• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.806-811
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• 2007

In this study, we propose a more systematic design process for the structure-borne noise. The proposed way consists of 4 steps: Problem definition, Cause analysis, Development of counter-measure and Validation. Especially, we improved the second step: Cause analysis. According to the PCA(Panel Contribution Analysis), a reduction in vibration of the panels of which panel contribution is positive and larger, results in a reduction in structure-borne noise. We have, however, met the case in which the concept of PCA is no valid in a few vehicle tests. In order to understand this phenomenon, we compared the major panels selected by PCA with the one chosen by DSA(Design Sensitivity Analysis). After investigating the difference between the two results, a more improved process is suggested. The proposed one for the second step in the design process consists of not only the previous way: PCA with deformation analysis results but also DSA. It is finally validated that the proposed design process decreases the sound pressure of the concerned noise transfer function more than 3.5 dB.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.694-698
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• 2006

This paper presents an optimal design process using beads on a body panel to improve interior noise of a passenger vehicle. Except modification of structural members, it is difficult to find effective countermeasures that can work for the intermediate frequency range from 100 Hz to 300 Hz which lies between the booming and low medium frequency. In this study, it is a major goal to find additional counter-measures for this intermediate frequency range by performing optimal design of beads on body panels. The proposed method for design optimization consists of 4 sub-steps, that is, a) problem definition, b) cause analysis, c) countermeasure development and d) validation. The objective function is minimization of interior noise level. The major design variables are the geometrical shape of a bead and combination of beads on the critical panels. Sensitivity analysis and optimization are performed according to the predefined process for an optimal design. It is verified that the proposed design decreases the level of noise transfer function above 5 dB.

• Journal of the Korean Society of Systems Engineering
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• v.13 no.1
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• pp.15-23
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• 2017

This paper describes verification of APR1400 reactor vessel by applying the system engineering approach, in which the design re-engineering method is used to check the design parameters of APR1400 RV (reactor vessel). The RV is classified as safety class 1 and therefore must adhere strictly to the rules of ASME BPVC section III, subsection NB and seismic category I. This study explores designing the RV by following the ASME guidelines and making a comparative study with the current design. To meet this objective we apply system engineering methodologies to structure the process and allow for verification and validation of the major RV design parameters such as thickness of RV. The structural thicknesses of various part of RV are determined as well as reinforcements on the RV major nozzles. A 3D virtual reality model was created based on the design parameters using CATIA V5 and animation using Dassault Composer V2016. A comparison of re-engineered ARP1400 RV and standard APR1400 RV was done to show which design parameters were taken more conservative approach.

• Journal of the Korean Society of Systems Engineering
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• v.12 no.2
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• pp.101-114
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• 2016

A model-based systems engineering (MBSE) approach to implementing hardware-based network cybersecurity controls for APR1400 non-safety data network is presented in this work. The proposed design was developed by implementing packet filtering and deep packet inspection functions to control the unauthorized traffic and malicious contents. Denial-of-Service (DoS) attack was considered as a potential cybersecurity issue that may threaten the data availability and integrity of DCS gateway servers. Logical design architecture was developed to simulate the behavior of functions flow. HDL-based physical architecture was modelled and simulated using Xilinx ISE software to verify the design functionality. For effective modelling process, enhanced function flow block diagrams (EFFBDs) and schematic design based on FPGA technology were together developed and simulated to verify the performance and functional requirements of network security controls. Both logical and physical design architectures verified that hardware-based cybersecurity controls are capable to maintain the data availability and integrity. Further works focus on implementing the schematic design to an FPGA platform to accomplish the design verification and validation processes.

• The Journal of the Korea Contents Association
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• v.13 no.1
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• pp.467-481
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• 2013

The smart education recognized new educational paradigm is going to be introduced in earnest in the field from 2015. We develop the instructional design model for smart education to be able to provide a guide to design the instruction which has a speciality compared to current instruction. Furthermore, we did an expert assessment to recognize the validation and applicability of developed instructional design model and then apply this model to the field. As a result, we drew a positive conclusion for this instructional design model, which provides a base of systemic instructional design in smart education environment and focuses on expanding the ability of students living on 21st century. We expect that this study will be useful for teachers in the field who perform the smart education.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.3
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• pp.205-213
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• 2023

An optimal design of a simple beam-shaped flexure hinge mount supporting an optical mirror is presented. An optical mirror assembly is an opto-mechanically coupled system as the optical and mechanical behaviors interact. This side-supporting mount is flexible in the radial direction and rigid for the remaining degrees of freedom to support the mirror without transferring thermal load. Through thermo-elastic, optical and eigenvalue analysis, opto-mechanical performance was predicted to establish the objective functions for optimization. The key design parameters for this flexure are the thickness and length. To find the optimal values of design parameters, response surface analysis was performed using the design of experiment based on nested FCD. Optimal design candidates were derived from the response surface analysis, and the optimal design shape was confirmed through Opto-mechanical performance validation analysis.

