• 한국전자통신학회논문지
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• 제19권1호
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• pp.137-142
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• 2024

최근, 의료 데이터 표현 분야에서 딥러닝 방법들이 사실상의 표준으로 자리잡고 있다. 하지만, 딥러닝 기술은 내재적으로 많은 양의 학습 데이터를 필요로 하므로 대규모의 데이터를 확보하기 쉽지 않은 의료 분야에서는 직접적인 적용이 어려운 실정이다. 특히 뇌신호 모달리티의 경우, 변동성이 크기 때문에 여전히 데이터 부족 문제를 가진다. 이에, 최근 연구에서는 뇌신호의 시간-공간-주파수 특징을 적절하게 추출할 수 있는 딥 뉴럴 네트워크 구조를 설계하거나, 혹은 자가-지도 학습 방법을 도입하여 뇌신호의 신경생리학적 특징을 미리 학습하도록 한다. 본 논문에서는, 최근 각광받는 기술인 뇌-컴퓨터 인터페이스 및 피험자 상태 예측 등의 관점에서 소규모데이터를 다루기 위해 적용되는 방법론에 대한 분석 및 향후 기술 방향성을 제시한다. 먼저 현재 제안되고 있는 뇌신호 표현을 위한 딥 뉴럴 네트워크 구조에 대해 분석한다. 또한 뇌신호의 특성을 잘 학습하기 위한 자가-지도 학습 방법론을 분석한다. 끝으로, 딥러닝 기반 뇌신호 분석을 위한 중요 시사점 및 방향성에 관하여 논한다.

• Journal of the Korean Data and Information Science Society
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• 제20권3호
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• pp.541-550
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• 2009

본 연구는 영어학습 영역별 (듣기쓰기능력, 단어수준, 스피킹, 단어기억, 리스닝) 성취도에 따른 대뇌피질 내의 뇌기능 활성화에 대한 관련성을 규명한 것이다. 좌뇌 기반으로 알려진 영어학습에 대한 우뇌적 요인에 대한 연구들이 진행되었다. 뇌기능 영상화 중에서 정량뇌파분석를 사용하여 영어학습에 관여하는 뇌 영역별 정량뇌파 결과를 분석함으로써 영어학습을 뇌 영역별 활성화로 변별할 수 있는 기준을 마련한 것이다. 영어학습의 좌우뇌 균형발달을 위한 지침을 제시하였으며 특정 학습영역과 연계한 뇌의 활성화를 제시함으로써 개인별 뇌 기능에 따른 영어학습 향상을 위한 뇌기능을 훈련할 수 있는 이론적 토대를 마련하였다 (권형규, 2008). 이를 통하여 단순한 이미지와 오감을 활용한 우뇌적 학습방향이 아니라 개인별 정량뇌파 데이터에 의한 통합뇌 훈련모형을 개발하였다. 정량뇌파 분석을 위해서는 피험자 개인별 영어능력 검사점수에 대한 뇌파지표를 도출하여 단계적 변수선택법에 의한 다중회귀분석을 실시하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.105-125
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• 2024

Gliomas are the most common malignant brain tumor and cause the most deaths. Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience. Manual brain tumor segmentation outcomes by different radiologists for the same patient may differ. Thus, more robust, and dependable methods are needed. Medical imaging researchers produced numerous semi-automatic and fully automatic brain tumor segmentation algorithms using ML pipelines and accurate (handcrafted feature-based, etc.) or data-driven strategies. Current methods use CNN or handmade features such symmetry analysis, alignment-based features analysis, or textural qualities. CNN approaches provide unsupervised features, while manual features model domain knowledge. Cascaded algorithms may outperform feature-based or data-driven like CNN methods. A revolutionary cascaded strategy is presented that intelligently supplies CNN with past information from handmade feature-based ML algorithms. Each patient receives manual ground truth and four MRI modalities (T1, T1c, T2, and FLAIR). Handcrafted characteristics and deep learning are used to segment brain tumors in a Global Convolutional Neural Network (GCNN). The proposed GCNN architecture with two parallel CNNs, CSPathways CNN (CSPCNN) and MRI Pathways CNN (MRIPCNN), segmented BraTS brain tumors with high accuracy. The proposed model achieved a Dice score of 87% higher than the state of the art. This research could improve brain tumor segmentation, helping clinicians diagnose and treat patients.

• 만화애니메이션 연구
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• 통권38호
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• pp.71-91
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• 2015

본 연구는 애니메이션의 전문적인 특성을 고려한 라이프 드로잉 교수법을 연구하는 과정으로 두뇌의 창작 기제를 고려한 전략을 적용한 모형과 교수방법 설계를 목적으로 한다. 최근 들어 창의성을 기반으로 하는 각 전문분야의 교육방법에 대한 대안적인 논의로 뇌 기반 학습원리를 적용한 융합적 교수법에 대한 연구결과들이 발표되고 있다. 즉, 뇌의 창의기제를 기반으로 한 융합적 교육은 미술과 드로잉 교육뿐만 아니라, 예술전반에서 적용되고 있는 것이다. 라이프 드로잉은 인체에 대한 구조적 지식을 넘어서 인지적 감각, 창의성, 그리고 동작을 통한 대상과의 소통방식을 이해한 생동감 표현법 등을 숙련할 수 있는 종합적인 교수법을 요하는 분야이다. 이에 본 연구에서는 연구의 앞선 단계에서 분석된 창의, 학습기제와 내용요소를 바탕으로 하여 라이프 드로잉 숙련을 위한 전략과 방법 그것을 정리한 교육모형 구조도를 설계하여 본다. 그 결과 이전 연구의 결과물인 뇌의 창의, 학습 기제를 기반으로 한 라이프드로잉의 능력요소와 두뇌기반 촉진요소가 유기적으로 결합되기 위해서는 5단계 인지전략단계인 뇌 활성화 준비단계, 대뇌피질 기능 활성화, 고등사고촉진단계, 고등사고단계, 통합단계를 거쳤을 때 가능하다는 결론에 도달하였다. 또한 이를 실행하기위한 전략적 방법으로는 브레인짐(brain gym), 우뇌활성화드로잉, HSP(고차인지)트레이닝으로 설계되었다. 이를 토대로 하여 설계된 교수학습모형 구조도는 이후의 연구에서 해당 회기 동안의 교수학습지도안 설계로 이어진다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4816-4834
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• 2020

