• 조명전기설비학회논문지
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• 제17권4호
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• pp.50-56
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• 2003

본 논문에서는 유입변압기 진단을 목적으로 부분방전시 발생하는 여러 가지 물리적 화학적 현상중 초음파를 검출하기 위하여 광섬유 Mach-Zehnder 간섭계를 구성하였다. 구성된 광섬유 센서의 초음파 검출 평가 실험을 실시하고, 절연유 중에 배치한 침-평판전극에 교류 고전압을 인가하여 방전시 발생하는 초음파를 측정하고 퓨리에 변환 및 wavelet 변환을 이용한 데이터 분석 결과를 나타냈다.

• 한국소음진동공학회논문집
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• 제13권6호
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• pp.474-483
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• 2003

In this study, the dynamic characteristics of a catenary system and pantograph supplying electrical power to high-speed trains are investigated. One of the most important issues accompanied by increasing the speed of high-speed rail is stabilization of current collection. To stabilize current collection, it is necessary the contact force between the catenary and the pantograph to be kept continuous without loss of contact. The analytical model of a catenary and a pantograph is constructed to simulate the behavior of an actual system. The analysis of the catenary based on the Finite Element Method (FEM) is performed to develop a catenary model suitable for high speed operation. The reliability of the models is verified by the comparison of the excitation test with Fast Fourier Transform (FFT) data of the actual system. The static deflection of the catenary, stiffness variation in contact lines, dynamic response of the catenary undergoing constant moving load, contact force, and each state of the pantograph model were calculated. It is confirmed that a catenary and pantograph model are necessary for studying the dynamic behavior of the pantograph system.

• 대한의용생체공학회:의공학회지
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• 제24권6호
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• pp.487-494
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• 2003

수면뇌파의 해석에 있어서 수면단계는 뇌파의 특성파 검출에 특히 중요하다. 수면단계는 여러 수면질환의 진단에 가장 기초적일 단서를 제공한다. 본 연구에서 수면뇌파 신호를 이산 웨이브렛 변환 뿐 만 아니라 퓨우리에 변환, 연속 웨이브렛 변환을 이용해서 해석하였다. 제안된 시스템 방범인 퓨우리에와 웨이브렛은 수면뇌파의 중요한 특성파(유파, 수면방추파, K복합, 구파 REM) 검출을 위해서 수면상태를 분석했다. 수면뇌파 분석에는 Daubechies 웨이브렛 변환 방법과 고속 퓨우리에를 이용했다. 모의실험결과 신경망 시스템이 특성 파형의 분류에 높은 성능을 발휘함을 알 수 있었다.

• Journal of Power Electronics
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• 제18권6호
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• pp.1670-1682
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• 2018

The further improvement of submarine propulsion is associated with the modularity of accumulator-fed inverters, such as cascaded inverters (CIs). CI technology guarantees smooth output voltages with reduced switch frequencies under linear loads. However, the output voltages of CIs are distorted under rectifier loads. This distortion requires harmonic suppression technology. One such technology is the repetitive controller (RC), which is commonly applied but suffers from poor performance in propulsion systems. In this study, the FFT spectrum of a CI under rectifier load is analyzed, and the harmonic contents are uneven in magnitude. For the purpose of harmonic suppression, the control gains at each harmonic frequency should be seriously considered. A RC with a specific harmonic gain compensation (SHGC) for CIs is proposed. This method provides additional control gains at low-order harmonic frequencies, which are difficult to achieve with conventional RCs. This SHGC consists of a band-pass filter (BPF) and proportional element and is easy to implement. These features make the proposed method suitable for submarine propulsion. Experimental results verify the feasibility of the improved RC.

• ETRI Journal
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• 제41권3호
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• pp.316-325
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• 2019

Accurate suppressive jamming is a prominent problem faced by radar equipment. It is difficult to solve signal detection problems for extremely low signal to noise ratios using traditional signal processing methods. In this study, a joint sensing dictionary based compressed sensing and adaptive iterative optimization algorithm is proposed to counter suppressive jamming in information domain. Prior information of the linear frequency modulation (LFM) and suppressive jamming signals are fully used by constructing a joint sensing dictionary. The jamming sensing dictionary is further adaptively optimized to perfectly match actual jamming signals. Finally, through the precise reconstruction of the jamming signal, high detection precision of the original LFM signal is realized. The construction of sensing dictionary adopts the Pei type fast fractional Fourier decomposition method, which serves as an efficient basis for the LFM signal. The proposed adaptive iterative optimization algorithm can solve grid mismatch problems brought on by undetermined signals and quickly achieve higher detection precision. The simulation results clearly show the effectiveness of the method.

• Smart Structures and Systems
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• 제29권1호
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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.

