order FAS (HOFAS) approach, an adaptive decentralized output feedback controller is designed and a closed-loop structure of the fully actuated subsystems is derived. This structure takes actuator loss of effectiveness, unknown control gains, and unstructured uncertainties into account in the interconnected time-delay subsystems. By selecting suitable Lyapunov-Krasovskii (L-K) functionals, the time-delay terms can be removed, ensuring that all signals of the overall closed-loop system converge to a bounded region. Finally, two simulation examples validate the efficacy of the proposed strategy.

Plug-and-play (PnP) operations of distributed generation units (DGUs) with constant power loads (CPLs) often destabilize dc microgrids (DCmGs). To address this issue, this article proposes a scalable neural network control strategy for nonlinear DCmGs with CPLs, enabling seamless PnP operations of DGUs. A radial basis function neural network is employed to handle the uncertain CPL nonlinearity without requiring any prior knowledge. A structured Lyapunov matrix is utilized to eliminate the coupling effects of power lines by reshaping them into a Laplacian matrix structure. Within this framework, a scalable neural network control approach is proposed, integrating a nominal controller with explicit gain inequalities and an adaptive controller governed by an adaptation law. This approach operates locally, independent of other DGUs and power lines, ensuring PnP operations and maintaining uniformly ultimately bounded stability. The effectiveness of the proposed method is validated through case studies on a modified IEEE 37-bus test system.

Coupling, as an important characteristic of cyber-physical systems (CPS), brings opportunities and challenges to the control and application of industrial systems. This article mainly analyses the role of the cyber layer in the process of controlling the physical layer and gives a security state assessment method for the CPS after the Denial-of-Service (DoS) attack. First, we develop an advanced CPS coupling model that intricately captures the interactions between the physical and cyber layers, emphasizing both data and topology coupling. Unlike existing models that often overlook these aspects, our model offers a more comprehensive understanding of the complex interdependencies within CPS. Second, we propose a unique method for partitioning network layer nodes based on the physical layer's control requirements. Unlike traditional approaches that treat network layer nodes as homogeneous, our method categorizes nodes into sensor, controller, and actuator areas, each with distinct roles in the control process. This differentiation ensures that data flow within the network layer follows a fixed and logical direction, enhancing the overall system efficiency and reliability. Third, we introduce a security state evaluation factor that leverages the network layer partitioning to assess the system's resilience under cyber-attacks. This evaluation provides a detailed analysis of the system's security status, ensuring that potential vulnerabilities are identified and mitigated. Together, these innovations contribute to a more robust and practical framework for CPS design and analysis, with significant implications for enhancing system security and performance in real-world applications.

Automated social behaviour analysis of mice has become an increasingly popular research area in behavioural neuroscience. Recently, pose information (i.e., locations of keypoints or skeleton) has been used to interpret social behaviours of mice. Nevertheless, effective encoding and decoding of social interaction information underlying the keypoints of mice has been rarely investigated in the existing methods. In particular, it is challenging to model complex social interactions between mice due to highly deformable body shapes and ambiguous movement patterns. To deal with the interaction modelling problem, we here propose a Cross-Skeleton Interaction Graph Aggregation Network (CS-IGANet) to learn abundant dynamics of freely interacting mice, where a Cross-Skeleton Node-level Interaction module (CS-NLI) is used to model multi-level interactions (i.e., intra-, inter- and cross-skeleton interactions). Furthermore, we design a novel Interaction-Aware Transformer (IAT) to dynamically learn the graph-level representation of social behaviours and update the node-level representation, guided by our proposed interaction-aware self-attention mechanism. Finally, to enhance the representation ability of our model, an auxiliary self-supervised learning task is proposed for measuring the similarity between cross-skeleton nodes. Experimental results on the standard CRMI13-Skeleton and our PDMB-Skeleton datasets show that our proposed model outperforms several other state-of-the-art approaches.

