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Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

Geo-Spatial Information Science 2019年第1期22卷 23-34页

作者： Ali Almagbile Department of Geography Yarmouk UniversityIrbidJordan

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings,crowd monitoring has taken a considerable attentions in many disciplines such as psychology,sociology,engineering,and computer vision.This is due to the fact that,monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents(e.***.sports).One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles(UAVs),because UAVs have the capability to acquiring fast,low costs,high-resolution and real-time images over crowd areas.In addition,geo-referenced images can also be provided through integration of on-board positioning sensors(e.g.GPS/IMU)with vision sensors(digital cameras and laser scanner).In this paper,a new testing procedure based on feature from accelerated segment test(FAST)algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions.The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order.A single pixel which takes the ranking number 9(for FAST-9)or 12(for FAST-12)was then compared with the center pixel.Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features.The results show that the proposed algorithms are able to extract crowd features from different UAV images.Overall,the values of Completeness range from 55 to 70%whereas the range of correctness values was 91 to 94%.

关键词： Unmanned Aerial Vehicle(UAV) crowd density corner detection Feature from Accelerated Segment Test(FAST)algorithm clustering analysis

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Building climate zones of major marine islands in China defined using two-stage zoning method and clustering analysis

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Frontiers of Architectural Research 2021年第1期10卷 134-147页

作者： Ziyang Hao Xiaojing Zhang Jingchao Xie Jianping Wang Jiaping Liu Key Laboratory of Green Built Environment and Energy Efficient Technology Beijing University of TechnologyBeijing 100124China Naval Institute of Engineering Design and Research PLABejjing 100070China

The current scheme of building climate zones in China generally assumes that building climate zones of island cities are identical to adjacent land stations.Consequently,building design strategies for island buildings usually refer to those developed for inland cities.This approach has to some extent hindered the energy-saving design and green development of island buildings in China.This research takes a first step on this issue by defining the building climate zones of 36 marine islands over China marine area using two-stage zoning methodology adopted by current building climate zoning standard(GB50178-1993).The meteorological data used for analysis was obtained from the National Climate Center of China over the 30-year period from 1985 to 2014.As comparison,40 coastal stations which are adjacent to the inves-tigated marine islands were also included in this study.Subsequently a more obiective techni-que-cluster analysis was operated as an effective supplement to discover the climate characteristics among different observations.The results of both methodologies consistentlyshow that among the 36 islands investigated,the majority of islands located in northern and eastern marine area belong to the same climate zones as their adjacent coastal cities.Howev-er,island cities in southern marine area cannot be assigned to any current climate zone,which was demonstrated by its distinctive climate features different from any other sites investi-gated through cluster analysis as well as different energy use patterns.Thus a new zone was defined to supplement the current building climate zoning scheme to cover marine area of China.

关键词： Building climate zoning Marine islands Two-stage zoning method clustering analysis Climate characteristics Building energy consumption

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Deep Learning,Feature Learning,and clustering analysis for SEM Image Classification

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Data Intelligence 2020年第4期2卷 513-528页

作者： Rossella Aversa Piero Coronica Cristiano De Nobili Stefano Cozzini National Research Council-Istituto Officina dei Materiali(CNR-IOM) 34136 TriesteItaly KIT-Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 176344 Eggenstein-LeopoldshafenGermany Research Software Engineering University of CambridgeCambridge CB30FAUK Freelance at *** Area Science Park Padriciano 9934149 TriesteItaly

In this paper,we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope(SEM).This is done by coupling supervised and unsupervised learning approaches.We first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training accuracy.Then,we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scales(from 1μm to 2μm).Finally,we compare different clustering methods to uncover intrinsic structures in the images.

关键词： Neural networks Feature learning clustering analysis Scanning Electron Microscope(SEM) Image classification

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Massive Power Device Condition Monitoring Data Feature Extraction and clustering analysis using MapReduce and Graph Model

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CES Transactions on Electrical Machines and Systems 2019年第2期3卷 221-230页

作者： Hongtao Shen Peng Tao Pei Zhao Hao Ma State Grid Hebei Energy Technology Service Limited Company Shijiazhuang050000China State Grid Hebei Electric Power Research Institute ShijiazhuangChina

Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun DTplus platform.First,power device condition monitoring data storage based on MaxCompute table and parallel permutation entropy feature extraction based on MaxCompute MapReduce are designed and implemented on DTplus platform.Then,Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering analysis.Finally,performance tests are performed to compare the execution time between serial program and parallel program.Performance is analyzed from CPU cores consumption,memory utilization and parallel granularity.Experimental results show that the designed framework and parallel algorithms can efficiently process massive power device condition monitoring data.