• Smart Structures and Systems
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• 제31권1호
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• pp.29-43
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• 2023

This study investigates the possibility of damage detection of a real bridge by means of a modal parameter-based finite element (FE) model update. Field moving vehicle experiments were conducted on an actual steel plate girder bridge. In the damage experiment, cracks were applied to the bridge to simulate damage states. A fast Bayesian FFT method was employed to identify and quantify uncertainties of the modal parameters then these modal parameters were used in the Bayesian model update. Material properties and boundary conditions are taken as uncertainties and updated in the model update process. Observations showed that although some differences existed in the results obtained from different model classes, the discrepancy between modal parameters of the FE model and those experimentally obtained was reduced after the model update process, and the updated parameters in the numerical model were indeed affected by the damage. The importance of boundary conditions in the model updating process is also observed. The capability of the MCMC model update method for application to the actual bridge structure is assessed, and the limitation of FE model update in damage detection of bridges using only modal parameters is observed.

• 대한조선학회지
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• 제27권2호
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• pp.87-95
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• 1990

본 논문에서는 충격시험에 의해 얻어진 구조물의 과도진동응답 신호로부터 구조물의 동특성치를 구하는 기존방법에 대하여 살펴보고 이들 기존 방법의 단점을 개선할 수 있는 방법으로 최대엔트로피방법(Maximum Entropy Method)과 최소자승 Prony법(Least Square Prony Method)를 도입하여 수치실험을 통한 성능시험을 수행하였다. 그리고 적용예로서 선박의 낙묘시험으로부터 얻은 시계열에 이들을 적용하여 그 결과 FFT법 결과와 비교하였다. 연구결과, 최대엔트로피방법은 구조물의 인접 고유진동수들이 가까이 있고 얻을 수 있는 동적응답 시계열의 data 길이가 짧을 때의 고유진동수 추정에 유용하나 감쇠비 산정에는 유용하지 못함이 밝혀졌다. 또한, 최소자승 Prony법은 과감쇠계의 고유진동수 및 감쇠비 추정에 유용하나 동적응답 시계열에 많은 잡음이 포함되어 있을 경우, 감쇠비 추정성능이 크게 저하되는 것으로 나타났다.

• 한국의학물리학회지:의학물리
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• 제8권1호
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• pp.75-82
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• 1997

인체의 생리기관의 운동은 매우 복잡하고 불규칙적인 운동을 보이고 있다. 특히 말초 혈관의 운동은 매우 민감하고 복잡한 운동특성을 보이고 있다. 그중에서도 당 (glucose)에 의한 운동은 매우 민감한 변화의 운동을 반응한다. 이런 운동을 분석하기에는 기존의 선형적인 분석방법으로 복잡한 혈류운동을 분석하고 예측하기에는 많은 문제점을 가지고 있다. 그래서 비선형적 운동계의 분석방법인 카오스이론의 시계열분석방법으로 분석하는 것이 적합하다. 이런 맥락으로 본 연구는 당의 주입에 의한 토끼의 말초혈류량의 스칼라적 데이터를 획득하여 시계열분석방법으로 다차원의 벡터로 재정의하여 말초혈관의 혈류운동이 카오스적 운동임을 재확인하고 비선형적분석방법의 적합성을 확인하고자 하였다. 그 결과 당 주입에 따른 혈당치의 변화에 따라 기존의 주파수분석 및 평균치분석에서 차이가 나타나지 않았지만 비선형적분석방법으로 분석한 결과 그 차이를 확인할수 있었고, 말초 혈류의운동이 카오스적현상을 보임을 확인하였다.

• 로봇학회논문지
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• 제7권3호
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• pp.171-181
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• 2012

Outdoor mobile robots are faced with various terrain types having different characteristics. To run safely and carry out the mission, mobile robot should recognize terrain types, physical and geometric characteristics and so on. It is essential to control appropriate motion for each terrain characteristics. One way to determine the terrain types is to use non-contact sensor data such as vision and laser sensor. Another way is to use contact sensor data such as slope of body, vibration and current of motor that are reaction data from the ground to the tire. In this paper, we presented experimental results on terrain classification using contact sensor data. We made a mobile robot for collecting contact sensor data and collected data from four terrains we chose for experimental terrains. Through analysis of the collecting data, we suggested a new method of terrain feature extraction considering physical characteristics and confirmed that the proposed method can classify the four terrains that we chose for experimental terrains. We can also be confirmed that terrain feature extraction method using Fast Fourier Transform (FFT) typically used in previous studies and the proposed method have similar classification performance through back propagation learning algorithm. However, both methods differ in the amount of data including terrain feature information. So we defined an index determined by the amount of terrain feature information and classification error rate. And the index can evaluate classification efficiency. We compared the results of each method through the index. The comparison showed that our method is more efficient than the existing method.