• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.8
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• pp.1241-1249
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• 1997

The problem of structural topology optimization can be relaxed and converted into the optimal density distribution problem. The optimal density distribution must be post-processed to get the real shape of the structure. The extracted shape can then be used for the next process, which is usually shape optmization based on the boundary movement method. In the practical point of view, it is very important to get the optimal density distribution from which the corresponding shape can easily be extracted. Among many other factors, the presence of checker-board patterns is a powerful barrier for the shape extraction job. The nature of checker-board patterns seems to be a numerical locking. In this paper, an efficient algorithm is presented to suppress the checker-board patterns. At each iteration, density is re-distributed after it is updated according to the optimization rule. The algorithm also results in the optimal density distribution whose corresponding shape has smooth boundary. Some examples are presented to show the performance of the density re-distribution algorithm. Checker-board patterns are successfully suppressed and the resulting shapes are considered very satisfactory.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.8
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• pp.1123-1128
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• 2012

The goal of this research is to develop an error-driven noun-connection rules which is used for breaking complicate nouns in Korean morphology analysis module. We collected complicate nouns from Web sites, and analyzed them by CnuMa. Whenever we find errors from outputs of the analyzer, we write noun-connection rules to correct the errors. The noun-connection rules are devised by considering left/right contexts in compound nouns. The error-driven noun-connection rules are helpful in improving precision and recall of a Korean morphology analyzer, CnuMa by 2.8% and 10.8%, respectively.

• Science of Emotion and Sensibility
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• v.10 no.2
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• pp.147-154
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• 2007

Electrocardiogram(ECG) being the recording of the heart's electrical activity provides valuable clinical information about heart's status. Many researches have been pursued for heart disease diagnosis using ECG so far. However, electrocardio-graph uses foreign diagnosis algorithm in the con due to inaccuracy of domestic diagnosis results for a heart disease. This paper proposes ST-segment extraction technique diagnosing heart disease parameter from raw ECG data. As the ST-segment is used for prediction of Coronary Artery Disease, we can predict heart disease using classification approach in data mining technique. We can also predict patient's clinical characterization from patient clinical data.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.297-306
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• 2007

지능정보시스템 구축에 있어서 자동화가 어려운 단계중의 하나인 규칙 습득을 위해 활용되는 방법중의 하나가 제한된 언어집합 기법을 이용하는 것이다. 그러나 제한된 언어집합 기법을 이용해 규칙을 생성하기 위해서는 규칙을 구성하는 변수와 그 값들에 대한 정보가 사전에 정의되어 있어야 하는데, 유동성이 큰 웹 환경에서 예상 가능한 모든 변수와 그 값을 사전에 정의하는 것이 매우 어렵다. 이에 본 연구에서는 이러한 한계를 극복하기 위해 제한된 언어집합 기법과 온톨로지를 이용한 규칙 생성 방법론을 제시하였다. 이를 위해 지식의 습득 대상이 되는 특정 문장은 문법구조 분석기를 이용해 파싱을 수행하며, 파싱된 단어들을 이용해 규칙의 구성 요소인 변수와 그 값을 식별한다. 그러나 규칙을 내포한 자연어 문장의 불완전성으로 인해 변수가 명확하지 않거나 완전히 빠져 있는 경우가 흔히 발생하며, 이로 인해 온전한 형식의 규칙 생성이 어렵게 된다. 이 문제는 도메인 온톨로지의 생성을 통해 해결하였다. 이 온톨로지는 특정 도메인을 구성하고 있는 개념들간의 관계를 포함하고 있다는 점에서는 기존의 온톨로지와 유사하지만, 규칙을 완성하는 과정에서 사용된 개념들의 사용빈도를 기반으로 온톨로지의 구조를 변경하고, 결과적으로 더 정확한 규칙의 생성을 지원한다는 점에서 기존의 온톨로지와 차별화된다. 이상의 과정을 통해 식별된 규칙의 구성요소들은 제한된 언어집합 기법을 이용해 구체화된다. 본 연구에서 제안하는 방법론을 설명하기 위해 임의의 인터넷 쇼핑몰에서 수행되는 배송관련 웹 페이지를 선정하였다. 본 방법론은 XRML에서의 지식 습득 과정의 효율성 제고에 기여할 수 있을 것으로 기대된다.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.999-1009
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• 2004

