Underdetermined Conditions Research Articles

A Single-Channel Blind Source Separation Technique Based on AMGMF and AFEEMD for the Rotor System

To improve the accuracy of the diagnosis of the composite failure of the rotor system under underdetermined conditions, a single-channel blind source separation method based on an adaptive multiscale generalized morphology filter (AMGMF) and an adaptive fast ensemble empirical mode decomposition (AFEEMD) algorithm is proposed. First, AMGMF is used to filter out the background noise in the signal and enhance the signal-to-noise ratio (SNR), and then, the filtered signal is decomposed by the AFEEMD algorithm to improve the accuracy of signal decomposition and reduce computation time. Second, the singular value decomposition-based method is used to estimate the number of source signals, and the main components highly correlated with the observed signal are selected according to the number of source signals. Finally, the effective separation of source signals is achieved by the fast independent component analysis (FastICA) algorithm. Simulation tests and multiple rotor fault signals are used to demonstrate the feasibility and effectiveness of the proposed method. Experimental results show that the proposed method has better separation performance than the existing EEMD-PCA-FastICA method.

Direction-of-arrival estimation of multipath signals using independent component analysis and compressive sensing

Multipath signal is often considered an interference that must be removed. The coherence between multipath and direct component makes it difficult to use conventional direction-of-arrival (DOA) estimation methods in a smart antenna system. This study demonstrates a new multipath signal DOA estimation technique. Unlike the common methods, without decoherence preprocessing, the proposed algorithm first apply a complex fast independent component analysis (cFastICA) algorithm to obtain the steering vectors with multipath information that corresponds to each source signal. Then, according to the special structure of the obtained steering vectors and spatial sparsity of the multipath signal components, the algorithm uses the solution of the sparse signal reconstruction problem in the compressive sensing (CS) theory, and the DOA estimation of the multipath signal is translated into an l1 norm minimization problem. Finally, we search the space spectrums to acquire the DOAs for each direct component and multipath component. Comparative simulation tests and analysis prove the effectiveness of the proposed algorithm in estimation accuracy in underdetermined conditions.

Diagnosis of Compound Fault Using Sparsity Promoted-Based Sparse Component Analysis.

Compound faults often occur in rotating machinery, which increases the difficulty of fault diagnosis. In this case, blind source separation, which usually includes independent component analysis (ICA) and sparse component analysis (SCA), was proposed to separate mixed signals. SCA, which is based on the sparsity of target signals, was developed to sever the compound faults and effectively diagnose the fault due to its advantage over ICA in underdetermined conditions. However, there is an issue regarding the vibration signals, which are inadequately sparse, and it is difficult to represent them in a sparse way. Accordingly, to overcome the above-mentioned problem, a sparsity-promoted approach named wavelet modulus maxima is applied to obtain the sparse observation signal. Then, the potential function is utilized to estimate the number of source signals and the mixed matrix based on the sparse signal. Finally, the separation of the source signals can be achieved according to the shortest path method. To validate the effectiveness of the proposed method, the simulated signals and vibration signals measured from faulty roller bearings are used. The faults that occur in a roller bearing are the outer-race flaw, the inner-race flaw and the rolling element flaw. The results show that the fault features acquired using the proposed approach are evidently close to the theoretical values. For instance, the inner-race feature frequency 101.3 Hz is very similar to the theoretical calculation 101 Hz. Therefore, it is effective to achieve the separation of compound faults utilizing the suggest method, even in underdetermined cases. In addition, a comparison is applied to prove that the proposed method outperforms the traditional SCA method when the vibration signals are inadequate.

Instantaneous Modelling and Reverse Engineering of Data-Consistent Prime Models in Seconds!

A theoretical framework that supports automated construction of dynamic prime models purely from experimental time series data has been invented and developed, which can automatically generate (construct) data-driven models of any time series data in seconds. This has resulted in the formulation and formalisation of new reverse engineering and dynamic methods for automated systems modelling of complex systems, including complex biological, financial, control, and artificial neural network systems. The systems/model theory behind the invention has been formalised as a new, effective and robust system identification strategy complementary to process-based modelling. The proposed dynamic modelling and network inference solutions often involve tackling extremely difficult parameter estimation challenges, inferring unknown underlying network structures, and unsupervised formulation and construction of smart and intelligent ODE models of complex systems. In underdetermined conditions, i.e., cases of dealing with how best to instantaneously and rapidly construct data-consistent prime models of unknown (or well-studied) complex system from small-sized time series data, inference of unknown underlying network of interaction is more challenging. This article reports a robust step-by-step mathematical and computational analysis of the entire prime model construction process that determines a model from data in less than a minute.

UBSS and blind parameters estimation algorithms for synchronous orthogonal FH signals

By using the sparsity of frequency hopping(FH) signals,an underdetermined blind source separation(UBSS) algorithm is presented. Firstly, the short time Fourier transform(STFT) is performed on the mixed signals. Then, the mixing matrix, hopping frequencies, hopping instants and the hooping rate can be estimated by the K-means clustering algorithm. With the estimated mixing matrix, the directions of arrival(DOA) of source signals can be obtained. Then, the FH signals are sorted and the FH pattern is obtained. Finally, the shortest path algorithm is adopted to recover the time domain signals. Simulation results show that the correlation coefficient between the estimated FH signal and the source signal is above 0.9 when the signal-to-noise ratio(SNR) is higher than 0 d B and hopping parameters of multiple FH signals in the synchronous orthogonal FH network can be accurately estimated and sorted under the underdetermined conditions.

