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A Study on Partial Discharges Pattern Recognition Under DC Voltage Through clustering algorithms and Cross Correlation Filter

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2023年第6期30卷 2543-2550页

作者： Imburgia, Antonino Di Fatta, Alessio Romano, Pietro Rizzo, Giuseppe Li Vigni, Vincenzo Ala, Guido Univ Palermo Dept Engn LEPRE HV Lab I-90133 Palermo Italy Prysmian Grp EOSS I-20126 Milan Italy

It is widely accepted that the presence of defects within insulation systems and the consequent triggering of partial discharges (PDs) leads to the gradual deterioration of the components up to its failure. Monitoring these phenomena is a widely used strategy to control health and integrity of an insulation system. The classification of the type of PD detected in the component under test is necessary to recognize the defect where the phenomenon is generated. In ac application, a widely used tool for this purpose is the phase resolved PD (PRPD) pattern. However, the same approach cannot be used in HVDC systems, because the phase reference is missing under dc voltage. Furthermore, an equally powerful technique for the dc case has not been developed yet, although several proposals are present in the literature. The aim of this article is to present a study for performing PD patterns recognition and noise separation, under dc voltage. The presented work is based on the comparison between a clustering algorithm and a cross correlation filter applied to the Time-frequency map (TF Map), proposed by other researchers. The results show that it is possible to distinguish noise from discharges and evaluate their behavior throughout the measurement phase.

关键词： clustering algorithm HVDC partial dis-charge (PD) pattern recognition time-frequency map (TF Map)

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Recursive partitioning clustering tree algorithm

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PATTERN ANALYSIS AND APPLICATIONS 2016年第2期19卷 355-367页

作者： Kang, Ji Hoon Park, Chan Hee Kim, Seoung Bum Korea Univ Sch Ind Management Engn Anam Seongbuk Seoul 136713 South Korea

clustering analysis elicits the natural groupings of a dataset without requiring information about the sample class and has been widely used in various fields. Although numerous clustering algorithms have been proposed and proven to perform reasonably well, no consensus exists about which one performs best in real situations. In this study, we propose a nonparametric clustering method based on recursive binary partitioning that was implemented in a classification and regression tree model. The proposed clustering algorithm has two key advantages: (1) users do not have to specify any parameters before running it;(2) the final clustering result is represented by a set of if-then rules, thereby facilitating analysis of the clustering results. Experiments with the simulations and real datasets demonstrate the effectiveness and usefulness of the proposed algorithm.

关键词： Unsupervised learning clustering algorithm Recursive binary partitioning Silhouette statistic

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Adaptive Asynchronous clustering algorithms for Wireless Mesh Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第3期35卷 2610-2627页

作者： Qiao, Cheng Brown, Kenneth N. Zhang, Fan Tian, Zhihong Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou 510006 Peoples R China Univ Coll Cork Insight Ctr Data Analyt Dept Comp Sci Cork T12 K8AF Ireland

It is a challenge to generate an accurate machine learning model in a distributed network due to the increased concern in data privacy and high cost in gathering all raw data. This paper presents an adaptive asynchronous distributed clustering algorithm and two centralised methods for agents in wireless network to learn the global models, while the privacy is protected. Moreover, the communication cost and clustering quality can be adaptively balanced. The proposed clustering algorithms do not require the number of clusters to be pre-defined, and we propose a bounding boxes based method to fully utilize the shape information of clusters to improve the accuracy of the global model. Furthermore, we consider different knowledge levels of agents and different requirements about the global model. In experiments on randomly generated network topologies, we demonstrate that methods which do all the iterations of clustering in each cycle, and which exchange descriptions of cluster shape and density instead of just centroids and data counts, achieve higher accuracy, in significantly shorter elapsed time.

关键词： clustering algorithms Computational modeling Data models Costs Context modeling Wireless networks Temperature sensors Distributed algorithm asynchronous clustering algorithm wireless mesh network

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Improved Kernel Possibilistic Fuzzy clustering algorithm Based on Invasive Weed Optimization

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Journal of Shanghai Jiaotong university(Science) 2015年第2期20卷 164-170页

作者：赵小强周金虎 College of Electrical Engineering and Information Engineering Lanzhou University of Technology

Fuzzy c-means(FCM) clustering algorithm is sensitive to noise points and outlier data, and the possibilistic fuzzy c-means(PFCM) clustering algorithm overcomes the problem well, but PFCM clustering algorithm has some problems: it is still sensitive to initial clustering centers and the clustering results are not good when the tested datasets with noise are very unequal. An improved kernel possibilistic fuzzy c-means algorithm based on invasive weed optimization(IWO-KPFCM) is proposed in this paper. This algorithm first uses invasive weed optimization(IWO) algorithm to seek the optimal solution as the initial clustering centers, and introduces kernel method to make the input data from the sample space map into the high-dimensional feature space. Then, the sample variance is introduced in the objection function to measure the compact degree of data. Finally, the improved algorithm is used to cluster data. The simulation results of the University of California-Irvine(UCI) data sets and artificial data sets show that the proposed algorithm has stronger ability to resist noise, higher cluster accuracy and faster convergence speed than the PFCM algorithm.

