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A k-Winners-Take-All (kWTA) Network With Noise Characteristics Captured
Jiexing Li, Yulin Cao, Zhengtai Xie, Long Jin
, Available online  , doi: 10.1109/JAS.2025.125153
[Abstract](35) [FullText HTML] (0) [PDF 13942KB](13)
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Competition-based $ k $-winners-take-all ($ k $WTA) networks play a crucial role in multi-agent systems. However, existing $ k $WTA networks either neglect the impact of noise or only consider simple forms, such as constant noise. In practice, noises often exhibit time-varying and nonlinear characteristics, which can be modeled using nonlinear functions and approximated by high-order polynomials. Such noises pose significant challenges for current $ k $WTA networks, limiting their practical applications. To address this, a $ k $WTA network with noise characteristics captured ($ k $WTA-NCC) is proposed in this article. Theoretical analyses demonstrate that the residual error of the proposed $ k $WTA-NCC network converges to zero globally, while simulation results confirm its robustness against polynomial noises. Additionally, a $ k $WTA coordination model is constructed by integrating the proposed network with a consensus estimator to achieve multi-agent tracking tasks. Finally, simulations and physical experiments are conducted further to demonstrate the validity and practicality of the $ k $WTA coordination model.
Variable Reconstruction for Evolutionary Expensive Large-Scale Multiobjective Optimization and Its Application on Aerodynamic Design
Jianqing Lin, Cheng He, Ye Tian, Linqiang Pan
, Available online  , doi: 10.1109/JAS.2024.124947
[Abstract](45) [FullText HTML] (0) [PDF 7670KB](15)
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Expensive multiobjective optimization problems (EMOPs) are complex optimization problems exacted from real-world applications, where each objective function evaluation (FE) involves expensive computations or physical experiments. Many surrogate-assisted evolutionary algorithms (SAEAs) have been designed to solve EMOPs. Nevertheless, EMOPs with large-scale decision variables remain challenging for existing SAEAs, leading to difficulties in maintaining convergence and diversity. To address this deficiency, we proposed a variable reconstruction-based SAEA (VREA) to balance convergence enhancement and diversity maintenance. Generally, a cluster-based variable reconstruction strategy reconstructs the original large-scale decision variables into low-dimensional weight variables. Thus, the population can be rapidly pushed towards the Pareto set (PS) by optimizing low-dimensional weight variables with the assistance of surrogate models. Population diversity is improved due to the cluster-based variable reconstruction strategy. An adaptive search step size strategy is proposed to balance exploration and exploitation further. Experimental comparisons with four state-of-the-art SAEAs are conducted on benchmark EMOPs with up to 1000 decision variables and an aerodynamic design task. Experimental results demonstrate that VREA obtains well-converged and diverse solutions with limited real FEs.
Multi-UAV Cooperative Pursuit Strategy With Limited Visual Field in Urban Airspace: A Multi-Agent Reinforcement Learning Approach
Zhe Peng, Guohua Wu, Biao Luo, Ling Wang
, Available online  , doi: 10.1109/JAS.2024.124965
[Abstract](5) [FullText HTML] (0) [PDF 8157KB](1)
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The application of multiple unmanned aerial vehicles (UAVs) for the pursuit and capture of unauthorized UAVs has emerged as a novel approach to ensuring the safety of urban airspace. However, pursuit UAVs necessitate the utilization of their own sensors to proactively gather information from the unauthorized UAV. Considering the restricted sensing range of sensors, this paper proposes a multi-UAV with limited visual field pursuit-evasion (MUV-PE) problem. Each pursuer has a visual field characterized by limited perception distance and viewing angle, potentially obstructed by buildings. Only when the unauthorized UAV, i.e., the evader, enters the visual field of any pursuer can its position be acquired. The objective of the pursuers is to capture the evader as soon as possible without collision. To address this problem, we propose the normalizing flow actor with graph attention critic (NAGC) algorithm, a multi-agent reinforcement learning (MARL) approach. NAGC executes normalizing flows to augment the flexibility of policy network, enabling the agent to sample actions from more intricate distributions rather than common distributions. To enhance the capability of simultaneously comprehending spatial relationships among multiple UAVs and environmental obstacles, NAGC integrates the “obstacle-target” graph attention networks, significantly aiding pursuers in supporting search or pursuit activities. Extensive experiments conducted in a high-precision simulator validate the promising performance of the NAGC algorithm.
Necessary and Sufficient Conditions for Controllability and Essential Controllability of Directed Circle and Tree Graphs
Jijun Qu, Zhijian Ji, Jirong Wang, Yungang Liu
, Available online  , doi: 10.1109/JAS.2024.124866
[Abstract](63) [FullText HTML] (0) [PDF 1114KB](19)
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The multi-agent controllability is intrinsically affected by the network topology and the selection of leaders. A focus of exploring this problem is to uncover the relationship between the eigenspace of Laplacian matrix and network topology. For strongly connected directed circle graphs, we elaborate how the zero entries in the left eigenvectors of Laplacian matrix L arise. The topologies arising from left eigenvectors with zero entries are filtered to construct essentially controllable directed circle graphs regardless of the choice of leaders. We propose two methods for constructing a substantial quantity of essentially controllable graphs, with a focus on utilizing essentially controllable circle graphs as the foundation. For a special directed graph-OT tree, the controllability is shown to be related with its substructure-paths. This promotes the establishment of a sufficient and necessary condition for controllability. Finally, a method is presented to check the controllable subspace by identifying the left eigenvectors and generalized left eigenvectors.
Accumulative-Error-Based Event-Triggered Control for Discrete-Time Linear Systems: A Discrete-Time Looped Functional Method
Xian-Ming Zhang, Qing-Long Han, Xiaohua Ge, Bao-Lin Zhang
, Available online  , doi: 10.1109/JAS.2024.124476
[Abstract](260) [FullText HTML] (5) [PDF 1585KB](23)
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This paper is concerned with event-triggered control of discrete-time systems with or without input saturation. First, an accumulative-error-based event-triggered scheme is devised for control updates. When the accumulated error between the current state and the latest control update exceeds a certain threshold, an event is triggered. Such a scheme can ensure the event-generator works at a relatively low rate rather than falls into hibernation especially after the system steps into its steady state. Second, the looped functional method for continuous-time systems is extended to discrete-time systems. By introducing an innovative looped functional that links the event-triggered scheme, some sufficient conditions for the co-design of control gain and event-triggered parameters are obtained in terms of linear matrix inequalities with a couple of tuning parameters. Then, the proposed method is applied to discrete-time systems with input saturation. As a result, both suitable control gains and event-triggered parameters are also co-designed to ensure the system trajectories converge to the region of attraction. Finally, an unstable reactor system and an inverted pendulum system are given to show the effectiveness of the proposed method.
Length-Variable Bionic Continuum Robot With Millimeter-Scale Diameter and Compliant Driving Force
Shiying Zhao, Qingxin Meng, Xuzhi Lai, Jinhua She, Edwardo Fumihiko Fukushima, Min Wu
, Available online  , doi: 10.1109/JAS.2024.125091
[Abstract](136) [FullText HTML] (0) [PDF 41993KB](32)
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Compared with conventional rigid-link robots, bionic continuum robots (CRs) show great potential in unstructured environments because of their adaptivity and continuous deformation ability. However, designing a CR to achieve miniaturization, variable length and compliant driving force remains a challenge. Here, inspired by the earthworm in nature, we report a length-variable bionic CR with millimeter-scale diameter and compliant driving force. The CR consists of two main components: the robot body and soft drives. The robot body is only 6 mm in diameter, and is composed of a backbone and transmission devices. The backbone is divided into three segments, and each segment is capable of adjusting its length and bending like the earthworm. The maximum length variation of the backbone can reach an astonishing 70 mm with a backbone’s initial length of 150 mm, and the maximum bending angle of each segment can reach 120 degrees. In addition, we develop soft drives using pneumatic soft actuators (PSAs) as a replacement for the rigid motors typically used in conventional CRs. These soft drives control the motions of the transmission devices, enabling length variation and bending of the backbone. By utilizing these soft drives, we ensure that the robot body has a compliant driving force, which addresses users’ concerns about human safety during interactions. In practical applications, we prove that this CR can perform delicate manipulations by successfully completing writing tasks. Additionally, we show its application value for detections and medical treatments by entering the narrow tube and the oral.
Neural Adaptive Sliding-Mode Control of Vehicular Cyber-Physical Systems With Uniformly Quantized Communication Data and Disturbances
Yuan Zhao, Mengchao Li, Zhongchang Liu, Lichuan Liu, Shixi Wen, Lei Ding
, Available online  , doi: 10.1109/JAS.2025.125186
[Abstract](6) [FullText HTML] (0) [PDF 3255KB](9)
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This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems (VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data. To reduce the adverse effects of quantization errors on system performance, a coupling sliding mode surface is established for each following vehicle. The radial basis function (RBF) neural networks are employed to approximate the unknown external disturbances. Then, a novel platoon control law is proposed for cooperative tracking in which each following vehicle only uses the uniformly quantized data of the neighboring vehicles. And the designed controllers in this paper are fully distributed due to the fact that the selection of each vehicle’s controller parameters is independent of the entire communication topology. The string stability of VCPSs in the entire control process is ensured rather than only ensuring the string stability after the sliding mode surface converges to zero. Compared with the existing controller design methods and quantization mechanisms, the neural adaptive sliding-mode platoon controller proposed in this paper is superior in performances including tracking errors, driving comfort and fuel economy. Numerical simulations illustrate the effectiveness and superiority of the designed control strategy.
