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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Zhang, Mengshi Cao, Yu Yang, Bo Huang, Jian School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute ShenZhen518060 China

ISBN: (纸本)9798350300178

Mobile wheeled inverted pendulum (MWIP) is a typical naturally unstable underactuated system and it is very necessary to constrain its transient response to improve the overall safety. Prescribed performance control (PPC) is an effective method, however the system initial conditions are usually required to be known and constraint, otherwise it can lead to singularity problems. In practical systems, it is often difficult to obtain the exact initial state leading to limitations in the application of PPC. To overcome this issue, this paper presented a composite error transformation function to address the problem of initial condition dependence. A continuous bounded function was designed to compress the arbitrary initial error into the predefined bound to avoid singularities. After a set time, it was degraded to an original system error. Based on this composite transformation, a state feedback controller was proposed for the underactuated MWIP system to achieve the constraint outputs. Then, by employing the Lyapunov theorem, it was guaranteed that the closed-loop system would remain stable. Various numerical simulations under different initial conditions were carried out to prove the validity of the proposed controller. © 2023 IEEE.

关键词： Closed loop systems

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image encryption algorithm based on Hilbert sorting vector and new spatiotemporal chaotic system

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OPTICS AND LASER TECHNOLOGY 2023年第1期167卷

作者： Zhang, Hangming Hu, Hanping Ding, Weiping Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China

The design of the lattice coupling mode in current spatiotemporal chaotic systems lacks dynamic characteristics. When it is applied to the cryptosystem, its chaotic performance defects weaken the security of the cryptosystem. In this context, it is urgent to design new coupling rules and secure cryptosystems based on fractal and chaos theory. In this paper, a kind of sorting vector based on the fractal sorting matrix and fractal curve, the Hilbert sorting vector (HSV), is proposed creatively, and its iterative generation process is introduced. HSV is irregular and infinitely iterable. According to the requirements of the actual situation, HSV has a flexible adjustment vector length, which improves the multiplicity and efficiency of changing information positions. Then, HSV is used to reconstitute the the interaction of nodes during iteration in a new spatiotemporal chaotic system named Hilbert-sorting-vector coupled map lattice (HSVCML). Using this new sorting vector, the dynamic characteristics of spatiotemporal chaotic systems can be effectively improved. This is proved by comparing their Lyapunov exponent, Kolmogorov-Sinai entropy, bifurcation diagram, and information entropy with the coupled map lattice (CML). Moreover, the rich spatio-temporal behaviours of HSVCML are studied. Therefore, HSVCML is more appropriate for image encryption than CML. Finally, HSV is combined with a spatiotemporal chaotic system to frame an image encryption method. For the purpose of proving the effectiveness and security of this algorithm against different types of attacks, a large number of tests related to security analysis and time complexity analysis are carried out. Simulation results prove that our encryption algorithm is more secure and efficient than the previous algorithms and can resist various attacks.

关键词： Hilbert sorting vector Fractal sorting matrix Fractal curve Spatiotemporal chaotic system Coupled map lattice image encryption

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Semi-Global Bounded Output Regulation of Linear Two-Time-Scale Systems With Input Saturation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2023年第11期70卷 4560-4569页

作者： Lei, Yan Wang, Yan-Wu Liu, Xiao-Kang Morarescu, Irinel-Constantin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Univ Lorraine CNRS CRAN F-54000 Nancy France

Standard output regulation design techniques cannot be applied for linear two-time-scale systems subject to saturated inputs. In this work, a state feedback output regulation is first proposed based on a classical stabilizing composite state feedback controller. Nevertheless, the corresponding design is difficult to implement due to numerical issues. Thus, the method of asymptotic power series expansion is applied to provide an approximate solution to the regulator equation. Then, a time-continuous state feedback controller is designed by combining the Chang transformation approach and the low-gain feedback technique, which results in a semi-global bounded output regulation of the closed-loop system. Furthermore, to reduce control updates, a dynamic event-triggered control scheme is proposed which ensures the exclusion of Zeno behavior by maintaining a strictly positive time between any two triggering moments, regardless of the initial state of the system. Additionally, an observer-based event-triggered control scheme is proposed to cater to the practical scenario in which system state information is unavailable. Finally, to demonstrate the effectiveness of our proposed technique, two examples are presented.

