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2011 IEEE International conference on Intelligent Computation and Bio-Medical Instrumentation, ICBMI 2011

作者： Sun, Yangguang Ding, Mingyue College of Computer Science South-Central University for Nationalities Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China College of Life Science and Technology Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769546230

A novel statistical method was proposed for boundary extraction of interested objects in this letter. The proposed method was inspired by the physical nature of quantum and the statistical property of wave function. By introducing the polymer representation of quantum particle and based on quantum particle motion in the gradient-based image potential field, the boundary extraction was realized in our method. Experimental results demonstrated that our method has the capability to make the boundary extraction with arbitrary initialization and topology changes possible. © 2011 IEEE.

关键词： Statistical methods

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Electrode localization in 3D Ultrasound images using 3D phase grouping and Randomized Hough transform

Electrode localization in 3D Ultrasound images using 3D phas...

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International Conference on Genetic and Evolutionary Computing

作者： Qiu, Wu Ding, Mingyue Yuchi, Ming School of Life Science and Technology Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780769542812

A method to locate the axis of radio frequency ablation electrode(RFAE) in 3D Ultrasound(US) image is presented based on 3D phase-grouping in this paper. Firstly, all voxels in 3D US images are categorized into different groups which are called Line Support Region(LSR) according to the outer products of adjacent orientation vectors. And then, the RFAE axis is extracted with 3D Randomized Hough transform in the maximal LSR, instead of least squares fitting method;Finally, the endpoint of the RFAE axis is determined by searching along the axis with the probability distribution of voxels. The proposed method was tested in synthetic and 3D US agar phantom datas, the results are promising. © 2010 IEEE.

关键词： Hough transforms

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Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification

Knowledge-Data Fusion Based Source-Free Semi-Supervised Doma...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Peng, Ruimin An, Jiayu Wu, Dongrui School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control Wuhan430074 China

ISBN: (纸本)9781665410205

Electroencephalogram (EEG)-based seizure sub-type classification enhances clinical diagnosis efficiency. Source-free semi-supervised domain adaptation (SF-SSDA), which transfers a pre-trained model to a new dataset with no source data and limited labeled target data, can be used for privacy-preserving seizure subtype classification. This paper considers two challenges in SF-SSDA for EEG-based seizure subtype classification: 1) How to effectively fuse both raw EEG data and expert knowledge in classifier design? 2) How to align the source and target domain distributions for SF-SSDA? We propose a Knowledge-Data Fusion based SF-SSDA approach, KDF-MutuaISHOT, for EEG-based seizure subtype classification. In source model training, KDF uses Jensen-Shannon Diver-gence to facilitate mutual learning between a feature-driven Decision Tree-based model and a data-driven Transformer-based model. To adapt KDF to a new target dataset, an SF-SSDA algorithm, MutualSHOT, is developed, which features a consistency-based pseudo-label selection strategy. Experiments on the public TUSZ and CHSZ datasets demonstrated that KDF-MutualSHOT outperformed other supervised and source-free domain adaptation approaches in cross-subject seizure subtype classification. © 2024 IEEE.

关键词： Self-supervised learning

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Short-Term Building Load Forecast Based on Patch Learning with Long Short-Term Memory Network and Support Vector Regression 1

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2nd International Conference on Neural Computing for Advanced Applications, NCAA 2021

作者： Dan, Zhaohui Wang, Bo Fan, Huijin Liu, Lei Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Artificial Intelligence and Automation School Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9789811651885

ISBN: (纸本)9789811651878

This paper proposes a novel short-term building load forecasting approach under the framework of patch learning, a novel data-driven model that aggregates a global model and several patch models to further reduce forecasting errors. A PL-LSTM-SVR model is hereby employed to address such a time-series based forecasting problem, where the long short-term memory network is considered as the global model and the support vector regression is selected as the patch model. To obtain satisfying performances, an infinity norm measurement is selected to evaluate load forecasting errors and identify patch locations. Furthermore, a genetic algorithm with elitist preservation strategy is introduced for hyperparameter tuning. The performances of the proposed PL-LSTM-SVR model are tested and verified on two different data sets, and compared with four advanced building load forecasting models on several common metrics. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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An Improved Reinforcement Learning for Security-Constrained Economic Dispatch of Battery Energy Storage in Microgrids 1

