• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.121-130
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• 2011

This paper highlights the reconstruction of the rectilinear type of 3D rooftop model from satellite image data using centroid neural network. The main idea of the proposed 3D reconstruction method is based on the grouping of 3D line segments. 3D lines are extracted by 2D lines and DEM (Digital Elevation Map) data evaluated from a pair of stereo images. Our grouping process consists of two steps. We carry out the first grouping process to group fragmented or duplicated 3D lines into the principal 3D lines, which can be used to construct the rooftop model, and construct the groups of lines that are parallel each other in the second step. From the grouping result, 3D rooftop models are reconstructed by the final clustering process. High-resolution IKONOS images are utilized for the experiments. The experimental result's indicate that the reconstructed building models almost reflect the actual position and shape of buildings in a precise manner, and that the proposed approach can be efficiently applied to building reconstruction problem from high-resolution satellite images of an urban area.

• Journal of Acupuncture Research
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• v.25 no.1
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• pp.155-167
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• 2008

Objectives : The objective of this study was to observe the positive effects of acupuncture and bee-venom acupuncture treatment on lumbar hypolordosis. Methods : Acupuncture and bee-venom acupuncture were performed twice a week for 30 days to treat patients with low back pain patients showing a Cobb's angle [L1-S1] of less than $35^{\circ}$(treatment group=20, control group=13). Lumbar lordosis(Cobb's angle [L1-S1], Centroid angle [L1-S1]) was measured using an L-spine Lat. X-ray before and after treatment. Pain scales(VAS scale, ODI) were also was measured before and after treatment. SPSS 12K for Windows was used for statistical analysis of the results. Results : 1. VAS and ODI decreased significantly in the treatment group. 2. Lumbar lordosis, presented by Cobb's angle, increased significantly from $25.00{\pm}7.06^{\circ}$ to $30.95{\pm}10.02^{\circ}$ (p = 0.006). The increase of Cobb's angle in the treatment group $5.95{\pm}8.60^{\circ}$, and that of the control group was $-3.08{\pm}8.41^{\circ}$(p = 0.006). The increase of the Centroid angle of the treatment group was $3.85{\pm}8.83^{\circ}$, and that of the control group was $-2.92{\pm}7.63^{\circ}$(p=0.031). 3. Cobb's angle and Vas scale showed a significant correlation coefficient of -0.250(p=0.043). Increase of Cobb's angle and decrease of Vas scale showed a significant correlation coefficient of 0.420(p=0.015). Conclusions : Acupuncture and bee-venom acupuncture were verified to have a positive effect on pain alleviation of lumbar lordosis decreased patients while also affecting lumbar lordosis to be increased as a result of structural changes. It was also shown that lumbar hypolordosis has significant correlation with pain.

• Journal of Korean Society of Transportation
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• v.29 no.1
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• pp.17-27
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• 2011

This paper investigates some critical errors influencing travel demand estimation in Korea Transportation Data Base (KTDB), and through this investigation reasonable traffic analysis zone (TAZ) size and internal trips ratio are analyzed. With varying zone size, the accuracy of travel demand estimation is studied and appropriate level of zone size in KTDB is also presented. For this purpose zonal structure consisting of location of zone centroid, number of centroid connecters has been constructed by social economic index, and then some descriptive statistical analyses such as F-test, coefficient of correlation are performed. From the results, this paper shows that the optimum levels of zone system were various according to the order and capacity of roads, and also shows that the smaller TAZ, the less error in this research. In conclusion, in order to improve accuracy of traffic demand estimation it is necessary to make zone size smaller.

• Journal of Biomedical Engineering Research
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• v.21 no.4
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• pp.333-338
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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 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 Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.551-559
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• 2017

In this paper, a real-time training method to improve the performance of bearing fault diagnosis. The traditional bearing fault diagnosis cannot classify a condition which is not trained by the classifier. The proposed 4-step method trains and recognizes new condition in real-time, thereby it can classify the condition accurately. In the first step, we calculate the maximum distance value for each class by calculating a Euclidean distance between a feature vector of each class and a centroid of the corresponding class in the training information. In the second step, we calculate a Euclidean distance between a feature vector of new acquired data and a centroid of each class, and then compare with the allowed maximum distance of each class. In the third step, if the distance between a feature vector of new acquired data and a centroid of each class is larger than the allowed maximum distance of each class, we define that it is data of new condition and increase count of new condition. In the last step, if the count of new condition is over 10, newly acquired 10 data are assigned as a new class and then conduct re-training the classifier. To verify the performance of the proposed method, bearing fault data from a rotating machine was utilized.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.137-140
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• 2000

We provide an useful method to design codebooks with better performance than conventional methods. In the proposed method, new codevectors obtained by learning iterations are not the centroid vectors which is the representatives of partitions, but the vectors manipulated by the distance between new codevectors and old codevectors in the early stages of learning iteration. Experimental results show that the codevectors in the obtained by the proposed method converge to a better locally optimal codebook.

• 한국전산유체공학회:학술대회논문집
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• 2009.11a
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• pp.228-230
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• 2009

The moment-of-fluid (MOF) method is a new volume-tracking method that accurately treats evolving material interfaces. Based on the moment data (volume and centroid) for each material, the material interfaces are reconstructed with second-order spatial accuracy in a strictly conservative manner. The MOF method is coupled with a stabilized finite element incompressible Navier-Stokes solver for two fluids, namely water and air. The effectiveness of the MOF method is demonstrated with a free-surface dam-break problem.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.04b
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• pp.447-454
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• 1998

The main object of the study is that axial force-moment relationships for high strength concrete structures using reliability theory(Linear statstical method, Monte Carlo Simulation) including probability conception. And mean stress factors and centroid factors proposed to high strength concrete structures using reliability theory(Linear statstical method, Monte Carlo Simulation). Finally, The established experimental data for axial force-moment relationships are compared to the analytical data(data for Linear statstical method and Monte Carlo Simulation) for axial force-moment relationships in this analytical method.

• Bulletin of the Korean Mathematical Society
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• v.39 no.2
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• pp.211-224
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• 2002

In this paper we show that if D is a continuous linear Jordan derivation on a Banach algebra A satisfying [[D($x^{n}$), $x^{n}$, $x^{n}$] $\in$ rad(A) for a positive integer n and for all x${\in}$A, then D maps A into rad(A).

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1907_1908
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• 2009

본 논문은 위성사진 데이터에서 경계선 추출에 대한 새로운 알고리즘을 제안한다. 새로운 알고리즘은 조각 선소들을 연결하기 위하여 몇 가지의 Heuristics를 사용하고, CNN(Centroid Neural Network)을 이용해 선소들을 군집화 하는 방법을 제시한다. 제안된 새로운 알고리즘은 실제의 위성영상 데이터에 대한 실험을 통해 그 유용성이 확인 되었다.