• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.1
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• pp.7-13
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• 2011

Accurate response analysis of long span bridges subjected to seismic excitation is important for earthquake hazard mitigation. In this paper, the performance of a typical four span continuous reinforced concrete bridge model subjected to asynchronous multiple seismic excitations at the supports is investigated in both the time and frequency domains and the results are compared with that from a relevant uniform support excitations. In the time domain analysis, a linear modal superposition approach is used to compute the peak response values. In the frequency domain analysis, linear random vibration theory is used to determine the root mean square response values where the cross correlation effects between the modal and the support excitations on the seismic response of the bridge model are included. From the two sets of results, a practical range of peak factors which are defined to be the ratio of peak and the root mean square responses are suggested for displacements and forces in members. With reliable practical values of peak factors, the frequency domain analysis is preferred for the performance based design of bridges because of the computational advantage and the generality of the results as the time domain analysis only yields results for the specific excitation input.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.9
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• pp.1029-1039
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• 1992

This paper presents the filtering method which is processed on the frequency domain among Hangul character recognition methods. It is processed the Hangul character parrern with spatial positioning modulation and mapped the Hangul character element which have spatial position variant feature onto frequency domain, at this time, normalized spatial position and so normalized the character size in frequency domain. And it is grouped the Hangul character element according to the generating position and set the standard pattern, and used each standard character element pattern with character element filter and filtering the character pattern of Hangul character, it is derived the normalized cross correlation function and the coherence function led to the filtering results, and calculated classification threshold.

• The Journal of the Acoustical Society of Korea
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• v.17 no.2E
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• pp.38-46
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• 1998

In this paper, we propose a remote speech input device, a new method of user-friendly speech input in spontaneous speech recognition system. We focus the user friendliness on hands-free and microphone independence in speech recognition applications. Our method adopts two algorithms, the automatic speech detection and the microphone array delay-and-sum beamforming (DSBF)-based speech enhancement. The automatic speech detection algorithm is composed of two stages; the detection of speech and nonspeech using the pitch information for the detected speech portion candidate. The DSBF algorithm adopts the time domain cross-correlation method as its time delay estimation. In the performance evaluation, the speech detection algorithm shows within-200 ms start point accuracy of 93%, 99% under 15dB, 20dB, and 25dB signal-to-noise ratio (SNR) environments, respectively and those for the end point are 72%, 89%, and 93% for the corresponding environments, respectively. The classification of speech and nonspeech for the start point detected region of input signal is performed by the pitch information-base method. The percentages of correct classification for speech and nonspeech input are 99% and 90%, respectively. The eight microphone array-based speech enhancement using the DSBF algorithm shows the maximum SNR gaing of 6dB over a single microphone and the error reductin of more than 15% in the spontaneous speech recognition domain.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.33-38
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• 2020

Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.71-77
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• 2018

The optical time domain reflectometer (OTDR) is the most widely used method to monitor problems with currently installed optical fibers. The OTDR is an instrument designed to test the FTTx network and evaluates the physical properties of the fiber, such as transmission loss and connection loss. It is important to improve the spatial resolution in order to accurately grasp the optical path problems by using the OTDR. When the pulse width is less than twice the distance between the two reflectors, the signals reflected from the two reflectors are reflected without overlap, so that the reflected signal can be distinguished. However, when the pulse width is larger than twice the distance between the two reflectors, so that the reflected signal can not be distinguished. In order to overcome these limitations, this paper proposed a method of improving spatial resolution by applying a super resolution algorithm. As a result of the simulation, the resolution is improved when the super resolution algorithm is applied, and the event interval can be analyzed more precisely.

• Geophysics and Geophysical Exploration
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• v.9 no.1
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• pp.102-107
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• 2006

Recently, rapid developments in computer hardware have enabled reverse-time migration to be applied to various production imaging problems. As a wave-equation technique using the two-way wave equation, reverse-time migration can handle not only multi-path arrivals but also steep dips and overturned reflections. However, reverse-time migration causes unwanted artefacts, which arise from the two-way characteristics of the hyperbolic wave equation. Zero-lag cross correlation with diving waves, head waves and back-scattered waves result in spurious artefacts. These strong artefacts have the common feature that the correlating forward and backward wavefields propagate in almost the opposite direction to each other at each correlation point. This is because the ray paths of the forward and backward wavefields are almost identical. In this paper, we present several tactics to avoid artefacts in shot-domain reverse-time migration. Simple muting of a shot gather before migration, or wavefront migration which performs correlation only within a time window following first arriving travel times, are useful in suppressing artefacts. Calculating the wave propagation direction from the Poynting vector gives rise to a new imaging condition, which can eliminate strong artefacts and can produce common image gathers in the reflection angle domain.

