• Title/Summary/Keyword: Learning-based approach

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Loading pattern optimization using simulated annealing and binary machine learning pre-screening

  • Ga-Hee Sim;Moon-Ghu Park;Gyu-ri Bae;Jung-Uk Sohn
    • Nuclear Engineering and Technology
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    • v.56 no.5
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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).

Superimposing 3D Models on Real Scenes Based on The Reinforcement Learning using Visual Observations

  • Yong-Ju Lee;Linh Nguyen;Man-Woo Park
    • International conference on construction engineering and project management
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    • 2024.07a
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    • pp.665-671
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    • 2024
  • This research presents a method for Augmented Reality (AR) object superimposition leveraging reinforcement learning techniques to significantly reduce manual input during the exploration of digital information on construction sites. A distinctive feature of this approach is the application of a reinforcement learning neural network, trained with pairs of real and virtual view images, for AR superimposition. This approach enables the precise adjustment of the virtual camera's position and orientation within a virtual scene, aiming to seamlessly integrate AR objects into real-world views. This research initially focuses on simpler scenarios involving 2 and 3 degrees of freedom for orientation and position adjustments. The purpose is to explore the feasibility of the application through those experiments, with the expectation that the results would be interpreted positively. These initial findings highlight the promise of the suggested method in improving AR applications, especially within the construction sector, by enabling more natural and precise merging of virtual and physical objects, without requiring user intervention.

Analysis of Structural Relationships Among Metaverse Characteristic Factors, Learning Immersion, and Learning Satisfaction: With Gather Town (메타버스 특성요인과 학습 몰입 및 학습 만족도 간의 구조적 관계 분석 : 게더타운을 대상으로)

  • Kim, Na Rang
    • The Journal of Information Systems
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    • v.31 no.1
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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.

A Study on Dual Response Approach Combining Neural Network and Genetic Algorithm (인공신경망과 유전알고리즘 기반의 쌍대반응표면분석에 관한 연구)

  • Arungpadang, Tritiya R.;Kim, Young Jin
    • Journal of Korean Institute of Industrial Engineers
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    • v.39 no.5
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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.

Particle Swarm Optimization based on Vector Gaussian Learning

  • Zhao, Jia;Lv, Li;Wang, Hui;Sun, Hui;Wu, Runxiu;Nie, Jugen;Xie, Zhifeng
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.4
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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.

An Artificial Intelligence Game Agent Using CNN Based Records Learning and Reinforcement Learning (CNN 기반 기보학습 및 강화학습을 이용한 인공지능 게임 에이전트)

  • Jeon, Youngjin;Cho, Youngwan
    • Journal of IKEEE
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    • v.23 no.4
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    • pp.1187-1194
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    • 2019
  • This paper proposes a CNN architecture as value function network of an artificial intelligence Othello game agent and its learning scheme using reinforcement learning algorithm. We propose an approach to construct the value function network by using CNN to learn the records of professional players' real game and an approach to enhance the network parameter by learning from self-play using reinforcement learning algorithm. The performance of value function network CNN was compared with existing ANN by letting two agents using each network to play games each other. As a result, the winning rate of the CNN agent was 69.7% and 72.1% as black and white, respectively. In addition, as a result of applying the reinforcement learning, the performance of the agent was improved by showing 100% and 78% winning rate, respectively, compared with the network-based agent without the reinforcement learning.

An Integrated Approach to Teaching and Learning College Mathematics

  • Ahuja, Om P.;Jahangiri, Jay M.
    • Research in Mathematical Education
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    • v.7 no.1
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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.

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Design and Implementation of a Web-based Simulation Courseware for Learning Kinetic Energy (웹 상에서 운동 에너지 탐구학습을 위한 시뮬레이션 코스웨어 설계 및 구현)

  • 송민석;인치호
    • Journal of Internet Computing and Services
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    • v.2 no.1
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    • pp.39-48
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    • 2001
  • In learning activities. research learning is mostly carried out in a laboratory. Learners can approach a learning process with ease and are given the chance to do the self-directed study one research experiments in advance by designing a process of research learning in the laboratory by a web-based simulation courseware. And this can offer a learner-centered model with which learners can share. exchange and interact information each other. Using a web in instructing and learning can be an appropriate implement of research and also gives learner-centered learning environment. This thesis is a study on design and implementation of a web-based simulation courseware for learning Kinetic energy through the research learning which is one of the self leading learning methods.

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A Study on the Experiential Learning-Based Education for the Development of Artificial Intelligence Competency (인공지능 역량 함양을 위한 경험학습 기반 교육에 관한 고찰)

  • Park Sangwoo;Cho Jungwon
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.19 no.1
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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.

Hierarchical Correlation-based Anomaly Detection for Vision-based Mask Filter Inspection in Mask Production Lines (마스크 생산 라인에서 영상 기반 마스크 필터 검사를 위한 계층적 상관관계 기반 이상 현상 탐지)

  • Oh, Gunhee;Lee, Hyojin;Lee, Heoncheol
    • IEMEK Journal of Embedded Systems and Applications
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    • v.16 no.6
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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.