• 한국콘텐츠학회논문지
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• 제22권5호
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• pp.633-642
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• 2022

이 논의는 고등평생학습생태계 구축에 초점을 두고 기존의 주장들에 대해서 비판적 고찰을 목적으로 진행하였다. 첫째, 현 한국의 대학교육 특성과 지방대학 위기 담론을 논의하였다. 둘째, 평생학습사회와 평생학습체제 구축에 대한 주장을 고찰하였다. 셋째, 고등평생학습생태계 구축에 대한 주장을 검토하고 실천적 방안에 대해 비판적 고찰을 하였다. 결론은 다음과 같다. 고등평생학습생태계 구축을 위한 방안은 상당히 구체적으로 논의 해왔고 이를 통한 지방대학의 위기 극복과 지역균형발전의 가능성은 충분하다. 다만 첫째, 대학이 중심적인 역할을 해야 하는데 이를 위해 대학 개혁이 이루어질 수 있는지 의문을 제기하였다. 둘째, 위기 극복을 위해 국가의 책임과 책무, 지속적인 지원이 요구되고 있다. 반면 현 한국의 정부 당국의 협조가 가능한지 의문을 제기하였다. 셋째, 지역사회 중심으로 거버넌스를 이루어 민주적인 방식에 따라 진행하는데 이해 관계자 합의를 위한 토대 형성 전략이 구성되어 있는지 의문을 제기하였다.

• 한국융합학회논문지
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• 제9권1호
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• pp.95-102
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• 2018

최근 지식 기반사회의 도래에 따라 직장인들에게도 끊임없는 자기 학습이 필요하다. 특히 희망이 강한 사람은 자기주도학습이 강한 것으로 나타났는데 그 사이에서 심리적인 특성이나 신념인 성장 마인드셋과 Grit이 매개역할을 할 것으로 판단된다. 이에 본 연구는 직장인들의 희망과 자기주도학습 사이에서 성장 마인드셋과 그릿의 매개효과를 파악하는데 연구의 목적을 두었다. 연구대상은 서울, 대전, 충남, 충북 지역에 위치하는 총 32개의 직장에서 선정하였고 총 368명이었다. 자료 분석은 빈도분석, 상관분석 및 구조방정식 모형 분석을 실시하여 수행하였고 주된 연구결과는 다음과 같다. 첫째, 상관분석 결과 희망과 성장 마인드셋, 그릿, 자기주도학습의 모든 하위 요인에서 유의한 정적인 상관관계가 있었다. 둘째, 경로분석 결과 희망은 자기주도학습에 직접적인 영향을 미치고 있었다. 셋째, 희망은 성장 마인드셋과 그릿을 매개로 하여 간접적인 영향을 미치고 있었다. 끝으로 본 연구의 제한과 직장인들의 자기주도학습을 높이기 위하여 희망, 성장 마인드셋, Grit을 촉진시키기 위한 정책적 함의를 하였다.

• 한국인공지능학회지
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• 제11권1호
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• pp.1-7
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• 2023

In this paper, a study was conducted to compare the prediction model of cardiovascular disease occurrence. It is the No.1 disease that accounts for 1/3 of the world's causes of death, and it is also the No. 2 cause of death in Korea. Primary prevention is the most important factor in preventing cardiovascular diseases before they occur. Early diagnosis and treatment are also more important, as they play a role in reducing mortality and morbidity. The Results of an experiment using Azure ML, Logistic Regression showed 88.6% accuracy, Decision Tree showed 86.4% accuracy, and Support Vector Machine (SVM) showed 83.7% accuracy. In addition to the accuracy of the ROC curve, AUC is 94.5%, 93%, and 92.4%, indicating that the performance of the machine learning algorithm model is suitable, and among them, the results of applying the logistic regression algorithm model are the most accurate. Through this paper, visualization by comparing the algorithms can serve as an objective assistant for diagnosis and guide the direction of diagnosis made by doctors in the actual medical field.

