• Journal of the Korea Society for Simulation
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• v.33 no.3
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• pp.13-26
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• 2024

The CBRN(Chemical, Biological, Radiological, and Nuclear) hazard prediction model is a system that supports commanders in making better decisions by creating contamination distribution and damage prediction areas based on the weapons used, terrain, and weather information in the events of biochemical and radiological accidents. NBC_RAMS(Nuclear, Biological and Chemical Reporting And Modeling S/W System) developed by ADD (Agency for Defense Development) is used not only supporting for decision making plan for various military operations and exercises but also for post analyzing CBRN related events. With the NBC_RAMS's core engine, we introduced a CBR hazard assessment scenario analysis system that can generate contaminant distribution prediction results reflecting various CBR scenarios, and described how to apply it in specific purposes in terms of input information, meteorological data, land data with land coverage and DEM, and building data with pologon form. As a practical use case, a technology development case is addressed that tracks the origin location of contaminant source with artificial intelligence and a technology that selects the optimal location of a CBR detection sensor with score data by analyzing large amounts of data generated using the CBRN scenario analysis system. Through this system, it is possible to generate AI-specialized CBRN related to training and analysis data and support planning of operation and exercise by predicting battle field.

• Journal of Biomedical Engineering Research
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• v.20 no.6
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• pp.515-521
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• 1999

Scintillator crystal is an important part and detcrmines performance characteristics of the gamma camera. We investigated the offects of scintillation crystal surface treatment on gamma camera imaging. Nal(TI) and Csl(Tl) scintillators. 20 mm diameter and 10 mm thickness, applied with two different surface treatments, white and black reflcetors, were applied to Nal(Tl) and Csl(Ti). The optical properties of generated scintillation light were evaluated by Monte Carlo simulation method and by actual measurement using a position sensitive photomultiplier tube (PSPMT). We measured sensitivity, energy resolution and spatial resolution of gamma camera with the various scintillators coupled to a PSPMT. In the simulation. Nal(Tl)-white prosented the best sensitivity. In the measurements, the sensitivities and the intrinsic spatial resolutions of Nal(Tl)-white, Nal(Tl)-black. CsI(Tl)-white, CsI(Tl)-black were 2920, 2322, 1754, 1401 cps/$\mu$ci and 5.2, 4.5, 7.0, 6.3 mm FWHM. respectively. Their intrinsic energy resolutions were mesured 12.5, 23.5, 20.5, 33.3% FWHM at 140 keV Tc-99m. In this study, we investigated the offects of a side surface treatment of the scintillator on the gamma camera imaging. Simulation and measurement prescnted similat trends. Based on the results, we concluded that the surface of th NaI(Tl)seintillator must be treated by absorptive materials in order to develop the gamma camera having good spatial resolution.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Proceedings of the KWS Conference
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• 2009.11a
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• pp.3-3
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• 2009

