Effect of Two Kinds of Similarity Factors on Principal Component Analysis Fault Detection in Air Conditioning Systems

Screening similar historical fault-free candidate data would greatly affect the effectiveness of fault detection results based on principal component analysis(PCA).In order to find out the candidate data,this study compares unweighted and weighted similarity factors(SFs),which measure the similarity of the principal component subspace corresponding to the first k main components of two datasets.The fault detection employs the principal component subspace corresponding to the current measured data and the historical fault-free data.From the historical fault-free database,the load parameters are employed to locate the candidate data similar to the current operating data.Fault detection method for air conditioning systems is based on principal component.The results show that the weighted principal component SF can improve the effects of the fault-free detection and the fault detection.Compared with the unweighted SF,the average fault-free detection rate of the weighted SF is 17.33%higher than that of the unweighted,and the average fault detection rate is 7.51%higher than unweighted.

Parallel Active Subspace Decomposition for Tensor Robust Principal Component Analysis

Tensor robust principal component analysis has received a substantial amount of attention in various fields.Most existing methods,normally relying on tensor nuclear norm minimization,need to pay an expensive computational cost due to multiple singular value decompositions at each iteration.To overcome the drawback,we propose a scalable and efficient method,named parallel active subspace decomposition,which divides the unfolding along each mode of the tensor into a columnwise orthonormal matrix(active subspace)and another small-size matrix in parallel.Such a transformation leads to a nonconvex optimization problem in which the scale of nuclear norm minimization is generally much smaller than that in the original problem.We solve the optimization problem by an alternating direction method of multipliers and show that the iterates can be convergent within the given stopping criterion and the convergent solution is close to the global optimum solution within the prescribed bound.Experimental results are given to demonstrate that the performance of the proposed model is better than the state-of-the-art methods.

Evaluation of Social Perception on Water Issues in Cameron Highlands （Malaysia） by Principle Factor Analysis

Dealing with water resources issues requires understanding of the community perception. It is important to create a communicative partnership between community and government towards sustainable water resources management. Opinion survey is an essential step to gather the point of view from local community. However, it always generates a large and complex dataset that are difficult to be interpreted by decision maker. In order to overcome this difficulty, statistical methods are applied to develop an interpretability model for decision maker. This study demonstrated the application of Descriptive Analysis and Principle Factor Analysis （PFA） to reduce the complexity of opinion survey dataset by revealing underlying information. A total of 106 respondents were interviewed; consisting of 68 male and 38 female respondents respectively. This study first applied descriptive analysis to identify the basic score for each variable, and these variables are soil erosion （68.9%）, degradation of water quality （65.1%）, degradation of freshwater ecosystem （61.0%）, water shortage （50%）, agricultural solid waste problem （46.2%）, water borne diseases （23.6%）, illegal land clearing （21.7%）, legal land clearing （15.1%）, uncontrolled river water abstraction in upstream （54.7%））, poor solid waste management （34.0%）, low awareness of local community （61.3%）, haphazard planning and development （74.5%） and administration mistake （37.0%）. Based on the PFA result, a total of four rotated factors were extracted, representing different aspects of water related issues in Cameron Highlands. Factor 1, 2, 3 and 4 were summarised to four topics namely： （1） water environment degradation caused by illegal solid waste disposal and low awareness of community, （2） agricultural development leading to negative impacts on water resources such as water shortage and ecosystem deterioration, （3） land clearing activity leading to serious land erosion （4） human health problem due to e-coli bacterial pollution and administration mistake on land development in Cameron Highlands.

Analysis and Optimisation of Halomonas Growth Factors Based on PCA and RSM

The biomass of petroleum-degrading bacteria, such as Halomonas spp., is crucial to the alleviation of severe oil spills through bioremediation. In this paper, the bacterium(HDMP1) was isolated and identified. Growth factors were analysed and optimised through the single-factor experiments, the factor analysis(FA), the principal component analysis(PCA), and the response surface methodology(RSM). Results indicated that HDMP1 was identified as genus Halomonas. In the single-factor experiments, the range of suitable growth conditions for HDMP1 covered: a salt concentration of 2%-4%, a medium pH value of approximately 9, an inoculum concentration of 1.0%, a substrate concentration of 1.0%-1.4%, and a rotation rate of 140 r/min. The evaluation by FA and PCA indicated that three significant growth factors were the salt concentration, the pH value, and the rotation rate. A maximum biomass of HDMP1 was obtained under the conditions covering a salt concentration of 3.5%, a medium pH of 8, and a rotation rate of 151 r/min by optimization.

