• Journal of the Korean Institute of Navigation
• /
• v.13 no.2
• /
• pp.75-95
• /
• 1989

The study presents the optimum design of B/C midship structure based on the classification society's Rule. The SUMT (Sequential Unconstrained Minimization Technique), using the Direct Search Methods (Hooke and Jeeves, Simplex) is applied to the solution of this nonlinear optimum design problem with constraints. Through the optimum designs of existing ships(60k, 186k, 220k), the amount 0.45-6.18% in weight of their midship structures are obtained on the viewpoint of minimum weight design.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.03a
• /
• pp.49-52
• /
• 1998

Neural Network has good performance in pattern classification, control and many other fields by learning ability. However, there is effective rule or systematic approach to determine optimal structure. In this paper, we propose a new method to find optimal structure of feed-forward multi-layer neural network as a kind of pruning method. That eliminating redundant elements of neural network. To find redundant elements we analysis error and weight changing with Rough Set Theory, in condition of executing back-propagation leaning algorithm.

• Journal of the Korea Safety Management & Science
• /
• v.4 no.2
• /
• pp.103-111
• /
• 2002

Nowdays cyber education based on the internet is actively developed. But the management of the customers in the cyber education field is not enough. Then, in this paper, we provide the learner with the proposals of lectures to be extremely matched by analyzing the learning capacity and the greatest concern of him(her) using the methods of data mining, such as RFM, prediction, slickness, association rule, classification, and so on.

• Proceedings of the Korean Information Science Society Conference
• /
• 2000.10b
• /
• pp.170-172
• /
• 2000

고유명사를 추출하고 그 범주를 파악할 수 있다면, 이는 정보 추출이나 정보 검색, 문서 요약과 같은 분야에 도움을 줄 수 있다. 본 논문에서는 고유명사를 추출하고 그 범주를 찾는 방법을 제시한다. 고유명사가 태깅된 코퍼스로부터, 고유명사의 내부와 주변에 반복적으로 나타나는 실마리들을 규칙 기반으로 학습한다. 이를 통하여 고유명사를 찾고 그 범주를 정한다. 구현한 시스템은 경제기사 코퍼스에서 4가지 범주로 고유명사를 추출하고 분류함에 있어 79.8%의 재현율과 92.9%의 정확률, 그리고 F 평가치에서 85.8의 성능을 보인다.

• Journal of KIISE:Information Networking
• /
• v.35 no.6
• /
• pp.482-496
• /
• 2008

Packet classification in the Internet routers requires multi-dimensional search for multiple header fields for every incoming packet in wire-speed, hence packet classification is one of the most important challenges in router design. Hierarchical packet classification is one of the most effective solutions since search space is remarkably reduced every time a field search is completed. However, hierarchical structures have two intrinsic issues; back-tracking and empty internal nodes. In this paper, we propose a new hierarchical packet classification algorithm which solves both problems. The back-tracking is avoided by using the set-pruning and the empty internal nodes are avoided by applying the binary search tree. Simulation result shows that the proposed algorithm provides significant improvement in search speed without increasing the amount of memory requirement. We also propose an optimization technique applying controlled rule copy in set-pruning.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.17 no.3
• /
• pp.162-173
• /
• 1997

Automatic on-line surface inspection systems have been applied for monitoring a quality of steel strip surfaces. One of the important issues in this application is the performance of on-line defect classifiers. Rule-based classification table methods which are conventionally used for this purpose have been suffered from their low performances. In this work, probabilistic neural networks and the enhanced classification tables which are newly proposed here are applied as alternative on-line classifiers to identify types of surface defects on cold rolled strips. Probabilistic neural networks have shown very excellent performance for classification of surface defects.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.14 no.4
• /
• pp.348-359
• /
• 1989

The multitree pattern recognition algorithm proposed by [1] and [2] is modified in order to improve its performance. The basic idea of the multitree pattern classification algorithm is that the binary dceision tree used to classify an unknow pattern is constructed for each feature and that at each stage, classification rule decides whether to classify the unknown pattern or to extract the feature value according to the feature ordet. So the feature ordering needed in the calssification procedure is simple and the number of features used in the classification procedure is small compared with other classification algorithms. Thus the algorithm can be easily applied to real pattern recognition problems even when the number of features and that of the classes are very large. In this paper, the wighting factor assignment scheme in the decision procedure is modified and various classification rules are proposed by means of the weighting factor. And the branch and bound method is applied to feature subset selection and feature ordering. Several experimental results show that the performance of the multitree pattern classification algorithm is improved by the proposed scheme.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.7
• /
• pp.1531-1539
• /
• 2014

Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. In other words, the design of algorithm that exactly detects abnormal signal and classifies PVC by analyzing the persons's physical condition and/or environment and variable QRS pattern is needed. Thus, PVC classification by personalized abnormal signal detection and QRS pattern variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and subtractive operation method and selected abnormal signal sets. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of abnormal beat detection and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 98.33% in abnormal beat classification error and 94.46% in PVC classification.

• Journal of Digital Contents Society
• /
• v.2 no.2
• /
• pp.103-115
• /
• 2001

This paper proposes the two algorithms which generate a minimal decision rule and approximate inference operation, adapted the rough set and the factor space theory in fuzzy knowledge base. The generation of the minimal decision rule is executed by the data classification technique and reduct applying the correlation analysis and the Bayesian theorem related attribute factors. To retrieve the specific object, this paper proposes the approximate inference method defining the membership function and the combination operation of t-norm in the minimal knowledge base composed of decision rule. We compare the suggested algorithms with the other retrieval theories such as possibility theory, factor space theory, Max-Min, Max-product and Max-average composition operations through the simulation generating the object numbers and the attribute values randomly as the memory size grows. With the result of the comparison, we prove that the suggested algorithm technique is faster than the previous ones to retrieve the object in access time.

• Korean Journal of Remote Sensing
• /
• v.31 no.3
• /
• pp.205-214
• /
• 2015

In this study, a novel land-cover classification framework for multi-temporal SAR data is presented that can combine multiple features extracted through data transforms and multiple classifiers. At first, data transforms using principle component analysis (PCA) and 3D wavelet transform are applied to multi-temporal SAR dataset for extracting new features which were different from original dataset. Then, three different classifiers including maximum likelihood classifier (MLC), neural network (NN) and support vector machine (SVM) are applied to three different dataset including data transform based features and original backscattering coefficients, and as a result, the diverse preliminary classification results are generated. These results are combined via a majority voting rule to generate a final classification result. From an experiment with a multi-temporal ENVISAT ASAR dataset, every preliminary classification result showed very different classification accuracy according to the used feature and classifier. The final classification result combining nine preliminary classification results showed the best classification accuracy because each preliminary classification result provided complementary information on land-covers. The improvement of classification accuracy in this study was mainly attributed to the diversity from combining not only different features based on data transforms, but also different classifiers. Therefore, the land-cover classification framework presented in this study would be effectively applied to the classification of multi-temporal SAR data and also be extended to multi-sensor remote sensing data fusion.