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Photovoltaic Module Fault Detection Technology Based on Remote Sensing Technology and Deeplabv3+Model

IEEE ACCESS 2024年 12卷 195472-195482页

作者： Xu, Xiaowei Liu, Mingxian Nie, Yongjie Wang, Ke Xu, Wenhua Yunnan Power Grid Co Ltd Elect Power Res Inst Kunming 650000 Peoples R China

With the rapid advancement of renewable energy, the fault detection of photovoltaic modules has become a key link to ensure their efficient operation. The study first utilizes remote sensing technology to obtain high-resolution images of photovoltaic power plants, and then uses the Deeplabv3+model to segment the images and identify faulty components. Combining remote sensing technology with Deeplabv3+ model, fast and accurate photovoltaic module fault detection can be achieved. The research results indicated that bilateral filtering and gamma filtering algorithms showed superior performance in testing, with the highest indicator evaluation results. Meanwhile, the structural similarity index value was also very close to 1. The error of the improved K-means was mainly concentrated between 0.000 and 0.014, while the traditional K-means was mainly distributed between 0.022 and 0.051. The statistical test results showed that the improved K-means algorithm was significantly better than the traditional K-means in clustering accuracy, and its average error was only 0.008, which was much lower than the 0.035 of the traditional K-means. The bilateral filtering showed the highest accuracy, with a classification result of 88%. This method can detect faults in photovoltaic modules, which has significant advantages over traditional methods. It provides a new and efficient method for photovoltaic module fault detection, which helps to optimize the operational efficiency and reliability of photovoltaic power plants and promote the sustainable development of renewable energy.

关键词： Solar panels Fault detection Remote sensing Accuracy Filtering Aircraft Maintenance Brightness Atmospheric modeling Object oriented modeling clustering algorithm Deeplabv3+model fault detection photovoltaic modules remote sensing technology

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Maximum Correntropy Criterion-Based UKF for Loosely Coupling INS and UWB in Indoor Localization

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2673-2703页

作者： Yan Wang You Lu Yuqing Zhou Zhijian Zhao Department of Computer and Communication Engineering Northeastern UniversityQinhuangdao066004China

Indoor positioning is a key technology in today’s intelligent environments,and it plays a crucial role in many application *** paper proposed an unscented Kalman filter(UKF)based on the maximum correntropy criterion(MCC)instead of the minimummean square error criterion(MMSE).This innovative approach is applied to the loose coupling of the Inertial Navigation System(INS)and Ultra-Wideband(UWB).By introducing the maximum correntropy criterion,the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise,thus enhancing its adaptability to diverse environmental localization *** in the presence of non-Gaussian noise,especially heavy-tailed noise,the MCCUKF exhibits superior accuracy and robustness compared to the traditional *** method initially generates an estimate of the predicted state and covariance matrix through the unscented transform(UT)and then recharacterizes the measurement information using a nonlinear regression method at the cost of ***,the state and covariance matrices of the filter are updated by employing the unscented transformation on the measurement ***,to mitigate the influence of non-line-of-sight(NLOS)errors positioning accuracy,this paper proposes a k-medoid clustering algorithm based on bisection k-means(Bikmeans).This algorithm preprocesses the UWB distance measurements to yield a more precise position *** results demonstrate that MCCUKF is robust to the uncertainty of UWB and realizes stable integration of INS and UWB systems.

关键词： Maximum correntropy criterion unscented Kalman filter inertial navigation system ultra-wideband bisecting kmeans clustering algorithm

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clustering based on adaptive local density with evidential assigning strategy

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INTELLIGENT DATA ANALYSIS 2022年第1期26卷 101-118页

作者： Wang, Qian Gong, Chaoyu Wang, Peihong Jiangsu Univ Sci & Technol Sch Energy & Power Zhenjiang 212003 Jiangsu Peoples R China Southeast Univ Sch Energy & Environm Nanjing Jiangsu Peoples R China

A new clustering algorithm, based on Adaptive Local Density (ALD) and Evidential K-Nearest Neighbors (EKNN), is proposed here. In density peaks clustering, many other density metrics fail to detect cluster centers on multi-density datasets, however the ALD deals with the tasks very well since it can better utilize the local information. To assign the remaining points after detecting the cluster centers, an assigning strategy in the framework of evidential theory, named EKNN, is created. The advantage of EKNN is twofold. Firstly, by fusing the information of K-Nearest Neighbors, it can reduce the risk of a phenomenon named domino effect: the drawback of one classical clustering, i.e., clustering by fast search and find of density peaks (always named as DPC). Secondly, it can detect border and noise points simultaneously since a credal partition is derived which can mine ambiguity and uncertainty of data structure. Simulations on both synthetic and real-world datasets demonstrate the outstanding performance of ALD-EKNN compared with DPC and some of its successors.