선박의 건조과정에서 필수적으로 선체 외판에는 선박의 안전과 운항 및 정비 등에 필요한 정보를 나타내기 위해 다양한 종류의 마크 및 문자가 마킹되어진다. 하지만, 단순한 도장 작업만으로는 해상과 같은 부식 환경에서 마크 및 문자가 쉽게 지워지거나 손상되기 때문에 마크 및 문자를 용접 비드(welding bead)로 표시하거나 미리 절단된 강판(steel plate)을 수동으로 용접한 뒤 도장을 함으로써 마크 및 문자의 손상을 방지하고 있다. 이러한 문자마킹작업을 하기 위해서는 작업자가 수작업으로 기준선과 마크 및 문자의 위치를 먹줄 등을 이용하여 마킹을 하고, 해당 마크 및 문자의 템플렛(template)을 이용하여 펀칭을 실시한 후 수동으로 용접을 실시한다. 하지만, 수작업을 통한 선체외판 문자마킹 작업은 작업자의 기량에 따라 품질이 상이하여 품질 저하의 원인이 된다. 또한 대조립 및 탑재 단계에서 문자 마킹 작업시 수직자세의 용접을 요구함으로써 작업자가 안전사고에 노출되어 있으며, 선박의 각 단계별 주요 공정보다 작업시간이 길어져 전체 선박 건조공정을 지연시키는 문제점 등을 야기시킬 수 있다. 이러한 문제점들을 해결하기 위해 조선업계에서는 선체 외판의 마크 및 문자를 자동으로 용접할 수 있는 장치를 개발하기 위해 노력해왔으며, 몇몇 개발 사례가 보고되고 있다. 하지만, 그 실효성 부분에서는 아직까지 해결하지 못한 문제점들로 인해 현장 적용에는 어려움을 보이고 있다. 본 연구에서는 선박외판 문자 자동용접장치의 기능성뿐만 아니라 현업 적용성을 가장 우선적으로 고려하여 문자마킹장치(Marking Robot for Shipbuilding) 개발을 진행하였다. 우선, 적절한 용접 재료를 선정하기 위해서 솔리드 와이어(Solid Wire)와 플럭스 코어드 와이어(Flux Cored Wire)에 대한 비드온 용접(Bead-On Welding)을 아래보기자세와 수직자세에 대해서 실시하여 적절한 용접 조건을 설정하였다. 본 연구에서 개발된 문자마킹 자동용접장치는 3축으로 구성되어 있으며 각 축들을 분리할 수 있도록 개발하여 이동성을 향상시켰으며, 작업면과 용접토치간의 거리를 일정하게 유지시킬 수 있도록 용접전류 센서(Welding Current Sensor)를 이용하여 토치 높이(Wire Extension)를 제어함으로써 균일한 품질의 용접비드를 얻을 수 있었다. 또한 문자마킹 자동용접장치는 본체 구동부와 제어부(Touch Screen)가 쉽게 분리되고 장착이 가능한 구조로 개발되었으며, 용접시 각 용접자세별로 용접전압, 전류 그리고 용접속도 설정이 가능하여 아래보기 자세뿐만 아니라 어떠한 자세에서도 같은 모양의 비드형상을 가지는 문자마킹용접이 가능하도록 개발하였으며, 이는 실험과 현장적용을 통해 검증하였다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.187-196
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• 2020

In this study, collapsed building detection using UAV (Unmanned Aerial Vehicle) and PlanetScope satellite images was carried out, suggesting the possibility of utilization of heterogeneous sensors in object detection located on the surface. To this end, the area where about 20 buildings collapsed due to forest fire damage was selected as study site. First of all, the feature information of objects such as ExG (Excess Green), GLCM (Gray-Level Co-Occurrence Matrix), and DSM (Digital Surface Model) were generated using high-resolution UAV images performed object-based segmentation to detect collapsed buildings. The features were then used to detect candidates for collapsed buildings. In this process, a result of the change detection using PlanetScope were used together to improve detection accuracy. More specifically, the changed pixels acquired by the bitemporal PlanetScope images were used as seed pixels to correct the misdetected and overdetected areas in the candidate group of collapsed buildings. The accuracy of the detection results of collapse buildings using only UAV image and the accuracy of collapse building detection result when UAV and PlanetScope images were used together were analyzed through the manually dizitized reference image. As a result, the results using only UAV image had 0.4867 F1-score, and the results using UAV and PlanetScope images together showed that the value improved to 0.8064 F1-score. Moreover, the Kappa coefficiant value was also dramatically improved from 0.3674 to 0.8225.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.469-469
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• 2017

