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2023 International Conference on Neuromorphic Computing, ICNC 2023

作者： Yang, Yong He, Dingxin Mao, Huantao Chen, Hong Wu, Hongtao Liu, Zhi-Wei School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent ControlMinistry of Education Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9798350316889

Compared to traditional area coverage algorithms, this paper proposes a re-plannable multi-agent coverage path planning algorithm to address issues such as low coverage rate of multiple agents in large-scale scenarios like warehouses and inability to cope with local environmental changes. Initially, the MSTC∗ algorithm is utilized to create the coverage path based on the available map information. To address scenarios like idle areas for material placement, the method introduces local area detection and path re-planning to enhance the coverage rate. This paper verifies the feasibility and effectiveness of the algorithm under different maps through simulation experiments. Compared with the coverage situation of other coverage path planning algorithms in environments with local area updates, the algorithm proposed in this paper has a higher coverage rate in this scenario. © 2023 IEEE.

关键词： Motion planning

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Learning Probabilistic Coordinate Fields for Robust Correspondences

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IEEE Transactions on Pattern Analysis and Machine intelligence 2023年第10期45卷 12004-12021页

作者： Zhao, Weiyue Lu, Hao Ye, Xinyi Cao, Zhiguo Li, Xin Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Wuhan430074 China West Virginia University The Lane Department of Computer Science and Electrical Engineering MorgantownWV26506-6109 United States

We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in correspondence-specific barycentric coordinate systems (BCS) with affine invariance. To know when and where to trust the encoded coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which parameterizes the distribution of coordinate fields as Gaussian mixture models. By jointly optimizing coordinate fields and their confidence conditioned on dense flows, PCF-Net can work with various feature descriptors when quantifying the reliability of PCFs by confidence maps. An interesting observation of this work is that the learned confidence map converges to geometrically coherent and semantically consistent regions, which facilitates robust coordinate representation. By delivering the confident coordinates to keypoint/feature descriptors, we show that PCF-Net can be used as a plug-in to existing correspondence-dependent approaches. Extensive experiments on both indoor and outdoor datasets suggest that accurate geometric invariant coordinates help to achieve the state of the art in several correspondence problems, such as sparse feature matching, dense image registration, camera pose estimation, and consistency filtering. Further, the interpretable confidence map predicted by PCF-Net can also be leveraged to other novel applications from texture transfer to multi-homography classification. © 1979-2012 IEEE.

关键词： image registration

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Bearing Remaining Useful Life Prediction Combining Trajectory Similarity and Relevance Vector Machine 41

Bearing Remaining Useful Life Prediction Combining Trajector...

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第41届中国控制会议

作者： Zhuyi Li Kesen Fan Cong Zhang Sihang Zhang Yiming Wan Key Laboratory of Image Processing and Intelligent Control Ministry of Education Chinaand School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Beijing Institute of Control Engineering

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Bearing remaining useful life(RUL) prediction is critical for safe operation of rotating *** this paper,we propose a combined RUL prediction approach that leverages both trajectory similarity and relevance vector machine(RVM).The similarity based prediction relies on historical degradation trajectories that are highly similar to the online data,hence would perform poorly if all historical trajectories have low similarity with the online *** RVM based prediction relies solely on a regression model learned from the available online data,thus gives an inaccurate prediction when insufficient data are available in the early stage of degradation.A weighted sum of these above two predictions is proposed to address the limitation of each single prediction method,whose weights are determined by solving a non-negative least squares fitting *** further improve RUL prediction accuracy,we distinguish between fast and slow degradation modes,so that each mode uses a different set of historical degradation trajectories and kernel *** doing so,we predict RUL under the identified *** case study using the PHM2012 dataset demonstrates the effectiveness of the proposed RUL prediction approach.

关键词： Bearing degeneration remaining useful life prediction relevance vector machine trajectory similarity

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Deep Learning-based Serialized Model and Feature Extraction Algorithm for Solid Oxide Fuel Cell State Prediction 43

Deep Learning-based Serialized Model and Feature Extraction ...

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43rd Chinese control Conference, CCC 2024

作者： Wu, Jiajian Zhu, Kanglin Wu, Han Xiang, Guohui 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 Hubei Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen China

ISBN: (纸本)9789887581581

Deep learning-based methods have shown their wide application prospects in the field of solid oxide fuel cell(SOFC) prediction. However, the irrationality of the prediction object and the lack of prediction accuracy have hindered the progress of the prediction methods for SOFC systems. Therefore, two algorithms are proposed in this study, a feature selection algorithm based on maximum information coefficient and Bayesian information criterion for identifying key variables that are sensitive to changes in the SOFC system, and a prediction model based on a genetic algorithm optimized for sequence to sequence architecture. The M/BIC algorithm reduces the number of features by 62.5% (from 40 to 15), which contributes to the acceleration of the algorithm training and the subsequent sensor layout optimization. The prediction model incorporates a two-dimensional convolutional neural network and a multi-head attention mechanism to capture more information in the input sequence, an encoder-decoder architecture and a Gated recurrent unit to better predict performance trends. Tests with real experimental data show significant changes in feature dimensionality reduction and prediction accuracy. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Dimensionality reduction

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FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud 26

FASTC: A Fast Attentional Framework for Semantic Traversabil...

