• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.2052-2053
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• 2007

Recently, the importance of supplying the reliable electric power is increasing. Breaking insulation of stator winding is major cause of fault in rotator machine. On-line PD detecting is useful technique to diagnose rotator machine. However, interpretation of its results in time domain is very complex because of the mixed results with PD(Partial Discharge) and noise signal. Therefore, the results were analyzed in frequency domain by FFT (Fast Fourier Transform) to detect precise PD signals. The purpose of this paper is to describe the optimal frequency range to discriminate the PD and noise signal.

• Journal of Broadcast Engineering
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• v.8 no.3
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• pp.278-287
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• 2003

Noise in an image degrades image quality and deteriorates coding efficiency. Recently, various edge-preserving noise filtering methods based on the nonstationary image model have been proposed to overcome this problem. In most conventional nonstationary image models, however, pixels are assumed to be uncorrelated to each other in order not to Increase the computational burden too much. As a result, some detailed information is lost in the filtered results. In this paper, we propose a computationally feasible adaptive noise smoothing algorithm which considers the nonstationary correlation characteristics of images. We assume that an image has a nonstationary mean and can be segmented into subimages which have individually different stationary correlations. Taking advantage of the special structure of the covariance matrix that results from the proposed image model, we derive a computationally efficient FFT-based adaptive linear minimum mean-square-error filter. Justification for the proposed image model is presented and effectiveness of the proposed algorithm is demonstrated experimentally.

• Composites Research
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• v.13 no.5
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• pp.1-9
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• 2000

An intensity-based optical fiber vibration sensor is applied to monitor the structural vibration and detect impact locations on a plate. Optical fiber vibration sensor is constructed by placing two cleaved fiber end, one of which is cantilevered in a hollow glass tube. The movement of the cantilevered section lags behind the rest of the sensor in response to an applied vibration and the amount of light coupled between the two fibers is thereby modulated. For vibration sensing, optical fiber vibration sensor is mounted on the carbon fiber composite beam and its response is investigated to free and forced vibration. In impact location detection, four optical fiber vibration sensors whose location is predetermined are placed at chosen positions and the different arrival times of impact-generated vibration signal are recorded by an FFT analyzer. Impact location can be calculated from these time delays. Experimental results show that optical fiber vibration sensor signals coincide with gap sensor in vibration sensing. The precise location of impact can be detected on an acrylate plate.

• Journal of Platform Technology
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• v.12 no.2
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• pp.12-22
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• 2024

With the rapid increase in digital data in modern society, digital forensics plays a crucial role, and file type identification is one of its integral components. Research on the development of identification models utilizing artificial intelligence is underway to identify file types swiftly and accurately. However, existing studies do not support the identification of file types with high domestic usage rates, making them unsuitable for use within the country. Therefore, this paper proposes a more accurate file type identification model using Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). To overcome limitations of existing methods, the proposed model demonstrates superior performance on the FFT-75 dataset, effectively identifying file types with high domestic usage rates such as HWP, ALZ, and EGG. The model's performance is validated by comparing it with three existing research models (CNN-CO, FiFTy, CNN-LSTM). Ultimately, the CNN and GRU based file type identification and classification model achieved 68.2% accuracy on 512-byte file fragments and 81.4% accuracy on 4096-byte file fragments.

• The Journal of the Acoustical Society of Korea
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• v.7 no.4
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• pp.31-53
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• 1988

In this work we study extensively the structures and performance characteristics of the block least mean-square (BLMS) adaptive digital filters (ADF's) that can be realized efficiently using the fast Fourier transform (FFT). The weights of a BLMS ADF realized using the FFT can be adjusted either in the time domain or in the frequency domain, leading to the time-domain BLMS(TBLMS) algorithm or the frequency-domain BLMS (FBLMS) algorithm, respectively. In Part Ⅰof the paper, we first present new results on the overlap-add realization and the number-theoretic transform realization of the FBLMS ADF's. Then, we study how we can incorporate the concept of different frequency-weighting on the error signals and the self-orthogonalization of weight adjustment in the FBLMS ADF's , and also in the TBLMS ADF's. As a result, we show that the TBLMS ADF can also be made to have the same fast convergence speed as that of the self-orthogonalizing FBLMS ADF. Next, based on the properties of the sectioning operations in weight adjustment, we discuss unconstrained FBLMS algorithms that can reduce two FFT operations both for the overlap-save and overlap-add realizations. Finally, we investigate by computer simulation the effects of different parameter values and different algorithms on the convergence behaviors of the FBLMS and TBLMS ADF's. In Part Ⅱ of the paper, we will analyze the convergence characteristics of the TBLMS and FBLMS ADF's.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.951-953
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• 2015

Learning for thinking says the behavior of the organism changes as a result of practice or experience. It is very difficult to identify focusing ability objectively when students study. But, brain of the body is not so. EEG signal means continuously electric records of brain potential variation between two points on the scalp when brain activities take place. In types of EEG, there are delta(0~4Hz), theta(4~8Hz), alpha(8~13Hz), beta(13~30Hz) and gamma waves(30~50Hz). SMR waves and Mid-beta waves appear when focused for studying. Part for the most influence on concentrating reported that Mid-beta waves. In relation to brain activities, EEG has been actively researched for evaluating brain focus index system during learning and study. So, By using Biopac system for this study, measured brain wave was converted into FFT for extracting Mid-beta domain signals that are related to learning after giving focus invoked subjects to a small number of people. When concentrating, we measured the change in the power of the Mid-beta frequency domain and presented a correlation. Based on these results, we analyzed whether students are concentrated objectively on learning or not. and hope to offer more efficient learning method.

