• Progress in Medical Physics
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• v.30 no.2
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• pp.49-58
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• 2019

Purpose: Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data. Deep-learning artificial intelligence (AI) architectures have been developed and applied to medical images, making high-precision diagnosis possible. Materials and Methods: For diagnosis, the medical images need to be labeled and standardized. After pre-processing the data and entering them into the deep-learning architecture, the final diagnosis results can be obtained quickly and accurately. To solve the problem of overfitting because of an insufficient amount of labeled data, data augmentation is performed through rotation, using left and right flips to artificially increase the amount of data. Because various deep-learning architectures have been developed and publicized over the past few years, the results of the diagnosis can be obtained by entering a medical image. Results: Classification and regression are performed by a supervised machine-learning method and clustering and generation are performed by an unsupervised machine-learning method. When the convolutional neural network (CNN) method is applied to the deep-learning layer, feature extraction can be used to classify diseases very efficiently and thus to diagnose various diseases. Conclusions: AI, using a deep-learning architecture, has expertise in medical image analysis of the nerves, retina, lungs, digital pathology, breast, heart, abdomen, and musculo-skeletal system.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.55-61
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• 2021

The character motion control based on physics simulation using reinforcement learning continue to being carried out. In order to solve a problem using reinforcement learning, the network structure, hyperparameter, state, action and reward must be properly set according to the problem. In many studies, various combinations of states, action and rewards have been defined and successfully applied to problems. Since there are various combinations in defining state, action and reward, many studies are conducted to analyze the effect of each element to find the optimal combination that improves learning performance. In this work, we analyzed the effect on reinforcement learning performance according to the state representation, which has not been so far. First we defined three coordinate systems: root attached frame, root aligned frame, and projected aligned frame. and then we analyze the effect of state representation by three coordinate systems on reinforcement learning. Second, we analyzed how it affects learning performance when various combinations of joint positions and angles for state.

• Journal of The Korean Association For Science Education
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• v.28 no.5
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• pp.409-423
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• 2008

Many researches have reported that it is difficult to solve students' difficulties in learning science with teaching models focused on certain aspects because of various reasons. Recently, in science education research, the integrated perceptive has been to put emphasis on understanding complex situations of real teaching and learning. In this research context, the integrated mental model theory that were considered as a whole factor related to learning has been studied by integrating previous studies that related to students' conceptions and learning in various fields. Thus, it is needed that the teaching model be based on the integrated mental model theory to help students to solve their difficulties. The purpose of this research was to develop a new teaching model based on the integrated mental model theory to address this issue. We reviewed current studies on student difficulties and teaching models. After this, we developed 4M learning cycle teaching model. In this paper, we described the process of developing a new teaching model and discussed how to apply this teaching model to the practices. We also discussed the effects of 4M learning cycle teaching model based on the integrated mental model theory in learning science with its implications.

• Journal of The Korean Association For Science Education
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• v.28 no.8
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• pp.796-813
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• 2008

A good understanding of how gifted science students understand physics is important to developing and delivering effective curriculum for gifted science students. This dissertation reports on a systematic investigation of gifted science students' reasoning model in learning physics. An analysis of videotaped class work, written work and interviews indicate that I will discuss the framework to characterize student reasoning. There are three main groups of students. The first group of gifted science students holds several different understandings of a single concept and apply them inconsistently to the tasks related to that concept. Most of these students hold the Aristotelian Model about Newton's second law. In this case, I define this reasoning model as the manifold model. The second group of gifted science students hold a unitary understanding of a single concept and apply it consistently to several tasks. Most of these students hold a Newtonian Model about Newton's second law. In this case, I define this reasoning model as the coherence model. Finally, some gifted science students have a manifold model with several different perceptions of a single concept and apply them inconsistently to tasks related to the concept. Most of these students hold the Aristotelian Model about Newton's second law. In this case, I define this reasoning model as the coherence model.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• Journal of The Korean Association For Science Education
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• v.37 no.6
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• pp.1051-1061
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• 2017

