• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.413-416
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• 2011

휴머노이드 로봇이 인간처럼 그림을 그리기 위해서는 순서를 가지는 드로잉 좌표 집합이 필요하다. 하지만 기존 영상 처리를 통한 윤곽선에서의 좌표 집합은 순서가 없고 로봇 암을 들어 올리는 좌표가 없다. 또한 불필요한 좌표가 다수 포함되어 있어서 효율적인 드로잉을 하기가 어려워 드로잉하는데 시간이 많이 걸린다. 따라서 본 논문에서는 3축으로 구성된 휴머노이드 로봇 암이 드로잉하기 위한 좌표 집합을 추출하는 알고리즘을 개발한다. 이를 구현하기 위해서는 로봇이 드로잉하기 위한 윤곽선 추출 알고리즘과 추출한 드로잉 좌표 집합에서 드로잉 순서와 로봇 암을 들어 올리는 점을 전체 좌표 리스트에 포함해야 한다. 제안하는 알고리즘이 추출하는 좌표 집합은 캠 영상으로부터 입력되는 컬러 이미지에서 이미지 프로세싱을 거친 윤곽선을 입력으로 하며, 추출한 좌표들의 순서와 로봇 암의 드로잉 시작점을 삽입함으로서 빠르고 효율적인 로봇 드로잉 좌표 집합 추출 알고리즘을 구현한다. 또한 제안하는 추출 알고리즘을 휴머노이드 로봇에 적용하여 실험하였으며, 좌표 추출 알고리즘의 정확성과 효율성을 비교하였다.

• Journal of Life Science
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• v.24 no.9
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• pp.1019-1024
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• 2014

Skeletal muscle atrophy can be defined as a decrease in or a disease of the muscle tissue, or as a disorder of the nerves that control the muscle, through injury or lack of use. This condition is associated with reactive oxygen species (ROS), resulting in various muscular disorders. Exposure to ROS induces muscle atrophy through several biological factors, such as SOD1 and HSP70. We found that cymbidium root extract reduced the $H_2O_2$-induced viability loss in C2C12 myoblasts and inhibited apoptosis. In addition, we showed that the cymbidium root extract increased the expression of HSP70 and decreased the expression of SOD1 in the $H_2O_2$-induced C2C12 myoblasts. These results suggest that cymbidium root extract might have therapeutic value in reducing ROS-induced muscle atrophy.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.49-50
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• 2018

본 논문에서는 음악 장르 분류를 위한 시간 및 주파수 기반 스파이크그램 특성 추출 기술을 제안한다. 기존의 음악 장르 분류 시스템에서는 푸리에 변환 기반의 입력 특성을 주로 사용해 왔다. 푸리에 변환은 시간 축에서 프레임 단위로 평균적인 주파수 정보를 취하므로 낮은 시간 해상도를 갖지만, 스파이크그램은 샘플 단위의 주파수 정보를 갖고 있어 고해상도의 특성을 추출할 수 있다. 제안하는 기술은 이러한 시간 기반 특성을 추출하여 주파수 기반 특성 및 SNR 특성과 함께 심층 신경망의 입력으로 사용한다. 제안하는 특성을 사용하여 시간 기반 특성을 사용하지 않은 기존 스파이크그램 특성 기반 분류기의 성능을 개선하였으며, 다른 특성 및 분류기에 비해 적은 수의 특성 입력으로도 우수한 성능을 얻는 것을 확인하였다.

• The Journal of the Acoustical Society of Korea
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• v.19 no.6
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• pp.10-16
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• 2000

A new algorithm for estimating bladder volume for portable bladder volume measurement system is proposed. Given the actual image of a bladder, edges between bladder wall and urine are extracted first. Axes are calculated from these data and actual cross section shape is obtained by filtering. Since ordinary shape of a bladder is irregular, two cross-sections(transverse and longitudinal) are considered. With the area of a longitudinal cross-section projected along the axes of transverse cross-section, or vice versa, two estimated volume are obtained. Averaging these two value yields the volume of the bladder. Applied to actual experiments, the proposed algorithm showed explicitly good results in comparison with the conventional techniques.