기후 변화 및 환경오염으로 인하여 물부족 국가가 세계적으로 증가하고 있는 추세이며, 특히 집중형 강우의 형태가 많아짐에 따라 홍수피해 및 상수공급의 문제가 사회적으로 큰 이슈가 되고 있다. 최근 20여 년간의 급속한 경제성장과 도시화 과정에서 인도네시아는 인구와 산업의 과도한 도시집중으로 지난 1960-80년대 한국이 산업화 과정에서 겪었던 것보다 훨씬 심각한 환경문제에 직면하고 있으며, 자카르타와 반둥을 포함하는 광역 수도권 지역의 물 부족과 수질 오염, 환경문제가 이미 매우 위험한 수준에 도달하고 있는 실정이다. 특히, 찌따룸강 중상류에 위치한 인도네시아 3대 도시인 반둥시는 고질적인 용수부족 문제를 겪고 있다. 2010년 현재 약 일평균 15 CMS의 용수가 부족한 상황이며, 2030년에는 지속적인 인구증가로 약 23 CMS의 용수가 추가로 더 필요한 것으로 전망된다. 이러한 용수공급 문제 해결을 위해 반둥시 및 찌따룸강 유역관리청은 댐 및 지하수 개발, 유역 간 물이동 등의 구조적인 대책뿐만 아니라 비구조적인 대책으로써 기존 및 신규 저수지 연계운영을 통한 용수이용의 효율성을 높이는 방안을 모색하고 있다. 이에 따라 본 연구에서는 해당유역의 용수공급 부족 문제를 해소할 수 있는 비구조적인 대책의 일환으로써 다양한 댐 및 보, 소수력 발전, 취수장 등 유역 내 수리 시설물의 운영 최적화를 위한 지능형 물관리 시스템 적용 방안을 제시하고자 한다. 본 연구의 지능형 물관리 시스템은 센서 및 사물 인터넷(Internet of Things, IoT), 네트워크 기술을 바탕으로 시설물 및 운영자, 유관기관 간의 양방향 통신을 통해 유기적인 상호연계 체계를 제공 할 수 있다. 또한 유역의 수문상황과 시설물의 운영현황, 용수공급 및 수요 현황을 실시간으로 확인함으로써 수요에 따른 즉각적인 용수공급량의 조절이 가능하다. 또한, 빅데이터 분석 및 기계학습(Machine Learning)을 통해 개별 물관리 시설물에 대한 최적 운영룰을 업데이트할 수 있으며, 유역의 수문상황과 용수 수요 현황을 고려하여 최적의 용수공급 우선순위를 선정할 수 있다. 지능형 물관리 시스템 개발의 목적은 찌상쿠이 유역의 수문현황을 실시간으로 모니터링하고, 하천시설물의 운영을 분석하여 최적의 용수공급 및 배분을 통해 유역의 수자원 활용 효율성을 향상시키는 데 있다. 이를 위해 수문자료의 수집체계를 구축하고 기관간 정보공유체계를 수립함으로써 분석을 위한 기반 인프라를 구성하며, 이를 기반으로 유역 유출을 비롯한 저수지 운영, 물수지 분석을 수행하고, 분석 및 예측결과, 과거 운영 자료를 토대로 새로운 물관리 시설 운영룰 및 시설물 간 연계운영 방안, 용수공급 우선순위 의사결정 등을 지원하고자 한다. 본 연구의 지능형 물관리 시스템은 통합 DB를 기반으로 수리수문 현상의 모의 분석을 통해 하천 시설물 운영의 합리적 기준을 제시함으로써 다양한 관리주체들의 시설물운영에 대한 이견 및 분쟁을 해소하고, 한정된 수자원과 다양한 수요 간의 효율적이고 합리적인 분배 및 시설물 운영문제를 해결하기 위한 의사결정도구로써 활용할 수 있을 것으로 기대된다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.587-593
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• 2019

The field of geospatial information is rapidly changing due to the development of sensor and data processing technology that can acquire location information. And demand is increasing in various related industries and social activities. The construction and utilization of three dimensional geospatial information that is easy to understand and easy to understand can be an essential element to improve the quality and reliability of related services. In recent years, 3D laser scanners are widely used as 3D geospatial information construction technology. However, 3D laser scanners may cause shadow areas where data acquisition is not possible when objects are large in size or complex in shape. In this study, 3D model of an object has been created by acquiring oblique images using an unmanned aerial vehicle and processing the data. The study area was selected, oblique images were acquired using an unmanned aerial vehicle, and point cloud type 3D model with 0.02 m spacing was created through data processing. The accuracy of the 3D model was 0.19m and the average was 0.11m. In the future, if accuracy is evaluated according to shooting and data processing methods, and 3D model construction and accuracy evaluation and analysis according to camera types are performed, the accuracy of the 3D model will be improved. In the point cloud type 3D model, Cross section generation, drawing of objects, and so on, it is possible to improve work efficiency of spatial information service and related work.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.587-598
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• 2021

