• Journal of Biosystems Engineering
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• 제43권4호
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• pp.352-361
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• 2018

Purpose: The objective of this study was to develop a multilayer perceptron (MLP) model to estimate solar radiation using a solar module. Methods: Data for the short-circuit current of a solar module and other environmental parameters were collected for a year. For MLP learning, 14,400 combinations of input variables, learning rates, activation functions, numbers of layers, and numbers of neurons were trained. The best MLP model employed the batch backpropagation algorithm with all input variables and two hidden layers. Results: The root-mean-squared error (RMSE) of each learning cycle and its average over three repetitions were calculated. The average RMSE of the best artificial neural network model was $48.13W{\cdot}m^{-2}$. This result was better than that obtained for the regression model, for which the RMSE was $66.67W{\cdot}m^{-2}$. Conclusions: It is possible to utilize a solar module as a power source and a sensor to measure solar radiation for an agricultural sensor node.

• 시스템엔지니어링학술지
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• 제17권2호
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• pp.91-97
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• 2021

User interface for cancer classification system is a software application with clinician's friendly tools and functions to diagnose cancer from pathology images. Pathology evolved from manual diagnosis to computer-aided diagnosis with the help of Artificial Intelligence tools and algorithms. In this paper, we explained each block of the project life cycle for the implementation of automated breast cancer classification software using AI and machine learning algorithms to classify normal and invasive breast histology images. The system was designed to help the pathologists in an automatic and efficient diagnosis of breast cancer. To design the classification model, Hematoxylin and Eosin (H&E) stained breast histology images were obtained from the ICIAR Breast Cancer challenge. These images are stain normalized to minimize the error that can occur during model training due to pathological stains. The normalized dataset was fed into the ResNet-34 for the classification of normal and invasive breast cancer images. ResNet-34 gave 94% accuracy, 93% F Score, 95% of model Recall, and 91% precision.

• 건축역사연구
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• 제31권1호
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• pp.41-50
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• 2022

Traditional wooden buildings deform over time and are vulnerable to fire or earthquakes. Therefore, traditional wooden buildings require continuous management and repair, and securing architectural drawings is essential for repair and restoration. Unlike modernized CAD drawings, traditional wooden building drawings scan and store hand-drawn drawings, and in this process, many noise is included due to damage to the drawing itself. These drawings are digitized, but their utilization is poor due to noise. Difficulties in systematic management of traditional wooden buildings are increasing. Noise removal by existing algorithms has limited drawings that can be applied according to noise characteristics and the performance is not uniform. This study presents deep artificial neural network based noised reduction for architectural drawings. Front/side elevation drawings, floor plans, detail drawings of Korean wooden treasure buildings were considered. First, the noise properties of the architectural drawings were learned with both a cycle generative model and heuristic image fusion methods. Consequently, a noise reduction network was trained through supervised learning using training sets prepared using the noise models. The proposed method provided effective removal of noise without deteriorating fine lines in the architectural drawings and it showed good performance for various noise types.

• 한국지구과학회지
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• 제25권2호
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• pp.63-73
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• 2004

연구의 목적은 협동학습이 중학생의 개념변화에 미치는 효과 그리고 협동학습 상황에서 일어나는 학생-학생간의 언어 상호작용의 유형 및 언어 상호작용과 개념변화와의 상호관련성을 분석하는 것이다. 중학교 2학년 2개 반을 각각 협동학습 집단(실험집단, n=37)과 전통적 학습 집단(통제집단, n=37)로 선정하였다. 협동학습 집단은 STAD 협동학습 모델을 사용하였으며 협동적 기능을 익혔다. 연구결과는 다음과 같이 요약할 수 있다: 첫째, 개념변화에 있어 협동학습 집단과 전통적 학습 집단간에 통계적으로 유의미한 차이는 없었다. 그러나 사전검사에서 중위 수준의 개념 이해를 보인 학생들의 경우 유의미한 차이를 보였다. 둘째, 협동학습의 언어 상호작용 유형을 보면 주로 상위와 중위 수준 학생 사이에 활발한 토론이 이루어지는 것으로 나타났다. 또한 성공적인 개념 변화를 보인 학생들의 경우 그렇지 않은 학생들보다 더 빈번한 언어 상호작용이 이루어졌음을 보여준다. 이 연구는 학생들의 오개념의 교정에 있어서는 교사-학생간의 상호작용이 필요함을 시사한다.

