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Human-Following control of Cane-Type Walking-Aid Robot Within Fixed Relative Posture

IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第1期27卷 537-548页

作者： Yan, Qingyang Huang, Jian Yang, Zhaohui Hasegawa, Yasuhisa Fukuda, Toshio Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Image Proc & Intelligent Control Key Lab Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Tongji Med Coll Union Hosp Dept Rehabil Wuhan 430022 Peoples R China Nagoya Univ Dept Micronano Syst Engn Nagoya Aichi 4648603 Japan Meijo Univ Fac Sci & Technol Dept Mechatron Engn Nagoya Aichi 4688502 Japan

Assisting and supervising daily long-durational walking is very crucial for patients with lower extremity dysfunction, especially in the stage of recovery toward a state of walking independently. However, due to the shortage of caregivers and high cost of nursing, long-term manual assistance and supervision is costly. Thus, in this article, we propose a cane-type walking-aid robot to follow a human user for the safety and supervision of independent walking during rehabilitation training. Strongly motivated by clinical suggestions and user experiences, the human-following rule that the robot should follow the user in the required position within a fixed relative posture (keeping a certain distance in front of the user, 15-20 cm lateral to the healthy side, and 0 degrees to the user's orientation) is established. To guarantee a reliable following, a quantitative human walking intention estimation method is proposed using the on-board laser ranger finder and Kalman filter. To effectively monitor the user's walking, a finite-time control method for the cane robot is also proposed to ensure its human-following performance. Experiments were conducted to demonstrate the effectiveness of the proposed system and methods. The experimental results show that the proposed system can obtain satisfactory performance of human following and can be made available for different users in different walking modes.

关键词： Legged locomotion Robots Robot kinematics Robot sensing systems Mechatronics Safety IEEE transactions Cane robot finite-time control human-following walking aid

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Optimal Design of Planar Spiral Coil for Uniform Magnetic Field to Wirelessly Power Position-Free Targets

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IEEE TRANSACTIONS ON MAGNETICS 2021年第2期57卷

作者： Xu, Qi Hu, Quankun Wang, Hao Mao, Zhi-Hong Sun, Mingui Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist Wuhan 430074 Peoples R China Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15261 USA Univ Pittsburgh Dept Neurosurg Pittsburgh PA 15213 USA

A large circular transmitter (power pad) is often utilized for inductively wireless charging so that the power receiver can position itself freely within the power pad. However, the uneven magnetic field distribution leads to the impedance mismatch and the variable transferred power due to the misalignment between a receiving coil and a relatively large transmitter coil. In this paper, the effects of the turn numbers, trace spacing, and coil size on the uniformity of magnetic field distribution over a planar spiral coil (PSC) are investigated based on an analytical model. Then, the genetic algorithm is used to optimize the trace width and spacing of a ten-turn PSC with the fixed outer and inner diameters, whereas the magnetic field is numerically calculated by HFSS software. The geometrically optimal PSC is obtained with the trace width approximately equal to trace spacing for each turn by minimizing the coefficient of variation (COV) of magnetic field within an effective charging area over the coil. The results showed that the simulated and measured COVs of magnetic field were 0.130 and 0.121, respectively, which are obviously less than that of the uniformly spaced regular coil and show a little less than that of the optimally spaced coil with fixed trace width. Therefore, the trace spacing is a major consideration to achieve the uniform magnetic field, whereas the trace width variation is applied to refine the evenness of field. It is also expected that the proposed GA-based optimization is well applied to design other Txs with conformable shape in inductively WPT systems for stable power delivery regardless of the receiver positions.

