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IEEE International Conference on Systems, Man, and Cybernetics

作者： Chun-Wei Tsai Tsung-Hsien Lin Ming-Chao Chiang Department of Computer Science and Information Engineering National Ilan University Department of Computer Science and Engineering National Sun Yat-sen University

ISBN: (纸本)9781479938414

clustering has always been playing a vital role in many different disciplines because it is an important tool for analyzing a set of unknown input patterns. However, some important issues related to clustering, such as automatically determining the number of clusters and partitioning non-linearly separable data, are never fully solved even though many researchers work on this subject for a long time. As such, a novel method based on the so-called elastic net clustering algorithm is presented in this paper to deal with exactly the two issues: partitioning non-linearly separable data and automatically determining the number of clusters. To evaluate the performance of the proposed algorithm, several well-known datasets are used. The experimental results show that not only can the proposed algorithm find the appropriate number of clusters, but it can also provide a higher accuracy rate than all the other methods compared in this study for most datasets.

关键词： Elastic net algorithm clustering algorithm Cluster validity index

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Ultrasonic signal enhancement for coarse grain materials by machine learning analysis

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ULTRASONICS 2021年 117卷 106550页

作者： Xu, Wanli Li, Xiaohong Zhang, Jun Xue, Zhixiang Cao, Jiancheng Wuhan Univ Sch Power & Mech Engn Key Lab Hydraul Machinery Transients Minist Educ Wuhan 430072 Hubei Peoples R China Southeast Univ Sch Mat Sci & Engn Nanjing 211189 Jiangsu Peoples R China

This paper aims at dealing with the dilemma of examining the existence of a defect in ultrasonic detection of coarse grain materials. In such cases, defect echoes can be drowned in a strong noise background resulting from intricate coarse grain scattering, that is, grain noise. To this end, we develop an innovative signal reconstruction methodology from polluted measurements which combines basic statistical analysis with a series of machine learning algorithms. The proposed methodology analyzes abundant information from numerous raw signals to distinguish the desired signal from grain noise, avoiding the limitation of information provided only by a single signal. The technique is achieved by collecting similar signals together through a clustering algorithm and subsequently inputting these similar signals to a denoising autoencoder to suppress the grain noise. It is successfully employed to ultrasonic signals obtained from an as-cast stainless steel specimen with coarse equiaxed grains, a stainless steel specimen with relatively homogeneous dendrite fabricated by additive manufacturing and a stainless steel weld with heterogeneous columnar grains having variation of grain sizes in various locations. The influence of material microstructure and probe frequency on denoising performance is investigated in detail. Based on this, the proposed methodology is applied to defect detection. Desired A-scan results and B-scan imaging are achieved by the proposed method, where defects are well revealed. The experimental results demonstrate the developed methodology has stable excellent performance and superior denoising capabilities for defect detection with respect to conventional techniques, especially in the case where the noise is almost the same as the desired signal.

关键词： Grain noise Ultrasonic testing Signal reconstruction Machine learning clustering algorithm

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Battlefield Target Grouping by a Hybridization of an Improved Whale Optimization algorithm and Affinity Propagation

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IEEE ACCESS 2021年 9卷 46448-46461页

作者： Duan, Yuxian Liu, Changyun Li, Song Air Force Engn Univ Air & Missile Def Coll Xian 710051 Peoples R China Air Force Engn Univ Grad Coll Xian 710051 Peoples R China

Target grouping, which is essentially a data clustering problem, is a research hotspot in the field of battlefield situation assessment. To address an unknown battlefield environment, affinity propagation based on an improved whale optimization algorithm (APBWOA) is proposed from the perspective of clustering. First, we propose a whale optimization algorithm based on a chaotic map and nonlinear inertia weight improvement called CPIW-WOA, which uses an improved circle map to generate initial populations and introduces nonlinear inertia weights to improve its convergence efficiency. The test results on nine benchmark functions show that the CPIW-WOA algorithm has superior performance to existing methods. Second, based on the fact that it fully considers the weights of the attributes in a given sample, the weighted Mahalanobis distance is adopted to replace the Euclidean distance. In addition, the silhouette index is introduced to determine the optimal number of clusters. By iteratively updating through CPIW-WOA to search for the optimal settings, the limitation of manually entering specified parameters can be overcome. Test results on real-world datasets show that the new method is more accurate and effective than other methods;therefore, it can provide effective solutions with respect to battlefield target grouping.