When traditional pole-dynamics attacks (TPDAs) are implemented with nominal models, model mismatch between exact and nominal models often affects their stealthiness, or even makes the stealthiness lost. To solve this problem, this article presents a novel stealthy measurement-aided pole-dynamics attacks (MAPDAs) method with model mismatch. First, the limitations of TPDAs using exact models are revealed. Second, to handle the limitations, the proposed MAPDAs method is designed by using an adaptive control strategy, which can keep the stealthiness. Moreover, it is easier to implement as only the measurements are needed in comparison with the existing methods requiring both measurements and control inputs. Third, the performance of the proposed MAPDAs method is explored using convergence of multivariate measurements, and MAPDAs with model mismatch have the same stealthiness and similar destructiveness as TPDAs. Finally, experimental results from a networked inverted pendulum system confirm the feasibility and effectiveness of the proposed method.

Existing scalable control methods mainly rely on a fixed block-diagonal structure for the Lyapunov matrix, potentially resulting in numerical infeasibility issues. To overcome this limitation, this article proposes a novel scalable and reliable control scheme for dc microgrids. Initially, a general model for dc microgrids is established to enhance reliability, considering scenarios involving loss of control effectiveness (LoCE) and offset faults. Subsequently, a structured free-weight matrix technique is introduced to mitigate negative coupling effects of power lines, and to address numerical infeasibility by avoiding the assumption about the Lyapunov matrix. Furthermore, the stability of the entire dc microgrid is guaranteed by checking local agent conditions, independently of power line couplings. Therefore, the proposed control scheme ensures plug-and-play scalability with varying number of agents. Finally, theoretical results are validated through numerical simulations using the MATLAB/SimPowerSystems toolbox.

Secure control for cyber-physical power systems (CPPSs) under cyber attacks is a challenging issue. Existing event-triggered control schemes are generally difficult to mitigate the impact of cyber attacks and improve communication efficiency simultaneously. To solve such two problems, this article studies secure adaptive event-triggered control for the CPPSs under energy-limited denial-of-service (DoS) attacks. A new DoS-dependent secure adaptive event-triggered mechanism (SAETM) is developed, where DoS attacks are taken into account when designing the trigger mechanisms. Sufficient conditions are derived to ensure the CPPSs to be uniformly ultimate boundedness stable, and the entering time when the state trajectories of the CPPSs are guaranteed to stay in the secure region is also given. Finally, numerical simulations are provided to illustrate the effectiveness of the proposed control method.

The plugging-in/-out of renewable distributed generation units (DGUs) often alters the microgrid size and coupling terms, resulting in computational burdens and voltage shocks. This article proposes a novel scalable fuzzy voltage control scheme for nonlinear direct current microgrids (DCmGs) composed of DGUs and constant power loads (CPLs) interconnected via power lines. First, a Takagi–Sugeno fuzzy DCmG model with CPL is formulated to capture nonlinear characteristics and diverse transient behaviors. Then, a scalable fuzzy control approach is developed to mitigate negative coupling effects of power lines. This is achieved by a novel argument that leverages dissipativity theory to transform such effects into linear matrix inequalities and subsequently imposes constraints on their sequential principal minors. Specifically, the proposed control method operates locally and independently of other DGUs and line couplings, enabling seamless plug-and-play operations without updating any controllers. Finally, theoretical results are validated through simulations using the MATLAB/SimPowerSystems toolbox.

Accelerated degradation testing (ADT) data typically exhibit a time-stress-dependent structure, as well as random uncertainties due to time-varying effects and unit-to-unit variations. Existing ADT models based on Brownian motion with drift have successfully represented the fault/failure-based degradation behavior and random uncertainty by assuming that the drift parameter follows a Gaussian distribution. However, these models often lack robustness to outliers, leading to distorted analysis, affecting parameter estimation, model accuracy, decision-making, risk assessment, and potentially overlooking the influence of stress factors. A novel robust ADT model based on the Wiener process and its corresponding lifetime analysis method are proposed to address these issues. The proposed approach improves upon traditional ADT models by making the drift parameter follow a$t$-distribution rather than a Gaussian distribution, which can reduce sensitivity to outliers in real degradation processes. In addition, the proposed method allows for the simultaneous consideration of time-stress-dependent factors in the ADT model, facilitating the derivation of a closed-form robust ADT formulation. Subsequently, the lifetime is analyzed based on the ADT model using the first hitting time method in a probabilistic framework. The proposed method is applied to stress relaxation data of electrical connectors and compared to three other common methods.

