• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.500-507
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• 2022

COVID-19 pandemic forces college education to be rapidly switched from face-to-face education into remote education. Two inconsistent findings exist in previous study about remote learning. First, studies before COVID-19 pandemic found remote learning is an effective method, which provided students with higher achievement and improved their work-life balance. However, studies showed remote learning during COVID-19 pandemic is not as effective as expected because of technical issues, lack of motivations and even mental health issues. Second, findings from studies about remote learning impacts on workload and productivity during COVID-19 are also inconsistent. Therefore, this study aims to quantitatively measure college students' workload and productivity during COVID-19 of different types of tasks to provide a comprehensive and latest evaluation on remote learning. The findings of this study show remote learning slightly increases college students' total listening and speaking tasks workload, total reading and writing tasks workload. Furthermore, phone call, in-person meeting, online meeting and email workload increase significantly in remote learning. However, productivity for both listening and speaking, reading and writing tasks decreases after remote learning but no significant changes of productivity are found.

• 인지과학
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• 제11권2호
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• pp.19-35
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• 2000

예제에 의한 학습은 프로그래밍 언어를 포함한 다양한 주제들을 숙지하는데 효과적인 방법으로 밝혀져 왔다. 그러나 어떤 예제를 어떻게 제공하는 것이 바람직한가에 대한 보다 심층적인 연구는 많지 않다. 본 연구는 예제가 제시되는 방식과 제시되는 예제의 형태가 예제에 의한 프로그래밍 언어의 학습 성과에 영향을 미치는 두 가지 중요한 차원이라는 가설을 세웠다. 이 가설들을 자바 프로그래밍 언어의 학습 과정을 통하여 검증하기 위하여 컴퓨터 상에서 실험을 실시하였다. 예제의 제시 방식에서는 두 종류의 예제들을 부가적 설명 없이 제공하는 것이 부가적 설명과 함께 하나의 예제를 제공하는 것보다 더 효과적이라는 결과를 얻었다. 예제의 형태에서는 두 종류의 예제를 제공받았더라고 두 예제가 주어진 과제와 기능적으로 유사한 경우가 기능적으로 상이한 경우보다 더 나은 수행 결과를 나타냈다. 이와 같은 수행 결과의 차이에 대한 이유를 밝히기 위해 개별 피험자들의 프로그래밍 행동의 유형을 시간과 빈도의 관점에서 분석하였으며 또한 피험자들의 행위에 대한 보다 체계적인 설명을 위하여 GOMS 모델을 제시하였다. 결론적으로, 본 연구의 결과들은 프로그래밍 언어를 효과적으로 지도할 수 있는 교육 시스템 개발에 기여할 수 있을 것으로 기대된다.

• 토지주택연구
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• 제8권3호
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• pp.123-130
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• 2017

This study aims to empirically verify the impacts of career learning on employability and the mediating effect of job expertise in a public corporation. For the empirical analysis, I surveyed 958 employees(valid respondents) working in a public corporation. And the structural equation modeling(SEM) was used to statistically analize and test the research hypotheses. The independent variable is 'career learning', the dependent variable is 'employability' and the mediating variable is 'job expertise'. The results are as follows. The empirical analysis shows that the positive effects of 'career learning ${\rightarrow}$ job expertise', 'job expertise ${\rightarrow}$ employability' and 'career learning ${\rightarrow}$ employability' are all verified. And the mediating effect of job expertise between career learning and employability is also partially verified. So, all the proposed hypotheses are accepted. From this result, I can clearly suggest that the employees can be growing to professionals with high employability when they retire if they are voluntarily and self-motivated to set up their career plan and to enhance their job expertise. In this context, it is expected that the company should support the employees to continue to strengthen their own expertise in their job place through their mid-long term career learning plan.

• 전자통신동향분석
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• 제35권5호
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• pp.14-29
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• 2020

The 6G mobile communication system will become a backbone infrastructure around 2030 for the future digital world by providing distinctive services such as five-sense holograms, ultra-high reliability/low-latency, ultra-high-precision positioning, ultra-massive connectivity, and gigabit-per-second data rate for aerial and maritime terminals. The recent remarkable advances in machine learning (ML) technology have recognized its efficiency in wireless networking fields such as resource management and cell-configuration optimization. Further innovation in ML is expected to play an important role in solving new problems arising from 6G network management and service delivery. In contrast, an approach to apply ML to a physical-layer (PHY) target tackles the basic problems in radio links, such as overcoming signal distortion and interference. This paper reviews the methodologies of ML-based PHY, relevant industrial trends, and candiate technologies, including future research directions and standardization impacts.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2022년도 춘계학술대회
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• pp.363-364
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• 2022

