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An improved method for k-means clustering based on internal validity indexes and inter-cluster variance

INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2022年第3期25卷 253-261页

作者： Zhu, Guangli Li, Xiaoqing Zhang, Shunxiang Xu, Xin Zhang, Biao Anhui Univ Sci & Technol Sch Comp Sci & Engn Huainan Peoples R China

It is difficult to get the best cluster number of k-means clustering algorithm by using the traditional internal validity indexes. Therefore, a good clustering result cannot be obtained usually. To solve this problem, this paper proposes an improved method for k-means clustering based on internal validity indexes and inter-cluster variance. Firstly, some integers distributed in the interval [2, root n] are selected as initial cluster numbers. Further, each initial cluster number is selected to carry out k-means clustering and obtain a clustering result. Secondly, two initial cluster numbers k(D) and k(C) are extracted respectively under two optimal validity index values (i.e., DB and CH). The k(D) and k(C) are extracted by comparing and analysing the validity index values. Finally, the validity index ICS-VAR is proposed to select the best cluster number k(B), while k(D) is not equal to k(C). Experimental results show that the improved method can obtain a better cluster number under a certain condition.

关键词： k-means clustering algorithm internal validity indexes inter-cluster variance initial cluster number

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Integrity-Management Characteristics and Efficiency Evaluation of Oil and Gas Pipelines

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China Petroleum Processing & Petrochemical Technology 2023年第4期25卷 139-150页

作者： Zhang Xiaodong Sun Jiazheng Fu Yong Lei Shaojuan School of Economics and Management University of Science and Technology BeijingBeijing 100083China PetroChina Planning and Engineering Institute Beijing 100083 China Nuclear and Radiation Safety Center Beijing 102444China

Pipeline integrity is a cornerstone of the operation of many industrial systems, and maintaining pipeline integrity is essential for preventing economic losses and ecological damage caused by oil and gas leaks. Based on integritymanagement data published by the US Pipeline and Hazardous Materials Safety Administration, this study applied the k-means clustering and data envelopment analysis(DEA) methods to both explore the characteristics of pipeline-integrity management and evaluate its efficiency. The k-means clustering algorithm was found to be scientifically valid for classifying pipeline companies as either low-, medium-, or high-difficulty companies according to their integrity-management requirements. Regardless of a pipeline company's classification, equipment failure was found to be the main cause of pipeline failure. In-line inspection corrosion and dent tools were the two most-used tools for pipeline inspection. Among the types of repair, 180-day condition repairs were a key concern for pipeline companies. The results of the DEA analysis indicate that only three out of 34 companies were deemed to be DEA-effective. To improve the effectiveness of pipeline integrity management, we propose targeted directions and scales of improvement for non-DEA-effective companies.

关键词： Integrity management k-means clustering algorithm data envelopment analysis safety management

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Combined relevance vector machine technique and subset simulation importance sampling for structural reliability

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APPLIED MATHEMATICAL MODELLING 2023年 113卷 129-143页

作者： Xie, Bin Peng, Chong Wang, Yanzhong Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China

Evaluating the failure probability of engineering structures involving implicit performance functions generally requires time-consuming finite element simulations. The computational effort is usually unbearable, especially for small failure probability. This paper proposes a new reliability analysis method combining relevance vector machine and subset simu-lation importance sampling, which guarantees the accuracy of results and improves the computational efficiency. In this method, relevance vector machine is first applied to ap-proximate relatively coarse limit states. Then, subset simulation importance sampling is executed based on the constructed relevance vector machine. To improve the prediction accuracy, samples from first and last level are used to refine relevance vector machine. Since only the samples of last level are involved in the calculation of the failure probabil-ity, and the updated relevance vector machine has a very high prediction accuracy for the samples of last level, the obtained failure probability is extremely accurate. In addition, the samples are predicted by the well-constructed relevance vector machine rather than eval-uated with real performance functions, which drastically decreases the calculation time. To reduce redundant iterations in the update process, a novel learning function based on the current design of experiment position and a stopping condition based on error estimation of reliability prediction are employed. Furthermore, the k-means clustering algorithm is utilized to implement the ability of surrogate model-based subset simulation importance sampling to handle multiple failure mode problems. The efficiency and accuracy of the proposed method are verified by four examples involving different types of features.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Reliability analysis Relevance vector machine Subset simulation importance sampling k-means clustering algorithm Small failure probability

