Contribution Rate Of Principal Component Research Articles

Comprehensive evaluation of ballastless-track sealants based on macro–micro tests and principal component analysis

In order to establish a comprehensive evaluation system of ballastless-track sealants, a method is put forward based on macro–micro tests and principal component analysis(PCA). In this study, the performance parameters of four different ballastless-track sealants (SE, NI, WQ, SY) were obtained by low-temperature performance test, atomic force microscope (AFM) and X-ray photoelectron spectroscopy (XPS) test. Then the PCA method was conducted to reveal the complex relationship between macro and micro performance parameters of sealants, such as the maximum tensile force, the maximum failure displacement, the surface roughness, the surface adhesion, the chemical bonding rate with SiO2, and the principal component contribution rate was calculated. Finally, the comprehensive score was utilized to evaluate the performance of different sealants. The results show that the macroscopic properties of the sealant are closely related to the microscopic parameters roughness, surface adhesion and chemical bonding rate of SiO2. The macro–micro performance parameters of sealant are comprehensively evaluated through PCA, and the results are as follows: NI > SE > WQ > SY. The PCA can comprehensively evaluate the performance of sealant intuitively and accurately, avoiding the subjective influence of evaluating the material performance only through macro-tests or micro-tests.

Data-driven analysis of battery electric vehicle energy consumption under real-world temperature conditions

Demand for battery electric vehicles has grown significantly in recent years as global oil prices have climbed. The current development of battery electric vehicles is still in its infancy, and its energy consumption also requires great attention. Five same model battery electric vehicles' driving condition data of a certain brand in Tianjin area are collected for a whole year, and their energy consumption is analyzed. The results show that the energy consumption of the actual driving conditions in the four seasons is generally higher than the test results under New European Driving Cycle, and only some vehicles' energy consumption in spring and autumn is consistent with the test results under Worldwide Harmonized Light Vehicles Test Cycle. Energy consumption tends to increase at both ends with the change of temperature. The impact of low temperature is greater, because the air conditioner usage rate and power in Winter are higher than those in Summer. Through principal component contribution rate analysis and K-means clustering calculation of micro-trips, the results show that the average energy consumption in urban, suburban and high-speed driving conditions decreases gradually. Driving habits such as rapid acceleration ratio and rapid deceleration ratio also have an impact on energy consumption. The decrease of energy braking recovery indirectly caused by rapid deceleration ratio increase makes the energy consumption per 100 km increase. And the increase of rapid acceleration ratio leads to an increase in energy consumption per 100 km. Finally, the Spearman rank correlation analysis shows that energy consumption has the greatest correlation with the average vehicle speed and air conditioner usage status, followed by the constant speed proportion, idle speed proportion and temperature, and finally the recovery of braking energy per 100 km, acceleration proportion, deceleration proportion and rapid acceleration proportion.

Component analysis of ancient glass products based on hierarchical analysis clustering algorithm

First, the attachment is pre-processed, and after the abnormal data are removed side by side, the bar chart is drawn to preliminarily analyze the relationship that lead-barium glass is easier to weathering than high potassium glass. Then the chi-square test is carried out to find that whether the weathering of the glass cultural relics surface is related to the glass type of the cultural relics, but there is no obvious relationship with the decoration and color of the cultural relics. Secondly, the statistical model of one-way ANOVA was established, and the difference analysis of various chemical components was conducted before and after the two types of glass weathering. The chemical components that passed the difference test had significant statistical rules. Finally, multiple linear regression equations are constructed to predict the chemical composition content before weathering. The 14 chemical components of glass were regarded as 14 indicators, and the principal component analysis method was used to calculate the principal component contribution rate and the cumulative contribution rate, and then the two principal components were determined. Then the principal component analysis was used to cluster the indicators, and the hierarchical clustering algorithm was used to generate lineage maps. According to the elbow principle, the number of subcategories of high potassium glass is 3, and the number of subcategories of lead-barium glass is 4, so as to divide the chemical composition of glass. The cluster group scatter plot is then plotted and the subclass results are highly reasonable. Then a stepwise regression model is established to analyze the classification sensitivity and obtain several indicators with high sensitivity, which can be used for more targeted protection and restoration of unearthed cultural relics.

