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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 *** 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(***).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 *** addition,geo-referenced images can also be provided through integration of on-board positioning sensors(***/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 *** 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 *** assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd *** results show that the proposed algorithms are able to extract crowd features from different UAV ***,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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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 *** first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training ***,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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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 ***,building design strategies for island buildings usually refer to those developed for inland *** approach has to some extent hindered the energy-saving design and green development of island buildings in *** 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 *** comparison,40 coastal stations which are adjacent to the inves-tigated marine islands were also included in this *** a more obiective techni-que-cluster analysis was operated as an effective supplement to discover the climate characteristics among different *** 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 ***-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 *** 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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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 ***,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 ***,Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering ***,performance tests are performed to compare the execution time between serial program and parallel *** is analyzed from CPU cores consumption,memory utilization and parallel *** 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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A NOVEL SVM ENSEMBLE APPROACH USING clustering analysis

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Journal of Electronics(China) 2008年第2期25卷 246-253页

作者： Yuan Hejin Zhang Yanning Yang Fuzeng Zhou Tao Du Zhenhua School of Computer Science Northwestern Polytechnical University Xi＇an 710072 China College of Mechanical ＆ Electronic Engineering Northwest Sci- Tech University of Agriculture and Forestry Yangling 712100 China Department of Maths Shaanxi University of Technology Hanzhong 723000 China

A novel Support Vector Machine(SVM) ensemble approach using clustering analysis is proposed. Firstly,the positive and negative training examples are clustered through subtractive clus-tering algorithm respectively. Then some representative examples are chosen from each of them to construct SVM components. At last,the outputs of the individual classifiers are fused through ma-jority voting method to obtain the final decision. Comparisons of performance between the proposed method and other popular ensemble approaches,such as Bagging,Adaboost and k.-fold cross valida-tion,are carried out on synthetic and UCI datasets. The experimental results show that our method has higher classification accuracy since the example distribution information is considered during en-semble through clustering analysis. It further indicates that our method needs a much smaller size of training subsets than Bagging and Adaboost to obtain satisfactory classification accuracy.

关键词： Support Vector Machine (SVM) Ensemble clustering analysis

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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 *** 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 ***:Human brucellosis data from the mainland of China between 2012 and 2016 were obtained from the China Information System for Disease Control and *** 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 ***:A total of 244348 human brucellosis cases were reported during the study period of *** average incidence of human brucellosis was higher in the 40-65 age *** temporal clustering analysis showed that the high incidence of brucellosis occurred between March and *** 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 *** 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 ***:Human brucellosis remains a widespread challenge,particularly in northern *** 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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Evaluation and classification of residential greenbelt quality based on factor analysis & clustering analysis:An example of Xinxiang City,China

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Journal of Forestry Research 2008年第4期19卷 311-314页

作者：乔丽芳张毅川齐安国河南科技学院园林学院

Five factors expressing greenbelt quality and one factor expressing quantity were adopted for evaluation of the residential greenbelt, and the AHP （Analytical Hierarchy Process） method was used to determine the value of factors. Thirty residential areas were selected as the samples. Two principal components were extracted and their expression was constructed by method of factor anlysis, therefore, quality evaluation of residential greenbelt was obtained. The accuracy of the function and implement quality classification toward the residential greenbelts in Xinxiang City were validated by clustering analysis method. The results showed that the greenbelt quality of fourteen residential areas was higher than the average level, of which eleven were newly-built residential areas. The 30 residential areas were classified into three types according to their greenbelt features and their formation by clustering analysis method. Finally rational proposal basing on aforesaid evaluating results was proposed for construction and renewal of residential greenbelt, upon which directive basis was provided for construction and renewal of residential greenbelt.

关键词： residential area greenbelt quality evaluation factor analysis clustering analysis

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Heavy minerals clustering analysis in application of provenance analysis of Kong 2 Member in Kongnan area

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Global Geology 2007年第1期10卷 44-47页

作者： Narenna WU College of Earth Sciences Jilin University

The main task of provenance analysis is to determine the source of sediments and the position of parent *** analysis may find out the relationship between erosion districts and sediment zone,between the uplift and the depression in the process of basin *** authors use the method of heavy mineral clustering analysis and estimate the provenance direction of Huanghua Depression in the Paleogene Kong 2 *** shows that there were five provenance areas of Kong 2 Member in Kongnan *** are western(Shenusi),northwestern(Cangzhou),eastern(Ganhuatun),northeastern and *** main provenance areas were northwestern and western,while the southern provenance could not be ruled *** these areas are consistent with the known provenance areas.

关键词： stable heavy minerals clustering analysis provenance analysis Kongnan area Kong 2 Member

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clustering analysis on Large Grained Brassica napus Materials Based on the Optimized ACGM Markers

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Agricultural Science & Technology 2012年第11期13卷 2265-2268页

作者：俎峰李静罗延青赵凯琴马芳陈苇王敬乔李劲峰董云松云南省农科院经济作物研究所云南昆明650205 华中农业大学作物遗传改良国家重点实验室湖北武汉430070 云南农业职业技术学院云南昆明650212

[Objective] This study aimed to develop ACGM markers for the clustering analysis of large grained Brassica napus materials. [Method] A total of 44 pairs of ACGM primers were designed according to 18 genes related to Arabidopsis grain development and their homologous rape EST sequences. After electrophoresis, 18 pairs of ACGM primers were selected for the clustering analysis of 16 larger grained samples and four fine grained samples of rapeseed. [Result] PCR result showed that 2-6 specific bands were respectively amplified by each pair of primes, and all the bands were polymorphic and repeatable, suggesting that the optimized ACGM markers were useful for clustering analysis of B. napus species. clustering analysis revealed that the 20 rapeseed samples were divided into three clusters A, B, and C at similarity coefficient 0.6. Then, the clusters A and B were further divided into five sub clusters A1, A2, A3, B1 and B2 at similarity coefficient 0.67. [Conclusion] This study will provide theoretical and practical values for rape breeding.

关键词： Brassica napus Large grain clustering analysis ACGM marker

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Genetic Diversity and clustering analysis of 48Cultivars of Olea euyopaea L.

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Agricultural Science & Technology 2013年第9期14卷 1215-1219页

作者：宁德鲁陈少瑜陈海云李瑞李勇杰毛云玲吴涛云南省林业科学院云南昆明650201 云南省林业科学院/云南省森林植物培育与开发利用重点实验室/国家林业局云南珍稀濒特森林植物保护和繁育重点实验室云南昆明650201

Inter-simple sequence repeat（ISSR） molecular markers were applied to analyze the genetic diversity and clustering of 48 introduced and bred cultivars of Olea euyopaea L. Totally 106 DNA bands were amplified by 11 screened primers, including 99 polymorphic bands; the percentage of polymorphic loci was 93.40%, indicating a rich genetic diversity in Olea euyopaea L. germplasm resources. Based on Nei＇s genetic distances between various cultivars, a dendrogram of 48 cultivars of Olea euyopaea L. was constructed using unweighted pair-group（UPMGA）method,which showed that 48 cultivars were clustered into four main categories; 84.6% of native cultivars were clustered into two categories; most of introduced cultivars were clustered based on their sources and main usages but not on their geographic origins. This study will provide references for the utilization and further genetic improvement of Olea euyopaea L. germplasm resources.

关键词： Olea euyopaea L. Genetic diversity clustering analysis

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