An image is a comprehensive result that you have experienced about an object and means the image that you have on the surface of your consciousness. The image of the subject has an important influence on learning the subject. The image analysis of the subjects that the learners have will be good data to decide the direction of teaching and learning. The purpose of this study is to develop and apply measurement tools for physics image and discuss its educational implications. The research method is to develop the measurement tools for the physics image by semantic analysis method and apply it to the secondary pre-service physics teacher. The subjects of the study were 39 first graders, 31 second graders, 37 third graders, and 38 fourth graders at the University of Education, a total of 145 students, 82 of whom were male and 63 were female. The study results show that the image measurement tools for physics consisted of 25 items from five elements: 'interest,' 'feeling,' 'scope,' 'evaluation,' and 'viewpoint.' There were statistically significant differences between the male and female students in applying the measurement tools developed for the physics image of secondary pre-service physics teachers. Male students showed significantly higher statistical significance than female students in the 'interest' and 'feeling' elements of measurement tools for the physics image. In the 'scope' element of measurement tools for the physics image the second grade was statistically higher than the fourth grade. Finally, we discussed educational implications for image analysis of physics and the usefulness of using measurement tools in physics image.

• Journal of The Korean Association For Science Education
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• v.24 no.1
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• pp.1-16
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• 2004

The purpose of this study was to examine the role of cognitive conflict in the conceptual change process. Ninety-seven high school students in Korea participated in this study. Before instruction, we conducted pretests to measure learning motivation and learning strategies. During instruction, we tested the students' preconceptions about Newton's 3rd Law and presented demonstrations. After this, we tested the students' cognitive conflict levels and provided students learning sessions in which we explained the results of the demonstrations. After these learning sessions, we tested the students' state learning motivation and state learning strategy. Posttests and delayed posttests were conducted with individual interviews. The result shows that cognitive conflict has direct/indirect effects on the conceptual change process. However, the effects of cognitive conflict are mediated by other variables in class, such as state learning motivation and state learning strategy. In addition, we found that there was an optimal level of cognitive conflict in the conceptual change process. We discuss the complex role of cognitive conflict in conceptual change, and the educational implications of these findings.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2452-2459
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• 2020

Neutron spectrum is essential to the safe operation of reactors. Traditional online neutron spectrum measurement methods still have room to improve accuracy for the application cases of wide energy range. From the application of artificial neural network (ANN) algorithm in spectrum unfolding, its accuracy is difficult to be improved for lacking of enough effective training data. In this paper, an adaptive deviation-resistant neutron spectrum unfolding method based on transfer learning was developed. The model of ANN was trained with thousands of neutron spectra generated with Monte Carlo transport calculation to construct a coarse-grained unfolded spectrum. In order to improve the accuracy of the unfolded spectrum, results of the previous ANN model combined with some specific eigenvalues of the current system were put into the dataset for training the deeper ANN model, and fine-grained unfolded spectrum could be achieved through the deeper ANN model. The method could realize accurate spectrum unfolding while maintaining universality, combined with detectors covering wide energy range, it could improve the accuracy of spectrum measurement methods for wide energy range. This method was verified with a fast neutron reactor BN-600. The mean square error (MSE), average relative deviation (ARD) and spectrum quality (Qs) were selected to evaluate the final results and they all demonstrated that the developed method was much more precise than traditional spectrum unfolding methods.

• Journal of Korean Elementary Science Education
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• v.41 no.2
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• pp.199-216
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• 2022

This study explored the possibility of introducing modern physics into the elementary school science curriculum. The study discussed the need of introducing modern physics to elementary school students and examined the results of certain projects and studies on teaching modern physics to elementary school students. Furthermore, this study proposes several teaching and learning techniques to introduce modern physics into the elementary school science curriculum. Modern physics can be linked to various everyday situations experienced by students and can increase their interest and curiosity in science. Additionally, introducing modern physics to elementary school students who are yet to establish a background on the classical view of nature can help them build a new perspective. Recently, several global projects to introduce modern physics at the elementary level have also reported positive results regarding the increase in student understanding and interest in modern physics. The study briefly proposed specific topics and teaching and learning techniques that could be suitable for the elementary school level. These proposals are expected to advance discussions on the possibility of introducing modern physics. However, appropriate follow-up studies are warranted to confirm the possibility and effectiveness of this initiative.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.810-813
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• 2002

We present the information theoretic learning based on the Tsallis entropy maximization principle for various q. The Tsallis entropy is one of the generalized entropies and is a canonical entropy in the sense of physics. Further, we consider the dependency of the learning on the parameter $\sigma$, which is a standard deviation of an assumed a priori distribution of samples such as Parzen window.