• KIEAE Journal
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• v.15 no.5
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• pp.95-105
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• 2015

Purpose: This paper presents a framework development for BIM (Building Information Modeling)-based OOPM (Object-Oriented Physical Modeling) for Building Thermal Simulation. The framework facilitates decision-making in the design process by integrating two object-oriented modeling approaches (BIM and OOPM) and efficiently providing object-based thermal simulation results into the BIM environment. Method: The framework consists of a system interface between BIM and OOPM-based building energy modeling (BEM) and the visualization of simulation results for building designers. The interface enables a BIM models to be translated into OOPM-based BEM automatically and the thermal simulation from the created BEM model immediately. The visualization module enables the simulation results to be presented in BIM for building designers to comprehend the relationships between design decisions and the building performances. For the framework implementation, we utilized the Modelica Buildings Library developed by the Lawrence Berkeley National Laboratory as a thermal simulation solver. We also conducted an experiment to validate the framework simulation results and demonstrate our framework. Result: This paper demonstrates a new methodology to integrate BIM and OOPM-based BEM for building thermal simulation, which enables an automatic translation BIM into OOPM-based BEM with high efficiency and accuracy.

• Transactions of the Korean Society of Mechanical Engineers
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• v.12 no.6
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• pp.1227-1237
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• 1988

A practically useful CAD system for mold design in the plastic injection molding processes has been developed. Even though many efforts have been tried to simulated the injection molding process, this is the first attempt toward an automatic mold design system instead of a manufacturing or a simulation system. In this system the computational routines, the data base for mold design, and the routines for three dimensional modeling are blended together so that the designed mold is obtained as a solid model. For this development, the following problems have been solved. First, the modeling capability of the plastic parts has been implemented by incorporating the modeling routines of a constructive solid geometric modeling system and developing a constant thickness modeling conditions, and that of standard mold bases have been established. Third, the experimental know-how and the empirical formulae have been collected and blended together with the modeling routines of a geometric modeling system to provide the high level commands for designing mold.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.510-518
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• 2020

With the promotion of digitalization in the construction industry, BIM has become an indispensable technology. On the other hand, it has not been actively utilized in practice because of the difficulty of BIM modeling. The reason is that 3D modeling is less productive not only because of the difficulty of learning BIM software but also the modeling work is done manually. Therefore, this study proposes a method and system that can improve the productivity of BIM-based modeling. For this reason, in the study, a slit caisson, which is a typical structure of a port, was selected as a development target, and various parameters were derived through interviews with experts so that it could be used in practice. This study presents a UI construction plan that considers user convenience for efficient management and operation of diverse and complex parameters. Based on this, this study used visual programming and Excel VBA to develop a BIM-based design automation system for slit caissons. The developed system can use many parameters to quickly develop slit caisson models suitable for various design conditions that can contribute to BIM-based modeling and productivity improvement.

• Korean Journal of Construction Engineering and Management
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• v.5 no.5 s.21
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• pp.151-162
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• 2004

Current methods for construction site modeling employ large, expensive laser range scanners that produce dense range point clouds of a scene from different perspectives. Days of skilled interpretation and of automatic segmentation may be required to convert the clouds to a finished CAD model. The dynamic nature of the construction environment requires that a real-time local area modeling system be capable of handling a rapidly changing and uncertain work environment. However, in practice, large, simple, and reasonably accurate embodying volumes are adequate feedback to an operator who, for instance, is attempting to place materials in the midst of obstacles with an occluded view. For real-time obstacle avoidance and automated equipment control functions, such volumes also facilitate computational tractability. In this research, a human operator's ability to quickly evaluate and associate objects in a scene is exploited. The operator directs a laser range finder mounted on a pan and tilt unit to collect range points on objects throughout the workspace. These groups of points form sparse range point clouds. These sparse clouds are then used to create geometric primitives for visualization and modeling purposes. Experimental results indicate that these models can be created rapidly and with sufficient accuracy for automated obstacle avoidance and equipment control functions.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.547-554
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• 2021

In order to quickly and accurately diagnose pneumonia on a chest X-ray image, different batch sizes of 4, 8, 16, and 32 were applied to the same Xception deep learning model, and modeling was performed 3 times, respectively. As a result of the performance evaluation of deep learning modeling, in the case of modeling to which batch size 32 was applied, the results of accuracy, loss function value, mean square error, and learning time per epoch showed the best results. And in the accuracy evaluation of the Test Metric, the modeling applied with batch size 8 showed the best results, and the precision evaluation showed excellent results in all batch sizes. In the recall evaluation, modeling applied with batch size 16 showed the best results, and for F1-score, modeling applied with batch size 16 showed the best results. And the AUC score evaluation was the same for all batch sizes. Based on these results, deep learning modeling with batch size 32 showed high accuracy, stable artificial neural network learning, and excellent speed. It is thought that accurate and rapid lesion detection will be possible if a batch size of 32 is applied in an automatic diagnosis study for feature extraction and classification of pneumonia in chest X-ray images using deep learning in the future.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.595-602
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• 2022

