A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

## Early Access

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This paper studies the connectivity-maintaining consensus of multi-agent systems. Considering the impact of the sensing ranges of agents for connectivity and communication energy consumption, a novel communication management strategy is proposed for multi-agent systems so that the connectivity of the system can be maintained and the communication energy can be saved. In this paper, communication management means a strategy about how the sensing ranges of agents are adjusted in the process of reaching consensus. The proposed communication management in this paper is not coupled with controller but only imposes a constraint for controller, so there is more freedom to develop an appropriate control strategy for achieving consensus. For the multi-agent systems with this novel communication management, a predictive control based strategy is developed for achieving consensus. Simulation results indicate the effectiveness and advantages of our scheme.
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With the rapid development of network technology and control technology, a networked multi-agent control system is a key direction of modern industrial control systems, such as industrial Internet systems. This paper studies the tracking control problem of networked multi-agent systems with communication constraints, where each agent does not know the dynamics of other agents except their outputs. A networked predictive proportional integral derivative (PPID) tracking scheme is proposed to achieve the desired tracking performance, compensate actively for communication delays, and simplify implementation in a distributed manner. This scheme combines the past, present and predictive information of neighbour agents to form a tracking error signal for each agent, and applies the proportional, integral, and derivative of the agent tracking error signal to control each individual agent. The criteria of the stability and output tracking consensus of multi-agent systems with the networked PPID tracking scheme are derived through detailed analysis on the closed-loop systems. The effectiveness of the networked PPID tracking scheme is illustrated via an example.
, Available online  , doi: 10.1109/JAS.2022.105932
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With the advent of digital therapeutics (DTx), the development of software as a medical device (SaMD) for mobile and wearable devices has gained significant attention in recent years. Existing DTx evaluations, such as randomized clinical trials, mostly focus on verifying the effectiveness of DTx products. To acquire a deeper understanding of DTx engagement and behavioral adherence, beyond efficacy, a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis. In this work, the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets, to investigate contextual patterns associated with DTx usage, and to establish the (causal) relationship between DTx engagement and behavioral adherence. This review of the key components of data-driven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets, which helps to iteratively improve the receptivity of existing DTx.
, Available online  , doi: 10.1109/JAS.2022.105995
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, Available online  , doi: 10.1109/JAS.2022.106019
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Safety critical control is often trained in a simulated environment to mitigate risk. Subsequent migration of the biased controller requires further adjustments. In this paper, an experience inference human-behavior learning is proposed to solve the migration problem of optimal controllers applied to real-world nonlinear systems. The approach is inspired in the complementary properties that exhibits the hippocampus, the neocortex, and the striatum learning systems located in the brain. The hippocampus defines a physics informed reference model of the real-world nonlinear system for experience inference and the neocortex is the adaptive dynamic programming (ADP) or reinforcement learning (RL) algorithm that ensures optimal performance of the reference model. This optimal performance is inferred to the real-world nonlinear system by means of an adaptive neocortex/striatum control policy that forces the nonlinear system to behave as the reference model. Stability and convergence of the proposed approach is analyzed using Lyapunov stability theory. Simulation studies are carried out to verify the approach.
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This paper presents an original theoretical framework to model steel material properties in continuous casting line process. Specific properties arising from non-Newtonian dynamics are herein used to indicate the natural convergence of distributed parameter systems to fractional order transfer function models. Data driven identification from a real continuous casting line is used to identify model of the electromagnetic actuator device to control flow velocity of liquid steel. To ensure product specifications, a fractional order control is designed and validated on the system. A projection of the closed loop performance onto the quality assessment at end production line is also given in this paper.
, Available online  , doi: 10.1109/JAS.2022.105959
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, Available online  , doi: 10.1109/JAS.2022.105950
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The uninterrupted operation of the quay crane (QC) ensures that the large container ship can depart port within laytime, which effectively reduces the handling cost for the container terminal and ship owners. The QC waiting caused by automated guided vehicles (AGVs) delay in the uncertain environment can be alleviated by dynamic scheduling optimization. A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems, in which the scheduling scheme determines the starting and ending nodes of paths, and the choice of paths between nodes affects the scheduling of subsequent AGVs. This work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime. A dynamic optimization algorithm, including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the Q-Learning algorithm, is designed to solve the optimal AGV scheduling and path schemes. A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between AGVs. Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.
