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Pointer-type instrument positioning method of intelligent inspection system for substation

JOURNAL OF ELECTRONIC IMAGING 2022年第1期31卷 013001-013001页

作者： Peng, Gang Du, Bing Cao, Chong He, Dingxin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China

Robot intelligent inspection is widely used in the positioning of various pointer instruments in power, petroleum, chemical, and other industries. Aiming at the technical problems of poor adaptability, poor real-time performance, and low positioning accuracy of the pointer instrument positioning method in the existing substation intelligent inspection robot system, we propose a simple and effective pointer instrument positioning detection algorithm. The algorithm first extracts locally adaptive regression kernels (LARK) features of the input image, and the dimension of the LARK feature is reduced using the principal components analysis algorithm. Then, the template image is slid in the input image, the cosine similarity is used as an evaluation index, and the Fourier transform is used to accelerate the convolution operation in the cosine similarity calculation. Finally, the accelerated-KAZE algorithm is used to extract the feature points of the pointer-type instrument area image and the template image, and the statistical method of grid motion was used to eliminate the wrong matching points. The remaining matching points were processed by random sample consensus algorithm, and the homography matrix was obtained. The image registration was completed by the homography matrix, and the pointer-type instrument region positioning was realized. The experimental results show that the proposed method has good adaptability, strong real-time performance, and high accuracy of pointer-type instrument positioning. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License.

关键词： pointer instrument positioning feature extraction registration intelligent inspection robot

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Adaptive Homophily Clustering: Structure Homophily Graph Learning With Adaptive Filter for Hyperspectral image

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Ding, Yao Zhang, Zhili Kang, Weijie Yang, Aitao Zhao, Junyang Feng, Jie Hong, Danfeng Zheng, Qinghe Xian Res Inst High Technol Key Lab Intelligent Control Xian 710025 Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Shandong Management Univ Sch Intelligent Engn Jinan 250357 Peoples R China

Hyperspectral image (HSI) clustering is a fundamental yet challenging task that typically operates without training labels. Recent advancements in deep graph clustering methods have shown promise for HSI due to their ability to effectively encode spatial structural information. However, limitations such as inadequate utilization of structural information, poor feature representation, and weak graph update capabilities hinder their performance. In this article, we propose an adaptive homophily structure graph clustering (AHSGC) method for HSI. Our approach begins with the generation of homogeneous regions to process HSI and construct the initial graph. Next, we design an adaptive filter graph encoder that captures both high and low-frequency features for subsequent processing. We then develop a graph embedding clustering self-training decoder using KL Divergence to generate pseudo-labels for network training. To enhance graph learning, we introduce homophily-enhanced structure learning, which updates the graph based on the clustering task. This involves estimating node connections through orient correlation estimation and dynamically adjusting graph edges via graph edge sparsification. Finally, we implement joint network optimization to facilitate self-training and graph updates, with K-means used to express latent features. The clustering accuracy on three datasets is 83.60%, 63.65%, and 86.03%, the FLOPs are 3.57G, 30.62G, and 2.95G. The source code will be available at https://***/DY-HYX.

关键词： Training Correlation Clustering methods Source coding Adaptive filters Estimation Reinforcement learning Decoding Optimization Hyperspectral imaging Adaptive filter graph encoder homophily-enhanced structure learning hyperspectral image (HSI) clustering joint network optimization

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Data-driven nested robust optimization for generation maintenance scheduling considering temporal correlation

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ENERGY 2023年第1期278卷

作者： Yang, Xiao Li, Yuanzheng Zhao, Yong Yu, Yaowen Lian, Yicheng Hao, Guokai Jiang, Lin Huazhong Univ Sci & Technol Autonomous Intelligent Unmanned Syst Engn Res Ctr Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England

