• The Journal of Engineering Geology
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• v.14 no.2
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• pp.147-168
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• 2004

Purpose of this study is to quantitatively characterize the fracture roughness which was measured with a confocal laser scanning microscope. The roughness discrete data measured by confocal laser microscope were analyzed by spectral analysis and fast Fourier transform (FFT).The roughness data by used noise reduction filter were applied for fractal analysis to describe roughness features quantitatively. Artificial fractures created by Brazilian test on granites were used to measure fracture roughness under the confocal laser scanning microscope. Measurements were performed along three scan lines on each fracture surface. 36 scan lines were determined on 12 specimens in total. Features of roughness showed that coarse and medium grained granites tend to more rough features than those of fine grained granites. Continuous analog data of roughness is possible to described as discrete data of measure roughness with a fixed interval under the confocal laser microscope. Results of FFT with the measured data showed the highest values on the second harmonics. Distribution of average amplitude of second harmonics was observed 0.9853 in coarse grained granite, 1.0792 in medium grained granite and 0.6794 in fine grained granite. This indicates that the larger roughness has the higher energy of harmonics as the result of fractal analysis in low frequency zone.

• Journal of KIISE
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• v.41 no.12
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• pp.1050-1057
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• 2014

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) can be used for a number of purposes in a variety of industries, such as to replace body parts like hands and feet or to improve user convenience. In this paper, we propose a method to decompose and extract motor imagery EEG signal using Empirical Mode Decomposition (EMD) and Fast Fourier Transforms (FFT). The EEG signal classification consists of the following three steps. First, during signal decomposition, the EMD is used to generate Intrinsic Mode Functions (IMFs) from the EEG signal. Then during feature extraction, the power spectral density (PSD) is used to identify the frequency band of the IMFs generated. The FFT is used to extract the features for motor imagery from an IMF that includes mu rhythm. Finally, during classification, the Support Vector Machine (SVM) is used to classify the features of the motor imagery EEG signal. 10-fold cross-validation was then used to estimate the generalization capability of the given classifier., and the results show that the proposed method has an accuracy of 84.50% which is higher than that of other methods.

• Journal of Digital Convergence
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• v.10 no.7
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• pp.123-128
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• 2012

In this paper, we propose a landmark-detection system of object for more accurate object recognition. The landmark-detection system of object becomes divided into a learning stage and a detection stage. A learning stage is created an interest-region model to set up a search region of each landmark as pre-information necessary for a detection stage and is created a detector by each landmark to detect a landmark in a search region. A detection stage sets up a search region of each landmark in an input image with an interest-region model created in the learning stage. The proposed system uses Fast Fourier Transform to detect landmark, because the landmark-detection is fast. In addition, the system fails to track objects less likely. After we developed the proposed method was applied to environment video. As a result, the system that you want to track objects moving at an irregular rate, even if it was found that stable tracking. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously methods.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.159-165
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• 2022

In this paper, a frame synchronization structure for the Binary Phase Shift Keying (BPSK) modulation method in underwater acoustic communication was proposed. The proposed frame synchronization structure is largely divided into two. First, the approximate position and frequency offset of the frame are obtained by non-coherent correlation and sliding Fast Fourier Transform (FFT) method. Second, after compensating for the frequency error to the received signal, the exact position of the frame is obtained by coherent correlation method. Maritime experiments were conducted to confirm the performance of the 2-STEP frame synchronization structure. It was showed that the limitations of the non-coherent correlation and sliding FFT method can be verified when the power of the received signal was greatly reduced due to the channel characteristics. As a result, stable frame synchronization could be obtained by compensating for the frequency error and then using the coherent correlation method.

• Proceedings of the KAIS Fall Conference
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• 2000.10a
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• pp.203-207
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• 2000

최근 신호 처리 분야에서 많은 연구가 되고 있는 웨이브렛 변환을 적용하고, DSP(Digital Signal Processor)인 TMS320C31을 사용하여 고속 처리 가능한 하드웨어를 구현하였다. 그리고, 컴퓨터하고 일정한 통신 대역을 유지하고 다른 장치에 영향을 주지 안기 위해서 ISA 버스를 사용하였다. 여기서는 웨이브렛 변환과 푸리에 변환의 차이 및 필터뱅크에 대해서 알아보고, DSP를 이용하여 웨이브렛 변환을 시키는 하드웨어를 구현했다.

