• 환경영향평가
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• 제19권6호
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• pp.647-656
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• 2010

As the $CO_2$ from buildings take up approximately 25% of the total $CO_2$ emission, the need for regulating and managing this emission is urgently required. Thus this study recognizes $CO_2$ emission status for diverse purposes and suggests accurate interpolation method for visualizing $CO_2$ emission as the basic data for regulating and managing $CO_2$ emission by applying IDW, Spline, and Kringing method. Results showed that Gaussian Function application among the Kriging methods had the highest accuracy in its estimations, with 3.049 with RMSE standards. This could be used as the basic data when visualizing $CO_2$ emission status, which is a necessity for many local and federal governments that are to regulate and manage $CO_2$ emission. This study shows that the interpolation is very appropriative method in recognizing $CO_2$ emission characteristics for regional climate change measures.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2007년도 춘계학술발표회 논문집
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• pp.341-344
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• 2007

National Geospatial-Intelligence Agency(NGA) developed VPF in mid 1980 to digitalize military geospatial information. However, because VPF is very complicated system and was severly inefficient in producing, maintaining, and managing the data, VPF was required to be replaced by more efficient data format. These requests resulted in an integrated schema, and eventually VPF. The main idea of using FDB in the production of digital map of non-accessible area is to increase the accuracy. This research focuses on the production of high accuracy digital map by utilizing the FDB. The accuracy of digital map by FDB and by DGN was individually compared with 1m CIB imagery of the Korean peninsula. By analyzing 38 check points based on CIB, DGN showed RMSE of 52m X axis and 49m Y axis. FDB showed 15m in X axis and 13m in Y axis. These results show that the digital map produced using FDB has much higher accuracy than DGN based digital map.

• 한국측량학회지
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• 제36권3호
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• pp.135-152
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• 2018

The purpose of this study is to propose an optimal fusion method of aerial multi - sensor data to improve the accuracy of land cover classification. Recently, in the fields of environmental impact assessment and land monitoring, high-resolution image data has been acquired for many regions for quantitative land management using aerial multi-sensor, but most of them are used only for the purpose of the project. Hyperspectral sensor data, which is mainly used for land cover classification, has the advantage of high classification accuracy, but it is difficult to classify the accurate land cover state because only the visible and near infrared wavelengths are acquired and of low spatial resolution. Therefore, there is a need for research that can improve the accuracy of land cover classification by fusing hyperspectral sensor data with multispectral sensor and aerial laser sensor data. As a fusion method of aerial multisensor, we proposed a pixel ratio adjustment method, a band accumulation method, and a spectral graph adjustment method. Fusion parameters such as fusion rate, band accumulation, spectral graph expansion ratio were selected according to the fusion method, and the fusion data generation and degree of land cover classification accuracy were calculated by applying incremental changes to the fusion variables. Optimal fusion variables for hyperspectral data, multispectral data and aerial laser data were derived by considering the correlation between land cover classification accuracy and fusion variables.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1194-1201
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• 2022

The existing literature has witnessed the importance of productivity assessment and deducing factors affecting it. However, yet many models have shown limitations in practical applications in actual construction sites for process planning due to uncertainty and lack of data. This research presents a productivity assessment and database generation framework using simulation and compares the results with RSMeans to derive appropriate equipment combinations alternatives for earthwork operations. Data of 15 different conditions was collected from 5 different construction sites. Prediction accuracy above 90% were achieved for the simulation models with average error rate of 7.4%. The construction productivity assessment framework presented in this study is expected to be highly applicable to operation planning for earthwork operations.

• 한국농림기상학회지
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• 제23권4호
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• pp.295-305
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• 2021

본 연구에서는 서리발생과 관련된 기상요인을 선정하여 랜덤포레스트(RF)를 이용한 서리발생 유무 분류모형을 구축하였고, 이와 더불어 기상인자의 중요도와 데이터 세트를 구성하는 방법들을 비교하는 실험을 수행하였다. 그 결과, 서리발생에 대한 분류 모형을 구축할 경우에 데이터 세트의 양이 많더라도 모형 구축을 위해 학습하기 위한 데이터 세트에서 특정 값이 월등히 많은 불균형은 모형의 예측력에 좋지 못한 영향을 미치는 것으로 분석되었다. 또한, 이번 연구에서 수집된 25지역의 서리발생과 관련된 기상요인에 대해 지역별로 그룹화하여 중요도가 높은 기상요인을 반영한 모형 구축하는 것보다 하나의 통합된 모형을 구축하는 것이 더 효율적인 것으로 나타났다. 이번 연구를 통해 분석된 결과와 서리예측을 위한 기상요인에 대한 추가분석 연구를 수행한다면 정확도 높은 서리발생 예측모형을 구축할 수 있을 것이라 예상한다.