, Available online  , doi: 10.1109/JAS.2022.105953
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, Available online  , doi: 10.1109/JAS.2022.105965
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Accurate estimation of the remaining useful life (RUL) of lithium-ion batteries is critical for their large-scale deployment as energy storage devices in electric vehicles and stationary storage. A fundamental understanding of the factors affecting RUL is crucial for accelerating battery technology development. However, it is very challenging to predict RUL accurately because of complex degradation mechanisms occurring within the batteries, as well as dynamic operating conditions in practical applications. Moreover, due to insignificant capacity degradation in early stages, early prediction of battery life with early cycle data can be more difficult. In this paper, we propose a hybrid deep learning model for early prediction of battery RUL. The proposed method can effectively combine handcrafted features with domain knowledge and latent features learned by deep networks to boost the performance of RUL early prediction. We also design a non-linear correlation-based method to select effective domain knowledge-based features. Moreover, a novel snapshot ensemble learning strategy is proposed to further enhance model generalization ability without increasing any additional training cost. Our experimental results show that the proposed method not only outperforms other approaches in the primary test set having a similar distribution as the training set, but also generalizes well to the secondary test set having a clearly different distribution with the training set. The PyTorch implementation of our proposed approach is available at https://github.com/batteryrul/battery_rul_early_prediction.
, Available online  , doi: 10.1109/JAS.2022.106001
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, Available online  , doi: 10.1109/JAS.2022.105524
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This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization. A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations. The feasibility of the formulated optimization problem is guaranteed under derived conditions. The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure. Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.
, Available online  , doi: 10.1109/JAS.2022.106010
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, Available online  , doi: 10.1109/JAS.2022.105998
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, Available online  , doi: 10.1109/JAS.2022.106013
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, Available online  , doi: 10.1109/JAS.2022.106016
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Autonomous marine vehicles (AMVs) have received considerable attention in the past few decades, mainly because they play essential roles in broad marine applications such as environmental monitoring and resource exploration. Recent advances in the field of communication technologies, perception capability, computational power and advanced optimization algorithms have stimulated new interest in the development of AMVs. In order to deploy the constrained AMVs in the complex dynamic maritime environment, it is crucial to enhance the guidance and control capabilities through effective and practical planning, and control algorithms. Model predictive control (MPC) has been exceptionally successful in different fields due to its ability to systematically handle constraints while optimizing control performance. This paper aims to provide a review of recent progress in the context of motion planning and control for AMVs from the perceptive of MPC. Finally, future research trends and directions in this substantial research area of AMVs are highlighted.
, Available online  , doi: 10.1109/JAS.2022.105425
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This study presents an autoencoder-embedded optimization (AEO) algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems (MEPs). A huge search space can be compressed to an informative low-dimensional space by using an autoencoder as a dimension reduction tool. The search operation conducted in this low space facilitates the population with fast convergence towards the optima. To strike the balance between exploration and exploitation during optimization, two phases of a tailored teaching-learning-based optimization (TTLBO) are adopted to coevolve solutions in a distributed fashion, wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary process. Also, a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence speed. The proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to 200. As indicated in our experiments, TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base optimizer. Compared with the state-of-the-art algorithms for MEPs, AEO shows extraordinarily high efficiency for these challenging problems, thus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.
, Available online  , doi: 10.1109/JAS.2022.105941
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Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities. This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks. A proportional-integral-observer (PIO) with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles. Then, a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks. In light of such a scheme and the common properties of Laplace matrices, the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one. Furthermore, some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory. The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies. Finally, a simulation example is provided to illustrate the effectiveness of the proposed control strategy.
, Available online  , doi: 10.1109/JAS.2022.105944
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This paper suggests the use of zonotopes for the design of watermark signals. The proposed approach exploits the recent analogy found between stochastic and zonotopic-based estimators to propose a deterministic counterpart to current approaches that study the replay attack in the context of stationary Gaussian processes. In this regard, the zonotopic analogous case where the control loop is closed based on the estimates of a zonotopic Kalman filter (ZKF) is analyzed. This formulation allows to propose a new performance metric that is related to the Frobenius norm of the prediction zonotope. Hence, the steady-state operation of the system can be related with the size of the minimal Robust Positive Invariant set of the estimation error. Furthermore, analogous expressions concerning the impact that a zonotopic/Gaussian watermark signal has on the system operation are derived. Finally, a novel zonotopically bounded watermark signal that ensures the attack detection by causing the residual vector to exit the healthy residual set during the replay phase of the attack is introduced. The proposed approach is illustrated in simulation using a quadruple-tank process.
