基于粗集模型的近似约简灰色区域表征法

利用粗集理论,通过建模构造了数据集在约简前后的信息损失表达的灰色区域,将少数优先和多数优先的统计策略包含到灰色区域之中,并利用该区域,提出了一种灰色区域表征的σδ—近似约简方法,通过仿真实验,对提出的方法进行了验证。该方法按照人的要求调整阈值获得问题所需解的思想体现了人机结合以人为主的思想。

基于定性和半定性方法的故障检测与诊断技术

首先介绍了基于定性和半定性方法的故障检测与诊断技术的产生背景和发展状况 ,然后分基于定性方法和基于半定性方法 .———两大类介绍了主要的故障检测和诊断技术 ,讨论了各种方法的优缺点 ,介绍了一些典型的应用实例 ,并对发展的趋向进行了探讨 .

A fuzzy logic model to predict the out-of-seam dilution in longwall mining

The longwall mining method is often affected by the out-of-seam dilution （OSD）. Therefore, predicting and controlling of dilution are important factors for reducing mining costs. In this study, the fuzzy set theory and multiple regression models with parameters, including variation in seam thickness, dip of seam, seam thickness, depth of seam, and hydraulic radius as inputs to the models were applied to pre- dict the OSD in the longwall coal panels. Field data obtained from Kerman and Tabas coal mines, lran were used to develop and validate the models. Three indices including coefficient of determination （R2）, root mean square error （RMSE） and variance account for （VAF） were used to evaluate the perfor- mance of the models. With 10 randomly selected datasets, for the linear, polynomial, power, exponential, and fuzzy logic models, R2, RSME and VAF are equal to （0.85, 4.4, 84.4）, （0.61, 7.5, 59.6）, （0.84, 4.5, 72.7）, （0.80, 4.1, 79.6）, and （0.97, 2.1, 95.7）, respectively. The obtained results indicate that the fuzzy logic model predictor with R2 = 0.97, RMSE = 2.1, and VAF = 95.7 performs better than the other models.

Research on an Integrated Enterprise Workflow Model

An integrated enterprise workflow model called PPROCE is presented firstly. Then, an enterprise’s ontology established by TOVE and Process Specification Language (PSL) is studied. Combined with TOVE’s partition idea, PSL is extended and new PSL Extensions is created to define the ontology of process, organization, resource and product in the PPROCE model. As a result, PPROCE model can be defined by a set of corresponding formal language. It facilitates the future work not only in the model verification, model optimization and model simulation, but also in the model translation.

Study based on ＂Situational Rationality＂ hypothesis for customer market classification model

The traditional market segmentation was based on ＂transcendental rationality＂ or ＂Situational Rationality＂, studies shows that it had disadvantages. This paper states the ＂Situational＂ integrated rationality hypothesis and then comes up with the market segmenting models and classification algorithm basing on this hypothesis. This algorithm combined the Rough Set theory and Neural Networks in application, which overcome the dilemma that caused complicated network structure and long training time by only using Neural Networks and influenced the classification precision caused by noise disturbance by only using Rough Set methods. Finally, the paper did a comparison experiment between the traditional method and the method we came up, the results shows that the model and algorithm has its advantage on every aspects.

Theoretical study of NO adsorbed on the surface of TiO2（110） cluster model

The chemisorption properties of N^18O adsorption on TiO2(110) surface were investigated by experimental and theoretical methods. The results of temperature programmed desorption (TPD) indicated that the temperatures of the three desorption peaks of the main N2 molecules were at (low) temperature of 230 K, 450 K, and (high) temperature of 980 K. This meant that N^18O decomposed and recombined during the process of N2 desorption after N^18O was exposed. Analysis of thestable combination and orbital theory calculation of the surface reaction of NO adsorption on the TiO2(110) cluster modelshowed that there was clear preference for the Ti-NO orientation.

Applying rough sets in word segmentation disambiguation based on maximum entropy model

To solve the complicated feature extraction and long distance dependency problem in Word Segmentation Disambiguation （WSD）, this paper proposes to apply rough sets ill WSD based on the Maximum Entropy model. Firstly, rough set theory is applied to extract the complicated features and long distance features, even frnm noise or inconsistent corpus. Secondly, these features are added into the Maximum Entropy model, and consequently, the feature weights can be assigned according to the performance of the whole disambiguation mnltel. Finally, tile semantic lexicou is adopted to build class-hased rough set teatures to overcome data spareness. The experiment indicated that our method performed better than previous models, which got top rank in WSD in 863 Evaluation in 2003. This system ranked first and second respcetively in MSR and PKU open test in the Second International Chinese Word Segmentation Bankeoff held in 2005.