Innovations and Refinements in LiDAR Odometry and Mapping: A Comprehensive Review
Guangjie Liu, Kai Huang, Xiaolan Lv, Yuanhao Sun, Hailong Li, Xiaohui Lei, Quanchun Yuan, Lei Shu
, Available online  , doi: 10.1109/JAS.2025.125198
[Abstract](90) [FullText HTML] (0) [PDF 14486KB](19)
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Since its introduction in 2014, the LiDAR odometry and mapping (LOAM) algorithm has become a cornerstone in the fields of autonomous driving and intelligent robotics. LOAM provides robust support for autonomous navigation in complex dynamic environments through precise localization and environmental mapping. This paper offers a comprehensive review of the innovations and optimizations made to the LOAM algorithm, covering advancements in multi-sensor fusion technology, frontend processing optimization, backend optimization, and loop closure detection. These improvements have significantly enhanced LOAM’s performance in various scenarios, including urban, agricultural, and underground environments. However, challenges remain in areas such as data synchronization, real-time processing, computational complexity, and environmental adaptability. Looking ahead, future developments are expected to focus on creating more efficient multi-sensor fusion algorithms, expanding application domains, and building more robust systems, thereby driving continued progress in autonomous driving, intelligent robotics, and autonomous unmanned systems.
Online Estimation of DC-link Capacitor Para-meters of Three-Level NPC Converters Using Inherent Signals Analysis
Ricardo Lucio de Araujo Ribeiro, Reuben Palmer Rezende de Sousa, Alexandre Cunha Oliveira, Antonio Marcus Nogueira Lima, Qing-Long Han
, Available online  , doi: 10.1109/JAS.2025.125159
[Abstract](6) [FullText HTML] (0) [PDF 2036KB](2)
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This paper presents a method for estimating the parameters of DC-link capacitors in three-level NPC voltage source inverters (3L-NPC-VSI) used in grid-tied systems. The technique uses the signals generated by the intermodulation caused by the PWM strategy and converter topology interaction to estimate the capacitor parameters of the converter DC-link. It utilizes an observer-based structure consisting of a recursive noninteger sliding discrete Fourier transform (rnSDFT) and an RLS filter improved with a forgetting factor (oSDFT-RLS) to accurately estimate the capacitance and equivalent series resistance (ESR). Importantly, this method does not require additional sensors beyond those already installed in off-the-shelf 3L-NPC-VSI systems, ensuring its noninvasiveness. Furthermore, the oSDFT-RLS estimates capacitor parameters in the time-frequency domain, enabling the tracking of capacitor degradation and predicting potential faults. Experimental results from the laboratory setup demonstrate the effectiveness of the proposed condition monitoring method.
Text2UA: Automatic OPC UA Information Modeling from Textual Data with Large Language Model
Rongkai Wang, Chaojie Gu, Shibo He, Jiming Chen
, Available online  , doi: 10.1109/JAS.2025.125114
[Abstract](13) [FullText HTML] (0) [PDF 5340KB](1)
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A Survey on Rough Feature Selection: Recent Advances and Challenges
Keyu Liu, Xibei Yang, Weiping Ding, Hengrong Ju, Tianrui Li, Jie Wang, Tengyu Yin
, Available online  , doi: 10.1109/JAS.2025.125231
[Abstract](22) [FullText HTML] (0) [PDF 2521KB](4)
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Advances in data acquisition and accumulation on a massive scale are fueling “the curse of dimensionality” which may deteriorate the generalization performance of machine learning models. Such a dilemma gives birth to the technique of feature selection excelling in the presence of high-dimensional data. As a specific method based on rough set theory, Rough Feature Selection (RFS) has been widely concerned and fruitfully applied. In this survey, we provide a comprehensive review of RFS algorithms that have proliferated in recent years. Firstly, we briefly introduce some typical rough set models especially neighborhood rough set and fuzzy rough set, as well as representative rough feature evaluation criteria. We then systematically discuss several emerging topics of RFS including accelerated, ensemble, incremental, label ambiguous, weakly-supervised, and multi-granularity RFS. Additionally, we illuminate the regular performance validation scheme of RFS and conduct a number of experiments to present benchmarking results of state-of-the-art RFS algorithms. Finally, we summarize the pros and cons of existing research efforts and outline the open challenges and opportunities of class imbalance, multi-modal scenario, causality inference, and high-level representation for RFS. By providing in-depth knowledge of RFS, we anticipate this survey will: 1) serve as a guidebook for newcomers intending to delve into RFS and a stepping-stone for researchers and practitioners to solve domain-specific problems; 2) gain insights into the state-of-the-art published findings, triggering a series of breakthroughs in RFS; 3) underscore some challenges ahead of RFS, directing future efforts toward punctuating advances beyond questions currently pursued.
MEET: A Million-Scale Dataset for Fine-Grained Geospatial Scene Classification With Zoom-Free Remote Sensing Imagery
Yansheng Li, Yuning Wu, Gong Cheng, Chao Tao, Bo Dang, Yu Wang, Jiahao Zhang, Chuge Zhang, Yiting Liu, Xu Tang, Jiayi Ma, Yongjun Zhang
, Available online  , doi: 10.1109/JAS.2025.125324
[Abstract](49) [FullText HTML] (0) [PDF 60970KB](17)
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Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications. However, existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples. This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios. To address this limitation, we introduce the Million-scale finE-grained geospatial scEne classification dataseT (MEET), which contains over 1.03 million zoom-free remote sensing scene samples, manually annotated into 80 fine-grained categories. In MEET, each scene sample follows a scene-in-scene layout, where the central scene serves as the reference, and auxiliary scenes provide crucial spatial context for fine-grained classification. Moreover, to tackle the emerging challenge of scene-in-scene classification, we present the Context-Aware Transformer (CAT), a model specifically designed for this task, which adaptively fuses spatial context to accurately classify the scene samples. CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes. Based on MEET, we establish a comprehensive benchmark for fine-grained geospatial scene classification, evaluating CAT against 11 competitive baselines. The results demonstrate that CAT significantly outperforms these baselines, achieving a 1.88% higher balanced accuracy (BA) with the Swin-Large backbone, and a notable 7.87% improvement with the Swin-Huge backbone. Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping. The source code and dataset will be publicly available at https://jerrywyn.github.io/project/MEET.html.
Collision-Free Maneuvering for a UAV Swarm Based on Parallel Control
Jiacheng Li, Wenhui Ma, YangWang Fang, Dengxiu Yu, C. L. Philip Chen
, Available online  , doi: 10.1109/JAS.2024.124674
[Abstract](29) [FullText HTML] (0) [PDF 8762KB](15)
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The maneuvering of a large-scale unmanned aerial vehicle (UAV) swarm, notable for flexible flight with collision-free, is still challenging due to the significant number of UAVs and the compact configuration of the swarm. In light of this problem, a novel parallel control method that utilizes space and time transformation is proposed. First, the swarm is decomposed based on a grouping-hierarchical strategy, while the distinct flight roles are assigned to each UAV. Then, to achieve the desired configuration (DCF) in the real world, a bijection transformation is conducted in the space domain, converting an arbitrarily general configuration (GCF) into a standard configuration (SCF) in the virtual space. Further, to improve the flexibility of the swarm, the time scaling transformation is adopted in the time domain, which ensures the desired prescribed-time convergence of the swarm independent of initial conditions. Finally, simulation results demonstrate that collision-free maneuvering, including formation changes and turning, can be effectively and rapidly achieved by the proposed parallel control method. Overall, this research contributes a viable solution for enhancing cooperation among large-scale UAV swarms.
Distributed Event-Triggered Nash Equilibrium Seeking for Aggregative Game With Second-Order Dynamics
Yi Huang, Jian Sun, Qing Fei
, Available online  , doi: 10.1109/JAS.2024.124830
[Abstract](31) [FullText HTML] (0) [PDF 451KB](12)
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Finite-Time Sliding-Mode Control for Semi-Markov Systems with Delayed Impulses
Fangmin Ren, Xiaoping Wang, Yangmin Li, Zhigang Zeng
, Available online  , doi: 10.1109/JAS.2024.125004
[Abstract](20) [FullText HTML] (0) [PDF 506KB](6)
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Efficient Knowledge-guided Self-evolving Intelligent Behavioral Control for Autonomous Vehicles
Qiao Peng, Kailong Liu, Jingda Wu, Amir Khajepour
, Available online  , doi: 10.1109/JAS.2024.124746
[Abstract](14) [FullText HTML] (0) [PDF 715KB](3)
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KT-RC: Kernel Time-Delayed Reservoir Computing for Time Series Prediction
Heshan Wang, Mengmeng Chen, Kunjie Yu, Jing Liang, Zhaomin Lv, Zhong Zhang
, Available online  , doi: 10.1109/JAS.2024.124986
[Abstract](52) [FullText HTML] (0) [PDF 9327KB](19)
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Reservoir computing (RC) is an efficient recurrent neural network (RNN) method. However, the performance and prediction results of traditional RCs are susceptible to several factors, such as their network structure, parameter setting, and selection of input features. In this study, we employ a kernel time-delayed RC (KT-RC) method for time series prediction. The KT-RC transforms input vectors linearly to obtain a high-dimensional set of time-delayed linear eigenvectors, which are then transformed by various kernel functions to represent the nonlinear characteristics of the input signal. Finally, the Bayesian optimization algorithm adjusts the few remaining weights and kernel parameters to minimize the manual adjustment process. The advantages of KT-RC can be summarized as follows: 1) KT-RC solves the problems of uncertainty in weight matrices and difficulty in large-scale parameter selection in the input and hidden layers of RCs. 2) The KT module can avoid massive reservoir hyperparameters and effectively reduce the hidden layer size of the traditional RC. 3) The proposed KT-RC shows good performance, strong stability, and robustness in several synthetic and real-world datasets for one-step-ahead and multistep-ahead time series prediction. The simulation results confirm that KT-RC not only outperforms some gate-structured RNNs, kernel vector regression models, and recently proposed prediction models but also requires fewer parameters to be initialized and can reduce the hidden layer size of the traditional RCs. The source code is available at https://github.com/whs7713578/RC.