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Process discovery, as one of the most challenging process analysis techniques, aims to uncover business process models from event logs. Many process discovery approaches were invented in the past twenty years; however, most of them have difficulties in handling multi-instance sub-processes. To address this challenge, we first introduce a multi-instance business process model (MBPM) to support the modeling of processes with multiple sub-process instantiations. Formal semantics of MBPMs are precisely defined by using multi-instance Petri nets (MPNs) that are an extension of Petri nets with distinguishable tokens. Then, a novel process discovery technique is developed to support the discovery of MBPMs from event logs with sub-process multi-instantiation information. In addition, we propose to measure the quality of the discovered MBPMs against the input event logs by transforming an MBPM to a classical Petri net such that existing quality metrics, e.g., fitness and precision, can be used. The proposed discovery approach is properly implemented as plugins in the ProM toolkit. Based on a cloud resource management case study, we compare our approach with the state-of-the-art process discovery techniques. The results demonstrate that our approach outperforms existing approaches to discover process models with multi-instance sub-processes.
, Available online  , doi: 10.1109/JAS.2020.1003408
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A classic kind of researches about the operational safety criterion for dynamic systems with barrier function can be roughly summarized as functional relationship, denoted by $\oplus$, between the barrier function and its first derivative for time $t$, where $\oplus$ can be “=”, “$<$”, or “$>$”, etc. This article draws on the form of the stable condition expression for finite time stability to formulate a novel kind of relaxed safety judgement criteria called exponential-alpha safety criteria. Moreover, we initially explore to use the control barrier function under exponential-alpha safety criteria to achieve the control for the dynamic system operational safety. In addition, derived from the actual process systems, we propose multi-hypersphere methods which are used to construct barrier functions and improved them for three types of special spatial relationships between the safe state set and the unsafe state set, where both of them can be spatially divided into multiple subsets. And the effectiveness of the proposed safety criteria are demonstrated by simulation examples.
, Available online  , doi: 10.1109/JAS.2022.105911
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The recent development of channel technology has promised to reduce the transaction verification time in blockchain operations. When transactions are transmitted through the channels created by nodes, the nodes need to cooperate with each other. If one party refuses to do so, the channel is unstable. A stable channel is thus required. Because nodes may show uncooperative behavior, they may have a negative impact on the stability of such channels. In order to address this issue, this work proposes a dynamic evolutionary game model based on node behavior. This model considers various defense strategies’ cost and attack success ratio under them. Nodes can dynamically adjust their strategies according to the behavior of attackers to achieve their effective defense. The equilibrium stability of the proposed model can be achieved. The proposed model can be applied to general channel networks. It is compared with two state-of-the-art blockchain channels: Lightning network and Spirit channels. The experimental results show that the proposed model can be used to improve a channel’s stability and keep it in a good cooperative stable state. Thus its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.
, Available online  , doi: 10.1109/JAS.2022.105980
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Deep Matrix Factorization (DMF) has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data. However, existing multi-view DMF methods mainly explore the consistency of multi-view data, while neglecting the diversity among different views as well as the high-order relationships of data, resulting in the loss of valuable complementary information. In this paper, we design a Hypergraph regularized Diverse Deep Matrix Factorization (HDDMF) model for multi-view data representation, to jointly utilize multi-view diversity and a high-order manifold in a multi-layer factorization framework. A novel diversity enhancement term is designed to exploit the structural complementarity between different views of data. Hypergraph regularization is utilized to preserve the high-order geometry structure of data in each view. An efficient iterative optimization algorithm is developed to solve the proposed model with theoretical convergence analysis. Experimental results on five real-world data sets demonstrate that the proposed method significantly outperforms state-of-the-art multi-view learning approaches.
