• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1424-1436
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• 2020

Although huge progress has been made in current image segmentation work, there are still no efficient segmentation strategies for tree image which is taken from natural environment and contains complex background. To improve those problems, we propose a method for tree image segmentation combining adaptive mean shifting with image abstraction. Our approach perform better than others because it focuses mainly on the background of image and characteristics of the tree itself. First, we abstract the original tree image using bilateral filtering and image pyramid from multiple perspectives, which can reduce the influence of the background and tree canopy gaps on clustering. Spatial location and gray scale features are obtained by step detection and the insertion rule method, respectively. Bandwidths calculated by spatial location and gray scale features are then used to determine the size of the Gaussian kernel function and in the mean shift clustering. Furthermore, the flood fill method is employed to fill the results of clustering and highlight the region of interest. To prove the effectiveness of tree image abstractions on image clustering, we compared different abstraction levels and achieved the optimal clustering results. For our algorithm, the average segmentation accuracy (SA), over-segmentation rate (OR), and under-segmentation rate (UR) of the crown are 91.21%, 3.54%, and 9.85%, respectively. The average values of the trunk are 92.78%, 8.16%, and 7.93%, respectively. Comparing the results of our method experimentally with other popular tree image segmentation methods, our segmentation method get rid of human interaction and shows higher SA. Meanwhile, this work shows a promising application prospect on visual reconstruction and factors measurement of tree.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.117-123
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• 2023

There are casualties due to inundation and flooding due to intensive typhoons or heavy rains in summer. Due to such damage, the biggest disaster is flood, and in order to reduce human damage, this paper proposes a shortest distance algorithm-based pedestrian path search study using Map API. This system selects Map API through comparative analysis and provides the shortest route. The route explored is in JSON format and the data of the shelter is stored in the database. The route search system designed and implemented based on this data locates pedestrians and provides evacuation routes in case of flash floods. In addition, if the route cannot be entered while moving to the evacuation route, the current location of the pedestrian is identified, the route is re-searched, and a new route is provided. Therefore, it is believed that the pedestrian route search system proposed in this paper will prevent negligent accidents.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.413-413
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• 2020

Godunov형 모형을 이용한 홍수모델링에서는 일반적으로 구조적 사각격자나 비구조적 삼각격자가 주로 적용된다. 2차원 수치모형을 이용한 홍수모델링에서 연구유역의 정보가 격자의 노드나 중심에 입력되므로 적용격자의 유형과 생성방법에 따라 모형의 입력자료 오차에 영항을 줄 수 있다. 따라서, 연구유역이 지형 변동성이 심한 지역이거나 흐름형상이나 흐름변동이 심한 구간이라면, 고해상도 격자를 통해 모형의 입력자료 오차를 최소화할 할 수 있다. 본 연구에서는 2가지 유형에 대한 연구를 수행하였다, 첫 번째는 홍수해석을 위한 2차원 모형의 격자형상과 해상도에 따른 홍수위 및 홍수범람범위를 비교·분석하는 연구를 수행하였다. 연구유역은 2000년 10월 29일부터 11월 19일까지 홍수가 발생한 영국의 Severn 강 유역이다. 연구유역의 홍수 모델링을 위한 지형자료는 3m 해상도의 LiDAR(Light Detection And Ranging)를 이용하여 구축하였으며, 격자유형 및 해상도에 따른 2차원 홍수위 및 홍수범람범위를 비교·분석하기 위해서 홍수 발생기간 동안 촬영된 4개(2000년 8월 11, 14, 15, 17일)의 ASAR(Advanced Synthetic Aperture Radar) 영상자료를 활용하였다. 즉, ASAR 영상으로 촬용된 최대범람시기 및 홍수류의 배수기를 활용하여 최대범람범위뿐만 아니라 홍수가 증가하는 시기와 하류단 배수로 인해 홍수가 감소하는 시기를 모두 포함하는 홍수범람범위에 대한 격자유형별 2차원 홍수범람모형의 계산 결과에 대해 비교하였다. 두 번째는 아마존 강 중류유역의 2,500K㎡ 면적에 해당하는 대규모 유역에 대해 SRTM(Shuttle Radar Topography Mission) 지형자료를 이용하여 홍수기와 갈수기에 대해 2차원 모델링을 수행하고 그 결과를 위성자료와 비교하였다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.55-68
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• 2014

Some infrastructure these days is usually constructed under the ground for it to not interfere the foot-traffic of pedestrians, and thus, it is difficult to visually confirm the accurate location of the site where the establishments must be buried. These technical difficulties increase the magnitude of the problems that could arise from over-reliance on the experience of the worker or a mere blueprint. Such problems include exposure to flood and collapse. This paper proposes a constructor-oriented visualization system via mobile gadgets in general construction sites with occluded structures. This proposal is consisted with three stages. First, "Stage of detecting manhole and extracting features" detects and extracts the basis point of occluded structures which is unoccluded manhole. Next, "Stage of tracking features" tracks down the extracted features in the previous stage. Lastly, "Stage of visualizing occluded constructions" analyzes and synthesizes the GPS data and 3D objects obtained from mobile gadgets in the previous stages. This proposal implemented ideal method through parallel analysis of manhole detection, feature extraction, and tracking techniques in indoor environment, and confirmed the possibility through occluded water-pipe augmentation in real environment. Also, it offers a practical constructor-oriented environment derived from the augmented 3D results of occluded water-pipings.

