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Nonlinear seismic site response classification using k-means clustering algorithm: Case study of the September 6, 2018 Mw6.6 Hokkaido Iburi-Tobu earthquake, Japan

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SOIL DYNAMICS AND EARTHQUAkE ENGINEERING 2020年 128卷 105907-000页

作者： Ji, kun Wen, Ruizhi Ren, Yefei Dhakal, Yadab P. China Earthquake Adm Key Lab Earthquake Engn & Engn Vibrat Inst Engn Mech 29 Xuefu Rd Harbin 150080 Heilongjiang Peoples R China Natl Res Inst Earth Sci & Disaster Resilience 3-1 Tennodai Tsukuba Ibaraki 3050006 Japan

Spectral ratio methods have been widely used in evaluation of nonlinear seismic site response. Nevertheless, it remains inefficient and subjective to identify stations with nonlinear site response according to empirical threshold values of spectral ratio nonlinear degree indicators. This study, which was the first to apply the machine learning clustering algorithm to address this problem, used the September 6, 2018 M(w)6.6 Hokkaido Iburi-Tobu earthquake (Japan) as an example. First, we calculated the surface/borehole and horizontal/vertical spectral ratios using strong ground motion data recorded by kik-net vertical array and k-NET stations, respectively. The degree of nonlinear site response (DNL) and percentage of nonlinear site response (PNL) were computed using the difference between the strong motion of the mainshock and weak aftershocks as the reference for linear site response. Then, the k-means clustering algorithm was incorporated in the identification of nonlinear site response using the DNL, PNL, strength of ground motion (PGA) and site condition (V-S20 or V-S30) as explanatory variables. After careful multicollinearity diagnosis and confirmation of the optimum clustering number, we successfully classified the stations into two clusters with nonlinear and linear site responses. Overall, the clustering results were found in good agreement with the classification results based on empirical thresholds of several nonlinear indicators. For the stations identified with nonlinear site response, the reduction of amplification and frequency shift could be observed from the spectral ratio curves regarding the ground motions in the mainshock and the reference weak aftershocks, demonstrating typical nonlinearity response characteristics. Furthermore, a comprehensive indicator of nonlinear site response occurrence probability (NLscore) was obtained from a linear weighted combination of the normalized variables (PGA, V-S30/V-S20, DNL and PNL). The NLscore ranking of th

关键词： Nonlinear site response Spectral ratio method k-means clustering algorithm 2018 Hokkaido Iburi-Tobu earthquake

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Research on Parallel Adaptive Canopy-k-means clustering algorithm for Big Data Mining Based on Cloud Platform

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JOURNAL OF GRID COMPUTING 2020年第2期18卷 263-273页

作者： Xia, Dongliang Ning, Feifei He, Weina Pingdingshan Univ Sch Software Pingdingshan 467000 Henan Peoples R China

Firstly, this paper introduces the types of clustering algorithm, and introduces the classical k-means algorithm and canopy algorithm in detail. Then, combining the map reduce computing model and spark cloud computing framework, this paper introduces the parallel Canopy-k-means algorithm after using Canopy algorithm to optimize the initial value of k-means algorithm. However, because Canopy algorithm needs to introduce a new distance threshold parameter T2, and the parameter needs to be set by human experience, it is difficult to determine the parameter artificially for large data, so this paper proposes a parallel adaptive Canopy-k-means algorithm, which can be used in cloud computing framework to determine the distance threshold parameter T2 adaptively based on statistical method. Using the parallelism of Map-Reduce computing model, the parallel Canopy-k-means algorithm is optimized by adaptive parameter estimation, which solves the problem that parameters depend on manual experience selection in Canopy process. After introducing the relevant theories and derivation process of this algorithm, cloud computing experiment platform is built based on the Spark framework, and the contrast experiments were performed using the Stanford Large Network Dataset Collection (SNAP) dataset and self-built Dimension Networks dataset. The experimental results show that the proposed method is effective.

关键词： Big data mining Parallel framework Cloud platform k-means clustering algorithm Canopy algorithm Spark framework

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Zoning of reservoir water temperature field based on k-means clustering algorithm

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JOURNAL OF HYDROLOGY-REGIONAL STUDIES 2022年 44卷

作者： Liu, Wei Zou, Peng Jiang, Dingguo Quan, Xiufeng Dai, Huichao China Three Gorges Univ Coll Hydraul & Environm Engn Yichang 443002 Peoples R China China Three Gorges Corp Beijing 100038 Peoples R China Hohai Univ Key Lab Coastal Disaster & Def Minist Educ Nanjing 210098 Peoples R China

Study region: Dongqing Reservoir located in Guizhou, China. Study focus: Zoning the RWTF (reservoir water temperature field) is of great significance and is an effective way to analyze RWTF's nature. In practice, the conditions of RWTF fluctuate greatly as time goes on, which leads to the existing RWTF zoning methods can't give a steady zoning result. In consequence, this paper creates a kind of zoning method to study the properties of RWTF in Dongqing Reservoir, which has two main steps: firstly, numerical simulation is used to obtain the whole data of RWTF, and then the k-means clustering algorithm is executed based on the numerical simulation results. New hydrological insights for the region: This paper proved that the zoning method developed in this paper, which combined numerical simulation and unsupervised machine learning, can be effectively applied and divided the RWTF into four zones without using experimental parameters in Dongqing Reservoir. Moreover, on the base of the four zones' spatial borders, the influencing factors of water temperature in each zone of Dongqing Reservoir were able to be found, which would be of value in further research of RWTF.

