• The KIPS Transactions:PartD
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• v.12D no.4 s.100
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• pp.507-520
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• 2005

The R-trees are usually used for an index of trajectories in moving-objects databases. However, they need to access a number of nodes to trace same trajectories because of considering only a spatial proximity. Overlaps and dead spaces should be minimized to enhance the performance of range queries in moving-objects indexes. Trajectories of moving-objects should be preserved to enhance the performance of the trajectory queries. In this paper, we propose the TP3DR-tree(Trajectory Preserved 3DR-tree) using clusters of trajectories for range and trajectory queries. The TP3DR-tree uses two split policies: one is a spatial splitting that splits the same trajectory by clustering and the other is a time splitting that increases space utilization. In addition, we use connecting information in non-leaf nodes to enhance the performance of combined-queries. Our experiments show that the new index outperforms the others in processing queries on various datasets.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.450-453
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• 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 steps. 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 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 it’s initial centroid value as the outstanding cluster’s centroid value. The proposed segmentation algorithm complements the defect of FCM algorithm, being influenced upon initial centroid, by calculating cluster’s centroid accurately And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the results of single spectral analysis.

• Journal of Korea Water Resources Association
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• v.34 no.6
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• pp.577-586
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• 2001

This study proposes a theoretical methodology for deriving a rainfall intensity-duration- frequency (I-D-F) curve using a simple rectangular pulses Poisson process model. As the I-D-F curve derived by considering the model structure is dependent on the rainfall model parameters estimated using the observed first and second order statistics, it becomes less sensitive to the unusual rainfall events than that derided using the annual maxima rainfall series. This study has been applied to the rainfall data at Seoul and Inchon stations to check its applicability by comparing the two I-D-F carves from the model and the data. The results obtained are as followed. (1) As the duration becomes longer, the overlap probability increases significantly. However, its contribution to the rainfall intensity decreases a little. (2) When considering the overlap of each rainfall event, especially for large duration and return period, we could see obvious increases of rainfall intensity. This result is normal as the rainfall intensity is calculated by considering both the overlap probability and return period. Also, the overlap effect for Seoul station is fecund much higher than that for Inchon station, which is mainly due to the different overlap probabilities calculated using different rainfall model parameter sets. (3) As the rectangular pulses Poisson processes model used in this study cannot consider the clustering characteristics of rainfall, the derived I-D-F curves show less rainfall intensities than those from the annual maxima series. However, overall pattern of both I-D-F curves are found very similar, and the difference is believed to be overcome by use of a rainfall model with the clustering consideration.

• The KIPS Transactions:PartA
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• v.13A no.5 s.102
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• pp.465-472
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• 2006

Recently, many suggestions have been made in image segmentation methods for extracting human organs or disease affected area from huge amounts of medical image datasets. However, images from some areas, such as brain, which have multiple structures with ambiruous structural borders, have limitations in their structural segmentation. To address this problem, clustering technique which classifies voxels into finite number of clusters is often employed. This, however, has its drawback, the influence from noise, which is caused from voxel by voxel operations. Therefore, applying image enhancing method to minimize the influence from noise and to make clearer image borders would allow more robust structural segmentation. This research proposes an efficient structural segmentation method by filtering based clustering to extract detail structures such as white matter, gray matter and cerebrospinal fluid from brain MR. First, coherence enhancing diffusion filtering is adopted to make clearer borders between structures and to reduce the noises in them. To the enhanced images from this process, fuzzy c-means clustering method was applied, conducting structural segmentation by assigning corresponding cluster index to the structure containing each voxel. The suggested structural segmentation method, in comparison with existing ones with clustering using Gaussian or general anisotropic diffusion filtering, showed enhanced accuracy which was determined by how much it agreed with the manual segmentation results. Moreover, by suggesting fine segmentation method on the border area with reproducible results and minimized manual task, it provides efficient diagnostic support for morphological abnormalities in brain.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1453-1461
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• 2022

The accuracy of peripheral object recognition algorithms using 3D data sensors such as LiDAR in autonomous vehicles has been increasing through many studies, but this requires high performance hardware and complex structures. This object recognition algorithm acts as a large load on the main processor of an autonomous vehicle that requires performing and managing many processors while driving. To reduce this load and simultaneously exploit the advantages of 3D sensor data, we propose 2D data-based recognition using the ROI generated by extracting physical properties from 3D sensor data. In the environment where the brightness value was reduced by 50% in the basic image, it showed 5.3% higher accuracy and 28.57% lower performance time than the existing 2D-based model. Instead of having a 2.46 percent lower accuracy than the 3D-based model in the base image, it has a 6.25 percent reduction in performance time.

