• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1071-1081
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• 2021

Remote Sensing using unmanned aerial vehicles(UAV) can acquire images with higher time resolution and spatial resolution than aerial and satellite remote sensing. However, UAV images are photographed at low altitude and the area covered by one image isrelatively narrow. Therefore multiple images must be processed to monitor large area. Since UAV images are photographed under different exposure conditions, there is difference in brightness values between adjacent images. When images are mosaicked, unnatural seamlines are generated because of the brightness difference. Therefore, in order to generate seamless mosaic image, a radiometric processing for correcting difference in brightness value between images is essential. This paper proposes a relative radiometric calibration and image blending technique. In order to analyze performance of the proposed method, mosaic images of UAV images in agricultural and mountainous areas were generated. As a result, mosaic images with mean brightness difference of 5 and root mean square difference of 7 were avchieved.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.535-544
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• 2018

Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. This study was conducted to segment and classify road objects using 3D point cloud data acquired by terrestrial mobile mapping system provided by National Geographic Information Institute. For this study, the original 3D point cloud data were pre-processed and a filtering technique was selected to separate the ground and non-ground points. In addition, the road objects corresponding to the lanes, the street lights, the safety fences were initially segmented, and then the objects were classified using the support vector machine which is a kind of machine learning. For the training data for supervised classification, only the geometric elements and the height information using the eigenvalues extracted from the road objects were used. The overall accuracy of the classification results was 87% and the kappa coefficient was 0.795. It is expected that classification accuracy will be increased if various classification items are added not only geometric elements for classifying road objects in the future.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.169-174
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• 2019

Because the warm and cozy demand of automotive driving seat increases, the research development of heating seat has been actively made. In this study, the thermal stress analysis and the structural analysis were carried out with three kinds of heating seats of A, B and C. By executing the thermal analysis with the same material, model A was shown to have the heat transfer better than model B or model C at the study result. So, it could be seen that the heat transfers became different each other though models had the same material according to the configuration of product. Adding the hot wire in order to expect the safer heating can be better heating, but there is the limit on the aspect considering the capability in contrast to the price of product. Generally, model B is thought to be safest thermally than model A or model C in every respect. As the design data of the automotive heating seat product with the durability and safety acquired by this study result are used, the artistic environment can be promoted by being grafted onto the automotive driving seat.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.315-316
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• 2019

전투복, 군용 천막 등에 사용되는 위장무늬는 군 작전 수행 시 주변 환경의 색상, 패턴을 모사하여 개인병사 및 무기체계의 위장 기능을 극대화하고, 이를 통해 아군의 생명과 시설피해를 최소화하기 위한 목적으로 사용된다. 특히 최근 들어 군의 작전환경과 임무가 복잡하고 다양해짐에 따라, 작전환경에 대한 데이터의 취득 및 정량적 분석을 통해 전장 환경에 최적화된 위장무늬 패턴 및 색상 추출에 대한 연구의 필요성이 증대되고 있다. 본 논문에서는 한반도 자연환경 영상에 대한 자기 조직화 지도(SOM, Self-organizing Map) 기반의 한반도 자연환경 대표 색상 군집화 연구 방법에 대해 서술한다. 이를 위해 한반도 내 위도를 고려한 장소에서 시간별, 계절별 자연환경 영상 수집을 진행하며, 수집된 영상 내 다수의 화소의 군집화를 위해 2차원 SOM을 활용한다. 영상 내 각 화소의 색상 값에 대한 SOM의 학습 시, RGB공간상의 색차/색상 인지 왜곡을 피하기 위하여 CIEDE2000 색차 식을 통해 군집화를 진행한다. 실험결과에서는 온라인상으로 수집한 여름 및 가을철 대표 색상 군집화 결과와, 현재까지 수집된 계절별 자연환경 사진 내 6억 7648개 화소에 대한 대표 색상 군집화 결과를 보여준다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.4
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• pp.531-540
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• 2019

