In this paper, we describe how to exploit a machine-readable dictionary (MRD) and domain-specific text corpus in supporting the construction of domain ontologies that specify taxonomic and non-taxonomic relationships among given domain concepts. In building taxonomic relationships (hierarchical structure) of domain concepts, some hierarchical structure can be extracted from a MRD with marked subtrees that may be modified by a domain expert, using matching result analysis and trimmed result analysis. In building non-taxonomic relationships (specification templates) of domain concepts, we construct concept specification templates that come from pairs of concepts extracted from text corpus, using WordSpace and an association rule algorithm. A domain expert modifies taxonomic and non-taxonomic relationships later. Through case studies with "the Contracts for the International Sales of Goods (CISG)" and "XML Common Business Library (xCBL)", we make sure that our system can work to support the process of constructing domain ontologies with a MRD and text corpus.

Software reuse has been recognized as important. According to our research, when a software product is reused, products correlated to the reused one may be reusable. This paper proposes a model for software products and a technique to retrieve correlated products. The paper also presents equations to evaluate correlation values, which is guidance for selecting reusable correlated products. Since correlated products can be identified by tracing product relationships, the proposed model manages both products and relationships.

Many fuzzy traffic controllers adjust the extension time of the green phase with the fuzzy input variables, arrival and queue. However, in our experiments, we found that the two input variables are not sufficient for an intersection where traffic flow rates change and thus, in this paper, traffic volume is used as an additional variable. Traffic volume is defined as the number of vehicles entering an intersection every second. In designing a fuzzy traffic controller, an ad-hoc approach is usually used to find membership functions and fuzzy control rules showing good performance. That is, initial ones are generated by human operators and modified many times based on the results of simulation. To partially overcome the limitations of the ad-hoc approach, we use genetic algorithms to automatically determine the membership functions for terms of each fuzzy variable when fuzzy control rules are given by hand. The experimental results indicate that a fuzzy logic controller with volume variable outperforms conventional ones with no volume variable in terms of the average delay and the average velocity. Also, the controller shows better performance when membership functions generated by a genetic algorithms instead of ones generated by hand are used.

Correct and quick generation of a membership function is the key point when we implement a real-time fuzzy logic controller. In this Letter, we presented two efficient VLSI architectures, one to generate triangle-shaped and the other to generate trapezoid-shaped membership functions. Simulation results show that our designs require lower hardware cost but achieve faster working rate.

The objective of this paper is to generate a wideband and temporal response from three-dimensional conducting structures. This is accomplished through the use of a hybrid method that involves generation of early time and low frequency information. These two are mutually complementary and contain all the necessary information for a sufficient record length. Utilizing orthogonal polynomials, time domain signal of scattering electromagnetic field could be expressed in an efficient way as well as the corresponding frequency domain responses. The available data is simultaneously extrapolated in both domains. Computational load for electromagnetic analysis, the Method of Moment (MOM), can be significantly reduced.

The plan of High Altitude Platform Station (HAPS) is considered as a revolutionary wireless system plan with several economic and technological advantages over both space- or ground-based counterparts. In this paper, we propose a joint system of terrestrial and HAPS cellular for Wideband-CDMA mobile communication. This system makes the conventional terrestrial W-CDMA cellular area smaller and the remainder area covered by HAPS to increase the total capacity. Furthermore in down link channel, we introduce the polarized wave and doughnut-like radiation. However, in the proposed system, the performance would be dependent on the terminal position especially near the boundary of doughnut-like cell zone. To overcome this, site diversity that uses both signals from terrestrial Base Station and HAPS Base Station is also introduced. To confirm the availability of the proposed system, we evaluate the system performance by computer simulation.

Many researches have been conducted on the recognition of Thai characters. Different approaches, such as neural network, syntactic, and structural methods, have been proposed. However, the success in recognizing Thai characters is still limited, compared to English characters. This paper proposes an approach to recognize the printed Thai characters using the hybrid of global feature, local features, fuzzy membership function and the neural network. The global feature classifies all characters into seven main groups. Then the local features and the neural network are applied to identify the characters.

