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Spatial Sampling and Grouping Information Entropy Strategy Based on kernel fuzzy c-means clustering Method for Hyperspectral Band Selection

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REMOTE SENSING 2022年第19期14卷 5058页

作者： Zhang, Zhou Wang, Degang Sun, Xu Zhuang, Lina Liu, Rong Ni, Li East China Univ Technol Fac Geomat Nanchang 330013 Jiangxi Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Computat Opt Imaging Technol Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China

The high spectral resolution of hyperspectral images (HSIs) provides rich information but causes data redundancy, which imposes a computational burden on practical applications. Band selection methods can select a subset of HSI without changing the main information to reduce the spectral dimension. clustering-based methods can reduce band correlation significantly, but traditional clustering methods are mostly hard clustering and are not accurate enough to partition the bands. An unsupervised band selection method based on fuzzy c-means clustering (FcM) was introduced to tackle this problem. However, FcM can easily obtain the local optimal solution and take a long time to process high-dimensional data. Hence, this work applies kernel function and a sampling strategy to reduce calculation time, and information entropy is used to initialize the cluster center. A kernel FcM algorithm based on spatial sampling and a grouping information entropy strategy is proposed and called SSGIE-KFcM. This method not only optimizes the calculation process and reduces the amount of computation data, accelerating the calculation efficiency, but also adopts grouping information entropy to improve the probability of obtaining a global optimal solution. classification experiments on two public HSI datasets show that: (1) The classification performance of the whole band can be achieved or even exceeded by using only a small number of bands to achieve the purpose of dimensionality reduction. (2) The classification accuracy can be improved compared with the FcM method. (3) With the introduction of sampling strategy and kernel function, the computational speed is at least 24 times faster than that of FcM. It has been proven that the SSGIE-KFcM method can significantly reduce the amount of HSI while retaining the primary information of the original data, which further promotes the research and application of HSI in the remote sensing area.

关键词： hyperspectral image band selection kernel fuzzy c-means clustering spatial sampling strategy information entropy

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A kernel fuzzy c-means clustering Algorithm Based on Firefly Algorithm 16th

A Kernel Fuzzy C-means Clustering Algorithm Based on Firefly...

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16th International Symposium on Neural Networks (ISNN)

作者： cheng, chunying Bao, chunhua Inner Mongolia Univ Nationalities Coll Comp Sci & Technol Tongliao 028043 Peoples R China

ISBN: (纸本)9783030227968;9783030227951

In this paper, the KFcM algorithm selects the Gaussian kernel function, maps the data into the high-dimensional feature space for clustering, and uses the optimal clustering center of the firefly algorithm as the initial value of KFcM, and then processes it through KFcM. Based on class analysis, a kernel fuzzy c-means clustering algorithm based on firefly algorithm (FAKFcM) is proposed. Numerical experiments results show that FA-KFcM is superior to other algorithms in clustering accuracy and time efficiency.

关键词： Firefly algorithm clustering kernel fuzzy c-means clustering The classical UcI data set

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Photovoltaic Array Fault Diagnosis Based on Gaussian kernel fuzzy c-means clustering Algorithm

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SENSORS 2019年第7期19卷 1520.-1520.页

作者： Liu, Shengyang Dong, Lei Liao, Xiaozhong cao, Xiaodong Wang, Xiaoxiao Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

In the fault diagnosis process of a photovoltaic (PV) array, it is difficult to discriminate single faults and compound faults with similar signatures. Furthermore, the data collected in the actual field experiment also contains strong noise, which leads to the decline of diagnostic accuracy. In order to solve these problems, a new eigenvector composed of the normalized PV voltage, the normalized PV current and the fill factor is constructed and proposed to characterize the common faults, such as open circuit, short circuit and compound faults in the PV array. The combination of these three feature characteristics can reduce the interference of external meteorological conditions in the fault identification. In order to obtain the new eigenvectors, a multi-sensory system for fault diagnosis in a PV array, combined with a data-mining solution for the classification of the operational state of the PV array, is needed. The selected sensors are temperature sensors, irradiance sensors, voltage sensors and current sensors. Taking account of the complexity of the fault data in the PV array, the kernel fuzzy c-means clustering method is adopted to identify these fault types. Gaussian kernel fuzzy c-means clustering method (GKFcM) shows good clustering performance for classifying the complex datasets, thus the classification accuracy can be effectively improved in the recognition process. This algorithm is divided into the training and testing phases. In the training phase, the feature vectors of 8 different fault types are clustered to obtain the training core points. According to the minimum Euclidean Distances between the training core points and new fault data, the new fault datasets can be identified into the corresponding classes in the fault classification stage. This strategy can not only diagnose single faults, but also identify compound fault conditions. Finally, the simulation and field experiment demonstrated that the algorithm can effectively diagnose the 8 commo

关键词： solar energy PV array fault diagnosis fill factor kernel fuzzy c-means clustering KFcM

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Indoor People counting Method based on Fingerprinting Localization with kernel fuzzy c-means clustering 17

Indoor People Counting Method based on Fingerprinting Locali...

