Source Separation of Diesel Engine Vibration Based on the Empirical Mode Decomposition and Independent Component Analysis

Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations,several blind source separation techniques are available for decomposing the signal into its components in the case of multichannel measurements,such as independent component analysis（ICA）.However,the source separation of vibration signal from single-channel is impossible.In order to study the source separation from single-channel signal for the purpose of source extraction,the combination method of empirical mode decomposition（EMD） and ICA is proposed in diesel engine signal processing.The performance of the described methods of EMD-wavelet and EMD-ICA in vibration signal application is compared,and the results show that EMD-ICA method outperforms the other,and overcomes the drawback of ICA in the case of single-channel measurement.The independent source signal components can be separated and identified effectively from one-channel measurement by EMD-ICA.Hence,EMD-ICA improves the extraction and identification abilities of source signals from diesel engine vibration measurements.

Single channel signal component separation using Bayesian estimation

A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC （Monte Carlo Markov Chain） algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.

Group separation of coal components and new ideas of coal utilization as petroleum

Four different groups of components were separated from coal under mild conditions of extraction and stripping process. Within these groups, and with pre-separation, individual utilization of all coal components can be realized, similar to petroleum components and enhance the inherent value and utilization value of coal, as well as increase environmental benefits. The characteristics of each component were analyzed with measurements by FTIR, GC/MS, TEM and the establishment of caking properties. The results show that coal can be separated into residues, ultra-pure coal, asphaltene components and light components by adding solvents for stripping into the CS2/NMP mixed extraction solution. Those four groups of components present great differences in the presence of carbon and hydrogen elements, in the structure of functional groups, in their macroscopic structure and micro-morphology and caking properties. Every component possesses its own inherent values and approaches. A new idea of coal processes and utilization, similar to the use of petroleum is proposed.

Epidural Analgesia Following Component Separation Hernia Repair: Is It Beneficial?

Purpose: Optimal pain management strategies for patients undergoing component separation hernia repair are not defined. Epidural analgesia (EA) has been shown to decrease pulmonary complications and duration of ileus and to improve pain control in other patient populations. In this study we examined outcomes of patients receiving EA after separation of components (SOC). Methods: After obtaining IRB approval, a retrospective review was performed of patients undergoing ventral hernia repair with SOC from January 2006 to October 2010 at the University of Kentucky. Patients were identified from hospital operative records. Pre-operative patient characteristics and operative data were obtained from the medical record. Information was collected relating to use of EA, complications, and length of hospitalization (LOS). Post-operative outcomes were compared between those that had epidurals and those that did not. Results: One hundred seventeen patients were identified that underwent SOC, 34 of whom had EA. These two groups were similar in relation to age, BMI, and co-morbidities. Three patients in the epidural group had complications limiting epidural duration—two with hypotension and one with refractory pruritus. There was no difference in pneumonia, deep vein thrombosis (DVT), wound infection, urinary tract infection (UTI), recurrence, or mortality (Table 1). There was an increase in LOS (6.68 vs. 6.06 days, p 0.01) in patients with EA. Conclusions: The use of EA results in increased LOS in patients undergoing SOC. EA associated morbidity occurs infrequently. The incidence of post-operative complications is unaffected by EA. Further studies are needed to delineate the benefit of EA in this patient population.

Blind Separation of Speech Signals Based on Wavelet Transform and Independent Component Analysis

Speech signals in frequency domain were separated based on discrete wavelet transform (DWT) and independent component analysis (ICA). First, mixed speech signals were decomposed into different frequency domains by DWT and the subbands of speech signals were separated using ICA in each wavelet domain; then, the permutation and scaling problems of frequency domain blind source separation (BSS) were solved by utilizing the correlation between adjacent bins in speech signals; at last, source signals were reconstructed from single branches. Experiments were carried out with 2 sources and 6 microphones using speech signals at sampling rate of 40 kHz. The microphones were aligned with 2 sources in front of them, on the left and right. The separation of one male and one female speeches lasted 2.5 s. It is proved that the new method is better than single ICA method and the signal to noise ratio is improved by 1 dB approximately.