LTDNet: A Lightweight Text Detector for Real-Time Arbitrary-Shape Traffic Text Detection
Runmin Wang, Yanbin Zhu, Ziyu Zhu, Lingxin Cui, Zukun Wan, Anna Zhu, Yajun Ding, Shengyou Qian, Changxin Gao, Nong Sang
, Available online  , doi: 10.1109/JAS.2024.125022
[Abstract](63) [FullText HTML] (0) [PDF 14194KB](19)
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Traffic text detection plays a vital role in understanding traffic scenes. Traffic text, a distinct subset of natural scene text, faces specific challenges not found in natural scene text detection, including false alarms from non-traffic text sources, such as roadside advertisements and building signs. Existing state-of-the-art methods employ increasingly complex detection frameworks to pursue higher accuracy, leading to challenges with real-time performance. In response to this issue, we propose a real-time and efficient traffic text detector named LTDNet, which strikes a balance between accuracy and real-time capabilities. LTDNet integrates three essential techniques to address these challenges effectively. First, a cascaded multilevel feature fusion network is employed to mitigate the limitations of lightweight backbone networks, thereby enhancing detection accuracy. Second, a lightweight feature attention module is introduced to enhance inference speed without compromising accuracy. Finally, a novel point-to-edge distance vector loss function is proposed to precisely localize text instance boundaries within traffic contexts. The superiority of our method is validated through extensive experiments on five publicly available datasets, demonstrating its state-of-the-art performance. The code will be released at https://github.com/runminwang/LTDNet.
E2AG: Entropy-Regularized Ensemble Adaptive Graph for Industrial Soft Sensor Modeling
Zhichao Chen, Licheng Pan, Yiran Ma, Zeyu Yang, Le Yao, Jinchuan Qian, Zhihuan Song
, Available online  
[Abstract](22) [FullText HTML] (0) [PDF 3899KB](3)
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Adaptive Graph Neural Networks (AGNNs) have achieved remarkable success in industrial process soft sensing by incorporating explicit features that delineate the relationships between process variables. This article introduces a novel GNN framework, termed Entropy-Regularized Ensemble Adaptive Graph (E2AG), aimed at enhancing the predictive accuracy of AGNNs. Specifically, this work pioneers a novel AGNN learning approach based on mirror descent, which is central to ensuring the efficiency of the training procedure and consequently guarantees that the learned graph naturally adheres to the row-normalization requirement intrinsic to the message-passing of GNNs. Subsequently, motivated by multi-head self-attention mechanism, the training of ensembled AGNNs is rigorously examined within this framework, incorporating an entropy regularization term in the learning objective to ensure the diversity of the learned graph. After that, the architecture and training algorithm of the model are then concisely summarized. Finally, to ascertain the efficacy of the proposed E2AG model, extensive experiments are conducted on real-world industrial datasets. The evaluation focuses on prediction accuracy, model efficacy, and sensitivity analysis, demonstrating the superiority of E2AG in industrial soft sensing applications.
An Emulation Approach to Semi-Global Robust Output Regulation for a Class of Nonlinear Uncertain Systems
Jieshuai Wu, Maobin Lu, Fang Deng, Jie Chen
, Available online  , doi: 10.1109/JAS.2024.125085
[Abstract](29) [FullText HTML] (0) [PDF 1184KB](9)
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In this paper, we investigate the semi-global robust output regulation problem of a class of nonlinear networked control systems. By the emulation approach, we propose a class of sampled-data output feedback control laws to solve this problem. In particular, we first develop a general sampled-data dynamic output feedback control law and characterize the closed-loop system by a hybrid system. Then, we design the internal model based on the sampled error output of the system. Based on the internal model principle, we convert the semi-global robust output regulation problem into a semi-global robust stabilization problem of an augmented hybrid system composed of the internal model and the original system. By proposing the sampled error output feedback control law and by means of Lyapunov analysis, we obtain the maximum allowable transmission interval for sampling and show that semi-global robust stabilization of the augmented hybrid system can be achieved by the proposed sampled-data control law and thus leading to the solution of the semi-global robust output regulation problem. Finally, we apply the proposed control approach to two practical applications to verify the effectiveness of the proposed control approach.
Instance by Instance: An Iterative Framework for Multi-Instance 3D Registration
Jiaqi Yang, Xinyue Cao, Xiyu Zhang, Yuxin Cheng, Zhaoshuai Qi, Siwen Quan
, Available online  , doi: 10.1109/JAS.2024.125058
[Abstract](20) [FullText HTML] (0) [PDF 27956KB](1)
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Multi-instance registration is a challenging problem in computer vision and robotics, where multiple instances of an object need to be registered in a standard coordinate system. Pioneers followed a non-extensible one-shot framework, which prioritizes the registration of simple and isolated instances, often struggling to accurately register challenging or occluded instances. To address these challenges, we propose the first iterative framework for multi-instance 3D registration (MI-3DReg) in this work, termed instance-by-instance (IBI). It successively registers instances while systematically reducing outliers, starting from the easiest and progressing to more challenging ones. This enhances the likelihood of effectively registering instances that may have been initially overlooked, allowing for successful registration in subsequent iterations. Under the IBI framework, we further propose a sparse-to-dense correspondence-based multi-instance registration method (IBI-S2DC) to enhance the robustness of MI-3DReg. Experiments on both synthetic and real datasets have demonstrated the effectiveness of IBI and suggested the new state-of-the-art performance with IBI-S2DC, e.g., our mean registration F1 score is 12.02%/12.35% higher than the existing state-of-the-art on the synthetic/real datasets. The source codes are availableonline at https://github.com/caoxy01/IBI.
A Survey of Distributed Algorithms for Aggregative Games
Huaqing Li, Jun Li, Liang Ran, Lifeng Zheng, Tingwen Huang
, Available online  , doi: 10.1109/JAS.2024.124998
[Abstract](25) [FullText HTML] (0) [PDF 1007KB](8)
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Game theory-based models and design tools have gained substantial prominence for controlling and optimizing behavior within distributed engineering systems due to the inherent distribution of decisions among individuals. In non-cooperative settings, aggregative games serve as a mathematical framework model for the interdependent optimal decision-making problem among a group of non-cooperative players. In such scenarios, each player’s decision is influenced by an aggregation of all players’ decisions. Nash equilibrium seeking in aggregative games has emerged as a vibrant topic driven by applications that harness the aggregation property. This paper presents a comprehensive overview of the current research on aggregative games with a focus on communication topology. A systematic classification is conducted on distributed algorithm research based on communication topologies such as undirected networks, directed networks, and time-varying networks. Furthermore, it sorts out the challenges and compares the algorithms’ convergence performance. It also delves into real-world applications of distributed optimization techniques grounded in aggregative games. Finally, it proposes several challenges that can guide future research directions.
A Multi-Type Feature Fusion Network Based on Importance Weighting for Occluded Human Pose Estimation
Jiahong Jiang, Nan Xia, Siyao Zhou
, Available online  
[Abstract](23) [FullText HTML] (0) [PDF 14610KB](5)
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Human pose estimation is a challenging task in computer vision. Most algorithms perform well in regular scenes, but lack good performance in occlusion scenarios. Therefore, we propose a multi-type feature fusion network based on importance weighting, which consists of three modules. In the first module, we propose a multi-resolution backbone with two feature enhancement sub-modules, which can extract features from different scales and enhance the feature expression ability. In the second module, we enhance the expressiveness of keypoint features by suppressing obstacle features and compensating for the unique and shared attributes of keypoints and topology. In the third module, we perform importance weighting on the adjacency matrix to enable it to describe the correlation among nodes, thereby improving the feature extraction ability. We conduct comparative experiments on the keypoint detection datasets of common objects in Context 2017 (COCO2017), COCO-Wholebody and CrowdPose, achieving the accuracy of 78.9%, 67.1% and 77.6%, respectively. Additionally, a series of ablation experiments are designed to show the performance of our work. Finally, we present the visualization of different scenarios to verify the effectiveness of our work.
