• ETRI Journal
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• 제25권5호
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• pp.401-406
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• 2003

A biometric system determines the identity of a person by measuring physical features that can distinguish that person from others. Since biometric features have many variations and can be easily corrupted by noises and deformations, it is necessary to apply machine learning techniques to treat the data. When applying the conventional machine learning methods in designing a specific biometric system, however, one first runs into the difficulty of collecting sufficient data for each person to be registered to the system. In addition, there can be an almost infinite number of variations of non-registered data. Therefore, it is difficult to analyze and predict the distributional properties of real data that are essential for the system to deal with in practical applications. These difficulties require a new framework of identification and verification that is appropriate and efficient for the specific situations of biometric systems. As a preliminary solution, this paper proposes a simple but theoretically well-defined method based on a statistical test theory. Our computational experiments on real-world data show that the proposed method has potential for coping with the actual difficulties in biometrics.

• 한국멀티미디어학회논문지
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• 제23권12호
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• pp.1486-1495
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• 2020

The analysis of digital microscopy images plays a vital role in computer-aided diagnosis (CAD) and prognosis. The main purpose of this paper is to develop a machine learning technique to predict the histological grades in prostate biopsy. To perform a multiclass classification, an AI-based deep learning algorithm, a multichannel convolutional neural network (MCCNN) was developed by connecting layers with artificial neurons inspired by the human brain system. The histological grades that were used for the analysis are benign, grade 3, grade 4, and grade 5. The proposed approach aims to classify multiple patterns of images extracted from the whole slide image (WSI) of a prostate biopsy based on the Gleason grading system. The Multichannel Convolution Neural Network (MCCNN) model takes three input channels (Red, Green, and Blue) to extract the computational features from each channel and concatenate them for multiclass classification. Stain normalization was carried out for each histological grade to standardize the intensity and contrast level in the image. The proposed model has been trained, validated, and tested with the histopathological images and has achieved an average accuracy of 96.4%, 94.6%, and 95.1%, respectively.

• 컴퓨터교육학회논문지
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• 제21권2호
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• pp.59-71
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• 2018

본 연구는 K-12의 구성원 및 관계자들에게 교수학습 자료를 제공하고 있는 시 도 교육청의 교수학습지원센터에 대한 웹 접근성 준수 실태 분석을 통해 웹 접근성 향상을 위한 방향을 제안하기 위한 목적이 있다. 목적 달성을 위해 9개의 교수학습지원센터를 대상으로 평가하였다. 평가는 전문가 2명에 의한 1차 전문가 수동평가와 18명의 사용자가 진행하였다. 사용자 평가는 청각장애인, 시각장애인, 뇌병변 장애인, 고령자, 다양한 브라우저 사용자, 다양한 운영체제 사용자 등으로 구성되었다. 분석 결과, 전문가와 사용자 평가에서 웹 접근성을 준수하고 있는 교수학습지원센터는 1곳도 없었다. 전문가 평가 결과, 웹 디자인 개선, HTML 소스 오류 수정, 장애인을 위한 대체 수단 제공 등에 대한 오류가 발견되었다. 본 연구는 개선한 웹 접근성 평가 방법을 통해 향후 국내 웹 접근성 평가 연구의 방향성에 대한 다양한 논의를 제공하였다는 점에 의의가 있다.

• Journal of Ginseng Research
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• 제47권5호
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• pp.662-671
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• 2023

Background: 20(S)-protopanaxadiol (PPD), a ginsenoside metabolite, has prominent benefits for the central nervous system, especially in improving learning and memory. However, its transcriptional targets in brain tissue remain unknown. Methods: In this study, we first used mass spectrometry-based drug affinity responsive target stability (DARTS) to identify the potential proteins of ginsenosides and intersected them with the transcription factor library. Second, the transcription factor PURA was confirmed as a target of PPD by biolayer interferometry (BLI) and molecular docking. Next, the effect of PPD on the transcriptional levels of target genes of PURA in brain tissues was determined by qRT-PCR. Finally, bioinformatics analysis was used to analyze the potential biological features of these target proteins. Results: The results showed three overlapping transcription factors between the proteomics of DARTS and transcription factor library. BLI analysis further showed that PPD had a higher direct interaction with PURA than parent ginsenosides. Subsequently, BLI kinetic analysis, molecular docking, and mutations in key amino acids of PURA indicated that PPD specifically bound to PURA. The results of qRT-PCR showed that PPD could increase the transcription levels of PURA target genes in brain. Finally, bioinformatics analysis showed that these target proteins were involved in learning and memory function. Conclusion: The above-mentioned findings indicate that PURA is a transcription target of PPD in brain, and PPD upregulate the transcription levels of target genes related to cognitive dysfunction by binding PURA, which could provide a chemical and biological basis for the study of treating cognitive impairment by targeting PURA.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.416-418
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• 2022

신경교종(glioma)은 신경교세포에서 발생하는 뇌 종양으로 low grade glioma와 예후가 나쁜 high grade glioma로 분류된다. 자기공명영상(magnetic Resonance Imaging, MRI)은 비침습적 수단으로 이를 이용한 신경교종 진단에 대한 연구가 활발히 진행되고 있다. 또한, 단일 modality의 정보 한계를 극복하기 위해 다중 modality를 조합하여 상호 보완적인 정보를 얻는 연구도 진행되고 있다. 본 논문은 네가지 modality(T1, T1Gd, T2, T2-FLAIR)의 MRI 영상에 입력단 fusion을 적용한 3D CNN 기반의 모델을 제안한다. 학습된 모델은 검증 데이터에 대해 정확도 0.8926, 민감도 0.9688, 특이도 0.6400, AUC 0.9467의 분류 성능을 보였다. 이를 통해 여러 modality 간의 상호관계를 학습하여 신경교종의 등급을 효과적으로 분류함을 확인하였다.

