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.

본 연구에서는 심부시추공 처분을 위한 밀봉시스템으로서 Gibb’s Group에 의해 제안된 화강암 용융 및 재결정화에 의한 시 추공 밀봉 방안에 대해 KURT 화강암을 대상으로 실현 가능성을 확인하였다. 화강암 용융 실험은 첨가제를 이용한 상압용 융시험과 물의 기화에 의한 수증기 고압용융시험 2가지로 수행되었다. 상압 용융시험 결과, KURT 화강암 분말에 NaOH를 첨가하여도 기본 융점보다 낮은 1,000℃에서 부분용융이 시작되었으며, 냉각된 용융물에서 침상결정의 형성을 확인하였다. 수증기 고압시험은 물의 첨가량에 따라 수증기압을 달리하며 최대 400 bar의 수증기압까지 용융 시험이 진행되었다. KURT 화강암은 낮은 수증기압에도 1,000℃에서 부분 용융이 시작되었으나, 물이 많이 첨가된 높은 수증기압에서 화강암의 부분 용융은 보이지 않았다. 따라서 소량의 수증기가 있는 고압상태가 화강암의 용융에 적합한 것으로 판단되었다. 한편, 고온고압의 수증기는 내부식성의 반응기 벽을 부식시켜, 고온의 수증기에 의한 처분용기의 부식 문제가 발생되었다.

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.

본 연구에서는 미급수지역의 주요 수원인 지하수위 현황을 이용한 가뭄 모니터링 기법을 개발하기 위해 256개의 국가지하수관측망 관측 자료를 이용하여 관측소별, 월별 수위분포를 핵밀도함수로 추정하였다. 추정된 누적분포함수를 이용하여 월별 지하수위의 분위수를 구하고, 분위수를 정규화 하여 표준지하수지수(SGI)를 산정하였다. 관측소별로 산정된 SGI는 티센망을 이용하여 167개 시군별 SGI로 변환하였다. SGI의 범위에 따른 가뭄등급을 설정하여 시군별 지하수 가뭄 정도를 모니터링 할 수 있는 기법을 제시하였다. 이를 통해 계측이 이루어지지 않는 미급수지역의 지하수 가뭄상황을 국가지하수관측망을 활용해 간접적으로 판단할 수 있도록 하였다.

본 연구는 SCAMPER 기법을 적용한 미술활동이 만 5세 유아의 창의성에 어떠한 영향을 미치는지를 밝힘으로써 유아의 창의성 향상에 효과적인 교수방법을 모색하고 개발 및 적용하는데 기초적 자료를 제공하는데 있다. 연구의 대상은 충청남도 아산시에 소재하고 있는 어린이집 2개소에 재원중인 만 5세 유아 총 36명을 연구대상으로 하였다. A 어린이집에 재원중인 유아 18명을 실험집단으로, B 어린이집에 재원중인 유아 18명을 비교집단으로 선정하였다. 대상 유아의 평균 연령은 5년 7개월이었다. 유아의 창의성을 측정하기 위하여 본 연구에서는 전경원(2014)이 개발한 유아종합창의성 검사 ‘K·CCTYC'를 사용하였다. 실험은 8주간 매주 3회씩, 21회에 걸쳐 미술활동을 실시하였으며, 모두 대 · 소그룹형태로 오전 · 오후 자유선택활동 시간에 실시하였다. 실험처치를 위하여 실험집단의 유아에게는 SCAMPER 기법을 적용한 미술활동을 경험하게 하고, 비교집단의 유아들에게는 실험집단과 같은 기간 동안 동일한 재료를 제공하였으며, 누리과정의 생활주제를 기반으로 SCAMPER 기법을 제외한 미술활동을 각각 실시하였다. 실험집단과 비교집단 간의 차이를 알아보기 위하여 자료의 처리는 연구문제에 따라 SPSS 18.0/ Window 10으로 통계처리 하였으며 사전, 사후검사를 이용하여 t-검증, 공분산분석을 실시하였다. 본 연구의 결과 SCAMPER 기법을 적용한 미술활동은 유아의 창의성을 향상시키는데 긍정적인 효과가 있는 것으로 나타났다. SCAMPER 기법을 적용한 미술활동은 유아교육 현장에서 유아들이 경험하는 미술활동의 지도방법에 교사의 발문이 창의성 향상에 긍정적인 영향을 미친다는 결론을 내릴 수 있다. 따라서 유아교육현장에서 SCAMPER 기법을 적용한 미술활동을 실시한다면 유아의 창의적 능력 향상을 위한 수업으로 효과적 일 것이다.

We report the good results of two stage treatment in split depression type pilon fractures. A retrospective study of 9 cases among the 12 cases of split depression type pilon fractures from January 2009 to December 2015, who underwent two stage treatment of pilon fractures with minimum 24 months follow-up. And mean follow-up periods are 29 (24-41) months. In the first stage of the operation, reduction of articular surface using minial incision and external fixation were performed. As soft tissue heals, locking compression plate fixation was done with MIPO (Minimally Invasive Plate Osteosynthesis) technique. Radiographic evaluation was graded by the criteria of Burwell and Charnley. And functional assessment of ankle were evaluated by American Orthopaedic Foot and Ankle Society ankle-hindfoot score. Fractures were united in all cases within 17 (12-24) weeks. Radiologic results were showed anatomical reduction in 8 cases and the mean AOFAS score is 87.8 (80-96). The mean range of ankle motion is 44 degree. There are one superficial wound complications and 3 cases of ankle osteoarthritis. Two stage treatment of split depression type pilon fractures is one of the good treatment methods, because of definitive second stage operation is more easier after first stage opertation designed to get early anatomical reduction, and shows good radiological and clinical outcomes.