• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.589-605
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• 1996

Recycling of used products and components has been considered as one of promising strategies for resolving environmental problems. In this respect, most manufacturing companies begin to consider possible recycling (e.q., reuse or re-production) of the components contained in their products. The primary objective of this research is to develop a multiple criteria decision making model for systematic management of recycle-oriented manufacturing components. The production planning problem of recycle-oriented manufacturing components is first formulated as a multiobjective mixed 0-1 integer programming model with three conflicting objectives. An interactive multiple criteria decision making method is then developed for solving the mathematical model. Also, an Input/Output analysis software is developed to help practitioners apply the model to real problems without much knowledge on computers and mathematical programming. A numerical example is used in examining the validity of the proposed model and to investigate the impact of the input variables on recycling production strategy.

• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1219-1225
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• 2017

In this work, a scalable algorithm for model calibration in nuclear engineering applications is presented and tested. The algorithm relies on the construction of surrogate models to replace the original model within the region of interest. These surrogate models can be constructed efficiently via reduced order modeling and subspace analysis. Once constructed, these surrogate models can be used to perform computationally expensive mathematical analyses. This work proposes a surrogate based model calibration algorithm. The proposed algorithm is used to calibrate various neutronics and thermal-hydraulics parameters. The virtual environment for reactor applications-core simulator (VERA-CS) is used to simulate a three-dimensional core depletion problem. The proposed algorithm is then used to construct a reduced order model (a surrogate) which is then used in a Bayesian approach to calibrate the neutronics and thermal-hydraulics parameters. The algorithm is tested and the benefits of data assimilation and calibration are highlighted in an uncertainty quantification study and requantification after the calibration process. Results showed that the proposed algorithm could help to reduce the uncertainty in key reactor attributes based on experimental and operational data.

• Structural Engineering and Mechanics
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• v.71 no.4
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• pp.329-339
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• 2019

Assessment of failure probability, especially for a complex structure, requires a considerable number of calls to the numerical model. Reliability methods have been developed to decrease the computational time. In this approach, the original numerical model is replaced by a surrogate model which is usually explicit and much faster to evaluate. The current paper proposed an efficient reliability method based on Monte Carlo simulation (MCS) and multi-gene genetic programming (MGGP) as a robust variant of genetic programming (GP). GP has been applied in different fields; however, its application to structural reliability has not been tested. The current study investigated the performance of MGGP as a surrogate model in structural reliability problems and compares it with other surrogate models. An adaptive Metropolis algorithm is utilized to obtain the training data with which to build the MGGP model. The failure probability is estimated by combining MCS and MGGP. The efficiency and accuracy of the proposed method were investigated with the help of five numerical examples.

• Earthquakes and Structures
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• v.16 no.2
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• pp.141-148
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• 2019

In this study, a well-established model and a new simplified version of it, that help avoid collapses in reinforced concrete structures during strong earthquakes, are presented and discussed. Using this model, the initial formation of plastic hinges and the final concentration of the damages only in beams are accurately assured. The model also assures that the columns and the beam-column joints can remain intact. This model can be applied for the design of modern R/C structures, as well as for the design of strengthening schemes of old R/C structures by the use of reinforced concrete jackets. The model can also predict the form of earthquake damages in old structures but also earthquake damages in the modern structures.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.12
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• pp.65-72
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• 2018

The purpose of this study is to predict vertical illuminance accurately at the design stage of a building without the help of simulation tools. Comparing two well-known vertical illuminance prediction algorithms with measured values, it is verified that the Igawa model is more consistent with the measured values than the Perez model. Using the DIVA program, we simulated the vertical illuminance at 30-degree intervals from south to north, compared with the vertical illuminance calculated with the Igawa model. The result of calculation values were verified from 120 degrees east to 120 degrees west. The vertical illuminance values with each of three shade devices were calculated using the Igawa model, and compared with the vertical illuminance simulated by DIVA program. As a result, all the errors when installing horizontal / vertical / grid shade divices were included in the error standard specified by ASHRAE.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.223-229
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• 2022

Early detection can help prevent heart disease, which is one of the most common reasons for death. This paper provides a clinical support model for predicting cardiac disease. The model is built using two publicly available data sets. The admissibility and application of the the model are justified by a sequence of tests. Implementation of the model and testing are also discussed

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.577-578
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• 2022

This study addresses the effectiveness of a VR model that is designed to relieve distress and improve communication during pediatric dental treatment for 3-9 year-old young patients. This is due to the challenging nature of the dental treatment for both young patients and pediatric dentists. The proposed model is designed to alleviate the unpleasant experience a young patient might go through during the dental treatment through providing a VR content to be displayed in on a head-mounted device during the treatment session. In addition, the model is designed to help decrease the frequent movement of the young patient during the dental treatment session by directing the patient's head to the immersive VR content. Furthermore, the model could solve the communication difficulties that might occur between pediatric dentists and young patients during the treatment session by showing live instructions related to adjusting body postures

• Trans-
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• v.14
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• pp.65-92
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• 2023

The knowledge of the narrative agent not only constitutes the content and meaning of the narrative itself, but is also closely related to the emotional response of the recipient. Also, the disparity of knowledge between narrative agents is an important factor in making a narrative richer and more interesting. But It tends to be treated as a sub-topic of narration theory or genre/style studies rather than an independent subject of narrative studies or criticism. In this paper, I propose a model that can systematically and quantitatively analyze the knowledge of narrative agents. The proposed model consists of the knowledge structure that represents a narrative, the knowledge state that expresses the knowledge of narrative agent as a degree of belief, and the knowledge flow that means changes in the knowledge state according to the development of events. In addition, the formal notation of the knowledge structure and a probabilistic inference model that could obtain the state of knowledge were proposed, and the knowledge structure and knowledge flow were analyzed by applying the model to the actual narrative. It is expected that the proposed model will be of practical help in the creation and evaluation of narratives.

• Journal of Engineering Education Research
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• v.17 no.4
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• pp.58-61
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• 2014

This paper introduces a tool for effective teaching of introductory computer programming. In order for the class to be effective, we try to attain attention, relevance, confidence, and satisfaction based on Keller's ARCS model. A web-based tool is developed to help both the students and the instructors.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.140-146
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• 2020

Since the current crime prevention systems have a standard mechanism that victims request for help by themselves or ask for help from a third party nearby, it is difficult to obtain appropriate help in situations where a prompt response is not possible. In this study, we proposed and developed an automatic rescue request model and system using Deep Learning and OpenCV. This study is based on the prerequisite that immediate and precise threat detection is essential to ensure the user's safety. We validated and verified that the system identified by more than 99% of the object's accuracy to ensure the user's safety, and it took only three seconds to complete all necessary algorithms. We plan to collect various types of threats and a large amount of data to reinforce the system's capabilities so that the system can recognize and deal with all dangerous situations, including various threats and unpredictable cases.