• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.1
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• pp.106-118
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• 2009

Satellite imagery with high resolution of about one meter is used widely in commerce and government applications ranging from earth observation and monitoring to national digital mapping. Due to the expensiveness of IKONOS Pro and Precision products, it is attractive to use the low-cost IKONOS Geo product with vendor-provided rational polynomial coefficients (RPCs), to produce highly accurate mapping products. The imaging geometry of IKONOS high-resolution imagery is described by RFs instead of rigorous sensor models. This paper presents four different polynomial models, that are the offset model, the scale and offset model, the Affine model, and the 2nd-order polynomial model, defined respectively in object space and image space to improve the accuracies of the RF-derived ground coordinates. Not only the algorithm for RF-based ground coordinates but also the algorithm for accuracy improvement of RF-based ground coordinates are developed which is based on the four models, The experiment also evaluates the effect of different cartographic parameters such as the number, configuration, and accuracy of ground control points on the accuracy of geopositioning. As the result of a experimental application, the root mean square errors of three dimensional ground coordinates which are first derived by vendor-provided Rational Function models were averagely 8.035m in X, 10.020m in Y and 13.318m in Z direction. After applying polynomial correction algorithm, those errors were dramatically decreased to averagely 2.791m in X, 2.520m in Y and 1.441m in Z. That is, accuracy was greatly improved by 65% in planmetry and 89% in vertical direction.

• Journal of Korean Society of Forest Science
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• v.102 no.4
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• pp.467-476
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• 2013

Site index information given from forest site map only exist in the sampled locations. In this study, site index for unsampled locations were estimated using kriging interpolation method which can interpolate values between point samples to generate a continuous surface. Site index of Pinus densiplora in Danyang area were calculated using Chapman-Richards model by plot unit. Then site index for unsampled locations were interpolated by theoretical variogram models and ordinary kriging. Also in order to assess parameter selection, cross-validation was performed by calculating mean error (ME), average standard error (ASE) and root mean square error (RMSE). In result, gaussian model was excluded because of the biggest relative nugget (37.40%). Then spherical model (16.80%) and exponential model (8.77%) were selected. Site index estimates of Pinus densiplora throughout the entire area in Danyang showed 4.39~19.53 based on exponential model, and 4.54~19.23 based on spherical model. By cross-validation, RMSE had almost no difference. But ME and ASE from spherical model were slightly lower than exponential model. Therefore site index prediction map from spherical model were finally selected. Average site index from site prediction map was 10.78. It can be expected that regional variance can be considered by site index prediction map in order to estimate forest biomass which has big spatial variance and eventually it is helpful to improve an accuracy of forest carbon estimation.

• 한국사회복지학회:학술대회논문집
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• 2001.10a
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• pp.639-669
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• 2001

• Proceedings of the KGS Conference
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• 2002.05a
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• pp.209-212
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• 2002

• 한국과학교육학회:학술대회논문집
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• 2008.08a
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• pp.101.2-101.2
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• 2008

• Korea Information Processing Society Review
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• v.17 no.3
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• pp.81-86
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• 2010

• 한국지형공간정보학회:학술대회논문집
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• 1994.10a
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• pp.2-5
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• 1994

• The Journal of Bigdata
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• v.6 no.1
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• pp.23-35
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• 2021

In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.433-437
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• 2003

고해상도 위성영상을 이용하여 수치표고모델(DEM) 및 정사영상을 제작해서 수치지도의 갱신 및 지형공간정보체계의 자료기반으로써 활용할 수 있다. 본 연구에서는 Sobel 연산자를 이용하여 경계추출을 행한 후 스크린 디지타이징 방법으로 경계선을 추출하였다 이렇게 추출된 벡터자료와 기존수치지도와의 중첩을 통해서 건물, 도로, 임야의 평균위치오차를 분석해 보았다. 평균위치오차가 공공측량의 작업규정에 대한 1 : 5,000 수치지도 제작의 허용오차범위에는 들지 못하였지만, 특정 부분의 지형·지물의 경우에는 수정, 보완이 가능한 것으로 나타났다. 그리고, 산악지역 보다는 도심지에서의 경계추출이 뚜렷하기 때문에 위치정밀도가 향상됨을 알 수 있었다.

• 한국정보교육학회:학술대회논문집
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• 2004.08a
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• pp.226-235
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• 2004

초등학생 프로그래밍 교육은 프로그래밍 활동을 통해 논리적 사고력과 문제 해결력을 신장시키는 데 의의를 두고 다양한 프로그래밍 교육 방법과 학습 시스템을 개발하려는 연구가 이루어지고 있다. 프로그래밍 교육의 목표가 프로그래밍적 사고력 함양이라면 프로그래밍적 사고를 언어로 표현하여 실제로 프로그램을 작성할 수 있는 프로그래밍 언어 사용 능력 함양도 필요하다. 초등학생 프로그래밍 언어 학습은 특정 언어의 문법적 설명과 해석을 지양하고 프로그래밍 언어에 대한 올바른 개념 이해와 활용을 통해 프로그램을 구현하는데 필요한 기초 소양 능력 함양에 중점을 두어야 한다. 따라서 초등학생을 위한 프로그래밍 언어 교육 방법의 체계화에 기여할 수 있는 하나의 모델로서, 프로그래밍 언어의 기본적인 개념 중 함수 개념을 효과적으로 지도할 수 있는 지도 원리와 학습 모형을 연구하였고, 함수가 가진 특성 즉 함수적 사고과정을 이용하여 프로그래밍 언어 기술 능력과 논리적 사고력 및 문제해결력의 고등인지기술 능력을 함께 신장시킬 수 있는 지도 방법을 제안하고자 한다.