• 한국과학교육학회지
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• 제25권5호
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• pp.563-570
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• 2005

이 연구는 중학생들의 과학 지식의 본성에 대한 이해와 개념 이해 및 학습 젼략 사이의 관계를 조사하였다. 인천 지역의 중학교 1학년 162명을 대상으로 과학 지식의 본성 검사와 학습 전략 검사를 실시하였다. 밀도에 대한 컴퓨터 보조 수업 후 개념 검사를 실시했다. 연구 결과, 학생들의 개념 이해와 심층적 및 피상적 학습 전략은 과학 지식의 본성에 대한 이해와 유의미한 상관이 있었다. 학생들의 복합적인 학습 전략의 유형을 확인하기 위해 군집분석을 실시하였다. 그 결과, '심층적 전략 점수는 높고, 피상적 전략 점수는 낮은 집단(군집 1)', '심층적 전략 점수는 낮고, 피상적 전략 점수는 높은 집단(군집 2)', '심층적, 피상적 전략 점수가 모두 높은 집단(군집 3)'으로 구분 되었다. 일원 변량 분석 결과, 과학 지식의 본성 검사와 개념 검사 모두에서 군집 3의 점수가 다른 군집들 보다 유의미하게 높았다. 또한 개념 검사에서는 군집1이 군집2에 비해 보다 높은 성적을 보였다. 이에 대한 교육학적 함의를 논의하였다.

• 영재교육연구
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• 제25권6호
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• pp.857-879
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• 2015

본 연구는 중학생 대상의 수학, 과학 온라인 영재교육 프로그램에서 튜터의 역할별 수행 수준을 측정하고, 그것이 학습자의 심층학습과 학업성취 수준 및 수업평가에 어떤 영향을 미치는지 알아보고자 수행되었다. 이를 위해 과학, 수학 온라인 수업을 수강한 중학생 249명을 대상으로 튜터 역할 수행 수준에 대한 설문을 실시하여 심층학습, 학업성취, 수업평가와의 관계를 살펴보았다. 분석 결과 심층학습의 하위변인인 종합적 학습, 반성적 학습에 긍정적인 영향을 미치는 변인은 공통적으로 학습내용 및 평가 전문가로서의 튜터의 역할인 반면, 고차적 학습에 영향을 미치는 튜터의 역할은 없는 것으로 나타났다. 다음으로 튜터 역할의 하위 변인 중 학습자의 탐구학습 점수와 전체 점수에 영향을 미치는 변인은 학습과정 및 방법 안내자로서의 역할이었으며, 튜터 역할과 개념학습 점수는 유의미한 관련이 없는 것으로 나타났다. 반면 온라인학습에 대한 전반적인 만족도, 참여도, 이해도에 유의미한 영향을 미치는 튜터 역할은 없는 것으로 나타났다.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.51-62
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• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제27권1호
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• pp.56-74
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• 2023

In order to obtain practical and high-quality satellite images containing high-frequency components, a large aperture optical system is required, which has a limitation in that it greatly increases the payload weight. As an attempt to overcome the problem, many multi-aperture optical systems have been proposed, but in many cases, these optical systems do not include high-frequency components in all directions, and making such an high-quality image is an ill-posed problem. In this paper, we use deep learning to overcome the limitation. A deep learning model receives low-quality images as input, estimates the Point Spread Function, PSF, and combines them to output a single high-quality image. We model images obtained from three rectangular apertures arranged in a regular polygon shape. We also propose the Modulation Transfer Function Loss, MTF Loss, which can capture the high-frequency components of the images. We present qualitative and quantitative results obtained through experiments.

• 4차산업연구
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• 제1권1호
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• pp.31-40
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• 2021

Purpose - According to the importance of business simulation learning as a new type of business learning tool, this study reviews the dimensions of business education and a brief history of business education simulation. At the end Capsim strategic management simulation program is introduce with its feature. Research design, data, and methodology - This study has been analyzed in a way that reviews at previous literature on simulation learning and looks at examples and features of Capsim simulation, online business simulation tools which has been used in the global market. Result - Capsim simulations are designed to offer focused opportunities for deep practice. That's why they are often more effective than passive tools such as textbooks, videos, or lectures. By the way, 'deep practice' is very different from 'ordinary practice'. After commuters who drive to school or work can accumulate thousands of hours of driving, but that doesn't make them expert drivers. The key to deep practice is self-awareness. That is, paying attention to what you are doing well and not so well. This is so important to learn that scientists use a specific term for it: 'metacognition', or thinking about the way you think and learn. Conclusion - The use of business simulation learning, such as Capsim, which is a given case, can create similar local systems by potentially engaging a large number of users in the virtual market. It could also be used as an individual to complete business training for students and those who are active in the business field of business.

• 한국멀티미디어학회논문지
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• 제23권2호
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• pp.146-154
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• 2020

Negative emotion causes stress and lack of attention concentration. The classification of negative emotion is important to recognize risk factors. To classify emotion status, various methods such as questionnaires and interview are used and it could be changed by personal thinking. To solve the problem, we acquire multi modal bio-signals such as electrocardiogram (ECG), skin temperature (ST), galvanic skin response (GSR) and extract features. The neural network (NN), the deep neural network (DNN), and the deep belief network (DBN) is designed using the multi modal bio-signals to analyze emotion status. As a result, the DBN based on features extracted from ECG, ST and GSR shows the highest accuracy (93.8%). It is 5.7% higher than compared to the NN and 1.4% higher than compared to the DNN. It shows 12.2% higher accuracy than using only single bio-signal (GSR). The multi modal bio-signal acquisition and the deep learning classifier play an important role to classify emotion.

