• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.637-640
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• 2022

3차원 물체의 탐지와 자세 추정은 실내외 환경에서 장면 이해, 로봇의 물체 조작 작업, 자율 주행, 증강 현실 등과 같은 다양한 응용 분야들에서 공통적으로 요구되는 매우 중요한 시각 인식 기술이다. 깊이 지도를 요구하는 기존 연구들과는 달리, 본 논문에서는 RGB 컬러 영상만을 이용해 미지의 물체들, 즉 3차원 CAD 모델을 가지고 있지 않은 새로운 물체들을 탐지해내고, 이들의 자세를 추정해낼 수 있는 새로운 신경망 모델을 제안한다. 제안 모델에서는 최근 빠른 속도로 발전하고 있는 깊이 추정 기술을 이용함으로써, 깊이 측정 센서 없이도 물체 자세 추정에 필요한 깊이 지도를 컬러 영상에서 구해낼 수 있다. 본 논문에서는 벤치마크 데이터 집합을 이용한 실험을 통해, 제안 모델의 유용성을 평가한다.

• Journal of Korean Society for Geospatial Information Science
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• v.2 no.1 s.3
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• pp.131-139
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• 1994

Geo-spatial information system technology and cartographic modeling techniques were used as a way to Improve the land use planning process. Off-road vehicle(ORV) trails, timber harvest sites, timber haul mads and wildlife ruffed grouse habitats areas should be allocated together in the Wayne National Forest in the United States. Automated spatial cartographic models were developed to resolve the conflicts of the multiple land use allocation. The techniques of descriptive and prescriptive modeling made the multiple land use allocation efficient and effective.

• The Microorganisms and Industry
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• v.20 no.1
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• pp.2-9
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• 1994

최근에 미국의 국립보건원과 에너지성에서는 1990년부터 합동으로 수행하고 있는 게놈연구 5개년 계획(1991-1995)을 보완하여 새로운 5개년(1994-1998) 계획을 마련하였다. 이 계획에는 인체게놈의 지도작성 및 염기서열결정, 물리지도에 알려진 유전자들의 위치결정, 게놈연구에 필요한 기술, 모델 organism, 정보의 축적, 윤리, 법 및 사회적인 문제, 게놈연구에 관련된 교육훈련, 기술이전, 축적된 기술 및 데이타의 상호교환 등에 관한 구체적인 계획들의 변경을 포함하고 있다. 본문에서는 인체게놈연구 수행을 위하여 필요로 하는 몇몇 게놈연구 관련 기술과 현재 이용되고 있는 유전자지도 작성방법 등에 대하여 알아보고 향후의 게놈연구 방향에 관하여 기술하려고 한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.413-419
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• 2022

As the development of the underground space becomes active, safety accidents related to the underground are frequently occurring in recent years. In this regard, the Ministry of Land, Infrastructure and Transport is enforcing the 『Special Act on Underground Safety Management』 (enforced on January 1, 2018, hereafter referred to as the Underground Safety Act). Among the core contents of the Underground Safety Act, underground facilities(water supply, sewage, gas, power, communication, heating) buried underground, underground structures(subway, underpass, underpass, underground parking lot, underground shopping mall, common area), ground (Drilling, wells, geology) of 15 types of underground information can be checked at a glance on a three-dimensional basis by constructing an integrated underground spatial map and using it. The purpose of this study is to develop a program that can quickly inspect the three-dimensional model after creating a three-dimensional underground structure data among the underground spatial integration maps. To this end, we first investigated and reviewed the domestic and foreign status of technology that generates and automatically inspects 3D underground structure data. A quality inspection program was developed. Through this study, it is judged that it will be meaningful as a basic research for improving the quality of underground structures on the integrated map of underground space by automating more than 98% of the 3D model inspection process, which is currently being conducted manually.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.407-418
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• 2023

The Multi-Object Goal Visual Navigation(MultiOn) is a visual navigation task in which an agent must visit to multiple object goals in an unknown indoor environment in a given order. Existing models for the MultiOn task suffer from the limitation that they cannot utilize an integrated view of multimodal context because use only a unimodal context map. To overcome this limitation, in this paper, we propose a novel deep neural network-based agent model for MultiOn task. The proposed model, MCFMO, uses a multimodal context map, containing visual appearance features, semantic features of environmental objects, and goal object features. Moreover, the proposed model effectively fuses these three heterogeneous features into a global multimodal context map by using a point-wise convolutional neural network module. Lastly, the proposed model adopts an auxiliary task learning module to predict the observation status, goal direction and the goal distance, which can guide to learn the navigational policy efficiently. Conducting various quantitative and qualitative experiments using the Habitat-Matterport3D simulation environment and scene dataset, we demonstrate the superiority of the proposed model.

