• Economic and Environmental Geology
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• v.53 no.4
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• pp.479-489
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• 2020

A fault is a geological structure that can be a migration path or a cap rock of hydrocarbon such as oil and gas, formed from source rock. The fault is one of the main targets of seismic exploration to find reservoirs in which hydrocarbon have accumulated. However, conventional fault detection methods using lateral discontinuity in seismic data such as semblance, coherence, variance, gradient magnitude and fault likelihood, have problem that professional interpreters have to invest lots of time and computational costs. Therefore, many researchers are conducting various studies to save computational costs and time for fault interpretation, and machine learning technologies attracted attention recently. Among various machine learning technologies, many researchers are conducting fault interpretation studies using the support vector machine, multi-layer perceptron, deep neural networks and convolutional neural networks algorithms. Especially, researchers use not only their own convolution networks but also proven networks in image processing to predict fault locations and fault information such as strike and dip. In this paper, by investigating and analyzing these studies, we found that the convolutional neural networks based on the U-Net from image processing is the most effective one for fault detection and interpretation. Further studies can expect better results from fault detection and interpretation using the convolutional neural networks along with transfer learning and data augmentation.

• Journal of the Korean Chemical Society
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• v.46 no.5
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• pp.456-465
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• 2002

The purpose of this study was to investigate the educational effects of an animation materials developed with the macroscopic particle moving sight. The 11 animations developed by the researchers showed the movements of molecules, ions, and electrons. The materials were developed for teachers to use when they taught "electrochemical cell' unit. The subjects were 151 students of 9th grade who were divided into the experimental and control group and were taught during 16 hours. In order to figure out the characteristics of each student before the instructions, a short-version GALT(Group Assessment of Logical Thinking) and the pretest of conceptions were carried out. After the instructions, students tested 3 types of exam; the posttest of conceptions, attitude test connected with science, cognition test. After 4 months later, students tested the posttest of conceptions agin for long-term memory effect. It was found that the exper-imental group using the developed animation materials had significantly higher scores of conceptual understanding than control group. The experimental group had also significantly higher scores of the long-term memory test and attitude test than control group. The results mean that animation materials which shows the macroscopic particle movement help stu-dents to understand scientific concepts and to elevate interests.

• Journal of the Korean Geotechnical Society
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• v.21 no.8
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• pp.73-84
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• 2005

A three layered neural network model was developed using back propagation algorithm to estimate the UU undrained shear strength of Korean soft soil based on the database of actual undrained shear strengths and piezocone measurements compiled from 8 sites over the Korea. The developed model was validated by comparing model predictions with measured values about new piezocone data, which were not previously employed during development of model. Performance of the neural network model was also compared with conventional empirical methods. It was found that the number of neuron in hidden layer is different for the different combination of transfer functions of neural network models. However, all piezocone neural network models are successful in inferring a complex relationship between piezocone measurements and the undrained shear strength of Korean soft soils, which give relatively high coefficients of determination ranging from 0.69 to 0.72. Since neural network model has been generalized by self-learning from database of piezocone measurements and undrained shear strength over the various sites, the developed neural network models give more precise and generally reliable undrained shear strengths than empirical approaches which still need site specific calibration.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.75-85
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• 2020

This study examines machine learning cross-validation to utilized create ROI for classification of land cover. The study area located in Sejong and one KOMPSAT-3A image was used in this analysis: procedure on October 28, 2019. We used four bands(Red, Green, Blue, Near infra-red) for learning cross validation process. In this study, we used K-fold method in cross validation and used SVM kernel type with cross validation result. In addition, we used 4 kernels of SVM(Linear, Polynomial, RBF, Sigmoid) for supervised classification land cover map using extracted ROI. During the cross validation process, 1,813 data extracted from 3,500 data, and the most of the building, road and grass class data were removed about 60% during cross validation process. Based on this, the supervised SVM linear technique showed the highest classification accuracy of 91.77% compared to other kernel methods. The grass' producer accuracy showed 79.43% and identified a large mis-classification in forests. Depending on the results of the study, extraction ROI using cross validation may be effective in forest, water and agriculture areas, but it is deemed necessary to improve the distinction of built-up, grass and bare-soil area.