Traditional power grids are gradually transitioning to smart grids with high penetration of renewable energy, which can realize the efficient utilization of power resources and low carbon emissions. However, the uncertainties of renewable energy (e.g., wind power) and load demand pose considerable challenges to secure operation and cost-effective planning in smart grids, such as generation maintenance scheduling (GMS). In this context, conventional methods including stochastic optimization and robust optimization are adopted to cope with the uncertainties and formulate the GMS plan. Unfortunately, these methods fail to consider the temporal information in uncertain variables, which can introduce extra operational costs brought by the uncertainties. To address this issue, we consider the temporal correlation of the uncertain wind power and load demand, and develop a data-driven two-stage nested robust optimization (NRO) approach for GMS to minimize the total costs of power system operation under uncertain scenarios. In our proposed approach, a temporal correlation Dirichlet process mixture model (TCDPMM) is developed to investigate the temporal information in the wind power and load demand datasets. Then, variational Bayesian inference (VBI) is employed to construct the data-driven uncertainty set, in which the temporal information for the uncertain variables and the correlations between the uncertain variables are considered. Subsequently, combined with this uncertainty set, a two-stage GMS problem is converted to a "min-max-max-min"optimization problem which is solved by the parallel Benders' decomposition algorithm. The effectiveness and superiority of the proposed approach are demonstrated with a six-bus power system and a practical power system in China.

关键词： Generation maintenance scheduling Nested robust optimization Temporal correlation Dirichlet process mixture model Data-driven uncertainty set

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Tight power and energy coupling constraints of energy storage resources for unit commitment

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IET RENEWABLE POWER GENERATION 2023年第9期17卷 2276-2289页

作者： Yu, Yaowen Yan, Bing Gao, Yijie Li, Yuanzheng Sun, Liangliang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Hubei Peoples R China Rochester Inst Technol Elect & Microelect Engn Rochester NY 14623 USA Northeastern Univ Sch Control Engn Shenyang Liaoning Peoples R China

Energy Storage Resources (ESRs) can help promote high penetrations of renewable generation and shift the peak load. However, the increasing number of ESRs and their features different from conventional generators bring computational challenges to operations of wholesale electricity markets. In order to improve the computational efficiency, this paper tightens the generic ESR formulation for unit commitment. To avoid the complexity caused by ESR operations in both discharge and charge directions, a novel "decoupled analysis" is conducted to analyze one direction at a time. For each direction, ESRs over two and three time periods are categorized into several types based on their parameters. For each type, our recent four-step systematic formulation tightening approach is used to construct the corresponding tight formulation. In order to consider more periods without analyzing all the drastically increased number of types, a series of major types are selected based on how many periods an ESR is able to discharge (charge) consecutively at the upper power limit. A related generic form of tight constraints over multiple periods is established. Moreover, validity and facet-defining proofs of our tight constraints have been provided. Numerical testing results illustrate the tightening process and demonstrate computational benefits of the tightened formulations.

关键词： energy storage formulation tightening mixed-integer linear programming unit commitment

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Distributed Event-Triggered Current Sharing control for Islanded DC Microgrids With Quantized State

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2023年第4期14卷 2147-2156页

作者： Liu, Xiao-Kang Cai, Jiong Xing, Lantao Wang, Yan-Wu 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 Shandong Univ Sch Control Sci & Engn Jinan 250012 Peoples R China

To eliminate voltage deviation of DC microgrids induced by droop control, distributed secondary control has been widely developed with lots of variants. Compared with most existing approaches, a digital communication network is considered where signals are quantized before transmitting into the channel. Then, a distributed secondary control strategy based on quantized signals is designed, to achieve current sharing and voltage restoration under a limited communication width. In this regard, the communication burden can be greatly reduced by using fewer information bits. For further reducing controller updating, we integrate signal quantization with an event-triggered mechanism. Finally, simulation and experimental results show the effectiveness of the proposed method.