• Journal of KSNVE
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• v.10 no.3
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• pp.410-422
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• 2000

This paper proposes a model of standardized noise map in korea, The standardization of noise map is accomplished by standardizing the way of map description and by applying international noise mapping criteria to Korean environment. The proposed model can be an useful star or the production of national standardized noise map in Korea.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.157-168
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• 2014

We propose an novel artificial visual attention model that is capable of automatic detection and segmentation of saliency region on natural images in this paper. The proposed model is based on human visual perceptions in biological vision and contains there are main contributions. Firstly, we propose a novel framework of artificial visual attention model based on the opponent process theory using intensity and color features, and an entropy filter is designed to perceive salient regions considering the amount of information from intensity and color feature channels. The entropy filter is able to detect and segment salient regions in high segmentation accuracy and precision. Lastly, we also propose an adaptive combination method to generate a final saliency map. This method estimates scores about intensity and color conspicuous maps from each perception model and combines the conspicuous maps with weight derived from scores. In evaluation of saliency map by ROC analysis, the AUC of proposed model as 0.9256 approximately improved 15% whereas the AUC of previous state-of-the-art models as 0.7824. And in evaluation of salient region segmentation, the F-beta of proposed model as 0.7325 approximately improved 22% whereas the F-beta of previous state-of-the-art models.

• Journal of Korean Society for Geospatial Information Science
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• v.2 no.2 s.4
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• pp.43-53
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• 1994

GSIS(geo-spatial information system), particularly when utilized in hazard management decision, is one of hazard analysis tool. Data of GSIS input from digitizing or scanning of map or aerial photos. This paper focuses upon the hazard prediction in GSIS and RS analysis to assess map, aerialphotos, satellite imagery and soil map. This study found computation of hazard area analysis. the results is formed as raster data model of quadtree. Authors knew more accurate results of overlay. This paper shows building up integrated data base as well as search of hazard area in aerial photographs.

• International Journal of Highway Engineering
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• v.13 no.3
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• pp.111-119
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• 2011

The noise prediction model, KRON-2006, in South Korea has been developed for obtaining the average noise level. The model is based on an outdoor sound propagation method based on ISO9613 and ASJ Model-1998 and supports the analysis of the linear noise source, such as highway, for obtaining Leq. Because of that, the model can't obtain Lmax, Lmin from the time series noise profile based on traffic at every moment. In order to address this problem, the real time noise prediction model based on traffic simulation using GIS model and algorithm is proposed. It can predict the vehicle point noise level based on vehicle type, speed generated from traffic simulation by using headway and obtain Lmax, Lmin as integrating the noise profile generated from it at every moment. An evalution of the noise prediciton model using field measurements finds good agreement between predicted and measured noise levels at 1m, 8m, 15m from curb of the near side lane.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.3
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• pp.229-236
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• 2001

One of the basic tasks in hydrological analysis is to delineate drainage basins and channel networks. Characteristics of channel networks and drainage basin parameters have been used widely in hydrologic calculation and modeling. DEMs(Digital Elevation Models) are generally used to automatically map the channel networks and to delineate drainage basins. This paper presents an effort to analyze basin characteristics using various DEMs. The quantitative analysis of channel networks begins with Horton's method of classifying stream according to Horton orders in hydro-geomorphy. HGSIS(Hydro-Geo-Spatial Information System) is possible to extract parameters. Usually, hydrologists. surveyors and GSIS researchers have some difficulties in accessing satellite images and in extracting DEMs from them. Therefore, the extracted DEMs from contours of digital map is widely used to have the basic works of hydrological analysis. This study presents proper DEMs to calculate Horton's orders, width function, drainage area, main channel length, total channel length, basin elevation and basin slope at digital map of 1:25,000 scale.