• Korean Journal of Environment and Ecology
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• v.13 no.3
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• pp.220-233
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• 1999

본 연구는 서열법(교호평균법)과 ZM학파의 식물사회학적 연구방법에 의해 대구, 구미, 김천 근처의 팔공산, 금오산, 황악산 일대의 참나무류 삼림을 분류하고 그 환경조건을 해석할 목적으로 실시되었다 교호평균법에서 추출된 stand의 제 1축상의 종의 배열은 식물사회학적 군락분류의 표징종이나 식별종의 후보 종군을 추출하는데 매우 효과적이었다. 이결과와 타지역과 본 연구지역의 낙엽수림의 조성을 비교 검토한 결과 이하의 2군집, 1군락, 2아군집을 식별하였다. 너도밤나무군강(Fagetea crenatae Miyawaki et al. 1968) ; 당단풍-신갈나무목(Acero-Quercetalia mongolicae Song 1988); 조록싸리-졸참나무군단(Lespedezo-Quercion ser-1-1 때죽나무아군집(Styracetosum japonicae subassoc. nov) 1-2 전형아군집(typicum subassoc. ) (Ainsliaeo-Quercetum mongolicae assoc. nov.) 3. 신갈나무-시닥나무군락(Quercus mongolica-Acer teschonoskii var, rubripes community) 본연구에서 식별된 군단은 우리나라의 냉온대 낙엽활엽수림의 북부형과 남부형에 대응하는 것으로 해석되었다. 서열법에 의해 계산된 제 1축과 제2축상에의 stand의 배열은 인위와 해발과 같은 환경경도상의 계열을 나타내었다. 이상의 연구와 함께 본 연구와 관련되는 사항으로 우리나라의 냉온대림의 군락분류학적 문제점을 종조성론의 입장에서 논하였다.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.137-143
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• 2018

DICOM images of patellofemoral bones were converted into a stereolithography file, and a Unigraphics CAD program was used to create a CAD modeling in which there exists point, line and facet information. The modeling extraction of joint facets was performed by linking two adjacent points into lines in the stereolithography file by using the Unigraphics rapid spacing function and then linking the lines into facets to complete the entire modeling. This modeling extraction was performed based on the anatomical knowledge of joint facet directions. As a result, a personalized space modeling and solid modeling were produced for the joint facets of patellofemoral bones. This was followed by a CAM control computing operation of solid modeling on graphite materials and 5-axis machining of patellofemoral bones. That is the description of a method for a personalized implant modeling by using DICOM images of patellofemoral bones.

• Journal of IKEEE
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• v.26 no.3
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• pp.408-415
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• 2022

In this paper, we propose a 3D point cloud reconstruction technique from 2D images using efficient feature map extraction network. The originality of the method proposed in this paper is as follows. First, we use a new feature map extraction network that is about 27% efficient than existing techniques in terms of memory. The proposed network does not reduce the size to the middle of the deep learning network, so important information required for 3D point cloud reconstruction is not lost. We solved the memory increase problem caused by the non-reduced image size by reducing the number of channels and by efficiently configuring the deep learning network to be shallow. Second, by preserving the high-resolution features of the 2D image, the accuracy can be further improved than that of the conventional technique. The feature map extracted from the non-reduced image contains more detailed information than the existing method, which can further improve the reconstruction accuracy of the 3D point cloud. Third, we use a divergence loss that does not require shooting information. The fact that not only the 2D image but also the shooting angle is required for learning, the dataset must contain detailed information and it is a disadvantage that makes it difficult to construct the dataset. In this paper, the accuracy of the reconstruction of the 3D point cloud can be increased by increasing the diversity of information through randomness without additional shooting information. In order to objectively evaluate the performance of the proposed method, using the ShapeNet dataset and using the same method as in the comparative papers, the CD value of the method proposed in this paper is 5.87, the EMD value is 5.81, and the FLOPs value is 2.9G. It was calculated. On the other hand, the lower the CD and EMD values, the better the accuracy of the reconstructed 3D point cloud approaches the original. In addition, the lower the number of FLOPs, the less memory is required for the deep learning network. Therefore, the CD, EMD, and FLOPs performance evaluation results of the proposed method showed about 27% improvement in memory and 6.3% in terms of accuracy compared to the methods in other papers, demonstrating objective performance.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.2
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• pp.12-27
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• 2023

