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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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A novel clustering-based method for reliability assessment of cyber-physical microgrids considering cyber interdependencies and information transmission errors

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APPLIED ENERGY 2022年 315卷

作者： Aslani, Mehrdad Faraji, Jamal Hashemi-Dezaki, Hamed Ketabi, Abbas Univ Kashan Dept Elect & Comp Engn 6 Km Ghotbravandi Blvd Kashan *** Iran Univ West Bohemia UWB Res & Innovat Ctr Elect Engn RICE Fac Elect Engn Plzen 30100 Czech Republic

In recent years, the evolution of microgrids (MGs) toward a complex interacted cyber-physical system is significant, which has received much attention. Cyber-physical MGs (CPMGs) are vulnerable to various cyberphysical interdependencies mainly. Therefore, the reliability evaluation of such systems is a crucial issue. Several research works have focused on the reliability assessment of CPMGs in the literature. However, there is a research gap in developing fast and accurate models to assess the reliability of CPMGs, simultaneously considering cyber and physical failures, cyber-power interdependencies, and information transmission errors. This article aims to fill such a knowledge gap by developing a new clustering-based method to evaluate the reliability of CPMGs. The scenarios for information transmission errors are generated by the Monte Carlo simulation (MCS), besides uncertain physical parameters, e.g., the output power of renewable distributed generations (DGs). Afterward, the clustering algorithms are used to reduce the number of scenarios. In this paper, the k-means clustering algorithm has been selected to cluster the scenarios. The introduced clustering-based method is applied to a CPMG, and test results are compared to available MCS-based studies. The inaccuracy of the proposed clustering-based method is less than 3%, while its execution time is about 22 times faster than MCS-based ones. The comparative test results under various scenarios like available methods, neglecting the cyber failures and information errors illustrate the advantages of this research. The comparative test results illustrate the accuracy of the reliability calculations, while the execution time is desired.

关键词： Cyber-physical systems Microgrids Reliability assessment clustering algorithm Monte Carlo simulation Information transmission errors

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Frontiers of Chinese entrepreneurship education research based on data visualization analysis 6

Frontiers of Chinese entrepreneurship education research bas...

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6th IEEE Advanced Information Technology, Electronic and Automation Control Conference (IEEE IAEAC)

作者： Shen, Yanhua Zhang, Yongzhong Gu, Zhenyu Dai, Yonghui Shanghai Xingjian Coll Shanghai Peoples R China Shanghai Open Univ Inst Sci & Technol Shanghai Peoples R China Shanghai Univ Int Business & Econ Sch Stat & Informat Shanghai Peoples R China Shanghai Univ Int Business & Econ Sch Management Shanghai Peoples R China

ISBN: (数字)9781665458641

ISBN: (纸本)9781665458641

In order to visualize the current frontiers of entrepreneurship education research in China, we selected 1196 relevant research literatures in the last 20 years from CNKI database, and used CiteSpace tool and clustering algorithm to analyze the authors and research institutions. It includes visual analysis of cooperation network map of author cooperation, innovation and entrepreneurship research institution, keyword co-occurrence map and keyword cluster map. In addition, we also discussed and analyzed the three stages of China's entrepreneurship education research. The results show that the current frontiers of entrepreneurship education research in China is integration and new engineering teaching, which helps to intuitively understand the frontiers of entrepreneurship education research in China.

关键词： Entrepreneurship education data visualization map analysis clustering algorithm

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Bi-level economic dispatch strategy for virtual power plants based on electric vehicles aggregation

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期225卷

作者： Guo, Jinrui Dou, Chunxia Zhang, Zhijun Yue, Dong Nanjing Univ Posts & Telecommun Inst Adv Technol Carbon Neutral Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Automat & Artificial Intelligence Nanjing 210023 Jiangsu Peoples R China

The rapid growth of electric vehicles (EVs) imposes great challenges to the flexible management and economic dispatch of virtual power plants. To overcome these obstacles, this paper proposes a bi-level economic dispatch strategy based on EVs aggregation. First, an aggregation method based on improved artificial bee colony Kmeans clustering algorithm is presented to aggregate the EVs with different dynamic characteristics in the upper level. Aiming at the issue that multi-parameter weights and aggregation effects are difficult to determine in the clustering process, information entropy and Silhouette metric are proposed to enhance the accuracy of clustering. Furthermore, pre-dispatch is conducted based on aggregation information and electricity market prices. Next, in the lower level, the charging demands information is utilized to formulate specific dispatch strategies for EVs users, and the pre-dispatch deviation in the upper level is modified. Finally, the simulation results show that the proposed aggregation method improves the clustering effect by 22.4% and guarantees the quality of aggregation and the flexibility of system management. Besides, the proposed strategy can reduce the total system operation cost and the computation time by 5.83% and 55.95% respectively and ensure the economy of the system operation and the computational efficiency of the dispatch.