关键词： Two-time-scale output regulation input saturation event-triggered control

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A Fused Deep Fuzzy Neural Network controller and Its Application to Pneumatic Flexible Joint

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2023年第6期28卷 3214-3225页

作者： Chen, Cheng Cao, Yu Chen, Xinxing Wu, Dongrui Xiong, Caihua Huang, Jian Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Shenzhen Key Lab Biomimet Robot & Intelligent Syst Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Guangdong Prov Key Lab Human Augmentat & Rehabil R Shenzhen 518055 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

With multilayered deep neural networks (DNNs) distinguished from the traditional single-layer neural networks (SNNs) by the depth, deep learning is a powerful paradigm possessing the capability of feature learning from big data. However, as a completely deterministic model, deep learning does not perform well in dealing with uncertain information and must be pretrained before being applied, which hinders the broad application of deep learning in real-time control. In this article, we introduce the knowledge-based fuzzy logic system (FLS) into deep learning and propose a fused deep fuzzy neural network (FDFNN) controller. The FDFNN controller derives the control signal from both deep fuzzy neural networks and the knowledge-based FLS. At the beginning of control, the FDFNN controller performs like a traditional fuzzy logic controller (FLC), which eliminates the pretraining procedure of deep learning. With another FLS providing supervised signal, the network weights of DFNN's hidden layers can be updated during the control process, which endows the FDFNN controller with online learning capability. A Lyapunov-based convergence analysis is conducted to guarantee the stability of the learning procedure. Both numerical simulations and real-world experiments validate that the proposed method effectively improves the real-time tracking control performance of a pneumatic flexible joint system.

关键词： Deep learning Fuzzy logic Real-time systems Knowledge based systems Neural networks Fuzzy neural networks Fuzzy control fuzzy logic systems (FLSs) tracking control

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A New True Random Number Generator Based on Chaotic Circuit Architecture

A New True Random Number Generator Based on Chaotic Circuit ...

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2022 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2022

作者： Lv, Fei Diao, Jin School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education Wuhan China

ISBN: (纸本)9781510657304

In the fields of information security, the application of random numbers is becoming more and more widespread. True random numbers (TRNs) tend to have better randomness and unpredictability than pseudo random numbers (PRNs). Chaotic systems are very suitable for constructing true random number generators (TRNG) due to their periodic infinity, randomicity, long-term unpredictability, high sensitivity to initial values and parameters, etc. However, when chaotic systems are implemented by finite state machines, their dynamic characteristics will disappear, such as periodic infinity and initial value sensitivity, and there are serious safety concerns about the random number generator designed on this basis. In order to address the degradation problem of the chaotic systems, this paper proposes a true random number generator based on the chaotic circuit, implements a complete hardware system, and tests its randomness. © 2022 SPIE.

关键词： Chaotic systems

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EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation

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Science China(Information Sciences) 2024年第8期67卷 130-146页

作者： Yingjian LIU Jiang LI Xiaoping WANG Zhigang ZENG School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Key Laboratory of Brain-inspired Intelligent Systems Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education Institute of Artificial Intelligence Huazhong University of Science and Technology

Emotion recognition in conversation(ERC) has attracted growing attention in recent years as a result of the advancement and implementation of human-computer interface technologies. In this paper,we propose an emotional inertia and contagion-driven dependency modeling approach(EmotionIC) for ERC tasks. Our EmotionIC consists of three main components, i.e., identity masked multi-head attention(IMMHA), dialogue-based gated recurrent unit(DiaGRU), and skip-chain conditional random field(SkipCRF).Compared to previous ERC models, EmotionIC can model a conversation more thoroughly at both the feature-extraction and classification levels. The proposed model attempts to integrate the advantages of attention-and recurrence-based methods at the feature-extraction level. Specifically, IMMHA is applied to capture identity-based global contextual dependencies, while Dia GRU is utilized to extract speaker-and temporal-aware local contextual information. At the classification level, SkipCRF can explicitly mine complex emotional flows from higher-order neighboring utterances in the conversation. Experimental results show that our method can significantly outperform the state-of-the-art models on four benchmark *** ablation studies confirm that our modules can effectively model emotional inertia and contagion.

关键词： emotion recognition in conversation emotional inertia and contagion multi-head attention gated recurrent unit conditional random field

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Cross-user Gesture Recognition Based on Deep Domain Adaptation Networks

Cross-user Gesture Recognition Based on Deep Domain Adaptati...

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第43届中国控制会议

作者： Zhongzheng Fu Xinrun He Haoyuan Wang Xingjian Chen Yixuan Li Jian Huang Ministry of Education Key Laboratory of Image Processing and Intelligent Control Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This study introduces an innovative approach for gesture recognition in smart wearable devices using a deep domain adaptation model, focusing on the challenges posed by heterogeneous user environments and the need for precise, adaptable, and personalized gesture recognition systems. Traditional methods based on machine learning and deep learning techniques, while effective, struggle with the variance in data distribution among different users, leading to accuracy challenges. To address this,the study proposes the Domain Kernel-Alignment Adaptation Network(DKAN) model, utilizing a new Kernel-Aligned Multi-kernel Maximum Mean Discrepancy(KAMMD) method within deep networks. By calculating kernel alignments between source and target domains and adjusting weights of Gaussian kernels accordingly, DKAN emphasizes features conducive to transfer learning, enabling more accurate cross-user gesture recognition. The model achieves a significant average accuracy of 97.3% across 21 gestures, highlighting its potential in practical applications. This advancement addresses challenges in traditional gesture recognition methods and sets a new direction for future research in smart wearable technology.