关键词： DC microgrid distributed control event-triggered control quantization

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Resilience patterns of urban road networks under the worst-case localized disruptions

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RISK ANALYSIS 2024年第10期44卷 2333-2347页

作者： Du, Chongyang Ouyang, Min Zhang, Hui Wang, Bo Wang, Naiyu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China Zhejiang Univ Coll Civil Engn & Architecture Hangzhou Peoples R China

Recent events, including COVID-19, extreme floods, and explosion accidents, commonly induced localized closures and disruptions of urban road networks (URNs), resulting in significant impacts on human mobility and socio-economic activities. Existing studies on URN resilience to those events mainly took few cases for empirical studies, limiting our understanding on the URN resilience patterns across different cities. By conducting a large-scale nationwide resilience analysis of URNs in 363 cities in mainland China, this study attempts to uncover the resilience patterns of URNs against the worst-case single (SLDs) and multiple localized disruptions (MLDs). Results show that the distance from the worst-case SLD to the city center would be less than 5 km in 62.3% cities, as opposed to more than 15 km in 14.3% cities. Moreover, the average road network resilience of cities in western China could be 7% and 13% smaller than that of the eastern cities under the worst-case SLDs and MLDs, respectively. This inequality in the worst-case resilience is partly attributable to variations in urban socio-economic, infrastructure-related, and topographic factors. These findings could inspire nationwide pre-disaster mitigation strategies to cope with localized disruptions and help transfer insights for mitigation strategies against disruptive events across cities.

关键词： localized disruption resilience pattern urban road network worst-case scenario

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Model-free algorithm for consensus of discrete-time multi-agent systems using reinforcement learning method

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JOURNAL OF THE FRANKLIN INSTITUTE 2023年第14期360卷 10564-10581页

作者： Long, Mingkang An, Qing Su, Housheng Luo, Hui Zhao, Jin Huazhong Univ Sci & Technol Educ Minist China Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Wuchang Univ Technol Artificial Intelligence Sch Wuhan 430223 Peoples R China

In this work, we investigate consensus issues of discrete-time (DT) multi-agent systems (MASs) with completely unknown dynamic by using reinforcement learning (RL) technique. Different from policy iteration (PI) based algorithms that require admissible initial control policies, this work proposes a value iteration (VI) based model-free algorithm for consensus of DTMASs with optimal performance and no requirement of admissible initial control policy. Firstly, in order to utilize RL method, the consensus problem is modeled as an optimal control problem of tracking error system for each agent. Then, we introduce a VI algorithm for consensus of DTMASs and give a novel convergence analysis for this algorithm, which does not require admissible initial control input. To implement the proposed VI algorithm to achieve consensus of DTMASs without information of dynamics, we construct actor-critic networks to online estimate the value functions and optimal control inputs in real time. At last, we give some simulation results to show the validity of the proposed algorithm.& COPY;2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.

关键词： Multi agent systems

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Construction of three-dimensional nickel-vanadium hydrotalcite with ball-flower architecture for screen-printed asymmetric supercapacitor

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APPLIED SURFACE SCIENCE 2023年第1期615卷

作者： Tu, Qian Zhang, Qiang Sun, Xinyu Wang, Jinglu Lin, Baoying Chen, Liangzhe Liu, Jin Deng, Zhonghua Jingchu Univ Technol Sch Elect Informat Engn Jingmen 448000 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist Wuhan 430074 Peoples R China

