Research on Investment Efficiency for Capital Construction in Coal Mine Based on Principal Component Analysis

2014 ◽  
Vol 687-691 ◽  
pp. 5124-5127 ◽  
Author(s):  
Xin Zhao ◽  
Ren Liang Shan ◽  
Jia Qi Li
Keyword(s):  
Principal Component Analysis ◽  
Coal Mine ◽  
Evaluation System ◽  
Coal Mines ◽  
Principal Component ◽  
Component Analysis ◽  
Shanxi Province ◽  
Investment Efficiency ◽  
Capital Construction ◽  
Construction Efficiency

In the paper, by designing evaluation system, capital construction investment efficiency of 13 coal mines in Shanxi province is analyzed through using principal component analysis. Meanwhile comprehensive scores of capital construction efficiency are calculated. The highest score and the lowest score are 3.813 and-1.141, respectively. Finally, 13 coal mines are divided into three levels, sufficiently distinguishing capital construction efficiency of those coal mines.

Tactile evaluation of down jacket fabric by the comprehensive handle evaluation system for fabrics and yarns

2020 ◽  
pp. 004051752097720
Author(s):  
Yuan Tian ◽  
Yi Sun ◽  
Zhaoqun Du ◽  
Dongming Zheng ◽  
Haochen Zou ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Principal Component Analysis ◽  
Evaluation System ◽  
Principal Component ◽  
Objective Evaluation ◽  
Component Analysis ◽  
Quality Level ◽  
Fabric Handle ◽  
Good Agreement

Down jacket fabric is greatly important in determining the quality of a down jacket. In order to enrich the research on fabric handle, subjective and objective evaluations were made for down jacket fabrics that were less studied. The comprehensive handle evaluation system for fabrics and yarns (CHES-FY) can be used to evaluate the tactile handle of the fabric by accurately and efficiently measuring the basic mechanical properties of the fabric. Therefore, the CHES-FY was used to link the objective evaluation with the subjective handle, so as to effectively estimate the total handle value of the down jacket fabric. Fifty-two kinds of down jacket fabrics were objectively tested through measuring 17 extracted parameters, and principal component analysis was adopted to establish the five main handle characteristics of fullness, softness, stiffness, smoothness, looseness and tightness to characterize basic style of the down jacket fabrics. The results showed that the subjective and objective results were in good agreement. These characteristics can be used as indicators to characterize fabric performance, and the principal component expression to characterize fabric handle can better predict the handle characteristics of down jacket fabrics. This also proves that the CHES-FY can quickly and accurately obtain the fabric handle value, and can also evaluate the fabric quality level.

Operational Risk Identification and Evaluation System of Power Grid Enterprises Based on Principal Component Analysis

2015 ◽  
Vol 738-739 ◽  
pp. 271-274
Author(s):  
Yi Xin Sun ◽  
Hong Xing Wei ◽  
Qing You Yan
Keyword(s):  
Principal Component Analysis ◽  
Asset Management ◽  
Evaluation System ◽  
Financial Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Risk Identification ◽  
Control Grid ◽  
Specific Analysis ◽  
Current Risk

This paper used financial analysis, facing on the current risk management of grid corporate assets, took the principal component analysis as the basic method. It identified five specific analysis of the control grid enterprise asset management risks, and choose the actual data for empirical analysis.

Impervious Surface Information Extracting Approach Using Landsat TM Remote Sensing Image

2013 ◽  
Vol 760-762 ◽  
pp. 1585-1589
Author(s):  
Shi Wei Li ◽  
Jason Yang ◽  
Wei Na Li
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Kappa Statistic ◽  
Impervious Surface ◽  
Component Analysis ◽  
Landsat Tm ◽  
Combination Method ◽  
Shanxi Province ◽  
Band Combination ◽  
Landsat Tm Image

In this paper, we present a new approach to improve extracting accuracy of impervious surfaces. One Landsat TM image of Taiyuan city, Shanxi Province of China was used. After doing test work and analyzing using optimum bands analysis, principal component analysis, and normalized difference impervious surface index, we present the method, optimized band combination. Both unsupervised and supervised classification methods were used to classify the original image, principal component analysis image, normalized difference impervious surface index image, and optimized band combination images we present. The accuracies result of these classifications were assessed by using 256 randomly selected sampling points, and it was found that the overall accuracy the accuracy of optimized band combination method can be reach 87.72%, with the Kappa statistic of 0.85 in impervious surface extraction, it was better than other three methods can get.

scholarly journals Risk Assessment of Coal Mine Water Inrush Based On PCA-DBN

2021 ◽  
Author(s):  
Zhang ye ◽  
Tang Shoufeng ◽  
Shi Ke
Keyword(s):  
Risk Assessment ◽  
Principal Component Analysis ◽  
Coal Mine ◽  
Water Inrush ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Model ◽  
Mine Safety ◽  
Training Time

Abstract To provide an effective risk assessment of water inrush for coal mine safety production, a BP neural network prediction method for water inrush based on principal component analysis and deep confidence network optimization was proposed. Because deep belief network (DBN) is disadvantaged by a long training time when establishing a high-dimensional data classification model, the principal component analysis (PCA) method is used to reduce the dimensionality of many factors affecting the water inrush of the coal seam floor, thus reducing the number of variables of the research object, redundancy and the difficulty of feature extraction and shortening the training time of the model. Then, a DBN network was used to extract secondary features from the processed nonlinear data, and a more abstract high-level representation was formed by combining low-level features to find the expression of the nonlinear relationship between the characteristics of water inbursts. Finally, a prediction model was established to predict the water inrush in coal mines. The superiority of this method was verified by comparing the prediction of the actual working face with the actual situation in typical mining areas of North China.