• 산업경영시스템학회지
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• 제33권3호
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• pp.176-183
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• 2010

The benefits of information technology cannot be obtained unless potential users utilize it for their work. This led to a lot of research works on computer system acceptance. But most of the works address the early stage of system introduction, leaving the learning effect on system acceptance unexplored. In this longitudinal study, two groups of novice and experienced users have been empirically investigated and compared for their acceptance of computer system and for the learning effect of actual usage. A research model based on the technology acceptance theory has been proposed and applied to the two groups. The result shows that the factor job relevance gets more important and the effect of user training on system acceptance diminishes as time passes. This finding may help introducing computer systems which can be easily accepted by users over the whole life cycle period of computer systems.

• Nuclear Engineering and Technology
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• 제56권5호
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• pp.1672-1678
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• 2024

We introduce a creative approach combining machine learning with optimization techniques to enhance the optimization of the loading pattern (LP). Finding the optimal LP is a critical decision that impacts both the reload safety and the economic feasibility of the nuclear fuel cycle. While simulated annealing (SA) is a widely accepted technique to solve the LP optimization problem, it suffers from the drawback of high computational cost since LP optimization requires three-dimensional depletion calculations. In this note, we introduce a technique to tackle this issue by leveraging neural networks to filter out inappropriate patterns, thereby reducing the number of SA evaluations. We demonstrate the efficacy of our novel approach by constructing a machine learning-based optimization model for the LP data of the Korea Standard Nuclear Power Plant (OPR-1000).

• 한국음향학회지
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• 제39권4호
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• pp.292-302
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• 2020

기계학습 알고리즘은 소나 및 레이더를 포함한 다양한 분야에서 사용되고 있다. 최근 개발된 GAN(Generative Adversarial Networks)의 변형인 Cycle-Consistency Generative Adversarial Network(CycleGAN)은 쌍을 이루지 않은 이미지-이미지 변환에 대해 검증된 네트워크이다. 본 논문에서는 높은 품질로 수중 선박 엔진음을 변환시킬 수 있는 변형된 CycleGAN을 제안한다. 제안된 네트워크는 수중 음향을 기존영역에서 목표영역으로 변환시키는 생성자 모델과 데이터를 참과 거짓으로 구분하는 개선된 식별자 그리고 변환된 수환 일관성(Cycle Consistency) 손실함수로 구성된다. 제안된 CycleGAN의 정량 및 정성분석은 공개적으로 사용 가능한 수중 데이터 ShipsEar을 사용하여 기존 알고리즘들과 Mel-cepstral분포, 구조적 유사 지수, 최소 거리 비교, 평균 의견 점수를 평가 및 비교함으로써 수행되었고, 분석결과는 제안된 네트워크의 유효성을 입증하였다.

• 한국보육지원학회지
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• 제15권6호
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• pp.139-167
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• 2019

Objective: This study aims to investigate the effects of a learning cycle model-based early childhood education program using physical motion on young children's scientific inquiry ability, scientific attitude, object manipulation ability and spatial ability. Methods: The subjects of this study were 60 five-year-old children who were attending K-G City Childcare Center. The SPSS Window 21.0 program and content analysis method were used, and post-validation Tukey was conducted to examine the differences between the one-way ANOVA and the group. Results: Activities using body movement were practiced systematically based on the circle learning. Children could revise their pre-concept and concept of error by interacting with other children, teachers and the environment. Furthermore, children were attaining new knowledge while they were doing body movement activities, assessing and applying them to actual activities. Conclusion/Implications: This study is investigated a cyclic learning-based early childhood science education program using physical motion, which has significance in systematic and practical early childhood centered education for young children.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제23권2호
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• pp.429-459
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• 2009

실행연구는 연구자가 문제의식을 가지고 실제를 개선하고 자신의 전문적 지식을 향상시켜 나가는 연구이다. 본 연구는 학생들이 학교와 가정에서 수학을 많이 접함에도 불구하고 수학적 문제해결력이 낮고 실생활에 적용시키는 수학적 이해력이 부족하다는 문제점을 인식한 교사가 학생들의 수학적 이해력을 높이고 교사 자신의 수학 교수법을 계발하려 데서 출발하였다. 본 연구를 실행한 교사는 수학적 지식을 적용할 수 있는 문제 상황을 학생들 스스로가 잦아보게 하여 수학을 실생활에 적용할 줄 알고 수학과 친숙해지도록 하는 수학적 이해력을 신장시키기 위한 방안으로 상황중심의 문제해결 모형을 고안하였다. 본문에서는 교사가 연구자가 되어 학생들의 이해를 촉진시키기 위하여 개발한 상황중심의 수업 모형을 설명하고, 이를 적용하는 과정과 수업의 반성을 통해서 얻은 연구자의 성찰적 지식을 정리하였다.