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2nd International Conference on Neural Computing for Advanced Applications, NCAA 2021

作者： Zha, Zhongyi Wang, Bo Fan, Huijin Liu, Lei Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Artificial Intelligence and Automation School Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9789811651885

ISBN: (纸本)9789811651878

Battery energy storage systems are widely used in microgrids integrated with volatile energy resources for their ability in peak load shifting. Security constrained economic dispatch over the system’s lifecycle is a constrained multi-period stochastic optimization problem, which is intractable. We propose an improved actor-critic-based reinforcement learning combined with a protection layer security control method for this issue, where the distributional critic net is applied to estimate the expected total reward value of a period more accurately, and the policy net with a mask action layer is used to make secure and real-time decision. Additionally, we propose a protection layer to assist the policy net as a secondary control to prevent the unsafe state of microgrids due to the trial-and-error learning of reinforcement learning. Numerical test results show the proposed algorithm can perform better than the conventional economic dispatch and other reinforcement learning algorithms while guaranteeing safe operation. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Renewable energy resources

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Enhancing Resilience of Microgrid-Integrated Power Systems in Disaster Events Using Reinforcement Learning 1

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Chinese Intelligent Automation Conference, CIAC 2023

作者： Zha, Zhongyi Wang, Bo Liu, Lei Fan, Huijin Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Artificial Intelligence and Automation School Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9789819961870

ISBN: (纸本)9789819961863

Enhancing the resilience of power systems has become increasingly important to mitigate the negative impact of risk attacks. Integrating microgrids, which are small-scale power grids that can operate independently or in conjunction with the main grid, has emerged as an effective energy source for emergency situations and can avoid power outages. Typhoons, which are inevitable and relatively easy to predict risk attacks, have become one of the contexts to examine power system resilience. In this paper, we propose a reinforcement learning algorithm that combines graph theory knowledge to make decisions for optimizing power system resilience with microgrids under the Typhoon event. The algorithm effectively utilizes graph theory knowledge and reduces the number of decisions required in the optimization process. Numerical experimental results show that compared to traditional methods, the reinforcement learning algorithm can more effectively respond to typhoon changes, achieve flexible economic control, and enhance the resilience of power systems with microgrids. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Numerical methods

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Lightweight Human Pose Estimation Using Loss Weighted by Target Heatmap 26th

Lightweight Human Pose Estimation Using Loss Weighted by Ta...

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26th International Conference on Pattern Recognition, ICPR 2022

作者： Li, Shiqi Xiang, Xiang Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783031376597

Recent research on human pose estimation exploits complex structures to improve performance on benchmark datasets, ignoring the resource overhead and inference speed when the model is actually deployed. In this paper, we lighten the computation cost and parameters of the deconvolution head network in SimpleBaseline and introduce an attention mechanism that utilizes original, inter-level, and intra-level information to intensify the accuracy. Additionally, we propose a novel loss function called heatmap weighting loss, which generates weights for each pixel on the heatmap that makes the model more focused on keypoints. Experiments demonstrate our method achieves a balance between performance, resource volume, and inference speed. Specifically, our method can achieve 65.3 AP score on COCO test-dev, while the inference speed is 55 FPS and 18 FPS on the mobile GPU and CPU, respectively. © 2023, Springer Nature Switzerland AG.

关键词： Benchmarking

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Super Twisting control of Passive Gait Training Exoskeleton Driven by Pneumatic Muscles 30

Super Twisting Control of Passive Gait Training Exoskeleton ...

