Rapid and real-time analysis of multi-component dissolved gas in seawater by Raman spectroscopy combined with continuous gas-liquid separator

Rapid and sensitive detection of dissolved gases in seawater is quite essential for the investigation of the global carbon cycle.Large quantities of in situ optical detection techniques showed restricted measurement efficiency,owing to the single gas sensor without the identification ability of multiple gases.In this work,a novel gas-liquid Raman detection method of monitoring the multi-component dissolved gases was proposed based on a continuous gas-liquid separator under a large difference of partial pressure.The limit of detection(LOD)of the gas Raman spectrometer could arrive at about 14 μl·L^(-1)for N_(2)gas.Moreover,based on the continuous gas-liquid separation process,the detection time of the dissolved gases could be largely decreased to about 200 s compared with that of the traditional detection method(30 min).Effect of equilibrium time on gas-liquid separation process indicated that the extracted efficiency and decay time of these dissolved gases was CO_(2)>O_(2)>N_(2).In addition,the analysis of the relationship between equilibrium time and flow speed indicated that the decay time decreased with the increase of the flow speed.The validation and application of the developed system presented its great potential for studying the components and spatiotemporal distribution of dissolved gases in seawater.

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.

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.

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.

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.

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.

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.

Evaluation of gas-liquid separation performance of natural gas filters

Fibrous filters are often used to remove contaminants including both dusts and liquid droplets from natural gas. This paper aims to evaluate the gas-liquid separation performance of three types of cartridge filters used in the West-East natural gas transmission project. The comparison of the original pressure drop of clean filters and the evolution of pressure drop as liquid droplets deposited in the filter media are described. The original pressure drops of these filters were similar but the pressure drops at a steady state were different. Fractional efficiency was used to study the separation performance of cartridge filters. Droplets at the outlet of the filters had small diameters, no more than 3 μm, but were very numerous. The effect of filtration velocity on gas-liquid separation performance was analyzed. Higher filtration velocity indicated better gas-liquid separation performance. Finally the quality factor related to pressure drop and filtration efficiency was applied to evaluate the gas-liquid separation performance.

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.

Measurment of gas-liquid two-phase slug flow with a Venturi meter based on blind source separation

We propose a novel flow measurement method for gas–liquid two-phase slug flow by using the blind source separation technique. The flow measurement model is established based on the fluctuation characteristics of differential pressure(DP) signals measured from a Venturi meter. It is demonstrated that DP signals of two-phase flow are a linear mixture of DP signals of single phase fluids. The measurement model is a combination of throttle relationship and blind source separation model. In addition, we estimate the mixture matrix using the independent component analysis(ICA) technique. The mixture matrix could be described using the variances of two DP signals acquired from two Venturi meters. The validity of the proposed model was tested in the gas–liquid twophase flow loop facility. Experimental results showed that for most slug flow the relative error is within 10%.We also find that the mixture matrix is beneficial to investigate the flow mechanism of gas–liquid two-phase flow.

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.

RESEARCH OF QUANTUM GENETIC ALGORITH AND ITS APPLICATION IN BLIND SOURCE SEPARATION

This letter proposes two algorithms: a novel Quantum Genetic Algorithm (QGA)based on the improvement of Han's Genetic Quantum Algorithm (GQA) and a new Blind Source Separation (BSS) method based on QGA and Independent Component Analysis (ICA). The simulation result shows that the efficiency of the new BSS method is obviously higher than that of the Conventional Genetic Algorithm (CGA).

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

To improve the separation performance of a supersonic gas separation device for the treatment of gas mixture with a single heavy component, a novel structure with shorter settlement distance was constructed and a method of droplet enlargement was applied. A series of experiments were carried out in the improved separation device under various conditions, using air-ethanol vapor as the medium and micro water droplets as nucleation cen- ters. The effects of the inlet pressure, temperature and relative humidity, the swirling intensity, and mass flow rate of water on the separation performance were investigated. The separation was improved by increasing the inlet pressure and relative humidity. With the decrease of swirling intensity and mass flow rate of water, the separation efficiency increased first and then decreased. The inlet temperature had a slight effect on the separation. The results showed that the separation performance was effectively improved using the proposed structure and method, and the best separation in this study was obtained with the ethanol removal rate about 55% and dew point depression 27 K. The addition of water had little pollution to the air-ethanol vapor system since the water carry-over rate was within the range of -2 %-0 in most cases.

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.