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.

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.

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.

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.

scholarly journals Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs

2021 ◽  
Vol 13 (20) ◽  
pp. 4123
Author(s):  
Hanqi Wang ◽  
Zhiling Wang ◽  
Linglong Lin ◽  
Fengyu Xu ◽  
Jie Yu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Pose Estimation ◽  
Point Cloud ◽  
Principal Component ◽  
Component Analysis ◽  
Evaluation Index ◽  
Evaluation Indexes ◽  
Learning Network ◽  
Estimation Algorithms

Vehicle pose estimation is essential in autonomous vehicle (AV) perception technology. However, due to the different density distributions of the point cloud, it is challenging to achieve sensitive direction extraction based on 3D LiDAR by using the existing pose estimation methods. In this paper, an optimal vehicle pose estimation network based on time series and spatial tightness (TS-OVPE) is proposed. This network uses five pose estimation algorithms proposed as candidate solutions to select each obstacle vehicle's optimal pose estimation result. Among these pose estimation algorithms, we first propose the Basic Line algorithm, which uses the road direction as the prior knowledge. Secondly, we propose improving principal component analysis based on point cloud distribution to conduct rotating principal component analysis (RPCA) and diagonal principal component analysis (DPCA) algorithms. Finally, we propose two global algorithms independent of the prior direction. We provided four evaluation indexes to transform each algorithm into a unified dimension. These evaluation indexes’ results were input into the ensemble learning network to obtain the optimal pose estimation results from the five proposed algorithms. The spatial dimension evaluation indexes reflected the tightness of the bounding box and the time dimension evaluation index reflected the coherence of the direction estimation. Since the network was indirectly trained through the evaluation index, it could be directly used on untrained LiDAR and showed a good pose estimation performance. Our approach was verified on the SemanticKITTI dataset and our urban environment dataset. Compared with the two mainstream algorithms, the polygon intersection over union (P-IoU) average increased by about 5.25% and 9.67%, the average heading error decreased by about 29.49% and 44.11%, and the average speed direction error decreased by about 3.85% and 46.70%. The experiment results showed that the ensemble learning network could effectively select the optimal pose estimation from the five abovementioned algorithms, making pose estimation more accurate.

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.

Principal Component Analysis the Economy of Shanghai in 2010

2011 ◽  
Vol 50-51 ◽  
pp. 404-408
Author(s):  
Xiao Qiang Guo ◽  
Zhen Dong Li ◽  
Dong Dong Hao ◽  
Xin Xie ◽  
Jian Min Wang
Keyword(s):  
Principal Component Analysis ◽  
Economic Development ◽  
Principal Component ◽  
Component Analysis ◽  
Evaluation Index ◽  
Short Term ◽  
Construction Costs ◽  
World Expo ◽  
Shanghai World Expo

This paper from the economic analysis, quantitative evaluation of the 2010 Shanghai World Exop impact. First, from the short-term and long-term benefits of the two considerations, the loss of earnings, base construction costs on the percentage of total funding, permanent building retained, the number of daily tours, the number of participating countries for the evaluation index, subjectively weight to the five indicators，calculate its scores to rank for five World Expos including Shanghai World Expo. Second, using principal component analysis, we get the five indicators of objective weighting and ranking for above five World Expos. The results show that the Shanghai World Expo will boost the economic development and has a huge influence on the economy

scholarly journals The Construction and Empirical Study on Evaluation Index System of International Low-Carbon Economy Development

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhang Zhongyu ◽  
Zhang Zhongxiang
Keyword(s):  
Index System ◽  
Global Climate ◽  
Principal Component ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Energy Structure ◽  
Principal Component Factor Analysis ◽  
Low Carbon ◽  
Low Carbon Economy ◽  
Carbon Economy

Global climate change has become one of the core issues of world governance. Many countries have put forward the goal of carbon neutrality one after another, leading to the intensification of international low-carbon economy competition. To assess the current low-carbon competitiveness among countries, this article constructs an evaluation index system of international low-carbon economy development, and obtains the scores and rankings of countries in energy, society, economy and environment, as well as overall. Taking 20 countries with the highest carbon emissions in the world in 2019 as samples, starting from the concept of low-carbon economy and five evaluation principles, this article selects 40 low-carbon evaluation indicators from five aspects, including economy, society, science and technology, environment, and energy structure. By using the principal component factor analysis method to calculate and test, the four factors, energy factor, society factor, economy factor, and environment factor, are finally extracted to construct the evaluation index system. Results show that South Korea, France, China, Canada, and Germany are among the world’s top five low-carbon economies. The overall competitiveness of China’s low-carbon economy is in a relatively favorable position (3rd overall), with the most outstanding performance in terms of economic strength (1st), but poor performance in terms of social development (9th) and environmental carrying capacity (9th), and the biggest disadvantage in terms of energy structure (13th).

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