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26th European Conference on Artificial intelligence, ECAI 2023

作者： Chen, Yirui Wei, Pengjin Liu, Zhenhuan Wang, Bingchao Yang, Jie Liu, Wei Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education China

ISBN: (纸本)9781643684369

Producing traversability maps and understanding the surroundings are crucial prerequisites for autonomous navigation. In this paper, we address the problem of traversability assessment using point clouds. We propose a novel pillar feature extraction module that utilizes PointNet to capture features from point clouds organized in vertical volume and a 2D encoder-decoder structure to conduct traversability classification instead of the widely used 3D convolutions. This results in less computational cost while even better performance is achieved at the same time. We then propose a new spatio-temporal attention module to fuse multi-frame information, which can properly handle the varying density problem of LIDAR point clouds, and this makes our module able to assess distant areas more accurately. Comprehensive experimental results on augmented Semantic KITTI and RELLIS-3D datasets show that our method is able to achieve superior performance over existing approaches both quantitatively and quantitatively. Our code is publicly available at https://***/chenyirui/FASTC. © 2023 The Authors.

关键词： Semantics

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Microgrid energy management strategy considering microgrid economy and rSOC stability: A model-based study 43

Microgrid energy management strategy considering microgrid e...

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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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A Memristive Spiking Neural Network Circuit for Bio-inspired Navigation Based on Spatial Cognitive Mechanisms

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IEEE Transactions on Biomedical Circuits and Systems 2024年第3期19卷 686-698页

作者： Chen, Zhanfei Wang, Xiaoping Wang, Zilu Yang, Chao Huang, Tingwen Lai, Jingang Zeng, Zhigang School of Mechanical Engineering and Automation Shenzhen518055 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation China Hubei Key Laboratory of Brain-inspired Intelligent Systems Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Shenzhen University of Advanced Technology School of Computer Science and Control Engineering Shenzhen518055 China

Cognitive navigation,a high-level and crucial function for organisms' survival in nature,enables autonomous exploration and navigation within the environment. However,most existing works for bio-inspired navigation are implemented with non-neuromorphic computing. This work proposes a bio-inspired memristive spiking neural network (SNN) circuit for goal-oriented navigation,capable of online decision-making through reward-based learning. The circuit comprises three primary modules. The place cell module encodes the agent's spatial position in real-time through Poisson spiking;the action cell module determines the direction of subsequent movement;and the reward-based learning module provides a bio-inspired learning method adaptive to delayed and sparse rewards. To facilitate practical application,the entire SNN is quantized and deployed on a real memristive hardware platform,achieving about a 21× reduction in energy consumption compared to a typical digital acceleration system in the forward computing phase. This work offers an implementation idea of neuromorphic solution for robotic navigation application in low-power scenarios. © 2007-2012 IEEE.

关键词： Intelligent robots

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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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Adaptive Neural Finite-Time Deployment of Heterogeneous Multi-agent Systems via a Cross-Species Bionic PDE-ODE Approach

Artificial Intelligence Science and Engineering

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Artificial intelligence Science and Engineering 2025年第1期1卷 52-63页

作者： Jingtao MAN Zhigang ZENG Jun LI 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

For large-scale heterogeneous multi-agent systems (MASs) with characteristics of dense-sparse mixed distribution, this paper investigates the practical finite-time deployment problem by establishing a novel cross-species bionic analytical framework based on the partial differential equation-ordinary differential equation (PDE-ODE) approach. Specifically, by designing a specialized network communication protocol and employing the spatial continuum method for densely distributed agents, this paper models the tracking errors of densely distributed agents as a PDE equivalent to a human disease transmission model, and that of sparsely distributed agents as several ODEs equivalent to the predator population models. The coupling relationship between the PDE and ODE models is established through boundary conditions of the PDE, thereby forming a PDE-ODE-based tracking error model for the considered MASs. Furthermore, by integrating adaptive neural control scheme with the aforementioned biological models, a “Flexible Neural Network” endowed with adaptive and self-stabilized capabilities is constructed, which acts upon the considered MASs, enabling their practical finite-time deployment. Finally, effectiveness of the developed approach is illustrated through a numerical example.

关键词： Adaptation models Uncertainty Biological system modeling Neural networks Stochastic processes Mathematical models Numerical models Topology Multi-agent systems Diseases

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Analysis of MFCS topology for high efficiency and long lifetime 43

Analysis of MFCS topology for high efficiency and long lifet...

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43rd Chinese control Conference, CCC 2024

作者： Xiang, Hongfu Wu, Jiajian Wang, Zhen 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 Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen518055 China

ISBN: (纸本)9789887581581

Multi-stack fuel cell system(MFCS) are an important basis for large-scale application of solid oxide fuel cell(SOFC) technology, MFCS can provide higher system power and longer service life. As the number of stacks increases, different topologies can be formed between stacks, including parallel structures and series structures, and the physical characteristics of MFCS will also change. In this study, the degradation of the stacks is first considered, and the system model of series structure and parallel structure including the degradation of the stacks is built. The parallel structure proposed in this paper is the key to ensure the high efficiency and long lifetime of MFCS. Through the simulation of systems with different topologies, it is found that the power generation efficiency of the systems caused by the topologies varies greatly in different output power stages. The efficiency of the parallel structure in the low power stage is significantly better than that of the series structure, and the parallel structure is more conducive to the high efficiency of the system in the whole stage. Different topological structures also have a greater impact on the degradation of stacks. The degradation rates of stacks in parallel structures are close to each other, and the state of stacks is more consistent. The overall life of the system in parallel structures is also better than that in series structures. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Topology

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