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Application of fusion of two classifiers based on principal component analysis method and time series comparison to recognize maritime objects upon FLIR images 12

Application of fusion of two classifiers based on principal ...

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12th Conference on Reconnaissance and Electronic Warfare Systems (CREWS)

作者： Pietkiewicz, Tadeusz Mil Univ Technol Inst Radioelect Gen W Urbanowicza Str 2 PL-00908 Warsaw Poland

ISBN: (数字)9781510627864

ISBN: (纸本)9781510627864

This paper presents a method of recognition of maritime objects based on FLIR (forward looking infra-red) sensor images using two methods: principal component analysis (PCA) and Dynamic Time Warping (DTW). A combination of the principal component analysis PCA with the eigenimages analysis method reduces the dimensionality of the recognition problem. DTW method finds the shortest distance between two time series allowing a transformation of time for both compared series. In the presented maritime objects FLIR images classifier the DTW method is used to compare the vertical brightness projection histograms of silhouettes for the recognized object and the object pattern. To determine the silhouette of a maritime object the Otsu thresholding algorithm is used. The paper describes the eigenimages method, the DTW method of comparing time series and the data fusion method combining conclusions both classifiers. In the final part of the paper are presented preliminary test results of the classification method for a set of maritime objects FLIR images registered in the Baltic Sea.

关键词： FLIR images recognition images classifiers segmentation principal component analysis method eigenimages method classifiers fusion

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TECHNOLOGIES OF THE FOURTH INDUSTRIAL REVOLUTION: RESEARCH OF TECHNOLOGICAL ACCEPTANCE OF INDUSTRIAL COMPANIES WITH THE HELP OF NONLINEAR principal component analysis method (NLPCA) 24

TECHNOLOGIES OF THE FOURTH INDUSTRIAL REVOLUTION: RESEARCH O...

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24th International Conference on Production Research (ICPR)

作者： Trachuk, A. Linder, N. Finance Univ Govt Russian Federat Dept Management 101 Prospect Mira Moscow 129085 Russia

ISBN: (纸本)9781605955070

Industrial companies' readiness to accept the industry technologies 4.0 is studied and results of industrial companies' questioning are analyzed in the work. The nonlinear principal component analysis method (NLPCA) is applied as a data processing instrument in understanding the Gifi system taking into account the variegated statistic nature of the questioning indexes.

关键词： industry 4.0 technological acceptance questioning principal component analysis method nonlinear principal component analysis method NLPCA the Gifi system

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principal component analysis method-Based Research on Agricultural Science and Technology Website Evaluation 9th

Principal Component Analysis Method-Based Research on Agricu...

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9th IFIP WG 5.14 International Conference on Computer and Computing Technologies in Agriculture (CCTA)

作者： Ma, Jian Chinese Acad Agr Sci Agr Informat Inst Beijing 100081 Peoples R China Minist Agr Key Lab Agr Informat Serv Technol 2006 2010 Beijing 100081 Peoples R China

ISBN: (纸本)9783319483542;9783319483535

Agricultural science and technology website is a very important supporter of driving agricultural information and servicing agriculture. An evaluation method is proposed on agricultural science and technology website based on objective data and artificial ratings, using principal component analysis method. Finally the author used the model to evaluate 18 agricultural science and technology websites, and proposed some suggestions on development of agricultural science and technology websites based on the evaluation result which would act as reference to agricultural science and technology website construction.

关键词： Agricultural science and technology website principal component analysis method Website evaluation

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principal component analysis method-Based Research on Agricultural Science and Technology Website Evaluation

Principal Component Analysis Method-Based Research on Agricu...

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The 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA 2015)

作者： Jian Ma Agricultural Information Institute of Chinese Academy of Agricultural Sciences Key Laboratory of Agricultural Information Service Technology (2006–2010) Ministry of Agriculture

ISBN: (纸本)9783319483535

Agricultural science and technology website is a very important supporter of driving agricultural information and servicing agricultureAn evaluation method is proposed on agricultural science and technology website based on objective data and arti ficial ratings, using principal component analysis methodFinally the author used the model to evaluate 18 agricultural science and technology websites, and proposed some suggestions on development of agricultural science and technology websites based on the evaluation result which would act as reference to agricultural science and technology website construction.

