• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.139-155
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• 2024

This study performed a multi-classification task to classify mesial temporal lobe epilepsy with left hippocampal sclerosis patients (left mTLE), mesial temporal lobe epilepsy with right hippocampal sclerosis (right mTLE), and healthy controls (HC) using magnetoencephalography (MEG) data. We applied various artificial neural networks and compared the results. As a result of modeling with convolutional neural networks (CNN), recurrent neural networks (RNN), and graph neural networks (GNN), the average k-fold accuracy was excellent in the order of CNN-based model, GNN-based model, and RNN-based model. The wall time was excellent in the order of RNN-based model, GNN-based model, and CNN-based model. The graph neural network, which shows good figures in accuracy, performance, and time, and has excellent scalability of network data, is the most suitable model for brain research in the future.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.2
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• pp.134-142
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• 2022

As industry and technology go through advancement, it is hard to search new materials which satisfy various standards through conventional trial-and-error based research methods. Crystal Graph Convolutional Neural Network(CGCNN) is a neural network which uses material's features as train data, and predicts the material properties(formation energy, bandgap, etc.) much faster than first-principles calculation. This report introduces how to train the CGCNN model which predicts the formation energy using open database. It is anticipated that with a simple programming skill, readers could construct a model using their data and purpose. Developing machine learning model for materials science is going to help researchers who should explore large chemical and structural space to discover materials efficiently.

• Journal of Aerospace System Engineering
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• v.16 no.4
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• pp.109-115
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• 2022

The CNN (Convolution Neural Network) algorithm which combines a deep learning technique, and a computer vision technology, makes image classification feasible with the high-performance computing system. In this thesis, the CNN algorithm is applied to the classification problem, by using a typical deep learning framework of TensorFlow and machine learning techniques. The data set required for supervised learning is generated with the same type of bolts. some of which have undamaged threads, but others have damaged threads. The learning model with less quantity data showed good classification performance on detecting damage in a bolt image. Additionally, the model performance is reviewed by altering the quantity of convolution layers, or applying selectively the over and under fitting alleviation algorithm.

• The Journal of the Korea Contents Association
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• v.14 no.9
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• pp.538-555
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• 2014

Systematic studies have been rarely conducted on the acceptance of big data technology despite the technology drawing much attention from academia, industry and general public. With big data technology still being in the infant stage in Korea, a study model was constructed in this paper by integrating the innovation diffusion theory and the task technology fit theory with this technology acceptance model (TAM) as the central framework to make big data technology more readily acceptable in the country, and the aim of making big data technology readily acceptable was expanded as the moderator variable of the TAM. The results of this study showed that "subjective norm" and "task technology fit" showed the most significant effect as the exogenous variables of the TAM. In addition, the "innovative characteristic of the organization" was the significant exogenous variable affecting the intention to accept big data technology to those "technology utilizers" that try to come up with new services or products that are technology-based; however, "subjective norm" was the rather significant factor affecting those simple "technology users". Finally, a significant difference was seen in the verification of mediation effect.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1385-1395
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• 2014

Economic traits of livestock are affected by environmental factors and genetic factors. In addition, it is not affected by one gene, but is affected by interaction of genes. We used a linear regression model in order to adjust environmental factors. And, in order to identify gene-gene interaction effect, we applied data mining techniques such as neural network, logistic regression, CART and C5.0 using five-SNPs (single nucleotide polymorphism) of FASN (fatty acid synthase). We divided total data into training (60%) and testing (40%) data, and applied the model which was designed by training data to testing data. By the comparison of prediction accuracy, C5.0 was identified as the best model. It were selected superior genotype using the decision tree.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.71-77
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• 2022

In this paper, a database is collected for extending the speech synthesis model to a model that synthesizes speech according to emotions and generating facial expressions. The database is divided into male and female data, and consists of emotional speech and facial expressions. Two professional actors of different genders speak sentences in Korean. Sentences are divided into four emotions: happiness, sadness, anger, and neutrality. Each actor plays about 3300 sentences per emotion. A total of 26468 sentences collected by filming this are not overlap and contain expression similar to the corresponding emotion. Since building a high-quality database is important for the performance of future research, the database is assessed on emotional category, intensity, and genuineness. In order to find out the accuracy according to the modality of data, the database is divided into audio-video data, audio data, and video data.

