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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Yan, Jiayu Li, Song Wan, Yiming Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China 29th Research Institute of China Electronics Technology Group Corporation Chengdu China

ISBN: (纸本)9781665401159

For battery management systems, it is significant to reliably estimate state-of-charge (SOC) from limited measurements in real time. Based on a nonlinear SOC-dependent equivalent circuit model, we propose a real-time moving horizon estimation (MHE) algorithm for SOC estimation. For efficient computation, the sequential quadratic programming strategy is applied, and each quadratic programming (QP) iteration is solved via the structure-exploiting forward and backward recursions. To account for the limited computation time allowed within each sampling interval, the proposed algorithm does not fully solve the receding horizon optimization problem over each sliding window, but performs fixed number of QP iterations. The simulation results show that compared to the existing fully solved MHE applied to SOC estimation, the proposed real-time MHE algorithm significantly saves computation time at the cost of a slight loss of estimation performance. © 2021 IEEE.

关键词： Quadratic programming

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InferEM: Inferring the Speaker’s Intention for Empathetic Dialogue Generation

arXiv

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arXiv 2022年

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

Current approaches to empathetic response generation typically encode the entire dialogue history directly and put the output into a decoder to generate friendly feedback. These methods focus on modelling contextual information but neglect capturing the direct intention of the speaker. We argue that the last utterance in the dialogue empirically conveys the intention of the speaker. Consequently, we propose a novel model named InferEM for empathetic response generation. We separately encode the last utterance and fuse it with the entire dialogue through the multi-head attention based intention fusion module to capture the speaker’s intention. Besides, we utilize previous utterances to predict the last utterance, which simulates human’s psychology to guess what the interlocutor may speak in advance. To balance the optimizing rates of the utterance prediction and response generation, a multitask learning strategy is designed for InferEM. Experimental results demonstrate the plausibility and validity of InferEM in improving empathetic expression. Copyright © 2022, The Authors. All rights reserved.

关键词： Encoding (symbols)

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GNet: 3D Object Detection from Point Cloud with Geometry-Aware Network

GNet: 3D Object Detection from Point Cloud with Geometry-Awa...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Hao Ruan Bingrong Xu Junbin Gao Lianguang Liu Junting Lv Yin Sheng Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China School of Automation Wuhan University of Technology Wuhan China

3D object detection based on point clouds in real-world applications has been a challenging task because the raw point cloud may be sparse or occluded. It may be caused by bad weather or limited sensor resolution, leading to decreasing detection performance. To this end, a novel highperformance Geometry-aware Network (GNet) is proposed and used to accurately detect 3D objects from sparse point clouds. Different from previous work that only regresses the offset of 3D bounding boxes numerically between ground truth (GT) and prediction, GNet considers the geometric information of 3D bounding boxes and combines the two effectively to achieve a better regression performance. In particular, Point Nets blocks learned voxel-wise features and the proposals are generated by the 3D voxel convolution neural network (voxel CNN). By utilizing the prior knowledge and the geometric properties of the 3D bounding boxes, the proposed novel OS-loss can improve detection performance and accelerate speed of inference. Furthermore, GNet takes advantage of prior geometry properties rather than only regressing the offset numerically. These speed up the convergence of the regression network. Finally, our proposed GNet was evaluated on the KITTI dataset for 3D objects and Bird’s Eye View (BEV) detection aspects. The results demonstrate that GNet can improve object detection performance for sparse point clouds.

关键词： Point cloud compression Location awareness Geometry Three-dimensional displays Neural networks Object detection Proposals

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Multimodal Representations Learning Based on Mutual Information Maximization and Minimization and Identity Embedding for Multimodal Sentiment Analysis

arXiv

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arXiv 2022年

作者： Zheng, Jiahao Zhang, Sen Wang, Xiaoping Zeng, Zhigang School of Artificial Intelligence and Automation The Key Laboratory of Image Processing Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China

Multimodal sentiment analysis (MSA) is a fundamental complex research problem due to the heterogeneity gap between different modalities and the ambiguity of human emotional expression. Although there have been many successful attempts to construct multimodal representations for MSA, there are still two challenges to be addressed: 1) A more robust multimodal representation needs to be constructed to bridge the heterogeneity gap and cope with the complex multimodal interactions, and 2) the contextual dynamics must be modeled effectively throughout the information flow. In this work, we propose a multimodal representation model based on Mutual information Maximization and Minimization and Identity Embedding (MMMIE). We combine mutual information maximization between modal pairs, and mutual information minimization between input data and corresponding features to mine the modal-invariant and task-related information. Furthermore, Identity Embedding is proposed to prompt the downstream network to perceive the contextual information. Experimental results on two public datasets demonstrate the effectiveness of the proposed model. Copyright © 2022, The Authors. All rights reserved.

