• 한국해양공학회지
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• 제15권3호
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• pp.87-92
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• 2001

In this studies, joint time-frequency analysis techniques were applied to analyze ultrasonic signals in the degraded austenitic 316 stainless steels, to study the evolution of damage in these materials. It was demonstrated that the nonstationary characteristics of ultrasonic signals could be analyzed effectively by these methods. The WVD was more effective for analyzing the attenuation and frequency characteristics of the degraded materials through ultrasonic. It is indicated that the joint time-frequency analysis, WVD method, should also be useful in evaluating various damages and defects in structural members.

• 한국해양공학회지
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• 제17권3호
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• pp.46-51
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• 2003

Acoustic emission (AE) signals, emanated during local failure of aluminum alloys, have been the subject of numerous investigations. It is well known that the characteristics of AE are strongly influenced by the previous thermal and mechanical treatment of the sample. Possible sources of AE during deformation have been suggested as the avalanche motion of dislocations, fracture of brittle particles, and debonding of these particles from the alloy matrix. The goal of the present study is to determine if AE occurring as the result of fatigue crack propagation could be evaluated by the joint time-frequency analysis method, short time Fourier transform (STFT), and Wigner-Ville distribution (WVD). The time-frequency analysis methods can be used to analyze non-stationary AE more effectively than conventional techniques. STFT is more effective than WVD in analyzing AE signals. Noise and frequency characteristics of crack openings and closures could be separated using STFT. The influence of various fatigue parameters on the frequency characteristics of AE signals was investigated.

• 비파괴검사학회지
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• 제19권4호
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• pp.270-276
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• 1999

지금까지 퓨리에 변환이 신호처리법으로 가장 널리 사용되고 있다. 그러나, 이 방법은 신호의 주파수성분이 시간에 대하여 어떻게 변화하는지를 표현하지 못한다. 따라서, 최근 이와 같이 비정상신호를 표현하지 못하는 퓨리에 변환의 단점을 보완하여, 신호의 시간과 주파수에 대한 정보를 동시에 표현할 수 있는 시간-주파수 해석법들이 개발되기 시작하였다. 본 연구에서는 인공열화된 SUS 316강의 초음파신호를 시간-주파수 해석법으로 분석하였다. 특히, 단시간 퓨리에 변화법과 위그너 빌 분포법을 이용하여 초음파 신호의 주파수 성분과 특성을 분석하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.310-312
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• 2004

The aim of this study was to analyze the characteristics of knee joint sound in frequency domain and classify the knee joint diseases. The spectral analysis of knee joint sounds was performed using LPC(Linear Predictive Coding) and Wigner-Ville distribution. Ten normal subjects and 5 patients with meniscal tearing were enrolled. Each subject was seated on a chair and underwent active knee flexion and extension for 60 seconds. Sampling frequency was 10kHz and electronic stethoscope and electro-goniometer were applied during the knee motion for data collection. The spectral analysis showed 3 peaks in both groups and the difference energy distribution in time-frequency domain. These results suggest that the diagnosis of knee joint pathology using the auscultation could be easier and more correct.

• 한국수산과학회지
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• 제48권2호
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• pp.221-232
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• 2015

Broadband echoes measured in live chub mackerel Scomber japonicus, goldeye rockfish Sebastes thompsoni, and fat greenling Hexagrammos otakii with different morphologies and internal characteristics were analyzed in time and frequency domains to understand the species-specific echo feature characteristics for classifying fish species. The mean echo image for each time-frequency representation dataset obtained as a function of orientation angle was extracted to mitigate the effect of fish orientation on acoustic scattering. The joint time-frequency content of the broadband echo signals was obtained using the smoothed pseudo-Wigner-Ville distribution (SPWVD). The SPWVDs were analyzed for each echo signature of the three fish species. The results show that the time-frequency analysis provided species-specific echo structure patterns and metrics of the broadband acoustic signals to facilitate fish species classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5527-5545
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• 2019