• 제어로봇시스템학회논문지
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• 제13권9호
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• pp.832-837
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• 2007

Electroencephalographic(EEG) is used to record activities of human brain in the area of psychology for many years. As technology develope, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study Emotion Recognition method which uses one of EEG signals and Gestures in the existing research. In this paper, we use together EEG signals and Gestures for Emotion Recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both EEG signals and gestures gets high recognition rates better than using EEG signals or gestures. Both EEG signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on a reinforcement learning.

• Journal of Information Processing Systems
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• 제17권1호
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• pp.178-190
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• 2021

Alzheimer's disease (AD) is an insidious and degenerative neurological disease. It is a new topic for AD patients to use magnetic resonance imaging (MRI) and computer technology and is gradually explored at present. Preprocessing and correlation analysis on MRI data are firstly made in this paper. Then kernel principal component analysis (KPCA) is used to extract features of brain gray matter images. Finally supervised classification schemes such as AdaBoost algorithm and support vector machine algorithm are used to classify the above features. Experimental results by means of AD program Alzheimer's Disease Neuroimaging Initiative (ADNI) database which contains brain structural MRI (sMRI) of 116 AD patients, 116 patients with mild cognitive impairment, and 117 normal controls show that the proposed method can effectively assist the diagnosis and analysis of AD. Compared with principal component analysis (PCA) method, all classification results on KPCA are improved by 2%-6% among which the best result can reach 84%. It indicates that KPCA algorithm for feature extraction is more abundant and complete than PCA.

• 스마트미디어저널
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• 제13권4호
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• pp.86-93
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• 2024

기능적 자기 공명영상(functional magnetic resonance imaging;fMRI)의 발전은 뇌 기능의 매핑, 휴식 상태에서 뇌 네트워크의 이해에 상당한 기여를 하였다. 본 논문은 알츠하이머의 진행상태를 분류하기 위해 CNN-LSTM 기반의 분류 모델을 제안한다. 첫 번째로 특징 추출 이전 fMRI 데이터에서 잡음을 제거하기 위해 4단계의 전처리를 수행한다. 두 번째, 전처리가 끝나면 U-Net 구조를 활용하여 공간적 특징을 추출한다. 세 번째, 추출된 공간적 특징은 LSTM을 활용하여 시간적 특징을 추출하여 최종적으로 분류하는 과정을 거친다. 실험은 데이터의 시간차원을 조절하여 진행하였다. 5-fold 교차 검증을 사용하여 평균 96.4%의 정확도를 달성하였고 이러한 결과는 제안된 방법이 fMRI 데이터를 분석하여 알츠하이머의 진행을 식별하는데 높은 잠재력을 가지고 있음을 보여준다.

• International Journal of Contents
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• 제14권4호
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• pp.24-29
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• 2018

EEG-based brain-computer interfaces has focused on explicitly expressed intentions to assist physically impaired patients. For EEG-based-computer interfaces to function effectively, it should be able to understand users' implicit information. Since it is hard to gather EEG signals of human brains, we do not have enough training data which are essential for proper classification performance of implicit intention. In this paper, we improve the subject independent classification of implicit intention through the generation of additional training data. In the first stage, we perform the PCA (principal component analysis) of training data in a bid to remove redundant components in the components within the input data. After the dimension reduction by PCA, we train ICA (independent component analysis) network whose outputs are statistically independent. We can get additional training data by adding Gaussian noises to ICA outputs and projecting them to input data domain. Through simulations with EEG data provided by CNSL, KAIST, we improve the classification performance from 65.05% to 66.69% with Gamma components. The proposed sample generation method can be applied to any machine learning problem with fewer samples.

• 한국초등과학교육학회지:초등과학교육
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• 제35권4호
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• pp.469-478
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• 2016

A brain-based evolutionary approach developed by reflecting the brain functions and authentic science is consisted of Affective, Behavioral, and Cognitive domains, and within each domain the processes of Diversifying, Evaluating, and Furthering are proceeded (ABC-DEF). Two core components of creativity of originality and usefulness are inherent in each step. So, this study analyzed scientific creativity with the originality and usefulness components in cognitive domain, which is composed of diversifying the meanings inherent in the results of observations or experiments (C-D), evaluating the meanings (C-E), and furthering (C-F) in learning of 'World of Plants' unit which includes two topics of 'Plants on Land' and 'Plants in Water and Special Environment'. A total of 20 fourth grade students at Y elementary school in Gyeonggi province participated in the study. The main results of this study are as follows. First, the scientific creativity in step C-D (Diversifying stage) was assessed according to the scientific creativity assessment formula. The scores of scientific creativity were quite different with topics and showed different pattern in the originality and usefulness components. Second, when the students compare and evaluate the values of each meaning (C-E stage), they weighed more on usefulness than originality, such as "because it is useful" or "because it solve many everyday problems". Third, the overall scores of scientific creativity in step C-F (Furthering stage), as compared with those of step C-D, were low and showed decrease in the average scores of originality from 9.8 to 7.5 points, whereas increase in the average scores of usefulness from 5.4 to 6.1 points. In conclusion, these results showed that, even though the levels were not so high, the students, as scientists, can exhibit the scientific creativity in the processes of diversifying, comparing and evaluating, and applying the meanings about the results obtained by observations or experiments. The specific and various strategies to help students express their potential scientific creativity more effectively need to be developed.