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Data-driven fault diagnosis method for the safe and stable operation of solid oxide fuel cells system

JOURNAL OF POWER SOURCES 2021年 490卷 229561-229561页

作者： Zheng, Yi Wu, Xiao-long Zhao, Dongqi Xu, Yuan-wu Wang, Beibei Zu, Yanmin Li, Dong Jiang, Jianhua Jiang, Chang Fu, Xiaowei Li, Xi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist Wuhan 430074 Hubei Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330031 Jiangxi Peoples R China Wuhan Intelligent Equipment Ind Inst Wuhan 430075 Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Prov Key Lab Intelligent Informat Proc & Re Coll Comp Sci & Technol Wuhan 430081 Hubei Peoples R China

Solid oxide fuel cell system is complex with multiple variables strongly coupled. Once a fault occurs, if it cannot be found in time, the initial minor fault may slowly evolve and spread to subsequent components. Therefore, fault diagnosis is a promising approach to guarantee the stability of the system. In this paper, the impact of air leakage and fuel starvation is investigated. To diagnose the two types of faults, a novel data-driven online fault diagnosis method based on principal component analysis and support vector machine is developed. Data comes from the entire stage of the solid oxide fuel cell system experiment. The results show that the proposed method can effectively identify the air leakage and fuel starvation fault in real time. Through comparison with traditional machine learning methods, this method shows higher accuracy and better generalization performance. Moreover, it combines prior knowledge and statistical characteristics to extract effective features, thereby reducing the calculation burden. Furthermore, with proper modifications, the proposed method can be extended to other types of solid oxide fuel cell system faults, which is significant in enhancing the reliability of the system.

关键词： Solid oxide fuel cell system Air leakage Fuel starvation Support vector machine Principal component analysis

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Coordinated Guiding Vector Field Design for Ordering-Flexible Multi-Robot Surface Navigation

arXiv

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arXiv 2024年

作者： Hu, Bin-Bin Zhang, Hai-Tao Yao, Weijia Sun, Zhiyong Cao, Ming The School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Robotics Hunan University Hunan410082 China The Institute of Engineering and Technology University of Groningen Groningen9747 AG Netherlands Department of Electrical Engineering Eindhoven University of Technology Netherlands

We design a distributed coordinated guiding vector field (CGVF) for a group of robots to achieve ordering-flexible motion coordination while maneuvering on a desired two-dimensional (2D) surface. The CGVF is characterized by three terms, i.e., a convergence term to drive the robots to converge to the desired surface, a propagation term to provide a traversing direction for maneuvering on the desired surface, and a coordinated term to achieve the surface motion coordination with an arbitrary ordering of the robotic group. By setting the surface parameters as additional virtual coordinates, the proposed approach eliminates potential singularity of the CGVF and enables both the global convergence to the desired surface and the maneuvering on the surface from all possible initial conditions. The ordering-flexible surface motion coordination is realized by each robot to share with its neighbors only two virtual coordinates, i.e. that of a given target and that of its own, which reduces the communication and computation cost in multi-robot surface navigation. Finally, the effectiveness of the CGVF is substantiated by extensive numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Navigation

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Hard sample mining makes person re-identification more efficient and accurate

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NEUROCOMPUTING 2020年第0期382卷 259-267页

作者： Chen, Kezhou Chen, Yang Han, Chuchu Sang, Nong Gao, Changxin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430074 Peoples R China

In recent years, the field of person re-identification has made significant advances riding on the wave of deep learning. However, owing to the fact that there are much more easy examples than those meaningful hard examples in a dataset, the training tends to stagnate quickly and the model may suffer from over-fitting, which leads to some error matching of models especially for some hard samples during the test process. Therefore, the hard sample mining method is fateful to optimize the model and improve the learning efficiency. In this paper, an Adaptive Hard Sample Mining algorithm is proposed for training a robust person re-identification model. No need for hand-picking the images in the batch or designing the loss function for both positive and negative pairs, we can briefly calculate the hard level by comparing the prediction result with the true label of the sample. Meanwhile, taking into account the change in the number of samples required for the model during training process, an adaptive threshold of hard level can make the algorithm not only stay in step with training process harmoniously but also alleviate the under-fitting and over-fitting problem simultaneously. Besides, the designed network to implement the approach is very efficient and has good generalization performance that can be combined with various existing models readily. Experimental results on Market-1501, DukeMTMC-reID and CUHK03 datasets clearly demonstrate the effectiveness of the proposed algorithm. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Person re-identification Hard sample mining Deep learning