This paper proposes transfer learning and fine-tuning techniques for a deep learning model to detect three distinct brain tumors from Magnetic Resonance Imaging (MRI) scans. In this work, the recent YOLOv4 model trained using a collection of 3064 T1-weighted Contrast-Enhanced (CE)-MRI scans that were pre-processed and labeled for the task. This work trained with the partial 29-layer YOLOv4-Tiny and fine-tuned to work optimally and run efficiently in most platforms with reliable performance. With the help of transfer learning, the model had initial leverage to train faster with pre-trained weights from the COCO dataset, generating a robust set of features required for brain tumor detection. The results yielded the highest mean average precision of 93.14%, a 90.34% precision, 88.58% recall, and 89.45% F1-Score outperforming other previous versions of the YOLO detection models and other studies that used bounding box detections for the same task like Faster R-CNN. As concluded, the YOLOv4-Tiny can work efficiently to detect brain tumors automatically at a rapid phase with the help of proper fine-tuning and transfer learning. This work contributes mainly to assist medical experts in the diagnostic process of brain tumors.

• Structural Engineering and Mechanics
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• 제87권4호
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• pp.305-315
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• 2023

The idea of the combination of the fractional-order operators with the brain emotional learning-based intelligent controller (BELBIC) is developed for implementation in seismic-excited structures equipped with active mass damper (AMD). For this purpose, a new design framework of the mentioned combination namely fractional-order BEBIC (FOBELBIC) is proposed based on a modified-teaching-learning-based optimization (MTLBO) algorithm. The seismic performance of the proposed controller is then evaluated for a 15-story building equipped with AMD subjected to two far-field and two near-field earthquakes. An optimal BELBIC based on the MTLBO algorithm is also introduced for comparison purposes. In comparison with the structure equipped with a passive tuned mass damper (TMD), an average reduction of 44.7% and 42.8% are obtained in terms of the maximum absolute and RMS top floor displacement for FOBELBIC, while these reductions are obtained as 30.4% and 30.1% for the optimal BELBIC, respectively. Similarly, the optimal FOBELBIC results in an average reduction of 42.6% and 39.4% in terms of the maximum absolute and RMS top floor acceleration, while these reductions are given as 37.9% and 30.5%, for the optimal BELBIC, respectively. Consequently, the superiority of the FOBELBIC over the BELBIC is concluded in the reduction of maximum and RMS seismic responses.

• Journal of the Korean Data and Information Science Society
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• 제18권3호
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• pp.721-733
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• 2007

The purpose of this research was to determine the effects of the functional lateralization of the brain variables related to the sex, the scientific attitude and the scientific exploration skills. The science instruction is divided in each type of the lecturing class with the experiment class. As for the degree of SMR brainwave activation in each stage are presented while accumulating the brain waves from the right, left and the whole brain waves are analyzed during the science learning activities. It is therefore reasonable to consider the science instruction types and brain lateralization to enhance the science learning effectiveness. Sensorimotor rhythm brainwave as the low Beta is represented well to show the thought process. Category quantification scores and objective scores are calculated to show the visual positioning map for the relationships of the categories by homogeneity analysis.

• 한국생산제조학회지
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• 제22권5호
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• pp.837-844
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• 2013

This study performs erection of a pendulum hanging at a free end of an arm by rotating the arm to the upright position. A mathematical model of a rotary inverted pendulum system (RIPS) is derived. A brain emotional learning based intelligent controller (BELBIC) is designed and used as a controller for swinging up and balancing the pendulum of the RIPS. In simulations performed in the study, a pendulum is initially inclined at $45^{\circ}$ with respect to the upright position. A simulation is also performed for evaluating the adaptiveness of the designed BELBIC in the case of system variation. In addition, a simulation is performed for evaluating the robustness of the designed BELBIC against a disturbance in the control input.

• Journal of Multimedia Information System
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• 제8권2호
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.

• 한국수학교육학회지시리즈A:수학교육
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• 제42권2호
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• pp.121-135
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• 2003

This article retrospects the developmental process of the psychology of learning and its' influence on mathematics education. At the end of the article, brain-based learning science is introduced to examine its possibility to improve the psychology of learning mathematics. Behaviorists points of views such as Skinner, Guthrie, and Gagne were summarized to discuss the influences on the learning and teaching of mathematics. Gestalt' theories and Constructivism are also included in the discussion of developmental process of learning psychology. In elaboration of the brain-based learning science, recent research findings and the possibility of it's impact on mathematics education were discussed. Since mathematics itself is the most abstract subject it could be more challenging to identify the teaming process of mathematics compared with other areas. The possibilities of identifying the teaming process of mathematics are cautiously anticipated with a help of new paradigm.