• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권6호
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• pp.2634-2648
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• 2020

In this paper, an adaptive sparse singular value decomposition (ASSVD) algorithm is proposed to estimate the signal matrix when only one data matrix is observed and there is high dimensional white noise, in which we assume that the signal matrix is low-rank and has sparse singular vectors, i.e. it is a simultaneously low-rank and sparse matrix. It is a structured matrix since the non-zero entries are confined on some small blocks. The proposed algorithm estimates the singular values and vectors separable by exploring the structure of singular vectors, in which the recent developments in Random Matrix Theory known as anisotropic Marchenko-Pastur law are used. And then we prove that when the signal is strong in the sense that the signal to noise ratio is above some threshold, our estimator is consistent and outperforms over many state-of-the-art algorithms. Moreover, our estimator is adaptive to the data set and does not require the variance of the noise to be known or estimated. Numerical simulations indicate that ASSVD still works well when the signal matrix is not very sparse.

• Current Optics and Photonics
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• 제2권5호
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• pp.413-421
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• 2018

We propose a novel methodology based on the multiplication function to improve the signal-to-noise ratio (SNR) for vibration detection in a phi optical time-domain reflectometer system (phi-OTDR). The extreme-mean complementary empirical mode decomposition (ECEMD) is designed to break down the original signal into a set of inherent mode functions (IMFs). The multiplication function in terms of selected IMFs is used to determine a vibration's position. By this method, the SNR of a phi-OTDR system is enhanced by several orders of magnitude. Simulations and experiments applying the method to real data prove the validity of the proposed approach.

• ETRI Journal
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• 제45권2호
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• pp.277-290
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• 2023

This paper proposes a method that can reduce the complexity of a system matrix by analyzing the characteristics of a pseudoinverse matrix to receive a binomial frequency division multiplexing (BFDM) signal and decode it using the least squares (LS) method. The system matrix of BFDM can be expressed as a band matrix, and as this matrix contains many zeros, its amount of calculation when generating a transmission signal is quite small. The LS solution can be obtained by multiplying the received signal by the pseudoinverse matrix of the system matrix. The singular value decomposition of the system matrix indicates that the pseudoinverse matrix is a band matrix. The signal-to-interference ratio is obtained from their eigenvalues. Meanwhile, entries that do not contribute to signal generation are erased to enhance calculation efficiency. We decode the received signal using the pseudoinverse matrix and the removed pseudoinverse matrix to obtain the bit error rate performance and to analyze the difference.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 추계학술대회논문집
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• pp.699-704
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• 2004

Empirical mode decomposition(EMD) method has been recently proposed to analyze non-linear and non-stationary data. This method allows the decomposition of one-dimensional signals into intrinsic mode functions(IMFs) and is used to calculate a meaningful multi-component instantaneous frequency. In this paper, it is assumed that each mode of damped vibration signal could be well separated in the form of IMF by EMD. In this case, we can have a new powerful method to calculate natural frequencies and dampings from damped vibration signal which usually has multiple modes. This proposed method has been verified by both simulation and experiment. The result by EMD method which has used only output vibration data is almost identical to the result by FRF method which has used both input and output data, thereby proving usefulness and accuracy of the proposed method.

• Wind and Structures
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• 제38권1호
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• The Journal of the Acoustical Society of Korea
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• 제25권4E호
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• pp.142-147
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• 2006

Blind signal separation is known as a powerful tool for enhancing noisy speech in many real world environments. In this paper, it is demonstrated that the performance of blind signal separation can be further improved by combining with a null beamformer (NBF). Cascading the blind source separation with null beamforming is equivalent to the decomposition of the received signals into the direct parts and reverberant parts. Investigation of beam patterns of the null beamformer and blind signal separation reveals that directional null of NBF reduces mainly direct parts of the unwanted signals whereas blind signal separation reduces reverberant parts. Further, it is shown that the decomposition of received signals can be exploited to solve the local stability problem. Therefore, faster and improved separation can be obtained by removing the direct parts first by null beamforming. Simulation results using real office recordings confirm the expectation.