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Planar Array Subarray Division Method in Microwave Wireless Power Transmission Based on PSO&k-means algorithm

IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION

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IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION 2023年 4卷 520-527页

作者： Zhang, Qian He Zhang, Qing He Shen, Zhao Yang China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hyd Yichang 443002 Hubei Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Yichang 443002 Hubei Peoples R China

For the problem of maximizing beam collection efficiency in microwave wireless power transmission, a clustering algorithm based on a combination of the particle swarm algorithm and the k-means algorithm is proposed for the subarray partitioning of planar arrays. By minimizing the difference between the subarray beam collection efficiency and the reference beam collection efficiency, the subarray partitioning problem is transformed into an excitation matching problem. Then, the optimal subarray configuration is obtained by using a clustering algorithm that combines the particle swarm algorithm and the k-means algorithm. The effectiveness of the proposed method is evaluated by integrating the beam collection efficiency with parameters such as the receiving area and the size of the planar array. The superiority of the proposed method is demonstrated by a comparison with other advanced methods.

关键词： clustering algorithms Microwave antenna arrays Transmitting antennas Partitioning algorithms Receiving antennas Planar arrays Wireless power transmission Microwave wireless power transmission (MWPT) planar array antenna INDEX TERMS particle swarm optimization algorithm (PSO) k-means clustering algorithm beam collection efficiency (BCE)

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A Low-Voltage Distribution Network Line Loss Calculation Method Based on Enhanced k-means clustering and BP Neural Network 3

A Low-Voltage Distribution Network Line Loss Calculation Met...

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Cong, Rili Man, Xiangyang Wang, Xiaoyan Fan, Jiaqi Liu, Jianfeng Economic and Technological Research Institute State Grid Inner Mongolia Eastern Electric Power Co. Ltd. Hohhot010020 China

ISBN: (纸本)9798350382426

As the energy revolution progresses and efforts to achieve the 'double carbon' target continue, the calculation of distribution network line losses as an important technical indicator will effectively reflect the economic stability and operational capacity of the network. This paper presents an effective approach to computing the wire loss in photovoltaic integrated low-voltage electrical substation systems, addressing issues such as precision and pace of computation. First, the wire loss indicators presented in this article are extracted using hierarchical analysis, and the enhanced k-means clustering method is then used to classify the wire loss data in accordance with the resulting metrics. Subsequently, a BP neural network optimized by LM is applied for the classification and training of the model data, and the correlation is fitted between the wire loss rate and exponent. Simulation calculations using sample data from a PV-integrated low-voltage distribution network in a region are undertaken to prove the feasibility of the proposal. © 2023 IEEE.

关键词： BP network k-means clustering algorithm low-voltage region power line loss

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Automatic Monitoring Alarm Method of Dammed Lake Based on Hybrid Segmentation algorithm

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SENSORS 2023年第10期23卷 4714-4714页