Through-wall ultra-wide band (UWB) radar has been considered as one of the preferred and non-contact technologies for the targets detection owing to the better time resolution and stronger penetration. The high time resolution is a result of a larger of bandwidth of the employed UWB pulses from the radar system, which is a useful tool to separate multiple targets in complex environment. The article emphasised on human subject localization and detection. Human subject usually can be detected via extracting the weak respiratory signals of human subjects remotely. Meanwhile, the range between the detection object and radar is also acquired from the 2D range-frequency matrix. However, it is a challenging task to extract human respiratory signals owing to the low signal to clutter ratio. To improve the feasibility of human respiratory signals detection, a new method is developed via analysing the standard deviation based kurtosis of the collected pulses, which are modulated by human respiratory movements in slow time. The range between radar and the detection target is estimated using joint time-frequency analysis (JTFA) of the analysed characteristics, which provides a novel preliminary signature for life detection. The breathing rates are obtained using the proposed accumulation method in time and frequency domain, respectively. The proposed method is validated and proved numerically and experimentally.

• 전기학회논문지
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• 제57권4호
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• pp.729-734
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• 2008

Vibroarthrographic(VAG) signals, emitted by human knee joints, are non-stationary and multi-component in nature and time-frequency distributions(TFD) provide powerful means to analyze such signals. The objective of this paper is to classify VAG signals, generated during joint movement, into two groups(normal and patient group) using the characteristic parameters extracted by time-frequency transform, and to evaluate the classification accuracy. Noise within TFD was reduced by singular value decomposition and back-propagation neural network(BPNN) was used for classifying VAG signals. The characteristic parameters consist of the energy parameter, energy spread parameter, frequency parameter, frequency spread parameter by Wigner-Ville distribution and the amplitude of frequency distribution, the mean and the median frequency by fast Fourier transform. Totally 1408 segments(normal 1031, patient 377) were used for training and evaluating BPNN. As a result, the average value of the classification accuracy was 92.3(standard deviation ${\pm}0.9$)%. The proposed method was independent of clinical information, and showed good potential for non-invasive diagnosis and monitoring of joint disorders such as osteoarthritis and chondromalacia patella.

• 지능정보연구
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• 제5권1호
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• pp.103-123
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• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.

• 한국전자파학회논문지
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• 제23권9호
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• pp.1100-1106
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• 2012

본 논문에서는 시간-주파수 동시 해석 기법 중 하나인 수정된 HHT 기법을 이용하여 회전하는 프로펠러의 날개에서 산란되는 마이크로 도플러 신호를 분석하였다. 프로펠러의 날개 모서리에 산란되는 산란파는 등가전류법(ECM)을 이용하여 구했다. 산란파의 시간 데이터를 얻은 후, 수정된 HHT 기법을 마이크로 도플러 신호의 해석에 적용하였다. 해석 결과는 실제 날개의 동특성에 잘 부합하였으며, 회전체로부터 발생하는 마이크로 도플러의 정편파적인 특성을 보였다. 수정된 HHT를 이용한 시간-주파수 동시 해석은 작은 레이더 단면적 값을 갖는 소형 비행체를 식별하기 위한 명확한 특성을 제공하였다.

• 한국생산제조학회지
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• 제8권2호
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• pp.73-79
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• 1999

Most of mechanical structures are combined of substructures such as beams and/or plates. There are few systems with unibody structures but are many systems with united body structures. Generally the dynamic a nalysis of whole structures is performed under alternation load. In the structure design, the analysis of each bolted joint is more important than others for zero severity. This paper presents the analysis method of contact stress distribution factor in the bolted joint with variable fastening torque on joints in the structure. At first, a static vibration test was performed to find out a nominal stress of bolt jointed plates from the relationship between natural frequency and nominal stress. Then a contact stress was computed at contact point between bolt and plate in the structure. It is believed that the proposed method has promisiong implications for safer design with index of contact stress distribution factor and has merits for cost-down and saving time at the beginning of vehicle development.