• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2003년도 춘계학술대회논문집
• /
• pp.1009-1009
• /
• 2003

VIPER a new tool for the VIsual PERception of sound quality and for sound design will be presented. Requirement for the visualization of sound quality is a signal analysis modeling the information processing of the ear. The first step of the signal processing implemented in VIPER, calculates an auditory spectrogram by a filter bank adapted to the time- and frequency resolution of the human ear. The second step removes redundant information by extracting time- and frequency contours from the auditory spectrogram in analogy to contours of the visual system. In a third step contours and/or auditory spectrogram can be resynthesised confirming that only aurally relevant information were extracted. The visualization of the contours in VIPER allows intuitively to grasp the important components of a signal. Contributions of parts of a signal to the overall quality can be easily auralized by editing and resynthesising the contours or the underlying auditory spectrogram. Resynthesis of time contours alone allows e.g. to auralize impulsive components separately from the tonal components. Further processing of the contours determines tonal parts in form of tracks. Audible differences between two versions of a sound can be visually inspected in VIPER through the help of auditory distance spectrograms. Applications are shown for the sound design of several interior noises of cars.

• 한국정보통신학회논문지
• /
• 제11권1호
• /
• pp.150-156
• /
• 2007

신호를 처리하는 과정에서 여러 가지 원인에 의해 잡음이 발생하고 있으며, 이에 따라 전송 데이터에서 오류가 발생하거나, 영상 및 음성 데이터의 인지도를 저하시킨다. 따라서 이러한 잡음들을 제거하여 신호를 복원하기 위한 다양한 방법들이 연구되고 있다. 현재, 시간-주파수 국부성을 갖고, 다중해상도 해석이 가능한 웨이브렛 변환이 많은 공학 분야에서 응용되고 있으며, 잡음 제거를 위해 문턱값과 상관관계를 이용한 방법 등이 제시되었다. 그러나 기존의 방법들은 여전히 많은 잡음들을 에지로서 판단하며, AWGN과 임펄스 잡음을 동시에 제거하기 위한 방법을 제공하지 않는다. 따라서 본 논문에서는 잡음에 의해 훼손된 신호를 복원하기 위하여, 새로운 웨이브렛기반 알고리즘을 제시하였으며, 기존의 방법들과 비교하였다.

• 한국해양공학회:학술대회논문집
• /
• 한국해양공학회 2002년도 춘계학술대회 논문집
• /
• pp.173-178
• /
• 2002

Fourier transform has been one of the most common tools to study the frequency characteristics of signals. With the Fourier transform alone, it is difficult to tell whether signal's frequency contents evolve in time or not. Except for a few special cases, the frequency contents of most signals encountered in the real world change with time. Time-frequency methods are developed recently to overcome the drawbacks of Fourier transform, which can represent the information of signals in time and frequency at the same time. In this study, heat damage process of a carbon fiber reinforced plastic(CFRP) and glass fiber reinforced plastic(GFRP) under monotonic tensile loading was characterized by acoustic emission. Different kinds of specimens were used to determine the characteristics of Strength and AE signals. Time-frequency analysis methods were employed for the analysis of fracture mechanism in CFRP such as matrix cracking, debonding and fiber fracture.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2004년도 ICCAS
• /
• pp.682-685
• /
• 2004

This paper investigated a method for classifying emotional states by using pulse wave signal. It focused on finding effective features for emotional state classification. The emptional states considered here consisted of interest and neutral. Classification experiments utilized 65 and 60 samples of interest and neutral states respectively. We have investigated 19 features derived from pulse wave signals by using both time domain and frequency domain analysis methods with 2 classifiers of minimum distance (normalized Euclidean distanece) and ${\kappa}$-Nearest Neighbour. The Leave-one-out cross validation was used as an evaluation mehtod. Based on experimental results, the most efficient features were a combination of 4 features consisting of (i) the mean of the first differences of the smoothed pulse rate time series signal, (ii) the mean of absolute values of the second differences of thel normalized interbeat intervals, (iii) the root mean square successive difference, and (iv) the power in high frequency range in normalized unit, which provided 80.8% average accuracy with ${\kappa}$-Nearest Neighbour classifier.