• Educational Technology International
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• v.17 no.1
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• pp.1-37
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• 2016

The purpose of this study was to develop a MOOC design model that would improve the current practice of MOOC development in Korea by specifying easy-to-use course development procedures and guiding strategies. Following Richey and Klein (2007)'s conceptual model development procedure, the first step was to perform critical review of relevant literature and observe typical MOOC development processes. As a result, the initial model was developed. The second step was to conduct the expert review with five educational technology and MOOC researchers to secure the internal validity of the model. Based on the experts' suggestions, the model was revised and once again reviewed by the same experts. This process resulted in the development of the 2^nd version of model. The third step was to carry out external validation research in order to test the effectiveness, efficiency, and usability of the model. A basic model may be confirmed or corrected based on examination of its results. Consequently, the model was elaborated as the final model. In the final model, 6 procedural phases and 9 specific steps were included. The six procedural phases are: Analysis (1st Iteration), Design, Development (Course Development), Implementation, Evaluation, and Analysis (2nd Iteration), a slight variation of ADDIE model. The specific steps include: 1) Goal Setting, 2) Environment Analysis, 3) Content Design, 4) Style Design, 5) Course Development, 6) Implementation Plan, 7) Course Implementation, 8) Summative Evaluation, and 9) Need Reflection. The study concluded with suggestions for further research and application of the MOOC design model.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.56 no.3
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• pp.230-237
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• 2020

In order to design a diesel engine system and predict its performance, it is necessary to analyze the gas flow of the intake and exhaust system. A gas flow analysis in three-dimensional (3D) format needs a high-resolution workstation and enormous time for analysis. Therefore, the method of characteristics (MOC) was used for a gas flow analysis with a fast calculation time and a low-resolution workstation. An experiment was conducted on a single cylinder diesel engine to measure pressure in cylinder, intake pipe and exhaust pipe. The one-dimensional (1D) gas flow was analyzed under the same conditions as the experiment. The engine speed, valve timing and compression ratio were the same conditions and the intake pressure was inputted as the experimental results. Bent pipe such as an exhaust port that cannot be realized in 1D was omitted. As results of validation, the cylinder pressure showed accuracy, but the exhaust pipe pressure exhibited inaccuracy. This is considered as an error caused by the failure to implement a bent pipe such as an exhaust port. When analyzed in 3D, calculation time required 61 hours more based on a model of this study. In the future, we intend to implement a bent pipe that cannot be realized in 1D using 3D and prepare a method to supplement reliability by using 1D-3D coupling.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.16 no.3
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• pp.315-330
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• 2018

A subcritical facility must ensure nuclear criticality safety under all circumstances. For this purpose, it is essential to have a procedure to validate that calculated values do not exceed upper subcritical limit (USL), determined by quantifying the bias and uncertainty. However, there are several validation methodologies of nuclear criticality safety and these can yield different USL. Therefore, it is necessary to analyze the validity of the methodologies to establish one methodology that can provide the most appropriate USL. In this study, two documents, a guide for validation of nuclear criticality safety calculational methodology (NUREG/CR-6698) and a criticality benchmark guide for light water reactor fuel in transport and storage package (NUREG/CR-6361), are compared and analyzed. In particular, the methodology in NUREG/CR-6361 is applied to the USLSTATS code. However, the analysis results show that the methodology in NUREG/CR-6698 is more appropriate, for several reasons. This is applied to decision of USL to design casks using SCALE code version 6.1.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.949-958
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• 2004

race detection using a boosting based algorithm requires a very large size of face and nonface data. In addition, the fact that there always occurs a need for adding additional training data for better detection rates demands an efficient incremental teaming algorithm. In the design of incremental teaming based classifiers, the final classifier should represent the characteristics of the entire training dataset. Conventional methods have a critical problem in combining intermediate classifiers that weight updates depend solely on the performance of individual dataset. In this paper, for the purpose of application to face detection, we present a new method to combine an intermediate classifier with previously acquired ones in an optimal manner. Our algorithm creates a validation set by incrementally adding sampled instances from each dataset to represent the entire training data. The weight of each classifier is determined based on its performance on the validation set. This approach guarantees that the resulting final classifier is teamed by the entire training dataset. Experimental results show that the classifier trained by the proposed algorithm performs better than by AdaBoost which operates in batch mode, as well as by ${Learn}^{++}$.