• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.760-765
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• 2013

An Interline Power Flow Controller (IPFC) is a converter based controller which compensates and balance the power flow among multi-lines within the same corridor of the multi-line subsystem. The Interline Power Flow Controller consists of a voltage source converter based Flexible AC Transmission System (FACTS) controller for series compensation. The reactive voltage injected by individual Voltage Source Converter (VSC) can be controlled to regulate active power flow in the respective line in which one VSC regulates the DC voltage, the other one controls the reactive power flows in the lines by injecting series active voltage. In this paper, a circuit model for IPFC is developed and simulation of interline power flow controller is done using the proposed circuit model. Simulation is done using MATLAB Simulink and PSPICE. The results obtained by MATLAB are compared with the results obtained by PSPICE and compared with theoretical values.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.1
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• pp.57-68
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• 2014

This paper propose a development of squirrel-cage induction motor(IM) for 5-phase 1.5kW, 220V, 60Hz in order to study a polyphase ac machinery that keep hold of advantages more than traditional three-phase a IM, such as reducing a amplitude of torque pulsation, decreasing electric noises, and increasing the reliability. Designed methods of the motor use a development tools with Maxwell 2D and Simplorer program. There are designed drawing of manufactured frames of the IM. amplitude and waveform of the generated electromotive force, FFT analysis of harmonics within output voltages and current, and reviewing a experiment results are shown by variable output. We are presenting a design and manufacture methods for the 5-phase squirrel-cage IM.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.9
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• pp.53-61
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• 2013

This paper propose a improved operating characteristics of the 5-phase 5kW within developed the surface permanent mount synchronous generator (SPMSG) in order to make a study of a polyphase ac motors keeping hold of more advantages. The developed manufacturing motor was necessary to do improvement of voltage regulation, efficiency, operating characteristics, and so on at the rated load. There are remake a redesigned and distributed stator winding connection without changing the frames of stator and rotor core in previous established generator by a repeat tests. There are shown a amplitude and waveform of the generated electromotive force, FFT analysis of harmonics within output voltages, and reviewing a experiment results in load of resistive and 5-phase induction motor by variable generator output frequency.

• Proceedings of the KSRS Conference
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• 2000.04a
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• pp.55-60
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• 2000

인공위성의 Radar Altimeter 자료를 통해 국지적인 중력이상을 조사하기 위하여 ERS-1 Altimeter를 이용하였다. ERS-1 Radar Altimeter는 조밀하게 인접한 데이터 간격(~8km)을 갖고 있어서 전지구적 규모뿐만 아니라 국지적인 연구에도 적합하다. 연구대상지역은 세 개의 판이 만나서 지진과 화산활동이 활발하게 진행되는 필리핀판 지역(동경1$10^{\circ}$~150$^{\circ}$, 북위 0$^{\circ}$~30$^{\circ}$)을 선정하였다. 이 지역에 대한 해저의 지형과 중력 이상 분석을 통해 판구조 운동의 여러 증거를 파악할 수 있다. ERS-1 Radar Altimeter를 통해 얻어진 지오이드 높이(Geoid geight)는 후리-에어 중력이상(Free-air gravity anomaly)으로 쉽게 전환시킬 수 있다. 본 연구에서는 Fast Fourier Transform(FFT)을 이용하여 지오이드기복을 직접 후리-에어 중력이상으로 전환시키는 Direct conversion method를 사용하였다. 후리-에어 중력이상은 지각평형과 직접적으로 연관되어 지각보상의 정도를 파악할 수 있게 하며 일반적으로 해양의 분지는 지각평형상태로 있어서 평균적인 중력이상은 0mgal 근처로 나타난다. 그러나 본 연구에서 살펴본 국지적인 후리-에어 중력이상은 판구조론과 관련한 해구난 호상열도에서는 해양분지에서의 평균적인 값과 다른 중력이상의 양상을 나타내었다.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.531-546
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• 2009

