본 연구는 CT를 이용한 뇌혈관 추출 검사에서 이중에너지 기법을 활용하여 각 에너지 준위별 뇌혈관 조 영술의 유용성을 평가하였다. 방법은 CT 뇌혈관 조영술을 시행한 환자 15명의 DE 영상과 SE 영상을 대상으로 하였다. 영상의 분석은 MCA, 뇌실직 조직, Background에 ROI를 설정하여 평균값, 표준편차 및 SNR, CNR 값을 구하고, SE영상과 비슷하게 구현되는 에너지 영역을 알아보았다. Likert 5점 척도 육안평가를 병행한 결과 DE 40 keV와 SE 120 kVp에서 가장 선명한 MCA 영상을 확인 하였다(p>0.05). SE영상의 SNR 값은 DE영상의 40 keV에너지 준위값과 비슷하게 측정되었고, 40 keV와 50 keV의 저에너지 준위의 영상이 SNR이 높게 측정되어 고에너지 준위의 영상에 비해 대조도가 높아 뇌혈관질환을 유용하게 관찰할 수 있을 것으로 사료된다.

현대 정보사회의 광고 홍수 속에서 특정 광고가 소비자들의 주목을 끌기는 매우 어렵다. 또한 광고를 회피할 수 있는 다양한 기술들까지 등장하고 있는 현실에서 소비자들에게 효과적으로 메시지를 전달할 수 있는 아이디어가 필요하다. 본 연구는 이와 같은 관점에서 광고 교육현장과 실무 현장에서 폭넓게 활용할 수 있는 다양한 아이디어 발상법들을 고찰해 보았다. 그리고 그중에서 직관적이면서도 시각적 표현 중심으로 단순하고 쉽게 아이디어 발상에 적용할 수 있는 스티븐 베이커 법을 선정하여 실제 광고 교육 현장에 발상 모델을 적용한 후 그 결과를 사례로 제시하였다. 연구 대상은 광고 전공 수업을 듣는 시각디자인학과 대학생들로 하였으며, 진행 과정은 먼저, 광고 콘셉트에 맞는 다양한 키워드를 도출한 후 스티븐 베이커법의 여러 가지 발상 기법 중에서 키워드와 연관성이 높은 기법들을 선정하였다. 이후 브레인스토밍을 통해 최적안에 적합한 아이디어들을 고른 후 섬네일 스케치를 진행하였으며, 그 중에서 최종적으로 정교화 작업을 진행할 안을 결정한 후 광고 시안을 제작하였다. 연구결과, 스티븐 베이커법을 활용하여 광고 발상 작업을 진행한 학생들은 직관적이면서도 광고 메시지를 효과적으로 전달할 수 있는 다양한 아이디어를 창출할 수 있었으며, 이를 바탕으로 만들어진 섬네일 스케치와 광고 시안 또한 완성도가 높았다. 스티븐 베이커법은 시각적 표현 중심의 아이디어 발상법으로 세부 기법들 자체가 단순하면 서도 크리에이티브 한 내용을 담고 있기 때문에 광고 교육 현장이나 실무 제작 환경에 도움이 될 것으로 기대한다.

To address the increase of weather hazards and the emergence of new types of such hazards, an optimization technique for three-dimensional (3D) representation of meteorological facts and atmospheric information was examined in this study as a novel method for weather analysis. The proposed system is termed as “meteorological and air quality information visualization engine” (MAIVE), and it can support several file formats and can implement high-resolution 3D terrain by employing a 30 m resolution digital elevation model. In this study, latest 3D representation techniques such as wind vector fields, contour maps, stream vector, stream line flow along the wind field and 3D volume rendering were applied. Implementation of the examples demonstrates that the results of numerical modeling are well reflected, and new representation techniques can facilitate the observation of meteorological factors and atmospheric information from different perspectives.