关键词： clustering algorithms Optimization algorithms Statistics Sociology Convergence Whales clustering algorithm affinity propagation whale optimization algorithm target grouping situation assessment

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Evaluation for block-scale solar energy potential of industrial block and optimization of application strategies: A case study of Wuhan, China

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SUSTAINABLE CITIES AND SOCIETY 2021年 72卷

作者： Xu, Shen Jiang, Haihua Xiong, Feng Zhang, Chen Xie, Mengju Li, Zhixin Sch Architecture & Urban Planning Hust Peoples R China Hubei New Urbanizat Engn & Technol Res Ctr Wuhan Peoples R China

Industrial blocks are an important part of the city which is very suitable for PV application because of its largescale, unified property rights and simple form. In this study, a multi-dimensional evaluation method of solar energy potential suitable for the block-scale is constructed. Then, 48 cases of industrial blocks in a typical city of china were selected as examples. Basing on morphological parameters, all cases were classified into 7 types by clustering algorithm. Afterwards, the total amount and distribution of the solar energy potential were calculated. Finally, the strategies of the photovoltaic system application in blocks were proposed according to types of blocks. It is found that there is significant difference in solar energy potential between different types of industrial blocks. Among them, solar energy potential of single-storey types is the highest up to 90.85 kW h/ m2 center dot year, while solar energy potential of high-rise type blocks is relatively lower which is only 55.64 kW h/ m2 center dot year. In addition, block shape has obvious impact on solar energy potential, among which building density having the greatest influence, as building density increases from 20 % to 55 %, the solar energy potential also increased by 138.74 % from 42.54 kW h/m2 center dot year to 101.56 kW h/m2 center dot year.

关键词： Block-scale solar energy potential Evaluation method Industrial block clustering algorithm Morphological parameter Optimization application strategies

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Exploring miRNA Sponge Networks of Breast Cancer by Combining miRNA-disease-lncRNA and miRNA-target Networks

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CURRENT BIOINFORMATICS 2021年第3期16卷 385-394页

作者： Tian, Lei Wang, Shu-Lin Hunan Univ Sch Informat Sci & Engn Changsha Peoples R China

Background: Recently, ample researches show that microRNAs (miRNAs) not only interact with coding genes but interact with a pool of different RNAs. Those RNAs are called miRNA sponges, including long non-coding RNAs (lncRNAs), circular RNA, pseudogenes and various messenger RNAs. Understanding regulatory networks of miRNA sponges can better help researchers to study the mechanisms of breast cancers. Objective: We develop a new method to explore miRNA sponge networks of breast cancer by combining miRNA-disease-lncRNA and miRNA-target networks (MSNMDL). Methods: Firstly, MSNMDL infers miRNA-lncRNA functional similarity networks from miRNAdisease-lncRNA networks. Secondly, MSNMDL forms lncRNA-target networks by using lncRNA to replace the role of matched miRNA in miRNA-target networks according to the lncRNA-miRNA pair of miRNA-lncRNA functional similarity networks. And MSNMDL only retains the genes of breast cancer in lncRNA-target networks to construct candidate miRNA sponge networks. Thirdly, MSNMDL merges these candidate miRNA sponge networks with other miRNA sponge interactions and then selects top-hub lncRNA and its interactions to construct miRNA sponge networks. Result: MSNMDL is superior to other methods in terms of biological significance and its identified modules might act as module signatures for prognostication of breast cancer. Conclusion: MiRNA sponge networks identified by MSNMDL are biologically significant and are closely associated with breast cancer, which makes MSNMDL a promising way for researchers to study the pathogenesis of breast cancer.