A New Method for Remote Sensing Satellite Observation Effectiveness Evaluation

The number of remote sensing satellites has increased rapidly in parallel with the advancement of space technology and the rising demand in the space industry. Consequently, the observation effectiveness evaluation of remote sensing satellites has received extensive attention. As the core content of the effectiveness evaluation, index systems are usually established and screened using qualitative or quantitative methods. They can hardly satisfy the construction principles such as completeness and independence simultaneously. To address this issue, we propose a new method for remote sensing satellite observation effectiveness evaluation that considers various principles. Firstly, a three-layer evaluation index system structure is constructed. The principle of completeness, hierarchy, and measurability of the index system is ensured by decomposition, clustering, and preliminary screening. Secondly, the principal component contribution rate is obtained through principal component analysis. Finally, we introduce a comprehensive scoring method (ICCLR) based on the combination of independence coefficient and principal component comprehensive loss rate. It realizes the screening of an index system from the index set containing correlation relationships. The validity and optimality of the proposed method are verified through experiments and analysis of three typical tasks.

Prospective study on the analysis of intestinal microflora changes and prediction on metabolic function in severe burn patients at early stage by 16S ribosomal RNA high-throughput sequencing

Objective: To analyze the changes of intestinal microflora and to predict the metabolic function of intestinal microflora in severe burn patients at early stage by 16S ribosomal RNA (rRNA) high-throughput sequencing. Methods: In this prospective observational study, 48 patients with severe burns who met the inclusion criteria were admitted to Department of Burns and Plastic Surgery of Affiliated Hospital of Jiangsu University from January 2018 to December 2019 were included in burn group, and 40 healthy volunteers who met the inclusion criteria and underwent physical examination at the Physical Examination Center of Affiliated Hospital of Jiangsu University in the same period were included in healthy group. Fecal samples were collected from patients in burn group in about 1 week after admission and from volunteers in healthy group on the day of physical examination. The 16S rRNA V4 gene sequencing was performed in the feces of patients in burn group and volunteers in healthy group to analyze the relative abundance of various bacteria. The operational classification unit (OTU) was divided by Mothur software to analyze the dominant bacteria. The OTU number, Chao1 index, Ace index, and Shannon index of fecal microflora were analyzed by QIIME1.9.0 software. The principal component analysis for relative abundance of fecal microflora was performed by Canoco Software 5.0. The metabolic function of fecal microflora was predicted by Kyoto Encyclopedia of Genes and Genomes. Data were statistically analyzed with independent sample t test, and Mann-Whitney U test, and Bonferroni correction. Results: The relative abundance of Bacteroides, Enterococcus, Acinetobacter, Macrococcus, and Staphylococcus in feces of patients in burn group was significantly higher than that of volunteers in healthy group (Z=-5.20, -2.37, -5.17, -4.41, -6.03, P<0.05 or P<0.01), and the relative abundance of unclassified-Helicobacillae, Prevotella, Cecobacteria, unclassified-Rumencocci, Pseudobutyrivibrio, Brautia, and unclassified-Digiestive Streptococcaceae (Z=-8.03, -3.21, -7.63, -5.88, -8.05, -8.05, -6.77, P<0.01) and other 12 species of bacteria in the feces of volunteers in healthy group was significantly higher than that of patients in burn group. The diversity of fecal microflora of volunteers in healthy group was better than that of patients in burn group, the main dominant microflora of volunteers in healthy group were Bacteroides, unclassified-Helicobacillae, Prevotella, unclassified- Enterobacteriaceae, Brautia, Parabacteroides, Escherichia coli, etc., and the main dominant microflora of patients in burn group were Bacteroides, Prevotella, unclassified-Enterobacteriaceae, and Parabacteroides. The OTU number, Ace index, Chao1 index, and Shannon index of fecal microflora of patients in burn group were 149±47, 199±45, 190±45, 2.0±0.9, which were significantly lower than 266±57, 323±51, 318±51, 3.8±0.5 of volunteers in healthy group (t=10.325, 11.972, 12.224, 11.662, P<0.01). The relative abundance of fecal microflora of patients in burn group and volunteers in healthy group was clearly divided into two groups by principal component 1, and the contribution rate of principal component 1 was 32.50%, P<0.01. The fecal microflora of volunteers in healthy group were more concentrated on principal component 2, the fecal microflora of patients in burn group were dispersed in principal component 2, and the contribution rate of principal component 2 was 13.44%, P>0.05. The metabolic levels of alanine-aspartate-glutamate, arginine- proline, cysteine-methionine, glycine-serine-threonine, phenylalanine, tryptophan, and tyrosine in amino acid, tricarboxylic acid cycle, glucose and mannose, galactolipin, glycolysis/gluconiogenesis, starch and sucrose in carbohydrate of fecal microflora of patients in burn group were significantly lower than those of volunteers in healthy group (Z=-4.75, -4.54, -4.75, -4.62, -3.71, -3.28, -4.19, -3.82, -4.72, -4.35, -4.75, -4.71, P<0.01). The levels of lipoic acid metabolism and coenzyme Q synthesis of fecal microflora of patients in burn group were significantly higher than those of volunteers in healthy group (Z=-6.07, -4.51, P<0.01). The metabolic level of arachidonic acid of fecal microflora of patients in burn group was similar to that of volunteers in healthy group (P>0.05). Conclusions: There are significant differences in intestinal microflora between severe burn patients at the early stage and healthy people, and the species and diversity of microflora are decreased, and the nutrient metabolism level is decreased in burn patients by 16S rRNA high-throughput sequencing.