Most of the existing signcryption schemes utilize a key generation center to generate pseudonyms without updating, and usually opt for bilinear pairing to design authentication schemes. The disadvantage is that these schemes not only incur heavy computation and communication overheads during information interaction, but also can not eliminate security risks arising from not updating pseudonyms. These limitations render them less effective for smart terminals (STs) with limited computation and communication resources in cyber-physical power systems. The main purpose of this article is to explore a streamlined signcryption scheme tailored for resource-limited STs. To achieve this, a dynamical pseudonym self-generation mechanism (DPSGM) is first introduced to prevent the source from being linked and protect privacy. In addition, a streamlined signcryption scheme is designed based on elliptic curve cryptography and certificateless cryptography, integrating seamlessly with DPSGM. This design significantly reduces computation and communication burdens during information interaction. Finally, a real experimental platform is established to demonstrate the feasibility and effectiveness of the proposed scheme. Visual interfaces show the entire secure interaction process and the resistance to attacks.

When networked control systems (NCSs) suffer from denial-of-service (DoS) attacks, system observability can be destroyed. To analyse this problem, this paper mainly investigates the observability of NCSs under DoS attacks. First, how DoS attacks destroy the observability of NCSs is revealed, where the original observability matrix needs to be reconstructed by considering the characteristics of DoS attacks. Second, the necessary and sufficient conditions for judging the observability of NCSs under arbitrary DoS attacks are proved, where the observability matrix needs to be calculated constantly. Third, the observability index and DoS attacks parameters are analysed, while the observability judging criteria under periodic and aperiodic DoS attacks are provided. Finally, the effectiveness and feasibility of the proposed method are verified by numerical examples.

The existing identity security schemes (e.g., based on bilinear pairing) have high computational complexity and large bytes of variables, which results in high computation and communication costs. It is difficult to apply the schemes to resource-constrained (i.e., computation and communication) devices. Moreover, most of these schemes adopt a fixed cycle key update strategy compromising the security of authentication schemes or dynamic (real-time) key update strategy with high computational cost. To solve these issues, this article explores a novel revocable lightweight authentication scheme for resource-constrained devices in cyber–physical power systems (CPPSs). First, a lightweight authentication scheme combined elliptic-curve cryptography (ECC) and certificateless cryptography (CLC) is proposed to negotiate a secure session key, which can achieve mutual authentication with low computation and communication costs. Second, aiming at security problem caused by key leakage, a real-time key update strategy with low computational cost is designed to improve the security of identity authentication. Third, according to a hardness assumption of the elliptic-curve discrete logarithm problem (ECDLP), theoretical analysis rigorously proves that the proposed authentication scheme ensures the security with respect to existential unforgeability against adaptively chosen message attacks (EUF-CMAs). Finally, experimental results confirm the feasibility and effectiveness of the proposed authentication scheme.

Dynamic watermarking schemes can enhance the cyberattack detection capability of networked control systems (NCSs). This article presents a linear event-triggered solution to conventional dynamic watermarking (CDW) schemes. First, the limitations of CDW schemes for event-triggered state estimation-based NCSs are investigated. Second, a new event-triggered dynamic watermarking (ETDW) scheme is designed by treating watermarking as symmetric key encryption, based on the limit convergence theorem in probability. Its security property against the generalized replay attacks (GRAs) is also discussed in the form of bounded asymptotic attack power. Third, finite sample ETDW tests are designed with matrix concentration inequalities. Finally, experimental results of a networked inverted pendulum system demonstrate the validity of our proposed scheme.