, Available online  , doi: 10.1109/JAS.2022.105962
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, Available online  , doi: 10.1109/JAS.2021.1003955
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Generalized eigenvector plays an essential role in the signal processing field. In this paper, we present a novel neural network learning algorithm for estimating the generalized eigenvector of a Hermitian matrix pencil. Differently from some traditional algorithms, which need to select the proper values of learning rates before using, the proposed algorithm does not need a learning rate and is very suitable for real applications. Through analyzing all of the equilibrium points, it is proven that if and only if the weight vector of the neural network is equal to the generalized eigenvector corresponding to the largest generalized eigenvalue of a Hermitian matrix pencil, the proposed algorithm reaches to convergence status. By using the deterministic discrete-time (DDT) method, some convergence conditions, which can be satisfied with probability 1, are also obtained to guarantee its convergence. Simulation results show that the proposed algorithm has a fast convergence speed and good numerical stability. The real application demonstrates its effectiveness in tracking the optimal vector of beamforming.
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This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential equation. Hence, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.
, Available online  , doi: 10.1109/JAS.2022.105845
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Connected automated vehicles (CAVs) serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety, and reducing fuel consumption and vehicle emissions. A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads. This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service (DoS) attacks that disrupt vehicle-to-vehicle communications. First, a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties, including diverse vehicle masses and engine inertial delays, unknown and nonlinear resistance forces, and a dynamic platoon leader. Then, a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability, attack resilience, platoon safety and scalability. Furthermore, a numerically efficient offline design algorithm for determining the desired platoon control law is developed, under which the platoon resilience against DoS attacks can be maximized but the anticipated stability, safety and scalability requirements remain preserved. Finally, extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.
, Available online  , doi: 10.1109/JAS.2022.105914
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Previous deep learning-based super-resolution (SR) methods rely on the assumption that the degradation process is predefined (e.g., bicubic downsampling). Thus, their performance would suffer from deterioration if the real degradation is not consistent with the assumption. To deal with real-world scenarios, existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative scheme. However, degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation errors. In this paper, we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples, respectively. Contrastive regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation space. Furthermore, instead of estimating the degradation, we extract global statistical prior information to capture the character of the distortion. Considering the coupling between the degradation and the low-resolution image, we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of distortions. We term our distortion-specific network with contrastive regularization as CRDNet. The extensive experiments on synthetic and real-world scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.
, Available online  , doi: 10.1109/JAS.2022.105920
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This paper addresses the problem of distributed secure state estimation for multi-agent systems under homologous sensor attacks. Two types of secure Luenberger-like distributed observers are proposed to estimate the system state and attack signal simultaneously. Specifically, the proposed two observers are applicable to deal with the cases in the presence and absence of time delays during network communication. It is also shown that the proposed observers can ensure the attack estimations from different agents asymptotically converge to the same value. Sufficient conditions for guaranteeing the asymptotic convergence of the estimation errors are derived. Simulation examples are finally provided to demonstrate the effectiveness of the proposed results.
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Permanent magnet synchronous motors (PMSMs) have been widely employed in the industry. Finite-control-set model predictive control (FCS-MPC), as an advanced control scheme, has been developed and applied to improve the performance and efficiency of the holistic PMSM drive systems. Based on the three elements of model predictive control, this paper provides an overview of the superiority of the FCS-MPC control scheme and its shortcomings in current applications. The problems of parameter mismatch, computational burden, and unfixed switching frequency are summarized. Moreover, other performance improvement schemes, such as the multi-vector application strategy, delay compensation scheme, and weight factor adjustment, are reviewed. Finally, future trends in this field is discussed, and several promising research topics are highlighted.
, Available online  , doi: 10.1109/JAS.2022.105917
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A new kind of group coordination control problem-group hybrid coordination control is investigated in this paper. The group hybrid coordination control means that in a whole multi-agent system (MAS) that consists of two subgroups with communications between them, agents in the two subgroups achieve consensus and containment, respectively. For multi-agent systems with both time-delays and additive noises, two group control protocols are proposed to solve this problem for the containment-oriented case and consensus-oriented case, respectively. By developing a new analysis idea, some sufficient conditions and necessary conditions related to the communication intensity between the two subgroups are obtained for the following two types of group hybrid coordination behavior: 1) agents in one subgroup and in another subgroup achieve weak consensus and containment, respectively; 2) agents in one subgroup and in another subgroup achieve strong consensus and containment, respectively. It is revealed that the decay of the communication impact between the two subgroups is necessary for the consensus-oriented case. Finally, the validity of the group control results is verified by several simulation examples.