关键词： clustering algorithm adaptive local density K-nearest neighbors evidential theory credal partition

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A Hybrid Composite Differential Evolution and Multiobjective Particle Swarm Optimization Evolutionary algorithm and Its Application

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IEEE ACCESS 2024年 12卷 74417-74431页

作者： Shang, Jin Li, Guiying Wuxi Inst Technol Control Technol Inst Wuxi 214121 Peoples R China Heilongjiang Univ Sch Mech & Elect Engn Harbin 150080 Peoples R China

The current multi-objective particle swarm algorithms excel in convergence speed for solving complex problems but often suffer from a loss of population diversity. Conversely, composite differential evolution algorithms maintain superior solution distribution but lag in convergence efficiency. This research introduces an improved hybrid algorithm, CoDE-MOPSO, which integrates multi-objective particle swarm optimization with composite differential evolution based on clustering technology. The clustering algorithm is used for all individual clusters to analyze the distribution constructs of populations, which determines whether the new solutions come from global or local populations at a mating restriction probability. The mating restriction probability is updated at each generation. To adapt the balance between the population solution diversities and the convergence speed of the algorithm, at each generation, the control probability is adjusted by a developed adaptive strategy according to the reproduction utility of the two mechanisms of generating new solutions over the last certain generations. This research introduces the CoDE-MOPSO algorithm, designed to transcend existing multi-objective optimization methods' limitations by optimally balancing exploration and exploitation. Our approach significantly advances evolutionary multi-objective optimization, demonstrating superior performance through lower Inverse Generational Distance and higher Hypervolume metrics, indicating enhanced efficiency in solving complex MOPs across various fields. In practical scenarios like gear reducer optimization, CoDE-MOPSO showcases remarkable effectiveness, highlighting its value in engineering applications and setting a foundation for sophisticated optimization strategies that combine speed with solution quality.

关键词： Statistics Social factors Optimization Convergence clustering algorithms Particle swarm optimization Interconnected systems Pareto optimization clustering algorithm composite differential evolution gear reducer multiobjective optimization particle swarm algorithm

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Adaptive Decentralized State Estimation for Multimachine Power Grids Under Measurement Noises With Unknown Statistics

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第2期21卷 1655-1664页

作者： Qu, Bogang Wang, Zidong Shen, Bo Dong, Hongli Peng, Daogang Shanghai Univ Elect Power Coll Automat Engn Shanghai 200090 Peoples R China Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH England Donghua Univ Coll Informat Sci & Technol Shanghai 200051 Peoples R China Minist Educ Engn Res Ctr Digitalized Text & Fash Technol Shanghai 201620 Peoples R China Northeast Petr Univ Artificial Intelligence Energy Res Inst Daqing 163318 Peoples R China Northeast Petr Univ Sanya Offshore Oil & Gas Res Inst Sanya 572024 Peoples R China

This article is concerned with the adaptive dynamic state estimation (DSE) problem for synchronous-generator-based multimachine power grids under measurement noise with unknown statistics. The statistical properties of the measurement noises are efficiently revealed by utilizing limited measurement data contained in a sliding window, and such data is employed to establish the base distribution of the noises, with the aid of the Gaussian mixture model and the kernel density estimation scheme. Subsequently, the component number of the base distribution of the measurement noises is reduced by designing a fuzzy C-means clustering algorithm with the Wasserstein distance criterion. An improved sliding-window-based adaptive cubature Kalman filtering scheme is then proposed, which leverages the already obtained statistical characteristics of the measurement noise and the concept of the Gaussian summation filter. Finally, the validity of the proposed adaptive DSE algorithm under various measurement noise statistics is illustrated by simulation studies conducted on the IEEE 39-bus system featuring three test scenarios.