关键词： Sentiment analysis

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GA2MIF: Graph and Attention Based Two-Stage Multi-Source Information Fusion for Conversational Emotion Detection

arXiv

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arXiv 2022年

作者： Li, Jiang Wang, Xiaoping Lv, Guoqing Zeng, Zhigang School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China

Multimodal Emotion Recognition in Conversation (ERC) plays an influential role in the field of human-computer interaction and conversational robotics since it can motivate machines to provide empathetic services. Multimodal data modeling is an up-and-coming research area in recent years, which is inspired by human capability to integrate multiple senses. Several graph-based approaches claim to capture interactive information between modalities, but the heterogeneity of multimodal data makes these methods prohibit optimal solutions. In this work, we introduce a multimodal fusion approach named Graph and Attention based Two-stage Multi-source Information Fusion (GA2MIF) for emotion detection in conversation. Our proposed method circumvents the problem of taking heterogeneous graph as input to the model while eliminating complex redundant connections in the construction of graph. GA2MIF focuses on contextual modeling and cross-modal modeling through leveraging Multi-head Directed Graph ATtention networks (MDGATs) and Multi-head Pairwise Cross-modal ATtention networks (MPCATs), respectively. Extensive experiments on two public datasets (i.e., IEMOCAP and MELD) demonstrate that the proposed GA2MIF has the capacity to validly capture intra-modal long-range contextual information and inter-modal complementary information, as well as outperforms the prevalent State-Of-The-Art (SOTA) models by a remarkable margin. Copyright © 2022, The Authors. All rights reserved.

关键词： Directed graphs

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Energy Management Strategies for PEMFC-Lithium Battery Hybrid Power Systems Based on Pt Degradation

Energy Management Strategies for PEMFC-Lithium Battery Hybri...

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Chinese control Conference (CCC)

作者： Ziang Guo Beijia Li Linghong Zeng Jun Fu Chuang Sheng Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Wuhan Hubei China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

Proton Exchange Membrane Fuel Cells(PEMFCs) are prone to decreased lifespan due to the degradation of the plat-inum(Pt) catalyst during operation. In this study, we have established a one-dimensional model to investigate the effects of different voltage conditions on Pt degradation. High potential is found to be the most significant factor affecting the degradation. Prolonged exposure to high potentials will significantly reduce the PEMFC's lifespan, and frequent voltage changes will exac-erbate the problem. To mitigate this issue, we have designed three energy management strategies with different limitations and simulated their performance in the operating scenario of a fuel-cell hybrid electric vehicle(FCHEV). The results demonstrate that strategies restricting high potential occurrences and voltage variations can effectively alleviate Pt degradation.

关键词：

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Thermoelectric Tracking control of Solid Oxide Fuel Cell System Using Adaptive Dynamic Programming

Thermoelectric Tracking Control of Solid Oxide Fuel Cell Sys...

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第34届中国控制与决策会议

作者： Xiao-long Wu Hongli Liu Hong Zhang Jinting Fu Bin Xie Han Hu Xi Li School of information engineering Nanchang University School of Artificial and Automation Key Laboratory of Education Ministry for Image Processing and Intelligent ControlHuazhong University of Science & Technology

An adaptive optimal control strategy for solid oxide fuel cell power generation systems(SOFCs) is proposed in this paper.A new estimator is proposed in a simple form to solve the modeling uncertainty,and express the control of SOFCs as an optimal control ***,a new system thermoelectric parameter tracking control strategy is *** strategy uses data-driven to build the recursive neural network model of the system thermoelectric *** order to ensure convergence,an adaptive law based on parameter estimation error is proposed to update the weights of neural networks *** simulation results show that the data-driven model can effectively identify the power generation process of solid oxide fuel cell *** the same time,the control method can realize the optimal tracking control of power,voltage,stack temperature and afterburner temperature with limited input.