关键词： Virtual power plants Electric vehicles aggregation Bi-level dispatch clustering algorithm Information entropy Silhouette metric

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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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A Method for Synthesizing Ultra-Large-Scale Clock Trees

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algorithmS 2025年第5期18卷

作者： Li, Ziheng Chen, Benyuan Wang, Wanting Lv, Hui Lv, Qinghua Chen, Jie Wang, Yan Li, Juan Zhang, Cheng Hubei Univ Technol Natl Res Ctr Microelect & Integrated Circuits 111 Sch Sci Wuhan 430068 Peoples R China Hubei Univ Educ Sch Phys & Electromech Engn Wuhan 430205 Peoples R China

As integrated circuit technology continues to advance, clock tree synthesis has become increasingly significant in the design of ultra-large-scale integrated circuits. Traditional clock tree synthesis methods often face challenges such as insufficient computational resources and buffer fan-out limitations when dealing with ultra-large-scale clock trees. To address this issue, this paper proposes an improved clock tree synthesis algorithm called incomplete balanced KSR (IB-KSR). Building upon the KSR algorithm, this proposed algorithm efficiently reduces the consumption of computational resources and constrains the fan-out of each buffer by incorporating incomplete minimum spanning tree (IMST) technology and a clustering strategy grounded in Balanced Split. In experiments, the IB-KSR algorithm was compared with the GSR algorithm. The results indicated that IB-KSR reduced the global skew of the clock tree by 43.4% and decreased the average latency by 34.3%. Furthermore, during program execution, IB-KSR maintained low computational resource consumption.

关键词： clock tree synthesis clustering algorithm k-nearest VLSI

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A Data Mining Approach Based on Grey Model and clustering algorithm

A Data Mining Approach Based on Grey Model and Clustering Al...

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2014年国际计算机科学与软件工程学术会议

作者： Zi-Shen LI Institute of VLSI Design School of Electrical EngineeringZhejiang UniversityHangzhouChina

This paper presents a new data mining approach based on grey model and clustering *** 2010 Shanghai World Expo was a great event,which has greatly boost China's development in many *** is necessary to dig useful information about its effects.A mathematical model is created in this paper,quantitatively estimating the economy revenue of the World Expo and assessing its influence on other province in China.

关键词： Data mining Grey model clustering algorithm Mathematical model

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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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Advanced Machine Learning on Cognitive Computing for Human Behavior Analysis

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2021年第5期8卷 1194-1202页

作者： Lv, Zhihan Qiao, Liang Singh, Amit Kumar Qingdao Univ Sch Data Sci & Software Engn Qingdao 266071 Peoples R China Natl Inst Technol Patna Comp Sci & Engn Dept Patna 800005 Bihar India

With the increasing size of data, massive amounts of data are being generated continuously. It is hoped to find a cognitive computing technology that can effectively learn and process large-scale data. The decision tree algorithm is optimized from the perspective of machine learning. A cognitive computing model based on context-aware data flow is constructed. Classification and regression tree (CART) algorithm is used in the data computing layer of the cognitive model. In addition, the clustering effectiveness index based on frequent patterns optimizes the K-means clustering method. The performance of the algorithm is analyzed through simulation experiments. The results show that the CART algorithm requires fewer training data sets while guaranteeing classification accuracy. Also, the algorithm has obvious advantages under large-scale data. In the application of actual data set, on Over, Over+Noise, and Bridge, only the clustering validity index based on frequent pattern (FPCVI) index proposed finds the correct number of clusters. The application of DPCK-K-means clustering algorithm can ensure the accuracy and stability of behavior classification, which is greatly significant for operators to analyze user behavior and develop personalized services.

关键词： clustering algorithms Machine learning Cognitive systems Machine learning algorithms Data mining Computational modeling Data models Behavior analysis clustering algorithm cognitive computing classification and regression tree~(CART) classification tree algorithm machine learning

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A non-uniform quantization scheme for visualization of CT images

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第4期18卷 4311-4326页

作者： Mehmood, Anam Khan, Ishtiaq Rasool Dawood, Hassan Dawood, Hussain Univ Jeddah Coll Comp Sci & Engn Dept Comp Sci & Artificial Intelligence Jeddah Saudi Arabia Univ Engn & Technol Dept Software Engn Taxila Pakistan Univ Jeddah Coll Comp Sci & Engn Dept Comp & Network Engn Jeddah Saudi Arabia

Medical science heavily depends on image acquisition and post-processing for accurate diagnosis and treatment planning. The introduction of noise degrades the visual quality of the medical images during the capturing process, which may result in false perception. Therefore, medical image enhancement is an essential topic of research for the improvement of image quality. In this paper, a clustering-based contrast enhancement technique is presented for computed tomography (CT) images. Our approach uses the recursive splitting of data into clusters targeting the maximum error reduction in each cluster. This leads to grouping similar pixels in every cluster, maximizing inter-cluster and minimizing intra-cluster similarities. A suitable number of clusters can be chosen to represent high precision data with the desired bit-depth. We use 256 clusters to convert 16-bit CT scans to 8-bit images suitable for visualization on standard low dynamic range displays. We compare our method with several existing contrast enhancement algorithms and show that the proposed technique provides better results in terms of execution efficiency and quality of enhanced images.

关键词： clustering algorithm computed tomography high dynamic range tone-mapping medical image enhancement

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