Analysis on the Influencing Factors of Low-carbon Economy and Its Mitigation Countermeasures in Sichuan Province

[Objective]The study aimed to analyze the influencing factors of low-carbon economy and its mitigation countermeasures in Sichuan Province.[Method]Taking Sichuan Province as an example,an extended STIRPAT model was established firstly,then the impacts of population,economy and technology on carbon emissions from 2000 to 2009 were analyzed econometrically by using the principal component analysis method.Finally,some corresponding countermeasures to reduce carbon dioxide emissions were put forward.[Result]At present,population scale had the greatest influence on carbon emissions in Sichuan Province,then energy consumption per industrial added value and the proportion of industrial added value to GDP.In addition,the influence of population scale on carbon emissions was still greater than that of population structure,and technical factor also has certain explanatory power on carbon emissions.Some countermeasures,like controlling population growth,advocating low-carbon life style and consumption model,paying more attention to the strategic adjustment of industrial structure to gradually reduce the proportion of high-carbon industries,encouraging energy consumption and emissions reduction plus scientific and technological innovation in a new energy technology filed,could be adopted to reduce carbon dioxide emissions,so as to adjust to the development of low-carbon economy in Sichuan Province.[Conclusion]The research could provide references for the establishment of policies for reducing carbon emissions.

Analysis of the Employment Situation of Non Private Enterprises in Various Regions of China

In the past 30 years, Chinese enterprises have been a hot topic of discussion and concern among the general public in terms of economic and social status, ownership structure, business mechanism, and management level. Solving the problem of employment for the people is an important prerequisite for their peaceful living and work, as well as a prerequisite and foundation for building a harmonious society. The employment situation of private enterprises has always been of great concern to the outside world, and these two major jobs have always occupied an important position in the employment field of China that cannot be ignored. With the establishment of the market economy system, individual and private enterprises have become important components of the socialist economy, making significant contributions to economic development and social progress. The rapid development of China’s economy, on the one hand, is the embodiment of the superiority of China’s socialist market economic system, and on the other hand, it is the role of the tertiary industry and private enterprises in promoting the national economy. Since the 1990s, China’s private enterprises have become a new economic growth point for local and even national countries, and are one of the important ways to arrange employment and achieve social stability. This paper studies the employment of private enterprises and individuals from the perspective of statistics, extracts relevant data from China statistical Yearbook, uses the relevant knowledge of statistics to process the data, obtains the conclusion and puts forward relevant constructive suggestions.

Kernel Factor Analysis Algorithm with Varimax

Kernal factor analysis （KFA） with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle component analysis （KPCA）. The results show that the best error rate in handwritten digit recognition by kernel factor analysis with vadmax （4.2%） was superior to KPCA （4.4%）. The KFA with varimax could more accurately image handwritten digit recognition.

Initial and Stopping Condition in Possibility Principal Factor Rotation

Uemura [1] discovered the mapping formula for Type 1 Vague events and presented an alternative problem as an example of its application. Since it is well known that the alternative problem leads to sequential Bayesian inference, the flow of subsequent research was to make the mapping formula multidimensional, to introduce the concept of time, and to derive a Markov (decision) process. Furthermore, we formulated stochastic differential equations to derive them [2]. This paper refers to type 2 vague events based on a second-order mapping equation. This quadratic mapping formula gives a certain rotation named as possibility principal factor rotation by transforming a non-mapping function by a relation between two mapping functions. In addition, the derivation of the Type 2 Complex Markov process and the initial and stopping conditions in this rotation are mentioned. .

Analysis to driving forces of land use change in Lu’an mining area

By selecting impact factors of driving force and formulating evaluation criteria of the impacts, the evaluation system of corresponding driving force impact of land use change was established. Taking Lu'an mining area as an example, the specific impact factors of coal mine were comprehensively evaluated and analyzed in order to carry out qualitative and quantitative analysis for the driving force of mining-land use change. The principal component analysis shows that the social and economic development in mining area from 2000 to 2007 demonstrates continuous accelerate trends, and the impacts of its overall driving force to land use change are increased gradually. The socio-economic factors have more impacts to mining-land use change than those of the natural resources. The main driving force of mining-land use change also include population, technological progress and policy.

Interpretation of water quality parameters for Villages of Sanganer Tehsil, by using Multivariate Statistical analysis

In this study, the factor analysis techniques is applied to water quality data sets obtained from the Sanganer Tehsil, Jaipur District, Rajasthan (India). The data obtained were standardized and subjected to principal components analysis (PCA) extraction to simplifying its interpretation and to define the parameters responsible for the main variability in water quality for Sanganer Tehsil in Jaipur District. The PCA analysis resulted in two factors explaining more than 94.5% of the total variation in water quality data set. The first factor indicates the variation in water quality is due to anthropogenic sources and second factor shows variation in water quality due to organic sources that are taking place in the system. Finally the results of PCA reflect a good look on the water quality monitoring and interpretation of the surface water.