Analysis of Students’ Positive Emotion and Smile Intensity Using Sequence-Relative Key-Frame Labeling and Deep-Asymmetric Convolutional Neural Network
Zhenzhen Luo, Xiaolu Jin, Yong Luo, Qiangqiang Zhou, Xin Luo
, Available online  
[Abstract](23) [FullText HTML] (0) [PDF 59344KB](5)
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Positive emotional experiences can improve learning efficiency and cognitive ability, stimulate students’ interest in learning, and improve teacher-student relationships. However, positive emotions in the classroom are primarily identified through teachers’ observations and postclass questionnaires or interviews. The expression intensity of students, which is extremely important for fine-grained emotion analysis, is not considered. Hence, a novel method based on smile intensity estimation using sequence-relative key-frame labeling is presented. This method aims to recognize the positive emotion levels of a student in an end-to-end framework. First, the intensity label is generated robustly for each frame in the expression sequence based on the relative key frames to address the lack of annotations for smile intensity. Then, a deep-asymmetric convolutional neural network learns the expression model through dual neural networks, to enhance the stability of the network model and avoid the extreme attention region learned. Further, dual neural networks and the dual attention mechanism are integrated using the intensity label based on the relative key frames as the supervised information. Thus, diverse features are effectively extracted and subtle appearance differences between different smiles are perceived based on different perspectives. Finally, comparative experiments for the convergence speed, model-training parameters, confusion matrix, and classification probability are performed. The proposed method was applied to a real classroom scene to analyze the emotions of students. Numerous experiments validated that the proposed method is promising for analyzing the differences in the positive emotion of students while learning in a classroom.
Robust Predefined-Time Control for Optimal Formation of Networked Mobile Vehicle Systems
Jing-Zhe Xu, Zhi-Wei Liu, Dingxin He, Ming-Feng Ge, Ming Chi
, Available online  
[Abstract](14) [FullText HTML] (0) [PDF 628KB](9)
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Bearings-Only Target Motion Analysis Via Deep Reinforcement Learning
Chengyi Zhou, Meiqin Liu, Senlin Zhang, Ronghao Zheng, Shanling Dong
, Available online  
[Abstract](14) [FullText HTML] (0) [PDF 334KB](2)
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Applications of Domain Generalization to Machine Fault Diagnosis: A Survey
Yongyi Chen, Dan Zhang, Ruqiang Yan, Min Xie
, Available online  , doi: 0.1109/JAS.2025.125120
[Abstract](40) [FullText HTML] (0) [PDF 2681KB](9)
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In actual industrial scenarios, the variation of operating conditions, the existence of data noise, and failure of measurement equipment will inevitably affect the distribution of perceptive data. Deep learning-based fault diagnosis algorithms strongly rely on the assumption that source and target data are independent and identically distributed, and the learned diagnosis knowledge is difficult to generalize to out-of-distribution data. Domain generalization (DG) aims to achieve the generalization of arbitrary target domain data by using only limited source domain data for diagnosis model training. The research of DG for fault diagnosis has made remarkable progress in recent years and lots of achievements have been obtained. In this article, for the first time a comprehensive literature review on DG for fault diagnosis from a learning mechanism-oriented perspective is provided to summarize the development in recent years. Specifically, we first conduct a comprehensive review on existing methods based on the similarity of basic principles and design motivations. Then, the recent trend of DG for fault diagnosis is also analyzed. Finally, the existing problems and future prospect is performed.
A Transactional-Behavior-Based Hierarchical Gated Network for Credit Card Fraud Detection
Yu Xie, MengChu Zhou, Guanjun Liu, Honghao Zhu, Lifei Wei, Pasquale De Meo
, Available online  , doi: 10.1109/JAS.2025.125243
[Abstract](34) [FullText HTML] (0) [PDF 11494KB](6)
Abstract:
The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system, as well as to enforce customer confidence in digital payment systems. Historically, credit card companies have used rule-based approaches to detect fraudulent transactions, but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms. Despite significant progress, the current approaches to fraud detection suffer from a number of limitations: for example, it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions, and they often neglect possible correlations among transactions, even though they could reveal illicit behaviour. In this paper, we propose a novel credit card fraud detection method based on a transaction behaviour-based hierarchical gated network. First, we introduce a feature-oriented extraction module capable of identifying key features from original transactions, and such analysis is effective in revealing the behavioural characteristics of fraudsters. Second, we design a transaction-oriented extraction module capable of capturing the correlation between users’ historical and current transactional behaviour. Such information is crucial for revealing users’ sequential behaviour patterns. Our approach, called Transactional-behaviour-based Hierarchical Gated Network model (TbHGN), extracts two types of new transactional features, which are then combined in a feature interaction module to learn the final transactional representations used for CCFD. We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42% and 6.53% and an improvement in average AUC between 0.63% and 2.78% over the state of the art.
Multi-Spacecraft Formation Control Under False Data Injection Attack: A Cross Layer Fuzzy Game Approach
Yifan Zhong, Yuan Yuan, Huanhuan Yuan, Mengbi Wang, Huaping Liu
, Available online  , doi: 10.1109/JAS.2024.124872
[Abstract](105) [FullText HTML] (0) [PDF 1684KB](34)
Abstract:
In this paper, we address a cross-layer resilient control issue for a kind of multi-spacecraft system (MSS) under attack. Attackers with bad intentions use the false data injection (FDI) attack to prevent the MSS from reaching the goal of consensus. In order to ensure the effectiveness of the control, the embedded defender in MSS preliminarily allocates the defense resources among spacecrafts. Then, the attacker selects its target spacecrafts to mount FDI attack to achieve the maximum damage. In physical layer, a Nash equilibrium (NE) control strategy is proposed for MSS to quantify system performance under the effect of attacks by solving a game problem. In cyber layer, a fuzzy Stackelberg game framework is used to examine the rivalry process between the attacker and defender. The strategies of both attacker and defender are given based on the analysis of physical layer and cyber layer. Finally, a simulation example is used to test the viability of the proposed cross layer fuzzy game algorithm.
Dissecting and Mitigating Semantic Discrepancy in Stable Diffusion for Image-to-Image Translation
Yifan Yuan, Guanqun Yang, James Z. Wang, Hui Zhang, Hongming Shan, Fei-Yue Wang, Junping Zhang
, Available online  , doi: 10.1109/JAS.2024.124800
[Abstract](135) [FullText HTML] (0) [PDF 96267KB](29)
Abstract:
Finding suitable initial noise that retains the original image’s information is crucial for image-to-image (I2I) translation using text-to-image (T2I) diffusion models. A common approach is to add random noise directly to the original image, as in SDEdit. However, we have observed that this can result in “semantic discrepancy” issues, wherein T2I diffusion models misinterpret the semantic relationships and generate content not present in the original image. We identify that the noise introduced by SDEdit disrupts the semantic integrity of the image, leading to unintended associations between unrelated regions after U-Net upsampling. Building on the widely-used latent diffusion model, Stable Diffusion, we propose a training-free, plug-and-play method to alleviate semantic discrepancy and enhance the fidelity of the translated image. By leveraging the deterministic nature of denoising diffusion implicit models (DDIMs) inversion, we correct the erroneous features and correlations from the original generative process with accurate ones from DDIM inversion. This approach alleviates semantic discrepancy and surpasses recent DDIM-inversion-based methods such as PnP with fewer priors, achieving a speedup of 11.2 times in experiments conducted on COCO, ImageNet, and ImageNet-R datasets across multiple I2I translation tasks. The codes are available at https://github.com/Sherlockyyf/Semantic_Discrepancy.
A Homotopy Method for Continuous-Time Model-Free LQR Control Based on Policy Iteration
Wenwu Fan, Junlin Xiong
, Available online  , doi: 10.1109/JAS.2025.125132
[Abstract](50) [FullText HTML] (0) [PDF 1038KB](16)
Abstract:
In recent years, reinforcement learning control theory has been well developed. However, model-free value iteration needs many iterations to achieve the desired precision, and model-free policy iteration requires an initial stabilizing control policy. It is significant to propose a fast model-free algorithm to solve the continuous-time linear quadratic control problem without an initial stabilizing control policy. In this paper, we construct a homotopy path on which each point corresponds to an linear quadratic regulator problem. Based on policy iteration, model-based and model-free homotopy algorithms are proposed to solve the optimal control problem of continuous-time linear systems along the homotopy path. Our algorithms are speeded up using first-order differential information and do not require an initial stabilizing control policy. Finally, several practical examples are used to illustrate our results.
An Intelligent Optimization Strategy for Blast Furnace Charging Operation Considering Three-Dimensional Burden Surface Shape
Jicheng Zhu, Zhaohui Jiang, Dong Pan, Haoyang Yu, Chuan Xu, Ke Zhou, Weihua Gui
, Available online  , doi: 10.1109/JAS.2025.125192
[Abstract](82) [FullText HTML] (0) [PDF 4132KB](21)
Abstract:
Today, a well-devised charging operation scheme is urgently needed by on-site workmen and is critical for building an intelligent blast furnace (BF). Previous research on charging operations always focused on the two-dimensional shape of the burden surface (i.e., a single radial profile) while neglecting the unique feature of global dissymmetry, severely restricting the development of precise charging. For this reason, this study proposes an innovative optimization strategy for the charging operation under the three-dimensional burden surface, which is the first attempt in this field. First, a practicable region partitioning scheme is introduced, and the partitioning results are then integrated with the charging mechanism to construct a three-dimensional burden surface prediction model. Next, the intrinsic relationship between the operational parameters and charging volume is revealed based on the law of mass conservation, which forms the basis for defining a novel operational parameter with variable-speed utility, referred to as the neotype charging matrix (NCM). To find the best NCM, a customized NCM optimization strategy, involving a dual constraint handling technique in conjunction with a two-stage hybrid variable differential evolution algorithm, is further developed. The industrial experiment results manifest that the partitioning scheme significantly enhances the accuracy of burden surface description. Moreover, the NCM optimization strategy offers greater flexibility and higher accuracy than current mainstream optimization strategies for the charging matrix (CM).