关键词： Agricultural science and technology website principal component analysis method Website evaluation

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APPLICATION ON STOCK PRICE PREDICTION OF ELMAN NEURAL NETWORKS BASED ON principal component analysis method 11

APPLICATION ON STOCK PRICE PREDICTION OF ELMAN NEURAL NETWOR...

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11th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Shi, Hongyan Liu, Xiaowei Shenyang Univ Technol Coll Sci Shenyang 110000 Peoples R China Shenyang Univ Technol Dept Grad Degree Shenyang Peoples R China

ISBN: (纸本)9781479972081

Study on the prediction of stock price has great theoretical significance and application value. Traditional stock forecasting methods cannot fit and analysis highly nonlinear, multi-factors of stock market well, there are problems such as the prediction accuracy is not high, the slow training speed etc. In order to improve the accuracy of stock price forecasting, this paper proposes a prediction method of Elman neural network model based on principal component analysis method. In order to better compare results, establish structure same BP network and Elman network, forecast for stock data;then using principal component analysis filter factors of significant effect on stock prices, Elman neural network model based on principal component analysis method, and compared with single Elman network and BP networks prediction results. Result shows BP network convergence is relatively slow, train for a long time, and could converge to a local minimum;Elman network training time is short, the error bars for smoother and more stable performance;Elman neural network model based on principal component analysis with higher accuracy, faster network speeds.

关键词： BP neural network Elman neural network principal component analysis method

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A joint configuration method for multiple flexible resources in low carbon distribution networks based on massive scene dimensionality reduction

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 534-543页

作者： Zheng, Zhen Zhang, Guanhua Wang, Duhong Mou, Kai He, Zhizhuo Xing, Haijun State Grid Shanghai Qingpu Elect Power Supply Co 6155 Wai Qingsong Highway Shanghai 201700 Peoples R China Shanghai Univ Elect Power Shanghai 200090 Peoples R China

Due to the extensive access of renewable sources in distribution networks, this research proposes a joint configuration method for multiple flexible resources in low carbon distribution networks based on massive scene dimensionality reduction to maximize the access capacity of renewable sources. The dimensionality reduction clustering is carried out on the wind-light-load mass high-dimensional scenes by the principal component Gaussian mixture clustering algorithm, and the typical scene set of wind-power-loads is obtained. Hereafter, a joint configuration model for multiple flexible resources of distribution network for massive scenarios is constructed, and using the second-order cone relaxation technique to convert the non-convex constraints in the model to convex processing. Last, a Portugal 54 distribution system is employed to verify the proposed method.

关键词： low carbon distribution network principal component analysis method Gaussian mixed clustering multiple flexible resources joint configuration

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Quality degradation evaluation of brick-timber structure houses built before 1950 in Shanghai

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PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-ENGINEERING HISTORY AND HERITAGE 2025年

作者： Xue, Bai Shanghai Res Inst Bldg Sci Co Ltd Shanghai Key Lab Engn Struct Safety Shanghai Peoples R China

The quality degradation of historical brick-timber structure houses in Shanghai is affected by various factors, like worsen environment, improper maintenance, and lack of repair. The inspection results of common problems involve a large amount of data. To quantitatively evaluate the quality degradation of historical brick-timber structure houses in Shanghai, this paper introduces principal component analysis method and complex correlation coefficient weighting method. First, material strength and component damage were selected as the evaluation indicators of housing quality degradation. Then the principal component analysis method and complex correlation coefficient weighting method were utilized to analyze the data of evaluation indicators. In this way, the final evaluation results could be obtained. The evaluation results of an example show that the evaluation values of the principal component analysis method and complex correlation coefficient weighting method were relatively similar, and well consistent with the actual safety elevation results of appraisal reports.