Comparative Analysis of the Course of Rural Urbanization and Urban Modernization——A Case Study of Jiangsu Province

According to the relevant data in Jiangsu Province during the period 2000-2005,this article conducts comparative analysis of the course of rural urbanization and urban modernization using factor analysis method and principal component analysis method.The results show that the factors influencing the course of rural urbanization and urban modernization in Jiangsu Province can be summarized as 3 common factors(economic urbanization,social urbanization,urbanization of life quality and environment);economic urbanization is still the main factor influencing the course of rural urbanization and urban modernization;social urbanization,urbanization of life quality and environment also have great impact on the course of rural urbanization and urban modernization.Finally this article draws the conclusion that the difference between rural urbanization and urban modernization in Jiangsu Province will be gradually reduced,and Jiangsu Province should achieve balanced development in urban and rural areas.

Statistical Analysis of Process Monitoring Data for Software Process Improvement and Its Application

Software projects influenced by many human factors generate various risks. In order to develop highly quality software, it is important to respond to these risks reasonably and promptly. In addition, it is not easy for project managers to deal with these risks completely. Therefore, it is essential to manage the process quality by promoting activities of process monitoring and design quality assessment. In this paper, we discuss statistical data analysis for actual project management activities in process monitoring and design quality assessment, and analyze the effects for these software process improvement quantitatively by applying the methods of multivariate analysis. Then, we show how process factors affect the management measures of QCD (Quality, Cost, Delivery) by applying the multiple regression analyses to observed process monitoring data. Further, we quantitatively evaluate the effect by performing design quality assessment based on the principal component analysis and the factor analysis. As a result of analysis, we show that the design quality assessment activities are so effective for software process improvement. Further, based on the result of quantitative project assessment, we discuss the usefulness of process monitoring progress assessment by using a software reliability growth model. This result may enable us to give a useful quantitative measure of product release determination.

Ecophysiology and multivariate analysis for production of Tachigali vulgaris in Brazil:Influence of rainfall seasonality and fertilization

Studies on fertilization management of species native to the Amazon for energy plantations contribute to the diversity of species use and reduce biological risk due to the excessive use of clones or hybrids of Eucalyptus.This study evaluates the effect of precipitation seasonality and phosphorus and potassium fertilization on gas exchange in a Tachigali vulgaris plantation.Three levels of P(zero,65.2,130.4 kg ha^(-1))and three of K(zero,100.0,200.0 kg ha^(-1))were applied in a 3×3 factorial randomized block design.Gas exchange measurements were conducted in April and November 2018.In low rainfall,high irradiance period,photo synthetic rates were up to four times higher than in the high rainfall period,reaching 20.3μmol m^(-2)s^(-1)in the treatment with 130.4 g kg^(-1)of P and 100.0 g kg^(-1)of K.Factor analysis and principal component analysis reduced the initial eight gas exchange variables to two and three principal components in periods of high and low rainfall,respectively.The multivariate method used in this study readily identified variations in the variables as a function of rainfall,with high reliability in explaining the data set.

Principal Component-Discrimination Model and Its Application

Having researched for many years, seismologists in China presented about 80 earthquake prediction factors which reflected omen information of earthquake. How to concentrate the information that the 80 earthquake prediction factors have and how to choose the main factors to predict earthquakes precisely have become one of the topics in seismology. The model of principal component-discrimination consists of principal component analysis, correlation analysis, weighted method of principal factor coefficients and Mahalanobis distance discrimination analysis. This model combines the method of maximization earthquake prediction factor information with the weighted method of principal factor coefficients and correlation analysis to choose earthquake prediction variables, applying Mahalanobis distance discrimination to establishing earthquake prediction discrimination model. This model was applied to analyzing the earthquake data of Northern China area and obtained good prediction results.

The Study of Processes Affecting Groundwater Hydrochemistry by Multivariate Statistical Analysis (Case Study: Coastal Aquifer of Ghaemshahr, NE-Iran)

To assess the quality of groundwater resources, samples were collected from 22 points for mean annual water years of 2003 and 2015 (mean minimum and maximum water table), and 19 parameters were examined and calculated. One of the objectives of this study was to evaluate the groundwater quality of the Ghaemshahr plain which includes the study of spatial and temporal changes of groundwater quality in different sectors and factors affecting it. In this study, combining statistical methods such as Pearson correlation coefficient, factor analysis, principal component analysis, and combined diagrams with hydrochemical methods are used to assess the chemical quality of groundwater. Samples were categorized by using cluster method and then the same samples were identified. Accordingly, samples were classified in four categories which represent the quality of groundwater in different districts. Factor analysis was used to identify the factors affecting the geochemical processes of the aquifer. Statistical methods showed that they can be used to complete the conventional methods in hydro-geochemistry as well as very precise results can be achieved. Based on the obtained results, saturation index of Ghaemshahr groundwater was super-saturated;and groundwater quality control of Ghaemshahr plain is hold by processes such as dissolution of halide (salt water intrusion of Caspian Sea and brackish fossil aquifers), calcite and dolomite (dissolution of limestone, dolomite, and marl in height), weathering sodium-rich plagioclases (clay minerals), and ion exchange.