关键词： Adaptive state estimation (SE) clustering algorithm cubature Kalman filter (CKF) multimachine power grids unknown measurement noises Adaptive state estimation (SE) clustering algorithm cubature Kalman filter (CKF) multimachine power grids unknown measurement noises

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A module partition method for complex product based on the knowledge hypergraph

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 152卷

作者： Wang, Pengchao Chu, Jianjie Yu, Suihuai Cheng, Fangmin Ding, Ning Cong, Yangfan Northwestern Polytech Univ Key Lab Ind Design & Ergon Minist Ind & Informat Technol Xian 710072 Peoples R China Xian Technol Univ Sch Art & Media Xian 710021 Peoples R China Hong Kong Polytech Univ Sch Design Hung Hom Hong Kong 999077 Peoples R China

The modular design can effectively control the development cycle and cost of complex product, with product module partition (PMP) serving as the foundation of modularization. However, models constructed based on expert knowledge are inadequate in effectively capturing the relationships within complex product, which undermines the efficiency and accuracy of PMP. To solve this problem, this paper introduces hypergraph theory into the field of PMP, specifically, proposes a PMP method based on the knowledge hypergraph (KHG). First, the multiple coupling relationships between complex product are defined from the perspective of "FunctionBehavior-Structure-Constraint", to form the pattern layer of the KHG. Then, the joint learning algorithm, which contains the pretraining model, Bi-directional Long Short-Term Memory network, Conditional Random Field and Attention layer, is proposed to automatically extract design knowledge from large-scale text data to form the data layer of the KHG. Furthermore, considering that the PMP model needs to learn nonlinear relationship features, achieve end-to-end optimization, and have strong anti-noise ability, hypergraph neural networks are used to partition the complex product modules, which contains the importance calculation, hypergraph convolution, modularity maximum and self-supervised module. Finally, a case study is conducted using a snow removal equipment as an example, the knowledge extraction accuracy reaches 91.67 %, and the PMP modularity is 0.68, thus validating the feasibility of the proposed method. Additionally, the comparison is made with other knowledge extraction and hypergraph clustering algorithms using public datasets, which further confirms the feasibility and superiority of the proposed method.

关键词： Product design Product module partition Knowledge hypergraph Hypergraph neural network clustering algorithm

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Dual-Task GPR Method: Improved Generative Adversarial Clutter Suppression Network and Adaptive Target Localization algorithm in GPR Image

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Hou, Feifei Fang, Mengqi Luo, Tess Xianghuan Fan, Xinyu Guo, Ying Cent South Univ Sch Automat Changsha 410083 Peoples R China Shenzhen Univ Coll Civil & Transportat Engn Shenzhen 518060 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Natl Pilot Software Engn Sch Beijing 100876 Peoples R China Hefei Natl Lab Hefei 230088 Peoples R China

Ground-penetrating radar (GPR), as a geophysical survey method, offers high resolution and fast detection capabilities obtaining rich information about the underground structure. However, under real conditions, GPR is susceptible to interference and clutter. Therefore, there is an urgent need to develop methods for suppressing clutter, highlighting and positioning targets in GPR images. This article proposed an integrated method consisting of two modules: GPR image decluttering and underground target localization, which are used for clutter suppression and hyperbola vertex estimation from GPR image. In the first module, an unsupervised generative adversarial network (GAN) model is developed to map cluttered GPR images to clutter-free GPR images, where the CycleGAN is adapted as the main network architecture. To further improve the performance of the model, the first module proposes a novel loss function that introduces a perceptual loss into the previous CycleGAN loss. It utilizes a multiscale structural similarity index (MS-SSIM) loss to obtain richer and more realistic context information. In the second module, based on the obtained decluttered image, a novel adaptive target characterization algorithm, called gradient points clustering-based localization (GPCL), is proposed for the recognition and localization of GPR target signatures. This algorithm is designed to leverage changes in target position and local characteristics, thereby achieving efficient target vertex evaluation. Experimental results conducted on field datasets demonstrate that the proposed methods can significantly improve the quality and clarity of various cluttered images and exhibit strong applicability for the localization of underground targets.

关键词： Clutter Location awareness Feature extraction clustering algorithms Transforms Generative adversarial networks Data mining clustering algorithm clutter elimination generative adversarial network (GAN) ground-penetrating radar (GPR) perceptual loss subsurface target localization

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Adaptive autonomous navigation system for coal mine inspection robots: overcoming intersection challenges

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INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION 2024年第6期51卷 922-935页

作者： Wang, Hongwei Li, Chao Liang, Wei Wang, Di Yao, Linhu Taiyuan Univ Technol Coll Mech & Vehicle Engn Taiyuan Peoples R China Taiyuan Univ Technol Coal Mine Intelligent Equipment Res Ctr Shanxi Pro Taiyuan Peoples R China Taiyuan Univ Technol Coll Mech & Vehicle Engn Taiyuan Peoples R China Taiyuan Univ Technol Taiyuan Peoples R China

