• Journal of KIISE:Software and Applications
• /
• v.36 no.10
• /
• pp.836-850
• /
• 2009

Human posture recognition has attracted tremendous attention in ubiquitous environment, performing arts and robot control so that, recently, many researchers in pattern recognition and computer vision are working to make efficient posture recognition system. However the most of existing studies is very sensitive to human variations such as the rotation or the translation of body. This is why the feature, which is extracted from the feature extraction part as the first step of general posture recognition system, is influenced by these variations. To alleviate these human variations and improve the posture recognition result, this paper presents 3D Star Skeleton and Principle Component Analysis (PCA) based feature extraction methods in the multi-view environment. The proposed system use the 8 projection maps, a kind of depth map, as an input data. And the projection maps are extracted from the visual hull generation process. Though these data, the system constructs 3D Star Skeleton and extracts the rotation invariant feature using PCA. In experimental result, we extract the feature from the 3D Star Skeleton and recognize the human posture using the feature. Finally we prove that the proposed method is robust to human variations.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.35C no.2
• /
• pp.71-79
• /
• 1998

In this paper, we propose a new mART(modified ART) neural network by combining the winner neuron definition method of SOM(self-organizing map) and the real-time adaptive clustering function of ART(adaptive resonance theory) and construct it in a modular structure, for the purpose of organizing the feature maps of three dimensional targets. Being constructed in a modular structure, the proposed modular mART can effectively prevent the clusters from representing multiple classes and can be trained to organze two dimensional distortion invariant feature maps so as to recognize targets with three dimensional distortion. We also present the recognition result and self-organization perfdormance of the proposed modular mART neural network after carried out some experiments with 14 tank and fighter target models.

• Journal of Korean Society of Forest Science
• /
• v.104 no.4
• /
• pp.519-526
• /
• 2015

This study was investigated to analyze stand structure in Daegu Dodong T.orientalis Forest for conservation Thuja orientalis forest. Results of cluster analysis, it was classified to Quercus variabilis group(A), Quercus variabilis-Quercus mongolica group(B), Pinus densiflora group(C), Thuja orientalis group(D). Charaters of location environments for D group were analyzed that altitude 99.3 m, slope $59^{\circ}$, rock exposure 68.3%, BHA $21.8m^2/ha$ and North West aspects. The MRPP-test, It classified groups, appropriately. Importance value of D group was T. orientalis 85.42, Q. variabilis 1.28, P.densiflora 1.30, Fraxinus rhynchophylla 3.56 etc. DBH classes of D group were expressed inverted-J-shaped curve. H' was resulted in 0.600~0.834, H'max 1.317~1.466, J' 0.456~0.594, D' 0.405~0.544. Indecator species Analysis were conducted that woody plants were 4 taxa, Herbal plants 9 taxa.

• The KIPS Transactions:PartC
• /
• v.13C no.2 s.105
• /
• pp.185-194
• /
• 2006

In this paper, we first propose a power-and-bandwidth efficient cooperative transmission protocol where a sensor node assists two others for their data transmission to a clusterhead in WSNs (Wireless Sensor Networks) using LEACH (Low-Energy Adaptive Clustering Hierarchy). Then we derive its closed-form BER expression which Is also a general BER one for the decode-and-forward protocol (DF) and Prove that the proposed protocol performs as same as the conventional DF but obtains higher spectral efficiency. A variety of numerical results reveal the cooperation can save the network power up to 11dB over direct transmission at BER of $10^{-3}$.

• Nuclear Engineering and Technology
• /
• v.52 no.11
• /
• pp.2667-2677
• /
• 2020

A domain decomposition (DD) scheme for GPU-based Monte Carlo (MC) calculation which is essential for whole-core depletion is introduced within the framework of the modified history-based tracking algorithm. Since GPU-offloaded MC calculations suffer from limited memory capacity, employing DDMC is inevitable for the simulation of depleted cores which require large storage to save hundreds of newly generated isotopes. First, an automated domain decomposition algorithm named wheel clustering is devised such that each subdomain contains nearly the same number of fuel assemblies. Second, an innerouter iteration algorithm allowing overlapped computation and communication is introduced which enables boundary neutron transactions during the tracking of interior neutrons. Third, a bank update scheme which is to include the boundary sources in a way to be adequate to the peculiar data structures of the GPU-based neutron tracking algorithm is presented. The verification and demonstration of the DDMC method are done for 3D full-core problems: APR1400 fresh core and a mock-up depleted core. It is confirmed that the DDMC method performs comparably with the standard MC method, and that the domain decomposition scheme is essential to carry out full 3D MC depletion calculations with limited GPU memory capacities.