关键词： miRNA sponge networks miRNA sponge modules breast cancer biological enrichment clustering algorithm

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Categorizing Car-Following Behaviors: Wavelet-Based Time Series clustering Approach

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JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 2020年第8期146卷

作者： Zheng, Yuan He, Shuyan Yi, Ran Ding, Fan Ran, Bin Wang, Ping Lin, Yangxin Southeast Univ Sch Transportat 2 Southeast Univ Rd Nanjing 211189 Peoples R China Univ Wisconsin Dept Civil & Environm Engn 1208 Engn Hall 1415 Engn Dr Madison WI 53706 USA Southeast Univ Joint Res Inst Internet Mobil Dept Joint Res Inst Internet Mobil 2 Southeast Univ Rd Nanjing 211189 Jiangsu Peoples R China Univ Wisconsin Madison WI USA Peking Univ Natl Engn Res Ctr Software Engn Beijing 10087 Peoples R China Peking Univ Sch Software & Microelect 5 Yiheyuan Rd Beijing 10087 Peoples R China

The categorization analysis of car-following behaviors is beneficial to enrich the current car-following models and the applications of connected and automated vehicles (CAVs) in a mixed traffic environment. Previous studies categorized the car-following behaviors during the traffic oscillations using artificially designed behavior patterns, but they are not quietly flexible and are limited to distinguish the complicated car-following behaviors. To address such a problem, the study proposes a wavelet-based time series clustering approach to automatically categorize the car-following behaviors. First, the response time series of the car-following behaviors are extracted using general Newell's car-following model. Second, the discrete wavelet transformation algorithm is employed to extract the following behavior features from the original time series. Finally, the hierarchical clustering algorithm is used to categorize the car-following behaviors according to the calculated similarity between the transformed time series. Numerical tests on Next Generation Simulation (NGSIM) show that the proposed algorithm can effectively and automatically categorize the typical car-following behavior patterns summarized in the previous studies. The proposed algorithm is also flexibly implemented to discover the potential car-following behavior patterns. Findings suggest that a wavelet-based time series clustering by combing the Haar wavelet transformation algorithm and hierarchical clustering algorithm is a superior approach to automatically categorize car-following behaviors with a time series trajectory.

关键词： Car-following Behavior features Discrete wavelet transformation clustering algorithm Time series

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Design of intelligent acquisition system for moving object trajectory data under cloud computing

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JOURNAL OF INTELLIGENT SYSTEMS 2021年第1期30卷 763-773页

作者： Zhang, Yang Asthana, Abhinav Asthana, Sudeep Khanna, Shaweta Mihai, Ioan-Cosmin JSS Acad Tech Educ Noida India Liaoning Equipment Mfg Vocat & Tech Coll Shenyang 110161 Peoples R China Liaoning Radio & TV Univ Shenyang 110034 Peoples R China HSBC Technol India Dept OSS Core Banking Pune Maharashtra India Natl Inst Technol Elect & Instrumentat Engn Dept Silchar Assam India Lovely Profess Univ Sch Chem Engn & Phys Sci Phagwara India Al I Cuza Police Acad Bucharest Romania

In order to study the intelligent collection system of moving object trajectory data under cloud computing, information useful to passengers and taxi drivers is collected from massive trajectory data. This paper uses cloud computing technology, through clustering algorithm and density-based DBSCAN algorithm combined with Map Reduce programming model and design trajectory clustering algorithm. The results show that based on the 8-day data of 15,000 taxis in Shenzhen, the characteristic time period is determined. The passenger hot spot area is obtained by clustering the passenger load points in each time period, which verifies the feasibility of the passenger load point recommendation application based on trajectory clustering. Therefore, in the absence of holidays, the number of passenger hotspots tends to be stable. It is reliable to perform cluster analysis. The recommended application has been demonstrated through experiments, and the implementation results show the rationality of the recommended application design and the feasibility of practice.

关键词： cloud computing clustering algorithm taxi data collection DBSCAN algorithm

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SNR Enhancement of Far-End Disturbances on Distributed Sensor Based on Phase-Sensitive Optical Time-Domain Reflectometry

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IEEE SENSORS JOURNAL 2021年第2期21卷 1957-1964页

作者： Li, Dandan Lou, Shuqin Xin, Qi Liang, Sheng Sheng, Xinzhi Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Sci Beijing 100044 Peoples R China

