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第36届中国控制会议

作者： Man Hao Wei-Hua Cao Min Wu Yan Yuan Zhen-Tao Liu School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (纸本)9781538629185

To diagnose the specific reasons that lead to deterioration of controller performance and improve the diagnostic accuracy,a performance diagnosis method based on eigenvector subspace k-mean clustering is ***,the number of standard deterioration performance subspace with different degrees of deterioration information is increased on the basis of the eigenvector subspace distance diagnosis method,in which multiple distance values between eigenvector subspaces are ***,k-mean clustering is used to divide these distance values into two classes,and the clustering center with a maximum number of points is selected as the representative ***,current performance degradation factor is attributed to the category that represented by the nearest standard performance *** simulation,a double-tank model is established,and simulation result shows that average accuracy of controller performance diagnosis reaches 99.4%approximately.

关键词： controller performance performance diagnosis eigenvector subspace k-mean clustering

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Urban Fire Risk clustering Method Based on Fire Statistics

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Tsinghua Science and Technology 2008年第S1期13卷 418-422页

作者：吴立志任爱珠 Institute of Engineering Disaster Prevention and Mitigation Tsinghua University

Fire statistics and fire analysis have become important ways for us to understand the law of fire, prevent the occurrence of fire, and improve the ability to control fire. According to existing fire statistics, the weighted fire risk calculating method characterized by the number of fire occurrence, direct economic losses, and fire casualties was put forward. On the basis of this method, meanwhile having improved k-mean clustering arithmetic, this paper established fire risk k-mean clustering model, which could better resolve the automatic classifying problems towards fire risk. Fire risk cluster should be classified by the absolute distance of the target instead of the relative distance in the traditional cluster arithmetic. Finally, for applying the established model, this paper carried out fire risk clustering on fire statistics from January 2000 to December 2004 of Shenyang in China. This research would provide technical support for urban fire management.

关键词： urban fire fire statistics fire risk k-mean clustering

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维普期刊数据库

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Is Social Distancing, and Quarantine Effective in Restricting COVID-19 Outbreak? Statistical Evidences from Wuhan, China

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Computers, Materials & Continua 2021年第2期66卷 1977-1985页

作者： Salman A.Cheema Tanveer kifayat Abdu R.Rahman Umair khan A.Zaib Ilyas khan kottakkaran Sooppy Nisar School of Mathematical and Physical Sciences University of NewcastleNewcastleAustralia Department of Statistics Quaid-i-Azam University IslamabadIslamabadPakistan Department of Mathematics and Social Sciences Sukkur IBA UniversitySukkur65200Pakistan Department of Mathematical Sciences Federal Urdu University of ArtsScience&TechnologyGulshan-e-IqbalKarachiPakistan Faculty of Mathematics and Statistics Ton Duc Thang UniversityHo Chi Minh CityVietnam Department of Mathematics College of Arts and SciencesWadi AldawaserPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia

The flow of novel coronavirus(COVID-19)has affected almost every aspect of human life around the *** the emerging ground and early sufferer of the virus,Wuhan city-data remains a case of multifold ***,it is of notable importance to explore the impact of unique and unprecedented public health response of Chinese authorities—the extreme lockdown of the *** this research,we investigate the statistical nature of the viral transmission concerning social distancing,extreme quarantine,and robust lockdown *** observed highly convincing and statistically significant evidences in favor of quarantine and social distancing *** findings might help countries,now facing,or likely to face the wave of the *** analyzed Wuhan-based data of“number of deaths”and“confirmed cases,”extracted from China CDC weekly database,dated from February 13,2020,to March 24,*** estimate the underlying group structure,the assembled data is further subdivided into three blocks,each consists of two ***,the complete data set is studied in three phases,such as,phase 1(Ph 1)=February 13,2020,to February 26,2020;phase 2(Ph 2)=February 27,2020 to March 11,2020;and phase 3(Ph 3)=March 12,2020 to March 24,*** observed the overall median proportion of deaths in those six weeks remained *** estimate is highly influenced by Ph1,when the early flaws of weak health response were still *** the time,we witnessed a median decline of 92.12%in the death ***,a non-parametric version of the variability analysis of death data,estimated that the average rank of reported proportions in Ph 3 remained 7,which was 20.5 in Ph 2,and stayed 34.5 in the first *** patterns were observed,when studying the confirmed cases *** estimated the overall median of the proportion of confirmed cases in Wuhan as 0.0041,which again,is highly inclined towards Ph 1 and Ph *** also witnessed minimum average rank proportions fo

关键词： COVID-19 k-mean clustering statistical methods variability analysis

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Blind localization of multiple primary users without number knowledge

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Journal of Harbin Institute of Technology(New Series) 2012年第5期19卷 113-117页

作者：邢志强宁士勇李炜宋鹏 College of Information Engineering North China University of Technology School of Computer and Information Engineering Harbin University of Commerce

A novel multiple PUs (Primary Users) localization algorithm was proposed, which estimates the number of PUs by SVD (Singular Value Decomposition) method and seeks non-cooperative PUs' position by executing k-mean clustering and iterative operations. The simulation results show that the proposed method can determined the number of PUs blindly and achieves better performance than traditional expectation-maximization (EM) algorithm.