• 한국지구과학회:학술대회논문집
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• 2005.02a
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• pp.203-212
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• 2005

The purpose of this study was to develop e-learning contents and to examine how ICT-powered instruction using the developed e-learning contents affects the science achievements of students and how the students respond to that. After an experiment in the 7th class of the weather condition unit in high school earth science, e-learning contents were prepared by using the videotaped material and flash animation to teach key learning points. The selected two different classes, experimental and control groups, shows almost the same final scores in the first semester. The experimental group received ICT-powered instruction with the contents developed in the study, and the control group received a typical expository lesson. And then the achievement test was done to these two groups, separately. The major findings of the study were as follows: As for the effects of ICT-powered instruction on the academic achievement, the average scores of the experimental group is higher than that of the control group, but the difference is insignificant. When each group was subdivided into the upper and lower groups, the upper group got higher average scores and the difference was significant. But there was no significant disparity between the lower groups. Therefore, the ICT-powered instruction using the e-learning contents gives a good effect on the students whose levels are higher than the average. In the questionaike about the ICT instruction, they generally had a positive opinion about its impact on learning interest and class participation and its learning effects.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.608-611
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• 2006

COSPAS-SARSAT 시스템은 위성체와 지상 설비를 이용하여 항공기 또는 선박 등이 조난 시에 탐색구조(SAR: Search and Rescue) 활동을 도울 수 있도록 조난경보와 위치정보를 제공하는 시스템이다. COSPAS-SARSAT 서비스의 경우, 조난신호 접수에서 조난위치확정까지 평균 1시간 이상이 소요되고, 위치정확도가 수 Km 정도로 범위가 넓은 편이다. 이러한 문제점을 개선하기 위해서 중궤도 위성을 이용한 차세대 탐색구조 시스템 개발이 추진 중에 있으며 EU에서 2011년 FOC(Full Operation Capability)를 목표로 개발중인 갈릴레오 항법위성 프로젝트의 경우 SAR 중계기를 탑재하여 탐색구조 서비스를 제공할 계획에 있다. 갈릴레오 탐색구조(SAR/Galileo) 서비스는 수 m급의 위치정확도, 10분 이내의 조난신호 접수에서 구조까지 소요시간, 및 조난자에게 회신링크 서비스 제공 등 보다 향상된 탐색구조 성능을 제공하기 위해 개발 중에 있으므로, 갈릴레오 위성 서비스가 시작되면 탐색구조시스템 체계에 보다 신속하고 정확한 구조가 가능할 것으로 예상된다. 우리나라에서는 COSPAS-SARSAT 회원국으로 가입하여 현재 송도 해양경찰청 내에 LEOLUT와 MCC가 설치되어 운용되고 있다. 날로 더해가는 다양한 재난에 대한 인명구조를 신속하고 효과적으로 대처하기 위해 차세대 갈릴레오 탐색구조 지상국 도입이 절실하다고 할 수 있다. 따라서, 탐색구조 단말기를 포함한 지상국 인프라의 구축 등 갈릴레오 탐색구조 지상시스템 개발의 참여 방안에 관한 연구는 매우 시기적절하고 중요한 연구이다. 본 논문은 갈릴레오 사업에 참여하여 SAR/Galileo 개발을 주관하고 있는 중국의 사례를 분석함으로 우리나라가 차세대 갈릴레오 탐색구조 지상시스템 개발에 참여하기 위해서 필요한 참여방법 및 절차 등을 도출하고, 참여 가능한 개발범위, 참여전략 및 추진체계에 대해서 제안한다.법의 성능을 평가를 위하여 원본 여권에서 얼굴 부분을 위조한 여권과 기울어진 여권 영상을 대상으로 실험한 결과, 제안된 방법이 여권의 코드 인식 및 얼굴 인증에 있어서 우수한 성능이 있음을 확인하였다.진행하고 있다.태도와 유아의 창의성간에는 상관이 없는 것으로 나타났고, 일반 유아의 아버지 양육태도와 유아의 창의성간의 상관에서는 아버지 양육태도의 성취-비성취 요인에서와 창의성제목의 추상성요인에서 상관이 있는 것으로 나타났다. 따라서 창의성이 높은 아동의 아버지의 양육태도는 일반 유아의 아버지와 보다 더 애정적이며 자율성이 높지만 창의성이 높은 아동의 집단내에서 창의성에 특별한 영향을 더 미치는 아버지의 양육방식은 발견되지 않았다. 반면 일반 유아의 경우 아버지의 성취지향성이 낮을 때 자녀의 창의성을 향상시킬 수 있는 것으로 나타났다. 이상에서 자녀의 창의성을 향상시키는 중요한 양육차원은 애정성이나 비성취지향성으로 나타나고 있어 정서적인 측면의 지원인 것으로 밝혀졌다.징에서 나타나는 AD-SR맥락의 반성적 탐구가 자주 나타났다. 반성적 탐구 척도 두 그룹을 비교 했을 때 CON 상호작용의 특징이 낮게 나타나는 N그룹이 양적으로 그리고 내용적으로 더 의미 있는 반성적 탐구를 했다용을 지원하는 홈페이지를 만들어 자료 제공 사이트에 대한 메타 자료를 데이터베이스화했으며 이를 통해 학생들이 원하는 실시간 자료를 검색하여 찾을 수 있고 홈페이지를 방분했을 때 이해하기 어려운 그래프나 각 홈페이지가 제공하는 자료들에 대한 처리 방법을 도움말로 제공받을 수 있게 했다. 실시간 자료들을 이용한 학습은 학생들의 학습 의욕과 탐구 능력을 향상시켰으며 컴퓨터 활용 능력과 외국어 자료 활용 능력을 향상 시키는데도 도움을 주었다.지역산업 발전을 위한 기술역량이 강화될 것이다.정 ${\rightarrow}$ 분배 ${\rightarrow}$ 최대다수의 최대행복이다.는 역할을 한다. 따라