Low light very likely leads to the degradation of an image’s quality and even causes visual task failures. Existing image enhancement technologies are prone to overenhancement, color distortion or time consumption, and their adaptability is fairly limited. Therefore, we propose a new single low-light image lightness enhancement method. First, an energy model is presented based on the analysis of membrane vibrations induced by photon stimulations. Then, based on the unique mathematical properties of the energy model and combined with the gamma correction model, a new global lightness enhancement model is proposed. Furthermore, a special relationship between image lightness and gamma intensity is found. Finally, a local fusion strategy, including segmentation, filtering and fusion, is proposed to optimize the local details of the global lightness enhancement images. Experimental results show that the proposed algorithm is superior to nine state-of-the-art methods in avoiding color distortion, restoring the textures of dark areas, reproducing natural colors and reducing time cost.

Payload-based anomaly detection (PAD) model is commonly built upon a big data of normal payload samples, and hence is able to discover zero-day attacks and unknown faults without the need of any negative samples in training phase. But such detection model encounters new challenges to adapt well to the emerging Industrial Internet of Things (IIoT). That is, the modern industrial processes are usually running in a very high complexity, resulting the payloads much more complex and diverse. Further, the industrial data is likely too sensitive to be shared in public, and thus induces a new privacy concern. To tackle these challenges, we propose${\mathrm{ P}}^{3}{\mathrm{ AD}}$, a novel privacy-preserved payload-based anomaly detection model for IIoT. The basic idea is to train a two-dimensional convolutional neural network (2D-CNN) based auto-encoder using normal payloads over a federated GAN (Generative Adversarial Network) architecture, and then to detect anomalies by an unexpected dissimilarity between the original payloads and the payloads reconstructed by the auto-encoder. By the 2D-CNN, we can model the normal payloads from both the request and response directions simultaneously, and thus have more opportunities to capture the complex and dynamic industrial behaviors that are possibly reflected in the bi-directional network communications. By the GAN, we can train a more generalized auto-encoder that is able to reconstruct more general payload samples without the need to have them in advance for model training. By the federated architecture, we can remove the need of direct sharing of normal payloads, and learn them indirectly by aggregating local models across different industrial data owners, hence ensuring the payload privacy. We have evaluated${\mathrm{ P}}^{3}{\mathrm{ AD}}$using four public industrial payload datasets as well as considering four typical IIoT PAD scenarios. The detection results achieve more than 0.966 in terms of F1 score for global condition and at least 0.753 for all kinds of federated settings, proving the effectiveness of our${\mathrm{ P}}^{3}{\mathrm{ AD}}$with privacy preserved.

Automated analysis of mouse behaviours is crucial for many applications in neuroscience. However, quantifying mouse behaviours from videos or images remains a challenging problem, where pose estimation plays an important role in describing mouse behaviours. Although deep learning based methods have made promising advances in human pose estimation, they cannot be directly applied to pose estimation of mice due to different physiological natures. Particularly, since mouse body is highly deformable, it is a challenge to accurately locate different keypoints on the mouse body. In this paper, we propose a novel Hourglass network based model, namely Graphical Model based Structured Context Enhancement Network (GM-SCENet) where two effective modules, i.e., Structured Context Mixer (SCM) and Cascaded Multi-level Supervision (CMLS) are subsequently implemented. SCM can adaptively learn and enhance the proposed structured context information of each mouse part by a novel graphical model that takes into account the motion difference between body parts. Then, the CMLS module is designed to jointly train the proposed SCM and the Hourglass network by generating multi-level information, increasing the robustness of the whole network. Using the multi-level prediction information from SCM and CMLS, we develop an inference method to ensure the accuracy of the localisation results. Finally, we evaluate our proposed approach against several baselines on our Parkinson’s Disease Mouse Behaviour (PDMB) and the standard DeepLabCut Mouse Pose datasets. The experimental results show that our method achieves better or competitive performance against the other state-of-the-art approaches.