PurposeIn response to the navigation challenges faced by coal mine tunnel inspection robots in semistructured underground intersection environments, many current studies rely on structured map-based planning algorithms and trajectory tracking techniques. However, this approach is highly dependent on the accuracy of the global map, which can lead to deviations from the predetermined route or collisions with obstacles. To improve the environmental adaptability and navigation precision of the robot, this paper aims to propose an adaptive navigation system based on a two-dimensional (2D) ***/methodology/approachLeveraging the geometric features of coal mine tunnel environments, the clustering and fitting algorithms are used to construct a geometric model within the navigation system. This not only reduces the complexity of the navigation system but also optimizes local positioning. By constructing a local potential field, there is no need for path-fitting planning, thus enhancing the robot's adaptability in intersection environments. The feasibility of the algorithm principles is validated through MATLAB and robot operating system simulations in this *** experiments demonstrate that this method enables autonomous driving and optimized positioning capabilities in harsh environments, with high real-time performance and environmental adaptability, achieving a positioning error rate of less than 3%.Originality/valueThis paper presents an adaptive navigation system for a coal mine tunnel inspection robot using a 2D LiDAR sensor. The system improves robot attitude estimation and motion control accuracy to ensure safe and reliable navigation, especially at tunnel intersections.

关键词： Mine robots Intersection environments Adaptive navigation clustering algorithm Geometric model Motion control

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A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN

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SENSORS 2024年第8期24卷 2611页

作者： Guo, Yihan Zocca, Simone Dabove, Paolo Dovis, Fabio Politecn Torino Dept Elect & Telecommun I-10129 Turin Italy Politecn Torino Dept Environm Land & Infrastruct Engn I-10129 Turin Italy

Positioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification of such events are still challenging. This research proposes a method for the post-processing estimation of pseudorange biases resulting from multipath/NLoS effects. Providing estimated pseudorange biases due to multipath/NLoS effects serves two main purposes. Firstly, machine learning-based techniques can leverage accurately estimated pseudorange biases as training data to detect and mitigate multipath/NLoS effects. Secondly, these accurately estimated pseudorange biases can serve as a benchmark for evaluating the effectiveness of the methods proposed to detect multipath/NLoS effects. The estimation is achieved by extracting the multipath/NLoS biases from pseudoranges using a clustering algorithm named Density-Based Spatial clustering of Applications with Noise (DBSCAN). The performance is demonstrated using two real-world data collections in multipath/NLoS scenarios for both static and dynamic conditions. Since there is no ground truth for the pseudorange biases due to the multipath/NLoS scenarios, the proposed method is validated based on the positioning performance. Positioning solutions are computed by subtracting the estimated biases from the raw pseudoranges and comparing them to the ground truth.

关键词： multipath non-line-of-sight pseudorange bias clustering algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 256卷

作者： Liu, Tao Cheng, Shuli Du, Anyu Xinjiang Univ Sch Comp Sci & Technol Urumqi 830046 Peoples R China

Currently, unsupervised person re-identification (Re-ID) typically directly computes the similarity matrix between samples and then applies clustering algorithms to generate pseudo labels for model training, achieving significant progress to some extent. However, distance matrices calculated from a single perspective cannot effectively handle complex and variable sample noise environments, leading to pseudo labels that are not comprehensive or accurate enough, with considerable noise that can misguide model optimization. To address this issue, this paper proposes a Multi-view Similarity Aggregation and Multi-level Gap Optimization (MSAMGO) to improve the accuracy of pseudo labels before and after clustering. Specifically, Multi-view Similarity Aggregation (MSA) involves integrating similarity matrices calculated from multiple perspectives to generate reliable pseudo labels for clustering, greatly reducing noise interference from the source. Additionally, the Multi-level Gap Optimization (MGO) is introduced to leverage the positive and negative centroids of similarity between cluster-level samples and the correlation between instance-level samples. Together, they ensure the overall quality of pseudo-labels, reduce noise interference, and collectively promote model optimization. Experiments were conducted on several datasets, demonstrating that our method surpasses the majority of existing unsupervised person re-identification to a large extent and exhibits excellent generalization.

关键词： clustering algorithm Similarity matrix Unsupervised person Re-ID Pseudo label purification

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