Rehabilitation work is required to increase well productivity, which decreases with the elapsed time of pumping owing to the clogging of the water well. Clogging causes not only a reduction in the well productivity but also a deterioration of the water quality. For unclogging and rehabilitating wells, several techniques are used such as brushing, air surging, surge blocks, and gas impulse. In this study, the high-pressure air impulse technique, which effectively and economically rehabilitates wells, was applied to a riverbank filtration site in Korea for the same objective. At most of the wells, the hydraulic parameters (transmissivity, storage coefficient, and specific capacity) were increased by the application of the high-pressure air impulse technique. The well loss change values also indicate an increase in the hydraulic parameters by the air impulse implementation. Thus, the high-pressure air impulse technique can be efficiently and economically applied to water and riverbank filtration wells for rehabilitating the decreased productivity.

강바닥판 교량의 교면포장 재료인 구스아스팔트는 약 240 ℃ 이상의 고온에서 포설되기 때문에 강바닥판에서 100 ℃ 이상 의 온도 상승을 유발하게 된다. 강바닥판 교량은 비교적 온도 변화에 민감하기 때문에 구조물의 내구성 및 안정성에 영향을 줄 수 있으므로 수치해석을 통하여 사전에 충분히 검토하여야 한다. 강바닥판 교량의 열영향 해석기법은 단순한 온도하중을 재하하는 방법과 열원을 적용하는 방법으로 대별될 수 있다. 본 연구에서는 다양한 시공환경에 적용 가능한 열원을 산정하여 열영향 해석을 수행하고, 기존 재래식 방법인 온도하중을 부여하는 방법과 상호 비교하여 검토하고자 한다.

본 연구에서는 2006년도의 교량 유지관리 빅데이터를 이용하여 선행연구에서 개발된 차종별 교통량 데이터와 연직 변위 데이터의 상관관계를 바탕으로 광안대교의 차종별 교통량 데이터를 이용한 연직 변위 추정 모델에 대하여 10여년이 경과한 현재적 적용성을 각각의 업데이트 방법으로 개발된 모델의 변위 추정 성능을 비교 분석하였다. 개발된 모델의 현재적 적용성은 추정된 변위는 실측 변위와 유사한 것으로 분석되었으며, 구조화 회귀 분석에 기반한 모델과 주성분 분석에 기반한 모델의 변위 추정 성능은 상호간에 큰 차이가 없다는 것을 알 수 있었다. 결론적으로 본 연구에서 개발한 차종별 교통량 데이터를 이용한 연직 변위 추정 모델은 광안대교의 교통하중에 따른 거동 분석 등에 유효하게 활용될 수 있을 것으로 사료된다.

The widespread sensors in a structural monitoring system provide vital support to its operation. Data is obtainedf rom sensors in a structural health monitoring system for integrity assessment of the structure, and false alarm will be frequently triggered if a faulty sensor is detected. In this study, a proposed method based on machine learning algorithm and Gaussian distribution is present to identify sensor fault.

In this paper, an experimental study on the data compression sensing technique optimized by Northridge earthquake waveform was performed to efficiently obtain the dynamic response of the civil structure. The optimized compression sensing technique is embedded in the wireless sensing system. Also, the dynamic response(acceleration) of the cable-stayed bridge model was acquired and the validity of the compression sensing technique was evaluated. The data compression sensing technique optimized by Northridge earthquake waveform was able to efficiently obtain the dynamic response of a flexible civil structure under 10Hz.

In this paper, an experimental study on the data compression sensing technique optimized by El-centro earthquake waveform was performed to efficiently obtain the dynamic response of the civil structure. The optimized compression sensing technique is embedded in the wireless sensing system. Also, the dynamic response(acceleration) of the cable-stayed bridge model was acquired and the validity of the compression sensing technique was evaluated. The data compression sensing technique optimized by El-centro earthquake waveform was able to efficiently obtain the dynamic response of a flexible civil structure under 10Hz.