关键词： data mining clustering algorithm possibilistic fuzzy c-means(PFCM) kernel possibilistic fuzzy c-means algorithm based on invasiv

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Identification of flow regimes in boiling flow with clustering algorithms: An interpretable machine-learning perspective

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APPLIED THERMAL ENGINEERING 2023年第1期228卷

作者： Zhu, Longxiang Ooi, Zhiee Jhia Zhang, Taiyang Brooks, Caleb S. Pan, Liangming Chongqing Univ Key Lab Low Grade Energy Utilizat Technol & Syst Minist Educ Chongqing 400044 Peoples R China Chongqing Univ Postdoctoral Stn Power Engn & Engn Thermophys Chongqing 400044 Peoples R China Chongqing Univ Dept Nucl Engn & Technol Chongqing 400044 Peoples R China Univ Illinois Dept Nucl Plasma & Radiol Engn Urbana IL 61801 USA Chongqing Univ Sch Energy & Power Engn Chongqing 400044 Peoples R China

The flow regime is the prerequisite to accurately modeling two-phase flow. Unsupervised machine learning techniques enable the identification of flow regimes objectively. Previous machine learning models are used as a "black box" tool without knowing the physical phenomena in the flow regime. Consequently, the cause of the identification error tends to be poorly understood and the model cannot be fundamentally improved. The paper develops an approach to better understand the identification result by creating a mapping relation between bubble distribution and the components in machine learning algorithms. The intrinsic interpretation generates the clustering principle to guide the feed-in feature extraction and clustering algorithm selection processes. Four features extracted from the bubble-size raw data recorded using conductivity probes are examined. Among them, the Cumulative Distribution Function of the chord length in seven dimensions is demonstrated to be the appropriate feed-in feature. Three major kinds of clustering algorithms are investigated, including partition-based, hierarchy-based, and model-based methods. After assigning physical meanings to the nodes in the algo-rithm and inspecting the clustering outcomes, the K-means, K-medoids, and Self-Organizing Maps are shown to succeed in the flow-regime identification problem. In addition, the local and the global flow regimes are generated by the well-designed machine learning model to assist the understanding of the boiling flow structure in a multi-dimensional way and in an area-averaged sense. The overall accuracy of the machine learning model for the three global flow regimes is 86%, which suggests the chosen algorithm with the selected feed-in feature is capable to capture the flow regime in the boiling flow. The flow regime map for the boiling dataset is compared with the existing flow regime criteria developed in the air-water flow, the result of which highlights the necessity of a new criterion to c

关键词： Two-phase flow Boiling flow Flow regime clustering algorithm Interpretable machine learning

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clustering Detection algorithm of Plant Leaf Relative Lesion Area Based on Improved GA

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INTELLIGENT AUTOMATION AND SOFT COMPUTING 2016年第2期22卷 249-254页

作者： Ren, Shun Yu, Haiye Wang, Linlin Zhang, Lei Jilin Univ Dept Biol & Agr Engn Changchun Peoples R China Jilin Univ Minist Educ Key Lab Bion Engn Changchun Peoples R China

In order to overcome the defects of traditional methods on measuring plant leaf diseases and achieve accurate detection of blade relative lesion area, clustering algorithm and the improved clustering algorithm are used to calculate the leaf relative lesion area with the knowledge of computer graphics technology. First, preprocess the image selectively by the image correction, color space conversion technology and so on, use the clustering algorithm to divide the target area. Finally calculate relative lesion area according to the partition determined by objective function. This paper proposed an improved genetic algorithm to improve the limitation problem of selecting the clustering algorithm initial value and enhance the searching capability and the robustness of the original algorithm by improving genetic operator of genetic algorithm. Considering the accuracy of image processing as the quota, the results show that the algorithm in this paper makes the lesion area calculated by the clustering center more accurate and lays more effective theoretical foundation for diagnosis of crop diseases and insects level.

关键词： Computer graphics clustering algorithm genetic algorithm relative lesion area (RLA)

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RETRACTED: Designing framework to secure data using K Means clustering based outlier Detection (KCOD) algorithm (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期44卷 1057-1068页

作者： Nithinsha, S. Anusuya, S. Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci Chennai Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Informat Technol Chennai Tamil Nadu India

The objective of the research work is to propose an intrusion detection system in a cloud environment using K-Means clustering-based outlier detection. In the open access and dispersed cloud architecture, the main problem is security and confidentiality because these are easily susceptible to intruders. Intrusion Detection System (IDS) is a commonly used method to identify the various attacks on the cloud which is easy to access from a remote area. The existing process cant provide the data to transmit securely. This work describes and notifies the modernly established IDS and alarm management methods by giving probable responses to notice and inhibit the intrusions in the cloud computing environment and to overcome the security and privacy issue. Proposed K-means clustering based Outlier Detection (KmCOD) is used to detect the intruders and efficiently secure the data from malicious activity, where it is formulated respectively to increase the trustworthiness of the system by using applying intrusion detection techniques to virtual machines thus keeping the system safe and free from intrusion also provides system reliability. The parametric measures such as the detection rate, trace preprocessing, and correctly identified and incorrectly identified malicious activity are chosen. The performance analysis shows the accuracy of outlier detection as 81%, detection rate achieves 76%, packet arrival rate reaches 79%, pre-processing trace achieves 74%, and malicious activity rate of 21%.