Simulation of Multi-component Multi-stage Separation Process ——An Improved Algorithm and Application

This paper presents a flexible model and a robust algorithm for simulation of multi-stage multi-component separation processes in which multiple feeds, side streams, strippers and/or side heat exchangers are involved. The improved algorithm effectively accelerates the speed of convergence and offers better stability by introducing a damping factor for updating the stripping factor, and also reduces the requirement on the initial estimates by updating the Joacobian matrix directly with the stripping factor and enthalpy. On the other hand, an efficient algorithm was proposed to solve the approximate tri-diagonal matrix (containing the off-band elements) derived from the material balance equations (M equations) and phase equilibrium equations (E equations), the advantages and simplicity of the “insideout” technique of the Russell are retained. The present algorithm was demonstrated to be effective in simulating complex separation columns with typical case studies.

Wideband dispersion removal and mode separation of Lamb waves based on two-component laser interferometer measurement

Ultrasonic Lamb waves are considered as a sensitive and effective tool for nondestructive testing and evaluation of plate-like or pipe-like structures. The nature of multimode and dispersion causes the wave packets to spread, and the modes overlap in both time and frequency domains as they propagate through the structures. By using a two-component laser interferometer technique, in combination with a priori knowledge of the dispersion characteristics and wave structure information of Lamb wave modes, a two-component signal processing technique is presented for implementing dispersion removal and mode separation simultaneously for two modes mixture signals of Lamb waves. The proposed algorithm is first processed and verified using synthetic Lamb wave signals. Then, the two-component displacements test experiment is conducted using different aluminum plate samples. Moreover, we confirm the effectiveness and robustness of this method.

Minimum Amount of Extracting Solvent of a Separation of Two Rare Earth Components

A significant development in the theory of countercurrent extraction will be presented in this article. New expressions of the term in countercurrent extraction process analysis, “Adjacent Stage Impurity Ratio” (ASIR), are deduced. Furthermore, based on the term together with mass balance and extraction equilibrium, the conditions where a given countercurrent extraction separation operation can have minimum amounts of both extracting solvent and scrubbing agent solution can be estimated, and the equations of the two minimum amounts can be deduced. It was found that the equations for a two-component separation using a single aqueous or organic feed are exactly the same as they appeared in the theory initially established in 1970s. Unlike its earlier version, the present derivation does not involve feed-stage-composition hypothesis, and also has the advantage of dealing with a double-feed system where both aqueous and organic feeds are simultaneously employed whereas the earlier theory can only analyze a separation using a single aqueous or organic feed.

Detection and Separation of Event-related Potentials from Multi-Artifacts Contaminated EEG by Means of Independent Component Analysis

Event-related potentials (ERP) is an important type of brain dynamics in human cognition research. However, ERP is often submerged by the spontaneous brain activity EEG, for its relatively tiny scale. Further more, the brain activities collected from scalp electrodes are often inevitably contaminated by several kinds of artifacts, such as blinks, eye movements, muscle noise and power line interference. A new approach to correct these disturbances is presented using independent component analysis (ICA). This technique can effectively detect and extract ERP components from the measured electrodes recordings even if they are heavily contaminated. The results compare favorably to those obtained by parametric modeling. Besides, auto-adaptive projection of decomposed results to ERP components was also given. Through experiments, ICA proves to be highly capable of ERP extraction and S/N ratio improving.