• Journal of The Korean Astronomical Society
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• v.35 no.3
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• pp.111-121
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• 2002

We have measured the correlation functions of the optically selected clusters of galaxies in the Abell and the APM catalogs, and of the X-ray clusters in the X-ray-Brightest Abell-type Clusters of galaxies (XBACs) catalog and the Brightest Clusters Sample (BCS). The same analysis method and the same method of characterizing the resulting correlation functions are applied to all observational samples. We have found that the amplitude of the correlation function of the APM clusters is much higher than what has been previously claimed, in particular for richer subsamples. The correlation length of the APM clusters with the richness R $\ge$ 70 (as defined by the APM team) is found to be $r_0 = 25.4_{-3.0}^{+3.1}\;h^{-1}$ Mpc. The amplitude of correlation function is about 2.4 times higher than that of Croft et al. (1997). The correlation lengths of the Abell clusters with the richness class RC $\ge$ 0 and 1 are measured to be $r_0 = 17.4_{-1.1}^{+1.2}$ and $21.0_{-2.8}^{+2.8}\;h^{-1}$ Mpc, respectively, which is consistent with our results for the APM sample at the similar level of richness. The richness dependence of cluster correlations is found to be $r_0= 0.40d_c + 3.2$ where $d_c$ is the mean intercluster separation. This is identical in slope with the Bahcall & West (1992)'s estimate, but is inconsistent with the weak dependence of Croft et al. (1997). The X-ray bright Abell clusters in the XBACs catalog and the X-ray selected clusters in the BCS catalog show strong clustering. The correlation length of the XBACs clusters with $L_x {\ge}0.65{\times} 10^{44}\;h^{-2}erg\;s^{-1}$ is $30.3_{-6.5}^{+8.2}\;h^{-1}$ Mpc, and that of the BCS clusters with $L_x {\ge}0.70{\times} 10^{44}\;h^{-2}erg\;s^{-1}$ is $30.2_{-8.9}^{+9.8}\;h^{-1}$ Mpc. The clustering strength of the X-ray clusters is much weaker than what is expected from the optical clusters.

• Journal of Korean Society of Transportation
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• v.36 no.3
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• pp.203-215
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• 2018

This study suggests a method to construct large scale dynamic O/D reflecting the characteristic that the passengers' travel patterns change according to the land use patterns of the destination. There are limitations in the existing research about dynamic O/D estimation method, such as the difficulty of collecting data, which can be applied only to a small area, or limiting to a specific transportation network such as highway networks or public transportation networks. In this paper, we propose a method to estimate dynamic O/D without limitation of analysis area based on transportation resources that can be easily collected and used according to the big data era. Clustering analysis was used to calculate the departure time trip distribution ratio based on arrival time and departure time trip distribution function was estimated by each cluster. As a result of the comparison test with the survey data, the estimated distribution function was statistically significant.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.1_2
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• pp.1-14
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• 2006

Sort-last parallel rendering using a cluster of GPUs has been widely used as an efficient method for visualizing large- scale volume datasets. The performance of this method is constrained by load balancing when data parallelism is included. In previous works static partitioning could lead to self-balance when only task level parallelism is included. In this paper, we present a load balancing scheme that adapts to the characteristic of volume dataset when data parallelism is also employed. We effectively combine the hierarchical data structures (octree and BSP tree) in order to skip empty regions and distribute workload to corresponding rendering nodes. Moreover, we also exploit a 3D clustering method to determine visibility order and save the AGP bandwidths on each rendering node. Experimental results show that our scheme can achieve significant performance gains compared with traditional static load distribution schemes.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.4
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• pp.353-362
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• 2012

3D building modeling from airborne Lidar without model regularization may cause positional errors or topological inconsistency in building models. Regularization of 3D building models, on the other hand, restricts the types of models which can be reconstructed. To resolve these issues, this paper modelled 3D buildings from airborne Lidar by building model regularization which considers more various types of buildings. Building points are first segmented into roof planes by clustering in feature space and segmentation in object space. Then, 3D building models are reconstructed by consecutive adjustment of planes, lines, and points to satisfy parallelism, symmetry, and consistency between model components. The experimental results demonstrated that the method could make more various types of 3d building models with regularity. The effects of regularization on the positional accuracies of models were also analyzed quantitatively.

• International Journal of Industrial Entomology and Biomaterials
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• v.31 no.2
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• pp.115-126
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• 2015

The tropical climate prevailing in India adversely affects temperate bivoltine silkworm rearing and causes crop loss especially during summer. Identification of high temperature tolerant bivoltine breeds by screening for thermotolerance in the silkworm, Bombyx mori (Lepidoptera: Bombycidae) is an essential prerequisite for the development of thermotolerant bivoltine breeds / hybrids. Therefore, in this study, 20 silkworm breeds were reared at different temperatures (25 ± 1℃,32 ± 1℃, 34 ± 1℃ and 36 ± 1℃) for 6 h every day from 3^rd d of 5^th instar to till spinning. Significant differences (p < 0.01) were found among all the rearing traits over temperature. Based on pupation percentage, SK4C and BHR3 were identified as thermotolerant bivoltine breeds. Hierarchical clustering analysis based on rearing traits at tested temperatures grouped 20 silkworm breeds in four clusters which included one cluster each of susceptible and tolerant, and two clusters of moderately tolerant silkworm breeds. This suggests that clustering based on rearing data at high temperatures by using Euclidean distance can be an effective approach in classifying the silkworm breeds on their thermotolerance capacity. The identified breeds would be used for development of thermo tolerant bivoltine silkworm breeds / hybrids.