• Fire Science and Engineering
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• v.30 no.4
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• pp.1-5
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• 2016

This research performed tests of Video Fire Detector and criteria of installation to make suggestions regarding the criteria that must be reflected in NFSC 203 by comparing the standards of FM Approvals, UL, ISO7240 and NFPA 72. FM Standard related to Video Fire Detector test has been classified as Smoke, Flame type, but the UL Standard has classified only as a Smoke type. This research examined 6 cases of fire phenomenon detection case in ISO 7240 and 3 cases in NFPA 72, respectively. There are 15 items required for the installation standard of a Video Fire Detector and each field standard is presented as a per installation method. To apply a Video Fire Detector, the pertinent items (the definition of term, detector's classification, structure and function among its test item) must be inserted. In addition, 7 items of the fire test, i.e., the sensitivity adjustment, prevent false alarm, ambient temperature test, the effective sensitivity and detection distance and viewing angle, aging test, flood test, must be applied to the actual test. For installation in the field, the operation environment and levels of illumination, and NFSC 203 must be set, and standards relevant to the sound system, indicators' installation distance, etc. need to be inserted.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.3
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• pp.113-127
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• 2015

Recently, there has been a growing interest in UAS(Unmanned Aerial System), and it is required to develop techniques to effectively detect water body from the recorded images in order to implement flood monitoring using UAS. This study used a UAS with RGB and NIR+RG bands to achieve images, and applied supervised classification method to evaluate the accuracy of water body detection. Firstly, the result for accuracy in water body image classification by RGB images showed high Kappa coefficients of 0.791 and 0.783 for the artificial neural network and minimum distance method respectively, and the maximum likelihood method showed the lowest, 0.561. Moreover, in the evaluation of accuracy in water body image classification by NIR+RG images, the magalanobis and minimum distance method showed high values of 0.869 and 0.830 respectively, and in the artificial neural network method, it was very low as 0.779. Especially, RGB band revealed errors to classify trees or grasslands of Songsan amusement park as water body, but NIR+RG presented noticeable improvement in this matter. Therefore, it was concluded that images with NIR+RG band, compared those with RGB band, are more effective for detection of water body when the mahalanobis and minimum distance method were applied.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.6
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• pp.469-477
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• 2008

Purpose: The goal of this paper is to present the design and performance of a position encoding circuit for $16{\times}16$ array of position sensitive multi-anode photomultiplier tube for small animal PET scanners. This circuit which reduces the number of readout channels from 256 to 4 channels is based on a charge division method utilizing a resistor array. Materials and Methods: The position encoding circuit was simulated with PSpice before fabrication. The position encoding circuit reads out the signals from H9500 flat panel PMTs (Hamamatsu Photonics K.K., Japan) on which $1.5{\times}1.5{\times}7.0\;mm^3$ $L_{0.9}GSO$ ($Lu_{1.8}Gd_{0.2}SiO_{5}:Ce$) crystals were mounted. For coincidence detection, two different PET modules were used. One PET module consisted of a $29{\times}29\;L_{0.9}GSO$ crystal layer, and the other PET module two $28{\times}28$ and $29{\times}29\;L_{0.9}GSO$ crystal layers which have relative offsets by half a crystal pitch in x- and y-directions. The crystal mapping algorithm was also developed to identify crystals. Results: Each crystal was clearly visible in flood images. The crystal identification capability was enhanced further by changing the values of resistors near the edge of the resistor array. Energy resolutions of individual crystal were about 11.6%(SD 1.6). The flood images were segmented well with the proposed crystal mapping algorithm. Conclusion: The position encoding circuit resulted in a clear separation of crystals and sufficient energy resolutions with H9500 flat-panel PMT and $L_{0.9}GSO$ crystals. This circuit is good enough for use in small animal PET scanners.

• Ecology and Resilient Infrastructure
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• v.2 no.4
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• pp.274-283
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• 2015

The reservoir shores seem to be vulnerable to biological invasion. The purpose of this research was to find out the floristic composition of alien plants and their relationship between environmental factors on reservoir shorelines in Korea. We investigated flora of alien plants and environmental factors of geomorphology, hydrology, water quality and soil on the shoreline of a total of 35 reservoirs with different water level managements. There were 56 species of alien plants, which was 15% of the total plant species identified in the study of reservoirs. A total of 57% of these alien species were the species which were introduced shortly after opening the port from 1876 to 1921 in Korea. More than 80% of the alien plants on the reservoir shores originated from America and Europe. The current distribution of Ambrosia trifida and Paspalum distichum were restricted in the central part and the southern region of the Korean Peninsula, respectively. The water level fluctuation, flood frequency at the median water level, water pollution index, coverage of rock exposure and mean degree of shoreline slope were determined as important environmental factors that have an effect on the characteristics of shoreline alien flora. Our results suggest that the reservoir shore was in danger of being invaded by alien plants due to the water level management and other human disturbances. For effective conservation of the reservoir ecosystem, periodic monitoring systems are required for the early detection of alien species on the reservoir shore.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.315-324
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• 2011

In Web environment, a flood of spam causes serious social problems such as personal information leak, monetary loss from fishing and distribution of harmful contents. Moreover, types and techniques of spam distribution which must be controlled are varying as days go by. The learning based spam classification method using Bag-of-Words model is the most widely used method until now. However, this method is vulnerable to anti-spam avoidance techniques, which recent spams commonly have, because it classifies spam documents utilizing only keyword occurrence information from classification model training process. In this paper, we propose a spam document detection method using a characteristic of repeating words occurring in spam documents as a solution of anti-spam avoidance techniques. Recently, most spam documents have a trend of repeating key phrases that are designed to spread, and this trend can be used as a measure in classifying spam documents. In this paper, we define six variables, which represent a characteristic of word repetition, and use those variables as a feature set for constructing a classification model. The effectiveness of proposed method is evaluated by an experiment with blog posts and E-mail data. The result of experiment shows that the proposed method outperforms other approaches.