• Journal of Korean Society of Water Science and Technology
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• v.26 no.6
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• pp.133-142
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• 2018

Increasing localized torrential rainfall caused by abnormal climate are making higher damage to human and property through urban inundation So The need of preventive measures is being highlighted. In this study, the methodology for calculating flood depth in domestic water map using an interpolation method in order to utilizing the results of flood analysis provided only in the form of a report is suggested. In the Incheon Metropolitan City S area as the test-bed, the flood depth was calculated using the interpolating the actual flood analysis by image and verification was performed. Verification results showed that the error rate was 5.2% for the maximum flooding depth, and that the water depth value was compared to 10 random points, which showed a difference of less than 0.030 m. Also, as the results of the flood analysis were presented in various ways, the flood depth was extracted from the image of the result of the flood analysis, which changed the presentation method, and then compared and analyzed. The results of this study could be available for the use of basic data from the research on the urban penetration of domestic consumption and for decision-making of policy.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.3030-3038
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• 2023

Nowadays, mobile robots have been used to search for uncontrolled radioactive source in indoor environments to avoid radiation exposure for technicians. However, in the indoor environments, especially in the presence of obstacles, how to make the robots with limited sensing capabilities automatically search for the radioactive source remains a major challenge. Also, the source search efficiency of robots needs to be further improved to meet practical scenarios such as limited exploration time. This paper proposes an automatic source search strategy, abbreviated as ACA: the location of source is estimated by a convolutional neural network (CNN), and the path is planned by the A-star algorithm. First, the search area is represented as an occupancy grid map. Then, the radiation dose distribution of the radioactive source in the occupancy grid map is obtained by Monte Carlo (MC) method simulation, and multiple sets of radiation data are collected through the eight neighborhood self-avoiding random walk (ENSAW) algorithm as the radiation data set. Further, the radiation data set is fed into the designed CNN architecture to train the network model in advance. When the searcher enters the search area where the radioactive source exists, the location of source is estimated by the network model and the search path is planned by the A-star algorithm, and this process is iterated continuously until the searcher reaches the location of radioactive source. The experimental results show that the average number of radiometric measurements and the average number of moving steps of the ACA algorithm are only 2.1% and 33.2% of those of the gradient search (GS) algorithm in the indoor environment without obstacles. In the indoor environment shielded by concrete walls, the GS algorithm fails to search for the source, while the ACA algorithm successfully searches for the source with fewer moving steps and sparse radiometric data.

• Korean Journal of Environment and Ecology
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• v.35 no.4
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• pp.377-386
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• 2021

This study was conducted to provide basic data for habitat management and preservation of Jeongmaek. A total of 18 priority research areas were selected with consideration to terrain and habitat environment, and 54 fixed plots were selected for three types of habits: development, valley, and forest road and ridge. The survey was conducted in each season (May, August, and October), excluding the winter season, from 2016 to 2018. The distribution analysis of birds observed in each habitat type using a self-organizing map (SOM) classified them into a total of four groups (MRPP, A=0.12, and p <0.005). The comparative analysis of the number of species, the number of individuals, and the species diversity index for each SOM group showed that they were all the highest in group III (Kruskal-Wallis, the number species: x² = 13.436, P <0.005; the number of individuals: x² = 8.229, P <0.05; the species diversity index: x² = 17.115, P <0.005). Moreover, the analysis by applying the land cover map to the random forest model to examine the index species of each group and identify the characteristics of the habitat environment showed a difference in the ratio of the habitat environment and the indicator species among the four groups. The index species analysis identified a total of 18 bird species as the indicator species in three groups except for group II. When applying the random forest model and indicator species analysis to the results of classification into four groups using the SOM, the composition of the indicator species by the group showed a correlation with the habitat characteristics of each group. Moreover, the distribution patterns and densities of observed species were clearly distinguished according to the dominant habitat for each group. The results of the analysis that applied the SOM, indicator species, and random forest model together can derive useful results for the characterization of bird habitats according to the habitat environment.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.457-461
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• 2013

This paper proposes an image feature-based real-time RGB-D (Red-Green-Blue Depth) 3D SLAM (Simultaneous Localization and Mapping) system. RGB-D data from Kinect style sensors contain a 2D image and per-pixel depth information. 6-DOF (Degree-of-Freedom) visual odometry is obtained through the 3D-RANSAC (RANdom SAmple Consensus) algorithm with 2D image features and depth data. For speed up extraction of features, parallel computation is performed with GPU acceleration. After a feature manager detects a loop closure, a graph-based SLAM algorithm optimizes trajectory of the sensor and builds a 3D point cloud based map.

• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.249-257
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• 2017

This paper proposes a method to segment urban scenes semantically based on location prior information. Since major scene elements in urban environments such as roads, buildings, and vehicles are often located at specific locations, using the location prior information of these elements can improve the segmentation performance. The location priors are defined in special 2D coordinates, referred to as road-normal coordinates, which are perpendicular to the orientation of the road. With the help of depth information to each element, all the possible pixels in the image are projected into these coordinates and the learned prior information is applied to those pixels. The proposed location prior can be modeled by defining a unary potential of a conditional random field (CRF) as a sum of two sub-potentials: an appearance feature-based potential and a location potential. The proposed method was validated using publicly available KITTI dataset, which has urban images and corresponding 3D depth measurements.

