• 한국환경과학회지
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• 제30권12호
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• pp.1101-1111
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• 2021

In this study, among the W-S-R(Wiper-Signal-Rainfall) relationship methods used to produce sensor-based rain information in real time, we sought to produce actual rainfall information by applying machine learning techniques to account for the effects of wiper operation. To this end, we used the gradient descent and threshold map methods for pre-processing the cumulative value of the difference before and after wiper operation by utilizing four sensitive channels for optical sensors which collected rain sensor data produced by five rain conditions in indoor artificial rainfall experiments. These methods produced rainfall information by calculating the average value of the threshold according to the rainfall conditions and channels, creating a threshold map corresponding to the 4 (channel) × 5 (considering rainfall information) grid and applying Optima Rainfall Intensity among the big data processing techniques. To verify these proposed results, the application was evaluated by comparing rainfall observations.

• 자원환경지질
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• 제39권4호
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• pp.485-494
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• 2006

1920년대의 온천조사에서부터 현재에 이르기까지 우리나라 지열연구의 역사를 간략히 요약하고, 우리나라의 지열류량 연구 결과 및 추세, 지열의 근원 연구, 그리고 지열에너지 개발 및 활용분야에 대한 연구활동을 정리하였다. 우리나라에서의 지열연구는 1970년대까지 주로 온천조사와 관련되어 있다. 1980년대에 들어서 연구소와 학계에서 온천조사 뿐만 아니라, 지열류량에 대한 연구도 많이 수행하게 되었으며 1996년도에는 우리나라 전국적인 지온경사 분포도와 지열류량 분포도를 발간하게 되었다. 또한 우리나라 온천수에 대한 지화학적 동위원소 분석과 화강암 지대의 열생산율 측정도 1990년대에 주로 이루어졌다. 지열개발과 활용에 대한 시도는 1990년대 초반부터 시도되었으나 실제 개발을 위한 시추로 이어지게 된 것은 2000년대에 들어와서 가능해졌다. 최근의 활발한 심부 지열수 자원 개발이나 천부 지중열을 활용한 냉난방 수요의 증가 등 주변여건이 호전됨에 따라 우리나라 지열연구개발의 전망은 밝다고 판단된다.

• 방송공학회논문지
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• 제22권1호
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• pp.62-69
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• 2017

신호등 검출은 첨단운전자보조시스템에서 매우 중요하며 최근 신호등 검출 알고리즘의 연구가 활발히 진행 중이다. 그러나 기존의 영상처리 기반의 신호등검출 알고리즘은 조명의 변화에 민감하다는 문제점이 있다. 이러한 문제점을 해결하기 위하여 본 논문에서는 다음과 같은 신호등 검출 알고리즘을 제안한다. 먼저 제안하는 컬러맵과 HSV(hue-saturation-value)를 이용하여 신호등의 후보영역을 검출한다. 이후 검출된 신호등 후보영역으로부터 HOG(histogram of oriented gradient) 서술자와 SVM(support vector machine)을 이용하여 신호등을 검출한다. 검출된 신호등 영상을 이용하여 제안하는 Multilayer HOG 서술자를 이용하여 신호등의 방향 정보를 결정한다. 실험결과에서 확인할 수 있듯이 제안하는 알고리즘은 높은 검출성능과 실시간 처리가 가능하다.

• 한국연소학회지
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• 제17권1호
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• pp.48-57
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• 2012

Laminar lifted propane coflow-jet flames diluted with nitrogen were experimentally investigated to determine heat-loss-related self-excitation regimes in the flame stability map and elucidate the individual flame characteristics. There exists a critical lift-off height over which flame-stabilizing effect becomes minor, thereby causing a normal heat-loss-induced self-excitation with O(0.01 Hz). Air-coflowing can suppress the normal heat-loss-induced self-excitation through increase of a Peclet number; meanwhile it can enhance the normal heat-lossinduced self-excitation through reducing fuel concentration gradient and thereby decreasing the reaction rate of trailing diffusion flame. Below the critical lift-off height. the effect of flame stabilization is superior, leading to a coflow-modulated heat-loss-induced self-excitation with O(0.001 Hz). Over the critical lift-off height, the effect of reducing fuel concentration gradient is pronounced, so that the normal heat-loss-induced self-excitation is restored. A newly found prompt self-excitation, observed prior to a heat-loss-induced flame blowout, is discussed. Heat-loss-related self-excitations, obtained laminar lifted propane coflow-jet flames diluted with nitrogen, were characterized by the functional dependency of Strouhal number on related parameters. The critical lift-off height was also reasonably characterized by Peclet number and fuel mole fraction.

• 대한공간정보학회지
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• 제24권1호
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• pp.17-24
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• 2016

현재 우리나라는 기후변화로 인해 여름철 집중호우와 국지성 호우가 증가하고 있다. 이에 토사유실 예방에 관한 연구들이 활발하게 이루어지고 있고 산림청에서는 산사태 위험지도를 제공하고 있다. 산사태 위험지도는 9가지의 요소들로 가중치를 주어 5등급화 하여 위험도를 나타낸다. 2014년 8월 25일 부산시 금정구 오륜터널 일대에서 국지성 호우로 인한 토사유실이 발생했으며 산사태위험지도와 비교해 본 결과 실제 토사유실이 발생한 지역은 위험지도상의 안전지대였다. 토사유실이 발생한 오륜터널 일대를 강우량, 토양도, 지질도, 임상도, 경사도, 하계망, 분수령, 유역형상, 유출량을 분석하였다. 분석한 결과 산사태가 발생한 지점의 토양, 임상, 다량의 유출량과 첨두유량, 수계망의 차수, 유역의 형상이 토사유실의 원인요소라고 판단된다. 이 요소 중 산사태 위험지도에서는 고려하지 않은 집중 강우량에 의한 유출량과 첨두유량, 수계망의 차수, 유역의 형상이 산사태의 가장 큰 원인이라고 분석되었다. 기록적인 국지성 호우로 인해 많은 재산피해가 있었던 발생지점 중 하나인 오륜터널 일대의 토사유실원인분석 연구를 통하여 강수량에 의한 유출량, 첨두유량, 유역의 형상이 중요하다고 판단된다. 이 요소들을 산사태 위험지역 판정 시 고려하여 집중호우 시 토사유실에 대비하여야 한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.312-316
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• 2004