Camera Planning for Physical Safety of Outdoor Electronic Devices: Perspective and Analysis
Qin Su, Lei Shu, Gerhard Petrus Hancke, Kai Huang, Edmond Nurellari, Qingsong Zhao, Nikumani Choudhury, Anakhi Hazarika
, Available online  , doi: 10.1109/JAS.2025.125129
[Abstract](99) [FullText HTML] (0) [PDF 2265KB](21)
Abstract:
Camera technology advancement and deployment continue to play a vital role in modern life and production, amongst other capabilities, generating relevant visual information. The challenge of optimizing the use and deployment of camera networks in various applications (e.g., surveillance, traffic monitoring, and public safety to name but just a few) has attracted considerable attention from both academia and industries. Camera planning is the first step in addressing this challenge. The surveillance objectives and scenes of a camera network dictate the modelling and optimization algorithms for camera planning. However, existing reviews have primarily focused on models or optimization algorithms, with insufficient attention given to surveillance scenes. This review aims to bridge this gap by 1) Classifying surveillance scenes into the urban environment and rural outdoor environment and comparing the surveillance requirements and challenges; 2) Summarizing the details of camera coverage optimization in the relevant literature from the perspective of deployment scenes; and 3) Proposing a new surveillance scene—Solar Insecticidal Lamps with the Internet of Things—as a case study to analyze the surveillance requirement and challenges in agricultural outdoor environment. Finally, we state the technical outlook on the physical safety of outdoor electronic devices in agriculture settings and provide insights to draw more attention and effort into this area.
Comprehensive Dynamic Model of Vertical Pneumatic Bellows Actuator System Considering Bidirectional Asymmetric Hysteresis
Huai Xiao, Xuzhi Lai, Qingxin Meng, Jinhua She, Edwardo F. Fukushima, Min Wu
, Available online  , doi: 10.1109/JAS.2025.125162
[Abstract](32) [FullText HTML] (0) [PDF 4366KB](6)
Abstract:
The complex nonlinear characteristics of pneumatic soft actuators, such as asymmetric hysteresis, rate-dependence, and mechanical load-dependence, pose a challenge in accurately modeling their dynamics. To address this challenge, this paper proposes a comprehensive dynamic model aimed at describing bidirectional asymmetric hysteresis, rate-dependent, and mechanical load-dependent characteristics of a vertical pneumatic bellows actuator (PBA) system. The dynamic model contains a hysteresis submodel and a load-dependent dynamic submodel. The hysteresis submodel consists of several sets of weighted double-side play (DSP) and weighted dead-zone (DZ) operators connected in series, and it is used to model the bidirectional asymmetric hysteresis of the system. The load-dependent dynamic submodel is built based on the gated recurrent unit (GRU) neural network, and it is used to fit the nonlinear relationship between the displacement of the system and the frequency of the input air pressure as well as the mechanical load. The model parameters of the hysteresis submodel and the load-dependent dynamic submodel are determined by intelligent optimization method and neural network training method, reseparately. The fitness value (FV) between the output of the dynamic model and the experimental data is calculated to be 96.1736%, demonstrating that the parameters of the dynamic model are valid. We conduct six set of experiments to compare the model output with the experimental data, and calculate the root-mean-square errors and the maximum error, respectively. The experimental results show that, the root-mean-square error remains consistently below 2.7700%, while the maximum error remains below 8.4000% across all experiments, thereby substantiating the validity and generality of the proposed model.
Path Following Control of Uncertain and Underactuated Autonomous Surface Vessels
Yang Wu, Yueying Wang, Zhiguang Feng, Xiangpeng Xie
, Available online  , doi: 10.1109/JAS.2024.124713
[Abstract](87) [FullText HTML] (0) [PDF 635KB](38)
Abstract:
This paper studies two critical issues in the path following of underactuated autonomous surface vessels (ASVs): 1) most existing vector field (VF) guidances only apply to straight lines or circular paths, limiting their practical applications when dealing with curves; and 2) while most existing barrier function-based sliding mode controllers (BFBSMCs) maintain strong robustness and reduce control conservatism, they often suffer from the issue of excessive initial control force/moment magnitudes. Thus, this paper proposes a novel VF guidance law and integrates a time based generator into the design of BFBSMC to tackle the problems above, significantly advancing the practical application of path following of ASVs in engineering contexts. Simulations verifies the theoretical findings above.
Self-cumulative Contrastive Graph Clustering
Xiaoqiang Yan, Kun Deng, Quan Zou, Zhen Tian, Hui Yu
, Available online  , doi: 10.1109/JAS.2024.125025
[Abstract](50) [FullText HTML] (1) [PDF 1455KB](18)
Abstract:
Contrastive graph clustering (CGC) has become a prominent method for self-supervised representation learning by contrasting augmented graph data pairs. However, the performance of CGC methods critically depends on the choice of data augmentation, which usually limits the capacity of network generalization. Besides, most existing methods characterize positive and negative samples based on the nodes themselves, ignoring the influence of neighbors with different hop numbers on the node. In this study, a novel self-cumulative contrastive graph clustering (SC-CGC) method is devised, which is capable of dynamically adjusting the influence of neighbors with different hops. Our intuition is that better neighbors are closer and distant ones are further away in their feature space, thus we can perform neighbor contrasting without data augmentation. To be specific, SC-CGC relies on two neural networks, i.e., autoencoder network (AE) and graph autoencoder network (GAE), to encode the node information and graph structure, respectively. To make these two networks interact and learn from each other, a dynamic fusion mechanism is devised to transfer the knowledge learned by AE to the corresponding GAE layer by layer. Then, a self-cumulative contrastive loss function is designed to characterize the structural information by dynamically accumulating the influence of the nodes with different hops. Finally, our approach simultaneously refines the representation learning and clustering assignments in a self-supervised manner. Extensive experiments on 8 realistic datasets demonstrate that SC-CGC consistently performs better over SOTA techniques.
CRDet: An Artificial Intelligence-Based Framework for Automated Cheese Ripeness Assessment From Digital Images
Alessandra Perniciano, Luca Zedda, Cecilia Di Ruberto, Barbara Pes, Andrea Loddo
, Available online  , doi: 10.1109/JAS.2024.125061
[Abstract](71) [FullText HTML] (0) [PDF 5395KB](23)
Abstract:
Assessing cheese quality and ripeness is a crucial challenge in the dairy industry, with significant implications for product quality, consumer satisfaction, and economic impact. Traditional evaluation methods relying on visual inspection and human expertise are susceptible to errors and time constraints. This study proposes an innovative approach leveraging machine learning and computer vision techniques for automated cheese ripeness detection to address these limitations.The key contributions of this work include the release of the first comprehensive public dataset of cheese wheel images depicting various products at different ripening stages comprising more than 775 images, CR-IDB, an extensive comparative analysis of the performance of machine learning classifiers trained with features extracted from convolutional neural networks and handcrafted descriptors, along with the evaluation of different feature selection techniques, and finally, a proposal of a novel AI-based framework built upon a Random Forest classifier for cheese ripeness detection, called CRDet.The novelty of CRDet lies in its enforceability across multiple types and dairy industries, which has not been previously addressed in the literature. Unlike earlier methodologies that focused on specific cheese types or relied on subjective visual inspections, this study introduces a comprehensive, noninvasive, and automated approach that demonstrates superior classification performance in differentiating ripeness phases. Thus, it overcomes the limitations of traditional methods and enhances the reliability of cheese ripening assessments.With performance in terms of F1 above 90%, the proposed approach reduces reliance on human expertise, ensuring efficient and reliable evaluation methods for the diverse cheese production landscape. The findings provide valuable insights into the potential of feature selection methods for advancing cheese quality analysis, with implications for the broader dairy industry.
