• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.71-78
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• 2022

This paper proposes an algorithm for 3D object detection by processing point cloud data of 3D LiDAR. Unlike 2D LiDAR, 3D LiDAR-based data was too vast and difficult to process in three dimensions. This paper introduces various studies based on 3D LiDAR and describes 3D LiDAR data processing. In this study, we propose a method of processing data of 3D LiDAR using clustering techniques for object detection and design an algorithm that fuses with cameras for clear and accurate 3D object detection. In addition, we study models for clustering 3D LiDAR-based data and study hyperparameter values according to models. When clustering 3D LiDAR-based data, the DBSCAN algorithm showed the most accurate results, and the hyperparameter values of DBSCAN were compared and analyzed. This study will be helpful for object detection research using 3D LiDAR in the future.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.50-56
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• 2011

This paper proposes 3D visualization method of compound knowledge which will be able to identify and search easily compound knowledge objects based the multidimensional relationship. For this, we structurized a compound knowledge with link and node which become the semantic network. and we suggested 3D visualization method using SOM. Also, to arrange compound knowledge from 3D space and to provide the chance of realistic and intuitional information retrieval to the user, we proposed compound knowledge 3D clustering methods using object similarity. Compound knowledge 3D visualization and clustering using SOM will be the optimum method to appear context of compound knowledge and connectivity in space-time.

• The Journal of the Korea Contents Association
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• v.8 no.2
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• pp.18-26
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• 2008

This paper presents a phased visualization method of facial expression space that enables the user to control facial expression of 3D avatars by select a sequence of facial frames from the facial expression space. Our system based on this method creates the 2D facial expression space from approximately 2400 facial expression frames, which is the set of neutral expression and 11 motions. The facial expression control of 3D avatars is carried out in realtime when users navigate through facial expression space. But because facial expression space can phased expression control from radical expressions to detail expressions. So this system need phased visualization method. To phased visualization the facial expression space, this paper use fuzzy clustering. In the beginning, the system creates 11 clusters from the space of 2400 facial expressions. Every time the level of phase increases, the system doubles the number of clusters. At this time, the positions of cluster center and expression of the expression space were not equal. So, we fix the shortest expression from cluster center for cluster center. We let users use the system to control phased facial expression of 3D avatar, and evaluate the system based on the results.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.717-730
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• 2004

Recently. many researches on the personalization of a web-site have been actively made. The web personalization predicts the sets of the most interesting URLs for each user through data mining approaches such as clustering techniques. Most existing methods using clustering techniques represented the web transactions as bit vectors that represent whether users visit a certain WRL or not to cluster web transactions. The similarity of the web transactions was decided according to the match degree of bit vectors. However, since the existing methods consider only whether users visit a certain URL or not, users' interestingness on the URL is excluded from clustering web transactions. That is, it is possible that the web transactions with different visit proposes or inclinations are classified into the same group. In this paper. we propose an enhanced transaction modeling with interestingness weight to solve such problems and a new similarity measuring method that exploits the proposed transaction modeling. It is shown through performance evaluation that our similarity measuring method improves the accuracy of the web transaction clustering over the existing method.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.293-298
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• 2006

Many data are stored in database. For getting any information from many data, we use the query sentences. These information is basic and simple. Data mining method is various. In this paper, we manage clustering and association rules. We present a method for finding the better association rules, and we solve a problem of the existing association rules. We propose and apply a new clustering method to fit for association rules. It is not clustering of the existing distance basis or category basis. If we find association rules of each clusters, we can get not only existing rules found in all transaction but also rules that will be characteristics of clusters. Through this study, we can expect that we will reduce the number of many transaction access in large databases and find association of small group.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.417-426
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• 2003

The problem of finding clusters in high dimensional data is well known in the field of data mining for its importance, because cluster analysis has been widely used in numerous applications, including pattern recognition, data analysis, and market analysis. Recently, a new framework, projected clustering, to solve the problem was suggested, which first select subdimensions of each candidate cluster and then each input point is assigned to the nearest cluster according to a distance function based on the chosen subdimensions of the clusters. We propose a new algorithm for subdimensional clustering of high dimensional data, each of the three major steps of which partitions the input points into several candidate clutters with proper numbers of points, filters the clusters that can not be useful in the next steps, and then merges the remaining clusters into the predefined number of clusters using a closeness function, respectively. The result of extensive experiments shows that the proposed algorithm exhibits better performance than the other existent clustering algorithms.

• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.77-83
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• 2010

Recently, IT-based environment is changing rapidly as changing in web services platform, evolution of cloud computing environments and expanding the base of a smart market. Accordingly, monitoring development of environment is changing quickly. So a customizable SaaS-based monitoring tool is required to provide monitoring services. It has to support a variety of environmental monitoring and a resource managers with requested information, and by an enhanced monitoring framework in clouding environment of management system. In this paper, the 3D viewer for the management of sensor node management system was designed and built. Through the 3D viewer by enhancing the accessibility and visibility, the sensor network will allow resources to be used efficiently.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.24-27
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• 1998

최근 컴퓨터의 속도 및 용량의 확장과 더불어 3차원 정보에 대한 연구의 필요성이 요구되고 있다. 본 논문에서는이 여기에 관한 연구의 하나로 FCV(Fuzzy c-Varieties)클러스터링의 방법을 써서 3차원 데이터의 변과 장점을 찾아 3차원 물체를 구성하여 중복된 자료의 크기를 압축하는 방법을 제시한다. 여기에 따른 문제점으로 클러스터의 개수를 결정하는 문제가 있는데 이는 fuzzy classification entropy로 해결하였다.

• Journal of KIISE:Software and Applications
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• v.26 no.3
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• pp.365-379
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• 1999

본 논문에서는 객체지향 데이터베이스의 클래스 계층에 대한 색인기법으로 이차원 색인구조를 이용하여 조율 가능한 이차원 클래스 색인기법인 2D-CHI를 제안한다. 2D-CHI 에서는 색인된 속성의 키값 도메인과 클래스 식별자 도메인으로 구성된 이차원 도메인상의 색인엔트리들에 대한 클러스터링 문제를 다룬다. 클러스터링 특성이 하나의 속성에 의해서 독점되는 B+-Tree 와 같은 일차원 색인구조를 이용하는 기존의 클래스 색인기법들은 특정 형태의 질의에 대해서만 적합한 색인기법들로서 다양한 형태의 질의들로 구성된 질의 패턴에 대해서 적절하게 대응하지 못한다. 2D-CHI에서는 질의 피턴에 따라 키값 도메인과 클래스 식별자 도메인 사이에서 색이 엔트리들의 클러스터링 정도를 조정함으로써 질의처리의 성능을 향상시킨다. 2D-CHI 의 성능평가를 위하여, 먼저 데이터의 균일 분포를 가정으로 비용 모델을 정립하여 기존의 색인기법들과 색인의 성능을 비교한다. 그리고, 계층 그리드 파일을 이용하여 구현한 2D-CHI의 실험으로 비용 모델을 검증하며, 다양한 실험을 통하여 데이터의 분포와 주어진 질의 형태에 따라 최적의 이차원 클래스 계층 색인구조를 구성할 수 있음을 보인다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.3A
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• pp.267-273
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• 2006

This paper improves the effects of clustered OFCDM scheme considering the frequency diversity effect over a frequency selective fading channel. In OFCDM with frequency domain spreading compared to OFDM, we can increase uncorrelated symbols by frequency allocation method of correspondent symbols over the same antenna and different antenna after spreading. The simulation results show that the performance of proposed system is improved by approximately 4 dB in ${\sigma}=0.02{\mu}sec$, the performance is improved by approximately 2.5dB in large delay spread in a 12-path Rayleigh fading channel with overall the root mean squared delay spread and the maximum Doppler frequency of 20 Hz. Also, the required average received Eb/No at the average BER of $10^{-3}$ by optimum method is improved by approximately 2.0 dB, compared to that of STA-OFCDM with frequency rearrange. The new method does not require any bandwidth expansion any feedback from the receiver to the transmitter and its computation complexity is similar to clustered OFCDM.