We here investigate the secure control of networked control systems developing a new dynamic watermarking (DW) scheme. First, the weaknesses of the conventional DW scheme are revealed, and the tradeoff between the effectiveness of false data injection attack (FDIA) detection and system performance loss is analyzed. Second, we propose a new DW scheme, and its attack detection capability is interrogated using the additive distortion power of a closed-loop system. Furthermore, the FDIA detection effectiveness of the closed-loop system is analyzed using auto/cross-covariance of the signals, where the positive correlation between the FDIA detection effectiveness and the watermarking intensity is measured. Third, the tolerance capacity of FDIA against the closed-loop system is investigated, and theoretical analysis shows that the system performance can be recovered from FDIA using our new DW scheme. Finally, the experimental results from a networked inverted pendulum system demonstrate the validity of our proposed scheme.

This article investigates an issue of distributed fusion estimation under network-induced complexity and stochastic parameter uncertainties. First, a novel signal selection method based on event trigger is developed to handle network-induced packet dropouts, as well as packet disorders resulting from random transmission delays, where the${H_{2}}/{H_{\infty } }$performance of the system is analyzed in different noise environments. In addition, a linear delay compensation strategy is further employed for solving the complex network-induced problem, which may deteriorate system performance. Moreover, a weighted fusion scheme is used to integrate multiple resources through an error cross-covariance matrix. Several case studies validate the proposed algorithm and demonstrate satisfactory system performance in target tracking.

Battery-based energy storage system is a key component to achieve low carbon industrial and social economy, where battery health status plays a vital role in determining the safety and reliability of energy-transportation nexus. This article proposes a transferred recurrent neural network (RNN)-based framework to achieve efficient calendar capacity prognostics under both witnessed and unwitnessed storage conditions. Specifically, this transferred RNN framework contains a base model part and a transfer model part. The base model is first trained by using the easily collected and time-saving accelerated ageing dataset from high temperature and state-of-charge (SOC) cases. Then the transfer part is tuned by using only a small portion of starting capacity data from unwitnessed condition of interest. The developed framework is evaluated under a well-rounded ageing dataset with three different storage SOCs (20%, 50%, and 90%) and temperatures (10 °C, 25 °C, and 45 °C). Experimental results demonstrate that the derived transferred RNN framework is capable of providing satisfactory calendar capacity health prognostics under different storage cases. A model structure with the impact factor terms of SOC and temperature outperforms other counterparts especially for the unwitnessed conditions. The proposed framework could assist engineers to significantly reduce battery ageing experiment burden and is also promising to capture future capacity information for battery health and life-cycle cost analysis of energy-transportation applications.

Aiming at the challenges of networked visual servo control systems, which rarely consider network communication duration and image processing computational cost simultaneously, we here propose a novel platform for networked inverted pendulum visual servo control using H∞ analysis. Unlike most of the existing methods that usually ignore computational costs involved in measuring, actuating, and controlling, we design a novel event-triggered sampling mechanism that applies a new closed-loop strategy to dealing with networked inverted pendulum visual servo systems of multiple time-varying delays and computational errors. Using the Lyapunov stability theory, we prove that the proposed system can achieve stability whilst compromising image-induced computational and network-induced delays and system performance. In the meantime, we use H∞disturbance attenuation level γ for evaluating the computational errors, whereas the corresponding H∞controller is implemented. Finally, simulation analysis and experimental results demonstrate the proposed system performance in reducing computational errors whilst maintaining system efficiency and robustness.

Multipopulation is an effective optimization component often embedded into evolutionary algorithms to solve optimization problems. In this paper, a new multipopulation-based multiobjective genetic algorithm (MOGA) is proposed, which uses a unique cross-subpopulation migration process inspired by biological processes to share information between subpopulations. Then, a Markov model of the proposed multipopulation MOGA is derived, the first of its kind, which provides an exact mathematical model for each possible population occurring simultaneously with multiple objectives. Simulation results of two multiobjective test problems with multiple subpopulations justify the derived Markov model, and show that the proposed multipopulation method can improve the optimization ability of the MOGA. Also, the proposed multipopulation method is applied to other multiobjective evolutionary algorithms (MOEAs) for evaluating its performance against the IEEE Congress on Evolutionary Computation multiobjective benchmarks. The experimental results show that a single-population MOEA can be extended to a multipopulation version, while obtaining better optimization performance.

The smart community (SC), as an important part of the Internet of Energy (IoE), can facilitate integration of distributed renewable energy sources and electric vehicles (EVs) in the smart grid. However, due to the potential security and privacy issues caused by untrusted and opaque energy markets, it becomes a great challenge to optimally schedule the charging behaviors of EVs with distinct energy consumption preferences in SC. In this paper, we propose a contract-based energy blockchain for secure EV charging in SC. First, a permissioned energy blockchain system is introduced to implement secure charging services for EVs with the execution of smart contracts. Second, a reputation-based delegated Byzantine fault tolerance consensus algorithm is proposed to efficiently achieve the consensus in the permissioned blockchain. Third, based on the contract theory, the optimal contracts are analyzed and designed to satisfy EVs' individual needs for energy sources while maximizing the operator's utility. Furthermore, a novel energy allocation mechanism is proposed to allocate the limited renewable energy for EVs. Finally, extensive numerical results are carried out to evaluate and demonstrate the effectiveness and efficiency of the proposed scheme through comparison with other conventional schemes.

Smart grid (SG) represents a large-scale network system with the tight integration of a physical power network and an information network, which makes it more vulnerable to hybrid cyber attacks against different regional subsystems. First, an alternating direction method of multipliers-based distributed state estimation method is developed to overcome the limitation of conventional state estimation and performance analysis of SG against a single type of cyber attacks. Regional subsystems are partitioned via the K-means method. Second, a novel distributed state estimation method integrated with the characteristics of data deception attacks and denial of service (DoS) attacks is proposed to account for the simultaneous presence of different cyber attacks on individual regional subsystems. Third, the convergence of a distributed state estimation algorithm under hybrid cyber attacks is proved theoretically. Furthermore, the relationships between the convergence and algorithm parameters as well as the occurring probability of DoS attacks are established. Finally, the simulations on a modified IEEE 118-bus system are given to demonstrate the feasibility and effectiveness of the proposed method.

This paper is concerned with a Takagi-Sugeno (T-S) fuzzy dynamic positioning controller design for an unmanned marine vehicle (UMV) in network environments. Network-based T-S fuzzy dynamic positioning system (DPS) models for the UMV are first established. Then, stability and stabilization criteria are derived by taking into consideration an asynchronous difference between the normalized membership function of the T-S fuzzy DPS and that of the controller. The proposed stabilization criteria can stabilize states of the UMV. The dynamic positioning performance analysis verifies the effectiveness of the networked modeling and the controller design.

Bandwidth measurement is important for many network applications and services, such as peer-to-peer networks, video caching and anonymity services. To win a bandwidth-based competition for some malicious purpose, adversarial Internet hosts may falsely announce a larger network bandwidth. Some preliminary solutions have been proposed to this problem. They can either evade the bandwidth inflation by a consensus view (i.e., opportunistic bandwidth measurements) or detect bandwidth frauds via forgeable tricks (i.e., detection through bandwidth's CDF symmetry). However, smart adversaries can easily remove the forgeable tricks and report an equally larger bandwidth to avoid the consensus analyses. To defend against the smart bandwidth inflation frauds, we design magic train, a new measurement method which combines an unpredictable packet train with estimated round-trip time (RTT) for detection. The inflation behaviors can be detected through highly contradictory bandwidth results calculated using different magic trains or a train's different segments, or large deviation between the estimated RTT and the RTT reported by the train's first packet. Being an uncooperative measurement method, magic train can be easily deployed on the Internet. We have implemented the magic train using RAW socket and LibPcap, and evaluated the implementation in a controlled testbed and the Internet. The results have successfully confirmed the effectiveness of magic train in detecting and preventing smart bandwidth inflation attacks.