Existing dozer productivity computation models use different input variables, formulas, productivity correction factors, and experimental data source. This paper presents a method that characterizes the productivity outputs obtained by the PLS model and the Caterpillar model that are accepted as industry standards. The method identifies the input variables to be collected from the site, the performance charts to be referenced, and the formulas and implements them in a single computational tool. This study verifies that the PLS model may replace the manual computational process of Caterpillar model by eliminating reliance on graphics manipulation. Replacing the Caterpillar model with the PLS model and implementing the process as a function contributes to assess the productivity of a dozer timely by encouraging to utilize real-time information collected directly from the site. This study allows researchers and practitioners to effectively deal with the values of productivity correction factors collected from the job site and to control the productivity. The practicality and effectiveness of the method have been validated by applying to a project case.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.2
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• pp.88-99
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• 2019

This study presents the results of analysis for the wave data that were consecutively collected from February 2013 to November 2018 at the location of 1.6 km offshore from Namhangjin beach. The water depth at the location is 30.5 m and waves were measured by AWAC (Acoustic Wave And Current meter). By using wave-by-wave analysis and spectral analysis, wave heights and periods were evaluated and then the relationships between the quantities obtained by the two methods were proposed based on linear regression analysis. In addition, monthly and yearly variations of the significant wave height and period, and the peak wave direction were analyzed. Moreover, the relationship between the significant wave height and period was newly suggested. Variability and probability distribution of the significant wave period with respect to the significant wave height were also examined.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.556-567
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• 2021

Purpose: In this study, various methods of deep learning-based automatic damage analysis technology were reviewed based on images taken through Unmanned Aerial Vehicle to more efficiently and reliably inspect the exterior inspection and inspection of railway bridges using Unmanned Aerial Vehicle. Method: A deep learning analysis model was created by defining damage items based on the acquired images and extracting deep learning data. In addition, the model that learned the damage images for cracks, concrete and paint scaling·spalling, leakage, and Reinforcement exposure among damage of railway bridges was applied and tested with the results of automatic damage analysis. Result: As a result of the analysis, a method with an average detection recall of 95% or more was confirmed. This analysis technology enables more objective and accurate damage detection compared to the existing visual inspection results. Conclusion: through the developed technology in this study, it is expected that it will be possible to analysis more accurate results, shorter time and reduce costs by using the automatic damage analysis technology using Unmanned Aerial Vehicle in railway maintenance.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.221-231
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• 2021

This paper identify factors of migration of employees' household who work for relocated public institutions. As a factors of migration, we consider individual and household characteristics, the gravity model of distance and population and so on. Considering discrete dependant variable and structure of data, we employ the logistic multilevel model and random intercept model. The result indicates employees' who are female, 30s and 40s, higher education level(PhD) and whose spouse are unemployed tend to transfer their residential registration to new city near relocated public institution. Regarding regional variable, the distance from employee's previous residential location and number of migration of prior year are statistically significant. Also the model indicate regional economy, educational and residential environment of new city influence employee's decision for transferring residential registration.

• Journal of Korean Society of Forest Science
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• v.100 no.1
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• pp.25-33
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• 2011

The purpose of this study is to develop a standard calculation program of forest road drainage facilities that may help forest road designers to design forest road drainage facilities more conveniently and precisely. Especially, the characteristics of this program is that the forest road designers may calculate the amount of outflow in the basin using rainfall intensity data conveniently, without the data designers should acquire through site measurements when they carry out indoor preliminary measurements before they go out for outdoor measurements. In this manner, excessive design may be restrained by offering minimum standard calculation for drainage structures. And also this study was designed to facilitate proper layout of drainage structures by calculating outflow discharge of each basin where forest roads will be installed. Especially, this study will contribute to leveling-up of forest design techniques as the researcher has prepared the reports on whole process of drain pipe installation and provided them in the form of computer file or printout, which show a rational design process, and make it possible to modify in case of an error.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1231-1240
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• 2020

Water pipes are laid on the ground, making it impossible to visually detect leaks due to aging of pipes, and technology to detect leaks in pipes is mainly used to detect leaks in pipes by detecting leaks. In this paper, two accelerometers were attached to both ends of the constant water piping to calculate the time difference between the acquired data to detect leakage points. The leak test of piping was performed by installing valves at 4.3m, 8.6m, and 12.9m points on piping 17.2m, and changing the development rate of valves to 30% and 70%. Leakage can be detected for pressure drop in piping, which is 30% and 70% open valve. It is very important to detect leakage in the early stage, and it is judged that detection of the initial leak point from the algorithm applied in this paper will be possible.