关键词： brickwork & masonry complex correlation coefficient weighting method data historical brick-timber structure houses principal component analysis method safety quality degradation degree

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Improving machining accuracy of complex precision turning-milling machine tools

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第1期131卷 211-227页

作者： Chan, Tzu-Chi Li, Jyun-De Farooq, Umar Ullah, Aman Natl Formosa Univ Dept Mech & Comp Aided Engn Yunlin Taiwan Natl Formosa Univ Dept Power Mech Engn Yunlin Taiwan

To improve stability and accuracy during machining, the structural performance of machine tools must be predicted in advance. This study aims to improve the performance of a large-scale turning-milling complex machine tool by analyzing its structural characteristics and health status. Modal and spatial accuracies of an actual machine tool were analyzed using the finite element method based on the working environment and material properties. Static analysis was used to determine spatial processing position deformation errors and an appropriate compensation value to improve accuracy. The modal analysis determined the characteristic parameters of the main machine structure in a fair frequency range, and the modal analysis software verified the machine frequency using modal shape curve fitting. Modal error percentages between the virtual and machine models determined the validation of the model. The prediction diagnosis performance system monitored the spindle vibration signal to evaluate its health status. However, the use and maintenance of each piece of equipment differed. Abnormal symptoms of the spindle are observed in specific frequency bands or vibration characteristics. The feature modeling can establish a health diagnosis model and use the principal component analysis method to observe vibration characteristics and identify machine health.

关键词： Large turning-milling complex machine tool Modal analysis Spatial precision analysis Error compensation Prediction diagnosis performance system principal component analysis method

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analysis of environmental impact factors in urban road traffic accidents: logistic regression model

Advances in Transportation Studies

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Advances in Transportation Studies 2024年第Special issue期3卷 39-50页

作者： Zhao, X.J. Li, S.Y. Guan, H.B. College of Mathematics and Information Science Anyang institute of Technology Anyang455000 China School of Computer Science and Information Engineering Anyang Institute of Technology Anyang455000 China College of Mathematics and Information Science Zhengzhou University of Light Industry Zhengzhou450002 China

The traditional methods for analyzing environmental impact factors in urban road traffic accidents suffer from issues such as low recall, poor precision, and time-consuming processes. To address these challenges, a novel analysis method based on the logistic regression model is proposed. This method utilizes the MIFP-Apriori algorithm to collect urban road traffic accident data and incorporates the EM algorithm for data filling and processing. principal component analysis is then employed to screen the environmental impact factors in urban road traffic accidents. These selected factors are input into the logistic regression model to conduct a comprehensive analysis. The results reveal that environmental impact factors like road conditions and meteorological factors have a significant influence on traffic accidents. Experimental results show that the proposed method achieves an average recall rate of 96.98%, an average precision rate of 97.66%, and the analysis time for environmental factors ranges from 0.12s to 0.52s. © 2024, Aracne Editrice. All rights reserved.

关键词： Environmental impact factors Logistic regression model Mifp-apriori algorithm principal component analysis method Traffic accident Urban road

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An English MOOC similar resource clustering method based on grey correlation

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International Journal of Business Intelligence and Data Mining 2024年第3-4期25卷 350-361页

作者： Mao, Xianzhuang Foreign Languages Department Henan University of Economics and Law Zhengzhou450046 China

Due to the problems of low clustering accuracy and efficiency in traditional similar resource clustering methods, this paper studies an English MOOC similar resource clustering method based on grey correlation. principal component analysis was used to extract similar resource features of English MOOC, and feature selection methods was used to pre-process similar resource features of English MOOC. On this basis, based on the grey correlation method, the pre-processed English MOOC similar resource features are standardised, and the correlation degree between different English MOOC similar resource features is calculated. The English MOOC similar resource correlation matrix is constructed to achieve English MOOC similar resource clustering. The experimental results show that the contour coefficient of the proposed method is closer to one, and the clustering accuracy of similar resources in English MOOC is as high as 94.2%, with a clustering time of only 22.3 ms. © 2024 Inderscience Enterprises Ltd.

关键词： grey correlation principal component analysis method English MOOC feature selection clustering of similar resources

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