• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04b
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• pp.359-364
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• 1993

As advanced method for the automatic generation of parametric models in computer-aided design systems is required for most of two-dimensional model which is represented as a set of geometric elements, and constr- aining scheme formulas. The development system uses geometirc constrainis and topology parameters which are derived from feature recognition and grouping the design entities into optimal ones from pre-designed drawings. The aim of this paper is to present guidelines for the application and development of parametric design modules for the standard parts in mechaniscal system, the basic constitutional part of mold base, and other 2D features.

• Journal of the Korean Statistical Society
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• v.35 no.1
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• pp.49-61
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• 2006

Wu et al. (1992) constructed some general classes of tight asymmetric orthogonal arrays of strength 2 using the method of grouping. Rains et al. (2002) obtained asymmetric orthogonal arrays of strength 2 using the concept of mixed spread in finite projective geometry. In this paper, we obtain some new tight asymmetric orthogonal arrays of strength 2 using the concept of mixed partition in finite projective geometry.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.107-110
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• 1997

본 논문에서는 선형 이진 신경회로망 (Linear Binary Neural Network)을 최소화 하기 위하여, 입력패턴의 그룹화 가능성을 측정하는 조건함수를 제시한다. 또한 이 조건식으로 그룹화 우선순위를 정하고 iteration을 통해 신경회로망을 합성하는 MSP Term Grouping Algorithm을 보인다. 여려가지 예제에 대한 실제적 합성 실험을 통해 기존의 알고리즘과 제시된 알고리즘을 비교한 결과는 제시된 알고리즘이 기존의 알고리즘 보다 작은 크기의 선형 이진 신경회로망을 합성할 수 있는 향상된 방법임을 보여준다.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.6
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• pp.1080-1086
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• 1987

SLAGEN, a system for symbolic cell layout based on artificial intelligence approach, takes as input a transistor connection description of CMOS standard cells and environment information, and outputs a symbolic layout description. SLAGEN performas transistor grouping by a heuristic search method, in order to minimize the number of separations, and then performs group reordering and transistor reordering with an eye toward minimizing routing. Next, SLAGEN creates a rough initial routing in order to guarantee functionality and correctness, and then improve the initial routing by a rule-based approach.

• 한국데이터정보과학회:학술대회논문집
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• 2004.10a
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• pp.77-86
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• 2004

Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper we used k-means clustering of several clustering techniques. The k-means Clustering is classified as a partitional clustering method. We analyze 2002 Gyeongnam social indicator survey data using k-means clustering techniques for environmental information. We can use these outputs given by k-means clustering for environmental preservation and environmental improvement.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.4
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• pp.236-247
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• 1993

An davanced method for the automatic generation of parametric models in computer- aided design systems is required for most of two-dimensional model which is represented as a set of geometric elements, and constraining scheme formulas. The development system uses geometric constraints and support of topology parameters from feature recognition and grouping the design entities into optimal ones from pre-designed drawings. The aim of this paper is to present guidelines for the application and development of parametric design modules for the standard parts in mechanical system, the basic constitutional part of mold base, and other 2D features.

• Journal of the military operations research society of Korea
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• v.37 no.1
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• pp.147-159
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• 2011

Recently, the military service period has been shortened continuously. Meanwhile, more skilled airmen are needed as the complexity of weapon systems increase. This phenomenon could lead to a disastrous result such as deteriorating the level of the readiness and the fighting power. We suggest a method to improve recruit's maintenance capability rapidly by assigning airmen to jobs appropriate to their characteristics using Datamining methods (K-menas and CART). We focus on the assigning method for air force's air-munition maintenance airmen since they are requested more skilled than other airmen. Grouping airmen with k-means method and devising classification rule with CART algorithm, we found that airmen's proficiency arrival period could be shortened by 1.79 months when they are assigned in the suggested way.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.488-494
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• 2001

Many literatures, so far, have concentrated on approaches employing dependent coordinates set resulting in computational burden of constraint forces, which is needless in many cases. Some researchers developed methods to remove or calculate it efficiently. But systematic generation of the motion equation using independent coordinates set by Kane's equation is possible for any closed loop system. Independent velocity transformation method builds the smallest size of motion equation, but needs practically more complicated code implementation. In this study, dependent velocity matrix is systematically transformed into independent one using dependent-independent transformation matrix of each body group, and then motion equation free of constraint force is constructed. This method is compared with the other approach by counting the number of multiplications for car model with 15 d.o.f..

