• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.1
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• pp.21-26
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• 2006

This paper deals with the dynamic analysis and experiments of moving-magnet linear actuator with/without spring. On the basis of two dimensional (2-D) analytical solutions and experiments, control parameters such as thrust constant, back-emf constant, inductance and resistance are obtained. And then, dynamic simulation algorithm is established from the voltage and motion equation. Finally, for various values of frequency, dynamic simulation results for characteristics of current and displacement of moving-magnet linear actuator with and without spring are presented and confirmed through the experiments. In particular, This paper applies the PWM voltage waveform obtained from a DSP for bidirectional voltage drive to the actuator.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4C
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• pp.229-238
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• 2006

Observed field behaviors are frequently different from the behaviors predicted in the design state due to several uncertainties involved in soil properties, numerical modeling, and error of measuring system even though a sophisticated numerical analysis technique is applied to solve the consolidation behavior of drainage-installed soft deposits. In this study, genetic algorithms are applied to back-analyze the soil properties using the observed behavior of soft clay deposit composed of multi layers that shows complex consolidation characteristics. Utilizing the program, one might be able to appropriately predict the subsequent consolidation behavior from the measured data in an early stage of consolidation of multi layered soft deposits. Example analyses for drainage-installed multi-layered soft deposits are performed to examine the applicability of proposed back-analysis method.

• Fashion & Textile Research Journal
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• v.25 no.5
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• pp.603-614
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• 2023

Body shape is the most influential factor in determining the quality of clothing fit. Women's body shape begins to change significantly in their mid-30s; therefore, this study aimed to classify and analyze the torso shapes of women aged 35-54 years. This study selected 200 3D body scans of women from the 8th Size Korea Survey database (2021). Using the Grasshopper algorithm developed in a previous study, 17 landmarks were automatically detected and 57 measurement values were generated. Using principal component analysis, 11 components (overall body size, upper body length, back protrusion, upper body slope, neck position, neck inclination, hip length, bust prominence, abdominal prominence, shoulder slope, and buttock prominence) were extracted. Three torso types were identified using K-means cluster analysis. The three body types were significantly different on nine component scores. Among the three torso types, Type 1 (37.5%) has the longest upper body and the flattest back and hips. Type 2 (31.0%) has the most curved back and forward upper body. Its abdomen is the flattest, and its shoulders are the most sloped. Type 3 (31.5%) has the shortest upper body, the most protruding hips, and the largest overall body size. This paper proposes two discriminant functions for identifying a new person's torso type.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.4
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• pp.112-117
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• 2004

This paper describes the analysis of the UHF signal characteristics due to the partial discharge sources which can exist in a GIS. For the experiment, a model GIS was made and 5 types of discharge source were created as follows; corona discharge, surface discharge, void discharge, discharge due to free particle, discharge from floating electrode. The frequency spectra and the phase characteristics of UHF signals were induced by UHF signal analysis. The results were quantified to systematically adapt to analyze the PD sources in the GIS and utilized as algorithm data based on the neural network for Back-Propagation Algorithm with a multi-layer structure. The perception rate of the constructed algorithm showed approximately 94[%] and 82[%] in learning and testing data, respectively.

• Proceedings of the KSRS Conference
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• v.1
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• pp.200-203
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• 2006

Cloud detection algorithm is being developed as major one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-1R and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithm and preliminary test result of both algorithms.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.231-236
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• 2001

The goal of multiphase optimization of machine structure is to obtain 1) light weight, 2) statically and dynamically rigid structure. The entire optimization process is carried out in two phases. In the first phase, multiple optimization problem with two objective functions is treated using pareto genetic algorithm. Two objective functions are weight of the structure, and static compliance. In the second phase, maximum receptance is minimized using genetic algorithm. The method is applied to design of quill type machine structure with back column.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.409-417
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• 2024

Excellent apparel design can increase market competitiveness. This article briefly introduced the theory of fractals and its application in the field of apparel design. The convolutional neural network (CNN) algorithm was used to assist in the evaluation of apparel designs. In the case analysis, the accuracy of the evaluation was validated by comparing the CNN algorithm with two other intelligent algorithms, support vector machine (SVM) and back propagation (BP). The evaluation of the proposed design showed that compared with SVM and BP algorithms, the CNN algorithm had higher accuracy in evaluating apparel designs. The evaluation result of the proposed apparel design not only further verifies the effectiveness of the CNN algorithm, but also demonstrates that the theory of fractals can be effectively applied in apparel design to provide more innovative designs.

• Journal of the Society of Disaster Information
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• v.15 no.4
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• pp.570-577
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• 2019

Purpose: The purpose of this study is to develop a system that enables quick on-site response using real-time decision-making by sharing the results of measurement and management performed in the field for safe temporary construction. Method: It is possible to take preemptive responses during construction by identifying the safety factors of construction conditions from measurement results and determining the risk factors such as soil properties and variability of climate change that can occur during construction by simultaneously using the back analysis method reflected in the measurement system and structural review. Result: we developed a back analysis algorithm of the SUNEX program to cope with the discrepancies between the design results and measured results due to inconsistency between site conditions and design properties, unexpected loads, and outdoor environment. The process of matching the measurement result with the analysis result can be confirmed in the safety management system. Conclusion: Gateway was used to communicate with real-time measurement results and safety management system program. It was made possible to preemptively respond to risk factors that may occur in the field.

• Journal of computational fluids engineering
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• v.14 no.1
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• pp.53-61
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• 2009

The objective of this research is to design blade planform to reduce high speed impulsive(HSI) noise from a non-lifting helicopter rotor using CFD method and optimization techniques. As for the aero-acoustic analysis, CFD technique for aerodynamic analysis and Kirchhoff's method for the acoustic analysis were used. As for the optimization method, Kriging-based genetic algorithm(GA) model as a high-fidelity optimization method was chosen. Design variables and constraints are determined for arbitrary blade planform. The result shows that the optimized blade planform with high swept-back and taper ratio can reduce HSI noise by suppressing generation of the strong shock wave on blade surface and propagation of the noise to the farfield flow region.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.367-372
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• 2006

Cloud detection algorithm is being developed as primary one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-IR and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithms and preliminary test results of both algorithms.