• 응용통계연구
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• 제22권3호
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• pp.499-513
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• 2009

An economic signal in the real world usually reflects complex phenomena. One may have difficulty both extracting and interpreting information embedded in such a signal. A natural way to reduce complexity is to decompose the original signal into several simple components, and then analyze each component. Spectral analysis (Priestley, 1981) provides a tool to analyze such signals under the assumption that the time series is stationary. However when the signal is subject to non-stationary and nonlinear characteristics such as amplitude and frequency modulation along time scale, spectral analysis is not suitable. Huang et al. (1998b, 1999) proposed a data-adaptive decomposition method called empirical mode decomposition and then applied Hilbert spectral analysis to decomposed signals called intrinsic mode function. Huang et al. (1998b, 1999) named this two step procedure the Hilbert-Huang transform(HHT). Because of its robustness in the presence of nonlinearity and non-stationarity, HHT has been used in various fields. In this paper, we discuss the applications of the HHT and demonstrate its promising potential for non-stationary financial time series data provided through a Korean stock price index.

• 한국음향학회지
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• 제26권3호
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• pp.123-128
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• 2007

본 논문에서는 수중 천이 신호에 대한 식별 알고리즘을 제안한다. 일반적으로 해양의 배경잡음은 스펙트럼 특성 및 에너지 변화가 적은 정재성을 갖는 반면에 천이 신호는 스펙트럼 및 에너지 변화가 큰 비정재성을 가진다. 따라서 수중 천이 신호 식별을 위하여 선행되어져야 하는 수중 천이 신호 탐지에서는 프레임 단위로 스펙트럼 변이와 에너지 변화를 이용한다. 제안한 수중 천이 신호 식별 알고리즘에서는 특징 벡터를 추출하기 위하여 위그너-빌 분포 함수를 기반으로 고유치 분해를 이용한다. 추출된 특징 벡터를 기반으로 탐지된 수중 천이 신호의 특징 벡터와 식별하고자 하는 데이터베이스에 있는 기준 신호의 특징 벡터와의 상관 값을 프레임 단위로 계산하고, 각 클래스별로 프레임 사상도를 산출하여 최대 값을 갖는 기준 신호로 탐지된 수중 천이 신호를 식별한다.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2001년도 하계 학술대회 논문집(KISPS SUMMER CONFERENCE 2001
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• pp.133-136
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• 2001

In this paper, we propose a calculation method for fuzzy control based on quantized $\alpha$ -cut decomposition of fuzzy sets. This method is easy to be implemented in analog hardware. The effect of quantization levels on defuzzified fuzzy inference result is investigated. A few quantization levels are sufficient for fuzzy control. The hardware implementation of this calculation method and the defuzzificaion by gravity center method by PWM are also presented.

• 전자공학회논문지
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• 제50권3호
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• pp.212-218
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• 2013

본 논문에서는 SAR (Synthetic Aperture Radar) 영상에 SVD (Singular Value Decomposition) - Pseudo Spectrum 알고리즘을 적용하고 그 성능을 기존 알고리즘과 비교한다. 이 논문의 목적은 SAR 영상의 해상도 및 목표물 분해능을 높이고자 하는 것이다. 본 논문에서는 신호 성분으로 이루어진 Hankel Matrix와 SVD (Singular Value Decomposition) 방법을 사용하여 잡음에 강인하고 sidelobe이 적으며 스펙트럼 추정에서 해상도를 높인 SVD-Pseudo Spectrum 방법을 제안하였다. 또한 분해될 목표물을 모델링하여 알고리즘의 성능을 분석하고 SVD-Pseudo Spectrum 방법이 기존의 퓨리에 변환 기반 방법과 고해상도 기술 기반의 MUSIC 방법보다 더 좋은 성능을 가짐을 보인다.