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43rd Chinese control Conference, CCC 2024

作者： Huang, Haizhou Xia, Zhiping Shan, Xinkai Wang, Jie Li, Xi Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China

ISBN: (纸本)9789887581581

Microgrids possess the ability to use renewable energy efficiently and play an increasingly significant role in environmental protection and sustainable development. Meanwhile, reversible solid oxide fuel cell (rSOC) is currently a new type of energy conversion and storage. Combining with microgrid systems is a great way to benefit from its high capacity, high efficiency, and cleanliness. However, rSOC's complex system of coupled heat, electricity, and gas presents a significant challenge in finding energy management strategies for microgrid applications containing rSOC. In this study, for a microgrid system combining rSOC, battery energy storage system (BESS), solar photovoltaic (PV), and wind turbine generator (WTG), we propose a model-based energy management strategy that simultaneously takes into account the economy of the microgrid system and the stability of the rSOC system. First of all, the microgrid is modeled with great detail, including the wind turbine, solar panels, battery storage system, and rSOC. In addition, the microgrid energy management problem is framed as a mathematical programming problem, with the attention focusing on the costs of BESS aging and operational energy loss of rSOC. In particular, this study also proposes a penalty strategy to reduce the power fluctuation amplitude of the rSOC system and reduce the mode switching frequency of the rSOC system. Using the solver, solving for each device's target power in a microgrid. The actual power operating curve is output by tracking the target power under slope constraints using the Simulink model. We then conduct a case study using data from Wuhan, China. Our research shows that compared with the control strategy based on rule control, the control strategy proposed in this study can reduce the power variation amplitude of the rSOC system by 79%, reduce the mode switching frequency by 85%, reduce the system operating cost by 4.5% and increase the energy self-sufficiency rate by 9%. © 2024 Techn

关键词： Wind turbines

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Dynamic Graph Dismantling with Reinforcement Supervised Learning 42

Dynamic Graph Dismantling with Reinforcement Supervised Lear...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Yumei Tang, Chuancong Zhang, Hai-Tao School of Artificial Intelligence and Automation Huazhong University of Science and Technology The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China

ISBN: (纸本)9789887581543

There are always some 'key' nodes in a big complex network, which can joint the most connected subgraphs. How to identify these nodes, finding a minimum set of nodes to attack for reducing the size of residual network's Largest Connected Component(LCC) to break up the original network, has become a research hotspot. Therefore, a method for determining the 'key' nodes based on reinforcement learning framework and supervised learning model is proposed. This algorithm can not only utilize the dynamic exploration ability of reinforcement learning to collect a rich training dataset, but also take advantage of the characteristics that supervised learning is adaptive and has strong generalization ability to possess high efficiency and strong robustness. In order to further improve the algorithm's performance, ϵ-greedy mechanism is used to explore more network states. The experiment results show that given the same fraction of removed nodes, our algorithm can make the residual LCC smaller in various networks which is superior to the state-of-the-art algorithms in terms of effectiveness and generalization. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Complex networks

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RBP-Former: Joint Prediction of RNA-protein Binding Sites on Full-length RNA Transcripts for Multiple RBPs

RBP-Former: Joint Prediction of RNA-protein Binding Sites on...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Yichong Liu, Xiaojian Cheng, Fan Pan, Xiaoyong Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions. © 2024 IEEE.

关键词： Binding sites

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Fabric Defect Defection based on Lightweight Convolutional Denoising Auto-Encoder 22

Fabric Defect Defection based on Lightweight Convolutional D...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Jin, Dong Li, Wenzheng Sun, Zhigang Li, Xiao School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450396110

In the industrial production environment, the scarcity of defect samples and the high labor cost of labeling defect samples make supervised machine learning models difficult to implement. In addition, defect detection using deep learning models usually requires high computation cost and advanced hardware platform, which is not suitable for running on embedded platforms. To solve this problem, this paper proposes an unsupervised fabric defect detection method based on lightweight convolutional denoising auto-encoder(LCDAE). The LCDAE model is constructed by the bottleneck layer in MobileNetV3. Experimental results show that the method can effectively detect and localize defects with less computation and has the potential to perform defect detection on edge-embedded platforms. © 2022 ACM.

关键词： Defects

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An Assist-as-Needed Variable Admittance controller for Adaptive Support of Mobile Walking Assistive Robots 30

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

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30th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Chen, Xinxing Zheng, Yue Su, Zongming Leng, Yuquan Huang, Jian Fu, Chenglong Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China Huawei Technologies Technology Planning Department 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: (纸本)9798350391916

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. © 2024 IEEE.

关键词： Assistive technology

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Energy management of solid oxide fuel cell/lithium battery hybrid system

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CSEE Journal of Power and Energy Systems 2022年 1-14页

作者： Li, Dong Zhong, Xiao Bo Xu, Yuan Wu Wei, Chong Yang Zhao, Song Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430081 China

The research of fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission. However, the lower efficiency of energy management has been a critical factor that obstruct the commercialization of hybrid system. In this work, based on finite element ideas, a solid oxide full cell node model is developed to accurately estimate the operating status of the fuel cell. As for lithium battery, previous energy management studies generally only focused on state of charge, however, state of health is also a key parameter for lithium battery. In this paper, considering the strong coupling between variables we use particle filter algorithm to jointly estimate the state of charge and state of health. To reasonably distribute and manage the energy in the hybrid system, a T-S fuzzy controller for the hybrid system is designed by controlling the state variables in the SOFC model and the state of lithium battery. Finally, our algorithm is verified by DSPACE simulation platform. The result shows that the hybrid system with the energy management strategy not only meets the real-time sharply changing demand but also ensures the hybrid system working efficiently and safely. © 2022 China Electric Power Research Institute. All rights reserved.

