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第40届中国控制会议

作者： Min Li Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

Multimodal information-based broad and deep learning model（MIBDL） for emotion understanding is proposed, in which facial expression and body gesture are used to achieve emotional states recognition for emotion understanding. It aims to understand coexistence multimodal information in human-robot interaction by using different processing methods of deep network and broad network, which obtains the features of depth and width dimensions. Moreover, random mapping in the initial broad learning network could cause information loss and its shallow layer network is difficult to cope with complex tasks. To address this problem, we use principal component analysis to generate the nodes of the broad learning, and the stacked broad learning network is adapted to make it easier for the existing broad learning networks to cope with complex tasks by creating deep variations of the existing network. To verify the effectiveness of the proposal, experiments completed on benchmark database of spontaneous emotion expressions are developed, and experimental results show that the proposal outperforms the state-of-theart methods. According to the simulation experiments on the FABO database, by using the proposed method, the multimodal recognition rate is 17,54%, 1.24%, and 0.23% higher than those of the temporal normalized motion and appearance features（TN）,the multi-channel CNN（MCCNN）, and the hierarchical classification fusion strategy（HCFS）, respectively.

关键词： Convolution neural network broad learning emotion understanding facial expression body gesture

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Improving Rate-of-Penetration and Reducing State Fluctuation of Drill-String System Based on Multiobjective Optimization

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IEEE Transactions on Industrial Informatics 2025年

作者： Yang, Xiao Wu, Min Lu, Chengda Huang, Hengyu Wu, Xiao Li, Wangnian Zhang, Youzhen China University of Geosciences School of Future Technology Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China China University of Geosciences School of Automation Wuhan430074 China Company Ltd. Xi'an710077 China

This article is concerned with the balance between improving rate-of-penetration and suppressing drill-string vibrations by optimizing weight-on-bit and rotational speed. The main contribution of this article lies in three points. First, due to the lack of downhole data, the problem of suppressing drill-string vibrations is transformed into reducing state fluctuation of drill-string system. Meanwhile, a multiobjective optimization scheme is established. Second, the temporal relations are revealed, including modeling time, optimization time, resampling time, controller response time, buffer time, and optimization interval. They act as constraints for subsequent modeling and optimization. Third, the improved extreme learning machine methods are developed to build the calculation models of rate-of-penetration and state fluctuation of drill-string system, separately. The nondominated sorting genetic algorithm II, technique for order preference by similarity to an ideal solution method, and moving window are used to obtain the optimal weight-on-bit and rotational speed. The effectiveness of the approach is demonstrated through an industrial case study based on the actual drilling data. © 2005-2012 IEEE.

关键词： Multiobjective optimization

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How Simulation Helps Autonomous Driving: A Survey of Sim2real, Digital Twins, and Parallel Intelligence

arXiv

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arXiv 2023年

作者： Hu, Xuemin Li, Shen Huang, Tingyu Tang, Bo Huai, Rouxing Chen, Long The School of Artificial Intelligence Hubei University Hubei Wuhan430062 China The Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Beijing Huairou Academy of Parallel Sensing Beijing101499 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100864 China The Waytous Inc. Beijing100083 China

Safety and cost are two important concerns for the development of autonomous driving technologies. From the academic research to commercial applications of autonomous driving vehicles, sufficient simulation and real world testing are required. In general, a large scale of testing in simulation environment is conducted and then the learned driving knowledge is transferred to the real world, so how to adapt driving knowledge learned in simulation to reality becomes a critical issue. However, the virtual simulation world differs from the real world in many aspects such as lighting, textures, vehicle dynamics, and agents' behaviors, etc., which makes it difficult to bridge the gap between the virtual and real worlds. This gap is commonly referred to as the reality gap (RG). In recent years, researchers have explored various approaches to address the reality gap issue, which can be broadly classified into three categories: transferring knowledge from simulation to reality (sim2real), learning in digital twins (DTs), and learning by parallel intelligence (PI) technologies. In this paper, we consider the solutions through the sim2real, DTs, and PI technologies, and review important applications and innovations in the field of autonomous driving. Meanwhile, we show the state-of-the-arts from the views of algorithms, models, and simulators, and elaborate the development process from sim2real to DTs and PI. The presentation also illustrates the far-reaching effects and challenges in the development of sim2real, DTs, and PI in autonomous driving. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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Extended Dissipativity Analysis and Synthesis for Discrete-Time Lur'e Delayed systems: Matrix-Separation-Based Lyapunov Functional Method