A denoising method based on singular value decomposition (SVD) with particle swarm optimization (PSO) is proposed to improve the signal-to-noise ratio (SNR) of far-end disturbances in distributed sensor system based on phase-sensitive optical time-domain reflectometry (Phi-OTDR). Also, an improved clustering algorithm is introduced to locate the position of disturbance. The effective sensing distance of the Phi-OTDR system is 25.05 km and four kinds of disturbance events, including watering, knocking, climbing and pressing, are applied on the sensing fiber respectively. A series of experiments of single-point far-end disturbances and five-point disturbances are carried out. Experimental results demonstrate that the SNR of far-end disturbance can be effectively improved to over 12dB, the processing time is less than 3 seconds, and the average location accuracy rate is more than 96%. Compared with the commonly used denoising methods, such as empirical mode decomposition (EMD), wavelet-1D and wavelet-2D, the SVD denoising with PSO method has better performance in SNR enhancement and real-time, which is beneficial for accurate positioning.

关键词： Signal-to-noise ratio (SNR) far-end disturbances denoising location accuracy rate phase-sensitive optical time-domain reflectometry (Phi-OTDR) singular value decomposition (SVD) particle swarm optimization (PSO) clustering algorithm

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An Optimized Cluster Structure Routing Method Based on LEACH in Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2021年第4期121卷 2719-2733页

作者： Ding, Xu-Xing Liu, Ya-Ni Yang, Liang-Yong Anhui Normal Univ Sch Phys & Elect Informat Wuhu 241000 Peoples R China Anhui Huamin Avion Syst Co Ltd Wuhu 241002 Peoples R China

In order to improve the accuracy and speed of traditional K-Nearest-Neighbor (KNN) algorithms and solve the problem of determining the most appropriate initial center and number of clusters of K-Means algorithm, an improved clustering dynamic threshold location algorithm (ICD-KNN) is proposed in this paper. The algorithm consists of two stages: clustering stage and position estimation stage. The clustering stage employs K-Means clustering algorithm based on Canopy. The Canopy is used as the preprocessing procedure to improve efficiency and clustering accuracy of K-Means algorithm. In the position estimation stage, threshold is set dynamically according to the dispersion of reference points to filter out singular reference points and improve positioning accuracy compared with the previous threshold algorithms. We compare proposed algorithm with several existing algorithms. The simulation results show that the positioning accuracy of ICD-KNN is improved by 10%, 38% and 39% respectively compared with dynamic threshold algorithms (DH-KNN), K-Means algorithms and KNN algorithms.

关键词： Indoor positioning Position fingerprint Dynamic threshold clustering algorithm K-nearest neighbor

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Some Information Measures Based on Centroid, Orthocenter, Circumcenter and Incenter Points of Transformed Triangular Fuzzy Numbers and their Applications

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COGNITIVE COMPUTATION 2021年第4期13卷 946-971页

作者： Garg, Harish Rani, Dimple Thapar Inst Engn & Technol Deemed Univ Sch Math Patiala 147004 Punjab India

Cognitive computing has deep extents, which embrace different features of cognition. In the decision-making process, multi-criteria decision making is credited as a cognitive-based human action. However, to treat and unite the information from several resources, the most vital stage is data collection. Intuitionistic fuzzy set (IFS) is one of the most robust and trustworthy tools to accomplish the imprecise information with the help of the membership degrees. In addition to this, an information measure plays an essential role in treating uncertain information to reach the final decision based on the degree of the separation between the pairs of the numbers. Motivated by these, this paper aims to present the novel information measures using four different centers namely centroid, orthocenter, circumcenter and incenter under the IFS environment to address the cognitive-based human decision-making problems. The present work is divided into three folds. The first fold is to propose a technique of transforming intuitionistic fuzzy values into general triangular fuzzy numbers (TFNs). The right-angled and isosceles TFNs are special cases of the proposed transformation technique. The second fold is to develop distance and similarity measures using four different centers namely centroid, orthocenter, circumcenter and incenter of transformed TFNs. The basic axioms of the proposed measures are investigated in detail. The third fold is to justify superiority and validity of the proposed measures. The effectiveness of the developed measures is examined by applying it in clustering as well as the pattern recognition problems, and their results are correlated with some prevailing studies. Additionally, a clustering technique is discussed based on the stated measures to classify the objects. A detailed comparative analysis is done with some of the existing measures and concludes that several existing measures fail to discriminate the results under the different instances such as di

关键词： Intuitionistic fuzzy set Information measures Centroid Orthocenter clustering algorithm Triangular fuzzy number

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