• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.753-756
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• 2014

파킨슨병은 도파민계 신경이 파괴되는 질병으로 알츠하이머병과 함께 대표적인 퇴행성 뇌 질환으로 병의 진행을 완화시킬 수 있는 치료법이 존재하기 때문에 병의 진단이 굉장히 중요하다. 파킨슨병을 진단하기 위한 과거의 연구는 대부분 단일 생체지표를 이용하는 것이었지만 이러한 방법에는 한계성이 존재한다. 따라서 본 연구에서는 생화학적 생체지표인 뇌척수액 내의 ${\alpha}-synuclein$ 단백질 수치와 영상학적 생체지표인 확산 텐서 영상의 여러 모수들을 결합한 융합 생체지표를 특징으로 사용하는 파킨슨병 진단 모델을 개발하고 성능을 평가하였다. 10-fold cross validation 에서 모든 성능지표에 대해 최고 100%를 보였으며, cross validation 의 과적합을 감안하더라도 파킨슨병의 조기진단에 유용하게 사용될 수 있는 가능성을 제시하였다.

• Asia pacific journal of information systems
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• v.30 no.3
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• pp.457-496
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• 2020

This study aims to extensively analyze the performance of various Machine Learning (ML) techniques for predicting version to version change-proneness of source code Java files. 17 object-oriented metrics have been utilized in this work for predicting change-prone files using 31 ML techniques and the framework proposed has been implemented on various consecutive releases of two Java-based software projects available as plug-ins. 10-fold and inter-release validation methods have been employed to validate the models and statistical tests provide supplementary information regarding the reliability and significance of the results. The results of experiments conducted in this article indicate that the ML techniques perform differently under the different validation settings. The results also confirm the proficiency of the selected ML techniques in lieu of developing change-proneness prediction models which could aid the software engineers in the initial stages of software development for classifying change-prone Java files of a software, in turn aiding in the trend estimation of change-proneness over future versions.

• Journal of information and communication convergence engineering
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• v.16 no.3
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• pp.173-178
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• 2018

Accurate classification of cloud images is a challenging task. Almost all the existing methods rely on hand-crafted feature extraction. Their limitation is low discriminative power. In the recent years, deep learning with convolution neural networks (CNNs), which can auto extract features, has achieved promising results in many computer vision and image understanding fields. However, deep learning approaches usually need large datasets. This paper proposes a deep learning approach for classification of cloud image patches on small datasets. First, we design a suitable deep learning model for small datasets using a CNN, and then we apply data augmentation and dropout regularization techniques to increase the generalization of the model. The experiments for the proposed approach were performed on SWIMCAT small dataset with k-fold cross-validation. The experimental results demonstrated perfect classification accuracy for most classes on every fold, and confirmed both the high accuracy and the robustness of the proposed model.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1257-1270
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• 2016

This study deals with the prediction of the total number of movie audiences as a measure for the box office. Prediction is performed by classification techniques of data mining such as decision tree, multilayer perceptron(MLP) neural network model, multinomial logit model, and support vector machine over time such as before movie release, release day, after release one week, and after release two weeks. Predictors used are: online word-of-mouth(OWOM) variables such as the portal movie rating, the number of the portal movie rater, and blog; in addition, other variables include showing the inherent properties of the film (such as nationality, grade, release month, release season, directors, actors, distributors, the number of audiences, and screens). When using 10-fold cross validation technique, the accuracy of the neural network model showed more than 90 % higher predictability before movie release. In addition, it can be seen that the accuracy of the prediction increases by adding estimates of the final OWOM variables as predictors.

• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.111-118
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• 2014

Current friend recommendation system on Twitter primarily recommends the most influential twitter. However, this way of recommendation has drawbacks where it does not recommend the users of which attributes of interests are similar to theirs. Since users want other users of which attributes are similar, this study implements follower recommendation system based on the similarity of twitter node informations. The data in this study is from SNAP(Stanford Network Analysis Platform), and it consists of twitter node information of which number of followers is over 10,000 and twitter link information. We used the SNAP data as a training data, and generated a classifier which recommends and predicts the relation between followers. We evaluated the classifier by 10-Fold Cross validation. Once two twitter node informations are given, our model can recommend the relationship of the two twitters as one of following such as: FoFo(Follower Follower), FoFr(Follower Friend), NC(Not Connected).