关键词： Reservoir water temperature field Unsupervised machine learning k-means clustering algorithm Numerical simulation Zoning method

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Decision Support System with k-means clustering algorithm for Detecting the Optimal Store Location Based on Social Network Events

Decision Support System with K-Means Clustering Algorithm fo...

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IEEE European Technology and Engineering Management Summit (E-TEMS)

作者： Hamada, Mohamed Ahmed Naizabayeva, Lyazat IITU Int IT Univ Informat Syst Dept Alma Ata Kazakhstan

ISBN: (纸本)9781728109039

Nowadays, the business market is more complicated and comprises many challenges;it became more competitive and surrounded by high-risk patterns. Seeking for new technologies and adopting innovation is becoming an important and crucial issue to eliminate the complexity of the decision-making process and failure probability. Decision support system (DSS) is a computerized system that encompasses mathematical and analytical models, knowledge base and a user interface to help managers for making better decisions. This research aims to develop a decision support system based on k-means clustering algorithm to detect the optimal store location through social network events. Also, this research explains how to extract data from one social network channel "Instagram" using the "Octoparse API" as a web data extraction tool. k-means algorithm identifies k-number of centroids, and allocates every data point to the nearest cluster. As a result, we analyzed 12754 posts started on the 1st of January 2019. Cleaned data are transformed using Minimax and k-means algorithms. As an output, we have got json format data file with centres which are placed on the map to provide a better understanding. The Result of this research is a visualized map pointed with places to define the optimal location of a specific store at the selected region. The practical value of this DSS tool is to help users to make a more valuable and accurate decision which lead to a decrease in the probability of ineffective business decision and minimize the business losses.

关键词： classification model decision support system machine learning optimal store location social network events k-means clustering algorithm

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Research on Load clustering Based on Singular Value Decomposition and k-means clustering algorithm

Research on Load Clustering Based on Singular Value Decompos...

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2020 Asia Energy and Electrical Engineering Symposium

作者： Wang, Jiao Wang, kaiyan Jia, Rong Chen, Xiaofei Xian Univ Technol Sch Elect Engn Xian Peoples R China Shaanxi Reg Elect Power Co LTD Xianyang Elect Power Supply Xianyang Peoples R China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167824

Considering the shortcomings of existing clustering algorithms in clustering quality, this paper proposes a load clustering research based on singular value decomposition and k-means clustering algorithm. First, eight characteristic indexes of load are extracted, and the singular value is used to decompose the load characteristics of the user side. The solved singular value reflects the importance of this type of load characteristic. The load characteristics corresponding to the data with large singular value are taken as the main load characteristics to complete the dimensionality reduction of the data. Then, the clustering evaluation index SSE is used to compare the effects of direct clustering of load characteristics and clustering after singular value decomposition. The results show that the proposed method has better clustering effect on load characteristics. Finally, k-means clustering algorithm is used to cluster the load characteristics.

关键词： singular value decomposition k-means clustering algorithm load characteristic Dimensionality reduction of data cluster evaluation index

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Integrated analysis of CFD simulation data with k-means clustering algorithm for soot formation under varied combustion conditions

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APPLIED THERMAL ENGINEERING 2019年 153卷 299-305页

作者： Yu, Wenbin Zhao, Feiyang Yang, Wenming Xu, Hongpeng Natl Univ Singapore Dept Mech Engn 9 Engn Dr 1 Singapore 117575 Singapore

Computational fluid dynamics (CFD) modelling is a scientific tool to provide fluid dynamics and chemical simulation that facilitates understanding of the complex combustion phenomenon in engine studies. With the advance of Machine Learning (ML) technology, the big data from CFD results can be intelligently recognized and classified, thus ease the data post-processing. This study proposed an integrated analysis that uses CFD simulation results of scalar distributions and k-means clustering algorithm to optimally partition engine combustion chamber into different zones. Therefore, the space of combustion chamber was automatically divided into light soot zones and heavy soot zones based on the clustering results on local equivalence ratio (ER) and temperature. Consequently, the surveys of soot mitigation by Reactivity Controlled Compression Ignition (RCCI) engines combustion mode were carried out as well as corresponding sooting tendency by CFD numerical study. The localized soot depositions in each zone under varied combustion boundaries were compared, hence improving the development of control strategy with numerical modellings and machine learning techniques.