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.471-479
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• 2018

Recently, the spread of smart TV based Android iOS Set Top box has become common. This paper propose a new approach to control the TV using gestures away from the era of controlling the TV using remote control. In this paper, the AdaBoost algorithm is applied to gesture recognition by using a mono camera. First, we use Camshift-based Body tracking and estimation algorithm based on Gaussian background removal for body coordinate extraction. Using global and local feature vectors, we recognized gestures with speed change. By tracking the time interval trajectories of hand and wrist, the AdaBoost algorithm with CART algorithm is used to train and classify gestures. The principal component feature vector with high classification success rate is searched using CART algorithm. As a result, 24 optimal feature vectors were found, which showed lower error rate (3.73%) and higher accuracy rate (95.17%) than the existing algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.55-64
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• 2013

Due to the popularization of digital high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy for anyone to make a high-quality counterfeit money. However, the probability of detecting a counterfeit money to the general public is extremely low. In this paper, we propose a counterfeit money detection algorithm using a general purpose scanner. This algorithm determines counterfeit money based on the different features in the printing process. After the non-local mean value is used to analyze the noises from each money, we extract statistical features from these noises by calculating a gray level co-occurrence matrix. Then, these features are applied to train and test the support vector machine classifier for identifying either original or counterfeit money. In the experiment, we use total 324 images of original money and counterfeit money. Also, we compare with noise features from previous researches using wiener filter and discrete wavelet transform. The accuracy of the algorithm for identifying counterfeit money was over 94%. Also, the accuracy for identifying the printing source was over 93%. The presented algorithm performs better than previous researches.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1719-1729
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• 2021

Leaf area index (LAI) provides valuable information necessary for sustainable and effective management of forests. Although global high resolution LAI data are provided by European Space Agency using Sentinel-2 satellite images, they have not considered forest characteristics in model development and have not been evaluated for various forest ecosystems in South Korea. In this study, we proposed a LAI estimation model combining machine learning and the PROSAIL radiative transfer model using Sentinel-2 satellite data over a local forest area in South Korea. LAI-2200C was used to measure in situ LAI data. The proposed LAI estimation model was compared to the existing Sentinel-2 LAI product. The results showed that the proposed model outperformed the existing Sentinel-2 LAI product, yielding a difference of bias ~ 0.97 and a difference of root-mean-square-error ~ 0.81 on average, respectively, which improved the underestimation of the existing product. The proposed LAI estimation model provided promising results, implying its use for effective LAI estimation over forests in South Korea.

• The Journal of Bigdata
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• v.6 no.1
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• pp.37-49
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• 2021

The number of dementia patients in Korea is estimated to be over 800,000, and the severity of dementia is becoming a social problem. However, no treatment or drug has yet been developed to cure dementia worldwide. The number of dementia patients is expected to increase further due to the rapid aging of the population. Currently, early detection of dementia and delaying the course of dementia symptoms is the best alternative. This study presented a methodology for early diagnosis of dementia by measuring and analyzing amyloid plaques. This vital protein can most clearly and early diagnose dementia in the retina through AI-based image analysis. We performed binary classification and multi-classification learning based on CNN on retina data. We also developed a deep learning algorithm that can diagnose dementia early based on pre-processed retinal data. Accuracy and recall of the deep learning model were verified, and as a result of the verification, and derived results that satisfy both recall and accuracy. In the future, we plan to continue the study based on clinical data of actual dementia patients, and the results of this study are expected to solve the dementia problem.