关键词： Coefficient of variation (COV) genetic algorithm (GA) inductively wireless power transfer (WPT) magnetic field distribution planar spiral coil (PSC)

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On the Tuning of the Computation Capability of Spiking Neural Membrane Systems with Communication on Request

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2022年第8期32卷 2250037页

作者： Wu, Tingfang Neri, Ferrante Pan, Linqiang Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China Univ Surrey NICE Res Grp Dept Comp Sci Stag HillUniv Campus Guildford GU2 7XH Surrey England Educ Minist China Key Lab Image Informat Proc & Intelligent Control Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Spiking neural P systems (abbreviated as SNP systems) are models of computation that mimic the behavior of biological neurons. The spiking neural P systems with communication on request (abbreviated as SNQP systems) are a recently developed class of SNP system, where a neuron actively requests spikes from the neighboring neurons instead of passively receiving spikes. It is already known that small SNQP systems, with four unbounded neurons, can achieve Turing universality. In this context, 'unbounded' means that the number of spikes in a neuron is not capped. This work investigates the dependency of the number of unbounded neurons on the computation capability of SNQP systems. Specifically, we prove that (1) SNQP systems composed entirely of bounded neurons can characterize the family of finite sets of numbers;(2) SNQP systems containing two unbounded neurons are capable of generating the family of semilinear sets of numbers;(3) SNQP systems containing three unbounded neurons are capable of generating nonsemilinear sets of numbers. Moreover, it is obtained in a constructive way that SNQP systems with two unbounded neurons compute the operations of Boolean logic gates, i.e., OR, AND, NOT, and XOR gates. These theoretical findings demonstrate that the number of unbounded neurons is a key parameter that influences the computation capability of SNQP systems.

关键词： Bio-inspired computing membrane computing neural computation spiking neural P system

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A fast sampling based evolutionary algorithm for million-dimensional multiobjective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 75卷

作者： Li, Lianghao He, Cheng Cheng, Ran Li, Hongbin Pan, Linqiang Jin, Yaochu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Bielefeld Univ Fac Technol D-33619 Bielefeld Germany

With their complexity and vast search space, large-scale multiobjective optimization problems (LSMOPs) chal-lenge existing multiobjective evolutionary algorithms (MOEAs). Recently, several large-scale multiobjective evolutionary algorithms have been developed to tackle LSMOPs. Unlike conventional MOEAs that concentrate on selection operations in the objective space, large-scale MOEAs emphasize operations in the decision space, such as offspring generation, to tackle the large number of decision variables. Nevertheless, most present large-scale MOEAs experience difficulty effectively and efficiently solving LSMOPs with tens of thousands or more decision variables or exhibit poor versatility in solving different LSMOPs. We propose a fast large-scale MOEA framework with reference-guided offspring generation, named FLEA, aiming at these issues. Generally, FLEA constructs several reference vectors in the decision space to steer the sampling of promising solutions during offspring generation. A parameter is used to allocate computation resources between the convergence and diversity of the offspring population adaptively. Without computationally expensive problem reformulation or decision variable analysis techniques, the proposed method can significantly accelerate the search speed of conventional MOEAs in solving LSMOPs. FLEA is examined on various LSMOPs with up to 1.6 million decision variables, demonstrating its superior effectiveness, efficiency, and versatility in large-scale multiobjective optimization.

关键词： Evolutionary algorithm Multiobjective optimization Offspring generation Large-scale optimization Reference vector

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Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition

arXiv

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arXiv 2024年

作者： Li, Jiang Wang, Xiaoping Zeng, Zhigang The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China The Hubei Key Laboratory of Brain-Inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

Multimodal emotion recognition in conversation (MERC) has garnered substantial research attention recently. Existing MERC methods face several challenges: (1) they fail to fully harness direct inter-modal cues, possibly leading to less-than-thorough cross-modal modeling;(2) they concurrently extract information from the same and different modalities at each network layer, potentially triggering conflicts from the fusion of multi-source data;(3) they lack the agility required to detect dynamic sentimental changes, perhaps resulting in inaccurate classification of utterances with abrupt sentiment shifts. To address these issues, a novel approach named GraphSmile is proposed for tracking intricate emotional cues in multimodal dialogues. GraphSmile comprises two key components, i.e., GSF and SDP modules. GSF ingeniously leverages graph structures to alternately assimilate inter-modal and intra-modal emotional dependencies layer by layer, adequately capturing cross-modal cues while effectively circumventing fusion conflicts. SDP is an auxiliary task to explicitly delineate the sentiment dynamics between utterances, promoting the model’s ability to distinguish sentimental discrepancies. Furthermore, GraphSmile is effortlessly applied to multimodal sentiment analysis in conversation (MSAC), forging a unified multimodal affective model capable of executing MERC and MSAC tasks. Empirical results on multiple benchmarks demonstrate that GraphSmile can handle complex emotional and sentimental patterns, significantly outperforming baseline models. Copyright © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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Consensus in Fractional-Order Multi-Agent Systems With Intermittence Sampled Data Over Directed Networks