作者： Cai, Ziming Sun, Liang An, Baosheng Zhong, Xin Yang, Wei Wang, Zhongyan Zhou, Yan Zhan, Feng Wang, Xinwei Guangxi Univ Sch Resources Environm & Mat Nanning 530004 Peoples R China Chinese Acad Sci Inst Semicond Optoelect Syst Lab Beijing 100083 Peoples R China Chinese Acad Sci Inst Tibetan Plateau Res Beijing 100101 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Mountainous regions are prone to dammed lake disasters due to their rough topography, scant vegetation, and high summer rainfall. By measuring water level variation, monitoring systems can detect dammed lake events when mudslides block rivers or boost water level. Therefore, an automatic monitoring alarm method based on a hybrid segmentation algorithm is proposed. The algorithm uses the k-means clustering algorithm to segment the picture scene in the RGB color space and the region growing algorithm on the image green channel to select the river target from the segmented scene. The pixel water level variation is used to trigger an alarm for the dammed lake event after the water level has been retrieved. In the Yarlung Tsangpo River basin of the Tibet Autonomous Region of China, the proposed automatic lake monitoring system was installed. We pick up data from April to November 2021, during which the river experienced low, high, and low water levels. Unlike conventional region growing algorithms, the algorithm does not rely on engineering knowledge to pick seed point parameters. Using our method, the accuracy rate is 89.29% and the miss rate is 11.76%, which is 29.12% higher and 17.65% lower than the traditional region growing algorithm, respectively. The monitoring results indicate that the proposed method is a highly adaptable and accurate unmanned dammed lake monitoring system.

关键词： dammed lake automatic monitoring alarm k-means clustering algorithm region growing algorithm water level recognition

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Mechanical response and damage mechanism of C/SiC composites impacted by high-velocity water jet

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JOURNAL OF THE EUROPEAN CERAMIC SOCIETY 2023年第8期43卷 3158-3171页

作者： Zhao, Renxi Hou, Naidan Wang, Xuan Yue, Yifan Wang, Bo Li, Yulong Zhang, Chengyu Northwestern Polytech Univ Sch Aeronaut Xian Peoples R China Joint Int Res Lab Impact Dynam & Engn Applicat Xian Peoples R China Shaanxi Key Lab Impact Dynam & Engn Applicat Xian Peoples R China Northwestern Polytech Univ Sch Civil Aviat Suzhou Peoples R China Northwestern Polytech Univ Sch Mat Sci & Engn Xian Peoples R China

Rain erosion is a potential hazard for supersonic vehicles, with severe damage to materials that may be impacted by raindrops. In this paper, a series of impact tests of 413-572 m/s are carried out on a 3 mm-thick 2D C/SiC composite specimen using a single impact waterjet apparatus. The typical morphology of C/SiC specimen is obtained by single jet impact test. Under the multi-drop impact, the stress wave interaction is enhanced, and the internal damage of the specimen is severe, showing a funnel-shaped damage. Moreover, the C/SiC specimen is penetrated after 5 drops of impact. Quasi-static tensile tests were employed to quantify the post-impact strength of the specimen, during which the digital image correlation (DIC) method was used to obtain the strain value, at the same time acoustic emission (AE) signal was detected and processed by the k-means to reveal the damage evolution.

关键词： Waterjet impact Ceramic matrix composites k-means clustering algorithm Residual tensile strength

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Ranked keyword Search Over Encrypted Cloud Data Through Machine Learning Method

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2023年第1期16卷 525-536页

作者： Miao, Yinbin Zheng, Wei Jia, Xiaohua Liu, Ximeng Choo, kim-kwang Raymond Deng, Robert H. Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China City Univ Hong Kong Hong Kong 999077 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong 999077 Peoples R China Fuzhou Univ Coll Math & Comp Sci Key Lab Informat Secur Network Syst Fuzhou 350108 Peoples R China Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX 78249 USA Singapore Management Univ Sch Informat Syst Singapore 178902 Singapore

Ranked keyword search over encrypted data has been extensively studied in cloud computing as it enables data users to find the most relevant results quickly. However, existing ranked multi-keyword search solutions cannot achieve efficient ciphertext search and dynamic updates with forward security simultaneously. To solve the above problems, we first present a basic Machine Learning-based Ranked keyword Search (ML-RkS) scheme in the static setting by using the k-means clustering algorithm and a balanced binary tree. ML-RkS reduces the search complexity without sacrificing the search accuracy, but is still vulnerable to forward security threats when applied in the dynamic setting. Then, we propose an Enhanced ML-RkS (called ML-RkS$<^>{+}$+) scheme by introducing a permutation matrix. ML-RkS$<^>{+}$+ prevents cloud servers from making search queries over newly added files via previous tokens, thereby achieving forward security. The security analysis proves that our schemes protect the privacy of indexes, query tokens and keywords. Empirical experiments using the real-world dataset demonstrate that our schemes are efficient and feasible in practical applications.

