• 한국자동차공학회논문집
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• 제20권2호
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• pp.85-89
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• 2012

This paper presents a study on the system identification of the in-situ output shaft torque measurement system using a non-contacting magneto-elastic torque transducer installed in a vehicle drivline. The frequency response (transfer) function (FRF) analysis is conducted to interpret the dynamic interaction between the output shaft torque and road side excitation due to the road roughness. In order to identify the frequency response function of vehicle driveline system, two power spectral density (PSD) functions of two random signals: the road roughness profile synthesized from the road roughness index equation and the stationary noise torque extracted from the original torque signal, are first estimated. System identification results show that the output torque signal can be affected by the dynamic characteristics of vehicle driveline systems, as well as the road roughness.

• 한국정보통신학회논문지
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• 제16권9호
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• pp.1870-1877
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• 2012

천이신호는 지속시간이 짧으면서 길이의 변화가 크고, 시변성 및 비정재성 특성을 갖는다. 이러한 천이신호의 식별에는 분석 프레임 단위로 참조신호에 대한 기준패턴을 만들어 입력신호와의 유사도를 비교하는 방법이 효과적일 수 있다. 본 연구에서는 참조신호의 기준패턴으로 프레임 기반의 특징벡터들에 대해 확률통계 모형인 정규혼합모델을 적용하는 방법을 제안하고, 다양한 수중 천이신호에 대한 식별 실험을 통해 제안한 방법의 타당성을 검증하였다.

• International Journal of Aeronautical and Space Sciences
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• 제6권1호
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• pp.8-16
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• 2005

This paper describes target detection performance of millimeter wave radar that exits on non-stationary target detection schemes in the ground clutter conditions. The comparison of various CFAR process schemes such as CA(Cell-Average)-CFAR, GO(Greatest Of)/SO(Smallest Of)-CFAR, and OS(Order Statistics)-CFAR performance are applied. Using matlab software, we show the performance and loss between target detection probability and signal to noise ratio. This paper concludes the OS-CFAR process performance is better than any others and satisfies the optimal detection probability without loss of detection in the homogeneous clutter, When range bins increase.

• 한국음향학회지
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• 제31권2호
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• pp.79-86
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• 2012

NLMS 알고리즘의 수렴 특성을 개선하기 위해 제안된 인접 투사 알고리즘의 성능을 향상시킨 알고리즘을 제안하였다. 기존의 인접 투사 또는 의사 인접 투사 알고리즘은 지연된 입력 신호 벡터 및 오차 신호 벡터를 기반으로 하기 때문에 복잡할 뿐만 아니라, 기존의 스칼라 오차를 기반으로 하는 LMS 형 알고리즘에서 개발된 기법을 적용하는데 어려움이 있다. 본 논문에서는 의사 인접 투사 알고리즘의 경우, 스칼라 오차 신호를 이용한다는 사실과 수렴 후 입력 신호가 오차 신호에 직교한다는 직교 원리에 근거하여 적은 연산량으로도 우수한 수렴 성능을 보이는 가변 스텝 사이즈 알고리즘을 제안하였다. 시스템 확인 모델 하에서 컴퓨터 모의 실험을 통하여 제안된 알고리즘이 기존의 알고리즘들에 비하여 적은 연산량에도 불구하고 정상 및 비정상 환경에서 수렴 특성이 우수함을 보였다.

• 디지털산업정보학회논문지
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• 제11권1호
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• pp.171-182
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• 2015

The general wavelet transform has profitable property in non-stationary signal analysis specially. The tunable Q-factor wavelet transform is a fully-discrete wavelet transform for which the Q-factor Q and the asymptotic redundancy r, of the transform are easily and independently specified. In particular, the specified parameters Q and r can be real-valued. Therefore, by tuning Q, the oscillatory behavior of the wavelet can be chosen to match the oscillatory behavior of the signal of interest, so as to enhance the sparsity of a sparse signal representation. The TQWT is well suited to fast algorithms for sparsity-based inverse problems because it is a Parseval frame, easily invertible, and can be efficiently implemented. The transform is based on a real valued scaling factor and is implemented using a perfect reconstruction over-sampled filter bank with real-valued sampling factors. The transform is parameterized by its Q-factor and its over-sampling rate, with modest over-sampling rates being sufficient for the analysis/synthesis functions to be well localized. This paper describes filter design of 2D discrete-time wavelet transform for which the Q-factor is easily specified. With the advantage of this transform, perfect reconstruction filter design and implementation for performance improvement are focused in this paper. Hence, the 2D transform can be tuned according to the oscillatory behavior of the image signal to which it is applied. Therefore, application for performance improvement in multimedia communication field was evaluated.