关键词： clustering analysis graph feature extraction mapreduce maxcompute power device condition monitoring.

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Human distribution and spatial-temporal clustering analysis of human brucellosis in China from 2012 to 2016

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Infectious Diseases of Poverty 2020年第5期9卷 124-124页

作者： Pei-Feng Liang Yuan Zhao Jian-Hua Zhao Dong-Feng Pan Zhong-Qin Guo 不详 Department of Epidemiology and Biostatistics School of Public Health and managementNingxia Medical UniversityYinchuan 750001China

Background:Brucellosis is a major public health issue in China,while its temporal and spatial distribution have not been studied in depth.This study aims to better understand the epidemiology of brucellosis in the mainland of China,by investigating the human,temporal and spatial distribution and clustering characteristics of the disease.Methods:Human brucellosis data from the mainland of China between 2012 and 2016 were obtained from the China Information System for Disease Control and Prevention.The spatial autocorrelation analysis of ArcGIS10.6 and the spatial-temporal scanning analysis of SaTScan software were used to identify potential changes in the spatial and temporal distribution of human brucellosis in the mainland of China during the study period.Results:A total of 244348 human brucellosis cases were reported during the study period of 2012-2016.The average incidence of human brucellosis was higher in the 40-65 age group.The temporal clustering analysis showed that the high incidence of brucellosis occurred between March and July.The spatial clustering analysis showed that the location of brucellosis clustering in the mainland of China remained relatively fixed,mainly concentrated in most parts of northern China.The results of the spatial-temporal clustering analysis showed that Heilongjiang represents a primary clustering area,and the Tibet,Shanxi and Hubei provinces represent three secondary clustering areas.Conclusions:Human brucellosis remains a widespread challenge,particularly in northern China.The clustering analysis highlights potential high-risk human groups,time frames and areas,which may require special plans and resources to monitor and control the disease.

关键词： Human brucellosis China clustering analysis Geographic information system SaTScan software

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情报研究方法的聚类分析

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图书馆学、信息科学、资料工作 2007年第1期 1561-1567页

作者：钱军杨欣杨娟

将判断和预测研究对象未知发展状态的各种情报研究方法，根据各自描述研究对象情报特征的途径与方法的不同，聚类为基于数据的方法、基于文献的方法、基于知识的方法、基于人的方法、基于组织的方法和基于认知的方法六种，详细分析了它... 详细信息

将判断和预测研究对象未知发展状态的各种情报研究方法，根据各自描述研究对象情报特征的途径与方法的不同，聚类为基于数据的方法、基于文献的方法、基于知识的方法、基于人的方法、基于组织的方法和基于认知的方法六种，详细分析了它们的共性特征、情报功能及关联性，为情报学方法论构建了一个全新的框架体系，使之得以更加有效有序、科学化的运用。

关键词：情报研究方法情报研究方法论聚类分析　　intelligence research methods intelligence research methodology clustering analysis

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clustering and Discrimination analysis of Telemetric Pulse Numbers of Inertial Navigation System

Clustering and Discrimination Analysis of Telemetric Pulse N...

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第30届中国控制与决策会议

作者： Jinhua Fan Taiyuan Satellite Launch Center

In order to automate the process of tackling the telemetric pulse numbers generated from an inertial navigation system, the classification issue of telemetric pulse numbers is studied. Firstly, examples for classification are extracted from the telemetric pulse number file, according to its inherent characteristics. Secondly, the clustering analysis is applied to verify that the outlier examples can be separated from the overflow examples. Finally, the discrimination analysis is conducted by the linear discrimination function and Bayesian approach respectively. Based on the data of a flight test, the analysis results indicates that better clustering results can be obtained when the cosine distance approach is adopted to measure the examples distance, and with the same training set and test set, better discrimination results are obtained by the Bayesian approach than by the linear discrimination function.

关键词： clustering analysis Discrimination analysis Telemetric Pulse Numbers Inertial Navigation System

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中国西部区域物流能力的灰色聚类分析

物流管理

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物流管理 2016年第6期 117-121页

作者：周泰袁波廖伟王丽丽

对不同区域的物流能力进行聚类分析，能使各区域客观地认识自身的物流能力现状以及相互间的差异，从而采取有效措施来提高其物流能力。将区域物流能力系统看作是灰色系统，运用灰色聚类分析方法对中国西部12省区的物流能力进行了客观综... 详细信息