Water Quality Evaluation Model Based on Principal Component Analysis and Information Entropy:Application in Jinshui River

水质评价对决策者决定水的使用功效尤为重要。水质综合评价系统中涉及到大量因子与指标,因子之间相互作用,致使水质的评价工作相对困难。主成分分析法可以消除因子间的相关性,因而被广泛应用于水质评价,但其忽略了数据离散程度的问题。熵值法则考虑了数据的离散特点。为更好地进行水质的综合评价,本文提出把主成分分析法和熵值法结合起来确定指标权重的方法,建立了水质评价模型,并采用该模型对郑州市金水河再生水2009年的水质情况进行评价,将评价结果与单独采用主成分分析或熵值法的结果进行了比较。结果表明了该方法的可行性与实用性,能够为非常规水资源利用提供理论依据和决策参考。

A comparative study on crash-influencing factors by facility types on urban expressway

This study aims at identifying crash-influencing factors by facility type of Nagoya Urban Expressway, considering the interaction of geometry, traffic flow, and ambient conditions. Crash rate （CR） model is firstly developed separately at four facility types： basic, merge, and diverge segments and sharp curve. Traffic flows are thereby categorized, and based on the traffic categories, the significances of factors affecting crashes are analyzed by principal component analysis. The results reveal that, the CR at merge segment is significantly higher than those at basic and diverge segments in uncongested flow, while the value is not significantly different at the three facility types in congested flow. In both un- and congested flows, sharp curve has the worst safety performance in view of its highest CR. Regarding influencing factors, geometric design and traffic flow are most significant in un- and congested flows, respectively. As mainline flow increases, the effect of merging ratio affecting crash is on the rise at basic and merge segments as opposed to the decreasing significance of diverging ratio at diverge segment. Mean- while, longer acceleration and deceleration lanes are adverse to safety in uncongested flow, while shorter acceleration and deceleration lanes are adverse in congested flow. Due to its special geometric design, crashes at sharp curve are highly associated with the large centrifugal force and heavy restricted visibility.

Influence of geological factors on coal permeability in the Sihe coal mine

Permeability of coal reservoirs influence the extraction of coal gas from coal seams.Twelve coal samples were collected at an anticline and a syncline of the No.3 coal seam in the Sihe coal mine.Porosity,permeability,pore size,vitrinite reflectance,and liquid nitrogen adsorption of the samples were evaluated.Structural curvatures at the sample locations,and the distance between the sampling locations and the nearest faults were calculated based on seismic data.The influences of the evaluated parameters on permeability were analyzed.Major factors that influence permeability of the No.3 coal seam were extracted using principal component analysis(PCA).Based on the porosity–permeability model derived from the Archie formula and classic Kozeny–Carman equation,we deduced that the permeability of coal increased with an increase in porosity.With an increase in average vitrinite reflectance,permeability decreases first and then increases.PCA results showed that coal permeability was regulated by three key components representing three modes.The first component included pore size,depth,and pore complexity accounting for 52.59%of the variability indicating that it was the most important in controlling permeability.The second component included specific surface area,structural curvature,and porosity,and the third component comprised of specific surface area,porosity,and average vitrinite reflectance.Overall,pore diameter and complexity had significant effects on coal permeability.The results show that researchers and stakeholders must consider the interactions among multiple factors rather than single factors to understand the influences on permeability to facilitate efficient utilization of coalbed methane resources.

Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks

In the paper, a general framework for large scale modeling of macroeconomic and financial time series is introduced. The proposed approach is characterized by simplicity of implementation, performing well independently of persistence and heteroskedasticity properties, accounting for common deterministic and stochastic factors. Monte Carlo results strongly support the proposed methodology, validating its use also for relatively small cross-sectional and temporal samples.

Effect of Multi-parameter Environmental Factors on Cucumber Leaf Surface Wetness

In this study, artificial leaf resistance was used to simulate leaf wetness. Specific to the solar greenhouse environment in Tianjin, microclimate monitoring equipment was installed for the collection of temperature group and humidity group data, as well as solar radiation and leaf wetness in the greenhouse. In order to reduce the complexity of multivariate factor prediction and ensure the richness of selected data types, correlation analysis was made to the 2 groups of data, screening 5 000 groups of data, including the humidity group data RH, RH_(20), RH_(40), temperature group data T, T_(20), T_(40), and solar radiation W. The data were then analyzed by principal component analysis, screening out 4 groups of principal components to show the leaf wetness index.