TOC estimation from logging data using principal component analysis

Total organic carbon(TOC)content is one of the most important parameters for characterizing the quality of source rocks and assessing the hydrocarbon-generating potential of shales.The Lucaogou Formation shale reservoirs in the Jimusaer Sag,Junggar Basin,NW China,is characterized by extremely complex lithology and a wide variety of mineral compositions with source rocks mainly consisting of carbonaceous mudstone and dolomitic mudstone.The logging responses of organic matter in the shale reservoirs is quite different from those in conventional reservoirs.Analyses show that the traditional△logR method is not suitable for evaluating the TOC content in the study area.Analysis of the sensitivity characteristics of TOC content to well logs reveals that the TOC content has good correlation with the separation degree of porosity logs.After a dimension reduction processing by the principal component analysis technology,the principal components are determined through correlation analysis of porosity logs.The results show that the TOC values obtained by the new method are in good agreement with that measured by core analysis.The average absolute error of the new method is only 0.555,much less when compared with 1.222 of using traditional△logR method.The proposed method can be used to produce more accurate TOC estimates,thus providing a reliable basis for source rock mapping.

Separation of P. and SV-wavefields from multi-componen seismic data

在多分量地震勘探资料中,水平和垂直分量都记录有P波和SV波。本文研究了P波和SV波波场分解的方法。当上行P波和S波分别入射时,通过研究它们引起的水平和垂直位移,把地震记录的水平和垂直分量进行分解,得到纵波和转换波;在已知海底介质中纵横波速度和介质密度的情况下,可以在τ-P域内实现波场分解,然后变换到时间域,得到时间域内的纵波和转换波剖面。把本文研究的波场分解方法应用于合成资料,能够有效地分解得到纵波波场和转换波波场。最后,海上多分量实际资料的实例应用表明,本文研究的波场分解方法是可行有效的。本方法也适用于自由表面的资料。

Speech Separation Methodology for Hearing Aid

In the design of hearing aids(HA),the real-time speech-enhancement is done.The digital hearing aids should provide high signal-to-noise ratio,gain improvement and should eliminate feedback.In generic hearing aids the perfor-mance towards different frequencies varies and non uniform.Existing noise can-cellation and speech separation methods drops the voice magnitude under the noise environment.The performance of the HA for frequency response is non uni-form.Existing noise suppression methods reduce the required signal strength also.So,the performance of uniform sub band analysis is poor when hearing aid is con-cern.In this paper,a speech separation method using Non-negative Matrix Fac-torization(NMF)algorithm is proposed for wavelet decomposition.The Proposed non-uniformfilter-bank was validated by parameters like band power,Signal-to-noise ratio(SNR),Mean Square Error(MSE),Signal to Noise and Dis-tortion Ratio(SINAD),Spurious-free dynamic range(SFDR),error and time.The speech recordings before and after separation was evaluated for quality using objective speech quality measures International Telecommunication Union-Telecommunication standard ITU-T P.862.

INVARIANT SUBSPACES AND GENERALIZED FUNCTIONAL SEPARABLE SOLUTIONS TO THE TWO-COMPONENT b-FAMILY SYSTEM

Invariant subspace method is exploited to obtain exact solutions of the two- component b-family system. It is shown that the two-component b-family system admits the generalized functional separable solutions. Furthermore, blow up and behavior of those exact solutions are also investigated.

FREQUENCY OVERLAPPED SIGNAL IDENTIFICATION USING BLIND SOURCE SEPARATION

The concepts, principles and usages of principal component analysis （PCA） and independent component analysis （ICA） are interpreted. Then the algorithm and methodology of ICA-based blind source separation （BSS）, in which the pre-whitened based on PCA for observed signals is used, are researched. Aiming at the mixture signals, whose frequency components are overlapped by each other, a simulation of BSS to separate this type of mixture signals by using theory and approach of BSS has been done. The result shows that the BSS has some advantages what the traditional methodology of frequency analysis has not.

