Intellectualization and Reliability Evaluation of Distribution Network Based on Principal Component Analysis

2014 ◽  
Vol 672-674 ◽  
pp. 1400-1404
Author(s):  
Yu Qing Feng ◽  
Jian Hua Yang ◽  
Lei Huang ◽  
Bin Ji ◽  
Jian Su
Keyword(s):  
Principal Component Analysis ◽  
Index System ◽  
Distribution Network ◽  
Principal Component ◽  
Distribution Networks ◽  
Original Data ◽  
Component Analysis ◽  
Evaluation Index System ◽  
Smart Distribution Network ◽  
Operation And Management

Principal component analysis is performed on the operation and management evaluation of smart distribution network because of its objectivity, synthesis and simplification to original data information. According to the demand on the evaluation that focuses on intellectualization and reliability of distribution network, an index system for intelligent and reliable evaluation is built. The performance indicators and the principal component analysis are used to analyze the intelligent and reliable level of distribution network operation and management. The feasibility of the evaluation index system is verified by the evaluation results of some distribution networks.

Effect Evaluation of Video Surveillance System on the Basis of Principal Component Analysis

2015 ◽  
Vol 713-715 ◽  
pp. 479-481
Author(s):  
Tao Zhu ◽  
Wei Jun Hong
Keyword(s):  
Principal Component Analysis ◽  
Video Surveillance ◽  
Index System ◽  
Surveillance System ◽  
Principal Component ◽  
Component Analysis ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Video Surveillance System ◽  
Effect Evaluation

The effect evaluation of video surveillance system is important for the effect of expected protection on the system. A comprehensive effect evaluation index system of video surveillance system is established. The Principal Component Analysis (PCA) method is applied on the established index system to obtain a new evaluation index system. It is proved in instances that the effect evaluation method of video surveillance system with the application of the index system is capable of evaluating the video surveillance system effectively and quantitatively. The protective effect of the video surveillance system is evaluated objectively on the basis of the new index system with the PCA.

scholarly journals Constructing a Method for an Evaluation Index System Based on Graph Distance Classification and Principal Component Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Keyou Shi ◽  
Yong Liu ◽  
Zhijun Zhang ◽  
Qing Yu ◽  
Qiucai Zhang
Keyword(s):  
Principal Component Analysis ◽  
Environmental Quality ◽  
Index System ◽  
Principal Component ◽  
Component Analysis ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Radiation Environment ◽  
Graph Distance ◽  
System A

Based on the importance of having an evaluation index system, a new method that combines PCA with graph distance classification is presented to make up the deficiencies of principal component analysis in the process of index screening, and this method is applied in the construction of an evaluation index system for the environmental quality of decommissioning uranium tailing. The seepage indexes were classified into six classes using graph distance classification, which selects the representative elements, including pH, ∑α, 210Pb, 210Po, F−, and NO3−. All of the representative elements were analyzed by PCA while determining the seepage indexes, including pH, U, Ra, ∑α, NH4-N, and F−, and establishing an index system for environmental quality evaluation that consists of two primary indexes (seepage and radiation environment) and 12 secondary indexes. The results showed that the model had ensured that the sifted indexes had a significant effect on the evaluation result and avoided the deletion of some important indexes and that it had stronger applicability and maneuverability.

The Competitiveness of Ecological Civilization

2011 ◽  
Vol 356-360 ◽  
pp. 2620-2623
Author(s):  
Yi Xia Tao ◽  
Xue Hua Zhang
Keyword(s):  
Principal Component Analysis ◽  
Index System ◽  
Comprehensive Evaluation ◽  
Principal Component ◽  
Component Analysis ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Ecological Civilization ◽  
Independent Components ◽  
Autonomous Regions

Abstract. According to the meaning of ecological civilization, we build an evaluation index system of ecological civilization competitiveness. We select 30 provinces or autonomous regions in China, collect the relevant data and through principal component analysis 9 independent components are picked up from 19 comprehensive evaluation indices which reflect the competitiveness of ecological civilization. Then we evaluate and rank the regions’ ecological civilization competitiveness. By comparing the results, we find out strengths and gaps of the regions as well as the related reasons.

scholarly journals Multi-Seam Coal Deposit Modeling via Principal Component Analysis & GIS

Minerals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 532
Author(s):  
Georgios Louloudis ◽  
Christos Roumpos ◽  
Konstantinos Theofilogiannakos ◽  
Nikolaos Stathopoulos
Keyword(s):  
Principal Component Analysis ◽  
Surface Mining ◽  
Principal Component ◽  
Original Data ◽  
Drill Hole ◽  
Component Analysis ◽  
Coal Deposit ◽  
Mining Operations ◽  
Maximum Variance ◽  
Mine Development