This paper describes glyph representation, that is, shape representation of inheritance relationships between a superclass and subclasses in an object-oriented programming language. The inheritance relationships in object-oriented programming languages are usually represented in a visual programming environment by a diagram of a tree graph or a nested structure. That diagram is not integrated with a code view showing control and data flows. Using the proposed representation, one can understand the inheritance relationships of classes and the assignment compatibility or type conformance just by seeing the glyphs. One thus does not need to look at a hierarchy diagram in order to recognize them. The inheritance relationships are represented by inclusion relationships of glyphs. Methods for generating suitable glyphs from a class hierarchy are also described, as is a prototype system for glyph generation. Experiments using the Java 2 Standard Edition (J2SE), which has more than 1,500 classes, show that one can recognize inheritance relationships in the proposed representation faster than in the usual textual representation. Consequently the proposed representation can facilitate the understanding of inheritance in visual object-oriented programming environments.

In this paper, a discrete-time convergence theorem for continuous-state Hopfield networks with self-interaction neurons is proposed. This theorem differs from the previous work by Wang in that the original updating rule is maintained while the network is still guaranteed to monotonically decrease to a stable state. The relationship between the parameters in a typical class of energy functions is also investigated, and consequently a "guided trial-and-error" technique is proposed to determine the parameter values. The third problem discussed in this paper is the post-processing of outputs, which turns out to be rather important even though it never attracts enough attention. The effectiveness of all the theorems and post-processing methods proposed in this paper is demonstrated by a large number of computer simulations on the assignment problem and the N-queen problem of different sizes.

Chen et al., have proposed a new estimation method for the membership values in fuzzy sets. The proposed scheme takes input from empirical/experimental data, which reflect the expert's knowledge on the relative degree of belonging of the members, and then searches for the best fit membership values of the element. Through the estimation of the practical case (Sect. 3 in [1]) the algorithm suggests to normalize the estimated membership values if there is any among them more than one and change some condition to guarantee its positiveness. In this paper, we show how to use the same imposed condition to guarantee that the estimated membership values will be within the unit interval without normalization.

Recently, two approaches investigated indexing and retrieving videos. One approach utilized the visual features of individual objects, and the other approach exploited the spatio-temporal relationships between multiple objects. In this paper, we integrate both approaches into a new video model, called the Visual-Spatio-Temporal (VST) model to represent videos. The visual features are modeled in a topological approach and integrated with the spatio-temporal relationships. As a result, we defined rich sets of VST relationships which support and simplify the formulation of more semantical queries. An intuitive query interface which allows users to describe VST features of video objects by sketch and feature specification is presented. The conducted experiments prove the effectiveness of modeling and querying videos by the visual features of individual objects and the VST relationships between multiple objects.

This paper presents a robust and nonblocking group membership protocol for large-scale distributed systems. This protocol uses the causal relation between membership-updating messages (i. e. , those specifying the adding and deleting of members) and allows the messages to be executed in a nonblocking manner. It differs from conventional group membership protocols in the following points: (1) neither global locking nor global synchronization is required; (2) membership-updating messages can be issued without being synchronized with each other, and they can be executed immediately after their arrival. The proposed protocol therefore is highly scalable, and is more tolerant to node and network failures and to network partitions than are the conventional protocols. This paper proves that the proposed protocol works properly as long as messages can eventually be received by their destinations. This paper also discusses some design issues, such as multicast communication of the regular messages, fault tolerance and application to reliable communication protocols (e. g. , TCP/IP).

The abilities of fuzzy inference methods in modeling of complicated systems are implemented to electromagnetics for the first time. The very popular and well known monopole antenna is chosen as a general example and a fast, simple and accurate fuzzy model for its input impedance is made by introducing a new point of view to impedance basic parameters. It is established that a surprisingly little number of input data points is sufficient to make a full model and also the system behavior (dominant rules) are saved as simple membership functions. The validity of the derived rules is confirmed through applying them to the case of thin-angled monopole antenna and comparing the results with the measured. Finally using the spatial membership function context, input impedance of thick-angled monopole antenna is predicted and a novel view point to conventional electromagnetic parameters is discussed to generalize the modeling method.

The novel method for automatic pattern recognition is presented. This method is based on Segment and Neighbors Matching algorithm which can be applied for recognizing of distinct well-known alphabets, complex glyphs, and Arabic scripts. In this work some different reported methods have been evaluated on Latin, Chinese characters, and Mayan glyphs with the principle objective to select those with the highest processing speed and recognition grade. The case of Mayan glyphs is more complicated due to a big number of elements in any glyph, significant variations of their representation or writing, there are more than 800 classes of glyphs and many of them with similar components and locations. The proposed method of Segments and Neighbors Matching has been developed on base of fuzzy sets and membership functions concept which can be defined during manipulation with the glyph skeleton. Next, levels of matching with predefined patterns are used for segments recognition and interpretation of whole glyph. The main characteristics of recognizing process are matching level, time of processing, grade of membership, and efficiency of interpretation that is important for incomplete glyphs images. On base of proposed method the special software RECGLYM (Mayan Glyphs Recognition) has been designed for the SUN and Intel PC computers platforms. The advantages of the proposed Segments and Neighbors Matching method are quick image processing and high probability of complex glyphs interpretation. The proposed method could be used in different applications, for example, for selection and diagnose of certain anomalies by means of processing of X-ray images or for Internet navigation and searching information searching by image similarity analysis with predefined pattern.

