Article
Automation & Control Systems
Xu Liu, Lingling Li, Fang Liu, Biao Hou, Shuyuan Yang, Licheng Jiao
Summary: The proposed deep group spatial-spectral attention fusion network is a novel classification method for PAN and MS images, which effectively combines spatial and spectral information to achieve comparable results in image interpretation through feature extraction, attention fusion, and classifier integration at pixel level.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Shan Li, Weihong Deng
Summary: This survey provides a comprehensive review of deep facial expression recognition (FER) research, including datasets, algorithms, and related issues. The authors introduce available datasets and data selection principles, describe the standard pipeline of a deep FER system and provide implementation suggestions. They also discuss the latest deep neural networks and training strategies, summarize the competitive performances and experimental comparisons, and explore additional related issues and application scenarios. The remaining challenges and future directions for robust deep FER systems are reviewed as well.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Automation & Control Systems
Wu Deng, Junjie Xu, Xiao-Zhi Gao, Huimin Zhao
Summary: In this paper, an enhanced MSIQDE algorithm based on mixing multiple strategies, called EMMSIQDE, is proposed to overcome the limitations of QDE in optimization problems. EMMSIQDE achieves better optimization performance by using new mutation strategies and evolution mechanisms, as well as a feasible solution space transformation strategy.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Tong Zhang, Xuehan Wang, Xiangmin Xu, C. L. Philip Chen
Summary: In recent years, emotion recognition has become a research focus in the field of artificial intelligence. This study proposes a Graph Convolutional Broad Network (GCB-net) for exploring deeper-level information of graph-structured data, and achieves better accuracy and performance compared to other methods.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Review
Computer Science, Artificial Intelligence
Giorgos Giannakakis, Dimitris Grigoriadis, Katerina Giannakaki, Olympia Simantiraki, Alexandros Roniotis, Manolis Tsiknakis
Summary: This review examines the impact of psychological stress on the human body, measured through biosignals. The objective of the study is to establish reliable biosignal indices that reveal the physiological mechanisms of the stress response. The study also investigates the relationship between various bodily responses and stress, and explores multimodal biosignal analysis and modeling methods.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Peixiang Zhong, Di Wang, Chunyan Miao
Summary: In this article, a regularized graph neural network (RGNN) is proposed for EEG-based emotion recognition. The RGNN captures both local and global relations among different EEG channels by considering the biological topology among different brain regions. The proposed adjacency matrix and two regularizers contribute to the improved performance of the RGNN model.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Automation & Control Systems
Zhaohui Zheng, Ping Wang, Dongwei Ren, Wei Liu, Rongguang Ye, Qinghua Hu, Wangmeng Zuo
Summary: The proposed CIoU loss and Cluster-NMS approach, which incorporates geometric factors, significantly improve average precision and average recall in object detection and instance segmentation, with notable gains without sacrificing inference efficiency.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Lianbo Ma, Min Huang, Shengxiang Yang, Rui Wang, Xingwei Wang
Summary: This article proposes an adaptive localized decision variable analysis approach under the decomposition-based framework to solve large-scale multiobjective and many-objective optimization problems. The algorithm incorporates the guidance of reference vectors into control variable analysis and optimizes decision variables using an adaptive strategy. Experimental results validate the effectiveness and efficiency of the proposed algorithm on the large-scale multiobjective and MaOPs.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Fuyuan Xiao
Summary: This article discusses the importance of uncertainty in decision-making processes, as well as the connections between quantum mechanics and the uncertainty reasoning tool CET. It introduces a new CEQD model to predict the impact of interference effects on human decision-making behaviors, along with the design of corresponding belief transformation functions to explain these effects, with experimental results confirming the effectiveness of the method proposed.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Wei Tao, Chang Li, Rencheng Song, Juan Cheng, Yu Liu, Feng Wan, Xun Chen
Summary: This article proposes an attention-based convolutional recurrent neural network (ACRNN) to extract more discriminative features from EEG signals and improve the accuracy of emotion recognition.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Automation & Control Systems
Nianyin Zeng, Zidong Wang, Weibo Liu, Hong Zhang, Kate Hone, Xiaohui Liu