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IET control Theory and Applications 2025年第1期19卷

作者： Xie, Ke-You Lee, Sangmoon Zhang, Chuan-Ke Jin, Li Liu, Yajuan He, Yong School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Electronic and Electrical Engineering Kyungpook National University Daegu Korea Republic of School of Control and Computer Engineering North China Electric Power University Beijing China

This paper develops a matrix-separation-based Lyapunov functional method to study the extended dissipativity analysis and synthesis issue of discrete-time Lur'e-type delayed systems. The advanced idea of matrix-separation is reflected in Lyapunov functional candidates and estimates summation terms as precisely as feasible. First, we introduce a novel summation inequality based on the matrix-separation method to provide a bound for the augmented summation that involves common state variables. An improved delay-product-type Lyapunov functional is devised by extending non-positive definite summations as separate subblocks of the state-augmented summation. Then, by using the matrix injection method to handle the cubic delay term in the functional forward difference, a delay-variation-dependent stability criterion and an extended dissipativity criterion are developed under the matrix-separation-based method. Subsequently, sufficient conditions for the control design of Lur'e-type systems are derived. Finally, the effectiveness and superiority of the proposed method are verified through its application to Chua's circuits, neural networks, and a stochastic numerical example. © 2025 The Author(s). IET control Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Stability criteria

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A Novel Multi-objective Evolutionary Algorithm Based on Space Partitioning 11th

A Novel Multi-objective Evolutionary Algorithm Based on Spac...

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11th International Symposium on Intelligence Computation and Applications, ISICA 2019

作者： Wu, Xiaofang Li, Changhe Zeng, Sanyou Yang, Shengxiang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

ISBN: (纸本)9789811555763

To design an effective multi-objective optimization evolutionary algorithms (MOEA), we need to address the following issues: 1) the sensitivity to the shape of true Pareto front (PF) on decomposition-based MOEAs;2) the loss of diversity due to paying so much attention to the convergence on domination-based MOEAs;3) the curse of dimensionality for many-objective optimization problems on grid-based MOEAs. This paper proposes an MOEA based on space partitioning (MOEA-SP) to address the above issues. In MOEA-SP, subspaces, partitioned by a k-dimensional tree (kd-tree), are sorted according to a bi-indicator criterion defined in this paper. Subspace-oriented and Max-Min selection methods are introduced to increase selection pressure and maintain diversity, respectively. Experimental studies show that MOEA-SP outperforms several compared algorithms on a set of benchmarks. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Drone Image Stitching Using Local Least Square Alignment

Drone Image Stitching Using Local Least Square Alignment

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Qi Wan Linbo Luo Jun Chen Yong Wang Donghai Guo School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

This paper proposes a strategy for drone image stitching using local least square alignment, which aims to effectively stitch multiple overlapping drone images into a natural panoramic image. Existing traditional methods using simple homography cannot handle the situation that the input drone images have parallax effect, and the mosaic result always suffers from artifacts. In order to achieve natural-looking stitching results without the above limitation, we divide the proposed method into the following two steps, namely, local least square alignment and global similarity constraint. Starting from initial feature sets obtained by traditional feature extraction methods, we construct a robust alignment energy based on parallax errors to adaptively eliminate parallax effects. The energy can be efficiently minimized used least square estimate. Combined with global similarity constraint, our proposed strategy can flexibly improve the naturalness of the results. Experiments show that our stitching strategy can more effectively eliminate parallax effects and achieve natural-looking results compared to other state-of-the-art methods.

关键词： Drones Image stitching Distortion Feature extraction Optimization Geology Adaptation models

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H∞ Consensus control for Discrete-Time Stochastic Multi-agent systems with Infinite Markov Jumps

H∞ Consensus Control for Discrete-Time Stochastic Multi-age...