This paper shows the design of multi-stage fuzzy inference system with smaller number of rules based upon the optimization of rules by using the genetic algorithm. Since the number of rules of fuzzy inference system increases exponentially in proportion to the number of input variables powered by the number of membership function, it is preferred to divide the inference system into several stages (multi-stage fuzzy inference system) and decrease the number of rules compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and the output of the stage is treated as an input to the next stage. If we use the simplified inference scheme and assume the shape of membership function is given, the same backpropagation algorithm is available to optimize the weight of each rule as is usually used in the single stage inference system. On the other hand, the shape of the membership function is optimized by using the GA (genetic algorithm) where the characteristics of the membership function is represented as a set of string to which the crossover and mutation operation is applied. By combining the backpropagation algorithm and the GA, we have a comprehensive optimization scheme of learning for the multi-stage fuzzy inference system. The inference system is applied to the automatic bond rating based upon the financial ratios obtained from the financial statement by using the prescribed evaluation of rating published by the rating institution. As a result, we have similar performance of the multi-stage fuzzy inference system as the single stage system with remarkably smaller number of rules.

In this letter, the novel mapping network named self-organizing relationship (SOR) network, which can approximate the desired I/O relationship by employing the modified Kohonen's learning law, is proposed. In the modified Kohonen's learning law, the weight vectors are updated to be attracted to or repulsed from the input vector.

Although there are many multimedia presentation systems on the market, they have some shortcomings and most of them only can work on one single computer, and few of them can work on Web. Thus, in the thesis we develop a network multimedia presentation system to let users easily design the multimedia presentation without restriction on technology or presentation time and place. Our system includes 3 main components: User Interface that includes temporal specification editor, spatial specification editor and multimedia object interface, Presentation Interface and Knowledge Base. There is a dynamic homepage generator in our system and we propose a displaying algorithm based on the Allen's theory, that there exist 13 temporal relationships between two intervals, for synchronizing the media objects.

Speech signals transmitted over telephone network often suffer from interference due to ambient noise and channel distortion. In this paper, a novel frame-dependent fuzzy channel compensation (FD-FCC) method employing two-stage bias subtraction is proposed to minimize the channel effect. First, through maximum likelihood (ML) estimation over the set of all word models, we choose the word model which is best matched with the input utterance. Then, based upon this word model, a set of mixture biases can be derived by averaging the cepstral differences between the input utterance and the chosen model. In the second stage, instead of using a single bias, a frame-dependent bias is calculated for each input frame to equalize the channel variations in the input utterance. This frame-dependent bias is achieved by the convex combination of those mixture biases which are weighted by a fuzzy membership function. Experimental results show that the channel effect can be effectively canceled even though the additive background noise is involved in a telephone speech recognition system.

Distributed web caching reduces retrieval latency of World Wide Web (WWW) objects such as text and graphics. Conventional distributed web caching methods, however, require many query messages among cache servers, which limits their scalability and reliability. To overcome these problems, we propose a query caching method in which each cache server caches not only WWW objects but also a query history. This method of finding cached objects can reduce the number of query messages among cache servers, making it possible to construct a large-scale distributed web cache server. We also propose an algorithm for constructing efficient query relationships among cache servers.

In this paper, we develop a novel method for tuning parameters known as the sensitivity parameters of membership functions used in a fuzzy classifier. The proposed method performs tuning by solving a set of inequalities. Each inequality represents a range of the ratio of the sensitivity parameters between the corresponding pair of classes. The range ensures the maximum classification rate for data of the two corresponding classes used for tuning. First, we discuss how such a set of inequalities is derived. We then propose an algorithm to solve the derived set of inequalities. We demonstrate the effectiveness of the proposed tuning method using two classification problems, namely, classification of commonly used iris data, and recognition of vehicle licence plates. The results are compared with those obtained by using the existing tuning method and with those by neural networks.