• Journal of the Korean Geosynthetics Society
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• v.21 no.2
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• pp.11-19
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• 2022

In this paper, we propose a GoogleNet transfer learning and CNN-LSTM combination method to improve the time-series prediction performance for crack detection using crack data captured inside the sewer pipes. LSTM can solve the long-term dependency problem of CNN, so spatial and temporal characteristics can be considered at the same time. The predictive performance of the proposed method is excellent in all test variables as a result of comparing the RMSE(Root Mean Square Error) for time series sections using the crack data inside the sewer pipe. In addition, as a result of examining the prediction performance at the time of data generation, the proposed method was verified that it is effective in predicting crack detection by comparing with the existing CNN-only model. If the proposed method and experimental results obtained through this study are utilized, it can be applied in various fields such as the environment and humanities where time series data occurs frequently as well as crack data of concrete structures.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.46-46
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• 2022

유기물의 복잡한 혼합물인 CDOM(Colored or Chromophoric Dissolved Organic Matter)은 하천 내 BOD(Biological Oxygen Demand), COD(Chemical Oxygen Demand) 및 유기 오염물질과 상당한 관련이 있다. CDOM은 가시광선 영역에서 빛을 흡수하는 성질을 가지고 있으며, 최근 원격감지 기술로 CDOM을 모니터링하기 위한 연구가 진행되고 있다. 본 연구에서는 백제보 상류 23km 구간에서 3년(2016~2018) 중 13일의 초분광영상을 활용하여 머신러닝 기반 CDOM을 추정 알고리즘을 개발하고자 한다. 초분광영상은 400~970 nm의 범위의 4 nm 간격 127개 대역의 분광해상도와 2 m의 공간해상도를 가진 항공기 탑재 AsiaFENIX 초분광 센서를 통해 수집하였으며 CDOM은 Millipore polycarbonate filter (𝚽47, 0.2 ㎛)에서 여과된 CDOM 샘플 자료를 200~800 nm의 흡수계수 스펙트럼으로 추출하여 사용하였다. CDOM 값은 전체기간 동안 2.0~11.0 m^-1의 값 분포를 보였으며 5 m^-1이상의 고농도 구간 자료개수가 전체 153개 샘플자료 중 21개로 불균형하다. 따라서 ADASYN(Adaptive Synthesis Sampling Approach)의 oversampling 방법으로 생성된 합성 데이터를 사용하여 원본 데이터의 소수계층 데이터 불균형을 해결하고 모델 예측 성능을 개선하고자 하였다. 생성된 합성 데이터를 입력변수로 하여 ANN(Artificial Neural Netowk)을 활용한 CDOM 예측 알고리즘을 구축하였다. ADASYN 기법을 통한 합성 데이터는 관측된 데이터의 불균형을 해결하여 기계학습 모델의 CDOM 탐지 성능을 향상시킬 수 있으며, 저수지 내 유기 오염물질 관리를 위한 설계를 지원하는데 사용할 수 있을 것으로 판단된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.75-78
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• 2005

텔레매틱스 서비스를 위한 많은 어플리케이션들이 개발 됨에 인해 테스트를 위한 도로 네트워크 기반의 이동객체 궤적데이터의 필요성이 증대되고 있다. 본 논문에서는 도로 네트워크 상의 이동객체들의 실 궤적 데이터와 유사한 합성 궤적 데이터를 구축하기 위한 방법론을 제안한다. 그리고 실제 구현 결과와 실 데이터와의 속도 패턴을 비교하여 실 데이터와의 유사성을 보인다.

• 전기의세계
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• v.53 no.5
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• pp.57-60
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• 2004

VTK란 Visualization ToolKit의 약자로서 그래픽 합성, 영상처리 및 3차원 데이터 가시화의 기능을 제공하는 소프트웨어 라이브러리이다. VTK는 1993년도에 William J. Schroeder, Ken Martin 및 Bill Lorensen이 공동 집필한 "The Visualization Toolkit： An Objectet-Oriented Approach to 3D Graphics" 책이 출판되면서 널리 알려지기 시작하였다. 이 책의 주저자이며 현재 Kitware 회사(http://www.kitware.com의 사장인 Schroeder는 이 책의 서문에서 데이터의 가시화 (visualization)이란 방대한 데이터를 단순한 숫자나 그림으로 표현하는 것 보다 시각적으로 표현하는 것이라고 정의를 하였으며, 또한 데이터의 시각화를 통하여 데이터의 특성을 이해하고 응용하기가 쉬우며 다양한 측면에서 데이터를 해석 또는 분석 이 가능하다고 주장하였다.(중략)