• Proceedings of the KSRS Conference
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• v.1
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• pp.212-215
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• 2006

This paper describes several reliability models to estimate COMS ground segment availability and shows assessed availability according to GS function. Due to a back-up concept among three ground center, SOC will have all H/W and S/W module to be installed in MSC and KOSC site. Therefore, all configurations and availability parameters for H/W and S/W modules in MSC and KOSC are assumed as equal with those in SOC, if related modules have same function. Prior to access availability over COMS GS function, Availability related to fundamental configuration such as series, parallel, partial operation, and module combined H/W and S/W is described. Consequently, all functions are expected to operate with more than 99% of availability.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2010-2014
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• 2005

Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.

• Korean Journal of Computational Design and Engineering
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• v.14 no.1
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• pp.10-17
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• 2009

Surface detection and parameter estimation in point cloud is a relevant subject in CAD/CAM, reverse engineering, computer vision, coordinate metrology and digital factory. In this paper we present a software for a fully automatic surface detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting, which work interactively. Our newly developed algorithms for orthogonal distance fitting(ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. We demonstrate the performance of the software on a variety of point clouds generated by laser radar, computer tomography, and stripe-projection method.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2009.05a
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• pp.74-77
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• 2009

Demands for high quality and productivity of precision mechanical parts are increasing greatly nowadays due to the rapid growth of information technologies and convergence industries. Therefore, core technologies for fabrication of precision mechanical parts are the fundamental issues, which are the precision machining, micro powder injection molding technologies, MR polishing, micro polymer processes, micro actuation modules and so on. These technologies are directly related to the mass production of high functional devices and machineries. Therefore, this study investigates the fabrication technologies of micro precision molds for advanced devices for possible commercialization in a near future.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.183-185
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• 1995

We present a visual programming system for signal analysis; it allows the user to construct signal processing algorithms by assembling visually fundamental signal processing blocks, and to observe the processed signals as well as the original signals in detail by magnifying a portion and measuring the time interval and amplitude between two points. Each fundamental signal processing block is implemented as an independent dynamically linked library module. Therefore, the user can expand the system processing capability by simply adding dynamically linked library modules without restructuring the entire system.

• Child Health Nursing Research
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• v.15 no.1
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• pp.15-23
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• 2009

Purpose: This research was done to provide fundamental data to improve learning methods in Pediatric nursing and meet the needs of the students in actual nursing by analyzing nurse student experiences with problem-based learning in Pediatric nursing. Method: Using the 31 Q-samples selected, 20 nursing students from J college were selected as p-samples. The students were personally interviewed in January or February 2008. Result: The result of the study showed 3 types. The first type was the "negative resister", who failed to adapt to the problem-based learning and resists negatively. The second type was the "active receiver", who participated in the process of the problem-based learning and received it actively. The third type was the "passive accepters", who accepted problem-based learning but worried because they were familiar only with traditional learning. Conclusions: In this study, problem-based learning was used for classes in the science of pediatric nursing. The findings indicate that preparation for learning and details should be considered when developing and using modules for pediatric nursing. Further study on the development of problem-based learning modules is also indicated.

• Educational Technology International
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• v.19 no.2
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• pp.175-197
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• 2018

Flipped classroom (FC) has gained attention as an active learning approach. Designing effective out-of-class activities to help prepare students for in-class activities is fundamental for successful implementation of FC. This study investigated the effectiveness of Goal-Based Scenarios (GBS) for out-of-class learning in FC. Four out of twelve units in a medical humanities course for Year 2 medical students was redesigned into a FC format, where e-learning modules were designed using a GBS approach for out-of-class activities and classroom debates were implemented for in-class activities. The other eight units were delivered in a conventional classroom debate format, which included reading text materials as pre-class assignments. A formative evaluation study was conducted using questionnaires and interview methods and students' academic achievements were evaluated by comparing their pre- and post-test scores between FC and conventional units. Students had positive perceptions of the e-learning modules in GBS approach and preferred the structure of learning in the FC format. Students' pre-test scores were slightly higher in the FC units, yet their post-test scores were comparable with conventional units. This study illustrates students' perceptions that the learning was bettered structured in FC and that the out-of-class learning using the GBS approach helped them better prepared for in-class activities.

• Journal of the Korean Solar Energy Society
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• v.27 no.1
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• pp.91-98
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• 2007

Perimeter zone is one of the weakest area in buildings and it makes an increase of heating and cooling loads, in addition to condensation or discomfort with cold-draft to residents in winter. Because of this, it needs to be reinforced by active systems. However, they use fossil fuel, and ultimately greenhouse effect is urged. Thus, we proposed BIPV system functioned as solar collector which can substitute active system. As an fundamental stage, heat balance equation in steady-state by Fortran was used not only, in winter for pre-heating effect and electric power capacity during the day, but also in summer, for the latter during the day and sky radiation effect during the night. Especially, we should have considered shading on PV by IES Suncast, since even a little bit of it makes the efficiency too low for the PV modules to work. As a result, in summer day, the PV panel should be tiled in 70 degrees to gain the most electric power. Moreover, we could verify that this model makes higher temperature and heat flux under 0.02 m/s. On the other hand, the PV had the high efficiency with high velocity because of cooling effect behind the PV. Therefore, we should regard the air current distribution later on.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.3 s.37
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• pp.39-46
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• 2006

This study is aimed at the development of ubiquitous based road disaster management system. The fundamental technologies used for developing this system are classified into three modules - wireless internet communication module, mobile module and server module. These fundamental technologies can be used not only for developing road disaster management system but also for developing various mobile or ubiquitous systems. With this system, workers can download many DB (Digital map, Attribute information etc.) from server to the field in realtime. The accuracy and objectivity of the DB could be improved with these informations collected at fields because these data can be used as basic data for road disaster information collection. Because in the web based server module - Web based Road Disaster Management System (URDMS) - field disaster information was showed link up with exist DB on road by absolute coordinate, the decision making with all of the field information was made and it sent to a field staff in realtime. The problems of current road disaster management rule ran be solved by this URDMS.