关键词： Gesture recognition Kernel-alignment Domain adaptation

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Interval Type-2 Fuzzy Hierarchical Adaptive Cruise Following-control for intelligent Vehicles

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1658-1672页

作者： Hong Mo Yinghui Meng Fei-Yue Wang Dongrui Wu the College of Electric and Information Engineering Changsha University of Science and TechnologyChangsha 410114China the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Qingdao Academy of Intelligent Industries Qingda 266109 the Center of China Economic and Social Security University of Chinese Academy of SciencesBeijing 100190China the Ministry of Education Key Laboratory on Image Information Processing and Intelligent Control the School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

intelligent vehicles can effectively improve traffic congestion and road traffic *** cruise followingcontrol(ACFC)is a vital part of intelligent *** this paper,a new hierarchical vehicle-following control strategy is presented by synthesizing the variable time headway model,type-2 fuzzy control,feedforward+fuzzy proportion integration(PI)feedback(F+FPIF)control,and inverse longitudinal dynamics model of ***,a traditional variable time headway model is improved considering the acceleration of the lead ***,an interval type-2 fuzzy logic controller(IT2 FLC)is designed for the upper structure of the ACFC system to simulate the driver's operating *** reduce the nonlinear influence and improve the tracking accuracy for the desired acceleration,the control strategy of F+FPIF is given for the lower control ***,the lower control method proposed in this paper is compared with the fuzzy PI control and the traditional method(no lower controller for tracking desired acceleration)***,the proportion integration differentiation(PID),linear quadratic regulator(LQR),subsection function control(SFC)and type-1 fuzzy logic control(T1 FLC)are respectively compared with the IT2 FLC in control performance under different ***,the simulation results show the effectiveness of IT2 FLC for the upper structure and F+FPIF control for the lower structure.

关键词： Adaptive cruise following-control(ACFC) feedforward+fuzzy proportion integration(PI)feedback(F+FPIF) type-2 fuzzy control variable time headway

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Optimizing Parameter Design with Frequency and Clock Drift Constraints in Microgrids∗ 63

Optimizing Parameter Design with Frequency and Clock Drift C...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Yu, Chang Lu, Xiaoqing Wuhan University of Science and Technology School of Information Science and Engineering Wuhan430081 China Huazhong University of Science and Technology Ministry of Education Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China Wuhan University School of Electrical Engineering and Automation Wuhan430000 China

ISBN: (纸本)9798350316339

Microgrids subjected to secondary cooperative control encounter significant challenges, including operational constraints and clock drifts, adversely affecting their stability and efficiency. This paper provides conditions that assure optimal microgrid performance in both transient and steady-state scenarios, focusing on the effects of clock drifts and fluctuations in load. Furthermore, we introduce a novel approach for designing secondary control parameters, specifically engineered to minimize steady-state discrepancies attributable to clock drifts while ensuring adherence to standards for transient operations. Comprehensive experimental validations corroborate the effectiveness of our proposed solutions. © 2024 IEEE.

关键词： Microgrids

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CSENet: Cascade semantic erasing network for weakly-supervised semantic segmentation

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NEUROCOMPUTING 2021年 453卷 885-895页

作者： Liu, Jiahui Yu, Changqian Yang, Beibei Gao, Changxin Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China

Weakly-supervised semantic segmentation based on image-level annotations has difficulty exploring pixel-level information. Most approaches adopt Class Activation Maps (CAM) to localize initial object regions, called seeds. To cover more potential object parts, seeds-expansion methods raise concern for artificial mask generation. Due to the seeds simply focus on discriminative regions, it is a challenge to spread seeds to the integral object. To tackle this problem, we propose a Cascade Semantic Erasing Network (CSENet) to expand seeds effectively and reasonably. In particular, CSENet sequentially stacks the semantic erasing stage to erase discriminative areas progressively. It forces the network to exploit rel-evant feature response for non-discriminative object districts. Moreover, CSENet directly suppresses seeds on the Class Activation Maps (CAM), which have stronger semantics, rather than on the Intermediate Feature Maps (IFM). Under semantic guidance, proposed erasing strategy correctly spreads seeds regions to the intra-class regions and meanwhile, prohibits from extending to the unexpected inter-class areas. Extensive experiments demonstrate the effectiveness of proposed CSENet. More specif-ically, our approach achieves 62.3% and 63.4% mIoU on PASCAL VOC 2012 validation and test set, respectively. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Weak supervision Semantic erasing Class activation maps

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