• 한국공간구조학회논문집
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• 제19권2호
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• pp.101-108
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• 2019

In this paper, the characteristic of intrinsic mode function(IMF) and its orthogonalization of ensemble empirical mode decomposition(EEMD), which is often used in the analysis of the non-linear or non-stationary signal, has been studied. In the decomposition process, the orthogonal IMF of EEMD was obtained by applying the Gram-Schmidt(G-S) orthogonalization method, and was compared with the IMF of orthogonal EMD(OEMD). Two signals for comparison analysis are adopted as the analytical test function and El Centro seismic wave. These target signals were compared by calculating the index of orthogonality(IO) and the spectral energy of the IMF. As a result of the analysis, an IMF with a high IO was obtained by GSO method, and the orthogonal EEMD using white noise was decomposed into orthogonal IMF with energy closer to the original signal than conventional OEMD.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.189-195
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• 2003

This paper represents an identification procedure for leading mechanism of a car A/V system which is composed of a DC motor and a set of plastic gears. In addition, we studied dominant noise source of rattle noise generated by external forced vibration as a car drives. we made a dynamometer to produce stationary operation on loading mechanism of A/V system because noise generated by actual loading mechanism is non-stationary signal. operating the dynamometer setup at various motor speeds, sound pressure spectra are measured and the results are analyzed. its dominant noise source is also identified by using a sound Intensity technique. we made use of multi-dimensional spectral analysis to rind a dominant rattle noise. this method is so useful to eliminate coherence between vibration sources and helps us obtain coherent output spectrum of individual vibration source which make a rattle noise.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 추계학술대회논문집
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• pp.513-517
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• 2005

The condition monitoring of rotating machinery such as turbines, pumps and compressors, determine what repairs are needed to avoid shutdown and disassembly of the machine in an industrial plant Many diagnosis methods have been developed for use when the machine is running at steady state, the stationary condition. But much information can be gained about a rotor's condition during non-stationary conditions such as run-up and run-down. Order tracking analysis is a powerful tool for analyzing the condition of a rotating machine when its speed changes over time. Powerful OTA using digital signal processing has some advantages(cheap hardware, the powerful methods, the accurate post processing) and also some disadvantages(calculation time, high speed sampling). New OTA tool based on the chirplet transform is similar to the short time Fourier transform. But, it has good resolution at high speed like other OTA methods based STFT and more resolution for constant frequency components than re-sampling OTA.

• 제어로봇시스템학회논문지
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• 제14권7호
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• pp.702-710
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• 2008

In this paper, the observability of the INS/GPS integrated system for a land vehicle is analyzed on measurements and different filters with respect to the measurements are designed. In the stationary case, it is shown that horizontal accelerometer biases and vertical attitude errors and gyro biases are unobservable. An 8-state filter is designed based on the observability analysis. When GPS signal is available, a 15-state filter is used with position and velocity measurements. To estimate the INS errors even in the case that GPS signal is blocked a filter is designed in consideration of the non-holonomic constraints of a land vehicle. In this case, the horizontal position and velocity errors and vertical attitude error are unobservable. However, a 12-state filter including the velocity states is designed to estimate the accelerometer biases. When GPS signal recovers, a 9-state filter is used excluding the sensor biases. This paper presents a multi-dimensional filter that switches the four filters according to the usable measurements and maneuver environments. A simulation is carried out to verify the performance of the proposed filter.

• International journal of advanced smart convergence
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• 제9권2호
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.