• 한국도로학회논문집
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• 제20권2호
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• pp.57-65
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• 2018

PURPOSES : The study aims to predict the service life of national highway asphalt pavements through deep learning methods by using maintenance history data of the National Highway Pavement Management System. METHODS : For the configuration of a deep learning network, this study used Tensorflow 1.5, an open source program which has excellent usability among deep learning frameworks. For the analysis, nine variables of cumulative annual average daily traffic, cumulative equivalent single axle loads, maintenance layer, surface, base, subbase, anti-frost layer, structural number of pavement, and region were selected as input data, while service life was chosen to construct the input layer and output layers as output data. Additionally, for scenario analysis, in this study, a model was formed with four different numbers of 1, 2, 4, and 8 hidden layers and a simulation analysis was performed according to the applicability of the over fitting resolution algorithm. RESULTS : The results of the analysis have shown that regardless of the number of hidden layers, when an over fitting resolution algorithm, such as dropout, is applied, the prediction capability is improved as the coefficient of determination ($R^2$) of the test data increases. Furthermore, the result of the sensitivity analysis of the applicability of region variables demonstrates that estimating service life requires sufficient consideration of regional characteristics as $R^2$ had a maximum of between 0.73 and 0.84, when regional variables where taken into consideration. CONCLUSIONS : As a result, this study proposes that it is possible to precisely predict the service life of national highway pavement sections with the consideration of traffic, pavement thickness, and regional factors and concludes that the use of the prediction of service life is fundamental data in decision making within pavement management systems.

• 한국전자통신학회논문지
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• 제18권6호
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• pp.1103-1108
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• 2023

본 논문에서는 음향신호의 배경잡음을 감쇠하기 위한 새로운 알고리즘을 제안한다. 이 알고리즘은 이산 웨이블릿 변환(DWT: Discrete Wavelet Transform) 후 기존의 적응필터를 대신 FNN(: Full-connected Neural Network) 심층학습 알고리즘을 이용하여 잡음감쇠 성능을 개선하였다. 입력신호를 단시간 구간별로 웨이블릿 변환한 다음 1024-1024-512-neuron FNN 딥러닝 모델을 이용하여 잡음이 포함된 단일입력 음성신호로부터 잡음을 제거한다. 이는 시간영역 음성신호를 잡음특성이 잘 표현되도록 시간-주파수영역으로 변환하고 변환 파라미터에 대해 순수 음성신호의 변환 파라미터를 이용한 지도학습을 통하여 잡음환경에서 효과적으로 음성을 예측한다. 본 연구에서 제안한 잡음감쇠시스템의 성능을 검증하기 위하여 Tensorflow와 Keras 라이브러리를 사용한 시뮬레이션 프로그램을 작성하고 모의실험을 수행하였다. 실험 결과, 제안한 심층학습 알고리즘을 사용하면 기존의 적응필터를 사용하는 경우보다 30%, STFT(: Short-Time Fourier Transform) 변환을 사용하는 경우보다는 20%의 평균자승오차(MSE: Mean Square Error) 개선효과를 얻을 수 있었다.

• 대한임베디드공학회논문지
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• 제17권3호
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• pp.129-138
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• 2022

Collecting and labeling sufficient training data, which is essential to deep learning-based visual inspection, is difficult for manufacturers to perform because it is very expensive. This paper presents a steel plate surface defect detection system with industrial-grade detection performance by training a small amount of steel plate surface images consisting of labeled and non-labeled data. To overcome the problem of lack of training data, we propose two data augmentation techniques: program-based augmentation, which generates defect images in a geometric way, and generative model-based augmentation, which learns the distribution of labeled data. We also propose a 4-step semi-supervised learning using pseudo labels and consistency training with fixed-size augmentation in order to utilize unlabeled data for training. The proposed technique obtained about 99% defect detection performance for four defect types by using 100 real images including labeled and unlabeled data.

• 한국간호교육학회지
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• 제27권3호
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• pp.251-260
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• 2021

Purpose: This study uses a descriptive research design to identify the influence of critical thinking disposition, deep approaches to learning, and interaction between learners on the degree of nursing process confidence for nursing students. Methods: The subjects of the study were second-year students in the Department of Nursing at a university in G city. The data included general characteristics, critical thinking disposition, deep approaches to learning, learner-to-learner interaction, and nursing process confidence were analyzed utilizing an independent t-test, one-way ANOVA, and Scheffe's test to identify differences in the variables according to general characteristics. To identify the correlation between the factors related to the nursing process and nursing process confidence, Pearson's correlation was analyzed, and hierarchical regression was used to determine the factors affecting the confidence of the subject's nursing process. Results: Gender, critical thinking disposition, and in-depth learning approach were statistically significant as factors affecting the nursing process confidence of nursing students, and these factors were shown to explain 62% of nursing course performance (F=23.80, p<.001), among which in-depth learning access has the greatest influence (β=.41, p<.001). Conclusion: Critical thinking disposition and deep approaches to learning arbitration program development are necessary to improve nursing students' nursing process confidence.