This study tried to suggest the most efficient learning rate for accurate and efficient automatic diagnosis of medical images for chest X-ray pneumonia images using deep learning. After setting the learning rates to 0.1, 0.01, 0.001, and 0.0001 in the Inception V3 deep learning model, respectively, deep learning modeling was performed three times. And the average accuracy and loss function value of verification modeling, and the metric of test modeling were set as performance evaluation indicators, and the performance was compared and evaluated with the average value of three times of the results obtained as a result of performing deep learning modeling. As a result of performance evaluation for deep learning verification modeling performance evaluation and test modeling metric, modeling with a learning rate of 0.001 showed the highest accuracy and excellent performance. For this reason, in this paper, it is recommended to apply a learning rate of 0.001 when classifying the presence or absence of pneumonia on chest X-ray images using a deep learning model. In addition, it was judged that when deep learning modeling through the application of the learning rate presented in this paper could play an auxiliary role in the classification of the presence or absence of pneumonia on chest X-ray images. In the future, if the study of classification for diagnosis and classification of pneumonia using deep learning continues, the contents of this thesis research can be used as basic data, and furthermore, it is expected that it will be helpful in selecting an efficient learning rate in classifying medical images using artificial intelligence.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.357-362
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• 2002

Most of battery industries are growing explosively as a core strategy industry for the development of the semi-conductor, the LCD, and the mobile communication device. In this thesis, dynamic characteristics of the steel can labeling machine on the automatic cell assembly line are studied. Dynamic characteristic analysis consists of dynamic behavior analysis and finite element analysis and is necessary for effective design of machines. In the dynamic behavior analysis, the displacement, velocity, applied force and angular velocity of each components are simulated according to each part. In the FEA, stress analysis, mode analysis, and frequency analysis are performed for each part. The results of these simulations are used for the design specification investigation and compensation for optimal design of cell manufacturing line. Therefore, Virtual Engineering of the steel can labeling machine on the automatic cell assembly line systems are modeled and simulated. 3D motion behavior is visualized under real-operating condition on the computer window. Virtual Prototype make it possible to save time by identifying design problems early in development, cut cost by reducing making hardware prototype, and improve quality by quickly optimizing full-system performance. As the first step of CAE which integrates design, dynamic modeling using ADAMS and FEM analysis using NASTRAN are developed.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.4
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• pp.16-21
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• 2011

This paper described an analysis of dynamic mechanism for the industrial two-step folding automatic door using commercial software packages. Two modeling types of operating on the guide rail, the sliding one and the rolling, were adopted to investigate effects of impact force when the door ascends the guide rail. The magnitude of impact force was found very peaklike large over an initial duration of the door's moving up. The amount of damping coefficient for alleviating this shock was controlled to such a moderate degree that the operating conditions can be obtained for the purpose of design. Moreover the behavior of both dynamic stress and deformation were observed for acquirement of structural reliabilities of the combined guide rail and rolling mechanism. This research will be a very useful tool in the near future for the dynamic analysis of the multi-step folding automatic door.

• Bulletin of the Korean Space Science Society
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• 2009.10a
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• pp.26.2-26.2
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• 2009

The Solar and Space Weather Research Group (SOS) in Korea Astronomy and Space Science Institute (KASI) is constructing the Space Weather Prediction Center since 2007. As a part of the project, we are developing automatic real-time system of the global 3-D magnetohydrodynamics (MHD) simulation. The MHD simulation model of earth's magnetosphere is designed as modified leap-frog scheme by T. Ogino, and it was parallelized by using message passing interface (MPI). Our work focuses on the automatic processing about simulation of 3-D MHD model and visualization of the simulation results. We used PC cluster to compute, and virtual reality modeling language (VRML) file format to visualize the MHD simulation. The system can show the variation of earth's magnetosphere by the solar wind in quasi real time. For data assimilation we used four parameters from ACE data; density, pressure, velocity of solar wind, and z component of interplanetary magnetic field (IMF). In this paper, we performed some initial tests and made a animation. The automatic real-time system will be valuable tool to understand the configuration of the solar-terrestrial environment for space weather research.

• Journal of Environmental Impact Assessment
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• v.20 no.3
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• pp.357-365
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• 2011

The QUAL2K has the same basic characteristics as the QUAL2E model, which has been widely used in stream water quality modeling; in QUAL2K, however, various functions are supplemented. The QUAL2Kw model uses a genetic algorithm(GA) for automatic calibration of QUAL2K, and it can search for optimum water quality parameters efficiently using the calculation results of the model. The QUAL2Kw model was applied to the Gangneung Namdaecheon River on the east side of the Korean Peninsula. Because of the effluents from the urban area, the middle and lower parts of the river are more polluted than the upper parts. Moreover, the hydraulic characteristics differ between the lower and upper parts of rivers. Thus, the river reaches were divided into seven parts, auto-calibration for the multiple reaches was performed using the function of the user-defined automatic calibration of the rates worksheets. Because GA parameters affect the optimal solution of the model, the impact of the GA parameters used in QUAL2Kw on the fitness of the model was analyzed. Sensitivity analysis of various factors, such as population size, crossover probability, crossover mode, strategy for mutation and elitism, mutation rate, and reproduction plan, were performed. Using the results of this sensitivity analysis, the optimum GA parameters were selected to achieve the best fitness value.