• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.114-120
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• 1999

본 연구는 Heuristic 알고리즘 및 유전자알고리즘(GA)을 이용하여 3단계의 통합차량운송계획 모델의 개발이다. 차량경로문제(VRP : Vehicle Routing Problem)를 해결하기 위한 접근방법으로 기존의 Saving 알고리즘을 개선하여 사용하였으며 유전자 알고리즘(Genetic Algorithm)의 각종 연산자 (Operators)들을 계산하여 사용하였다. 본 모델은 다음 3단계의 접근방법을 사용하였다 ; 1) 다 물류 센터의 문제해결을 위한 영역활당(Sector Clustering) 모델, 2) 경로계획모델(VRP Model), 및 3) 최적 운송계획모델(GA-TSP Model). 본 모델들을 다양한 운송환경에서, 거리산정방법, 가용운송장비 대수, 운송시간의 제한, 물류센터 및 운송지점의 위치 및 수요량 등 다양한 파라메터들을 고려한 통합시스템으로 3개의 Component로 구성된 GUI-Type 프로그램을 개발하고 Sample 응용결과를 보였으며 기존의 모델들 보다 우수한 결과를 보였다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1536-1544
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• 1995

We propose a design scheme of a binary decision tree and apply it to the tire tread pattern recognition problem. In this scheme, a binary decision tree is constructed by using fuzzy C-means( FCM ) algorithm. All the available features are used while clustering. At each node, the best feature or feature subset among these available features is selected based on proposed similarity measure. The decision tree can be used for the classification of unknown patterns. The proposed design scheme is applied to the tire tread pattern recognition problem. The design procedure including feature extraction is described. Experimental results are given to show the usefulness of this scheme.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.416-426
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• 2003

In this paper we propose a new method for choosing the initial values of Expectation-Maximization(EM) algorithm that has been used in various applications for clustering. Conventionally, the initial values were chosen randomly, which sometimes yields undesired local convergence. Later, K-means clustering method was employed to choose better initial values, which is currently widely used. However the method using K-means still has the same problem of converging to local points. In order to resolve this problem, a new method of initializing values for the EM process. The proposed method not only strengthens the characteristics of EM such that the number of iteration is reduced in great amount but also removes the possibility of falling into local convergence.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.439-448
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• 2006

Previous recognition/clustering algorithms such as Kohonen SOM(Self-Organizing Map), MLP(Multi-Layer Percecptron) and SVM(Support Vector Machine) might not adapt to unexpected input pattern. And it's recognition rate depends highly on the complexity of own training patterns. We could make up for and improve the weak points with lowering complexity of original problem without losing original characteristics. There are so many ways to lower complexity of the problem, and we chose a kernel concepts as an approach to do it. In this paper, using a kernel concepts, original data are mapped to hyper-dimension space which is near infinite dimension. Therefore, transferred data into the hyper-dimension are distributed spasely rather than originally distributed so as to guarantee the rate to be risen. Estimating ratio of recognition is based on a new similarity-probing and learning method that are proposed in this paper. Using CEDAR DB which data is written in cursive letters, 0 to 9, we compare a recognition/clustering performance of kSOM that is proposed in this paper with previous SOM.

• Journal of Computing Science and Engineering
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• v.6 no.3
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• pp.227-242
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• 2012

Vehicular Ad Hoc Networks (VANETs) are highly dynamic and unstable due to the heterogeneous nature of the communications, intermittent links, high mobility and constant changes in network topology. Currently, some of the most important challenges of VANETs are the scalability problem, congestion, unnecessary duplication of data, low delivery rate, communication delay and temporary fragmentation. Many recent studies have focused on a hybrid mechanism to disseminate information implementing the store and forward technique in sparse vehicular networks, as well as clustering techniques to avoid the scalability problem in dense vehicular networks. However, the selection of intermediate nodes in the store and forward technique, the stability of the clusters and the unnecessary duplication of data remain as central challenges. Therefore, we propose an adaptable destination-based dissemination algorithm (DBDA) using the publish/subscribe model. DBDA considers the destination of the vehicles as an important parameter to form the clusters and select the intermediate nodes, contrary to other proposed solutions. Additionally, DBDA implements a publish/subscribe model. This model provides a context-aware service to select the intermediate nodes according to the importance of the message, destination, current location and speed of the vehicles; as a result, it avoids delay, congestion, unnecessary duplications and low delivery rate.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.541-546
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• 2006