关键词： Indexes keyword search Cryptography Security Binary trees Complexity theory Servers Ranked keyword search k-means clustering algorithm balanced binary tree permutation matrix forward security

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SF6 Pointer Pressure Meter Reading Method Based on Fusion Attention Feature UNet

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IEEE ACCESS 2023年 11卷 107451-107462页

作者： Wu, Hao Qi, Ziyuan Tian, Haipeng Ni, Zhihao Chen, Weizhe Sichuan Univ Sci & Engn Automat & Informat Engn Zigong 643000 Sichuan Peoples R China Artificial Intelligence Key Lab Sichuan Prov Zigong 643000 Sichuan Peoples R China

Aiming at the problem of decreasing the accuracy of meter reading caused by manual reading, an SF6 pointer-type pressure meter reading method based on the fusion attention feature UNet is proposed to improve the accuracy of pointer-type pressure meter reading. Firstly, design the fusion attention feature UNet neural network to segment the pointer and dense scale of the SF6 pressure meter. The Ghost convolutional module used in the coding layer of the neural network can reasonably utilize redundant features to strengthen the inference ability of the network. Deep semantic information feature fusion module built to extract deep potential feature information. Also, introduce the pyramid split attention mechanism to strengthen the information interaction between the network coding and decoding layers. Then use the minimum outer rectangle algorithm and k-means clustering algorithm to determine the circle's center of the SF6 pressure meter for the segmented data. Finally, use the circle's center to fit the initial scale and the pointer in two straight lines. It calculates the angle between the two straight lines. The angle conversion formula obtains the accurate reading of the SF6 pressure meter. It is proved by experiment that the proposed intelligent reading algorithm will not be affected by environmental factors and can better divide the pointer and dense scale in the SF6 pointer pressure meter to complete the accurate reading of the meter.

关键词： Pressure meter UNet neural networks deep feature fusion attentional mechanisms k-means clustering algorithm

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Three-Dimensional Segmentation of Equine Paranasal Sinuses in Multidetector Computed Tomography Datasets: Preliminary Morphometric Assessment Assisted with clustering Analysis

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SENSORS 2024年第11期24卷 3538页

作者： Borowska, Marta Lipowicz, Pawel Daunoraviciene, kristina Turek, Bernard Jasinski, Tomasz Pauk, Jolanta Domino, Malgorzata Bialystok Tech Univ Inst Biomed Engn Fac Mech Engn PL-15351 Bialystok Poland Vilnius Gediminas Tech Univ Dept Biomech Engn LT-03224 Vilnius Lithuania Warsaw Univ Life Sci Inst Vet Med Dept Large Anim Dis & Clin PL-02787 Warsaw Poland

The paranasal sinuses, a bilaterally symmetrical system of eight air-filled cavities, represent one of the most complex parts of the equine body. This study aimed to extract morphometric measures from computed tomography (CT) images of the equine head and to implement a clustering analysis for the computer-aided identification of age-related variations. Heads of 18 cadaver horses, aged 2-25 years, were CT-imaged and segmented to extract their volume, surface area, and relative density from the frontal sinus (FS), dorsal conchal sinus (DCS), ventral conchal sinus (VCS), rostral maxillary sinus (RMS), caudal maxillary sinus (CMS), sphenoid sinus (SS), palatine sinus (PS), and middle conchal sinus (MCS). Data were grouped into young, middle-aged, and old horse groups and clustered using the k-means clustering algorithm. Morphometric measurements varied according to the sinus position and age of the horses but not the body side. The volume and surface area of the VCS, RMS, and CMS increased with the age of the horses. With accuracy values of 0.72 for RMS, 0.67 for CMS, and 0.31 for VCS, the possibility of the age-related clustering of CT-based 3D images of equine paranasal sinuses was confirmed for RMS and CMS but disproved for VCS.

关键词： horse 3D segmentation volume surface area k-means clustering algorithm

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