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Joint Masked Reconstruction and Contrastive Learning for Mining Interactions Between Proteins

arXiv

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arXiv 2025年

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

Protein-protein interaction (PPI) prediction is an instrumental means in elucidating the mechanisms underlying cellular operations, holding significant practical implications for the realms of pharmaceutical development and clinical treatment. Presently, the majority of research methods primarily concentrate on the analysis of amino acid sequences, while investigations predicated on protein structures remain in the nascent stages of exploration. Despite the emergence of several structure-based algorithms in recent years, these are still confronted with inherent challenges: (1) the extraction of intrinsic structural information of proteins typically necessitates the expenditure of substantial computational resources;(2) these models are overly reliant on seen protein data, struggling to effectively unearth interaction cues between unknown proteins. To further propel advancements in this domain, this paper introduces a novel PPI prediction method jointing masked reconstruction and contrastive learning, termed JmcPPI. This methodology dissects the PPI prediction task into two distinct phases: during the residue structure encoding phase, JmcPPI devises two feature reconstruction tasks and employs graph attention mechanism to capture structural information between residues;during the protein interaction inference phase, JmcPPI perturbs the original PPI graph and employs a multi-graph contrastive learning strategy to thoroughly mine extrinsic interaction information of novel proteins. Extensive experiments conducted on three widely utilized PPI datasets demonstrate that JmcPPI surpasses existing optimal baseline models across various data partition schemes. The associated code can be accessed via https://***/lijfrankopen/JmcPPI. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Different Set Domain Adaptation for Brain-Computer Interfaces: A label Alignment Approach

引用

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2020年第5期28卷 1091-1108页

作者： He, He Wu, Dongrui Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

A brain-computer interface (BCI) system usually needs a long calibration session for each new subject/task to adjust its parameters, which impedes its transition from the laboratory to real-world applications. Domain adaptation, which leverages labeled data from auxiliary subjects/tasks (source domains), has demonstrated its effectiveness in reducing such calibration effort. Currently, most domain adaptation approaches require the source domains to have the same feature space and label space as the target domain, which limits their applications, as the auxiliary data may have different feature spaces and/or different label spaces. This paper considers different set domain adaptation for BCIs, i.e., the source and target domains have different label spaces. We introduce a practical setting of different label sets for BCIs, and propose a novel label alignment (LA) approach to align the source label space with the target label space. It has three desirable properties: 1) LA only needs as few as one labeled sample from each class of the target subject;2) LA can be used as a preprocessing step before different feature extraction and classification algorithms;and, 3) LA can be integrated with other domain adaptation approaches to achieve even better performance. Experiments on two motor imagery datasets demonstrated the effectiveness of LA.

关键词： Covariance matrices Electroencephalography Manifolds Feature extraction Calibration Silicon Pipelines Brain-computerinterface EG label alignment Riemannian geometry domain adaptation transfer learning

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Kinematic control for Crossed-Fiber-Reinforced Soft Manipulator Using Sparse Bayesian Learning

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第2期27卷 611-622页

作者： Shen, Yi Deng, Linan Yuan, Ye Zhang, Fumin Ding, Han Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30308 USA

Muscular antagonism caused by muscle fibers and muscle layers is vital for the hydrostatic skeleton of soft creatures. This article introduces a crossed-fiber arrangement and a hybrid material structure to a hydrostatic-skeleton-inspired soft manipulator that mimics the bending motion of worms. Deformation tests on radial expansion, torsion, and bending motion verify that the proposed design is controllable under large deformations to the manipulator. To achieve accurate control, this article proposes a sparse-Bayesian-learning-based piecewise constant curvature kinematic model and designs a feedback controller based on the kinematic model. The proposed controller demonstrates superior accuracy and reduces vibration compared with the linear least-squares-based controller and the neural-network-based controller in tracking desired movements during experiments.

关键词： Bending Kinematics Strain Grippers Fabrication Atmospheric modeling Aerospace electronics Inverse kinematics soft manipulator sparse Bayesian learning (SBL)

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Optimize TSK Fuzzy Systems for Classification Problems: Minibatch Gradient Descent With Uniform Regularization and Batch Normalization

引用

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第12期28卷 3065-3075页

作者： Cui, Yuqi Wu, Dongrui Huang, Jian Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Takagi-Sugeno-Kang (TSK) fuzzy systems are flexible and interpretable machine learning models;however, they may not be easily optimized when the data size is large, and/or the data dimensionality is high. This article proposes a minibatch gradient descent (MBGD) based algorithm to efficiently and effectively train TSK fuzzy classifiers. It integrates two novel techniques: First, uniform regularization (UR), which forces the rules to have similar average contributions to the output, and hence to increase the generalization performance of the TSK classifier;and, second, batch normalization (BN), which extends BN from deep neural networks to TSK fuzzy classifiers to expedite the convergence and improve the generalization performance. Experiments on 12 UCI datasets from various application domains, with varying size and dimensionality, demonstrated that UR and BN are effective individually, and integrating them can further improve the classification performance.