Data Mining is the process of finding hidden patterns inside a large data set. Cluster analysis has been used as a popular technique for data mining. It is a fundamental process of data analysis and it has been Playing an important role in solving many problems in pattern recognition and image processing. If fuzzy cluster analysis is to make a significant contribution to engineering applications, much more attention must be paid to fundamental decision on the number of clusters in data. It is related to cluster validity problem which is how well it has identified the structure that Is present in the data. In this paper, we design an adaptive data mining model using fuzzy performance measures. It discovers clusters through an unsupervised neural network model based on a fuzzy objective function and evaluates clustering results by a fuzzy performance measure. We also present the experimental results on newsgroup data. They show that the proposed model can be used as a document classifier.

• Journal of KIISE:Databases
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• v.32 no.5
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• pp.498-508
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• 2005

A shape data is used one oi most important feature for image retrieval as data to reflect meaning of image. Especially, structural feature of shape is widely studied because it represents primitive properties of shape and relation information between basic units well. However, most structural features of shape have the problem that it is not able to guarantee an efficient search time because the features are expressed as graph or tree. In order to solve this problem, we generate curvature-based morphological graph, End design key to cluster shapes from this graph. Proposed this graph have contour features and morphological features of a shape. Shape retrieval is accomplished by stages. We reduce a search space through clustering, and determine total similarity value through pattern matching of external curvature. Various experiments show that our approach reduces computational complexity and retrieval cost.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.201-208
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• 2005

Recently, the container terminal requires the study of method to increase efficiency through change of its operation method. Loading plan is a very important part to increase the efficiency of container terminal. Loading Plan is largely divided into two cases, deciding loading location and loading scheduling and this Paper proposes a more efficient method of container loading scheduling. Container loading scheduling is a problem of combination optimization to consider several items of loading location and operation equipments. etc. An existing method of cluster composition that decides the order of container loading scheduling has a restriction to increase the efficiency of work owing to rehandling problem. Therefore, we Propose a more efficient method of container loading scheduling which composes containers with identical attribution, based on ship loading list and yard map, into stack units of cluster, applying to hierarchical clustering method, and defines the restriction of working order. In this process, we can see a possible working path among clusters by defining the restriction of working order and search efficiency will be increased because of restricted search for working path.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.777-781
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• 2001

This paper presents an image contrast enhancement technique for improving the low contrast images using the improved IAFC(Integrated Adaptive Fuzzy Clustering) model. The low pictorial information of a low contrast image is due to the vagueness or fuzziness of the multivalued levels of brightness rather than randomness. Fuzzy image processing has three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. Using a new model of automatic crossover point selection, optimal crossover point is selected automatically. The problem of crossover point selection can be considered as the two-category classification problem. The improved IAFC model is used to classify the image into two classes. The proposed method is applied to several experimental images with 256 gray levels and the results are compared with those of the histogram equalization technique. We utilized the index of fuzziness as a measure of image quality. The results show that the proposed method is better than the histogram equalization technique.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.251-260
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• 2011

In this paper, we proposed a scheme that it safely exchanges encrypted keys without Trust Third Party (TTP) and Pre-distributing keys in multi-hop clustering sensor networks. Existing research assume that it exists a TTP or already it was pre-distributed a encrypted key between nodes. However, existing methods are not sufficient for USN environment without infrastructure. Some existing studies using a random number Diffie-Hellman algorithm to solve the problem. but the method was vulnerable to Replay and Man-in-the-middle attack from the malicious nodes. Therefore, authentication problem between nodes is solved by adding a ��TESLA. In this paper, we propose a modified Diffie-Hellman algorithm that it is safe, lightweight, and robust pair-wise agreement algorithm by adding One Time Password (OTP) with timestamp. Lastly, authentication, confidentiality, integrity, non-impersonation, backward secrecy, and forward secrecy to verify that it is safe.