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.1
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• pp.9-15
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• 2016

Source localization has developed in various fields of signal processing including radar, sonar, and wireless communication, etc. Source localization can be found by estimating the time difference of arrival between the each of sensors. Several methods like the NLS(Nonlinear Least Square) cost function have been proposed in order to improve the performance of time delay estimation. In this paper, we propose a clustering method using the four sensors with the same aperture as previous methods of using the three sensors. Clustering method can be improved the source localization performance by grouping similar estimated values. The performance of source localization using clustering method is evaluated by Monte Carlo simulation.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.111-125
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• 2011

Clustering is a process of grouping similar or relevant documents into a cluster and assigning a meaningful concept to the cluster. By this process, clustering facilitates fast and correct search for the relevant documents by narrowing down the range of searching only to the collection of documents belonging to related clusters. For effective clustering, techniques are required for identifying similar documents and grouping them into a cluster, and discovering a concept that is most relevant to the cluster. One of the problems often appearing in this context is the detection of a complex concept that overlaps with several simple concepts at the same hierarchical level. Previous clustering methods were unable to identify and represent a complex concept that belongs to several different clusters at the same level in the concept hierarchy, and also could not validate the semantic hierarchical relationship between a complex concept and each of simple concepts. In order to solve these problems, this paper proposes a new clustering method that identifies and represents complex concepts efficiently. We developed the Hierarchical Overlapping Clustering (HOC) algorithm that modified the traditional Agglomerative Hierarchical Clustering algorithm to allow overlapped clusters at the same level in the concept hierarchy. The HOC algorithm represents the clustering result not by a tree but by a lattice to detect complex concepts. We developed a system that employs the HOC algorithm to carry out the goal of complex concept detection. This system operates in three phases; 1) the preprocessing of documents, 2) the clustering using the HOC algorithm, and 3) the validation of semantic hierarchical relationships among the concepts in the lattice obtained as a result of clustering. The preprocessing phase represents the documents as x-y coordinate values in a 2-dimensional space by considering the weights of terms appearing in the documents. First, it goes through some refinement process by applying stopwords removal and stemming to extract index terms. Then, each index term is assigned a TF-IDF weight value and the x-y coordinate value for each document is determined by combining the TF-IDF values of the terms in it. The clustering phase uses the HOC algorithm in which the similarity between the documents is calculated by applying the Euclidean distance method. Initially, a cluster is generated for each document by grouping those documents that are closest to it. Then, the distance between any two clusters is measured, grouping the closest clusters as a new cluster. This process is repeated until the root cluster is generated. In the validation phase, the feature selection method is applied to validate the appropriateness of the cluster concepts built by the HOC algorithm to see if they have meaningful hierarchical relationships. Feature selection is a method of extracting key features from a document by identifying and assigning weight values to important and representative terms in the document. In order to correctly select key features, a method is needed to determine how each term contributes to the class of the document. Among several methods achieving this goal, this paper adopted the $x^2$�� statistics, which measures the dependency degree of a term t to a class c, and represents the relationship between t and c by a numerical value. To demonstrate the effectiveness of the HOC algorithm, a series of performance evaluation is carried out by using a well-known Reuter-21578 news collection. The result of performance evaluation showed that the HOC algorithm greatly contributes to detecting and producing complex concepts by generating the concept hierarchy in a lattice structure.