As a promising energy storage matter, two-dimensional (2D) layered double hydroxides (LDHs) suffer from a lower specific capacitance and poor retention. Morphology engineering is deemed to be an effective means. Herein, three-dimensional (3D) nickel-vanadium hydrotalcite (NiV-LDHs) with ball-flower structure are syn-thesized successfully via a facile dynamic-refluxing route, such a green strategy dispenses with template and high pressure, and generates a specific surface area as high as 61.6 m2/g. Then, the screen-printed inks with outstanding rheological performances are formulated and the resulting electrodes display superior hydrophilic performance. Benefitted from the 3D architecture and shear-flow process, the NiV-LDHs electrode can deliver an enhanced specific capacitance of 1069 F/g at 1 A/g with cycling stability of-68.0 % after 1500 cycles at 20 A/g, in contrast with that of NiV-LDHs prepared by coprecipitation (848 F/g and-5.1 %). Furthermore, an asym-metric NiV-LDHs//activated carbon supercapacitor (ASC) is assembled, which can yield a remarkable energy density of 75.8 mu Wh/cm2 at a power density of 0.80 mW/cm2, and two ASCs in series can illuminate a red light (2.5 V) for more than 270 s. Therefore, this study proposes a facile and economic strategy to prepare 3D nanomaterial for advanced and flexible printable energy storage devices.

关键词： Nickel-vanadium hydrotalcite Flower-like nanostructure Asymmetric supercapacitors Screen printing Flexible

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Time-efficient identification of lithium-ion battery temperature-dependent OCV-SOC curve using multi-output Gaussian process

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ENERGY 2023年第1期268卷

作者： Fan, Kesen Wan, Yiming Wang, Zhuo Jiang, Kai Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Sch Elect & Elect Engn Wuhan 430074 Hubei Peoples R China

For lithium-ion batteries, the functional dependence of open circuit voltage (OCV) on state of charge (SOC) varies with temperature and aging, which plays a significant role in accurate SOC estimation and state of health monitoring. To identify the OCV-SOC curve at a given condition, OCVs usually need to be either measured by a time-consuming OCV test, or estimated with inevitable errors that eventually propagate into the identified OCV-SOC curve. In this paper, we investigate time-efficient identification of temperature-dependent OCV-SOC curve from current-voltage data, without measuring or estimating OCVs. In particular, we identify the complete OCV-SOC curve from data over a partial SOC range at a given temperature, by fusing available OCV-SOC curve data at other temperatures. In the proposed approach, a multi-output Gaussian process (MOGP) model is first built to capture correlations among OCV-SOC curves at different temperatures, and then used to construct the OCV-SOC curve at the given temperature. Using experimental datasets, our proposed approach reduces the root mean square error (RMSE) of OCV predictions by at least 29.4% compared to three existing methods. Besides, with the updated OCV-SOC curve, the RMSE of SOC estimates is reduced by at least 14.0%, compared to using a non-updated OCV-SOC curve.

关键词： Lithium-ion battery Equivalent circuit model Open circuit voltage State of charge Multi-output Gaussian process

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Modeling and identification of rate-dependent and asymmetric hysteresis of soft bending pneumatic actuator based on evolutionary firefly algorithm

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MECHANISM AND MACHINE THEORY 2023年 181卷

作者： Ru, Hongge Huang, Jian Chen, Wenbin Xiong, Caihua Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan Hubei Peoples R China

Soft-bending pneumatic actuators (SBPA) have shown great potential in various applications owing to their intrinsic compliance. However, motion control is still challenging because of the complicated hysteresis of the elastic material and pneumatic system, which unlike the general hysteresis found in the rigid body mechanism, is rate-dependent and asymmetric. A precise hysteresis model is crucial for improving the performance of SBPA. This study proposed a rate-dependent modified generalized Prandtl-Ishlinskii (RMGPI) model for a vulcanized silicone rubber-based fast pneu-net soft bending pneumatic actuator (fp-SBPA) to describe complicated hysteresis characteristics. A visual feedback system obtained the bending angle in real time. With the increase in the number of operators, the identification of the proposed model parameters became more complicated and time-consuming;thus, an evolutionary firefly algorithm (EFA) was developed to improve identification efficiency. A series of experiments and comparison studies were conducted to verify the effectiveness of the proposed model and identification method.

关键词： Bending pneumatic muscle Rate-dependent and asymmetric hysteresis Prandtl-Ishlinskii (PI) model

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