关键词： Cloud intrusion detection data security clustering algorithm outlier detection data privacy anomaly

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Enhancing CURE algorithm with Stochastic Neighbor Embedding (CURE-SNE) for Improved clustering and Outlier Detection

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第12期15卷 382-391页

作者： Ginting, Dewi Sartika Br Efendi, Syahril Amalia Sihombing, Poltak Univ Sumatera Utara Dept Comp Sci & Technol Informat Medan Indonesia

This study focuses on analyzing stunting data using the CURE and CURE-SNE algorithms for clustering and outlier detection. The primary challenge is identifying patterns in stunting data, which includes variables such as age, gender, height, weight, and nutritional status. Both algorithms were employed to group the data and detect outliers that may affect the results of the analysis. The evaluation methods included determining the optimal number of clusters using the silhouette score and assessing cluster quality using the Davies-Bouldin Index (DBI). The results showed that both algorithms formed four clusters, with CURESNE detecting 6,050 outliers, while CURE detected 5,047 outliers. Silhouette score analysis revealed that both algorithms formed four optimal clusters. However, when validated using DBI, CURE achieved a score of 0.523, while CURE-SNE produced a lower score of 0.388, indicating that CURE-SNE outperformed CURE in terms of cluster quality. This suggests that CURE-SNE not only detects more outliers but also produces clusters with better separation and compactness. The findings highlight that both algorithms are effective for clustering stunting data, but CURESNE excels in terms of outlier detection and overall cluster quality. Thus, CURE-SNE is more suitable for handling complex datasets with potential outliers, providing more accurate insights into the structure of the data. In conclusion, CURE-SNE demonstrates superior performance compared to CURE, offering a more reliable and detailed clustering solution for stunting data analysis.

关键词： Stunting clustering algorithm CURE CURESNE outliers

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A clustering-based surrogate-assisted evolutionary algorithm (CSMOEA) for expensive multi-objective optimization

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SOFT COMPUTING 2023年第15期27卷 10665-10686页

作者： Wang, Wenxin Dong, Huachao Wang, Peng Wang, Xinjing Shen, Jiangtao Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710068 Peoples R China

This paper presents a novel surrogate-assisted evolutionary algorithm, CSMOEA, for multi-objective optimization problems (MOPs) with computationally expensive objectives. Considering most surrogate-assisted evolutionary algorithms (SAEAs) do not make full use of population information and only use population information in either the objective space or the design space independently, to address this limitation, we propose a new strategy for comprehensive utilization of population information of objective and design space. The proposed CSMOEA adopts an adaptive clustering strategy to divide the current population into good and bad groups, and the clustering centers in the design space are obtained, respectively. Then, a bi-level sampling strategy is proposed to select the best samples in both the design and objective space, using distance to the clustering centers and approximated objective values of radial basis functions. The effectiveness of CSMOEA is compared with five state-of-the-art algorithms on 21 widely used benchmark problems, and the results show high efficiency and a good balance between convergence and diversity. Additionally, CSMOEA is applied to the shape optimization of blend-wing-body underwater gliders with 14 decision variables and two objectives, demonstrating its effectiveness in solving real-world engineering problems.

关键词： Expensive multi-objective optimization Radial basis function Bi-level sampling strategy clustering algorithm

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Cerebral hemorrhage extraction with modified shuffled frog leaping algorithm based on the blood clot clustering

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第2期83卷 5653-5681页

作者： Fang, Lingling Jiang, Yumeng Liaoning Normal Univ Dept Comp & Informat Technol Dalian Liaoning Peoples R China

Rapid extraction of brain lesions can help doctors speed up clinical diagnosis and provide help for follow-up treatment. The spatial location, shape, and distribution of Cerebral hemorrhage are very changeable. Besides, there is a particular case where a blood clot adhesion to the skull (called Skull adhesion). Based on the features of a blood clot, combined with the modified shuffled frog leaping algorithm (MSFLA) and clustering ideas, this paper proposes a C-MSFLA based on the cerebral hemorrhage clot clustering algorithm and establishes the intracranial blood clot extraction framework. Here, the brain uncorrelated tissue is removed by the two-dimensional prefix summation eliminate algorithm, and then the complete blood clot is extracted by regional morphological operation. The proposed method can automatically and accurately extract blood clots. The experiments are tested using clinical cerebral hemorrhage CT images of patients in the Second Affiliated Hospital of Dalian Medical University and verified by the evaluation indicators of JAC, Dice, and Acc. Experiments verify that the proposed method has good performance. It can assist doctors in detecting lesions in time, which can make an efficient, automatic, and accurate clinical diagnosis.

关键词： Blood clot extraction Cerebral hemorrhage clustering algorithm CT images Shuffled frog leaping algorithm

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