Event-Triggered Adaptive Horizon DMPC for Discrete-Time Coupled Nonlinear Systems
Rui Guo, Jianwen Feng, Jingyi Wang, Yi Zhao
, Available online  , doi: 10.1109/JAS.2024.124704
[Abstract](38) [FullText HTML] (0) [PDF 643KB](13)
Abstract:
Deep Reinforcement Learning for Zero-Shot Coverage Path Planning With Mobile Robots
José Pedro Carvalho, A. Pedro Aguiar
, Available online  , doi: 10.1109/JAS.2024.125064
[Abstract](193) [FullText HTML] (0) [PDF 1606KB](71)
Abstract:
The ability of mobile robots to plan and execute a path is foundational to various path-planning challenges, particularly Coverage Path Planning. While this task has been typically tackled with classical algorithms, these often struggle with flexibility and adaptability in unknown environments. On the other hand, recent advances in Reinforcement Learning offer promising approaches, yet a significant gap in the literature remains when it comes to generalization over a large number of parameters. This paper presents a unified, generalized framework for coverage path planning that leverages value-based deep reinforcement learning techniques. The novelty of the framework comes from the design of an observation space that accommodates different map sizes, an action masking scheme that guarantees safety and robustness while also serving as a learning-from-demonstration technique during training, and a unique reward function that yields value functions that are size-invariant. These are coupled with a curriculum learning-based training strategy and parametric environment randomization, enabling the agent to tackle complete or partial coverage path planning with perfect or incomplete knowledge while generalizing to different map sizes, configurations, sensor payloads, and sub-tasks. Our empirical results show that the algorithm can perform zero-shot learning scenarios at a near-optimal level in environments that follow a similar distribution as during training, outperforming a greedy heuristic by sixfold. Furthermore, in out-of-distribution environments, our method surpasses existing state-of-the-art algorithms in most zero-shot and all few-shot scenarios, paving the way for generalizable and adaptable path-planning algorithms.
FDTs: A Feature Disentangled Transformer for Interpretable Squamous Cell Carcinoma Grading
Pan Huang, Xin Luo
, Available online  
[Abstract](115) [FullText HTML] (0) [PDF 6689KB](20)
Abstract:
Global Sampled-Data Output Feedback Stabilization for Nonlinear Systems via Intermittent Hold
Le Chang, Cheng Fu, Huanjun Zhang
, Available online  
[Abstract](75) [FullText HTML] (0) [PDF 1318KB](31)
Abstract:
This paper introduces a sampled-data and intermittent-hold controller for nonlinear feedforward systems. The intermittent hold allows the control signal to be held in a portion of each sampled period, which does not require the control to be persistently implemented, and thus has less control time. But, less control time degrades the performance of a continuous-time control system or even destabilizes it, especially when the holding portion is sufficiently small. To tackle this obstacle, we first introduce the notion of activating rate to describe the intermittent hold, and give the sampled-data and intermittent-hold controller based on some tuning parameters. Then it is proved that for any activating rate, these parameters can be designed to achieve the stability of the considered systems under appropriately choosing the sampling size. Finally, simulation examples are given to illustrate the effectiveness of the proposed method.
Fixed-Time Stability of Random Nonlinear Systems
Fuyong Wang, Jiayi Gong, Zhongxin Liu, Fei Chen
, Available online  , doi: 10.1109/JAS.2024.124353
[Abstract](61) [FullText HTML] (0) [PDF 471KB](14)
Abstract:
Hazard-Aware Weighted Advantage Combination for UAV Target Tracking and Obstacle Avoidance
Lele Xu, Jian Liu, Xiaoguang Chang, Xuping Liu, Changyin Sun
, Available online  , doi: 10.1109/JAS.2024.124920
[Abstract](89) [FullText HTML] (0) [PDF 4176KB](23)
Abstract:
In recent years, the rapid evolution of unmanned aerial vehicles (UAVs) has brought about transformative changes across various industries. However, addressing fundamental challenges in UAV technology, particularly target tracking and obstacle avoidance, remains crucial for wildlife protection, military industry security, etc. Many existing methods based on reinforcement learning to solve UAV multi-tasks need to be redesigned and retrained, and cannot be quickly and effectively extended to other scenarios. To this end, we propose a novel solution based on a hazard-aware weighted advantage combination for UAV target tracking and obstacle avoidance. First, we independently trained the UAV target tracking and obstacle avoidance using the Dueling Double Deep Q-Network reinforcement learning algorithm. Subsequently, in a multitasking scenario, we introduce the two pre-trained networks. Meanwhile, we design a weight determined by the present risk level encountered by the UAV. This weight is utilized to perform a weighted summation of the advantage values from both networks, eliminating the need for retraining to obtain the final action. We validate our approach through extensive simulation experiments in the robotics simulator known as CoppeliaSim. The results demonstrate that our method outperforms current state-of-the-art techniques, achieving superior performance in both tracking accuracy and avoidance of collisions.
DoS Attack Schedules for Remote State Estimation in CPSs with two-hop relay networks Under Round-Robin Protocol
Shuo Zhang, Lei Miao, Xudong Zhao
, Available online  , doi: 10.1109/JAS.2024.124755
[Abstract](58) [FullText HTML] (0) [PDF 369KB](16)
Abstract:
A Self-Healing Predictive Control Method for Discrete-Time Nonlinear Systems
Shulei Zhang, Runda Jia
, Available online  , doi: 10.1109/JAS.2024.124620
[Abstract](82) [FullText HTML] (0) [PDF 1270KB](22)
Abstract:
In this work, a self-healing predictive control method for discrete-time nonlinear systems is presented to ensure the system can be safely operated under abnormal states. First, a robust MPC controller for the normal case is constructed, which can drive the system to the equilibrium point when the closed-loop states are in the predetermined safe set. In this controller, the tubes are built based on the incremental Lyapunov function to tighten nominal constraints. To deal with the infeasible controller when abnormal states occur, a self-healing predictive control method is further proposed to realize self-healing by driving the system towards the safe set. This is achieved by an auxiliary soft-constrained recovery mechanism that can solve the constraint violation caused by the abnormal states. By extending the discrete-time robust control barrier function theory, it is proven that the auxiliary problem provides a predictive control barrier bounded function to make the system asymptotically stable towards the safe set. The theoretical properties of robust recursive feasibility and bounded stability are further analyzed. The efficiency of the proposed controller is verified by a numerical simulation of a continuous stirred-tank reactor process.
Convex Optimization-Based Model Predictive Control for Mars Ascent Vehicle Guidance System
Kun Li, Yanning Guo, Guangtao Ran, Yueyong Lyu, Guangfu Ma
, Available online  , doi: 10.1109/JAS.2024.124587
[Abstract](50) [FullText HTML] (0) [PDF 486KB](14)
Abstract:
Improving Control Performance by Cascading Observers: Case of ADRC With Cascade ESO
Ahmed T.-E. Benyahia, Momir Stanković, Rafal Madonski, Oluleke Babayomi, Stojadin M. Manojlović
, Available online  , doi: 10.1109/JAS.2024.124995
[Abstract](85) [FullText HTML] (0) [PDF 3229KB](26)
Abstract:
In this paper, we show the performance benefits of connecting multiple observers within a control system. We focus here on a particular observer-based control approach, namely the active disturbance rejection control (ADRC) with cascade extended state observer (ESO). For this framework, we analyze the control performance in terms of quality of observer estimation, reference tracking, disturbance rejection, sensitivity to measurement noise/unmodeled dynamics, and overall stability. A comprehensive frequency response analysis is performed to study the influence of cascading the observers on the selected quality criteria. To make the inquiry beneficial also to practitioners, FPGA-in-the-loop tests are conducted using a guided missiles gimbaled seeker. They validate the theoretical findings in discrete-time settings, where the sampling time and hardware resource requirements become a factor. The results of the investigation are distilled into guidelines for prospective users on when and how a cascade observer structure can be useful for controls.
A Learning-Based Passive Resilient Controller for Cyber-Physical Systems: Countering Stealthy Deception Attacks and Complete Loss of Actuators Control Authority
Liang Xin, Zhi-Qiang Long
, Available online  , doi: 10.1109/JAS.2024.124683
[Abstract](97) [FullText HTML] (0) [PDF 2804KB](24)
Abstract:
Cyber-physical systems (CPSs) are increasingly vulnerable to cyber-attacks due to their integral connection between cyberspace and the physical world, which is augmented by Internet connectivity. This vulnerability necessitates a heightened focus on developing resilient control mechanisms for CPSs. However, current observer-based active compensation resilient controllers exhibit poor performance against stealthy deception attacks (SDAs) due to the difficulty in accurately reconstructing system states because of the stealthy nature of these attacks. Moreover, some non-active compensation approaches are insufficient when there is a complete loss of actuator control authority. To address these issues, we introduce a novel learning-based passive resilient controller (LPRC). Our approach, unlike observer-based state reconstruction, shows enhanced effectiveness in countering SDAs. We developed a safety state set, represented by an ellipsoid, to ensure CPS stability under SDA conditions, maintaining system trajectories within this set. Additionally, by employing deep reinforcement learning (DRL), the LPRC acquires the capacity to adapt and diverse evolving attack strategies. To empirically substantiate our methodology, various attack methods were compared with current passive and active compensation resilient control methods to evaluate their performance.
