• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.05a
• /
• pp.556-558
• /
• 2019

기존 초음파 지방간 분석은 Hepatorenal sonographic index(HI)를 사용하여 지방간을 진단하여 왔다. 이러한 HI 기법에서는 Hepato(간)과 Renal(신장), 두 부분의 영상데이터를 비교 활용하였다면, 본 논문에서는 신장의 영상데이터만을 이용하여, 이의 통계적 특징 벡터만을 활용하여 지방간을 진단을 함으로서 기존의 HI기반 분석대비 편리성과 정확도를 개선코자 Kidney Index(KI) 기반의 분석 기법을 제안한다. 본 논문에서 제안된 KI는 정상간과 지방간을 가진 실제 환자의 초음파 사진(정상간, 지방간 각 30명)을 학습 데이터를 구성하고, 이들 데이터군으로부터 특징 벡터들을 선별하여 머신러닝 기법 중 서포트 벡터 머신(Support Vector Machine)을 통해 학습시켜, 제안된 알고리즘의 유효성을 입증하였다.

• Journal of Korea Multimedia Society
• /
• v.9 no.11
• /
• pp.1435-1444
• /
• 2006

We present an effective and real-time face detection method based on Principal Component Analysis(PCA) and Support Vector Machines(SVMs). We extract simple Haar-like features from training images that consist of face and non-face images, reinterpret the features with PCA, and select useful ones from the large number of extracted features. With the selected features, we construct a face detector using an SVM appropriate for binary classification. The face detector is not affected by the size of a training data set in a significant way, so that it showed 90.1 % detection rates with a small quantity of training data. it can process 8 frames per second for $320{\times}240$ pixel images. This is an acceptable processing time for a real-time system.

• 한국가스학회:학술대회논문집
• /
• 2003.10a
• /
• pp.126-130
• /
• 2003

• Environmental and Resource Economics Review
• /
• v.28 no.4
• /
• pp.467-495
• /
• 2019

Uncertainty of renewable energy such as photovoltaic(PV) power is detrimental to the flexibility of the power system. Therefore, precise prediction of PV power generation is important to make the power system stable. The purpose of this study is to forecast PV power generation using meteorological data including particulate matter(PM). In this study, PV power generation is predicted by support vector machine using RBF kernel function based on machine learning. Comparing the forecasting performances by including or excluding PM variable in predictor variables, we find that the forecasting model considering PM is better. Forecasting models considering PM variable show error reduction of 1.43%, 3.60%, and 3.88% in forecasting power generation between 6am~8pm, between 12pm~2pm, and at 1pm, respectively. Especially, the accuracy of the forecasting model including PM variable is increased in daytime when PV power generation is high.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.6
• /
• pp.535-544
• /
• 2018

Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. This study was conducted to segment and classify road objects using 3D point cloud data acquired by terrestrial mobile mapping system provided by National Geographic Information Institute. For this study, the original 3D point cloud data were pre-processed and a filtering technique was selected to separate the ground and non-ground points. In addition, the road objects corresponding to the lanes, the street lights, the safety fences were initially segmented, and then the objects were classified using the support vector machine which is a kind of machine learning. For the training data for supervised classification, only the geometric elements and the height information using the eigenvalues extracted from the road objects were used. The overall accuracy of the classification results was 87% and the kappa coefficient was 0.795. It is expected that classification accuracy will be increased if various classification items are added not only geometric elements for classifying road objects in the future.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.5
• /
• pp.576-583
• /
• 2020

This paper proposes a technique to determine the spam comments on YouTube, which have recently seen tremendous growth. On YouTube, the spammers appeared to promote their channels or videos in popular videos or leave comments unrelated to the video, as it is possible to monetize through advertising. YouTube is running and operating its own spam blocking system, but still has failed to block them properly and efficiently. Therefore, we examined related studies on YouTube spam comment screening and conducted classification experiments with six different machine learning techniques (Decision tree, Logistic regression, Bernoulli Naive Bayes, Random Forest, Support vector machine with linear kernel, Support vector machine with Gaussian kernel) and ensemble model combining these techniques in the comment data from popular music videos - Psy, Katy Perry, LMFAO, Eminem and Shakira.