Multichannel Sound Source Dereverberation and Separation for Arbitrary Number of Sources Based on Bayesian Nonparametrics

Multichannel signal processing using a microphone array provides fundamental functions for coping with multi-source situations, such as sound source localization and separation, that are needed to extract the auditory information for each source. Auditory uncertainties about the degree of reverberation and the number of sources are known to degrade performance or limit the practical application of microphone array processing. Such uncertainties must therefore be overcome to realize general and robust microphone array processing. These uncertainty issues have been partly addressed-existing methods focus on either source number uncertainty or the reverberation issue, where joint separation and dereverberation has been achieved only for the overdetermined conditions. This paper presents an all-round method that achieves source separation and dereverberation for an arbitrary number of sources including underdetermined conditions. Our method uses Bayesian nonparametrics that realize an infinitely extensible modeling flexibility so as to bypass the model selection in the separation and dereverberation problem, which is caused by the source number uncertainty. Evaluation using a dereverberation and separation task with various numbers of sources including underdetermined conditions demonstrates that (1) our method is applicable to the separation and dereverberation of underdetermined mixtures, and that (2) the source extraction performance is comparable to that of a state-of-the-art method suitable only for overdetermined conditions.

Segmenting, removing and ranking partial blur

Image blur is a common phenomenon in daily life. Due to the great challenge, image restoration fascinates researchers to find out the solutions. Considering different types of blur, we propose a framework to segment the partial blur from a single image and then restore the latent information. In general, some morphological technologies are applied to separate the blur area. Traditionally, blind deconvolution method is applied in underdetermined conditions. In this research, we marginalize the kernel estimation by separating the problem into two stages, both of which are combined with different useful priors. A criterion of ranking the blur degree of a partial blur image is also proposed at the end of this paper. Experimental results demonstrate the accuracy and superiority of our approach.

Ship’s Horizontal Plane Determination by Means of Three Laser Range Sensors in Pilot Navigation and Docking System / WYZNACZENIE POŁOŻENIA UMOWNEJ WODNICY STATKU ZA POMOCĄ TRZECH DALMIERZY LASEROWYCH W PILOTOWYM SYSTEMIE NAWIGACYJNO-DOKUJĄCYM

Abstract Modern ship’s navigation support systems relay mainly on GNSS technology, as prime source of position. The advantages of such solution are obvious and undeniable. On the other hand in critical situations the dependency of the one system presents a great deal of risk. Taking above into consideration the idea of building GNSS independent Pilot Navigation and Docking System (PNDS) was put into practice. PNDS utilizes the range measurements between laser head and ship’s side to determine the ship’s horizontal plane presented on the screen. The study comprises the case in which three networked rangefinders were deployed on the berth side in a line. Such location of sensors causes uncertainty in determining of ship’s outline contour and speed in relation to assigned coordinate reference system. The final algorithm presented in the article, taking into account all underdetermined conditions, has been tested in PNDS system constructed in Maritime University in Szczecin.

Empirical Methods to Determine the Number of Sources in Single-Channel Musical Signals

We present a sequence of empirical methods to determine the number of sources in musical signals when only one channel is available. Rather than building evidence through a statistical model-based approach, we instead develop a carefully tuned and tested two-stage system that is able to function effectively even in extremely underdetermined conditions. A first, more general procedure accurately determines the number of sources that are not closely harmonically related, while the second stage subsequently detects the presence of any remaining sources. The main advantages of this approach lie in its avoidance of the restrictive assumptions that can accompany more complex models in underdetermined cases, and in its use of robust heuristics to identify and exploit as much source-specific information as possible. These features make it possible to address even the most difficult cases in which sources are closely harmonically related, or even share the same fundamental frequency. We report an overall accuracy of nearly 80% on average, using both random and harmonically related mixtures of one to six sources taken from two widely available musical instrument databases-a notable result that demonstrates both the efficiency and the robustness of our proposed procedure.

Nonlinear underdetermined blind signal separation using Bayesian neural network approach

Nonlinear signal separation and underdetermined signal separation have received much attention in blind signal separation literature. However, neither of them can solve the situation where both nonlinear and underdetermined characteristics exist at the same time. In this paper, a new learning algorithm based on Bayesian statistics is proposed to solve the separation problem of the blind nonlinear underdetermined mixtures. We suppose that the observations are post-nonlinear mixtures of the sources and the number of observations is less than the number of sources. Due to the characteristics of Bayesian statistics, the generalized Gaussian distribution model is utilized to approximate the prior probability distribution of the source signals and the mixing variables. Formal derivation shows that the source signals, mixing matrix and nonlinear functions can be estimated through an iterative technique based on alternate optimization. The nonlinear mismatch problem is also considered by applying a multilayer perceptron with a typical least square error problem. Simulations have been given to demonstrate the effectiveness in separating signals under nonlinear and underdetermined conditions.

Maximum a posteriori-based approach to blind nonlinear underdetermined mixture

A new learning algorithm is proposed to solve the separation problem of the blind nonlinear underdetermined mixtures. The mixing system is characterised by the post-nonlinear structure and concurrently the number of sensors is less than the number of sources. The proposed algorithm utilises the generalised Gaussian distribution to model the prior probability distribution of the source signals and the mixing variables. A novel iterative technique based on alternate optimisation within the Bayesian framework has been developed for estimating the source signals, mixing matrix and the nonlinear distortion. It is shown that through formal Bayesian derivation, the update of the mixing matrix can be decomposed into two separate constituents given by linear and nonlinear parts. Furthermore, the post-nonlinear distortion functions in the mixing model are approximated by a set of polynomials and the coefficients are found by solving a least square error problem. Simulations have been carried out to verify the effectiveness in separating signals under nonlinear and underdetermined conditions. An average margin of 130% improvement has been obtained when compared with the existing linear algorithm.