• 한국컴퓨터정보학회논문지
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• 제23권4호
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• pp.41-49
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• 2018

The purpose of this study is to investigate the effect of computer pointing game on the visual perception ability of children with severe intellectual disability. Based on a literature review, we developed a computer pointing game to improve visual perception ability, which consisted of three stages; catching a hamburger, catching a hamburger and a soda, and catching various foods. At each stage, different instructional models were applied by difficulty level of the contents. Experiments were performed among four children with severe intellectual disabilities for three weeks. They belonged to H public school in Kyeonggi, Korea. Their visual perceptions were quantitatively measured four times by utilizing the Korean Developmental Test of Visual Perception tool (K-DTVP-2). For qualitative evaluation, an observation assessment diary was written and analyzed. All four children at the fourth test showed better visual perception ability, compared with the ability at the first test. As a result of the analysis of the observation assessment, they were considered successful in their learning and ordinary life related to visual perception. It can be concluded that the computer pointing game may play a role in helping children with severe intellectual disabilities improve their visual perception ability.

• Nuclear Engineering and Technology
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• 제56권7호
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• pp.2610-2624
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• 2024

Liquid metal heat pipes play a critical role in various high-temperature applications, with their optimization being pivotal to achieving optimal thermal performance. In this study, a deep learning based genetic algorithm is suggested to optimize the operating conditions of liquid metal heat pipes. The optimization performance was investigated in both single and multi-variable optimization schemes, considering the operating conditions of heat load, inclination angle, and filling ratio. The single-variable optimization indicated reasonable performance for various conditions, reinforcing the potential applicability of the optimization method across a broad spectrum of high-temperature industries. The multi-variable optimization revealed an almost congruent performance level to single-variable optimization, suggesting that the robustness of optimization method is not compromised with additional variables. Furthermore, the generalization performance of the optimization method was investigated by conducting an experimental investigation, proving a similar performance. This study underlines the potential of optimizing the operating condition of heat pipes, with significant consequences in sectors such as high temperature field, thereby offering a pathway to more efficient, cost-effective thermal solutions.

• 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.

• 기본간호학회지
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• 제22권3호
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• pp.308-317
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• 2015

Purpose: The aim of this study was to examine the effects of case-based learning (CBL) on clinical decision making and nursing performance. Methods: This research was conducted between September, 2011 and January, 2012 as a nonequivalent comparison group design. The participants were 55 third year nursing students who were enrolled in a college of nursing in a university in Korea. The intervention was the CBL procedures which involved role-play practice videoed by camera and watched on the computer by the students. Questionnaires were used before and after the intervention to measure clinical decision-making. Nursing performance tests were done after the intervention. Results: Statistically significant group differences were observed in clinical decision-making. Nursing performance was significantly higher in the CBL group than in the control group. Conclusion: CBL focused on the solving problem process and clinical cases which are based on clinical setting allowing students to develop efficiency in clinical practice and adaptation to the clinical situation.

• 드라이브 ㆍ 컨트롤
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• 제19권4호
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• pp.104-109
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• 2022

For autonomous driving research using AI, datasets collected from road environments play an important role. In other countries, various datasets such as CityScapes, A2D2, and BDD have already been released, but datasets suitable for the domestic road environment still need to be provided. This paper analyzed and verified the dataset reflecting the Korean driving environment. In order to verify the training dataset, the class imbalance was confirmed by comparing the number of pixels and instances of the dataset. A similar A2D2 dataset was trained with the same deep learning model, ConvNeXt, to compare and verify the constructed dataset. IoU was compared for the same class between two datasets with ConvNeXt and mIoU was compared. In this paper, it was confirmed that the collected dataset reflecting the driving environment of Korea is suitable for learning.

• 품질경영학회지
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• 제45권4호
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• pp.917-932
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• 2017

Purpose: This study aims to identify the most important quality construct among system quality, information quality, and service quality, which are integrated as the second-order construct; perceived quality, and to investigate the relationship between perceived quality, learner satisfaction, learner enjoyment, switching cost, and learner loyalty. Method: Data were collected from learners who had taken e-learning course, and the analysis was conducted in two phases. The first phase described demographic characteristics using SPSS23.0; the second phase involved the second order CFA of perceived quality and the analysis of measurement model and structural model through AMOS 23.0. Results: (1) The explanatory power of system quality, information quality, and service quality appears to be almost equal; (2) Perceived quality positively influences only both learner satisfaction and switching cost; (3) Only learner satisfaction positively influences learner loyalty and switching cost negatively influences learner loyalty. Conclusion: Learner enjoyment does not play an important role in this study, which could be extrapolated in regard to the characteristics of sample. The respondents are over high school students, who emphasize on the acquisition of knowledge rather than enjoyment. Additionally, the result implies that respondents show low loyalty in the high switching cost.

• Smart Structures and Systems
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• 제29권1호
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.