• 유체기계공업학회:학술대회논문집
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• 2000.12a
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• pp.200-210
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• 2000

본 연구에서는 고속의 원심압축기에서 스톨 발단에 관한 연구를 수행하였다. 스톨을 일으키는 요인과, 스톨이 발생하기 전에 이를 미리 경고할 수 있는 방법을 주된 연구 주제로 삼았다. 원주방향으로 균일하게 분포된 8개의 고속응답 압력변환기를 사용하여 순간압력을 측정하였으며, 이 신호를 공간 푸리에 변환(space Fourier transform)을 사용하여 스톨의 발단을 알리는 신호를 측정하였고, 회전하는 파의 에너지(Traveling Wave Energy) 방법을 사용하여 스톨을 미리 경고하는 방법에 대하여 연구하였다. 회전하는 파의 에너지 방법은 스톨을 경고하는 데 좋은 성능을 보였으며, 저속에서는 약 임펠러 100회전, 중간속도에서는 약 200회전, 그리고 고속에서는 약 임펠러 1000회전의 경고시간을 보였다. 그리고 스톨 발단 근처에서 공간 푸리에 계수의 위상이 임펠러 주파수의 속도로 선형적인 증가를 보이는 구간이 나타났으며, 또한 임펠러 주파수의 스펙트럼이 스톨로 접근하면서 증가하는 것으로부터, 임펠러 주파수가 스톨을 일으키는 중요한 요인으로 작용함을 알 수 있었다. 또한 임펠러의 회전속도에 관계없이 스톨로 접근하면서 임펠러 주파수의 스펙트럼이 증가하므로, 이 값이 스톨을 경고하는 방법으로 사용될 수 있음을 보였으며, 약 임펠러 2n회전의 경고시간을 얻을 수 있었고, 임펠러의 속도가 빠를수록 긴 경고시간을 얻었다. 이 방법의 개발로 하나의 센서의 측정만으로도 효과적으로 스톨을 경고할 수 있는 기반을 마련하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.4B
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• pp.418-422
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• 2001

4-점 리버스 자켓 변환 (4-Point Reverse Jacket transform)의 장점 중의 하나는 4-점 fast Fourier transform(FFT)시 야기되는 실수 또는 복소수 곱셈을 행렬분해(matrix decomposition)를 이용, 곱셈인자를 모두 대각행렬에만 집중시킨, 매우 간결하고 효율적인 알고리즘이라는 점이다. 본 논문에서는 이를 N 점 FFT에 적용하는 알고리즘을 제안한다. 이 방법은 기존의 다른 변환형태보다 확장하거나 구조를 파악하기에 매우 용이하다.

• Korean Security Journal
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• no.61
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• pp.377-400
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• 2019

Photography is the most commonly used method of documenting the crime and incident scene as it helps maintaining chain of custody (COC) and prove integrity of the physical evidence. It can also capture phenomena as they are. However, digital images can be manipulated and lose their authenticity as admissible evidence. Thus only limited techniques can be used to enhance images, and one of them is Fourier transform. Fourier transform refers to transformation of images into frequency signals. Fast Fourier transform (FFT) is used in this study. In this experiment, we overlapped fingerprints with graph paper or other fingerprints and separated the fingerprints. Then we evaluated and compared quality of the separated fingerprints to the original fingerprints, and examined whether the two fingerprints can be identified as same fingerprints. In the case of the fingerprints on graph paper and general pattern-overlapping fingerprints, fingerprint ridges are enhanced. On the other hand, in case of separating complicated fingerprints such as core-to-core overlapping and delta-to-delta overlapping fingerprints, quality of fingerprints can be deteriorated. Quality of fingerprints is known to possibly bring negative effects on the credibility of examiners. The result of this study may be applicable to other areas using digital imaging enhancement technology.

• Proceedings of the Optical Society of Korea Conference
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• 2004.02a
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• pp.294-295
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• 2004

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.27-32
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• 2011

This paper presents a methodology for classifying sleep disturbance using electroencephalogram (EEG) signal at sleep stage 2 and principal component analysis. For extracting initial features, fast Fourier transforms(FFT) were carried out to remove some noise from EEG signal at sleep stage 2. In the second phase, we used principal component analysis to reduction from EEG signal that was removed some noise by FFT to 5 features. In the final phase, 5 features were used as inputs of NEWFM to get performance results. The proposed methodology shows that accuracy rate, specificity rate, and sensitivity were all 100%.