The COVID-19 pandemic had substantial negative impacts and caused several disruptions to the global supply chain of the shipping industry. The key challenges identified in terms of maritime manpower are the Certificates of Competency (CoC) or the expiration and/or failure to complete refresher and/or revalidation courses, which directly hinder employment retention and lost opportunities at sea. To tackle this issue directly and swiftly, the creation of the APEC SEN Distance Learning Platform was suggested and approved by APEC as part of an official project. This paper introduces the APEC-wide accessible distance learning platform with the following key topics: the organisation and operation of the platform, the themes and content to be prioritised, the process of education, training, certification, and the ways to promote accreditation, mutual recognition on CoC, education and training videos by taking collaborative actions, and the development of content.

• 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).

• 한국산업융합학회 논문집
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• 제27권4_2호
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• pp.1001-1007
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• 2024

Recent deep learning algorithms for object detection in real-time play a crucial role in various applications such as autonomous driving, traffic monitoring, health care, and water quality monitoring. The size of small objects, in particular, significantly impacts the accuracy of detection models. However, data containing small objects can lead to underfitting issues in models. Therefore, this study developed a deep learning model capable of quickly detecting small objects to provide more accurate predictions. The RE-SOD (Residual block based Small Object Detector) developed in this research enhances the detection performance for small objects by using RGB separation preprocessing and residual blocks. The model achieved an accuracy of 1.0 in image classification and an mAP50-95 score of 0.944 in object detection. The performance of this model was validated by comparing it with real-time detection models such as YOLOv5, YOLOv7, and YOLOv8.

• 지식경영연구
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• 제18권2호
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• pp.85-102
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• 2017

With rapid development of information and communication technologies, open collaboration can be eased through the Internet. Open source software, as a representative area of open collaboration, is developed and adopted to various fields. In this research, based on organizational learning theory, we examine the impacts of exploration and exploitation on innovation performance in open source software development projects. We define knowledge exploration as a number of developers from outside organization and knowledge exploitation as the ratio of member of an organization who participated in an open source software project managed by the organization. For analysis, we collect data of 4794 projects from github which is a representative open source software development platform using Web crawler developed by Python. As a result, we find that excessive exploration has curvilinear (invers U-shape) relationship on project performance. On the other hand, exploitation with enough external developers will positively impact on project performance.

• 컴퓨터교육학회논문지
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• 제9권3호
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• pp.67-74
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• 2006

본 연구에서는 학습자가 특정 e-러닝 프로그램을 선호하는데 영향을 미치는 다양한 요인들을 탐색해 보았다. 선호도에 영향을 미치는 다양한 변인들을 열거하고 그러한 변인들을 상관 정도에 따라 공통요인을 추출하는 탐색적 요인분석(exploratory factor analysis)를 실시하였다. 분석 결과, 학습자들이 특정한 형태의 e-러닝 프로그램을 선호하게 되는 주된 요인들은 e-러닝 프로그램의 설계방식(1 요인), e-러닝 프로그램 활용목적(2 요인), 사회문화적 쟁점(3요인), 인구학적 요소(4 요인), 조직의 요구(5요인), e-러닝 프로그램 활용결과(6요인), e-러닝 프로그램 운영관리(7 요인), e-러닝 프로그램의 기술적 환경(8 요인)으로 나타났다.

• Journal of Information Technology Applications and Management
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• 제27권3호
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• pp.19-36
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• 2020

The Internet of things (IoT) is a system of interrelated computed devices, digital machines and any physical objects which are provided with unique identifiers and the potential to transmit data to people or machine (M2M) without requiring human interaction. IoT devices can be used to monitor and control the electrical and electronic systems used in different fields like smart home, smart city, smart healthcare and etc. In this study we introduce four imaginary IoT devices as a learning support assistants according to students' dominant learning styles measured by Honey and Mumford Learning Styles: Activists, Reflectors, Theorists and Pragmatists. This research emphasizes the association between students' strong learning styles and a preference to appropriate IoT devices with specific characteristics. Moreover, different levels of IoT devices' architecture are clearly explained in this study where all the artificial devices are designed based on this structure. Data analysis of experiment were measured by the use of chi square test for association and research results showed the statistical significance of the estimated model and the impacts of each category over the model where we finally got accurate estimates for our research variables. This study revealed the importance of considering the students' dominant learning styles before inventing a new IoT device.