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2020年第2期67卷 365-369页

作者： Ye, Yanyan Su, Housheng Chen, Jinhe Peng, Yaxin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Min Wuhan 430074 Peoples R China

The leaderless consensus of fractional-order multi-agent systems (FOMASs) by intermittence sampled data control method is investigated in this brief, for which a distributed intermittence sampled data control protocol is presented to reduce the updating rate and working time of controllers. Subsequently, the Laplace transform and the stability theory are utilized to derive some necessary and sufficient consensus criteria that show the relations among the fractional order, sampling period, communication width, coupling strengths, and network topology. What is more, it can be discovered that both the communication width and sampling period have the lower and upper bounds for given networks to attain leaderless consensus. Finally, a numerical simulation example is presented to prove the theoretical results.

关键词： Laplace equations Protocols Multi-agent systems Distributed databases Couplings Kinetic theory Circuits and systems Fractional-order multi-agent system consensus sampled data communication width

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TasselNetV3: Explainable Plant Counting With Guided Upsampling and Background Suppression

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷

作者： Lu, Hao Liu, Liang Li, Ya-Nan Zhao, Xiao-Ming Wang, Xi-Qing Cao, Zhi-Guo Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Wuhan Inst Technol Sch Comp Sci & Engn Wuhan 430205 Peoples R China China Agr Univ Coll Biol Sci Ctr Crop Functional Genom & Mol Breeding Beijing 100193 Peoples R China

Fast and accurate plant counting tools affect revolution in modern agriculture. Agricultural practitioners, however, expect the output of the tools to be not only accurate but also explainable. Such explainability often refers to the ability to infer which instance is counted. One intuitive way is to generate a bounding box for each instance. Nevertheless, compared with counting by detection, plant counts can be inferred more directly in the local count framework, while one thing reproaching this paradigm is its poor explainability of output visualization. In particular, we find that the poor explainability becomes a bottleneck limiting the counting performance. To address this, we explore the idea of guided upsampling and background suppression where a novel upsampling operator is proposed to allow count redistribution, and segmentation decoders with different fusion strategies are investigated to suppress background, respectively. By integrating them into our previous counting model TasselNetV2, we introduce TasselNetV3 series: TasselNetV3-Lite and TasselNetV3-Seg. We validate the TasselNetV3 series on three public plant counting data sets and a new unmanned aircraft vehicle (UAV)-based data set, covering maize tassels counting, wheat ears counting, and rice plants counting. Extensive results show that guided upsampling and background suppression not only improve counting performance but also enable explainable visualization. Aside from state-of-the-art performance, we have several interesting observations: 1) a limited-receptive-field counter in most cases outperforms a large-receptive-field one;2) it is sufficient to generate empirical segmentation masks from dotted annotations;3) middle fusion is a good choice to integrate foregroundx2013;background a priori knowledge;and 4) decoupling the learning of counting and segmentation matters.

关键词： image segmentation Agriculture Plants (biology) Annotations Tools Data visualization Ear Explainable counting local count network maize tassel phenotyping plant counting regression rice plant segmentation unmanned aircraft vehicle (UAV) wheat ear

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A large-scale group Success Likelihood Index Method to estimate human error probabilities in the railway driving process

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年 228卷

作者： Zhou, Jian-Lan Yu, Ze-Tai Xiao, Ren-Bin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518057 Peoples R China Univ Southern Calif Viterbi Sch Engn Dept Comp Sci Los Angeles CA 90089 USA