SILIC: Intelligent On/Off Control for Networked Solar Insecticidal Lamps
Heyang Yao, Lei Shu, Yuli Yang, Miguel Martínez-García, Wei Lin
, Available online  
[Abstract](106) [FullText HTML] (0) [PDF 1873KB](17)
Abstract:
The solar insecticidal lamp (SIL) is an innovative green control device. Nevertheless, a major challenge is often encountered when carrying out insecticidal work is low energy utilization efficiency. The substantial energy consumption required to turn on the SIL, coupled with the extension of insecticidal working time during the low pest activity periods, can result in low energy efficiency. Especially when the energy storage level is below 50%, the inefficient use of energy significantly reduces the effectiveness of pest control. Consequently, an ineffective on/off scheme for these lamps may lead to suboptimal energy utilization. In this paper, we present the solar insecticidal lamp intelligent energy management scheme (SIL-IEMS) to address the challenge of inefficient energy utilization in the solar insecticidal lamp internet of things (SIL-IoT). SIL-IEMS primarily utilizes genetic algorithm (GA) and greedy algorithms to optimize insecticidal working time by considering constraints such as residual energy and the number of trap pests. Comparing SIL-IEMS to the traditional remote switching method (TRSM) and the solar insecticidal lamp genetic algorithm (SILGA), our simulation results showcase its superior energy efficiency and pest control effectiveness. Particularly noteworthy is the SILIEMS’s 17.6% increase in insecticidal efficiency compared to TRSM and 6% improvement over SILGA when the SIL begins with a remaining energy level of 15%.
From Singleton to Collaboration: Robust 3D Cooperative Positioning for Intelligent Connected Vehicles Based on Hybrid Range-Azimuth-Elevation Under Zero-Trust Driving Environments
Zhenyuan Zhang, Heng Qin, Darong Huang, Xin Fang, Mu Zhou, Shenghui Guo
, Available online  
[Abstract](223) [FullText HTML] (0) [PDF 9820KB](56)
Abstract:
Reliable and accurate cooperative positioning is vital to intelligent connected vehicles (ICVs), in which vehicle-vehicle relative measurements are integrated to provide stable location-aware services. However, in zero-trust autonomous driving environments, the possibility of measurement failures and malicious communication attacks tends to reduce positioning performance. With this in mind, this paper presents an ultra-wide bandwidth (UWB) based cooperative positioning system with the specific objective of ICV localization in zero-trust driving environments. Firstly, to overcome measurement degradation under non-line-of-sight (NLOS) propagation conditions, this study proposes a decentralized 3D cooperative positioning method based on a distributed Kalman filter (DKF) by integrating relative range-azimuth-elevation measurements, unlike the state-of-the-art methods that rely on only one single relative range information to update motion states. More specifically, in contrast to pioneering studies that mainly focus on the positioning problem arising from only one single type of communication attack (either false data injection (FDI) or denial of service (DoS)), we consider a more challenging case of secure cooperative state estimation under mixed FDI and DoS attacks. To this end, a singular-value decomposition (SVD)-assisted decoupled DKF algorithm is proposed in this work, in which a novel update-triggered inter-vehicular communication mechanism is introduced to ensure robust positioning performance against communication attacks while maintaining low transmission load between individuals. To verify the effectiveness in practical 3D NLOS scenarios, we design an intelligent connected multi-robot platform based on a robot operating system (ROS) and UWB technology. Consequently, extensive experimental results demonstrate its superiority and feasibility by achieving a high positioning accuracy of 0.68 m under adverse attacks, especially in the case of hybrid FDI and DoS attacks. In addition, several critical discussions, including the impact of attack parameters, resilience assessment, and a comparison with event-triggered methods, are provided in this work. Moreover, a demo video has been uploaded in the supplementary materials for a detailed presentation.
Nonlinear Integral-Ameliorated Model for Dynamic Convex Optimization With Perturbance Considered
Kangze Zheng, Yunong Zhang
, Available online  
[Abstract](43) [FullText HTML] (0) [PDF 8944KB](8)
Abstract:
This work presents a nonlinear integral-ameliorated model for handling dynamic optimization problems with affine constraints. They pose a challenge as their optimal solutions evolve with time. Traditional iteration-based methods that exactly solve the problem at each time instant, fail to precisely and real-time track the solution due to computational and communication bottlenecks. Our model, through rigorous theoretical analyses, is able to reduce the optimality gap (i.e., the difference between the model state and optimal solution) to zero in a finite time, and thus, track the solution online. Besides, perturbance is taken into account. We prove that under certain conditions, our model can totally tolerate an important kind of noise that we call “error-related noise”. In numerical experiments, compared with six existing methods, our model exhibits superior robustness when contaminated by the error-related noise. The key techniques in the model design involve employing the zeroing neural network to leverage time-derivative information, and introducing an integral term as well as the class $ {{{\mathrm{C}}}^0_\text{L}} $ functions to enhance convergence and noise resistance. Finally, we establish a model-free control framework for a surgical manipulator with the remote-center-of-motion constraint and compare the performances of the framework based on different models in simulations. The results indicate that our model achieves the best performance among various models employed within the framework.
Joint Super-Resolution and Nonuniformity Correction Model for Infrared Light Field Images Based on Frequency Correlation Learning
You Du, Yong Ma, Jun Huang, Xiaoguang Mei, Jinhui Qin, Fan Fan
, Available online  , doi: 10.1109/JAS.2024.124881
[Abstract](112) [FullText HTML] (0) [PDF 30190KB](19)
Abstract:
Super-resolution (SR) for the camera array-based infrared light field (IRLF) images aims to reconstruct high-resolution sub-aperture images (SAIs) from their low-resolution counterparts. Existing SR methods mainly focus on exploiting the spatial and angular information of SAIs and have achieved promising results in the visible band. However, they fail to adaptively correct the nonuniform noise in IRLF images, resulting in over-smoothness or artifacts in their results. This study proposes a novel method that reconstructs high-resolution IRLF images while correcting the nonuniformity. The main idea is to decompose the structure and nonuniform noise into high- and low-frequency components and then learn the frequency correlations to help correct the nonuniformity. To learn the frequency correlation, intra- and inter-frequency units are designed. The former learns the correlation of neighboring pixels within each component, aiming to reconstruct the structure and coarsely remove nonuniform noise. The latter models the correlation of contents between different components to reconstruct fine-grained structures and reduce residual noise. Both units are equipped with our designed triple-attention mechanism, which can jointly exploit spatial, angular, and frequency information. Moreover, we collected two real-world IRLF-image datasets with significant nonuniformity, which can be used as a common base in the field. Qualitative and quantitative comparisons demonstrate that our method outperforms state-of-the-art approaches with a clearer structure and fewer artifacts. The code is available at https://github.com/DuYou2023/IRLF-FSR.
GT-A2T: Graph Tensor Alliance Attention Network
Ling Wang, Kechen Liu, Ye Yuan
, Available online  , doi: 10.1109/JAS.2024.124863
[Abstract](94) [FullText HTML] (0) [PDF 592KB](23)
Abstract:
Fuzzy Prescribed-Time Control for Uncertain Nonlinear Pure Feedback Systems
Qidong Li, Changchun Hua, Kuo Li
, Available online  , doi: 10.1109/JAS.2024.124848
[Abstract](87) [FullText HTML] (0) [PDF 1116KB](21)
Abstract:
Multi-Phase Degradation Modeling Based on Uncertain Random Process for Remaining Useful Life Prediction Under Triple Uncertainties
Xuerui Cao, Kaixiang Peng, Ruihua Jiao
, Available online  
[Abstract](35) [FullText HTML] (0) [PDF 3338KB](7)
Abstract:
Due to abrupt changes in the intrinsic degradation mechanism or shock from external environmental pressure, degradations of some equipment are characterized by multi-phase and jumps. Meanwhile, equipment is subject to inherent fluctuations, limited data and imperfect measurements resulting in aleatory, epistemic and measurement uncertainties of the degradation process. This paper proposes a degradation model and remaining useful life (RUL) prediction method under triple uncertainties for a category of complex equipment with multi-phase degradation and jumps. First, a multi-phase degradation model with random jumps and measurement errors is constructed based on uncertain random processes. Afterward, the analytic expression of RUL prediction considering the heterogeneity is derived by modeling the uncertainty of degradation states at change points under the concept of first hitting time. A stochastic uncertain approach is utilized for the proposed multi-phase degradation model to identify model parameters based on historical data. Furthermore, the implied degradation features are adaptively updated in online stage using similarity-based weighted stochastic uncertain maximum likelihood estimation and Kalman filtering. Finally, the effectiveness of the method is verified by simulation example and practical case.
Feature-Driven Variational Mesh Denoising
Jianbin Yang, Cong Wang, Hui Hou, Mingyuan Wang, Xuelong Li
, Available online  , doi: 10.1109/JAS.2024.124923
[Abstract](30) [FullText HTML] (0) [PDF 1811KB](6)
Abstract:
This work elaborates an innovative mesh denoising approach that combines feature recovery and denoising in an alternating manner. It proposes a feature-driven variational model and introduces an iterative scheme that alternates between feature recovery and the denoising process. The main idea is to estimate feature candidates, filter noisy face normals in the smooth (non-feature) domain, and utilize erosion and dilation operators on the feature candidates. By imposing connectivity constraints on normal vectors with large amplitude variations, the proposed scheme effectively removes noise and progressively recovers both sharp and small-scale features during the iterative process. To validate its effectiveness, this work conducts extensive numerical experiments on both simulated and real-scanned data. The results demonstrate significant improvements in noise reduction and feature preservation compared to existing methods.