This paper is concerned with the stability for visual servo inverted pendulum system with variable image processing time delay. Firstly, the image processing time delay is inevitably introduced in visual servo inverted pendulum system due to the visual feedback, where its upper and lower bounds are obtained via the statistics analysis on time delay data. Specially, a Markov chain is then employed to describe the characters of image processing time delay. Furthermore, a discrete model of inverted pendulum system with time-varying image processing time delay is presented and the state-feedback controller is designed. Finally, simulation results confirm the feasibility and effectiveness of the proposed method.

This paper introduces a Markov model for evolutionary algorithms (EAs) that is based on interactions among individuals in the population. This interactive Markov model has the potential to provide tractable models for optimization problems of realistic size. We propose two simple discrete optimization search strategies with population-proportion-based selection and a modified mutation operator. The probability of selection is linearly proportional to the number of individuals at each point of the search space. The mutation operator randomly modifies an entire individual rather than a single decision variable. We exactly model these optimization search strategies with interactive Markov models. We present simulation results to confirm the interactive Markov model theory. We show that genetic algorithms and biogeography-based optimization perform better with the addition of population-proportion-based selection on a set of real-world benchmarks. We note that many other EAs, both new and old, might be able to be improved with this addition, or modeled with this method.

Biogeography-based optimization (BBO) is an evolutionary algorithm inspired by biogeography, which is the study of the migration of species between habitats. This paper derives a mathematical description of the dynamics of BBO based on ideas from statistical mechanics. Rather than trying to exactly predict the evolution of the population, statistical mechanics methods describe the evolution of statistical properties of the population fitness. This paper uses the one-max problem, which has only one optimum and whose fitness function is the number of 1s in a binary string, to derive equations that predict the statistical properties of BBO each generation in terms of those of the previous generation. These equations reveal the effect of migration and mutation on the population fitness dynamics of BBO. The results obtained in this paper are similar to those for the simple genetic algorithm with selection and mutation. The paper also derives equations for the population fitness dynamics of general separable functions, and we find that the results obtained for separable functions are the same as those for the one-max problem. The statistical mechanics theory of BBO is shown to be in good agreement with simulation.

This paper proposes a higher energy-efficient mixed sampling scheme (MSE) for networked control systems (NCSs) over IEEE 802.15.4 wireless networks. Compared with some existing periodic event-triggered sampling (ETS) schemes with a delayed sampling estimation, this delay is no longer existing in MSE since there is a dynamic adjustable threshold in MSE to compensate for this delay. Compared with some existing self-triggered sampling (STS)/ETS schemes, MSE does not require continuous measurement of the system states and does not suffer from the conservativeness induced by a self-triggered estimation. By using the proposed MSE, one can improve the energy efficiency in energy-constrained wireless NCSs (WiNCSs) by reducing the number of transmitted packets and increasing the idle-listening period of wireless sensor nodes. An inverted pendulum (Feedback 33-005-PCI) controlled over IEEE 802.15.4 wireless networks is given to demonstrate the effectiveness of the proposed method.

Due to the rapid increases in both the population of mobile social users and the demand for quality of experience (QoE), providing mobile social users with satisfied multimedia services has become an important issue. Media cloud has been shown to be an efficient solution to resolve the above issue, by allowing mobile social users to connect to it through a group of distributed brokers. However, as the resource in media cloud is limited, how to allocate resource among media cloud, brokers, and mobile social users becomes a new challenge. Therefore, in this paper, we propose a game theoretic resource allocation scheme for media cloud to allocate resource to mobile social users though brokers. First, a framework of resource allocation among media cloud, brokers, and mobile social users is presented. Media cloud can dynamically determine the price of the resource and allocate its resource to brokers. A mobile social user can select his broker to connect to the media cloud by adjusting the strategy to achieve the maximum revenue, based on the social features in the community. Next, we formulate the interactions among media cloud, brokers, and mobile social users by a four-stage Stackelberg game. In addition, through the backward induction method, we propose an iterative algorithm to implement the proposed scheme and obtain the Stackelberg equilibrium. Finally, simulation results show that each player in the game can obtain the optimal strategy where the Stackelberg equilibrium exists stably.