Multi-dimensional blind separation method for STBC systems

Intercepted signal blind separation is a research topic with high importance for both military and civilian communication systems. A blind separation method for space-time block code （STBC） systems is proposed by using the ordinary independent component analysis （ICA）. This method cannot work when specific complex modulations are employed since the assumption of mutual independence cannot be satisfied. The analysis shows that source signals, which are group-wise independent and use multi-dimensional ICA （MICA） instead of ordinary ICA, can be applied in this case. Utilizing the block-diagonal structure of the cumulant matrices, the JADE algorithm is generalized to the multidimensional case to separate the received data into mutually independent groups. Compared with ordinary ICA algorithms, the proposed method does not introduce additional ambiguities. Simulations show that the proposed method overcomes the drawback and achieves a better performance without utilizing coding information than channel estimation based algorithms.

Blind source separation of ship-radiated noise based on generalized Gaussian model

When the distribution of the sources cannot be estimated accurately, the ICA algorithms failed to separate the mixtures blindly. The generalized Gaussian model （GGM） is presented in ICA algorithm since it can model non- Ganssian statistical structure of different source signals easily. By inferring only one parameter, a wide class of statistical distributions can be characterized. By using maximum likelihood （ML） approach and natural gradient descent, the learning rules of blind source separation （BSS） based on GGM are presented. The experiment of the ship-radiated noise demonstrates that the GGM can model the distributions of the ship-radiated noise and sea noise efficiently, and the learning rules based on GGM gives more successful separation results after comparing it with several conventional methods such as high order cumnlants and Gaussian mixture density function.

Blind source separation of multichannel electroencephalogram based on wavelet transform and ICA

Combination of the wavelet transform and independent component analysis （ICA） was employed for blind source separation （BSS） of multichannel electroencephalogram （EEG）. After denoising the original signals by discrete wavelet transform, high frequency components of some noises and artifacts were removed from the original signals. The denoised signals were reconstructed again for the purpose of ICA, such that the drawback that ICA cannot distinguish noises from source signals can be overcome effectively. The practical processing results showed that this method is an effective way to BSS of multichannel EEG. The method is actually a combination of wavelet transform with adaptive neural network, so it is also useful for BBS of other complex signals.

A contourlet-transform based sparse ICA algorithm for blind image separation

A contourlet-transform （CT） based sparse independent component analysis for blind image separation is proposed. The images are first decomposed into sets of local features with various degrees of sparsity, and then the intrinsic property is used to select the best （sparsest） subsets of features for further separation. Based on sparse description of the contourlet- transform, the proposed approach is able to yield better performance, including faster convergence and the certain order for the separated signals. Simulation results confirm the validity of the proposed method.

Performance of Inner-core Supersonic Gas Separation Device with Droplet Enlargement Method

与一个单个重部件为煤气的混合的处理改进一台超声的煤气的分离设备的分离表演，有更短的解决距离的新奇结构被构造，微滴增大的一个方法被使用。一系列实验在各种各样的条件下面在改进分离设备被执行，用象象成核中心的中等、微的水微滴的空气乙醇蒸汽。入口压力，温度和相对湿度，打漩的紧张，和分离表演上的水的集体流动率的效果被调查。分离被增加入口压力和相对湿度改进。与紧张和集体流动水评估的打漩的减少，分离效率增加了第一然后减少。入口温度在分离上有细微效果。结果证明分离表演有效地用建议结构和方法被改进，并且在这研究的最好的分离与乙醇移动率被获得大约 55% 和露水点消沉 27 K。自从水存留之物率在 &#x2212 的范围以内，水的增加有很少污染到空气乙醇蒸汽系统；在大多数情况中的 2%-0。

Research on Blind Source Separation for Machine Vibrations

Blind source separation is a signal processing method based on independent component analysis, its aim is to separate the source signals from a set of observations (output of sensors) by assuming the source signals independently. This paper reviews the general concept of BSS firstly;especially the theory for convolutive mixtures, the model of convolutive mixture and two deconvolution structures, then adopts a BSS algorithm for convolutive mixtures based on residual cross-talking error threshold control criteria, the simulation testing points out good performance for simulated mixtures.