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.421-428
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• 2014

Like Alzheimer's disease, Parkinson's Disease(PD) is one of the most common neurodegenerative brain disorders. PD results from the deterioration of dopaminergic neurons in the brain region called the substantia nigra. Currently there is no cure for PD, but diagnosing in its early stage is important to provide treatments for relieving the symptoms and maintaining quality of life. Unlike many diagnosis methods of PD which use a single biomarker, we developed a diagnosis method that uses both biochemical biomarkers and imaging biomarkers. Our method uses ${\alpha}$-synuclein protein levels in the cerebrospinal fluid and diffusion tensor images(DTI). It achieved an accuracy over 91.3% in the 10-fold cross validation, and the best accuracy of 72% in an independent testing, which suggests a possibility for early detection of PD. We also analyzed the characteristics of the brain fiber pathways of Parkinson's disease patients and normal elderly people.

• Journal of the Korean Society for Heat Treatment
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• v.34 no.5
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• pp.211-217
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• 2021

To predict mechanical properties of secondary hardening martensitic steels, a machine learning ensemble model was established. Based on ANN(Artificial Neural Network) architecture, some kinds of methods was considered to optimize the model. In particular, interaction features, which can reflect interactions between chemical compositions and processing conditions of real alloy system, was considered by means of feature engineering, and then K-Fold cross validation coupled with bagging ensemble were investigated to reduce R2_score and a factor indicating average learning errors owing to biased experimental database.

• Annual Conference on Human and Language Technology
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• 2009.10a
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• pp.213-218
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• 2009

본 논문에서는 무제한 텍스트 입력이 가능한 파서에서 오류분석을 통한 성능 향상을 이루고자 한다. 우선 코퍼스로부터 자동학습에 의해서 구문 분석 모델을 만들고 이를 평가하여 발생하는 오류를 분석한다. 오류를 감소시킬 수 있는 언어 특성이 반영된 자질을 추가하여 성능을 향상시키고자 한다. 세종 코퍼스를 10-fold cross validation으로 평가할 때, 한국어의 특성을 반영한 자질 추가로 1%이상의 성능 향상을 이루었다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.606-611
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• 2012

An analysis and design method for reducing aerodynamic noise in high-speed trains based on biomimetics of noiseless flight of owl is proposed. Wind tunnel testing and numerical CFD (Computational Fluid Dynamics) simulation for the basic inter-coach spacing model are carried out, and their results compared. To determine the effect of scaling of the owl's flight feather on the noise reduction, two-fold and a four-fold scaled up model of the feather are constructed, and the numerical simulations are carried out to obtain the aerodynamic noise levels for each scale. Original model is found to reduce the noise level by 10 dB, while two-fold increase in length dimensions reduces the noise by 12 dB. Validation of numerical solution using wind tunnel experimental measurements are presented as well.

• International Journal of Railway
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• v.4 no.4
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• pp.109-115
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• 2011

An analysis and design method for reducing aerodynamic noise in high-speed trains based on biomimetics of noiseless flight of owl is proposed. Five factors related to the morphology of the flight feather have been selected, and the candidate optimal shape of the flight feather is determined. The turbulent flow field analysis demonstrates that the optimal shape leads to diminished vortex formation by causing separation of the flow as well as allowing the fluid to climb up along the surface of the flight feather. To determine the effect of scaling of the owl's flight feather on the noise reduction, a two-fold and a four-fold scaled up model of the feather are constructed, and the numerical simulations are carried out to obtain the aerodynamic noise levels for each scale. Original model is found to reduce the noise level by 10 dBA, while two-fold increase in length dimensions reduces the noise by 12 dBA. Validation of numerical solution using wind tunnel experimental measurements is presented as well.