• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1267-1276
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• 2024

The emerging technologies at the industrial level have deployed rapidly within the energy transition process innovations. The nuclear industry incorporates several technologies like Artificial Intelligence (AI), Machine Learning (ML), Digital Twins, High-Performance-Computing (HPC) and Quantum Computing (QC), among others. Factors identifications are explained to set up a regulatory framework in the digitalization era, providing new capabilities paths for nuclear technologies in the forthcoming years. The Analytical Network Process (ANP) integrates the quantitative-qualitative decision-making analysis to assess the implementation of different aspects in the digital transformation for the New-Energy Transition Era (NETE) with a Nuclear Power Infrastructure Development (NPID).

• Journal of Information Technology Applications and Management
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• v.14 no.1
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• pp.145-160
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• 2007

The knowledge based economy requires more and more people to learn new knowledge and skills in a timely and effective manner. These needs and new technology such as computer and Internet are fueling a transition in e-learning. We did the case study of K company, which is leading the business to business e-learning in Korea. We investigated prior studies about e-learning and deduced the major variables composed of learner, tutor, infrastructure, contents, and practice. And then we suggested the successful way of doing the operation for the company's e-learning. We hope that this research will help the companies that have introduced or consider the adoption of e-learning.

• Journal of the Korean Chemical Society
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• v.65 no.2
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• pp.145-150
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• 2021

The present study describes a hands-on experiment to help students understand the concept of phase change or phase transition and its application in a phase change material (PCM). PCMs are substances that have the capability of storing and releasing large amounts of thermal energy. They act as energy storage materials that provide an effective way to save energy by reducing the electricity required for heating and cooling. Lauric acid (LA) was selected as an example of the PCM. Students investigated the temperature change of LA and the temperature (of air) inside the test tube. The differences in the temperatures of the systems helped students understand how PCMs work. A one-group pretest and posttest design was implemented with 34 grade-11 students in science and mathematics. Students' understanding was assessed using a multiple-choice test and a questionnaire. The findings revealed that the designed activity helped students understand the concept of phase change and its application to materials for thermal energy storage.

• Journal of Gastric Cancer
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• v.18 no.2
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• pp.172-181
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• 2018

Purpose: This study aimed to evaluate the surgical outcomes and investigate the feasibility of reduced-port laparoscopic gastrectomy using learning curve analysis in a small-volume center. Materials and Methods: We reviewed 269 patients who underwent laparoscopic distal gastrectomy (LDG) for gastric carcinoma between 2012 and 2017. Among them, 159 patients underwent reduced-port laparoscopic gastrectomy. The cumulative sum technique was used for quantitative assessment of the learning curve. Results: There were no statistically significant differences in the baseline characteristics of patients who underwent conventional and reduced-port LDG, and the operative time did not significantly differ between the groups. However, the amount of intraoperative bleeding was significantly lower in the reduced-port laparoscopic gastrectomy group (56.3 vs. 48.2 mL; P<0.001). There were no significant differences between the groups in terms of the first flatus time or length of hospital stay. Neither the incidence nor the severity of the complications significantly differed between the groups. The slope of the cumulative sum curve indicates the trend of learning performance. After 33 operations, the slope gently stabilized, which was regarded as the breakpoint of the learning curve. Conclusions: The surgical outcomes of reduced-port laparoscopic gastrectomy were comparable to those of conventional laparoscopic gastrectomy, suggesting that transition from conventional to reduced-port laparoscopic gastrectomy is feasible and safe, with a relatively short learning curve, in a small-volume center.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.276-284
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• 2022

The research aims to study the dynamics of academic procrastination and its impact on the mental health of students during the transition to distance learning during the COVID-19 pandemic. At the beginning of the COVID-19 pandemic, it was identified a declining tendency of overall rates of academic procrastination and at the same time increase in the number of carriers of mid and high levels of academic procrastination. The decline in the general rates of academic procrastination at the beginning of 2021 testifies to the adaptation processes experienced by students to the conditions of distance learning. It was documented that students' academic procrastination is accompanied by a steady negative emotional tension. During the transition to distance learning, the intensity of students' learning activity has increased, which altogether causes stress as one of the main reasons for the academic procrastination among future psychologists. The study identified a risk of academic procrastination manifestation among students for their mental health, which provides a basis for developing and testing a program to prevent the phenomenon of academic procrastination among degree-seeking students.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1618-1624
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• 2018

The undergraduate structure based on flipped learning should be a necessary course to cultivate value creation capability based on students' problem solving capability through the change of university education in the fourth industrial revolution era. Flipped learning stimulated the learner's high order thinking and activates communication between the faculty-student and the students through the use of activity oriented teaching strategy. Introduction and spread of Flipping Learning combining project-based learning with MOOC is required. The professor should be able to apply net teaching and learning methods using flipping learning and active learning, and develop class contents reflecting new knowledge, information and technology. As the introduction and spread of AI-based(E-Advisor, chat bot et al) learning consulting, Which is becoming increasingly advanced, the transition to "personalized education" that meets the 4th Industrial Revolution should be made.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1234-1239
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• 1990

In this paper, we propose a new type of decentralized learning automata for the control finite state Markov chains with unknown transition probabilities and rewards. In our scheme a .betha.-type learning automaton is associated with each state in which two or more actions(desisions) are available. In this decentralized learning automata system, each learning automaton operates, requiring only local information, to improve its performance under local environment. From simulation results, it is shown that the decentralized learning automata will converge to the optimal policy that produces the most highly total expected reward with discounting in all initiall states.

• The Journal of Korean Medical History
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• v.28 no.2
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• pp.15-22
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• 2015

Objectives : The each college of Korean medicine in Korea adopts diverse textbooks for the medical history class, resulting in educational contents variations. This proposal aimed for the standardization of educational contents. Methods : The transition of medical history curriculum will be attempted based on the understanding of paradigm change in modern education. The first step is investigation on the course credit and curriculum grade of medical history class presented in education status reports of all Korean medicine schools. The next step is study on the various methods about changes of medical history education base on the learning objectives of colleges of Korean medicine. Results : The researchers of medical history should make an agreement on modification of learning objectives of the curriculum, and then educational standardization must be achieved by publishing a medical history textbook in accordance with the modified learning objectives. Conclusions : The researchers of medical history must collaborate to standardize medical history education by developing and applying internet-based flipped learning model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.370-373
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• 2022

Recently, various deep learning framework models such as Tensorflow, Pytorch, Keras, etc. have appeared. In addition, CNN (Convolutional Neural Network) is applied to image recognition using frameworks such as Tensorflow, Pytorch, and Keras, and the optimization model in image classification is mainly used. In this paper, based on the results of training the CNN model with the Paitotchi and tensor flow frameworks most often used in the field of deep learning image recognition, the two frameworks are compared and analyzed for image analysis. Derived an optimized framework.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.155-162
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• 2002

Reinforcement learning is a learning method that uses trial-and-error to perform Learning by interacting with dynamic environments. It is classified into online reinforcement learning and delayed reinforcement learning. In this paper, we propose an online reinforcement learning system (ONRELS : Outline REinforcement Learning System). ONRELS updates the estimate-value about all the selectable (state, action) pairs before making state-transition at the current state. The ONRELS learns by interacting with the compressed environments through trial-and-error after it compresses the state space of the mage environments. Through experiments, we can see that ONRELS can search the shortest path faster than Q-learning using TD-ewor and $Q(\lambda{)}$-learning using $TD(\lambda{)}$ in the maze environments.