• 한국지반환경공학회 논문집
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• 제19권1호
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• pp.5-14
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• 2018

건설 현장에서 생산되는 각종 계측 데이터 내에는 다양한 원인에서 생성된 각종 이상 데이터가 포함되어 있다. 본 연구에서는 시계열 데이터 내에 포함된 이상 데이터의 효과적 판정을 위한 합성신호 생성 기법과 그를 이용한 회귀분석, 최종적인 이상 데이터 판단과 평가 등에 관한 연구를 수행하였다. 방대한 데이터로 구성된 다수 데이터셋에 대한 이상 데이터 평가 시 다수의 데이터셋 간의 상관성을 가중치로 한 합성신호는 특정 데이터셋 과의 상관성을 크게 향상 시키는 효과를 보였으며, 이를 통해 효과적인 이상 데이터 판정이 가능하였다. 인위적 이상 데이터가 포함된 인공 오류 데이터를 생성하고 이에 합성신호 기법을 적용한 결과, 이상 데이터 판정 정확도가 크게 증가 하였으며 이러한 결과는 이종 시계열 모델의 경우에서도 동일하게 확인되었다. 이상 데이터 판정의 정확도는 신호 합성에 이용되는 데이터셋 수가 많고 시계열 모델 특성이 유사할수록 크게 증가하였다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2023년도 가을학술발표대회논문집
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• pp.209-210
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• 2023

In this study, the accuracy of the assembly rate of fine aggregate and the cleavage rate of coarse aggregate was analyzed using the constructed learning data. As a result, it was possible to predict the distribution of assembly rate for fine aggregate through a simple sample collection image, showing an accuracy of 96%. The classification of the aggregates could be confirmed by analyzing the fracture shape of the gravel, showing an accuracy of 97%.

• Computers and Concrete
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• 제20권5호
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• pp.555-562
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• 2017

The accuracy and integrity of stress data acquired by bridge heath monitoring system is of significant importance for bridge safety assessment. However, the missing and abnormal data are inevitably existed in a realistic monitoring system. This paper presents a data reconstruction approach for bridge heath monitoring based on the wavelet multi-resolution analysis and support vector machine (SVM). The proposed method has been applied for data imputation based on the recorded data by the structural health monitoring (SHM) system instrumented on a prestressed concrete cable-stayed bridge. The effectiveness and accuracy of the proposed wavelet-based SVM prediction method is examined by comparing with the traditional autoregression moving average (ARMA) method and SVM prediction method without wavelet multi-resolution analysis in accordance with the prediction errors. The data reconstruction analysis based on 5-day and 1-day continuous stress history data with obvious preternatural signals is performed to examine the effect of sample size on the accuracy of data reconstruction. The results indicate that the proposed data reconstruction approach based on wavelet multi-resolution analysis and SVM is an effective tool for missing data imputation or preternatural signal replacement, which can serve as a solid foundation for the purpose of accurately evaluating the safety of bridge structures.

• 한국측량학회지
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• 제35권4호
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• pp.261-268
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• 2017

The topographic volume estimation is carried out for the earth work of a construction site and quarry excavation monitoring. The topographic surveying using instruments such as engineering levels, total stations, and GNSS (Global Navigation Satellite Systems) receivers have traditionally been used and the photogrammetric approach using drone systems has recently been introduced. However, these methods cannot be adopted for inaccessible areas where high resolution satellite images can be an alternative. We carried out experiments using Kompsat-3/3A data to estimate topographic volume for a quarry and checked the accuracy. We generated DEMs (Digital Elevation Model) using newly acquired Kompsat-3/3A data and checked the accuracy of the topographic volume estimation by comparing them to a reference DEM generated by timely operating a drone system. The experimental results showed that geometric differences between stereo images significantly lower the quality of the volume estimation. The tested Kompsat-3 data showed one meter level of elevation accuracy with the volume estimation error less than 1% while the tested Kompsat-3A data showed lower results because of the large geometric difference.

• 대한원격탐사학회지
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• 제23권5호
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• pp.483-491
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• 2007

One of the major obstacles to classify and validate Land Cover maps is the high cost of acquiring reference data. In case of inaccessible areas such as North Korea, the high resolution satellite imagery may be used for reference data. The objective of this paper is to investigate the possibility of utilizing QuickBird high resolution imagery of North Korea that can be obtained from Google Earth data via internet for reference data of land cover classification. Monthly MODIS NDVI data of nine months from the summer of 2004 were classified into L=54 cluster using ISODATA algorithm, and these L clusters were assigned to 7 classes - coniferous forest, deciduous forest, mixed forest, paddy field, dry field, water, and built-up areas - by careful use of reference data obtained through visual interpretation of the high resolution imagery. The overall accuracy and Kappa index were 85.98% and 0.82, respectively, which represents about 10% point increase of classification accuracy than our previous study based on GCP point data around North Korea. Thus we can conclude that Google Earth may be used to substitute the traditional reference data collection on the site where the accessibility is severely limited.