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Automation & Control Systems
Xian-Fang Song, Yong Zhang, Dun-Wei Gong, Xiao-Zhi Gao
Summary: This study proposes a new three-phase hybrid feature selection algorithm that effectively integrates three different feature selection methods to address the "curse of dimensionality" and high computational cost in high-dimensional feature selection problems. Experimental results demonstrate that the algorithm performs well in obtaining good feature subsets with the lowest computational cost on 18 real-world datasets.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
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Automation & Control Systems
Tie Li, Gang Kou, Yi Peng, Philip S. Yu
Summary: The goal of this study is to develop an integrated approach to detect clusters in financial data and optimize their scope for easy interpretation. The proposed algorithm efficiently finds a reasonable number of clusters and is suitable for large-scale financial datasets and financial mining tasks.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Yufeng Tian, Zhanshan Wang
Summary: This article investigates the finite-time extended dissipative filtering for singular T-S fuzzy Markov jump systems with time-varying transition probabilities. The authors propose a unified framework for solving the H-infinity, L-2 - L-infinity, passive, and dissipative performance using a generalized performance index. They also introduce a new condition for the existence of the fuzzy filter using linear matrix inequalities and the decoupling principle. The results are shown to be more practical and less conservative compared to existing works.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Guangxu Li, Gang Kou, Yi Peng
Summary: As the number of participants in decision-making increases, the complexity of the group decision-making process also increases. Traditional methods divide large groups into smaller ones and translate heterogeneous information into a uniform format. This article uses fuzzy cluster analysis to integrate heterogeneous information for large-scale GDM problems and develops a feedback mechanism to adjust decision matrices when consensus cannot be reached.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Zhao Kang, Zhiping Lin, Xiaofeng Zhu, Wenbo Xu
Summary: This work proposes a scalable graph learning framework based on anchor points and bipartite graphs to address drawbacks in graph-based subspace clustering methods, ensuring clustering effectiveness and processing efficiency.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Jiayue Sun, Huaguang Zhang, Yingchun Wang, Shaoxin Sun
Summary: This article solves the fault-tolerant control problem of uncertain time-delayed systems with signal quantization. It proposes an observer-based scheme that enhances the robust stability of the systems and can estimate incomplete measurable variables. The article also introduces a novel method for efficiently seeking the upper bound solution of time-varying delay, reducing conservativeness. The effectiveness of the design method is verified through simulated analysis.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Xin Huo, Hamid Reza Karimi, Xudong Zhao, Bohui Wang, Guangdeng Zong
Summary: The article studies a decentralized event-triggered control problem for a class of constrained nonlinear interconnected systems. It is proven that the system is stable in the sense of uniformly ultimate boundedness. The control scheme is demonstrated through a simulation example.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Xin Li, Haidong Shao, Siliang Lu, Jiawei Xiang, Baoping Cai
Summary: This article proposes a new fault diagnosis method using the least square interactive support matrix machine (LSISMM) and infrared thermal images. The LSISMM is constructed as a matrix-form classifier to leverage the structure information of the thermal images, addressing the issues of vibration analysis and computation efficiency in existing methods. Experimental results demonstrate that this method outperforms state-of-the-art methods in terms of diagnosis accuracy and efficiency.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Siddharth, Tzyy-Ping Jung, Terrence J. Sejnowski
Summary: This research applies novel deep-learning-based methods to analyze and evaluate four publicly available multi-modal emotion datasets containing bio-sensing and video data. The algorithms outperform previous studies in emotion classification and set benchmarks for new datasets. The research also overcomes inconsistencies between datasets using transfer learning and proposes a new technique for identifying salient brain regions corresponding to affective states.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Automation & Control Systems
Ding Wang, Junfei Qiao, Long Cheng
