• Annual Conference on Human and Language Technology
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• 2001.10d
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• pp.175-182
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• 2001

본 논문은 사전에 기반한 질의변환 교차언어 문서검색에서, 대역어 중의성 문제를 해결하기 위한, 질의어 가중치 부여 및 구조화 방법을 제안한다. 제안하는 방법의 질의 변환 과정은 다음의 세 단계로 이루어진다. 첫째, 대역어 클러스터링을 통해 먼저 질의어 단어의 적합한 의미를 결정짓고, 둘째, 문맥정보와 지역정보를 이용하여 후보 대역어들간의 상호관계를 분석하며, 셋째, 각 후보 대역어들을 연결하여, 후보 질의어를 만들고 각각에 가중치를 부여하여 weighted Boolean 질의어로 생성하게 된다. 이를 통해, 단순하고 경제적이지만, 높은 성능을 낼 수 있는 사전에 의한 질의변환 교차언어 문서검색 방법을 제시하고자 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.254-257
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• 2015

차량 인식 기술은 운전자에게 차량 충돌과 같은 위험요소를 사전에 인식시키거나 차량을 자동으로 제어하는 기술로 각광 받고 있다. 본 논문에서는 입력 영상에서 차량이 나타날 수 있는 관심 영역을 설정한 다음 미리 학습된 검출기를 통한 Haar-like와 Adaboost 알고리즘으로 차량 후보 영역을 검출하고 중복된 영역을 제거하기 위인식 기술해 클러스터링 기법을 적용하고, 칼만필터로 프레임 영상에서 차량을 추적 하고, 다시 중복된 영역에 대해 클러스터링 기법을 적용하는 방법을 제안하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.523-528
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• 2015

In this paper, we propose a new multiresolutional and dynamic approach of the EM algorithm. EM is a very popular and powerful clustering algorithm. EM, however, has problems that indexes multiresolution data and requires a priori information on a proper number of clusters in many applications, To solve such problems, the proposed EM algorithm can impose a multiresolution kd-tree structure in the E-step and allocates a cluster based on sequential data. To validate clusters, we use a merge criteria for cluster merging. We demonstrate the proposed EM algorithm outperforms for texture image segmentation.

• The KIPS Transactions:PartC
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• v.17C no.2
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• pp.165-172
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• 2010

Sensor network can be applied in many areas in our life, and security is very important in applying sensor network. For secure sensor communication, pairwise key establishment between sensor nodes is essential. In this paper, we cluster the network field in hexagonal shapes and preassign three different kinds of key information for each sensor according to its expected location. We adopt overlapped key string pool concept for our clustered network architecture and every node uses the part of sub-strings for setting up pairwise keys with all neighboring nodes in its own cluster and from different clusters according to respective position with small amount of information. Our proposal decreases the memory requirement and increases security level efficiently.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.1-10
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• 2024

Attackers tend to use similar vulnerabilities when finding their next target IT assets. They also continuously search for new attack targets. Therefore, it is essential to find the potential targets of attackers in advance. Our method proposes a novel approach for efficient vulnerable asset management and zero-day response. In this paper, we propose the ability to detect the IT assets that are potentially infected by the recently discovered vulnerability based on clustering and similarity results. As the experiment results, 86% of all collected assets are clustered within the same clustering. In addition, as a result of conducting a similarity calculation experiment by randomly selecting vulnerable assets, assets using the same OS and service were listed.

• Information Systems Review
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• v.16 no.1
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• pp.37-50
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• 2014

Conventional case-based reasoning (CBR) does not perform efficiently for high volume dataset because of case-retrieval time. In order to overcome this problem, some previous researches suggest clustering a case-base into several small groups, and retrieve neighbors within a corresponding group to a target case. However, this approach generally produces less accurate predictive performances than the conventional CBR. This paper suggests a new hybrid case-based reasoning method which dynamically composing a searching pool for each target case. This method is applied to diagnose for the heart disease dataset. The results show that the suggested hybrid method produces statistically the same level of predictive performances with using significantly less computational cost than the CBR method and also outperforms the basic clustering-CBR (C-CBR) method.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.159-168
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• 2004

This paper presents hybrid classification method to improve the performance of satellite images classification by combining Bayesian maximum likelihood classifier, ISODATA clustering and fuzzy C-Means algorithm. In this paper, the training data of each class were generated by separating the spectral signature using ISODATA clustering. We can classify according to pixel's membership grade followed by cluster center of fuzzy C-Means algorithm as the mean value of training data for each class. Bayesian maximum likelihood classifier is performed with prior probability by result of fuzzy C-Means classification. The results shows that proposed method could improve performance of classification method and also perform classification with no concern about spectral signature of the training data. The proposed method Is applied to a Landsat TM satellite image for the verifying test.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.29-43
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• 2023

It is necessary to grasp the characteristics of traffic flow passing through a specific road section and the topological structure of the road in advance in order to quickly prepare a movement management strategy in the event of a disaster or disaster. It is because it can be an essential basis for road managers to assess vulnerabilities by microscopic road units and then establish appropriate monitoring and management measures for disasters or disaster situations. Therefore, this study presented spatial density, time occupancy, and betweenness centrality index to evaluate vulnerabilities by road link in the city department and defined spatial-temporal and topological vulnerabilities by clustering analysis based on distance and density. From the results of this study, road administrators can manage vulnerabilities by characterizing each road link group. It is expected to be used as primary data for selecting priority control points and presenting optimal routes in the event of a disaster or disaster.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.11-20
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• 2019

Numerous enterprises, organizations and individual users are exposed to large DDoS (Distributed Denial of Service) attacks. DDoS attacks are performed through a BotNet, which is composed of a number of computers infected with a malware, e.g., zombie PCs and a special computer that controls the zombie PCs within a hierarchical chain of a command system. In order to detect a malware, a malware detection software or a vaccine program must identify the malware signature through an in-depth analysis, and these signatures need to be updated in priori. This is time consuming and costly. In this paper, we propose a botnet detection scheme that does not require a periodic signature update using an artificial neural network model. The proposed scheme exploits Word2Vec and accelerated hierarchical density-based clustering. Botnet detection performance of the proposed method was evaluated using the CTU-13 dataset. The experimental result shows that the detection rate is 99.9%, which outperforms the conventional method.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.7-16
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• 2009

Some clustering algorithms have a problem that an image is over-segmented since both the spatial information between the segmented regions is not considered and the number of the clusters is defined in advance. Therefore, they are difficult to be applied to the applicable fields. This paper proposes the new post-processing methods, a reclassification of the inhomogeneous clusters and a region merging using Baysian algorithm, that improve the segmentation results of the clustering algorithms. The inhomogeneous cluster is firstly selected based on variance and between-class distance and it is then reclassified into the other clusters in the reclassification step. This reclassification is repeated until the optimal number determined by the minimum average within-class distance. And the similar regions are merged using Baysian algorithm based on Kullbeck-Leibler distance between the adjacent regions. So we can effectively solve the over-segmentation problem and the result can be applied to the applicable fields. Finally, we design a classification system for the segmented regions to validate the proposed method. The segmented regions are classified by SVM(Support Vector Machine) using the principal colors and the texture information of the segmented regions. In experiment, the proposed method showed the validity for various real-images and was effectively applied to the designed classification system.