In this study, among the construction-type parks in Gangdong-gu, targeting parks with high environmental and ecological value located on the urban green axis, a plan was prepared for the ecological renewal of urban parks, and a design that applied to them was proposed. The renewal target site was selected by analyzing the general condition of Gangdong-gu and urban parks, the land use and green area ratio, park green area, and the green axis of Gangdong-gu. Gangdong-gu has 54 parks, including 2 neighborhood parks and 52 children's parks. In the first stage of the current status review, 17 parks were extracted through locational value analysis, such as location and adjacency to the natural axis and green axis. In the second stage, eight parks were selected among the first-stage extraction parks based on the ratio of green spaces and open spaces within each park service area. In the third stage, two of the second stage extraction parks were selected based on whether the legal standard of the park area was met, and in the fourth stage, one of the third stage extraction parks was selected through an aging survey of the park. As for the urban ecological status of the renewal target site, the status of land use in the aspect of entropy reduction, the status of soil cover in the aspect of water circulation, and the status of planting structure in the aspect of biodiversity were investigated. As for the status of the three renewal sites, the green area was insufficient at 18.3-45.3%, and the facility area was 54.7%-81.7%, which was judged to have low urban temperature reduction effects. The impervious pavement area accounted for 34.5% to 48.9% of the park area, accounting for most of the facility area, and it was judged that the water circulation function was insufficient. The planting structure consisted of a single layer and a double layer structure, and although the tree layer was good, the lower vegetation was poor, and there was no planting site of edible plants or large hardwood trees, so the biodiversity was low. After the ecological renewal design of Seonrin Children's Park, Dangmal Children's Park, and Saemmul Children's Park, which were selected as the renewal targets in this study, the ecological area ratio of each park increased by 1.4 to 3 times than before the renewal. If the urban parks located on the urban green axis are examined from the perspective of the urban ecosystem and renewed ecologically, it is judged that the expected effect will be high in reducing entropy, improving water circulation, and laying the foundation for biodiversity in terms of the urban ecosystem.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.461-469
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• 2020

In this paper, we propose a PCA-based feature feedback method for extracting important areas of handwritten numeric data sets and face data sets. A PCA-based feature feedback method is proposed by extending the previous LDA-based feature feedback method. In the proposed method, the data is reduced to important feature dimensions by applying the PCA technique, one of the dimension reduction machine learning algorithms. Through the weights derived during the dimensional reduction process, the important points of data in each reduced dimensional axis are identified. Each dimension axis has a different weight in the total data according to the size of the eigenvalue of the axis. Accordingly, a weight proportional to the size of the eigenvalues of each dimension axis is given, and an operation process is performed to add important points of data in each dimension axis. The critical area of the data is calculated by applying a threshold to the data obtained through the calculation process. After that, induces reverse mapping to the original data in the important area of the derived data, and selects the important area in the original data space. The results of the experiment on the MNIST dataset are checked, and the effectiveness and possibility of the pattern recognition method based on PCA-based feature feedback are verified by comparing the results with the existing LDA-based feature feedback method.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.267-273
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• 2011

In this paper, we proposed the method to detect brain tumor region in MR images. Our method is composed of 3 parts, detection of tumor slice, detection of tumor region and tumor boundary detection. In the tumor slice detection step, a slice which contains tumor regions is distinguished using symmetric analysis in 3D brain volume. The tumor region detection step is the process to segment the tumor region in the slice distinguished as a tumor slice. And tumor region is finally detected, using spatial feature and symmetric analysis based on the cluster information. The process for detecting tumor slice and tumor region have advantages which are robust for noise and requires less computational time, using the knowledge of the brain tumor and cluster-based on symmetric analysis. And we use the level set method with fast marching algorithm to detect the tumor boundary. It is performed to find the tumor boundary for all other slices using the initial seeds derived from the previous or later slice until the tumor region is vanished. It requires less computational time because every procedure is not performed for all slices.