关键词： multiple primary user localization SVD iterative k-mean clustering

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Remote sensing of bark beetle damage in Norway spruce individual tree canopies using thermal infrared and airborne laser scanning data fusion

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Forest Ecosystems 2022年第5期9卷 710-723页

作者： Agata Zakrzewska Dominik kopec Department of Biogeography Paleoecology and Nature ProtectionFaculty of Biology and Environmental ProtectionUniversity of LodzŁodzPoland MGGP Aero Sp.z o.o. TarnowPoland

Background:Increasing threat to Central Europe’s forests from the growing population of the European spruce bark beetle Ips typographus (L.) calls for developing highly effective methods of detection of the infestation spots. The main goal of this study was to establish an automatic workflow for detection of dead trees and trees in poor condition of Picea abies using Middle Wave Infrared spectral range obtained from the ***:The studies were conducted in Wigry National Park (Poland) in 2020. A fusion of aircraft thermal data and laser scanning was used. Synchronous with thermal data acquisition ground reference data were obtained for P. abies in different health conditions. Determination of the range of canopy temperatures characteristic of the three condition states (‘healthy’,‘poor condition’,‘dead’) was performed using k-mean clustering. The accuracy of the method was evaluated on two validation sets:(1) individual tree canopies determined by photointerpretation, and (2) automatic segmentation of laser scanning ***:The results showed that the average temperature of ‘healthy’trees was 27.70℃, trees in ‘poor condition’28.57℃, and ‘dead’trees 30.17℃. High temperature differences between ‘healthy’and ‘dead’P. abies made it possible to distinguish these two condition classes with high accuracy. Lower accuracies were obtained for the class of ‘poor condition’, which was found to be confusing with both ‘healthy’and ‘dead’trees. According to results from the first validation set, a high overall accuracy of 0.60 was obtained. For the second validation set, the overall accuracy was reduced by 11%.Conclusions:This study indicates that canopy temperature recorded from the airborne level is a variable that differentiates ‘healthy’spruces from those in ‘poor condition’and the ‘dead’trees. The results confirmed that thermal and airborne laser scanning data fusion allows for creating a quick and simple workflow, which can successfully separate individual tre

关键词： Infrared thermography Picea abies Ips typographus Middle wave infrared Forest invasion Forest health k-mean clustering

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基于多线激光雷达的道路和障碍物检测

基于多线激光雷达的道路和障碍物检测

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作者： Aloshyna Valentyna 北京工业大学

学位级别：硕士

Today there is a huge number of vehicles(cars, trains, planes) in the world. In one hand, it greatly simplifies our daily lives but in the other hand, it poses many problems that must be a solved. Research about vehicle recognition and adaptive cruise control is recently emerging. Many well-known vehicle manufacturers introduce new technologies in their production that gives the opportunity to increase the safety of our lives. Vehicle recognition and adaptive cruise control using various sensory resources. There are many sensors like cameras, GPS, accelerometers, radars etc., that are used for detecting edges of the road, traffic signs, cars or pedestrians. There are many different sensors to access the objects' data *** paper presents obstacle road detection. To accomplish this task, from the entire set of available sensors the multilayer LIDAR(Light, Detection and Ranging) with four levels was chosen. This allows obtaining reliable information about the location of objects. The so-obtained data have parameters relative to the coordinate system, the distance of each point from the sensor, which opens up great opportunities for ***, this paper considers how to obtain data from the laser, with detailed consideration of its options. At this stage, the important step is data preprocessing, which includes consideration of only those points that are included in the scan range. The elimination of *** second step is the most important task is how to detect the road and objects on it. The solution to this problem requires the use of a variety of methods. It was viewed a large number of sources in this field and the following data processing methods. It is known that for the detection of the road can be considered a method of determining direct. Today, one of the most widely used methods is the Hough method. Its detailed implementation is proposed in this work. For the detection of objects is regarded, the least squares method. This metho

关键词： drivable road recognition Theil–Sen estimator multilayer LIDAR k-mean clustering Hough method Сanny edge detection

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