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Internet of things (IoT) devices make up 30% of all network-connected endpoints, introducing vulnerabilities and novel attacks that make many companies as primary targets for cybercriminals. To address this increasing threat surface, every organization deploying IoT devices needs to consider security risks to ensure those devices are secure and trusted. Among all the solutions for security risks, firmware security analysis is essential to fix software bugs, patch vulnerabilities, or add new security features to protect users of those vulnerable devices. However, firmware security analysis has never been an easy job due to the diversity of the execution environment and the close source of firmware. These two distinct features complicate the operations to unpack firmware samples for detailed analysis. They also make it difficult to create visual environments to emulate the running of device firmware. Although researchers have developed many novel methods to overcome various challenges in the past decade, critical barriers impede firmware security analysis in practice. Therefore, this survey is motivated to systematically review and analyze the research challenges and their solutions, considering both breadth and depth. Specifically, based on the analysis perspectives, various methods that perform security analysis on IoT devices are introduced and classified into four categories. The challenges in each category are discussed in detail, and potential solutions are proposed subsequently. We then discuss the flaws of these solutions and provide future directions for this research field. This survey can be utilized by a broad range of readers, including software developers, cyber security researchers, and software security engineers, to better understand firmware security analysis.
, Available online  , doi: 10.1109/JAS.2022.105863
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Few-shot semantic segmentation aims at training a model that can segment novel classes in a query image with only a few densely annotated support exemplars. It remains a challenge because of large intra-class variations between the support and query images. Existing approaches utilize 4D convolutions to mine semantic correspondence between the support and query images. However, they still suffer from heavy computation, sparse correspondence, and large memory. We propose axial assembled correspondence network (AACNet) to alleviate these issues. The key point of AACNet is the proposed axial assembled 4D kernel, which constructs the basic block for semantic correspondence encoder (SCE). Furthermore, we propose the deblurring equations to provide more robust correspondence for the aforementioned SCE and design a novel fusion module to mix correspondences in a learnable manner. Experiments on PASCAL-5i reveal that our AACNet achieves a mean intersection-over-union score of 65.9% for 1-shot segmentation and 70.6% for 5-shot segmentation, surpassing the state-of-the-art method by 5.8% and 5.0% respectively.
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, Available online  , doi: 10.1109/JAS.2022.105935
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In recent years, intelligent data-driven prognostic methods have been successfully developed, and good machinery health assessment performance has been achieved through explorations of data from multiple sensors. However, existing data-fusion prognostic approaches generally rely on the data availability of all sensors, and are vulnerable to potential sensor malfunctions, which are likely to occur in real industries especially for machines in harsh operating environments. In this paper, a deep learning-based remaining useful life (RUL) prediction method is proposed to address the sensor malfunction problem. A global feature extraction scheme is adopted to fully exploit information of different sensors. Adversarial learning is further introduced to extract generalized sensor-invariant features. Through explorations of both global and shared features, promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor malfunctions. The experimental results suggest the proposed approach is well suited for real industrial applications.
, Available online  , doi: 10.1109/JAS.2022.105857
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This paper is concerned with distributed Nash equilibrium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization of players' objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized information flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respectively. Through Lyapunov stability analysis, it is shown that players’ actions can be steered to a neighborhood of the Nash equilibrium with logarithmic and uniform quantizers, and the quantified convergence error depends on the parameter of the quantizer for both undirected and directed cases. A numerical example is given to verify the theoretical results.
, Available online  , doi: 10.1109/JAS.2022.105929
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Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state responses. This paper presents a deep observation on and a comparison between two of those methods: the generalized extended-state observer (GESO) and the equivalent input disturbance (EID) from assumptions, system configurations, stability conditions, system design, disturbance-rejection performance, and extensibility. A time-domain index is introduced to assess the disturbance-rejection performance. A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method. A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.