关键词： Hybrid systems

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Location and Capacity of Charging Station with Random Spatial-Temporal Distribution of Charging Demands 13

Location and Capacity of Charging Station with Random Spatia...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： Jiang, Man Cao, Zhong Niu, Yinsheng Zhang, Xueyu Liu, Xiao-Kang Yuan, Qi-Fan CentralChina Branch of State Grid Corporation of China Wuhan China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798350315196

This paper studies the problem of location and capacity for direct charging (DC) fast charging stations (CS). A new model with the random spatial-temporal distribution of charging demands is proposed to plan both the location and capacity for CSs and is modeled as a nonlinear optimization problem with a comprehensive cost function and multi-variable that can be solved by an improved genetic algorithm. Further, the trip chain of EV users is considered to describe the traveling characteristics based on the origin-destination (OD) matrix, and then the Voronoi diagram is used to divide the service area of each CS. The effectiveness of both the model and the improved genetic algorithm is demonstrated through the final simulation results and comparative analyses. © 2023 IEEE.

关键词： Location

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A CSP-based retraining framework for motor imagery based brain-computer interfaces

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Science China(Information Sciences) 2024年第8期67卷 349-350页

作者： Xue JIANG Lubin MENG Xinru CHEN Dongrui WU Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Shenzhen Huazhong University of Science and Technology Research Institute

Motor imagery(MI) is a classic paradigm of electroencephalogram(EEG)-based brain-computer interfaces(BCIs). It entails individuals mentally imagining the movement of a body part without physically performing it. EEG s... 详细信息

关键词： Classification (of information)

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A time-coded multi-concentration microfluidic chemical waveform generator for high-throughput probing suspension single-cell signaling

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Chinese Chemical Letters 2022年第6期33卷 3091-3096页

作者： Yiran Guo Peng Chen Zhaolong Gao Yiwei Li Shunji Li Xiaojun Feng Bi-Feng Liu The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics and Molecular Imaging Key Laboratory Systems Biology ThemeDepartment of Biomedical EngineeringCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

The cellular response to the complex extracellular microenvironment is highly dynamic in time and type of extracellular *** reconstructing this process and analyzing the changes in receptor conformation on the cell membrane surface and intracellular or intercellular signaling has been a major challenge in analytical chemistry and biophysical *** this paper,a time-coded multiconcentration microfluidic chemical waveform generator was developed for the dynamic signaling probing with single-cell array of high temporal resolution,high throughput,and multi-concentration combination *** on innovative microchannel structure,sophisticated external control methods and multiplexing technology,the system not only allowed for temporally sequential permutations of the four concentrations of stimuli(time code),but also generated pulsed and continuous waveforms at different frequencies in a highly controllable ***,the single-cell trap array was set up to efficiently capture cells in suspension,dramatically increasing throughput and reducing experiment preparation *** maximum frequency of the platform was 1 Hz,and one cell could be stimulated at multiple *** show the ability of the system to investigate rapid biochemical events in high throughput,pulse stimulation and continuous stimulation of different frequencies and different time codes,combined with four concentrations of histamine(HA),were generated for probing G protein-coupled receptor(GPCR)signaling in He La ***,statistical analysis was performed for the mean peak height and mean peak area of the cellular *** believe that the time-coded multi-concentration microfluidic chemical waveform generator will provide a novel strategy for analytical chemistry,biophysics,cell signaling,and individualized medicine applications.

关键词： Microfluidics Dynamic stimulation Time code High-throughput Single-cell array

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Bimodal Feature Propagation and Fusion for Real-time Semantic Segmentation on RGB-D images 7

Bimodal Feature Propagation and Fusion for Real-time Semanti...

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7th International Conference on Intelligent Computing and Signal processing, ICSP 2022

作者： He, Ying Xiao, Li Sun, Zhigang Wang, Zhuo Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9781665478571

Semantic Segmentation is the foundation of scene understanding and automatic driving tasks. One of the challenges of semantic segmentation is the reduction of feature resolution as the network goes deep. In this paper, the low-resolution features are integrated into high-resolution features progressively, enabling high-resolution prediction. Currently, incorporating the auxiliary depth information into a semantic segmentation framework has proven to be helpful to improve accuracy. However, additional process of depth data immensely increases computational complexity, resulting in limitation for practical application. Here, a real-time bimodal semantic segmentation network that effectively extracts and fuses complementary information is investigated. Our network extends a light-weight architecture which can seamlessly take advantage of various prevalent backbone networks. Our model extracts RGB and depth features from two branches in parallel and integrates multi-level features of two modalities through bimodal feature fusion blocks. To reduce computation complexity, we employ depth-wise separable convolution while keep performance intact. Meanwhile, an attention mechanism is exploited to recalibrate the fusion modality features adaptively. Refinement components are retained to refine multi-resolution feature maps. We conduct ablative study to demonstrate the availability of our bimodal fusion blocks. Comprehensive experiments validate that the proposed network outperforms the state-of-the-arts and achieves 18 FPS on the challenging RGB-D dataset, NYUDv2. © 2022 IEEE.

关键词： Semantic Segmentation

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