关键词： Training Fuzzy systems Neural networks Convergence Optimization Evolutionary computation Standards Batch normalization (BN) minibatch gradient descent Takagi– Sugeno– Kang (TSK) fuzzy classifier uniform regularization (UR)

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Tissue P systems with evolutional communication rules with two objects in the left-hand side

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NATURAL COMPUTING 2023年第1期22卷 119-132页

作者： Orellana-Martin, David Valencia-Cabrera, Luis Song, Bosheng Pan, Linqiang Perez-Jimenez, Mario J. Univ Seville Dept Comp Sci & Artificial Intelligence Res Grp Nat Comp Avda Reina Mercedes E-41012 Seville Spain Univ Seville SCORE Lab I3US Avda Reina Mercedes E-41012 Seville Spain Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Natl Supercomp Ctr Changsha 410082 Hunan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Hubei Peoples R China

In the framework of Membrane Computing, several efficient solutions to computationally hard problems have been given. To find new borderlines between families of P systems that can solve them and the ones that cannot is an important task to tackle the P versus NP problem. Adding syntactic and/or semantic ingredients can mean passing from non-efficiency to presumed efficiency. Here, we try to get narrow frontiers, setting the stage to adapt efficient solutions from a family of P systems to another one. In order to do that, a solution to the SAT problem is given by means of a family of tissue P systems with evolutional symport/antiport rules and cell separation with the restriction that both the left-hand side and the righthand side of the rules have at most two objects;that is, with recognizer P systems from TSEC(2, 2). This result improves a previous one, when 3 objects could be used in the left-hand side of the evolutional communication rules

关键词： Membrane computing Symport/antiport rules The P versus NP problem SAT problem

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Two-Archive-Based Competitive Swarm Optimizer for Constrained Multiobjective Optimization

Two-Archive-Based Competitive Swarm Optimizer for Constraine...

引用

Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Lianghao Li Cheng He Jianqing Lin Linqiang Pan 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 Wuhan China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

Constrained multiobjective optimization problems (CMOPs) are prevalent in various real-world applications, presenting a formidable challenge to existing evolutionary algorithms when faced with intricate constraints. When solving CMOPs, a crucial concern is achieving a balance between convergence, diversity, and feasibility. To address these challenges, this paper proposes a two-archive-based constrained multiobjective competitive swarm optimizer. The algorithm preserves two collaborative and complementary archives to improve population convergence and feasibility. By implementing a competitive particle swarm mechanism, offspring solutions are generated by drawing upon solutions from both archives, thus capitalizing on their synergistic effect and exceptional information. An adaptive parameter is also used to adjust the bias in choosing the winner in the paired competition during the evolution. The experimental results demonstrate the effectiveness of the proposed algorithm in handling constrained multiobjective optimization problems.

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Learning from Noisy labels with Coarse-to-Fine Sample Credibility Modeling

arXiv

引用

arXiv 2022年

作者： Zhang, Boshen Li, Yuxi Tu, Yuanpeng Peng, Jinlong Wang, Yabiao Wu, Cunlin Xiao, Yang Zhao, Cairong YouTu Lab Tencent Shanghai China Tongji University Shanghai China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China

Training deep neural network (DNN) with noisy labels is practically challenging since inaccurate labels severely degrade the generalization ability of DNN. Previous efforts tend to handle part or full data in a unified denoising flow via identifying noisy data with a coarse small-loss criterion to mitigate the interference from noisy labels, ignoring the fact that the difficulties of noisy samples are different, thus a rigid and unified data selection pipeline cannot tackle this problem well . In this paper, we first propose a coarse-to-fine robust learning method called CREMA, to handle noisy data in a divide-and-conquer manner. In coarse-level, clean and noisy sets are firstly separated in terms of credibility in a statistical sense. Since it is practically impossible to categorize all noisy samples correctly, we further process them in a fine-grained manner via modeling the credibility of each sample. Specifically, for the clean set, we deliberately design a memory-based modulation scheme to dynamically adjust the contribution of each sample in terms of its historical credibility sequence during training, thus alleviating the effect from noisy samples incorrectly grouped into the clean set. Meanwhile, for samples categorized into the noisy set, a selective label update strategy is proposed to correct noisy labels while mitigating the problem of correction error. Extensive experiments are conducted on benchmarks of different modality, including image classification (CIFAR, Clothing1M etc) and text recognition (IMDB), with either synthetic or natural semantic noises, demonstrating the superiority and generality of CREMA. © 2022, CC BY.

关键词： Deep neural networks

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