, Available online  , doi: 10.1109/JAS.2022.105926
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A critical component of visual simultaneous localization and mapping is loop closure detection (LCD), an operation judging whether a robot has come to a pre-visited area. Concretely, given a query image (i.e., the latest view observed by the robot), it proceeds by first exploring images with similar semantic information, followed by solving the relative relationship between candidate pairs in the 3D space. In this work, a novel appearance-based LCD system is proposed. Specifically, candidate frame selection is conducted via the combination of Super-features and aggregated selective match kernel (ASMK). We incorporate an incremental strategy into the vanilla ASMK to make it applied in the LCD task. It is demonstrated that this setting is memory-wise efficient and can achieve remarkable performance. To dig up consistent geometry between image pairs during loop closure verification, we propose a simple yet surprisingly effective feature matching algorithm, termed locality preserving matching with global consensus (LPM-GC). The major objective of LPM-GC is to retain the local neighborhood information of true feature correspondences between candidate pairs, where a global constraint is further designed to effectively remove false correspondences in challenging sceneries, e.g., containing numerous repetitive structures. Meanwhile, we derive a closed-form solution that enables our approach to provide reliable correspondences within only a few milliseconds. The performance of the proposed approach has been experimentally evaluated on ten publicly available and challenging datasets. Results show that our method can achieve better performance over the state-of-the-art in both feature matching and LCD tasks. We have released our code of LPM-GC at https://github.com/jiayi-ma/LPM-GC.
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Aimed at infinite horizon optimal control problems of discrete time-varying nonlinear systems, in this paper, a new iterative adaptive dynamic programming algorithm, which is the discrete-time time-varying policy iteration (DTTV) algorithm, is developed. The iterative control law is designed to update the iterative value function which approximates the index function of optimal performance. The admissibility of the iterative control law is analyzed. The results show that the iterative value function is non-increasingly convergent to the Bellman-equation optimal solution. To implement the algorithm, neural networks are employed and a new implementation structure is established, which avoids solving the generalized Bellman equation in each iteration. Finally, the optimal control laws for torsional pendulum and inverted pendulum systems are obtained by using the DTTV policy iteration algorithm, where the mass and pendulum bar length are permitted to be time-varying parameters. The effectiveness of the developed method is illustrated by numerical results and comparisons.
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Due to the non-standardization and complexity of the farmland environment, it is always a huge challenge for tractors to achieve fully autonomy (work at Self-driving mode) all the time in agricultural industry. Whereas, when tractors work in the Tele-driving (or Remote driving) mode, the operators are prone to fatigue because they need to concentrate for long periods of time. In response to these, a dual-mode control strategy was proposed to integrate the advantages of both approaches, i.e., by combing Self-driving at most of the time with Tele-driving under special (complex and hazardous) conditions through switching control method. First, the state switcher was proposed, which is used for smooth switching the driving modes according to different working states of a tractor. Then, the state switching control law and the corresponding subsystem tracking controllers were designed. Finally, the effectiveness and superiority of the dual-mode control method were evaluated via actual experimental testing of a tractor whose results show that the proposed control method can switch smoothly, stably, and efficiently between the two driving modes automatically. The average control accuracy has been improved by 20% and 15% respectively, compared to the conventional Tele-driving control and Self-driving control with low-precision navigation. In conclusion, the proposed dual-mode control method can not only satisfy the operation in the complex and changeable farmland environment, but also free drivers from high-intensity and fatiguing work. This provides a perfect application solution and theoretical support for the intelligentization of unmanned farm agricultural machinery with high safety and reliability.
, Available online  , doi: 10.1109/JAS.2022.105854
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, Available online  , doi: 10.1109/JAS.2022.105848
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, Available online  , doi: 10.1109/JAS.2022.105674
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This paper investigates the problem of path tracking control for autonomous ground vehicles (AGVs), where the input saturation, system nonlinearities and uncertainties are considered. Firstly, the nonlinear path tracking system is formulated as a linear parameter varying (LPV) model where the variation of vehicle velocity is taken into account. Secondly, considering the noise effects on the measurement of lateral offset and heading angle, an observer-based control strategy is proposed, and by analyzing the frequency domain characteristics of the derivative of desired heading angle, a finite frequency H index is proposed to attenuate the effects of the derivative of desired heading angle on path tracking error. Thirdly, sufficient conditions are derived to guarantee robust H performance of the path tracking system, and the calculation of observer and controller gains is converted into solving a convex optimization problem. Finally, simulation examples verify the advantages of the control method proposed in this paper.