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30th International Symposium on Micro-NanoMechatronics and Human Science, MHS 2019

作者： Zhang, Mengshi Huang, Jian Cao, Yu Zhang, Hai-Tao Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728126746

Pneumatic Muscles (PMs) - driven exoskeleton has a promising prospect in the field of rehabilitation and assistance, because of the PMs intrinsic features of compliance and high force-to-weight ratio. However, the precise control of PMs-driven exoskeleton still remains a challenging problem due to the hysteresis, time-varying parameters of PMs and complexity of the mechanism. To solve this problem, this paper presents a super twisting controller (STC), essentially a second-order sliding mode control, to realize the human gait tracking control of lower limb exoskeleton. As a result, the disturbances and uncertainties of the system can be handled, and the chattering caused by the traditional sliding mode control (SMC) could be eliminated. The stability of the closed-loop system is ensured according to the Lyapunov theorem. In addition, we conduct experiments in real exoskeleton system and the results illustrate the validity of the super twisting algorithm. Pneumatic Muscles (PMs) - driven exoskeleton has a promising prospect in the field of rehabilitation and assistance, because of the PMs' intrinsic features of compliance and high force-to-weight ratio. However, the precise control of PMs-driven exoskeleton still remains a challenging problem due to the hysteresis, time-varying parameters of PMs and complexity of the mechanism. To solve this problem, this paper presents a super twisting controller (STC), essentially a second-order sliding mode control, to realize the human gait tracking control of lower limb exoskeleton. As a result, the disturbances and uncertainties of the system can be handled, and the chattering caused by the traditional sliding mode control (SMC) could be eliminated. The stability of the closed-loop system is ensured according to the Lyapunov theorem. In addition, we conduct experiments in real exoskeleton system and the results illustrate the validity of the super twisting algorithm.. © 2019 IEEE.

关键词： Sliding mode control

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Bagging and Boosting Fine-Tuning for Ensemble Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2024年第4期5卷 1728-1742页

作者： Zhao, Changming Peng, Ruimin Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074 China and alsowith the ShenzhenHuazhong University of Science and Technology Research Institute Shenzhen 518029 China

Ensemble learning aggregates outputs from multiple base learners for better performance. Bootstrap aggregating (bagging) and boosting are two popular such approaches. They are suitable for integrating unstable base learners with large variance and weak base learners with large bias, respectively, but not base learners with small variance and/or bias, e.g., support vector machine, regularized logistic regression, and ridge regression. This article proposes two novel ensemble-learning-based fine-tuning approaches, boosting fine-tuning (BF) and bagging and boosting fine-tuning (BBF), to fine-tune learners with small variance and/or bias for better performance. BF embeds boosting in a single hidden layer neural network. In each iteration, BF first uses the Newton's method to generate a temporary training set, and then trains a boosting learner on it. BBF combines BF and bagging. It first uses bootstrap to obtain multiple replicas of the training set, and then trains a BF learner on each replica. Extensive experiments on 46 real-world datasets demonstrated that BBF is flexible, robust, and effective, and can fine-tune many popular classifiers to achieve better generalization performance. © 2020 IEEE.

关键词： Support vector machines

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Optimal ascent guidance based on direct optimization method

Optimal ascent guidance based on direct optimization method

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2012 International Conference on Modelling, Identification and control, ICMIC 2012

作者： Lu, Xuefang Wang, Yongji Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780956715715

In this paper, an optimal guidance algorithm is proposed for atmospheric ascent. The optimal guidance algorithm updates the reference trajectory to deal with the impact of disturbance by solving an optimal control problem on-board. For the strong nonlinear ascent dynamic, the optimal control problem is transformed into nonlinear programming problem by trajectory discretization. The direct optimization method is implemented for this nonlinear programming problem. Only a segment of the reference trajectory is updated by the optimal guidance algorithm. Due to the small amount of the discrete nodes and the initial guess solution which is close to the optimization solution, the direct optimization method is fast enough to generate a new reference trajectory. Numerical simulation for the Generic Hypersonic Vehicle model and scramjet engine is done for different cases of the aerodynamic coefficient bias. The results show the accuracy and the effectiveness of this optimal guidance algorithm. © 2012 Huazhong Univ of Sci & Tec.

关键词： Trajectories

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