Robust Pose Graph Optimization Against Outliers Using Consistency Credibility Factor
Jie Cai, Guoliang Wei, Wangyan Li, Yaolei Wang
, Available online  , doi: 10.1109/JAS.2023.123897
[Abstract](55) [FullText HTML] (0) [PDF 513KB](6)
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A Game-Theoretic Approach to Solving the Roman Domination Problem
Xiuyang Chen, Changbing Tang, Zhao Zhang, Guanrong Chen
, Available online  , doi: 10.1109/JAS.2023.123840
[Abstract](57) [FullText HTML] (0) [PDF 2230KB](12)
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The Roman domination problem is an important combinatorial optimization problem that is derived from an old story of defending the Roman Empire and now regains new significance in cyber space security, considering backups in the face of a dynamic network security requirement. In this paper, firstly, we propose a Roman domination game (RDG) and prove that every Nash equilibrium (NE) of the game corresponds to a strong minimal Roman dominating function (S-RDF), as well as a Pareto-optimal solution. Secondly, we show that RDG is an exact potential game, which guarantees the existence of an NE. Thirdly, we design a game-based synchronous algorithm (GSA), which can be implemented distributively and converge to an NE in $ O(n)$ rounds, where n is the number of vertices. In GSA, all players make decisions depending on local information. Furthermore, we enhance GSA to be enhanced GSA (EGSA), which converges to a better NE in $ O(n^2)$ rounds. Finally, we present numerical simulations to demonstrate that EGSA can obtain a better approximate solution in promising computation time compared with state-of-the-art algorithms.
Release Power of Mechanism and Data Fusion: A Hierarchical Strategy for Enhanced MIQ-Related Modeling and Fault Detection in BFIP
Siwei Lou, Chunjie Yang, Zhe Liu, Shaoqi Wang, Hanwen Zhang, Ping Wu
, Available online  , doi: 10.1109/JAS.2024.124821
[Abstract](113) [FullText HTML] (0) [PDF 18758KB](33)
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Data-driven techniques are reshaping blast furnace iron-making process (BFIP) modeling, but their “black-box” nature often obscures interpretability and accuracy. To overcome these limitations, our mechanism and data co-driven strategy (MDCDS) enhances model transparency and molten iron quality (MIQ) prediction. By zoning the furnace and applying mechanism-based features for material and thermal trends, coupled with a novel stationary broad feature learning system (StaBFLS), interference caused by nonstationary process characteristics are mitigated and the intrinsic information embedded in BFIP is mined. Subsequently, by integrating stationary feature representation with mechanism features, our temporal matching broad learning system (TMBLS) aligns process and quality variables using MIQ as the target. This integration allows us to establish process monitoring statistics using both mechanism and data-driven features, as well as detect modeling deviations. Validated against real-world BFIP data, our MDCDS model demonstrates consistent process alignment, robust feature extraction, and improved MIQ modeling—yielding better fault detection. Additionally, we offer detailed insights into the validation process, including parameter baselining and optimization. Details of the code are available online.1
Federated Experiments: Generative Causal Inference Powered by LLM-based Agents Simulation and RAG-based Domain Docking
De-Yu Zhou, Xiao Xue, Qun Ma, Chao Guo, Li-Zhen Cui, Yong-Lin Tian, Jing Yang, Fei-Yue Wang
, Available online  , doi: 10.1109/JAS.2024.124671
[Abstract](168) [FullText HTML] (0) [PDF 682KB](61)
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Data-Driven Iterative Learning Consensus Tracking Based on Robust Neural Models for Unknown Heterogeneous Nonlinear Multiagent Systems With Input Constraints
Chong Zhang, Yunfeng Hu, TingTing Wang, Xun Gong, Hong Chen
, Available online  
[Abstract](79) [FullText HTML] (0) [PDF 409KB](19)
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Optimal Secure Control of Networked Control Systems Under False Data Injection Attacks: A Multi-Stage Attack-Defense Game Approach
Dajun Du, Yi Zhang, Baoyue Xu, Minrui Fei
, Available online  , doi: 10.1109/JAS.2023.124005
[Abstract](87) [FullText HTML] (0) [PDF 878KB](24)
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Distributed Optimal Formation Control of Unmanned Aerial Vehicles: Theory and Experiments
Gang Wang, Zhenhong Wei, Peng Li
, Available online  , doi: 10.1109/JAS.2024.124518
[Abstract](129) [FullText HTML] (0) [PDF 4081KB](46)
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Non-Singular Practical Fixed-time Prescribed Performance Adaptive Fuzzy Consensus Control for Multi-Agent Systems Based on an Observer
Chi Ma, Dianbiao Dong
, Available online  , doi: 10.1109/JAS.2024.124428
[Abstract](125) [FullText HTML] (0) [PDF 2534KB](49)
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In this paper, the problem of non-singular fixed-time control with prescribed performance is studied for multi-agent systems characterized by uncertain states, nonlinearities, and non-strict feedback. To mitigate the nonlinearity, a fuzzy logic algorithm is applied to approximate the intrinsic dynamics of the system. Furthermore, a fuzzy logic system state observer based on leader state information is designed to address the partial unobservability of followers. Subsequently, the power integral method is incorporated into the backstepping approach to avoid singularities in the fixed-time controller. A command filter method is introduced into the standard backstepping approach to reduce the computational complexity of controller design. Then, a non-singular fixed-time adaptive control strategy with prescribed performance is proposed by constraining the tracking error within a prescribed range. Rigorous theoretical analysis ensures the convergence of consensus error in the multi-agent system to the prescribed performance region within a fixed time. Finally, the practicality of the algorithm is validated through numerical simulations.
Semi-Decentralized Convex Optimization on \begin{document}$ {\cal{SO}}(3)$\end{document}
Weijian Li, Peng Yi
, Available online  , doi: 10.1109/JAS.2024.124356
[Abstract](76) [FullText HTML] (0) [PDF 377KB](15)
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New Controllability Criteria for Linear Switched and Impulsive Systems
Jiayuan Yan, Bin Hu, Zhi-Hong Guan, Yandong Hou, Lei Shi
, Available online  , doi: 10.1109/JAS.2024.124272
[Abstract](73) [FullText HTML] (0) [PDF 356KB](11)
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Set-Valued State Estimation of Nonlinear Discrete-Time Systems and Its Application to Attack Detection
Hao Liu, Qing-Long Han, Yuzhe Li
, Available online  
[Abstract](132) [FullText HTML] (0) [PDF 1560KB](30)
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This paper investigates set-valued state estimation of nonlinear systems with unknown-but-bounded (UBB) noises based on constrained polynomial zonotopes which is utilized to characterize non-convex sets. First, properties of constrained polynomial zonotopes are provided and the order reduction method is given to reduce the computational complexity. Then, the corresponding improved prediction-update algorithm is proposed so that it can be adapted to non-convex sets. Based on generalized intersection, the utilization of set-based estimation for attack detection is analyzed. Finally, an example is given to show the efficiency of our results.
Event-Based Networked Predictive Control of Cyber-Physical Systems With Delays and DoS Attacks
Wencheng Luo, Pingli Lu, Changkun Du, Haikuo Liu
, Available online  
[Abstract](96) [FullText HTML] (0) [PDF 457KB](17)
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A Multi-Constrained Matrix Factorization Approach for Community Detection Relying on Alternating-Direction-Method of Multipliers
Ying Shi, Zhigang Liu
, Available online  
[Abstract](124) [FullText HTML] (0) [PDF 826KB](22)
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Distributed Finite-Time Event-Triggered Formation Control Based on a Unified Framework of Affine Image
Yan-Jun Lin, Yun-Shi Yang, Li Chai, Zhi-Yun Lin
, Available online  , doi: 10.1109/JAS.2023.123885
[Abstract](161) [FullText HTML] (1) [PDF 644KB](46)
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Global Stabilization Via Adaptive Event-Triggered Output Feedback for Nonlinear Systems With Unknown Measurement Sensitivity
Yupin Wang, Hui Li
, Available online  , doi: 10.1109/JAS.2023.123984
[Abstract](169) [FullText HTML] (0) [PDF 389KB](46)
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Synchronous Membership Function Dependent Event-Triggered H∞ Control of T-S Fuzzy Systems Under Network Communications
Bo-Lin Xu, Chen Peng, Wen-Bo Xie
, Available online  , doi: 10.1109/JAS.2023.123729
[Abstract](122) [FullText HTML] (0) [PDF 414KB](26)
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Intra-independent Distributed Resource Allocation Game
Jialing Zhou, Guanghui Wen, Yuezu Lv, Tao Yang, Guanrong Chen
, Available online  , doi: 10.1109/JAS.2023.123906
[Abstract](242) [FullText HTML] (0) [PDF 552KB](48)
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Efficient Centralized Traffic Grid Signal Control Based on Meta-Reinforcement Learning
Jia Wu, Yican Lou
, Available online  , doi: 10.1109/JAS.2023.123270
[Abstract](231) [FullText HTML] (0) [PDF 703KB](33)
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Supplementary File of “Push-Sum Based Algorithm for Constrained Convex Optimization Problem and Its Potential Application in Smart Grid”
Qian Xu, Zao Fu, Bo Zou, Hongzhe Liu, Lei Wang
, Available online  
[Abstract](292) [FullText HTML] (0) [PDF 131KB](1)
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Supplementary Material for “Collision and Deadlock Avoidance in Multi-Robot Systems Based on Glued Nodes”
Zichao Xing, Xinyu Chen, Xingkai Wang, Weimin Wu, Ruifen Hu
, Available online  
[Abstract](331) [FullText HTML] (0) [PDF 8129KB](6)
Abstract: