This paper gives on overview of register windowing structure and presents advantages and limitations. Based on these advantages and disadvantages, an original approach for the design of large register file is presented, analyzed and compared with existing approaches. The advantages and disadvantages of this new approach to register file design are discussed, and conditions under which it works better than the existing approaches are outlined. Design tradeoffs are examined in an analytic and empirical study, and the results of which are summarized in the conclusion of this paper.

In this paper, a new indexing technique is proposed for efficient evaluation of nested queries on aggregation hierarchy in object-oriented data model. As an index data structure, an extended $B^{+}$ tree is introduced in which instance identifier to be searched and path information used for update of index record are stored in leaf node and subleaf node, respectively. the retrieval and update algorithm on the introduced index data structure is provided. Comparisons under a variety of conditions are given with current indexing techniques, showing improved performance in cost, i.e., the total number of pages accessed for retrieval and update.

In this paper, a method of representing the pattern classifier as a collection of hypercubic regions is proposed. This representation has following advantages over the conventional ones : 1) a simple form of human knowledge can be used in designing the classifier, 2) the form of the classifier is suit for the rule-based system, and 3) this can reduce the classification time. A method of synthesis of the classifier under this representation is also proposed and the experimental result shows that the proposed method is faster than the well-known nearest neighbor classifier.

As a general model of rule-based systems, we propose a model for a fuzzy production system having chaining rules and an inference engine associated with the model. The concept of so-called 'fuzzy petri nets' is used to model the fuzzy production system and the inference engine is designed to be capable of handling inexact knowledge. The fuzzy logic is adopted to represent vagueness in the rules and the certainty factor is used to express uncertainty of each rules given by a human expert. Parallel, inference schemes are devised by transforming Fuzzy Petri nets to matrix formula. Futher, the inference engine mechanism under the Mamdani's implication method can be desceribed by a simple algebraic formula, which makes real time inference possible.

This paper suggests a measure constraint locus which is the loci of points satisfying the necessary constraint for optimality of a measure in the configuration space. The characterization of four measures for singularity avoidance is worked out by using the measure constraint locus. It gives a global look at the performance of an inverse kinematic algorithm whien each of measures in a kinematically redundant robot is used. The invertible workspace without singularities and the topological properties both on the configuration and operational spaces are analyzed. We discuss also some limitations, based on the topological arguments of measure constraint locus, of the inverse kinematic algorithms, and compare global properties of each of measure. Therfore, a new concept called measure constraint locus gives a methodology for obtaining a conservative joint trajectory without singularities for almost entire workspace.

본 논문은 단시안에 투영된 3차원 물체의 Image에서 측정된 명암강도의 차이를 이용하여 3차원 물체의 절대거리 z 및 형상을 유출하는 수치적인 방법을 연구, 단시안에 의해서도 Camera와 물체사이의 3차원 절대거리가 구해질 수 있음을 보여주고 있다. 기발표된 이론과는 다르게 본 논문에서는 점광원을 이용하여 투영된 명암강도와 3차원 물체의 절대거리 및 형상과의 관계를 물체가 Uniform Lambertian이라는 가정하에서 새로운 관계식으로 정립하였다. 정립된 Non-Linear 관계식은 Smoothness 조건아래 $'Calculus of Variation$'방법을 사용하여 수학적 Algorithm으로 정리되어 Programming 되었고 간단한 실험방법을 이용하여 실제 Data에 적용시켜 그 타당성을 조사하였다.당성을 조사하였다.

The conventional BMA is performed with the fixed block size. For better performance at low bitrate, the block size is required to be large in relatively stationary area, while small in moving area. Thus, in this paper, a video coding technique using variable block size model is proposed. It decides the block size based on the degree of local motion defined by the local mean and variance of blocks. Computer simulation shows that the proposed method gives comparable performance to the conventional one with less bits required for motion vector coding.

In this paper, an approach for 3-D object segmentation and classification, which is based on eigen-values of polynomial function as their surface features, using neural network is proposed. The range images of 3-D objects are classified into surface primitives which are homogeneous in their intrinsic eigenvalue properties. The misclassified regions due to noise effect are merged into correct regions satisfying homogeneous constraints of Hopfield neural network. The proposed method has advantage of processing both segmentation and classification simultaneously.

Recent research on the application of fuzzy set theory to the design of control systems has led to interest in the theory of decomposition of multivariable fuzzy systems. Decomposition of multivariable control rules is preperable since it alleviates the complexity of the problem. However inference error is inevitable because of its approximate nature. In this paper we define an index of applicability which can classify whether the Gupta et. al's method can be applied to multivariable fuzzy system or not. We also propose a modified version of the decomposition which can reduce inference error and improve performance of the system.

Fuzzy controllers have been successfully applied to many cases to which conventional control algorithms are difficult to be applied. Even though the representations and the processings of data and information in the fuzzy controller are quite different from those in other control algorithms, the information processing operation that it caries out is basically a function ∫: $A{\subset}R^n{\to}R^m$, from a bounded subset A of an n-dimensional Euclidean space to a bounded subset f[A] of an m-dimensional Euclidean space, where n and m are the number of measured states and the number of control inputs of the controlled system, respectively. Under the assumptions of Mamdani's direct reasoning method and C.O.G.(center of gravity) defuzzification method, the fuzzy controllers are proven to perform the mapping of any given functions f with appropriately defined fuzzy sets. The mapping capabilities of fuzzy controllers are analyzed in detail for two cases, ∫: $R^{1}{\to}R^{1}$ and g: $R^{2}{\to}R^{1}$. Also, it will be shown that the results can be extended to multiple dimensional cases.