• 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).

• 한국정보시스템학회지:정보시스템연구
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• 제31권1호
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• pp.219-238
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• 2022

Purpose The purpose of this study is to investigate the structural relationships between interest, interaction level, presence, which are the characteristics of metaverse, learning immersion, and learning satisfaction, which are learning factors. Design/methodology/approach A questionnaire survey technique was used to achieve the purpose of the study. A questionnaire survey was conducted from November 22 to December 5, 2021, with students with experience in non-face-to-face classes using Gather Town and a total of 114 copies of the questionnaire excluding those with insincere answers were used for empirical analysis. SPSS Win ver.23.0 was used for basic statistical analysis, and AMOS 22.0 was used for the establishment and analysis of a structural equation model. Findings According to the study findings, interest and interaction levels had effects on learning immersion and learning presence, self-efficacy on learning presence, and learning immersion and learning presence on learning satisfaction. This study is meaningful in that it conducted an empirical study to find variables for improving learning immersion by conducting classes based on metaverse. Based on the findings of this study, it was found that interest and interaction, which are the biggest characteristics of metaverse, sustain learning participation and immersion and increase presence thereby enhancing learning satisfaction so that the possibilities of metaverse as a next generation education platform passing the limit of existing real time video platforms can be peeped.

• 대한산업공학회지
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• 제39권5호
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• pp.361-366
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• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권4호
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• pp.2038-2057
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• 2017

Gaussian learning is a new technology in the computational intelligence area. However, this technology weakens the learning ability of a particle swarm and achieves a lack of diversity. Thus, this paper proposes a vector Gaussian learning strategy and presents an effective approach, named particle swarm optimization based on vector Gaussian learning. The experiments show that the algorithm is more close to the optimal solution and the better search efficiency after we use vector Gaussian learning strategy. The strategy adopts vector Gaussian learning to generate the Gaussian solution of a swarm's optimal location, increases the learning ability of the swarm's optimal location, and maintains the diversity of the swarm. The method divides the states into normal and premature states by analyzing the state threshold of the swarm. If the swarm is in the premature category, the algorithm adopts an inertia weight strategy that decreases linearly in addition to vector Gaussian learning; otherwise, it uses a fixed inertia weight strategy. Experiments are conducted on eight well-known benchmark functions to verify the performance of the new approach. The results demonstrate promising performance of the new method in terms of convergence velocity and precision, with an improved ability to escape from a local optimum.

• 전기전자학회논문지
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• 제23권4호
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• pp.1187-1194
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• 2019

본 논문에서는 인공지능 오델로 게임 에이전트를 구현하기 위해 실제 프로기사들의 기보를 CNN으로 학습시키고 이를 상태의 형세 판단을 위한 근거로 삼아 최소최대탐색을 이용해 현 상태에서 최적의 수를 찾는 의사결정구조를 사용하고 이를 발전시키고자 강화학습 이론을 이용한 자가대국 학습방법을 제안하여 적용하였다. 본 논문에서 제안하는 구현 방법은 기보학습의 성능 평가 차원에서 가치평가를 위한 네트워크로서 기존의 ANN을 사용한 방법과 대국을 통한 방법으로 비교하였으며, 대국 결과 흑일 때 69.7%, 백일 때 72.1%의 승률을 나타내었다. 또한 본 논문에서 제안하는 강화학습 적용 결과 네크워크의 성능을 강화학습을 적용하지 않은 ANN 및 CNN 가치평가 네트워크 기반 에이전트와 비교한 결과 각각 100%, 78% 승률을 나타내어 성능이 개선됨을 확인할 수 있었다.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제7권1호
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• pp.11-24
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• 2003

The key features of our integrated approach to teaching and loaming college mathematics include interactive and discussion-based teaching, small group work, computer as a tool, problem solving approach, open approach, mathematics in context, emphasis on mathematical thinking and creativity, and writing/communicating about mathematics. In this paper we report a few examples to illustrate the type of problems we use in our integrated approach.