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This article addresses the leader-following output consensus problem of heterogeneous linear multi-agent systems with unknown agent parameters under directed graphs. The dynamics of followers are allowed to be non-minimum phase with unknown arbitrary individual relative degrees. This is contrary to many existing works on distributed adaptive control schemes where agent dynamics are required to be minimum phase and often of the same relative degree. A distributed adaptive pole placement control scheme is developed, which consists of a distributed observer and an adaptive pole placement control law. It is shown that under the proposed distributed adaptive control scheme, all signals in the closed-loop system are bounded and the outputs of all the followers track the output of the leader asymptotically. The effectiveness of the proposed scheme is demonstrated by one practical example and one numerical example.
, Available online  , doi: 10.1109/JAS.2022.105530
Abstract:
High-speed rail (HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.
, Available online  , doi: 10.1109/JAS.2022.105665
Abstract:
The attitude tracking operations of an on-orbit spacecraft with degraded performance exhibited by potential actuator uncertainties (including failures and misalignments) can be extraordinarily challenging. Thus, the control law development for the attitude tracking task of spacecraft subject to actuator (namely reaction wheel) uncertainties is addressed in this paper. More specially, the attitude dynamics model of the spacecraft is firstly established under actuator failures and misalignment (without a small angle approximation operation). Then, a new non-singular sliding manifold with fixed time convergence and anti-unwinding properties is proposed, and an adaptive sliding mode control (SMC) strategy is introduced to handle actuator uncertainties, model uncertainties and external disturbances simultaneously. Among this, an explicit misalignment angles range that could be treated herein is offered. Lyapunov-based stability analyses are employed to verify that the reaching phase of the sliding manifold is completed in finite time, and the attitude tracking errors are ensured to converge to a small region of the closest equilibrium point in fixed time once the sliding manifold enters the reaching phase. Finally, the beneficial features of the designed controller are manifested via detailed numerical simulation tests.
, Available online  , doi: 10.1109/JAS.2022.105521
Abstract:
Weighted vertex cover (WVC) is one of the most important combinatorial optimization problems. In this paper, we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted networks. We first model the WVC problem as a general game on weighted networks. Under the framework of a game, we newly define several cover states to describe the WVC problem. Moreover, we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums (SNEs) of the game. Then, we propose a game-based asynchronous algorithm (GAA), which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial time. Subsequently, we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms, termed the improved game-based asynchronous algorithm (IGAA), in which we prove that it can obtain a better solution to the WVC problem than using a the GAA. Finally, numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.
, Available online  , doi: 10.1109/JAS.2022.105437
Abstract:
During the last three decades, evolutionary algorithms (EAs) have shown superiority in solving complex optimization problems, especially those with multiple objectives and non-differentiable landscapes. However, due to the stochastic search strategies, the performance of most EAs deteriorates drastically when handling a large number of decision variables. To tackle the curse of dimensionality, this work proposes an efficient EA for solving super-large-scale multi-objective optimization problems with sparse optimal solutions. The proposed algorithm estimates the sparse distribution of optimal solutions by optimizing a binary vector for each solution, and provides a fast clustering method to highly reduce the dimensionality of the search space. More importantly, all the operations related to the decision variables only contain several matrix calculations, which can be directly accelerated by GPUs. While existing EAs are capable of handling fewer than 10 000 real variables, the proposed algorithm is verified to be effective in handling 1 000 000 real variables. Furthermore, since the proposed algorithm handles the large number of variables via accelerated matrix calculations, its runtime can be reduced to less than 10% of the runtimes of existing EAs.
, Available online  , doi: 10.1109/JAS.2022.105509
Abstract:
This paper is concerned with the cooperative target tracking of multiple autonomous surface vehicles (ASVs) under switching interaction topologies. For the target to be tracked, only its position can be measured/received by some of the ASVs, and its velocity is unavailable to all the ASVs. A distributed extended state observer taking into consideration switching topologies is designed to integrally estimate unknown target dynamics and neighboring ASVs' dynamics. Accordingly, a novel kinematic controller is designed, which takes full advantage of known information and avoids the approximation of some virtual control vectors. Moreover, a disturbance observer is presented to estimate unknown time-varying environmental disturbance. Furthermore, a distributed dynamic controller is designed to regulate the involved ASVs to cooperatively track the target. It enables each ASV to adjust its forces and moments according to the received information from its neighbors. The effectiveness of the derived results is demonstrated through cooperative target tracking performance analysis for a tracking system composed of five interacting ASVs.