A Decision Model Based on Grey Rough Sets Integration with Incomplete Information

In this paper, for multiple attribute decision-making problem in which attribute values are interval grey numbers and some of them are null values, a decision model based on grey rough sets integration with incomplete information is proposed. We put forward incidence degree coefficient formula for grey interval, by information entropy theory and analysis technique, the method and principle is presented to fill up null values. We also establish the method of grey interval incidence cluster. Because grey system theory and Rough set theory are complementary each other, decision table with preference information is obtained by the result of grey incidence cluster. An algorithm for inducing decision rules based on rough set theory and the dominance relationship is presented. In some extent, this algorithm can deal with decision-making problem in which the attribute values are interval grey numbers and some of them are null values. Contrasted with classical model of cluster decision-making, the algorithm has an advantage of flexibility and compatibility to new information.

A fast method to evaluate water eutrophication

Water eutrophication has become a worldwide environmental problem in recent years.Once a water body is eutrophicated,it will lose its primary functions and subsequently influence sustainable development of society and economy.Therefore,analysis of eutrophication becomes one of the most essential issues at present.With the ability to deal with vague and uncertain information,and express knowledge in a rule form,the rough set theory(RST) has been widely applied in diverse domains.The advantage of RST is that it can compress the rule and remove needless features by reduction inference rule.By this way,the rule gets effectively simplified and inference efficiency gets improved.However,if data amount is relatively big,it could be a process with large calculated amount to search rules by looking up tables.Petri nets(PNs) possesses so powerful parallel reasoning ability that inference result could be obtained rapidly merely by simple matrix manipulation with no need for searching rules by looking up tables.In this work,an integrated RPN model combining RST with PN was used to analyze relations between degrees of water eutrophication level and influence factors in the Pengxi River of Three Gorges Reservoir.It was shown that the RPN model could analyze water eutrophicaion accurately and quickly,and yield decision rules for the decision-makers at water purification plants of the water quality and assist them in making more cost-effective decisions.

Fuzzy TOPSIS method to primary crusher selection for Golegohar Iron Mine(Iran)

Selection of the crusher required a great deal of design regarding to the mine planning. Selection of suitable primary crusher from all of available primary crushers is a multi-criterion decision making(MCDM) problem. The present work explores the use of technique for order performance by similarity to ideal solution(TOPSIS) with fuzzy set theory to select best primary crusher for Golegohar Iron Mine in Iran. Gyratory, double toggle jaw, single toggle jaw, high speed roll crusher, low speed sizer, impact crusher, hammer mill and feeder breaker crushers have been considered as alternatives. Also, the capacity, feed size, product size, rock compressive strength, abrasion index and application of primary crusher for mobile plants were considered as criteria for solution of this MCDM problem. To determine the order of the alternatives, closeness coefficient is defined by calculating the distances to the fuzzy positive ideal solution(FPIS) and fuzzy negative ideal solution(FNIS). Results of our work based on fuzzy TOPSIS method show that the gyratory is the best primary crusher for the studied mine.

A Model on the Source of Language Vagueness

Vagueness of language has long been explored in the fields of philosophy and logic. Although Zadeh put forward fuzzy sets theory which was considered to be a decent quantitative instrument for the study of language vagueness, the source of vagueness still remains a disputed issue. As the study of vagueness goes further, researchers attached more and more attention to the relation between language-cognition- reality, especially in the cognitive field. Thus we found that it would be more satisfied with the issue to construct a relation-model between five factors： reality, concept, human, language, and context. This model, which is different from the semantic triangle in explicating the factors, human and context, may help to explain the nature of vagueness and reclassify the language vagueness.

A Nonlinear Interval Portfolio Selection Model and Its Application in Banks

In classical Markowitz＇s Mean-Variance model, parameters such as the mean and covari- ance of the underlying assets＇ future return are assumed to be known exactly. However, this is not always the case. The parameters often correspond to quantities that fall within a range, or can be known ambiguously at the time when investment decision must be made. In such situations, investors determine returns on investment and risks etc. and make portfolio decisions based on experience and economic wisdom. This paper tries to use the concept of interval numbers in the fuzzy set theory to extend the classical mean-variance portfolio selection model to a mean-downside semi-variance model with consideration of liquidity requirements of a bank. The semi-variance constraint is employed to control the downside risk, filling in the existing interval portfolio optimization model based on the linear semi-absolute deviation to depict the downside risk. Simulation results show that the model behaves robustly for risky assets with highest or lowest mean historical rate of return and the optimal investment proportions have good stability. This suggests that for these kinds of assets the model can reduce the risk of high deviation caused by the deviation in the decision maker＇s experience and economic wisdom.

The Future of Cosmology and the Role of Non-Linear Perturbations

Cosmological perturbation theory is a key tool to study the universe.The linear or first order theory is well understood,however,developing and applying the theory beyond linear order is at the cutting edge of current research in theoretical cosmology.In this article,I will describe some signatures of non-linear perturbation theory that do not exist at linear order,focusing on vorticity generation at second order.In doing so,we discuss why this,among other features such as induced gravitational waves and non-Gaussianities,shows that cosmological perturbation theory is crucial for testing models of the universe.