Success Likelihood Index Method (SLIM) is widely used to estimate human error probability. However, the traditional SLIM relies on a small number of experts, which may cause biases and undermine its effectiveness. To overcome this drawback, a large number of experts participate to provide fuzzy ratings and negotiate to reduce biases, which forms a large-scale group SLIM (LG-SLIM) problem. The LG-SLIM model is used to negotiate among experts to reach a unanimous agreement of the group. We improve the consensus model by constructing two mathematical programming models that provide the recommendations of both optimum preference adjustments and weight adjustment for the non-cooperative subgroup, which establishes an effective balance between the subjectivities and compromises of experts. Some typical human errors concerning target tasks are estimated in the railway driving process. To verify the proposed LG-SLIM model, the results obtained from it and individuals are compared with the simulation values from the Bayesian network respectively. The comparison shows that the results are consistent with the simulation results and conform to the experience and knowledge in railway driving. The LG-SLIM model is helpful to human error assessment when critical operations need enormous ex-perts to obtain reliable and accurate results.

关键词： Railway operation Success likelihood index method Human error assessment Large-scale group decision -making Consensus model Bayesian network

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Bipartite Consensus for Second-Order Multiagent Systems With Matrix-Weighted Signed Network

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第12期52卷 13038-13047页

作者： Miao, Suoxia Su, Housheng East China Jiaotong Univ Dept Elect & Automat Engn Nanchang 330013 Jiangxi Peoples R China Nanchang Inst Technol Sch Sci Nanchang 330099 Jiangxi Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

The second-order scalar-weighted consensus problem of multiagent systems has been well explored. However, in some practical antagonistic interaction networks, the interdependencies of multidimensional states of the agents must be described by matrix coupling. In order to highlight the influence of matrix coupling in the antagonistic interaction network, we investigate the second-order matrix-weighted bipartite consensus problem on undirected structurally balanced signed networks. Under the proposed bipartite consensus protocol, an algebraic condition is obtained for achieving second-order bipartite consensus via utilizing matrix-valued Gauge transformation and stability theory. Then, using the obtained criteria, a more direct algebraic graph condition is given for reaching bipartite consensus. Besides, because of the existence of negative (positive) semidefinite connections, the matrix-weighted network may have clustering phenomena, which means that matrix weights play a critical role in achieving consensus. An algebraic graph condition for admitting cluster bipartite consensus is provided. By designing matrix weights in practical scenarios, the required number of clusters can be obtained. Finally, the theoretical results are verified by five simulation examples.

关键词： Symmetric matrices Laplace equations Multi-agent systems Couplings Social networking (online) Network topology Dynamical systems Cluster bipartite consensus matrix-weighted network multiagent systems (MASs) second-order bipartite consensus signed graph structural balance

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Operator-Adapted Evolutionary Large-Scale Multiobjective Optimization for Voltage Transformer Ratio Error Estimation 11th

Operator-Adapted Evolutionary Large-Scale Multiobjective Opt...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Huang, Changwu Li, Lianghao He, Cheng Cheng, Ran Yao, Xin Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783030720612

Large-scale multiobjective optimization problems (LSMOPs) exist widely in real-world applications, and they are challenging for existing evolutionary algorithms due to their massive volume of search space. Despite that a number of large-scale multiobjective evolutionary algorithms (LSMOEAs) have been proposed in recent years, their effectiveness in solving LSMOPs remains unsatisfactory. One main reason is that most existing LSMOEAs may fail to balance convergence enhancement and diversity maintenance, especially for solving real-world problems. To address this issue, we propose to use a hybridized LSMOEA with adaptive operator selection (AOS) to handle real-world LSMOPs. Specifically, the proposed hybridized LSMOEA with AOS (AOS-LSMOEA) includes multiple different offspring generation and environmental selection strategies extracted from some existing LSMOEAs. Then it uses the AOS to adaptively determine the application rates of different offspring generation and environmental selection operators in an online manner. The proposed approach is capable of taking advantage of existing LSMOEAs, and the AOS enables the algorithm to choose suitable operators for solving different LSMOPs. In this study, the proposed algorithm is expected to solve the voltage transformer ratio error estimation (TREE) problems effectively. Experimental results show that AOS-LSMOEA achieves significant performance improvement due to the hybridization of different operators and the adoption of AOS method. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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