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17th International Symposium on Distributed computing and Applications for Business Engineering and Science (DcABES)

作者： Li, Junhuai Hu, Zhiyong Wang, Huaijun Wang, Kan Wang, Zhixiao Xian Univ Technol Sch Comp Sci & Engn Xian Shaanxi Peoples R China Xian Univ Technol Shaanxi Key Lab Network Comp & Secur Technol Xian Shaanxi Peoples R China

ISBN: (纸本)9781538674451

Efficient and accurate people counting has become significant as it is essential in many applications such as smart guide and intelligent building. In this paper, a people counting approach based on fingerprinting localization is proposed for multiple regions in indoor environment. We obtain the number of people in each subarea by localizing each target in the environment. The approach is composed of the offline training phase and online counting phase. In offline phase, we construct the fingerprint database which is also called radiomap, and transform it through DIFF method to solve the problem of device diversity. Then we divide the whole region into different subareas utilizing kernel fuzzy c-means (KFcM) clustering algorithm to reduce the computational complexity. In online phase, we first select the nearest subarea of the target and then estimate its location. The location is compared with the boundaries of all subareas to determine the subarea it belongs to. When applied to multiple targets, the number of people in each region will be counted. Experimental results show that our proposed method is effective and the average people counting accuracy can achieve 90.24%.

关键词： People counting Indoor localization Fingerprinting kernel fuzzy c-means clustering

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Fault Diagnosis Method Based on kernel fuzzy c-means clustering with Gravitational Search Algorithm 7

Fault Diagnosis Method Based on Kernel Fuzzy C-Means Cluster...

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IEEE 7th Data Driven control and Learning Systems conference (DDcLS)

作者： Wu, Biyuan Li, Xiangshun Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781538626184

The main drawback of the traditional fuzzy c-means clustering algorithm (FcM) is the randomness of the initial clustering center, which usually leads to the local optimal solutions and have a great influence on the clustering results. It also has to mention the FcM cannot deal with the non-linear data effectively. In this paper, gravitational search algorithm (GSA) is proposed to solve the randomness of the clustering centers. In addition, kernel fuzzy c-means clustering (KFcM) is introduced, which can improve the clustering result of the fuzzy c-means clustering for non-linear data. Finally, the proposed improved algorithm are verified with the three-tank system, and the results show that the concurrent faults can be diagnosed effectively.

关键词： Gravitational Search Algorithm kernel fuzzy c-means clustering concurrent Fault Diagnosis Three-Tank System

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Local feature driven fuzzy local information c-means clustering with kernel metric for blurred and noisy image segmentation

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JOURNAL OF REAL-TIME IMAGE PROcESSING 2023年第6期20卷 116页

作者： Wu, chengmao Qi, Xiao Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Peoples R China

kernel fuzzy weighted local information c-means clustering is a widely used robust segmentation algorithm for noisy images. However, it cannot effectively solve the segmentation problem of blurred and noisy images. A local feature driven fuzzy local information c-means clustering with kernel metric for blurred and noisy image segmentation is proposed in this paper. Firstly, a local ternary pattern is used to extract the feature information of the blurred and noisy images;Secondly, the image feature information is embedded into the objective function of fuzzy local information clustering and an optimization model for blurred and noisy image segmentation is established. Thirdly, Lagrange multiplier method is used to solve this optimization problem, and a dual-level alternating iterative clustering algorithm for blurred and noisy image segmentation is obtained. Experimental results demonstrate that the proposed algorithm has better segmentation performance for blurred and noisy images than the latest robust fuzzy clustering-related algorithms, and its PSNR and Acc values increase by about 0.09 similar to 1.07 and 0.08 similar to 0.13, respectively.

关键词： Image segmentation kernel fuzzy c-means clustering Local ternary pattern Local information

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Fault diagnosis of wind turbine gearbox based on kernel fuzzy c-means clustering

Fault diagnosis of wind turbine gearbox based on kernel fuzz...