• Journal of Internet Computing and Services
• /
• v.20 no.4
• /
• pp.21-27
• /
• 2019

Selection of feature pattern gathered from the observation of the RNA sequencing data (RNA-seq) are not all equally informative for identification of differential expressions: some of them may be noisy, correlated or irrelevant because of redundancy in Big-Data sets. Variable selection of feature pattern aims at differential expressed gene set that is significantly relevant for a special task. This issues are complex and important in many domains, for example. In terms of a computational research field of machine learning, selection of feature pattern has been studied such as Random Forest, K-Nearest and Support Vector Machine (SVM). One of most the well-known machine learning algorithms is SVM, which is classical as well as original. The one of a member of SVM-criterion is Support Vector Machine-Recursive Feature Elimination (SVM-RFE), which have been utilized in our research work. We propose a novel algorithm of the SVM-RFE with Q-learning in reinforcement learning for better variable selection of feature pattern. By comparing our proposed algorithm with the well-known SVM-RFE combining Welch' T in published data, our result can show that the criterion from weight vector of SVM-RFE enhanced by Q-learning has been improved by an off-policy by a more exploratory scheme of Q-learning.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.6
• /
• pp.405-416
• /
• 2019

Due to the development of information and communication technology, the production and processing speed of data is getting faster. To classify objects using machine learning, which is a field of artificial intelligence, data required for training can be easily collected due to the development of internet and geospatial information technology. In the field of geospatial information, machine learning is also being applied to classify or recognize objects using images and point clouds. In this study, the problem of manually constructing training data using existing digital map version 1.0 was improved, and the technique of classifying roads, buildings and vegetation using image and point clouds were proposed. Through experiments, it was possible to classify roads, buildings, and vegetation that could clearly distinguish colors when using true ortho-image with only RGB (Red, Green, Blue) bands. However, if the colors of the objects to be classified are similar, it was possible to identify the limitations of poor classification of the objects. To improve the limitations, random forest and support vector machine techniques were applied after band fusion of true ortho-image and normalized digital surface model, and roads, buildings, and vegetation were classified with more than 85% accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.11
• /
• pp.126-136
• /
• 2018

The purpose of this study was to develop a severity-adjustment model for predicting mortality in acute stroke patients using machine learning. Using the Korean National Hospital Discharge In-depth Injury Survey from 2006 to 2015, the study population with disease code I60-I63 (KCD 7) were extracted for further analysis. Three tools were used for the severity-adjustment of comorbidity: the Charlson Comorbidity Index (CCI), the Elixhauser comorbidity index (ECI), and the Clinical Classification Software (CCS). The severity-adjustment models for mortality prediction in patients with acute stroke were developed using logistic regression, decision tree, neural network, and support vector machine methods. The most common comorbid disease in stroke patients were hypertension, uncomplicated (43.8%) in the ECI, and essential hypertension (43.9%) in the CCS. Among the CCI, ECI, and CCS, CCS had the highest AUC value. CCS was confirmed as the best severity correction tool. In addition, the AUC values for variables of CCS including main diagnosis, gender, age, hospitalization route, and existence of surgery were 0.808 for the logistic regression analysis, 0.785 for the decision tree, 0.809 for the neural network and 0.830 for the support vector machine. Therefore, the best predictive power was achieved by the support vector machine technique. The results of this study can be used in the establishment of health policy in the future.

• The Bulletin of The Korean Astronomical Society
• /
• v.37 no.2
• /
• pp.129.1-129.1
• /
• 2012

서포트 벡터 머신(Support Vector Machine, SVM)과 인공신경망 모형(Neural Network, NN)을 사용하여 태양 양성자 현상(Solar proton event, SPE)의 플럭스 세기를 예측해 보았다. 이번 연구에서는 1976년부터 2011년까지 10MeV이상의 에너지를 가진 입자가 10개 cm-1 sec-1 ster -1 이상 입사할 경우를 태양 양성자 현상으로 정의한 NOAA의 태양 고에너지 입자 리스트와 GOE위성의 X-ray 플레어 데이터를 사용하였다. 여기에서 C, M, X 등급의 플레어와 관련있는 178개 이벤트를 모델의 훈련을 위한 데이터(training data) 89개와 예측을 위한 데이터(prediction data) 89개로 구분하였다. 플러스 세기의 예측을 위하여, 우리는 로그 플레어 세기, 플레어 발생위치, Rise time(플레어 시작시간부터 최대값까지의 시간)을 모델 입력인자로 사용하였다. 그 결과 예측된 로그 플럭스 세기와 관측된 로그 플럭스 세기 사이의 상관계수는 SVM과 NN에서 각각 0.32와 0.39의 값을 얻었다. 또한 두 값 사이의 평균 제곱근 오차(Root mean square error)는 SVM에서 1.17, NN에서는 0.82로 나왔다. 예측된 플럭스 세기와 관측된 플럭스 세기의 차이를 계산해 본 결과, 오차 범위가 1이하인 경우가 SVM에서는 약 68%이고 NN에서는 약 80%의 분포를 보였다. 이러한 결과로부터 우리는 NN모델이 SVM모델보다 플럭스 세기를 잘 예측하는 것을 알 수 있었다.