• 인터넷정보학회논문지
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• 제2권1호
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• pp.39-48
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• 2001

학습 활동에서 탐구학습은 실험실에서 주로 이루어진다. 이러한 실험실에서 탐구학습과정이 웹을 기반으로 하는 시뮬레이션 코스웨어로 설계하므로써 학생들에게 학습과정을 보다 쉽게 접근해 갈 수 있도록 하며 자기 스스로 사전학습과 탐구실험을 할 수 있는 공간을 제공하고 정보의 공유, 교환 및 상호 작용적인 학습자 중심의 교육 모델을 제공할 수 있다. 웹의 활용은 탐구학습에 적합한 학습도구가 될 뿐만 아니라, 학생들의 흥미를 유발시켜 보다 나은 교수학습 환경을 만들어준다. 이에 본 논문에서는 웹을 이용하여 역학적 에너지를 자기 스스로 학습할 수 있는 환경을 제공하고 탐구실험과정을 가상실험으로 실시할 수 있도록 학습모형을 시뮬레이션 코스웨어로 설계하고 구현하고자 한다.

• 디지털산업정보학회논문지
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• 제19권1호
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• pp.153-172
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• 2023

We look into the theory of experiential learning, which allows learners to design and organize their own lives, as well as to develop the necessary competencies for students who will be living in intelligent information society. We also investigate the teaching and learning methods, as well as the educational contents of artificial intelligence education, and develop an approach to artificial intelligence education that will develop learners' capabilities. As a result, we have investigated the pedagogical needs for artificial intelligence education in elementary and secondary schools, critically reviewed the discussions on experiential learning-based education for artificial intelligence education in elementary and secondary schools, and proposed a plan. Experiential learning achieves comprehension and knowledge acquisition naturally, as well as subject connection and integration. When preparing for artificial intelligence education, practical methods and procedures for developing capabilities in artificial intelligence education, focusing on in-depth learning, inter-subject linkage and integration, life-related learning, and reflection on the learning process, should be considered unavoidable.

• 대한임베디드공학회논문지
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• 제16권6호
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• pp.277-283
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• 2021

This paper addresses the problem of vision-based mask filter inspection for mask production systems. Machine learning-based approaches can be considered to solve the problem, but they may not be applicable to mask filter inspection if normal and anomaly mask filter data are not sufficient. In such cases, handcrafted image processing methods have to be considered to solve the problem. In this paper, we propose a hierarchical correlation-based approach that combines handcrafted image processing methods to detect anomaly mask filters. The proposed approach combines image rotation, cropping and resizing, edge detection of mask filter parts, average blurring, and correlation-based decision. The proposed approach was tested and analyzed with real mask filters. The results showed that the proposed approach was able to successfully detect anomalies in mask filters.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권4호
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• pp.244-249
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• 2017

스테그아날리시스(Steganalysis)란 이미지 등 일반적인 자료에 암호화된 정보를 은닉하는 스테가노그래피(Steganography)에 대한 검출 및 분석 방법으로, 기계학습 기반 방법론을 포함한다. 기존 기계학습 기반 스테그아날리시스는 영상(Image)의 특징(Feature) 추출 및 모델링에 기반하며, 최근 딥러닝(Deep Learning)의 적용으로 검출 정확도가 큰 폭으로 향상되었다. 하지만 현존하는 스테그아날리시스 모델은 단일 스테가노그래피 기법에 대해 국한되어 있어 학습에 사용되지 않은 스테고(Stego) 이미지의 경우 검출이 불가능한 결정적 한계를 가진다. 본 연구에서는 다양한 스테가노그래피 기법으로 생성된 스테고 이미지에 딥러닝을 적용하여 스테그아날리시스를 학습하는 범용적 모델을 제안한다. 다양한 실험을 통해 제안 기법의 효용성 및 가능성을 확인하고, 범용적 스테그아날리시스 모델이 각각에 특화된 검출 기법과 유사한 정확도로 스테고 이미지를 검출할 수 있음을 보인다.