关键词： Solid oxide fuel cell adaptive optimal control adaptive dynamic programming system thermoelectric performance

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An Assist-as-Needed Variable Admittance controller for Adaptive Support of Mobile Walking Assistive Robots

An Assist-as-Needed Variable Admittance Controller for Adapt...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Xinxing Chen Yue Zheng Zongming Su Yuquan Leng Jian Huang Chenglong Fu Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China Technology Planning Department Huawei Technologies Shenzhen China Key Laboratory for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Mobile walking assistive robots have been widely used for monitoring patients’ gait patterns and providing support to prevent falls. Traditional approaches often separate the operational modes of these robots into human-following and supporting modes, which can lead to discontinuities in assistance and potentially compromise safety. This paper proposes a novel assist-as-needed control strategy that incorporates a Balance Index (BI) to dynamically adjust the robot’s support based on the user’s balance capability. The BI is calculated using the interaction force between the user and the robot along the z-axis and the gait symmetry index (GSI). The proposed variable admittance controller modifies its parameters based on the BI, ensuring seamless transitions between compliance and support modes. Experimental validation was conducted with a healthy subject walking both with and without a knee fixation brace. The results demonstrate that the robot provides minimal interference during natural gait and increased support during impaired gait, validating the effectiveness of the proposed control strategy in enhancing user safety and promoting natural walking patterns.

关键词： Legged locomotion Force Stability criteria Interference Assistive robots User experience Safety Admittance Indexes Robots

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Optimizing Binary Sequence Excitation Signals for Rapid Impedance Spectrum Measurement in Proton Exchange Membrane Fuel Cells

Optimizing Binary Sequence Excitation Signals for Rapid Impe...

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第43届中国控制会议

作者： Jingzhi Lei Chuang Sheng Jingjing Wang Liansong Yu Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and Technology State Grid Electric Power Research Institute Wuhan Nanrui Limited Liability Company Shenzhen Huazhong University of Science and Technology Research Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

With the increasing demand for efficient and reliable renewable energy sources,Proton Exchange Membrane Fuel Cells(PEMFCs) have emerged as a promising *** experiments demonstrate that Electrochemical Impedance Spectroscopy(EIS) effectively differentiates various states of *** broadband signals as testing excitations significantly reduces EIS measurement time,facilitating real-time monitoring of PEMFCs' health under dynamic *** signal design requires maintaining adequate amplitude across frequency components for noise resistance while minimizing signal peak value to reduce operational *** paper introduces an EIS simulation environment,leveraging PEMFCs' impedance and electrochemical noise *** focuses on optimizing the excitation signal's amplitude-frequency distribution using discrete interval binary *** results indicate that our method achieves minimal perturbation amplitude while preserving measurement accuracy in amplitude-frequency signal design.

关键词： Proton exchange membrane fuel cell Electrochemical impedance spectroscopy Electrochemical noise Excitation signal optimization

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Wind Speed Prediction Based on Combined Deep Learning Model Woa-Vmd-Gru-Tcn

SSRN

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SSRN 2024年

作者： Zeng, Chuxi Xia, Zhipnig Wang, Zhuo Zhou, Yihuan Zeng, Linghong Yin, Zishan Li, Xi Huang, Haizhou Qi, Xiaolan Deng, Zhonghua School of Artificial Intelligence and Automation Key Laboratory of Image Processing Intelligent Control of Education Ministry Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China Huazhong University of Science and Technology China

Accurate wind speed prediction is essential for the integration of wind turbines into microgrids. This paper proposes a combined prediction model, firstly, the key parameters of the variational modal decomposition (VMD) is optimized by whale optimization algorithm (WOA), and the original wind speed sequence is decomposed by using the WOA-VMD to obtain the intrinsic mode functions (IMFs) with different frequencies;secondly, the Pearson correlation coefficient is performed between IMFs and the original wind speed sequence, classifying the IMFs into two groups according to their correlation coefficients, the IMFs with strong correlation are predicted using the temporal convolutional network (TCN), and the IMFs with weak correlation are predicted using the gate recurrent unit (GRU);finally, the prediction results of each component are superimposed to obtain the final prediction results. The combined model is applied to real data from six wind farms in China with different climatic conditions and geographic locations, and different comparison models are established for validation. The experimental results show that the combined WOA-VMD-GRU-TCN prediction model can significantly improve the prediction accuracy, reduce the wind speed sequence error, and have better robustness than other methods. © 2024, The Authors. All rights reserved.

关键词： Intrinsic mode functions

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