Most existing networked filtering methods are proposed to solve the filtering problem under single (wireless or wired) network environment. However, with the development of the networked technology, hybrid networks that integrate wireless and wired networks are gradually employed in industrial fields. The main objectiveness of the paper is to investigate H2/H∞filtering of hybrid networks with random packet losses. Firstly, two different Bernoulli random processes are used to describe the characters of the random packet losses of wireless and wired networks, respectively. A model of filtering error dynamic system is then proposed, and a sufficient condition of stochastic stability is further obtained, where the relationship between the system stability, the filter parameters and the packet losses parameters is established. Finally, simulation results confirm the effectiveness and feasibility of the proposed method.

This paper presents relaxed stability and stabilization conditions for Takagi-Sugeno (T-S) fuzzy systems under network environments subject to asynchronous grades of membership. Because of the introduction of a communication network, the favorable property in the point-to-point connection, that is, sharing the identical premises in the fuzzy plant and the fuzzy controllers cannot be arbitrarily employed. To widen the applicability of the fuzzy control method under network environments, a novel method is provided to reconstruct the synchronous time scale grades of membership at the controller as those in the plant. As a result, the aforementioned favorable property can be conditionally used in the derivation of the stability and stabilization criteria for the system under consideration controlled over a communication network. Numerical examples have been given to illustrate the effectiveness of the proposed approach.

This study provides an event‐triggered ℋ ∞ filtering design method for a discrete linear system under network environments. First, a periodic event‐triggered communication scheme and a networked filter error system model for networked ℋ ∞ filtering are presented. In this scheme and model: (i) the sensor is time‐triggered; (ii) the transmitter is event‐triggered in a periodic manner; and (iii) the closed‐loop system is modelled as a time‐delay dependent filter error system. Second, under consideration of the proposed communication scheme, an ℋ ∞ filtering analysis criterion and a stabilisation criterion are derived. Compared with those where the communication scheme and the filter must be individually designed in some existing ones, the communication and filtering parameters can be obtained simultaneously. In particular, a co‐design algorithm is provided to obtain the communication and filtering parameters in a unified framework for using less network bandwidth. Finally, two examples are given to show the advantages of the proposed method.

Biogeography-based optimization (BBO) is a new evolutionary algorithm inspired by biogeography. BBO has demonstrated good performance on various benchmark functions and real-world optimization problems. However, the performance of BBO is sensitive to the migration model which provides the most important control parameters, immigration rate and emigration rate. According to no free lunch theorem, it is impossible for BBO with a single migration model to obtain always good performance. In this paper, BBO with an ensemble of migration models (BBO-EMM) is introduced and is realized using in three parallel populations. The performance is tested on a set of 25 benchmark functions of CEC 2005 and compared with variant versions of BBO with a single migration model with respect to optimization ability and running time. Results show that the proposed BBO-EMM is better than other BBO algorithms for the problems that we studied in this paper.

Image segmentation plays a key role in many image content analysis applications, and a lot of effort has aimed at improving the performance of established segmentation algorithms. In this paper, we present a mean shift-based combined Dirichlet process mixture (MDP)/Markov Random Field (MRF) image segmentation algorithm. Our method incorporates a mean shift process to iteratively reduce the difference between the mean of cluster centres and image pixels within the standard MDP/MRF procedure. Experimental results show that the proposed segmentation technique outperforms the classical MDP/MRF algorithm.