As a new approach to the diagnosis of cancers, bioinformatics attracts great interest these days. Machine teaming techniques have produced valuable results, but the field of medicine requires not only highly accurate classifiers but also the effective analysis and interpretation of them. Since gene expression data in bioinformatics consist of tens of thousands of features, it is nearly impossible to represent their relations directly. In this paper, we propose a method composed of a feature selection method and genetic programming. Rank-based feature selection is adopted to select useful features and genetic programming based arithmetic operators is used to generate classification rules with features selected. Experimental results on Lymphoma cancer dataset, in which the proposed method obtained 96.6% test accuracy as well as useful classification rules, have shown the validity of the proposed method.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1968-1971
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• 2003

We propose a knowledge-based algorithm for extracting an object boundary from low-quality image like the forward looking infrared image. With the multi-classes training data set, the global shape is modeled by multispace KL(MKL)[1] and curvature model. And the objective function for fitting the deformable boundary template represented by the shape model to true boundary in an input image is formulated by Bales rule. Simulation results show that our method has more accurateness in case of multi-classes training set and performs better in the sense of computation cost than point distribution model(PDM)[2]. It works well in distortion under the noise, pose variation and some kinds of occlusions.

• Journal of KIISE:Databases
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• v.32 no.3
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• pp.219-227
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• 2005

In this paper, we propose an efficient algorithm which generates frequent itemsets by only one database scanning. A frequent itemset is subset of an itemset which is accessed by a transaction. For each item, if informations about transactions accessing the item are exist, it is possible to generate frequent itemsets only by the extraction of items haying an identical transaction ID. Proposed method in this paper generates the data structure which stores transaction ID for each item by only one database scanning and generates 2-frequent itemsets by using the hash technique at the same time. k(k$\geq$3)-frequent itemsets are simply found by comparing previously generated data structure and transaction ID. Proposed algorithm can efficiently generate frequent itemsets by only one database scanning .

• Journal of Internet Computing and Services
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• v.1 no.1
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• pp.63-72
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• 2000

Data mining means a process of nontrivial extraction of hidden knowledge or potentially useful information from data in large databases. Data mining algorithm is a multi-disciplinary field of research; machine learning, statistics, and computer science all make a contribution. Different classification schemes can be used to categorize data mining methods based on the kinds of tasks to be implemented and the kinds of application classes to be utilized, and classification has been identified as an important task in the emerging field of data mining. Since classification is the basic element of human's way of thinking, it is a well-studied problem in a wide varietyof application. In this paper, we propose a classifier system based on genetic algorithm with robust property, and the proposed system is evaluated by applying it to nDmC problem related to classification task in data mining.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1083-1094
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• 2020

Recognizing plant species based on leaf images is challenging because of the large inter-class variation and inter-class similarities among different plant species. The effective extraction of leaf descriptors constitutes the most important problem in plant leaf recognition. In this paper, a multi-scale angular description method is proposed for fast and accurate leaf recognition and retrieval tasks. The proposed method uses a novel scale-generation rule to develop an angular description of leaf contours. It is parameter-free and can capture leaf features from coarse to fine at multiple scales. A fast Fourier transform is used to make the descriptor compact and is effective in matching samples. Both support vector machine and k-nearest neighbors are used to classify leaves. Leaf recognition and retrieval experiments were conducted on three challenging datasets, namely Swedish leaf, Flavia leaf, and ImageCLEF2012 leaf. The results are evaluated with the widely used standard metrics and compared with several state-of-the-art methods. The results and comparisons show that the proposed method not only requires a low computational time, but also achieves good recognition and retrieval accuracies on challenging datasets.

• Journal of Internet Computing and Services
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• v.9 no.1
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• pp.129-135
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• 2008

This paper presents a methodology to forecast KOSPI index by extracting fuzzy rules based on the neural network with weighted fuzzy membership functions (NEWFM) and the minimized number of input features using the distributed non-overlap area measurement method. NEWFM classifies upward and downward cases of KOSPI using the recent 32 days of CPPn,m (Current Price Position of day n for n-1 to n-m days) of KOSPI. The five most important input features among CPPn,m and 38 wavelet transformed coefficients produced by the recent 32 days of CPPn,m are selected by the non-overlap area distribution measurement method. For the data sets, from 1991 to 1998, the proposed method shows that the average of forecast rate is 67.62%.