• Journal of Environmental Impact Assessment
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• v.17 no.4
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• pp.255-262
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• 2008

For the Han-river estuary and DMZ where white-naped crane (Grus vipio; endangered migratory bird) stopover or spend winter, the habitat composition and the habitat use pattern of white-naped crane were analyzed with the position data obtained by the satellite tracking method. By the use of geographic information system (GIS), the percent composition of seven habitat categories of white naped-crane data points (n=228) was analyzed. The chi-square test showed that the white-naped crane habitat use pattern was significantly different (p<0.05) from that of random points (n=228). It means that white-naped crane select and use particular habitat area in the Han-river estuary and DMZ.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.06a
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• pp.101-104
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• 2014

3차원 영상 제작을 위해서는 장면의 색상 영상과 함께 깊이 정보가 필요하다. 일반적으로 깊이를 측정하는 TOF 카메라에 의해 획득된 깊이 영상은 컬러 영상에 비해 매우 작은 해상도의 영상을 갖게 되는 문제가 있다. 따라서 색상 영상과 함께 3차원 영상 제작에 깊이 영상을 사용하기 위해서는 저해상도 깊이 영상의 업샘플링 방법이 필요하다. 특히 컬러 영상에서 사물 간의 경계에 해당하는 부분에서 색상 차이를 인지하지 못하여 깊이 맵을 부적절하게 처리하게 되는 경우가 발생한다. 본 논문에서는 색상 영상에서 경계부분에 해당하는 영역을 이용하여 저해상도 깊이 영상을 업샘플링 하는 방법을 제안한다. 깊이 영상을 업샘플링 할 때 중요하게 다루어야 할 경계 부분을, 고해상도 색상 영상과 저해상도 깊이 영상을 이용하여 찾아낸다. 색상 경계 부분을 고려하여 깊이 영상 업샘플링을 위한 에너지 함수를 MRF를 이용하여 모델링하고, 신뢰 확산(belief propagation)방법을 이용하여 에너지 함수 최적화를 수행한다. 제안한 방법은 기존의 다른 에너지 함수나 필터 기반 업샘플링 방법보다 우수한 성능을 나타내었다.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.356-360
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• 2006

Recently, many power analysis attacks have been proposed. Since the attacks are powerful, it is very important to implement cryptosystems securely against the attacks. We propose countermeasures against power analysis attacks for elliptic curve cryptosystems based on Koblitz curves (KCs), which are a special class of elliptic curves. That is, we make our countermeasures be secure against SPA, DPA, and new DPA attacks, specially RPA, ZPA, using a random point at each execution of elliptic curve scalar multiplication. And since our countermeasures are designed to use the Frobenius map of KC, those are very fast.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.256-261
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• 2007

본 논문은 이미지 완성(Image Completion)을 위한 근사적 에너지 최적화 알고리즘을 제안한다. 이미지 완성이란 이미지의 특정영역이 지워진 상태에서, 그 지워진 부분을 나머지 부분과 시각적으로 어울리도록 완성시키는 기법을 말한다. 본 논문에서 이미지 완성은 유사-확률적(pseudo-probabilistic) 시스템인 Markov Random Field로 모델링된다. MRF로 모델링된 이미지 완성 시스템에서 사후 확률(posterior probability)을 최대로 만드는 MAP(Maximum A Posterior) 문제는 결국 시스템의 전체 에너지를 낮추는 에너지 최적화 문제와 동일하다. 본 논문에서는 MRF의 최적화 알고리즘들 중에서 Belief Propagation 알고리즘을 이용한다. BP 알고리즘이 이미지 완성 분야에 적용될 때 다음 두 가지가 계산시간을 증가시키는 요인이 된다. 첫 번째는 완성시킬 영역이 넓어 MRF를 구성하는 정점의 수가 증가할 때이다. 두 번째는 비교할 후보 이미지 조각의 수가 증가할 때이다. 기존에 제안된 Priority-Belief Propagation 알고리즘은 우선순위가 높은 정점부터 메시지를 전파하고 불필요한 후보 이미지 조각의 수를 제거함으로써 이를 해결하였다. 하지만 우선순위를 정점에 할당하기 위한 최초 메시지 전파의 경우 Belief Propagation의 단점은 그대로 남아있다. 이를 개선하기 위해 본 논문에서는 이미지 완성을 위한 MRF 모델을 피라미드 구조와 같이 층위로 나누어 정점의 수를 줄이고, 계층적으로 메시지를 전파하여 시스템의 적합성(fitness)을 정교화 해나가는 Hierarchical Priority Belief Propagation 알고리즘을 제안한다.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.35.2-35.2
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• 2016

Lyman alpha photons emitted from the early generation galaxies are scattered through the intergalactic medium, and can be observed as Lyman alpha emitting sources. We examine the Lyman alpha line transfer mechanism by tracing the random scattering histories of Lyman alpha photons in the intergalactic medium of the early universe. The density and ionization fields are based on the 3D map by N-body + radiation transfer simulations of the epoch of reionization. The calculation is compared with analytical models, too. The emergent line profile and the size of the Lyman alpha blob are strongly tied to the density and ionization environment, likely to give constraints when high-z Lyman alpha blobs are observed.