In this paper, an experimental study on the data compression sensing technique optimized by Kobe earthquake waveform was performed to efficiently obtain the dynamic response of the civil structure. The optimized compression sensing technique is embedded in the wireless sensing system. Also, the dynamic response(acceleration) of the cable-stayed bridge model was acquired and the validity of the compression sensing technique was evaluated. The data compression sensing technique optimized by Kobe earthquake waveform was able to efficiently obtain the dynamic response of a flexible civil structure under 10Hz.

Construction safety is one of the significant problems on the world. Deep learning is an emerging term that acquires, processes and analyses image or video data to help computers have a high-level visual understanding of the world. In recent years, it has been introduced into the construction industry for improvements of occupational health and safety. This research contributes in solving this problem by using deep learning only RGB images that output detects the hazard zone on construction sites. The main goal of this study is to use different computer vision and deep learning to develop for different cases concerning fall related hazards.

This research aims to detect delamination-like damage from asphalt-concrete interface using contactless ultrasonic technique. FEM simulation and experiments demonstrate that surface waves and S mode are strongly present when there is delamination-like damage. It is believed that the measurement of S mode by non-contact ultrasonic system will allow one to determine the existence of delamination-like damage from bridge decks.

The damage detection method of blade systems largely depends on the personal ability of an inspector using a camera. Thus, this paper proposes a deep learning-based detection method that can rapidly and reliably identify and evaluate the damages on the blades.

This paper proposes a deep learning-based underground object classification technique incorporated with phase analysis of ground penetrating radar (GPR) for enhancing the underground object classification capability. Deep convolutional neural network (CNN) using the combination of the B- and C-scan images has recently emerged for automated underground object classification. However, it often leads to misclassification because arbitrary underground objects may have similar signal features. To overcome the drawback, the combination of B- and C-scan images as well as phase information of GPR are simultaneously used for CNN in this study, enabling to have more distinguishable signal features among various underground objects. The proposed technique is validated using in-situ GPR data obtained from urban roads in Seoul, South Korea. The validation results show that the false alarm is significantly reduced compared to the CNN results using only B- and C-scan images.

실제증발산 자료를 융합하기 위한 Modified Kling-Gupta efficiency Fusion (KGF)방법을 제시하였고, 인공위성 및 재분석 증발산 자료인 Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), MODIS Global Evapotranspiration Project (MOD16)를 활용하여 Simple Taylor skill’s Score (STS)와 비교하였다. 한반도와 중국의 세가지 land cover type(i.e., cropland, grassland, forest)을 가진 flux tower에서 비교 검증을 실시하였다. 실제증발산의 융합 방법인 STS와 KGF로 계산된 가중치의 결과를 확인하면, cropland와 grassland에서 재분석 자료(GLDAS, GLEAM)가 높은 가중치 영향을 나타내지만, forest에서 융합 방법에 따라 가중치 영향이 다르게 나타났다. 전반적으로 실제증발산 융합 방법 적용 결과의 비교에서는 cropland에서는 융합에 사용된 자료에 비하여 높은 개선이 이뤄지지 않았지만, grassland와 forest 에서는 개선이 이뤄졌다. 두 방법 중 KGF의 결과가 STS의 결과에 비하여 약간 개선되는 결과를 나타내었다

For the purposes of enhancing usability of Numerical Weather Prediction (NWP), the quantitative precipitation prediction scheme by machine learning has been proposed. In this study, heavy rainfall was corrected for by utilizing rainfall predictors from LENS and Radar from 2017 to 2018, as well as machine learning tools LightGBM and XGBoost. The results were analyzed using Mean Absolute Error (MAE), Normalized Peak Error (NPE), and Peak Timing Error (PTE) for rainfall corrected through machine learning. Machine learning results (i.e. using LightGBM and XGBoost) showed improvements in the overall correction of rainfall and maximum rainfall compared to LENS. For example, the MAE of case 5 was found to be 24.252 using LENS, 11.564 using LightGBM, and 11.693 using XGBoost, showing excellent error improvement in machine learning results. This rainfall correction technique can provide hydrologically meaningful rainfall information such as predictions of flooding. Future research on the interpretation of various hydrologic processes using machine learning is necessary.

