Patches of reverse tilt in a twisted nematic liquid crystal display device may be removed by inducing the same small angle of pretilt at both solid surfaces. Continuum theory is used to investigate the effect of this ...
Patches of reverse tilt in a twisted nematic liquid crystal display device may be removed by inducing the same small angle of pretilt at both solid surfaces. Continuum theory is used to investigate the effect of this initial tilt upon a Freedericksz transition in a twisted nematic in an electric field. In particular, the author describes the various initial alignments that can occur and verify that the field has little influence until it approaches the threshold value corresponding to a similar cell without tilt.
Consider the second order self-adjoint neutral difference equation of form Δ(an|Δ(xn - pnx τ;n)|αsgnΔ(xn - p nxτn)) + f(n, xgn) = 0. In this paper, we will give the classification of nonoscillatory solutions of ...
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Consider the second order self-adjoint neutral difference equation of form Δ(an|Δ(xn - pnx τ;n)|αsgnΔ(xn - p nxτn)) + f(n, xgn) = 0. In this paper, we will give the classification of nonoscillatory solutions of the above equation;and by the fixed point theorem, we present some existence results for some kinds of nonoscillatory solutions of the equation.
Fuzzy variable is a function from a possibility space to the real line. By analogy of the independence of random variables, there are similar terms for fuzzy variables such as unrelated fuzzy variables, noninteractive...
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ISBN:
(纸本)0780384032
Fuzzy variable is a function from a possibility space to the real line. By analogy of the independence of random variables, there are similar terms for fuzzy variables such as unrelated fuzzy variables, noninteractive fuzzy variables, and independent fuzzy variables. The purpose of this work is to discuss the relations among these terms. Toward this end, the work first defines the marginal possibility distributions of a fuzzy vector, and then gives a simple approach to define the independent fuzzy variables. After that, the equivalence of unrelated, noninteractive, and independent fuzzy variables are proved in the framework of possibility theory.
This paper first presents multicriteria fuzzy random expected value models via expected value operators, and variance of fuzzy random variables. Then, three types of minimization (maximization) fuzzy random expected v...
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ISBN:
(纸本)0780384032
This paper first presents multicriteria fuzzy random expected value models via expected value operators, and variance of fuzzy random variables. Then, three types of minimization (maximization) fuzzy random expected value models are built based on a decision-maker's preference or intention. After that, the interconnections among the three types of optimization problems are discussed. At the end of the paper, a numerical example is provided to illustrate the modeling ideas of the proposed optimization problems.
Approximately a decade ago, engineering educators at several institutions began introducing the concept of a freshman engineering or introduction to engineering course. Today there is hardly any undergraduate engineer...
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Approximately a decade ago, engineering educators at several institutions began introducing the concept of a freshman engineering or introduction to engineering course. Today there is hardly any undergraduate engineering program that does not require the freshman student to take such a course. With its large undergraduate engineering program the University of Wisconsin-Platteville (UW-Platteville) now offers 12-15 sections of a similar course entitled Introduction to Engineering every fall semester. Earlier assessments indicated that the course was generally well received, and several key issues were addressed. The issue at hand now, is the content of the course. Because of the wide range of background in math.science, and computing of our freshmen group, it is a challenge for any instructor to go in depth on any engineering concept without running the risk of losing those at the lower competency level and at the same time keeping the course interesting and challenging for those who are well into the advanced sequence. Faculty with varied backgrounds teaching the course are grappling to find innovative ways to fulfill the main objectives of the course, viz., retention, offer a better understanding of engineering disciplines, and prepare students well for the intended course of study. Nine years after the first offering of this course, it is time to reflect on what this course has accomplished, and the dilemmas faced by the instructors.
Statistical learning theory has investigated the conditions for consistency of the learning processes based on the empirical risk minimization induction principle. But it deals with the unrealistic, i.e. noise-free ca...
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ISBN:
(纸本)0780384032
Statistical learning theory has investigated the conditions for consistency of the learning processes based on the empirical risk minimization induction principle. But it deals with the unrealistic, i.e. noise-free case. In this paper, we will give the key theorem when the outputs are corrupted by noise.
Fuzzy integral is a valid method for combining multiple classifiers. However in the fusion based on fuzzy integral, how to choose an appropriate fuzzy measure is a difficult but important problem. The system's per...
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ISBN:
(纸本)0780384032
Fuzzy integral is a valid method for combining multiple classifiers. However in the fusion based on fuzzy integral, how to choose an appropriate fuzzy measure is a difficult but important problem. The system's performance is largely dependent of the fuzzy measure. An appropriate fuzzy measure can make the system's performance better than the best individual classifier, while an inappropriate fuzzy measure will result in worse performance than the individual classifiers. This paper investigates the fusion mechanism based on the fuzzy integral for multiple classifiers, and discusses the impact of fuzzy measures or nonnegative set functions on the fusion. The study is useful to obtain an appropriate fuzzy measure for improving the performance of the system.
Statistical Learning Theory or SLT which was introduced by Vladimir N. Vapnik, is a small sample statistics, which concerns mainly with the statistic principles when samples are limited, especially the properties of l...
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ISBN:
(纸本)0780384032
Statistical Learning Theory or SLT which was introduced by Vladimir N. Vapnik, is a small sample statistics, which concerns mainly with the statistic principles when samples are limited, especially the properties of learning procedure in such cases. The key theorem of learning theory plays an important role in SLT, which is the foundation for the subsequent theories and applications. However, this theory only suits to fixed probability measure, which reduces the application range of theorem. Thus, we will generalize the application range by means of changing the probability space into g λ measure space.
With the rapid development of Internet, Web has been becoming a main information source. Search engine is the most important information retrieval tool. However, most popular search engines are based on HTML documents...
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ISBN:
(纸本)0780384032
With the rapid development of Internet, Web has been becoming a main information source. Search engine is the most important information retrieval tool. However, most popular search engines are based on HTML documents and lack of semantic retrieval and personalized service. This paper introduces the conception of XML search engine, gives a framework of an intelligent XML search engine, and discusses the key techniques in intelligent XML search engine.
We investigate in this paper the standard k-means clustering algorithm and give our improved version by selecting better initial centroids that the algorithm begins with. First we evaluate the distances between every ...
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ISBN:
(纸本)0780384032
We investigate in this paper the standard k-means clustering algorithm and give our improved version by selecting better initial centroids that the algorithm begins with. First we evaluate the distances between every pair of data-points;then try to find out those data-points which are similar;and finally construct initial centroids according to these found data-points. Different initial centroids lead to different results. If we can find initial centroids which are consistent with the distribution of data, the better clustering can be obtained. According to our experimental results, the improved k-means Clustering Algorithm has the accuracy higher than the original one.
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