Establishment and comparative analysis of HPLC fingerprints of deer tissues

2020 ◽  
Vol 60 (10) ◽  
pp. 1343
Author(s):  
Mengjie Yao ◽  
Haiping Zhao ◽  
Xiaoyan Qi ◽  
Yuan Xu ◽  
Wenyuan Liu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Evaluation System ◽  
High Performance ◽  
Principal Component ◽  
Component Analysis ◽  
Chemometric Methods

Context With the increasing use of velvet antlers (VA) as functional food or traditional Chinese medicine, the quality control has become more and more important. Aims Establish an effective method to provide a way of distinguishing VA from other types of deer tissue. Methods In the present study, 18 samples from three types of deer tissue were analysed on the basis of high-performance liquid chromatography, and a chromatogram of each sample was obtained. Then, these chromatograms were processed using the similarity evaluation system for chromatographic fingerprints of traditional Chinese medicine, to give the fingerprints of three deer tissues. The chemometric methods were used to analyse the fingerprint results, so as to identify the three types of deer tissue. Key results Shared peaks of VA, venison and deer bone were identified using similarity evaluation system. The results showed that, in total, 19 peaks were identified among these three types of deer tissue. Compared with venison, VA lacked three peaks (Numbers 3, 4 and 17); compared with deer bone, VA had six extra peaks (Numbers 2, 5, 8, 9, 14 and 19). The results of chemometric methods showed that different tissue samples could be classified into three categories by using both cluster analysis and principal component analysis. After principal component analysis and partial least-square discrimination analysis, seven peaks were selected, which had significant influence on the classification of VA, venison and deer bone. Conclusions The high-performance liquid-chromatography fingerprints in combination with chemometric methods can be used to effectively distinguish three deer tissue types, namely, VA, venison and deer bone. Implications We believe the method offers a useful tool much needed in the current Chinese velvet market.

scholarly journals Evaluation of Water Resources Carrying Capacity Using Principal Component Analysis: An Empirical Study in Huai’an, Jiangsu, China

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2587
Author(s):  
Fan Wu ◽  
Zhicheng Zhuang ◽  
Hsin-Lung Liu ◽  
Yan-Chyuan Shiau
Keyword(s):  
Principal Component Analysis ◽  
Water Resources ◽  
Carrying Capacity ◽  
Evaluation System ◽  
Rapid Development ◽  
Supply And Demand ◽  
Principal Component ◽  
Component Analysis ◽  
Research Perspective ◽  
Water Resources Carrying Capacity

With the rapid development of urbanization, problems such as the tight supply and demand of water resources and the pollution of the water environment have become increasingly prominent, and the pressure on the carrying capacity of water resources has gradually increased. In order to better promote the sustainable development of cities, it is extremely important to coordinate the relationship between water resources and economic society. This study analyzed the current research status of water resources carrying capacity from two aspects, i.e., research perspective and research methodology, established an innovative evaluation system, and used the principal component analysis to analyze the water resources carrying capacity in Huai’an City, an important city in China’s Huaihe River Ecological Economic Zone. Based on the results, it is found that the water resources carrying capacity of Huai’an City has been declining year by year from 2013 to 2019. Based on the evaluation results, suggestions and measures to improve the water resources carrying capacity of the empirical city are proposed to provide an important decision basis for the coordinated development of urban economy, society, and water resources.

scholarly journals Prediction and analysis of water resources demand in Taiyuan City based on principal component analysis and BP neural network

2021 ◽  
Author(s):  
Junhao Wu ◽  
Zhaocai Wang ◽  
Leyiping Dong
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Water Resources ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Principal Component ◽  
Component Analysis ◽  
Shanxi Province ◽  
Economic Resource ◽  
Bp Model

Abstract Water is a fundamental natural and strategic economic resource that plays a vital role in promoting economic and social development. With the accelerated urbanization and industrialization in China, the potential demand for water resources will be enormous. Therefore, accurate prediction of water resources demand is important for the formulation of industrial and agricultural policies, development of economic plans, and many other aspects. In this study, we develop a model based on principal component analysis (PCA) and back propagation (BP) neural network to predict water resources demand in Taiyuan, Shanxi Province, a city with severe water shortage in China. The prediction accuracy is then compared with PCA-ANN, ARIMA, NARX, Grey–Markov, serial regression, and LSTM models, and the results showed that the PCA-BP model outperformed other models in many evaluation factors. The proposed PCA-BP model reduces the dimensionality of high-dimensional variables by PCA and transformed them into uncorrelated composite data, which can make them easier to compute. More importantly, BP and weight threshold adjustment in model training further improve the prediction accuracy of the model. The model analysis will provide an important reference for water demand assessment and optimal water allocation in other regions.

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