, Available online
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, Available online
Abstract:
Knowledge graphs (KGs) have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services. In recent years, researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids. With multiple power grid dispatching knowledge graphs (PDKGs) constructed by different agencies, the knowledge fusion of different PDKGs is useful for providing more accurate decision supports. To achieve this, entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step. Existing entity alignment methods cannot integrate useful structural, attribute, and relational information while calculating entities’ similarities and are prone to making many-to-one alignments, thus can hardly achieve the best performance. To address these issues, this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments. This model proposes a novel knowledge graph attention network (KGAT) to learn the embeddings of entities and relations explicitly and calculates entities’ similarities by adaptively incorporating the structural, attribute, and relational similarities. Then, we formulate the counterpart assignment task as an integer programming (IP) problem to obtain one-to-one alignments. We not only conduct experiments on a pair of PDKGs but also evaluate our model on three commonly used cross-lingual KGs. Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.
, Available online  , doi: 10.1109/JAS.2022.105428
Abstract:
This paper is concerned with anti-disturbance Nash equilibrium seeking for games with partial information. First, reduced-order disturbance observer-based algorithms are proposed to achieve Nash equilibrium seeking for games with first-order and second-order players, respectively. In the developed algorithms, the observed disturbance values are included in control signals to eliminate the influence of disturbances, based on which a gradient-like optimization method is implemented for each player. Second, a signum function based distributed algorithm is proposed to attenuate disturbances for games with second-order integrator-type players. To be more specific, a signum function is involved in the proposed seeking strategy to dominate disturbances, based on which the feedback of the velocity-like states and the gradients of the functions associated with players achieves stabilization of system dynamics and optimization of players’ objective functions. Through Lyapunov stability analysis, it is proven that the players’ actions can approach a small region around the Nash equilibrium by utilizing disturbance observer-based strategies with appropriate control gains. Moreover, exponential (asymptotic) convergence can be achieved when the signum function based control strategy (with an adaptive control gain) is employed. The performance of the proposed algorithms is tested by utilizing an integrated simulation platform of virtual robot experimentation platform (V-REP) and MATLAB.
, Available online  , doi: 10.1109/JAS.2022.105413
Abstract:
The finite/fixed-time stabilization and tracking control is currently a hot field in various systems since the faster convergence can be obtained. By contrast to the asymptotic stability, the finite-time stability possesses the better control performance and disturbance rejection property. Different from the finite-time stability, the fixed-time stability has a faster convergence speed and the upper bound of the settling time can be estimated. Moreover, the convergent time does not rely on the initial information. This work aims at presenting an overview of the finite/fixed-time stabilization and tracking control and its applications in engineering systems. Firstly, several fundamental definitions on the finite/fixed-time stability are recalled. Then, the research results on the finite/fixed-time stabilization and tracking control are reviewed in detail and categorized via diverse input signal structures and engineering applications. Finally, some challenging problems needed to be solved are presented.
, Available online  , doi: 10.1109/JAS.2022.105407
Abstract:
Load frequency regulation is an essential auxiliary service used in dealing with the challenge of frequency stability in power systems that utilize an increasing proportion of wind power. We investigate a load frequency control method for multi-area interconnected power systems integrated with wind farms, aimed to eliminate the frequency deviation in each area and the tie-line power deviation between different areas. The method explores the derivative and integral terminal sliding mode control technology to solve the problem of load frequency regulation. Such technology employs the concept of relative degrees. However, the subsystems of wind-integrated interconnected power systems have different relative degrees, complicating the control design. This study develops the derivative and integral terminal sliding-mode-based controllers for these subsystems, realizing the load frequency regulation. Meanwhile, closed-loop stability is guaranteed with the theory of Lyapunov stability. Moreover, both a thermal power system and a wind power system are applied to provide frequency support in this study. Considering both constant and variable external disturbances, several numerical simulations were carried out in a two-area thermal power system with a wind farm. The results demonstrate the validity and feasibility of the developed method.
, Available online
Abstract:
, Available online  , doi: 10.1109/JAS.2021.1004144
Abstract:
Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter (KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements. The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation. The proposed algorithm exhibits good robustness, adaptability, and value on applications.