Spatial modeling and evaluation is a critical step for planning the exploitation of mineral deposits. In this work, a methodology for the investigation of a multi-seam coal deposit spatial variability is proposed. The study area includes the Klidi (Florina, Greece) multi-seam lignite deposit which is suitable for surface mining. The analysis is based on the original data of 76 exploratory drill-holes in an area of 10 km2, in conjunction with the geological and geomorphological data of the deposit. The analytical methods include drill-hole data analysis and evaluation based on an appropriate algorithm, principal component analysis and geographic information techniques. The results proved to be very satisfactory for the explanation of the maximum variance of the initial data values as well as the identification of the deposit structure and the optimum planning of mine development. The proposed analysis can be also helpful for minimizing cost and optimizing efficiency of surface mining operations. Furthermore, the provided methods could be applied in other areas of geosciences, indicating the theoretical value as well as the important practical implications of the analysis.

Damage detection in nonlinear civil structures using kernel principal component analysis

2020 ◽  
Vol 23 (11) ◽  
pp. 2414-2430
Author(s):  
Khaoula Ghoulem ◽  
Tarek Kormi ◽  
Nizar Bel Hadj Ali
Keyword(s):  
Principal Component Analysis ◽  
Damage Detection ◽  
Structural Model ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Correlated Data ◽  
Data Driven ◽  
Kernel Principal Component Analysis ◽  
Structural Systems

In the general framework of data-driven structural health monitoring, principal component analysis has been applied successfully in continuous monitoring of complex civil infrastructures. In the case of linear or polynomial relationship between monitored variables, principal component analysis allows generation of structured residuals from measurement outputs without a priori structural model. The principal component analysis has been widely used for system monitoring based on its ability to handle high-dimensional, noisy, and highly correlated data by projecting the data onto a lower dimensional subspace that contains most of the variance of the original data. However, for nonlinear systems, it could be easily demonstrated that linear principal component analysis is unable to disclose nonlinear relationships between variables. This has naturally motivated various developments of nonlinear principal component analysis to tackle damage diagnosis of complex structural systems, especially those characterized by a nonlinear behavior. In this article, a data-driven technique for damage detection in nonlinear structural systems is presented. The proposed method is based on kernel principal component analysis. Two case studies involving nonlinear cable structures are presented to show the effectiveness of the proposed methodology. The validity of the kernel principal component analysis–based monitoring technique is shown in terms of the ability to damage detection. Robustness to environmental effects and disturbances are also studied.

scholarly journals Study on the Ability for Jilin Province of China to Undertake Outsourced International Service

2011 ◽  
Vol 2 (3) ◽  
pp. 86
Author(s):  
Liu Liqin
Keyword(s):  
Principal Component Analysis ◽  
Human Capital ◽  
Influencing Factors ◽  
Index System ◽  
Principal Component ◽  
Component Analysis ◽  
Jilin Province ◽  
Service Outsourcing ◽  
International Service

Technology, economy, human capital and policy are essential facilities of undertaking international service outsourcing for an area based on analyzing the influencing factors. With principal component analysis, this paper evaluates the ability to undertake international service outsourcing in Jilin Province of China with the purpose of constructing an index system. It shows that the ability in Jilin Province is weak. It is essential for Jilin province of China to improve the technology, to train and introduce talents, and to perfect the soft environment in order to further develop the ability to undertake international service outsourcing.

scholarly journals Measuring the Performance of Hospitals in Lebanese qadas Using PCA- DEA Model

2019 ◽  
Vol 12 (1) ◽  
pp. 23
Author(s):  
Alissar Nasser
Keyword(s):  
Principal Component Analysis ◽  
Relative Efficiency ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Data Envelopment ◽  
Hospital Level ◽  
Discriminating Power ◽  
Input Model ◽  
Decision Making Units

We study in this paper the performance of Hospitals in Lebanon. Using the nonparametric method Data Envelopment Analysis (DEA), we are able to measures relative efficiency of Hospitals in Lebanon. DEA is a technique that uses linear programming and it measures the relative efficiency of similar type of organizations termed as Decision Making Units (DMUs). In this study, due to the lack of individual data on hospital level, each DMU refers to a qada in Lebanon where the used data represent the aggregation of input and outputs of different hospitals within the qada. In DEA, the inclusion of more number of inputs and /or outputs results in getting a more number of efficient units. Therefore, selecting the appropriate inputs and outputs is a major factor of DEA results. Therefore, we use here the Principal Component Analysis (PCA) in order to reduce the data structure into certain principal components which are essential for identifying efficient DMUs. It is important to note that we have used the basic BCC-input model for the entire analysis. We considered 24 DMUs for the study, using DEA on original data; we got 17 DMUs out of 24 DMUs as efficient. Then we considered 1 PC for inputs and 1 PC for output with almost 80 percent variances, resulting in 3 DMUs as efficient and 21 as inefficient. Using 1 PC for input and 2 PCs for output with 90 percent variance for both input and output, we got 9 DMUs as efficient and 15 DMUs as inefficient. Finally, we have attempted to identify the efficient units with 2 PCs and for 2 PCs for input and outputs with variance more than 95 percent, resulting in 10 efficient DMUs and 14 inefficient DMUs. In Principal Component analysis, if the variance lies between 80 percent to-90 percent it is judged as a meaningful one. It is concluded that Principal Component Analysis plays an important role in the reduction of input output variables and helps in identifying the efficient DMUs and improves the discriminating power of DEA.