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International conference on Renewable Power Generation (RPG 2015)

作者： Zhuang Li Rui Jiang Zhiyong Ma Yibing Liu School of Energy Power and Mechanical Engineering North China Electric Power University Beijing 102206 China

ISBN: (纸本)9781510821415

conventional methods for fault diagnosis need a process of supervised training based on historical samples of known fault. However, it is time-consuming and costly to collect all kinds of known fault samples. In practice, it is lack of complete known samples for supervised training. These methods fail to diagnose a new or unknown fault. In this paper, a method based on kernel fuzzy c-means clustering (KFcM) was proposed to diagnose the known and unknown faults for fault diagnosis of wind turbine gearbox. At first, the samples of known samples were classified by KFcM and the class centre of each known fault was acquired. Similarity parameters in kernel space between new data samples and known class centres were employed in this paper. Thereafter similarity parameters were calculated for diagnosing whether the new data samples belong to knows faults. The proposed method was applied in fault diagnosis of wind turbine gearbox. The results show that the proposed method can diagnose both the known faults and unknown faults accurately and effectively.

关键词： fault diagnosis gearbox kernel fuzzy c-means clustering wind turbine Wind Energy conversion Systems similarity parameter Fault diagnosis Seeds faults Transmissions

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Automatic Segmentation of Multiple Sclerosis Lesions in Multispectral MR Images Using kernel fuzzy c-means clustering

Automatic Segmentation of Multiple Sclerosis Lesions in Mult...

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IEEE International conference on Medical Imaging Physics and Engineering (IcMIPE)

作者： Xiang, Yan He, Jianfeng Shao, Dangguo Ma, Lei Kunming Univ Sci & Technol Dept Biomed Engn Kunming 650051 Peoples R China

ISBN: (纸本)9781467358873

Magnetic resonance (MR) images can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. An automatic method is presented for segmentation of MS lesions in multispectral MR images. Firstly a PD-w image is subtracted from its corresponding T1-w image to get an image in which the cerebral spinal fluid (cSF) is enhanced. Then based on kernel fuzzy c-means (KFcM) algorithm, the enhanced image and the corresponding T2-w image are segmented respectively to extract the cSF region and the cSF combining MS lesions region. A raw MS lesions image is obtained by subtracting the cSF region from cSF combining MS region. By applying median filter and thresholding to the raw image, the MS lesions are detected finally. Results are quantitatively evaluated on BrainWeb images using Dice similarity coefficient (DSc). Finally, the potential of the method as well as its limitations are discussed.

关键词： Multiple sclerosis kernel fuzzy c-means clustering Magnetic resonance Segmentation

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Short-Term Power Load Prediction Based on cluster Analysis and Temporal convolutional Networks of Attention Mechanism

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cONcURRENcY AND cOMPUTATION-PRAcTIcE & EXPERIENcE 2025年第9-11期37卷

作者： Niu, Shuqi Zhang, Zhao Zhou, Hongyan chen, Xue-Bo Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Peoples R China

Short-term power load prediction has become one of the important contents of smart grid management. Accurate power load prediction can provide a safer, more reliable, and more efficient direction for power system operation. This article proposes a short-term power load forecasting method. Mainly based on the improved fuzzy c-means clustering (FcM) algorithm and a temporal convolutional network (TcN) model combined with an attention mechanism (AM). First, to cluster the load data with the same power consumption behavior into one class, a kernel FcM algorithm based on particle swarm optimization is used. Meanwhile, external factors with high correlation are selected as inputs. The Pearson correlation coefficient can be used to measure the degree of correlation between load data and external factors. Second, by analyzing the degree of correlation between external influencing factors and load data, the channel AM and time AM are introduced into the TcN model. Finally, the effectiveness of the proposed method was verified through a real electricity load dataset. The experimental results indicate that this method can accurately predict future changes in power load. compared with other models, it also has high accuracy and generalization ability.

关键词： attentional mechanism kernel fuzzy c-means clustering short-term power load forecasting temporal convolutional networks

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Enhancing Multicriteria-Based Recommendations by Alleviating Scalability and Sparsity Issues Using collaborative Denoising Autoencoder

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computers, Materials & continua 2024年第2期78卷 2269-2286页

作者： S.Abinaya K.Uttej Kumar School of Computer Science&Engineering Vellore Institute of TechnologyChennaiTamilnadu600127India

A Recommender System(RS)is a crucial part of several firms,particularly those involved in *** conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer ***,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’*** the other hand,the collaborative filtering(cF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested ***,the cost of these computations increases nonlinearly as the number of items and users *** triumph over the issues,a novel expanded stacked autoencoder(ESAE)with kernel fuzzy c-means clustering(KFcM)technique is proposed with two *** the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the ESAE approach and users are clustered using the KFcM *** the next phase of online,the top-N recommendation prediction is made by the ESAE approach involving only the most similar user from multiple *** the ESAE_KFcM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation *** experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the ESAE_KFcM model constantly outperforms conventional RS algorithms on a variety of assessment measures.

关键词： Recommender systems multicriteria rating collaborative filtering sparsity issue scalability issue stacked-autoencoder kernel fuzzy c-means clustering

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