In this study, orbit determination (OD) simulation for the Korea Pathfinder Lunar Orbiter (KPLO) was accomplished for investigation of the observational arc-length effect using a sequential estimation algorithm. A lunar polar orbit located at 100 km altitude and 90° inclination was mainly considered for the KPLO mission operation phase. For measurement simulation and OD for KPLO, the Analytical Graphics Inc. Systems Tool Kit 11 and Orbit Determination Tool Kit 6 software were utilized. Three deep-space ground stations, including two deep space network (DSN) antennas and the Korea Deep Space Antenna, were configured for the OD simulation. To investigate the arc-length effect on OD, 60-hr, 48-hr, 24-hr, and 12-hr tracking data were prepared. Position uncertainty by error covariance and orbit overlap precision were used for OD performance evaluation. Additionally, orbit prediction (OP) accuracy was also assessed by the position difference between the estimated and true orbits. Finally, we concluded that the 48-hr-based OD strategy is suitable for effective flight dynamics operation of KPLO. This work suggests a useful guideline for the OD strategy of KPLO mission planning and operation during the nominal lunar orbits phase.

Currently in the drought evaluation, which is a supplier-oriented standard that applies storage rates of reservoirs, evaluation for users that use agricultural water is not done. Therefore, this study established drought evaluation items for drought evaluation based on farmers' judgement, conducted a survey on farmers and experts, compared and analyzed weighted value between two groups, and then classified the evaluation standards per each evaluation item. The agricultural drought evaluation items are 5 major items of water supply lapse rate, agricultural weather, agricultural irrigation facility, crop and soil, and 12 subsections for regional characteristics and opinions of consumers that use water to be reflected. The result of analyzing weighted value of farmers and experts' major items shows that farmers is agricultural irrigation facility(0.219), water supply lapse rate(0.211), agricultural weather(0.204), crop(0.183) and soil(0.183). Experts is agricultural weather(0.297), agricultural irrigation facility(0.202), water supply lapse rate(0.189), crop(0.162) and soil(0.150), which displays difference between the two groups. The agricultural drought criteria standards are established based on precedent studies and cases, and grades of evaluation items are 1st grade(extreme stage), 2nd grade(warning stage), 3rd grade(alert stage) and 4th grade(attention stage). The above analysis per each consumer-oriented agricultural drought evaluation item and the analysis on the standards of evaluation grades are expected to be used as a basic resource for establishing agriculture drought policy and selecting drought area in the future.

본 연구에서는 미급수지역의 주요 수원인 지하수의 수위 변동 상황을 기반으로 한 미급수지역 가뭄 예보 기법 개발을 목적으로 하였다. 이를 위해 지역화된 표준지하수지수(SGI)와 표준강수지수들(SPIs)의 상관관계를 분석하였다. 관측 지하수위로부터 산정된 SGI의 자기회귀 특성 및 지속기간별 SPI와 SGI의 상관관계를 동시에 고려할 수 있는 NARX (nonlinear autoregressive exogenous model) 인공신경망 모형을 이용하여 지역별 예측모형을 구축하였다. 학습기간 동안 관측 SGI와 모델 출력 SGI의 상관계수는 0.7 이상인 곳이 전체 167개 지역별 모형 중 146개(87%)로 상관성이 높은 것으로 분석되었다. 적용기간에 대해서는 평균제곱근오차와 상관계수로 모형을 평가하였다. 본 연구를 통해 기상청에서 제공하는 59 개 관측소별 강수량 전망 값으로부터 산정된 지속기간별 SPI와 관측된 지하수위를 이용한 지역별 SGI 전망이 가능하도록 하였으며, 미급수지역의 가뭄 예‧경보를 위한 기초자료로 활용이 가능토록 하였다.