关键词： CFD modelling k-means clustering algorithm RCCI engine combustion Soot formation

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Application of k-means clustering algorithm in human resource data informatization 2020

Application of K-means clustering algorithm in human resourc...

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Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Yang Zhao Business management and human resources Wuhan Business University Wuhan China

ISBN: (纸本)9781450387828

Information based human resource management is a complete solution to enterprise human resource management through information technology. It is a new human resource management mode based on advanced software and high-speed and large-capacity hardware. It can reduce costs, improve efficiency and improve employees through centralized information base, automatic processing of information, self-service, outsourcing and service sharing. The purpose of the service model. It is connected with the existing network technology of enterprises and institutions to ensure the synchronous development of the technological environment with the rapid development of human resources. Human resource management is to optimize the allocation of human resource management in a planned way according to the development needs of the organization. Through the recruitment, training, assessment, incentive and other aspects of the staff, the enthusiasm of the staff can be brought into play, so as to create maximum benefits for the organization. Human resources are the most important resources in today's society. How to optimize the allocation and efficient use of human resources management departments should fully consider the problem. In today's data and information technology developed today, enterprises and institutions should use the Internet, big data and other means to scientifically transform, optimize and innovate the human resources management of institutions, and expand more new paths.

关键词： informatization data human resources k-means clustering algorithm

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Improving performance of classification on severity of ill effects (SEV) index on fish using k-means clustering algorithm with various distance metrics

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WATER PRACTICE AND TECHNOLOGY 2019年第1期14卷 101-117页

作者： Hamid, khakzad RusPers Grp Co Dept Engn Moscow 109263 Russia

The severity of ill effects (SEV) index is based on the limited meta-analysis of previous peer reviewed reports and consultations, and described as a function of duration of exposure to turbid conditions in fisheries or fish life stages by fish adapted to life in clear water ecosystems. In this study, the performance of classification by SEV index was investigated using the k-means clustering algorithm. This study is based on 303 tests undertaken on aquatic ecosystem quality over a wide range of sediment concentrations (1-50,000 mg SS/L) and durations of exposure (1-35,000 h). Training and testing data includes concentration of suspended sediment, duration of exposure, species and life stages as the input variables and the SEV index for fish as the output variable. Results indicate that the k-means clustering algorithm, as an efficient novel approach with an acceptable range of error, can be used successfully for improving the performance of classification by SEV index.

关键词： k-means clustering algorithm scale of the severity (SEV) sediment management turbidity currents

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Dk-means: a deterministic k-means clustering algorithm for gene expression analysis

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PATTERN ANALYSIS AND APPLICATIONS 2019年第2期22卷 649-667页

作者： Jothi, R. Mohanty, Sraban kumar Ojha, Aparajita Pandit Deendayal Petr Univ Sch Technol Dept Comp Engn Gandhinagar India Indian Inst Informat Technol Design & Mfg Jabalpu Jabalpur Madhya Pradesh India

clustering has been widely applied in interpreting the underlying patterns in microarray gene expression profiles, and many clustering algorithms have been devised for the same. k-means is one of the popular algorithms for gene data clustering due to its simplicity and computational efficiency. But, k-means algorithm is highly sensitive to the choice of initial cluster centers. Thus, the algorithm easily gets trapped with local optimum if the initial centers are chosen randomly. This paper proposes a deterministic initialization algorithm for k-means (Dk-means) by exploring a set of probable centers through a constrained bi-partitioning approach. The proposed algorithm is compared with classical k-means with random initialization and improved k-means variants such as k-means++ and MinMax algorithms. It is also compared with three deterministic initialization methods. Experimental analysis on gene expression datasets demonstrates that Dk-means achieves improved results in terms of faster and stable convergence, and better cluster quality as compared to other algorithms.

关键词： k-means clustering algorithm Initial cluster centers Gene expression clustering Microarray data analysis

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Airport Role Orientation Based on Improved k-means clustering algorithm 3rd

Airport Role Orientation Based on Improved K-means Clusterin...

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3rd European-Alliance-for-Innovation (EAI) International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Xia, Qingjun Zhang, Zhaoyue Zhang, Baochen Civil Aviat Univ China Coll Air Traff Management Tianjin Peoples R China

ISBN: (纸本)9783030364052;9783030364045

This paper aims to provide an insight into the roles of the different types of airports in China by improved k-means clustering algorithm. The first part of the work analyzed the characteristics of Chinese airline network and pointed out that the key to construct hub-and-spoke airline network is determining the function of each airport. The index system of airport function orientation was established from airport operation index, airport hinterland index and airport growth index. The airports in China were classified into four classes by the k-means clustering algorithm. In order to improve reliability of clustering algorithm, a formula was used to normalize the value of each index, and the airports were clustered by improved k-means clustering algorithm. The algorithm was simulated by the MATLAB and the clustered results show the airports have obvious hierarchy.

关键词： k-means clustering algorithm Hub airport Trunk airport Line airport

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