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. .

Determination of the principal factors of river water quality through cluster analysis method and its prediction

In this paper, an artificial neural network model was built to predict the Chemical Oxygen Demand （CODMn） measured by permanganate index in Songhua River. To enhance the prediction accuracy, principal factors were determined through the analysis of the weight relation between influencing factors and forecasting object using cluster analysis method, which optimized the topological structure of the prediction model input items of the artificial neural network. It was shown that application of the principal factors in water quality prediction model can improve its forecasting skill significantly through the comparison between results of prediction by artificial neural network and the measurements of the CODMn. This methodology is also applicable to various water quality prediction targets of other water bodies and it is valuable for theoretical study and practical application.

Type 2 Possibility Factor Rotation in No-Data Problem

Uemura [1] discovered a mapping formula that transforms and maps the state of nature into fuzzy events with a membership function that expresses the degree of attribution. In decision theory in no-data problems, sequential Bayesian inference is an example of this mapping formula, and Hori et al. [2] made the mapping formula multidimensional, introduced the concept of time, to Markov (decision) processes in fuzzy events under ergodic conditions, and derived stochastic differential equations in fuzzy events, although in reverse. In this paper, we focus on type 2 fuzzy. First, assuming that Type 2 Fuzzy Events are transformed and mapped onto the state of nature by a quadratic mapping formula that simultaneously considers longitudinal and transverse ambiguity, the joint stochastic differential equation representing these two ambiguities can be applied to possibility principal factor analysis if the weights of the equations are orthogonal. This indicates that the type 2 fuzzy is a two-dimensional possibility multivariate error model with longitudinal and transverse directions. Also, when the weights are oblique, it is a general possibility oblique factor analysis. Therefore, an example of type 2 fuzzy system theory is the possibility factor analysis. Furthermore, we show the initial and stopping condition on possibility factor rotation, on the base of possibility theory.

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.

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.

Study and Application of Objective Evaluation Model on Fabric Style

Based on 31 fabric property parameters tested by FAST test system and other test instruments, the principal factors of fabric style are obtained through the principal factor analysis method and computer program. According to the correlation between each parameter and principal factor, the selected positive or negative coefficient, the objective evaluation model of fabric style has been established based on the percentage of variance. And wool fabrics have been taken for example to show how to use the objective evaluation model for fabric design.

A VERIFICATION ON DIVISION OF HYDROCLIMATIC AREA IN CHINA SEAS USING DIGITAL CHARACTERISTIC OF FREQUENCY DISTRIBUTION

oceanographic data files on the China Seas prepared by the National Marine Data and Information Service, SOA, China and the '30-year (1953-1982) Reports of Sea Surface Monthly Mean Temperature in the East China Sea by the Meteorological Agency, Japan,' were used to calculate the digital characteristics of frequency distribution of sea and air temperature in 153 areas in the China Seas. Principal factor analysis and fuzzy cluster ISODATA were used to divide the China hydroclimatic area into three climatic zones including ten climatic regions. It is concluded that the characteristic values derived by this method may completely show the characteristics of frequency distribution of sea and air temperature in the studied area and the final division of hydroclimatic area is fully coincident with the author's former result [2].

贵州省岩溶洼地发育的空间分布格局及控制因素

Karst depressions are common negative topographic landforms formed by the intense dissolution of soluble rocks and are widely developed in Guizhou province.In this work,an inventory of karst depressions in Guizhou was established,and a total of approximately 256,400 karst depressions were extracted and found to be spatially clustered based on multidistance spatial cluster analysis with Ripley’s K function.The kernel density(KD)can transform the position data of the depressions into a smooth trend surface,and five different depression concentration areas were established based on the KD values.The results indicated that the karst depressions are clustered and developed in the south and west of Guizhou,while some areas in the southeast,east and north have poorly developed or no clustering.Additionally,the random forest(RF)model was used to rank the importance of factors affecting the distribution of karst depressions,and the results showed that the influence of lithology on the spatial distribution of karst depressions is absolutely dominant,followed by that of fault tectonics and hydrological conditions.The research results will contribute to the resource investigation of karst depressions and provide theoretical support for resource evaluation and sustainable utilization.

Trace metals in atmospheric fine particles in one industrial urban city: Spatial variations, sources, and health implications

Trace metals in PM2.5 were measured at one industrial site and one urban site during September, 2010 in Ji＇nan, eastern China. Individual aerosol particles and PM2.5 samples were collected concurrently at both sites. Mass concentrations of eleven trace metals （i.e., Al, Ti, Cr, Mn, Fe, Ni, Cu, Zn, Sr, Ba, and Pb） and one metalloid （i.e., As） were measured by inductively coupled plasma atomic emission spectroscopy （ICP-AES）. The result shows that mass concentrations of PM2.5 （130μg/m3） and trace metals （4.03 μg/m3） at the industrial site were 1.3 times and 1.7 times higher than those at the urban site, respectively, indicating that industrial activities nearby the city can emit trace metals into the surrounding atmosphere. Fe concentrations were the highest among all the measured trace metals at both sites, with concentrations of 1.04 ixg/m 3 at the urban site and 2.41 Itg/m3 at the industrial site, respectively. In addition, Pb showed the highest enrichment factors at both sites, suggesting the emissions from anthropogenic activities existed around the city. Correlation coefficient analysis and principal component analysis revealed that Cu, Fe, Mn, Pb, and Zn were originated from vehicular traffic and industrial emissions at both sites; As, Cr, and part of Pb from coal-fired power plant; Ba and Ti from natural soil. Based on the transmission electron microscopy analysis, we found that most of the trace metals were internally mixed with secondary sulfate/organic particles. These internally mixed trace metals in the urban air may have different toxic abilities compared with externally mixed trace metals.