• Journal of Biomedical Engineering Research
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• v.20 no.1
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• pp.81-90
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• 1999

This paper describes an EEG(electroencephalogram) software for two-channel cerebral function monitoring system to detect the cerebral ischemia. In the software, two-channel bipolar analog EEG signals are digitized and from the signals various EEG parameters are extracted and displayed on a monitor in real-time. Digitized EEG signal is transformed by FFT(Fast Fourier transform) and represented as CSA(compressed spectral array) and DSA(density spectral array). Additional 5 parameters, such as alpha ratio, percent delta, spectral edge frequency, total power, and difference in total power, are estimated using the FFT spectra. All of these are effectively merged in a monitor and displayed in real-time. Through animal experiments and clinical trials on men, the software is modified and enhanced. Since the software provides raw EEG, CSA, DSA, simultaneously with additional 5 parameters in a monitor, it is possible to observe patients multilaterally. For easy comparison of patient's status, reference patterns of CSA, DSA can be captured and displayed on top of the monitor. And user can mark events of surgical operation and patient's conditions on the software, this allow him jump to the points of events directly, when reviewing the recorded EEG file afterwards. Other functions, such as forward/backward jump, gain control, file management are equipped and these are operated by simple mouse click. Clinical tests in a university hospital show that the software responds accurately according to the conditions of patients and medical doctors can use the software easily.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.241-248
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• 2011

In this paper we proposed and implemented a diagnosis system to control nuclear fuel handling machine. The proposed system consists of data acquisition system, diagnosis algorithm and faults simulator. Since the test on real operation of the fuel handling machine is impossible, we evaluated the proposed system by diagnosis experiments using the faults simulator, with which test signals on abnormal states of the bearing ball and the inner race of the bearing are generated. The experiments showed that resulting diagnosis analysis are consistent with the theoretical expectations.

• Proceedings of the KIEE Conference
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• 2004.11d
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• pp.122-126
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• 2004

직격뇌가 높은 건축물에 치거나 인접 건물로부터 뇌전류가 유입되었을 때 잘못된 피뢰설비로 인한 피해는 매우 심각한 실정이다. 낙뢰가 치는 순간에 반도체와 같은 민감한 전자부품을 사용하는 전자 및 통신기기는 뇌전류로 인한 전자기장의 영향으로 오동작이 발생하거나 부품의 손상을 입기가 쉽다. 본 논문에서는 건축물 구조체에 직격뇌가 유입되었을 때 건축물 구조체 및 건물 주위에 나타나는 전위분포특성을 연구하였다. 본 논문에서 30m 높이 건축물의 상부 모서리와 중앙부 그리고 건축물 하부 모서리와 중앙부로 뇌전류가 유입된다고 가정하여 건축물의 전계분포특성을 시뮬레이션하였으며, 뇌전류는 2중 지수함수형태로 모의된 20kA 임펄스 서지 전류를 주입하였다. 뇌서지 전류의 주파수 특성은 Fast Fourier Transform(FFT)을 이용하여 얻었으며, 얻어진 주파수 값을 이용하여 건축물 구조체와 인접지역의 Scalar Potentials과 Electric Fields의 특성을 시뮬레이션하였다. 또한 철골 빔 건축물의 철골 빔에 직접 뇌전류가 유입되는 경우와 건물 하부의 접지전극에 뇌전류가 유입되는 경우로 분리 하여 연구하였다. 그 결과 뇌전류의 유입경로가 건축물의 모서리부분 보다는 중심부에 위치될 때 전위 및 전계 크기가 작았으며 건축 철골구조물보다 건축물 하부에 접지전극이 설치될 때 더 낮은 전계 값을 갖는 것을 확인하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4A
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• pp.303-311
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• 2011

In this paper, we propose the adaptive multibeamforming algorithm to remove the same subcarrier interference and multipath signals in the MIMO(Multi-Input Multi-Out)-OFDMA (Orthogonal Frequency Division Multiplexing Access)system allocated the same subcarrier partially in order to improve spectrum efficiency. In addition to removing the interference, we can get diversity gain and combat the detriment of the performance according to time delay by the proposed approach. BER performance improvement and combating the delay spread detrimental effects of the proposed approach is investigated through computer simulation by applying it to MIMO-OFDMA.