• Journal of information and communication convergence engineering
• /
• 제5권1호
• /
• pp.1-6
• /
• 2007

In the process of the acquisition, storage, transmission of signals, noises are generated by various causes and the degradation phenomenon by noises tends to generate serious errors for the signal with information. So, in order to analyze and remove these noises, studies on numerous mathematical methods such as the Fourier transform have been implemented. And recently there have been many ongoing wavelet-based denoising algorithms representing excellent characteristics in time-frequency localization and multiresolution analysis, but the method to remove additive white Gaussian noise (AWGN) and the impulse noise simultaneously was not given. So, to reconstruct the corrupted signal by noises, in this paper a novel wavelet-based denoising algorithm was proposed and using signal-to-noise ratio (SNR) this method was compared to conventional methods.

• 한국해양공학회지
• /
• 제17권3호
• /
• pp.46-51
• /
• 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.

• 한국멀티미디어학회논문지
• /
• 제16권11호
• /
• pp.1325-1337
• /
• 2013

음악은 사람의 감성, 감정에 영향을 끼치며, 최근 음악의 정서적인 힘은 여러 분야에 응용되고 있다. 특히, 음악을 듣는 것뿐만 아니라 시각적으로 가시화하여 여러 분위기를 연출할 수 있다. 본 논문에서는 음악과 조명을 연계하여 사람과의 상호작용을 위한 감성 제어 시스템을 제안한다. 기존의 FT(Fourier Transform)는 시간에 대한 정보를 가지고 있지 않기 때문에 시간에 따라 주파수 성분이 변하는 신호의 정보를 효율적으로 분석하기 어렵다. 이와 같은 문제점을 해결하기 위해 음악 신호 분석은 STFT(Short Time Fourier Transform)을 사용하였고, 세 가지의 장르로 음악을 분류하여 장르에 따른 주파수 특성을 비교해 보았으며, 분석된 범위내의 주파수 성분을 바탕으로 컬러 LED조명의 색상과 밝기 제어를 하였다. 기존의 음악을 이용한 LED조명 제어 연구와 다르게 감성적인 조명의 색 제어와 음악 신호의 변화량을 이용하여 밝기 제어를 하였으며, 제안된 조명 제어 방식은 감성 조명뿐만 아니라 여러 산업분야로 활용 될 수 있을 것이다.

• 한국음향학회지
• /
• 제12권5호
• /
• pp.79-85
• /
• 1993

• 한국전기전자재료학회논문지
• /
• 제20권10호
• /
• pp.907-911
• /
• 2007

This paper dealt with the frequency spectrum analysis of acoustic signal produced by partial discharge (PD) in insulation oil and the positioning of PD occurrence to apply in diagnosis of oil insulated transformers. Three types of electrode system ; the needle-plane, the plane-plane, and the wire-wire structure were assembled to simulate partial discharges in oil insulated transformers. A low-noise amplifier and a decoupling circuit were designed to detect acoustic signal with high sensitivity The frequency spectrum of the acoustic signal were 50 kHz ${\sim}$ 260 kHz in the needle-plane, 50 kHz ${\sim}$ 250 kHz in the plane-plane, and 45 kHz${\sim}$195 kHz in the wire-wire electrode system. Their peak frequencies were 145 kHz, 130 kHz and 114 kHz, respectively The position of PD occurrence was calculated by the time difference of arrival (TOA) using three acoustic emission (AE) sensors, and we could find the position within the error of 1 % in the experimental apparatus.

• 정보보호학회논문지
• /
• 제31권1호
• /
• pp.1-9
• /
• 2021

본 논문은 RSA 복호화 과정에서 발생한 전력 신호로부터 암호연산을 예측하는 과정을 주파수 분석과 K-means 알고리즘을 이용하여 자동화하는 것을 제안한다. RSA 복호화 과정은 제곱 연산과 곱셈 연산으로 나뉘며, 시간에 따른 연산의 종류를 예측하게 되면, RSA 암호의 키(key)값을 알 수 있게 된다. 본 논문은 복호화 과정에서 발생한 전력 파형을 2차원 주파수 신호로 변환한 후, K-means algorithm을 이용하여 연산의 종류에 따라 주파수 벡터를 분류하였다. 이후, 이러한 분류된 주파수 벡터를 이용하여 연산의 종류를 예측한다.