Multiple Process Capability Improvement Method based on Principal Component Analysis

For the traditional multi-process capability construction method based on principal component analysis, the process variables are mainly considered, but not the process capability, which leads to the deviation of the contribution rate of principal component. In response to the question, this paper first clarifies the problem from two aspects: theoretical analysis and example proof. Secondly, aiming at the rationality of principal components degree, an evaluation method for pre-processing data before constructing MPCI using PCA is proposed. The pre-processing of data is mainly to standardize the specification interval of quality characteristics making the principal components degree more reasonable and optimizes the process capability evaluation method. Finally, the effectiveness and feasibility of the method are proved by an application example.

Quantitative Taxonomy of Osmanthus fragrans Cultivars in Jingzhou

This investigation had been implemented for checking Osmanthus fragrans Resources. In this paper, 29 classified characters were selected based on our preliminary research foundation of O. fragrans in Jingzhou city parks, then O. fragrans quantitative classification of 24 cultivars was carried out by SPSS analysis. The results showed principal component contribution rate was scattered, which is not an ideal method for quantitative classification of O. fragrans cultivars. The method of Walder was the best among seven clustering methods. The clustering results showed that there was a long relationship between autumn Osmanthus and Asiaticus Group. In addition, flowering season, flower color and fertility were very important for O. fragrans cultivars classification. The evolution of O. fragrans petals color might be complex and multi-way. The O. fragrans cultivars classification should not be too entangled with their color, but mainly based on flowering date. The comprehensive evaluation of inflorescence type, flower color, leaf color and other morphological characteristics played necessary reference role for the traditional variety classification method and the modern quantitative classification method.

The determination of kernel function parameter initial value of the KPCA based on adaptive contribution rate

In this article, we start from the samples and the kernel principal component contribution rate adaptive point of view. Our aim is to research that how to choose the initial parameters of the mixed kernel function in comprehensive evaluation of KPCA. To some extent, it will overcome the shortcomings that the kernel function parameter is difficult to determine, and it provides an effective method that KPCA applies to determine the kernel function initial parameter of the multi-indexes comprehensive evaluation. We believe that it will be more widely applied to feature extraction with its advantages appear in specific issues, and makes the evaluation method more and more scientific and effective.

Typical Insulation Defects Partial Discharge Feature Extraction Research Based on AE Method

Gas insulated switchgears (GIS) occupy an important position in the power system. Feature extraction is the key to GIS partial discharge pattern recognition, but the dimension of feature space is high, based on this, the article introduce the principal component sparse thoughts, firstly, through the 252 kV GIS partial discharge simulation experiment platform, set up the typical GIS partial discharge models, and uses AE method to obtain the signals, and through the principal component contribution rate to decide the degree of sparse, and according to the sparse component and the number of original features to decide the characteristics of related components, the results show that using this method can realize effective extraction of characteristic, and enhance the clustering results.

Effects of different cropping system and fertilization on functional diversity in soil microbial community of Chrysanthemum morifolium

To study the effects of Chrysanthemum morifolium based on functional diversity in soil microbial community with different cropping system and fertilization and offer scientific basis for the establishment of the reasonable planting patterns. Combined yield and quality of Ch. morifolium, 8 treatments of different cropping system and fertilization on functional diversity in soil microbial community of Ch. morifolium were investigated by the Biolog. The AWCD of Ch. morifolium paddy-dryland rotation was higher than that of the continuous cropping, the AWCD of organic fertilizer and compound NPK > single organic fertilizer > single compound NPK > no fertilizer. The principal component analysis about Ch. morifolium soil microbial carbon source use showed that the contribution rate of principal component 1 was 45.5% and principal component 2 was 12.1%, which could explain most information about the variation. Different cropping system of Ch. morfolium differentiated in principal component 1 axis, different fertilization differentiated in principal component 2 axis. The yield of Ch. morifolium and volatile oil content paddy-dryland rotation was significantly higher than that of continuous cropping. The Ch. morifolium should be cultivated with organic fertilizer and compound NPK by paddy-dryland rotation patters.