Summary: In this article, a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is proposed with improved velocity update mechanism and learning strategy. The differential evolution algorithm is successfully hybridized with the particle swarm optimization algorithm to enhance the solution accuracy for multimodal optimization problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Yang Liu, Deyin Yao, Hongyi Li, Renquan Lu
Summary: This article introduces a definition of compound tracking control and a finite-time performance function, and utilizes methods such as adaptive neural networks to design a distributed cooperative regulation protocol. Simulation experiments confirm the effectiveness of the theoretical findings.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Huanqing Wang, Wen Bai, Xudong Zhao, Peter Xiaoping Liu
Summary: This article discusses finite-time-prescribed performance-based adaptive fuzzy control for a class of strict-feedback systems in the presence of actuator faults and dynamic disturbances. An adaptive fuzzy fault-tolerant control strategy is introduced to deal with the difficulties associated with actuator faults and external disturbances. A modified performance function called the finite-time performance function (FTPF) is presented, which ensures that all signals of the closed-loop system are bounded and the tracking error converges to a predetermined region in finite time. The effectiveness of the controller is verified through simulation results.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Chunyong Yin, Sun Zhang, Jin Wang, Neal N. Xiong
Summary: This article proposes an integrated model for anomaly detection, using convolutional neural network (CNN) and recurrent autoencoder as the basis, and extracting features through two-stage sliding window data preprocessing. Empirical results show that the proposed model performs better on multiple classification metrics and achieves excellent results in anomaly detection.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Peng Cheng, Shuping He, Vladimir Stojanovic, Xiaoli Luan, Fei Liu
Summary: This article addresses the design issue of fuzzy asynchronous fault detection filter for a class of nonlinear Markov jump systems by using an event-triggered scheme. The asynchronous phenomenon between system and filter is characterized via a hidden Markov model with partly accessible mode detection probabilities, and sufficient conditions for the presence of FAFDF are obtained applying Lyapunov function methods.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Guozeng Cui, Jinpeng Yu, Qing-Guo Wang
Summary: This article investigates the problem of finite-time adaptive fuzzy tracking control for multi-input and multi-output (MIMO) nonlinear systems with input saturation. A new finite-time command filter and modified error compensation mechanism are introduced to address complexity explosion and filter error effects. The proposed finite-time adaptive control scheme guarantees finite-time bounded signals and regulation of output tracking errors to a small neighborhood of the origin. Numerical comparison example verifies the effectiveness of the proposed control scheme.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Tong Yang, Ning Sun, Yongchun Fang
Summary: This article addresses the control problem of underactuated systems by using elaborately constructed finite-time convergent surfaces, overcoming the main obstacle in sliding-mode surface analysis and demonstrating its importance in real applications.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Xiaoming Xue, Kai Zhang, Kay Chen Tan, Liang Feng, Jian Wang, Guodong Chen, Xinggang Zhao, Liming Zhang, Jun Yao
Summary: Evolutionary multitasking (EMT) is a new research topic that aims to improve convergence across multiple optimization tasks by facilitating knowledge transfer. Existing EMT algorithms are limited to homogeneous problems, and little effort has been made to generalize EMT for solving heterogeneous problems. This article proposes a novel rank loss function to achieve superior intertask mapping and derive an analytical solution for affine transformation. The proposed technique can seamlessly integrate with EMT paradigms, and its effectiveness is demonstrated through experiments on synthetic multitasking and many-tasking benchmark problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Tianfu Li, Zhibin Zhao, Chuang Sun, Li Cheng, Xuefeng Chen, Ruqiang Yan, Robert X. Gao
Summary: The article introduces a novel wavelet-driven deep neural network, WaveletKernelNet (WKN), for mechanical fault diagnosis, which is more effective than traditional CNN in terms of accuracy and convergence speed. The network is designed with a continuous wavelet convolutional layer to discover more meaningful kernels and provides a customized kernel bank for extracting defect-related impact components.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Peng Cheng, Hai Wang, Vladimir Stojanovic, Shuping He, Kaibo Shi, Xiaoli Luan, Fei Liu, Changyin Sun
Summary: This article discusses the design of asynchronous fault detection (FD) observer for 2-D Markov jump systems expressed by a Roesser model. By employing a hidden Markov model (HMM) and a multiobjective solution, sufficient conditions for the existence of asynchronous FD are obtained using linear matrix inequality technology. An asynchronous FD algorithm is generated to achieve optimal performance indices.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