对不同区域的物流能力进行聚类分析，能使各区域客观地认识自身的物流能力现状以及相互间的差异，从而采取有效措施来提高其物流能力。将区域物流能力系统看作是灰色系统，运用灰色聚类分析方法对中国西部12省区的物流能力进行了客观综合评价，得到了灰色聚类评价结果，并依据这些省区物流业的发展现状印证了聚类结果的合理性。研究表明，灰色聚类方法计算简便，结果客观，在区域物流能力综合评价中具有较好的可操作性和实用价值。

关键词：区域物流能力灰色系统聚类分析西部地区　　regional logistics capability grey system clustering analysis western area

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clustering Seismic Activities Using Linear and Nonlinear Discriminant analysis

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Journal of Earth Science 2014年第1期25卷 140-145页

作者： H Serdar Kuyuk Eray Yildirim Emrah Dogan Gunduz Horasan Department of Civil Engineering Faculty of EngineeringSakarya University Department of Geophysical Engineering Faculty of EngineeringSakarya University

Identification and classification of different seismo-tectonic events with similar character- istics in a region of interest is one of the most important subjects in seismic hazard studies. In this study, linear and nonlinear discriminant analyses have been applied to classify seismic events in the vicinity of Istanbul. The vertical components of the digital velocity seismograms are used for seismic events with magnitude （Md） between 1.8 and 3.0 that occurred between 2001 and 2004. Two, time dependent pa- rameters, complexity and S/P peak amplitude ratio are selected as predictands. Linear, quadratic, diag- linear and diagquadratic discriminant functions are investigated. Accuracy of methods with an addi- tional adjusted quadratic models are 96.6%, 96.6%, 95.5%, 96.6%, and 97.6%, respectively with a vari- ous misclassified rate for each class. The performances of models are justified with cross validation and resubstitution error. Although all models remarkably well performed, adjusted quadratic function achieved the best success rate with just 4 misclassified events out of 179, even better compared to com- plex methods such as, self organizing method, k-means, Gaussion mixture models that applied to same dataset in literature.

关键词： discriminant analysis clustering analysis self organizing map k-means Gaussion mix- ture models.

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Cluster analysis reveals a homogeneous subgroup of PCOS women with metabolic disturbance associated with adverse reproductive outcomes

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Chinese Medical Journal 2024年第5期137卷 604-612页

作者： Hanxiao Chen Rujun Zeng Xun Zeng Lang Qin The Reproductive Medical Center Department of Obstetrics and GynecologyWest China Second University HospitalSichuan UniversityChengduSichuan 610041China Key Laboratory of Birth Defects and Related of Women and Children of Ministry of Education West China Second University HospitalSichuan UniversityChengduSichuan 610041China

Background:Polycystic ovarian syndrome(PCOS)is a heterogeneous and complex reproductive endocrinological disease that could lead to infertility.There were many attempts to classify PCOS but it remains unclear whether there is a specific subgroup of PCOS that is associated with the best or worst reproductive outcomes of assisted reproductive techniques(ART).Methods:Infertile PCOS patients who underwent their first cycle of in vitro fertilization(IVF)in West China Second University Hospital,Sichuan University from January 2019 to December 2021 were included.Basic clinical and laboratory information of each individual were extracted.Unsupervised cluster analysis was performed.Controlled ovarian stimulation parameters and reproductive outcomes were collected and compared between the different clusters of PCOS.Results:Our analysis clustered women with PCOS into"reproductive","metabolic",and"balanced"clusters based on nine traits.Reproductive group was characterized by high levels of testosterone(T),sex hormone-binding globulin(SHBG),follicular stimulation hormone(FSH),luteinizing hormone(LH),and anti-Müllerian hormone(AMH).Metabolic group was characterized by high levels of body mass index(BMI),fasting insulin,and fasting glucose.Balanced group was characterized by low levels of the aforementioned reproductive and metabolic parameters,except for SHBG.Compared with PCOS patients in reproductive and balanced clusters,those in metabolic cluster had lower rates of good quality day 3 embryo and blastocyst formation.Moreover,PCOS patients in the reproductive cluster had greater fresh embryo transfer(ET)cancelation rate and clinical pregnancy rate after fresh ET than metabolic cluster(odds ratio[OR]=3.37,95%confidence interval[CI]:1.77-6.44,and OR=6.19,95%CI:1.58-24.24,respectively).And compared with PCOS of metabolic cluster,PCOS of balanced cluster also had higher chance for fresh ET cancelation(OR=2.83,95%CI:1.26-6.35).Conclusion:Our study suggested that PCOS patients in metabolic cluster may be associated with adverse re

关键词： Polycystic ovarian syndrome Assisted reproductive techniques clustering analysis Metabolic disturbance Clinical pregnancy

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