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Annual Conference of Industrial Electronics Society

作者： Jie Wang Xin Chen School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9781728154145

ISBN: (纸本)9781728154152

This paper focuses on multi-agent systems under the disturbances of parameter uncertainties, noises and external disturbances simultaneously, which are more suitable for the changing environments than most ideal models. For this case, the robust H∞ consensus control problem is studied to attenuate the influence of disturbances below a given level. We aim to design a consensus controller such that the closed-loop muti-agent systems reach the desired H ∞ performance. By tools of Kronecker product, graph theory, and infinite horizon bounded real lemma, sufficient conditions for existence of H ∞ consensus protocol design are obtained in terms of linear matrix inequalities (LMIs). Finally, a numerical example is given to show the validity of the method.

关键词： Multi-agent systems Symmetric matrices Markov processes Stochastic systems Robot sensing systems Eigenvalues and eigenfunctions Topology

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EID-based Robust Stabilization for a Class of Nonlinear Uncertain systems with Disturbances 38

EID-based Robust Stabilization for a Class of Nonlinear Unce...

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第三十八届中国控制会议

作者： Shengnan Tian Min Wu Luefeng Chen Jinhua She Manli Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

ISBN: (纸本)9781728123295;9789881563972

This paper concerns robust stabilization for a class of nonlinear systems with uncertainties and disturbaces. The equivalent-input-disturbance(EID) approach is utilized to estimate the influence of nonlinearities and exogenous disturbances on the output of the system. A robust stability condition is derived in terms of linear matrix inequalities. Moreover, two parameters are used to tune the robustness of the system. Simulation results show that it is more effective to stabilize the system using the developed method than using the sliding-mode control(SMC) method.

关键词： Robust stabilization equivalent-input-disturbance nonlinear uncertain system disturbance linear matrix inequality(LMI)

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Learning Multiple Network Embeddings for Social Influence Prediction ⁎

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IFAC-PapersOnLine 2020年第2期53卷 2868-2873页

作者： Feng Wang Jinhua She Yasuhiro Ohyama Min Wu School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China School of Engineering Tokyo University of Technology Tokyo 192-0982 Japan

How to effectively predict social influence is an essential issue in social network analysis. Almost all reported methods for social influence prediction are mainly concerned with estimating influence probabilities for each linking edge. However, all of this past work cannot accurately predict influence probabilities for all edges due to the problem of data sparsity. Unlike conventional approaches, this work focuses on exploring a cross problem for multiple network embeddings and social influence prediction. This study developed a new end-to-end approach, Multi-Influor, that learns multiple influence vectors for each user in social networks, instead of estimating influence probabilities for each edge. The multiple network embeddings consider multi-dimensional influence factors that incorporate pairwise node interactions, network structures, and global similarity comparisons. Moreover, this study solves the problem of influence evaluation caused by sparse observations. Extensive comparisons based on large-scale datasets indicate that the Multi-Influor approach outperforms several state-of-the-art baselines, and the experimental results demonstrate that the Multi-Influor approach is more practical on real-world social networks.

关键词： Multiple Network Embeddings Representation Learning Online Social Networks Neural Network Social Influence Prediction

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A Unified Framework for Finite-/Fixed-Time Synchronization of Memristor Chaotic systems and Its Application in Image Encryption

Research Square

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Research Square 2021年

作者： Wang, Leimin Jiang, Shan Ge, Ming-Feng Hu, Junhao China University of Geosciences China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China College of Mathematics and Statistics South-Central University for Nationalities Wuhan430074 China

This paper proposes a sliding-mode-based unified control framework to solve the synchronization problem of memristor chaotic systems. Both finite- and fixed-time synchronization of the memristor chaotic systems can be obtained in the uniform framework. According to the Lyapunov stability and finite-time stability theories, we demonstrate that the trajectories of error system reach the presented sliding-mode surface and converge to the origin along the surface in a finite/fixed time. Moreover, an image encryption algorithm is developed based on the presented control framework. Finally, the numerical simulations and the statistical performance analyses are discussed to illustrate the correctness of synchronization results, the effectiveness of the proposed encryption algorithm, and its potential applications in the scope of secure communication. © 2021, CC BY.

关键词： Chaotic systems

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