The main objective of this study is to analyse the polarimetric characteristics of the various terrain targets by ground-based polarimetric SAR system and to confirm the compatible and effective polarimetric analysis method to reveal the polarization properties of different terrain targets by the GB-SAR. The fully polarimetric GB-SAR data with HH, HV, VH, and VV components were focused using the Deramp-FFT (DF) algorithm. The focused GB-SAR images were processed by the H/A/$\alpha$ polarimetric decomposition and the combined H/$\alpha$ or H/A/$\alpha$ and Wishart classification method. The segmented image and distribution graphs in H/$\alpha$ plane using Cloude and Pottier's method showed a reliable result that this quad-polarization GB-SAR data could be useful to classified corresponding scattering mechanism. The H/$\alpha$-Wishart and H/A/$\alpha$-Wishart classification results showed that a natural media and an artificial target were discriminated by the combined classification, in particular, after applying multi-looking and the Lee refined speckle filter.

• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.566-574
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• 2021

The arc is one of factors causing electrical fires. Over past decades, various researches have been carried out to detect arc occurrences. Even though frequency analysis, wavelet and statistical features have been used, arc detection performance is degraded due to diverse arc waveforms. On the contray, Deep neural network (DNN) direcly utilizes raw data without feature extraction, based on end-to-end learning. However, a disadvantage of the DNN is processing complexity, posing the difficulty of being migrated into a termnial device. To solve this, this paper proposes an arc detection method using a logistic regression that is one of simple machine learning methods.

• Journal of IKEEE
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• v.22 no.4
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• pp.1195-1201
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• 2018

This paper interprets the relationship between the physical activity of the human and the signal of the brain to show the meaningful results in the process of sending and receiving information to the connected muscles. When a person works or thinks, a specific brain signal is generated from the brain and being trasmmited to the connected part. The EMG signal, which has muscle activity information, outputs the result of the muscle activation as an electrical signal, which outputs muscle activity information usually due to muscle contraction and relaxation. The purpose of this study is to analyze the relationship between the two signals, which are difficult to identify easily by visual data extraction and data acquisition by extracting such EMG and EMG in real time.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.8
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• pp.25-32
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• 2021

Washboarding is a crucial problem occurring on unpaved roads. This phenomenon involves the formation of ripples on the surface of the unpaved road and causes a critical problem to vehicles and riders. The phenomenon is affected by several parameters, but we focused on the velocity and the size of the rotating body. In the precedent research, we observed that a critical velocity existed for the occurrence of the phenomenon, and the phenomenon's grade was related to the velocity. Therefore, this study, using a discrete element method, aimed to analyze the relation between the velocity and the size of the rotating body for the occurrence of the phenomenon and perform a fast Fourier transform (FFT) analysis to determine the correlation between the phenomenon and the period. The study observed that the critical velocity could vary from the velocity and the size of the rotating body, and there was a certain range of frequency for the occurrence of the Washboarding phenomenon.

• Journal of the Korean Society of Visualization
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• v.18 no.3
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• pp.103-108
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• 2020

RBC (red blood cell) deformability is one of factors inducing blood shear thinning effect. Reduction of RBC deformability increases blood viscosity in high shear region. In this study, 3D printed chip with proper distribution of wall shear rate (WSR) was proposed to measure RBC deformability of blood samples. To fabricate 3D printed chip, the design of 3D printed chip determined through numerical simulation was modified based on the resolution of the 3D printer. For the estimation of pressure drop in the 3D printed chip, two bypass outlets with low and high WSR are exposed to atmospheric pressure through the needles. By positioning the outlet of needles in the gravity direction, the formation of droplets at bypass outlets can be captured by smartphone. Through image processing and fast Fourier transform (FFT) analysis, the frequency of droplet formation was analyzed. Since the frequency of droplet formation is related with the pressure at bypass, high pressure drop caused by reduction of RBC deformability can be estimated by monitoring the formation of blood droplets using the smartphone.

• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.