Summary: This article investigates adaptive optimal feedback stabilization for discounted guaranteed cost control of uncertain nonlinear dynamical systems. The problem is transformed to design a discounted optimal control policy for the nominal plant, and an improved self-learning algorithm under the framework of adaptive critic designs is established for deriving the approximate optimal solution. The effectiveness of the method is illustrated through simulation verification on the F16 aircraft plant and other dynamics.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Zhen Zhang, Zhuolin Li
Summary: Consistency and consensus are crucial in linguistic group decision making, where personalized individual semantics play a significant role. This study focuses on developing models to control consistency and reach consensus, introducing the concept of personalized individual semantics to improve decision making processes.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Qiaolin Ye, Peng Huang, Zhao Zhang, Yuhui Zheng, Liyong Fu, Wankou Yang
Summary: This article presents a new multiview learning approach, MvRDTSVM, to improve classification performance and robustness by introducing double-sided constraints and using L1-norm as the distance metric. Experimental results confirm the effectiveness of the proposed methods.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Ambreen Nazir, Yuan Rao, Lianwei Wu, Ling Sun
Summary: This article investigates the issues and challenges in aspect-based sentiment analysis and summarizes recent progress. It also discusses future research directions to assist researchers and improve sentiment classification.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Mingyue Niu, Jianhua Tao, Bin Liu, Jian Huang, Zheng Lian
Summary: Physiological studies indicate differences in speech and facial activities between depressive and healthy individuals. To predict the individual depression level, this study proposes a spatio-temporal attention network and a multimodal attention feature fusion strategy. The approach integrates spatial and temporal information and emphasizes audio/video frames related to depression detection. Experimental results demonstrate the effectiveness of the proposed method on depression databases.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Philipp Werner, Daniel Lopez-Martinez, Steffen Walter, Ayoub Al-Hamadi, Sascha Gruss, Rosalind W. Picard
Summary: This paper assesses the current state of pain recognition technology and provides guidance for researchers. It provides an overview of pain's mechanisms, responses, and assessment methods, discusses challenges in technology development, and surveys existing datasets and evaluation methods. The paper then summarizes the latest advances in automated pain recognition, highlighting different approaches and tools used, as well as the challenges faced. It also identifies underexplored areas and promising opportunities for future advancements.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Review
Automation & Control Systems
Xinrong Gong, Tong Zhang, C. L. Philip Chen, Zhulin Liu
Summary: The emergence of broad learning system (BLS) in recent years has the potential to revolutionize conventional artificial intelligence methods, with remarkable efficiency and flexibility, being applied in various domains. This survey provides a comprehensive overview of BLS in data mining and neural networks, summarizing different BLS methods and future research directions.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Juntao Fei, Huan Wang, Yunmei Fang
Summary: In this article, a fractional-order sliding-mode control scheme based on a two-hidden-layer recurrent neural network (THLRNN) is proposed for a single-phase shunt active power filter. The new THLRNN structure improves fitting ability and more adjustable space in the sliding-mode controller, which proves effective in current compensation.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Maolong Lv, Wenwu Yu, Jinde Cao, Simone Baldi
Summary: This paper investigates the consensus tracking problem for high-power nonlinear multiagent systems with partially unknown control directions, and proposes a hybrid Nussbaum technique to address these issues.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Wenhai Qi, Xu Yang, Ju H. Park, Jinde Cao, Jun Cheng
Summary: This article presents a quantized sliding-mode control design methodology for nonlinear stochastic switching systems, considering semi-Markovian switching parameters, T-S fuzzy strategy, uncertainty, signal quantization, and nonlinearity. The approach involves using a mode-independent sliding surface and Lyapunov function to analyze and control the stability of the sliding-mode dynamics.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Jiehao Li, Junzheng Wang, Hui Peng, Yingbai Hu, Hang Su
Summary: This article proposes a flexible control scheme for lateral motion control of mobile robots, using a Kalman algorithm and a fuzzy compensation preview angle-enhanced sliding model controller to improve tracking accuracy and robustness. Through simulations and experimental demonstrations, the effectiveness and robustness of the method in high-precision trajectory tracking and stability control of mobile robots have been validated.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