• The KIPS Transactions:PartC
• /
• v.13C no.3 s.106
• /
• pp.317-322
• /
• 2006

This paper presents an efficient user-cooperation protocol associated with cyclic coding for WSNs (Wireless Sensor Networks) using LEACH(Low-Energy Adaptive Clustering Hierarchy). Since the proposed user-cooperation requires no CSI(Channel State Information) at both transmitter and receiver, and encoding and decoding of cyclic codes are simple, the processing complexity of sensor nodes is significantly reduced. Simulation results reveal such a combination can save the network energy up to 10dB over single-hop transmission at BER of $10^{-4}$.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.3
• /
• pp.619-630
• /
• 2015

The purpose of this study is to provide basic information to develop a game strategy plan of a team in a future by identifying the patterns of attack and pass of national men's professional volleyball teams and extracting core key words related with volleyball game performance to evaluate game performance using 'social network analysis' and 'text mining'. As for the analysis result of 'social network analysis' with the whole data, group '0' (6 players) and group '1' (11 players) were partitioned. A point of view the degree centrality and betweenness centrality in 'social network analysis' results, we can know that the group '1' more active game performance than the group '0'. The significant result for two group (win and loss) obtained by 'text mining' according to two groups ('0' and '1') obtained by 'social network analysis' showed significant difference (p-value: 0.001). As for clustering of each network, group '0' had the tendency to score points through set player D and E. In group '1', the player K had the tendency to fail if he attack through 'dig'; players C and D have a good performance through 'set' play.

• The KIPS Transactions:PartD
• /
• v.11D no.3
• /
• pp.495-508
• /
• 2004

Recently, in content-based music information retrieval systems, to enhance the response time of retrieving music data from large music database, some researches have adopted the indexing mechanism that extracts and indexes the representative melodies. The representative melody of music data must stand for the music itself and have strong possibility to use as users' input queries. However, since the previous researches have not considered the musical composition forms, they are not able to correctly catch the contrast, repetition and variation of motif in musical forms. In this paper, we use an index automatically constructed from representative melodies such like first melody, climax melodies and similarly repeated theme melodies. At first, we expand the clustering algorithm in order to extract similarly repeated theme melodies based on the musical composition forms. If the first melody and climax melodies are not included into the representative melodies of music by the clustering algorithm, we add them into representative melodies. We implemented a prototype system and did experiments on comparison the representative melody index with other melody indexes. Since, we are able to construct the representative melody index with the lower storage by 34％ than whole melody index, the response time can be decreased. Also, since we include first melody and climax melody which have the strong possibility to use as users' input query into representative melodies, we are able to get the more correct results against the various users' input queries than theme melody index with the cost of storage overhead of 20％.

• Journal of Biomedical Engineering Research
• /
• v.21 no.4
• /
• pp.333-338
• /
• 2000

In this paper, an automated segmentation algorithm is proposed for MR brain images using T1-weighted, T2-weighted, and PD images complementarily. The proposed segmentation algorithm is composed of 3 step. In the first step, cerebrum images are extracted by putting a cerebrum mask upon the three input images. In the second step, outstanding clusters that represent inner tissues of the cerebrum are chosen among 3-dimensional(3D) clusters. 3D clusters are determined by intersecting densely distributed parts of 2D histogram in the 3D space formed with three optimal scale images. Optimal scale image is made up of applying scale space filtering to each 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram. In the final step, cerebrum images are segmented using FCM algorithm with its initial centroid value as the outstanding clusters centroid value. The proposed cluster's centroid accurately. And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the method of single spectral analysis.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.2 no.3
• /
• pp.103-122
• /
• 2007

Innovation clusters developed or evolved around a specific region IS the key element of national innovation system and determine national competitiveness. Recognizing the importance of innovation clusters, Korean government has made "Daedeok Special R&D Zone" in 2005. This paper examines the success factors of famous Cluster in advanced countries and China, and proposes the strategy for regional development in Daejeon through boosting Daedeok Innovation Cluster. Although 30.5% of government R&D investment is being concentrated in Daedeok along with 10% of the country's doctorate degree holders, it is lack of increasing revenue by linking corporate R&D with a creative and challenging entrepreneur spirit. The core of the innovation cluster is the integration and mutual networking of the main participants. This paper suggests strategies for developing as a world class innovation cluster, global networking and clustering, venture ecosystem formation, commercialization the knowledge by interacting with market. It also explains the necessity of regional integration for cluster to cluster linkages in the East Asia Region.