The paper presents a technique to identify the boundary of the optic disc in infant retinal digital images using an approach based on active contours (snakes). The technique can be used to be develop a automate system in order to help the ophthalmologist's diagnosis the retinopathy of prematurity (ROP) disease which may occurred on preterm infant,. The optic disc detection is one of the fundamental step which could help to create an automate diagnose system for the doctors we use a new kind of active contour (snake) method has been developed by Chenyang et. al. [1], based on a new type of external force field, called gradient vector flow, or GVF. GVF is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. The testing results on a set of infant retinal ROP images verify the effectiveness of the proposed methods. We show that GVF has a large capture range and it's able to move snakes into boundary concavities of optic disc and finally the optic disk boundary was determined.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.576-590
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• 2023

Machine learning is widely used in various academic fields, and recently it has been actively applied in the medical research. In the medical field, machine learning is used in a variety of ways, such as speeding up diagnosis, discovering new biomarkers, or discovering latent traits of a disease. In the respiratory field, a relative regional air volume change (RRAVC) map based on quantitative inspiratory and expiratory computed tomography (CT) imaging can be used as a useful functional imaging biomarker for characterizing regional ventilation. In this study, we seek to predict RRAVC using various regular machine learning models such as extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP). We experimentally show that MLP performs best, followed by XGBoost. We also propose several relative coordinate systems to minimize intersubjective variability. We confirm a significant experimental performance improvement when we apply a subject's relative proportion coordinates over conventional absolute coordinates.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.775-779
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• 2002

Currently, high-resolution satellite imagery such as KOMPSAT and IKONOS has been tentatively utilized to various types of urban engineering problems such as transportation planning, site planning, and utility management. This approach aims at software development and followed applications of remotely sensed imagery to transportation geographic analysis. At first, GDPA (Gradient Direction Profile Algorithm) and main modules in it are overviewed, and newly implemented results under MS visual programming environment are presented with main user interface, input imagery processing, and internal processing steps. Using this software, road network are automatically generated. Furthermore, this road network is used to transportation geographic analysis such as gamma index and road pattern estimation. While, this result, being produced to do-facto format of ESRI-shapefile, is used to several types of road layers to urban/transportation planning problems. In this study, road network using KOMPSAT EOC imagery and IKONOS imagery are directly compared to multiple road layers with NGI digital map with geo-coordinates, as ground truth; furthermore, accuracy evaluation is also carried out through method of computation of commission and omission error at some target area. Conclusively, the results processed in this study is thought to be one of useful cases for further researches and local government application regarding transportation geographic analysis using remotely sensed data sets.

• 대한원격탐사학회지
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• 제40권2호
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• pp.123-139
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• 2024

Forest vertical structure is vital for comprehending ecosystems and biodiversity, in addition to fundamental forest information. Currently, the forest vertical structure is predominantly assessed via an in-situ method, which is not only difficult to apply to inaccessible locations or large areas but also costly and requires substantial human resources. Therefore, mapping systems based on remote sensing data have been actively explored. Recently, research on analyzing and classifying images using machine learning techniques has been actively conducted and applied to map the vertical structure of forests accurately. In this study, Sentinel-2 and digital surface model images were obtained on two different dates separated by approximately one month, and the spectral index and tree height maps were generated separately. Furthermore, according to the acquisition time, the input data were separated into cases 1 and 2, which were then combined to generate case 3. Using these data, forest vetical structure mapping models based on random forest, support vector machine, and extreme gradient boost(XGBoost)were generated. Consequently, nine models were generated, with the XGBoost model in Case 3 performing the best, with an average precision of 0.99 and an F1 score of 0.91. We confirmed that generating a forest vertical structure mapping model utilizing bi-seasonal data and an appropriate model can result in an accuracy of 90% or higher.

• 한국압력기기공학회 논문집
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• 제19권2호
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• pp.93-101
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• 2023

In this study, a deep learning algorithm was used to diagnose electric potential signals obtained through CIPS and DCVG, used indirect inspection methods to confirm the soundness of buried pipes. The deep learning algorithm consisted of CNN(Convolutional Neural Network) model for diagnosing the electric potential signal and Grad CAM(Gradient-weighted Class Activation Mapping) for showing the flaw prediction point. The CNN model for diagnosing electric potential signals classifies input data as normal/abnormal according to the presence or absence of flaw in the buried pipe, and for abnormal data, Grad CAM generates a heat map that visualizes the flaw prediction part of the buried pipe. The CIPS/DCVG signal and piping layout obtained from the 3D finite element model were used as input data for learning the CNN. The trained CNN classified the normal/abnormal data with 93% accuracy, and the Grad-CAM predicted flaws point with an average error of 2m. As a result, it confirmed that the electric potential signal of buried pipe can be diagnosed using a CNN-based deep learning algorithm.