This study aims to develop a multi-camera based MMS (Mobile Mapping System) technology for building a HD (High Definition) map for autonomous driving and for quick update. To replace expensive lidar sensors and reduce long processing times, we intend to develop a low-cost and efficient MMS by applying multiple cameras and real-time data pre-processing. To this end, multi-camera storage technology development, multi-camera time synchronization technology development, and MMS prototype development were performed. We developed a storage module for real-time JPG compression of high-speed images acquired from multiple cameras, and developed an event signal and GNSS (Global Navigation Satellite System) time server-based synchronization method to record the exposure time multiple images taken in real time. And based on the requirements of each sector, MMS was designed and prototypes were produced. Finally, to verify the performance of the manufactured multi-camera-based MMS, data were acquired from an actual 1,000 km road and quantitative evaluation was performed. As a result of the evaluation, the time synchronization performance was less than 1/1000 second, and the position accuracy of the point cloud obtained through SFM (Structure from Motion) image processing was around 5 cm. Through the evaluation results, it was found that the multi-camera based MMS technology developed in this study showed the performance that satisfies the criteria for building a HD map.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.989-1006
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• 2020

Building change monitoring based on building detection is one of the most important fields in terms of monitoring artificial structures using high-resolution multi-temporal images such as CAS500-1 and 2, which are scheduled to be launched. However, not only the various shapes and sizes of buildings located on the surface of the Earth, but also the shadows or trees around them make it difficult to detect the buildings accurately. Also, a large number of misdetection are caused by relief displacement according to the azimuth and elevation angles of the platform. In this study, object-based building detection was performed using the azimuth angle of the Sun and the corresponding main direction of shadows to improve the results of building change detection. After that, the platform's azimuth and elevation angles were used to detect changed buildings. The object-based segmentation was performed on a high-resolution imagery, and then shadow objects were classified through the shadow intensity, and feature information such as rectangular fit, Gray-Level Co-occurrence Matrix (GLCM) homogeneity and area of each object were calculated for building candidate detection. Then, the final buildings were detected using the direction and distance relationship between the center of building candidate object and its shadow according to the azimuth angle of the Sun. A total of three methods were proposed for the building change detection between building objects detected in each image: simple overlay between objects, comparison of the object sizes according to the elevation angle of the platform, and consideration of direction between objects according to the azimuth angle of the platform. In this study, residential area was selected as study area using high-resolution imagery acquired from KOMPSAT-3 and Unmanned Aerial Vehicle (UAV). Experimental results have shown that F1-scores of building detection results detected using feature information were 0.488 and 0.696 respectively in KOMPSAT-3 image and UAV image, whereas F1-scores of building detection results considering shadows were 0.876 and 0.867, respectively, indicating that the accuracy of building detection method considering shadows is higher. Also among the three proposed building change detection methods, the F1-score of the consideration of direction between objects according to the azimuth angles was the highest at 0.891.

• Journal of the Korea Institute of Building Construction
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• v.9 no.5
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• pp.79-86
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• 2009

In a steel structure, spray-applied fire resistive materials are crucial in preventing structural strength from being weakened in the event of a fire. The quality control of such materials, however, is difficult for manual workers, who can frequently be in short supply. These skilled workers are also very likely to be exposed to environmental hazards. Problems with construction work such as this, which are specifically the difficulty of achieving quality control and the dangerous nature of the work itself, can be solved to some degree by the introduction of automated equipment. It is, however, very difficult to automate the work process, from operation to the selection of a location for the equipment, as the environment of a construction site has not yet been structured to accommodate automation. This is a fundamental study on the possibility of the automation of spray-applied fire resistive coating work. In this study, the linkability of the cutting-edge RTLS to an automation system is reviewed, and a scenario for the automation of spray-applied fire resistive coating work and system composition is presented. The system suggested in this study is still in a conceptual stage, and as such, there are many restrictions still to be resolved. Despite this fact, automation is expected to have good effectiveness in terms of preventing fire from spreading by maintaining a certain level of strength at a high temperature when a fire occurs, as it maintains the thickness of the fire-resistive coating at a specified level, and secures the integrity of the coating with the steel structure, thereby enhancing the fire-resistive performance. It also expected that if future research is conducted in this area in relation to a cutting-edge monitoring TRS, such as the ubiquitous sensor network (USN) and/or building information model (BIM), it will contribute to raising the level of construction automation in Korea, reducing costs through the systematic and efficient management of construction resources, shortening construction periods, and implementing more precise construction