• Journal of electromagnetic engineering and science
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• v.18 no.2
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• pp.94-100
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• 2018

Partial discharges (PDs) are electrical sparks that occur inside insulation between two conducting electrodes and can lead to the disastrous failure of insulation systems. To determine the location of a PD, a distributed array of UHF PD sensors is used to detect the electromagnetic (EM) signals emitted from the PD source, and the localization of the PD source can be estimated using the time difference of arrival (TDOA) between EM signals captured by the UHF PD sensor array. There are four popular methods to estimate the TDOA-the first peak method, the cross-correlation method, the energy criterion method, and the average time window threshold method. In this work, we numerically investigate the influence of noise on estimating the TDOA for the four different methods. Numerical results show that the energy criterion method is more robust against noise than other methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.11
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• pp.1559-1563
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• 2014

Recently, the prevention of crime using CCTV draws special in accordance with the higher crime incidence rate. Therefore security systems like a CCTV with audio capability are developing for giving an instant alarm. This paper proposes a scream detecting engine from various ambient noises in real environment for surveillance systems. The proposed engine detects scream signals among the various ambient noises using the features extracted in time/frequency domain. The experimental result shows the performance of our engine is very promising in comparison with the traditional engines using the model based features like LPC, LPCC and MFCC. The proposed method has a low computational complexity by using FFT and cross correlation coefficients instead of extracting complex features like LPC, LPCC and MFCC. Therefore the proposed engine can be efficient for audio-based surveillance systems with low SNRs in real field.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2262-2264
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• 2003

본 논문에서는 도선의 결함 유무와 결함 위치를 측정하는 방법으로 널리 사용되고 있는 시간 영역 반사파 처리 기법(Time Domain Reflectometry : TDR)의 성능 향상을 위하여 가우시안 형태를 가지는 입력 신호와 상호 상관 관계 함수를 이용한 신호 처리 방법을 제안한다. 일반적으로 TDR은 입력 신호와 반사 신호의 시간 지연을 측정해서 결함 위치를 측정하게 되므로, TDR 방법으로 결함 위치를 측정하는데 있어 시간축 분해능의 정도에 따라 측정 방법의 성능이 크게 좌우된다. 따라서, 본 논문에서는 제한된 시간축 분해능에서 결함 위치 측정의 정확도를 향상시키기 위해 가우시안 형태를 갖는 입력 신호 및 반사 신호와의 상호 상관관계 함수를 사용한다. 한편, 실제 도선에 적용하여 기존의 TDR 방법과 측정 성능을 비교 분석함으로써 본 논문에서 제안하는 방법의 우수성을 검증한다.

• Wind and Structures
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• v.7 no.2
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• pp.107-130
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• 2004

This paper presents a time-domain approach for analyzing nonlinear random vibrations of long-span suspended cables under transversal wind. A consistent continuous model of the cable, fully accounting for geometrical nonlinearities inherent in cable behavior, is adopted. The effects of spatial correlation are properly included by modeling wind velocity fluctuation as a random function of time and of a single spatial variable ranging over cable span, namely as a one-variate bi-dimensional (1V-2D) random field. Within the context of a Galerkin's discretization of the equations governing cable motion, a very efficient Monte Carlo-based technique for second-order analysis of the response is proposed. This procedure starts by generating sample functions of the generalized aerodynamic loads by using the spectral decomposition of the cross-power spectral density function of wind turbulence field. Relying on the physical meaning of both the spectral properties of wind velocity fluctuation and the mode shapes of the vibrating cable, the computational efficiency is greatly enhanced by applying a truncation procedure according to which just the first few significant loading and structural modal contributions are retained.