• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2942-2960
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• 2022

The core of cognitive radio is the problem concerning intelligent decision-making for communication parameters, the objective of which is to find the most appropriate parameter configuration to optimize transmission performance. The current algorithms have the disadvantages of high dependence on prior knowledge, large amount of calculation, and high complexity. We propose a new decision-making model by making full use of the interactivity of reinforcement learning (RL) and applying the Q-learning algorithm. By simplifying the decision-making process, we avoid large-scale RL, reduce complexity and improve timeliness. The proposed model is able to find the optimal waveform parameter configuration for the communication system in complex channels without prior knowledge. Moreover, this model is more flexible than previous decision-making models. The simulation results demonstrate the effectiveness of our model. The model not only exhibits better decision-making performance in the AWGN channels than the traditional method, but also make reasonable decisions in the fading channels.

• Journal of information and communication convergence engineering
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• v.17 no.2
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• pp.128-134
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• 2019

Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4607-4616
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• 2023

We implement machine learning regression models to predict peak pressures of primary and secondary systems, a major safety concern in Loss Of Condenser Vacuum (LOCV) accident. We selected the Multi-dimensional Analysis of Reactor Safety-KINS standard (MARS-KS) code to analyze the LOCV accident, and the reference plant is the Korean Optimized Power Reactor 1000MWe (OPR1000). eXtreme Gradient Boosting (XGBoost) is selected as a machine learning tool. The MARS-KS code is used to generate LOCV accident data and the data is applied to train the machine learning model. Hyperparameter optimization is performed using a simulated annealing. The randomly generated combination of initial conditions within the operating range is put into the input of the XGBoost model to predict the peak pressure. These initial conditions that cause peak pressure with MARS-KS generate the results. After such a process, the error between the predicted value and the code output is calculated. Uncertainty about the machine learning model is also calculated to verify the model accuracy. The machine learning model presented in this paper successfully identifies a combination of initial conditions that produce a more conservative peak pressure than the values calculated with existing methodologies.

• Journal of Engineering Education Research
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• v.21 no.1
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• pp.3-13
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• 2018

The purpose of this study is to propose a teaching and learning model that can effectively manage convergence education, which is one of the concerns of university education, at the level of course. The pre-collaborative instructional design stage is to prepare the operation of the convergence project course. It shares the common goal and establishes a team of relevant professors to set up the actual convergence project topic and establishes cooperation relationships with industry or community as needed. In the convergence project activity, students will be able to understand the learning objectives, learning activities, evaluation methods, and explain the subject of the convergence project by proceeding with the whole orientation. Students organize teams of interest and conduct learning and design activities on convergence technologies and present their results. In the educational improvement activities, professors will share the lesson process and results and discuss improvements through the improvement seminar. As a result of analyzing the effectiveness of the proposed convergence project based teaching and learning model, the convergence project experience has improved the cooperative self - efficacy for the learners and the results were confirmed that students perceived to achieve the expected learning goal and satisfied with their experience.

• Journal of Information Processing Systems
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• v.13 no.1
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• pp.57-67
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• 2017

In this paper, Atanassov's intuitionistic fuzzy set theory is used to handle the uncertainty of students' knowledgeon domain concepts in an E-learning system. Their knowledge on these domain concepts has been collected from tests that were conducted during their learning phase. Atanassov's intuitionistic fuzzy user model is proposed to deal with vagueness in the user's knowledge description in domain concepts. The user model uses Atanassov's intuitionistic fuzzy sets for knowledge representation and linguistic rules for updating the user model. The scores obtained by each student were collected in this model and the decision about the students' knowledge acquisition for each concept whether completely learned, completely known, partially known or completely unknown were placed into the information table. Finally, it has been found that the proposed scheme is more appropriate than the fuzzy scheme.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1227-1236
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• 2021

Adversarial attack, one of the attacks on deep learning classification model, is attack that add indistinguishable perturbations to input data and cause deep learning classification model to misclassify the input data. There are various adversarial attack algorithms. Accordingly, many studies have been conducted to detect adversarial attack but few studies have been conducted to classify what adversarial attack algorithms to generate adversarial input. if adversarial attacks can be classified, more robust deep learning classification model can be established by analyzing differences between attacks. In this paper, we proposed a model that detects and classifies adversarial attacks by constructing a random forest classification model with input features extracted from a target deep learning model. In feature extraction, feature is extracted from a output value of hidden layer based on class predicted by the target deep learning model. Through Experiments the model proposed has shown 3.02% accuracy on clean data, 0.80% accuracy on adversarial data higher than the result of pre-existing studies and classify new adversarial attack that was not classified in pre-existing studies.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.23-27
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• 2007

Many trials have been used to model room impulse responses, all attempting to provide efficient representations of room acoustics. The traditional model designs for room impulse response seem to fail in accuracy, controllability, or computational efficiency. In time domain, a room impulse response is generally considered as the combination of three parts having different acoustic characteristics, initial time delay, early reflection, and late reverberation. This paper introduces new learning hybrid model for the room impulse response. In this proposed model, those three parts are modeled using different models with learning algorithms that determine the length or boundary of each model in the hybrid model. By the simulation with measured room impulse responses, it was examined that the performance of proposed model shows the best efficiency in views of both the parameter numbers and modeling error.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.4
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• pp.915-927
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• 2013

This study aimed to discover the affective ability of students by applying a thematic project model concerning the marine field unit of science for third grade middle school students from among diverse methods of realizing STEAM education. Also, based on this, STEAM education is a new type of learning including the process of exploring by oneself, presenting, discussing and mutually evaluating by becoming an independent person in learning As the results of the study are as follows: First, the STEAM class applied with a thematic project model was discovered to promote a learning attitude toward science by learners. Second, the STEAM class applied with a thematic project model was shown to be effective in improving the self-directed learning characteristics of learners. Third, STEAM education applied with a thematic project model was found to promote learners' motivation for learning science. This may be an effective method for learners who have felt difficulty in the science curriculum or have not been interested in the curriculum by triggering voluntary motivation.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1679-1686
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• 2007

In this paper, we introduce the sharable and reusable learning objects which are suitable for XML Web services in e-learning systems. These objects are extracted from the principles of pedagogical paradigms for reusable learning units. We call them LIO (Learning Item Object) objects. Existing models, such as Web-hosted and ASP-oriented service model, are difficult to cooperate and integrate among the different kinds of e-learning systems. So we developed the LIO objects that are suitable for XML Web services. The reusable units that are extracted from pedagogical paradigms are tutorial item, resource, case example, simulation, problems, test, discovery and discussion. And these units correspond to the LIO objects in our learning object model. As a result, the proposed model is that learner and instruction designer should increase the power of understanding about learning contents that are based on pedagogical paradigms. By using XML Web services, this guarantees the integration and interoperation of the different kinds of e-learning systems in distributed environments and so educational organizations can expect the cost reduction in constructing e-learning systems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3308-3314
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• 2009

The purpose of this research is to develop a model of teacher education system in u-Learning age. To achieve the purpose, first, the current teacher education system was analyzed. Second, ubiquitous technologies which could be used in future teacher education system were analyzed. Third, functions of ubiquitous technologies supporting communication between the elements of teacher education system were explored based on the analysis of the technologies. Forth, researchers analyzed pre-service teachers' understanding, needs, and demands for the future teacher education system in u-Learning age. The model of teacher education system which is appropriate for u-Learning age was proposed based on the results of analyses. The developed model could help to educate pre-service teachers who will lead the education in u-Learning age.