scholarly journals PENGELOMPOKKAN DATA WAJAH MENGGUNAKAN METODE AGGLOMERATIVE CLUSTERING DENGAN ANALISIS KOMPONEN UTAMA

2011 ◽  
Vol 15 (1) ◽  
pp. 178
Author(s):  
Altien J Rindengan ◽  
Deiby Tineke Salaki
Keyword(s):  
Principal Component Analysis ◽  
Data Clustering ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Matlab Program

PENGELOMPOKKAN DATA WAJAH MENGGUNAKAN METODE AGGLOMERATIVE CLUSTERING DENGAN ANALISIS KOMPONEN UTAMA Altien J. Rindengan1) dan Deiby Tineke Salaki1) 1)Program Studi Matematika FMIPA Universitas Sam Ratulangi Manado 95115 ABSTRAK Pada penelitian ini dilakukan analisis pengelompokkan data wajah dengan analisis komponen utama untuk mengambil beberapa akar ciri yang cukup mewakili data tersebut dan pengelompokkannya menggunakan metode agglomerative clustering. Dengan menggunakan program Matlab, data wajah yang terdiri dari 6 orang dengan 10 image dapat dikelompokkan sesuai data aslinya.  Pengelompokkannya cukup menggunakan 3 akar ciri pada selang 68 %. Kata kunci: agglomerative clustering, analisis komponen utama, data wajah  FACE DATA CLUSTERING USING AGGLOMERATIVE CLUSTERING METHODS WITH PRINCIPAL COMPONENT ANALYSIS ABSTRACT In this research, face data is grouped using principal component analysis by getting some of its eigenvalues which are representative enough to describe the data and then by using agglomerative clustering the data is clustered.  By running the Matlab program, face data which is consist of 6 people with 10 images can be clustered to fit the original data.  The clustering is enough using 3 eigenvalues with 68 % of interval. Keywords: agglomerative clustering, principal component analysis, face data

scholarly journals Initial Dataset Dimension Reduction Using Principal Component Analysis

2020 ◽  
Vol 23 ◽  
pp. 41-44
Author(s):  
Oļegs Užga-Rebrovs ◽  
Gaļina Kuļešova
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Initial Data ◽  
Dimension Reduction ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Preliminary Processing ◽  
Implicit Form ◽  
Initial Dataset

Any data in an implicit form contain information of interest to the researcher. The purpose of data analysis is to extract this information. The original data may contain redundant elements and noise, distorting these data to one degree or another. Therefore, it seems necessary to subject the data to preliminary processing. Reducing the dimension of the initial data makes it possible to remove interfering factors and present the data in a form suitable for further analysis. The paper considers an approach to reducing the dimensionality of the original data based on principal component analysis.

scholarly journals Dimensionality Reduction with Sparse Locality for Principal Component Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Pei Heng Li ◽  
Taeho Lee ◽  
Hee Yong Youn
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Sparse Representation ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Reconstruction Error ◽  
Dimensional Representation ◽  
Weighted Matrix ◽  
Low Dimensional

Various dimensionality reduction (DR) schemes have been developed for projecting high-dimensional data into low-dimensional representation. The existing schemes usually preserve either only the global structure or local structure of the original data, but not both. To resolve this issue, a scheme called sparse locality for principal component analysis (SLPCA) is proposed. In order to effectively consider the trade-off between the complexity and efficiency, a robust L2,p-norm-based principal component analysis (R2P-PCA) is introduced for global DR, while sparse representation-based locality preserving projection (SR-LPP) is used for local DR. Sparse representation is also employed to construct the weighted matrix of the samples. Being parameter-free, this allows the construction of an intrinsic graph more robust against the noise. In addition, simultaneous learning of projection matrix and sparse similarity matrix is possible. Experimental results demonstrate that the proposed scheme consistently outperforms the existing schemes in terms of clustering accuracy and data reconstruction error.

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