Mixture gas component concentration analysis based on support vector machine and infrared spectrum

A novel quantitative analysis method of multi-component mixture gas concentration based on support vector machine (SVM) and spectroscopy is proposed. Through transformation of the kernel function, the seriously overlapped and nonlinear spectrum data are transformed in high-dimensional space, but the high-dimensional data can be processed in the original space. Some factors, such as kernel function, range of the wavelength, and penalty coefficient, are discussed. This method is applied to the quantitative analysis of natural gas components concentration, and the component concentration maximal deviation is 2.28%.     

Hepatic CT image retrieval based on the combination of Gabor filters and support vector machine

Content-based image retrieval has been an active area of research for more than ten years.Gabor schemes and support vector machine (SVM) method have been proven effective in image representation and clas- sification.In this paper,we propose a retrieval scheme based on Gabor filters and SVMs for hepatic computed tomography (CT) images query.In our experiments,a batch of hepatic CT images containing several types of CT findings are used for the retrieval test.Precision comparison between our scheme and existing methods is presented.     

Speech compression scheme based on wavelet transform and vector quantization

lIntroductionThemethodoflinearpredictionhasbeendondnatedinspeechcompressiollsince197o's-However,thefreqencyresolutionoflinearpredictionisnotmatchedwellwiththeactualphysicalprocessinthehUInanear-Thewavelettransform,asakindofnewmethodforsignalprocessing,overcomesthedritationsofshort-tAneFourierThansformonthefrequencyresoutfon-Itcanuselilghfrequencyresolutionatlowfreqllencyrangeandusefowfrequencyresohitionathighfrequencyrange-Basedonthispoint,weusewavlettransformtothespeechcomnresslon.Firstthe…     

Source identification of gasoline engine noise based on continuous wavelet transform and EEMD–RobustICA

In order to separate noise source of gasoline engine, ensemble empirical mode decomposition (EEMD), robust independent component analysis (RobustICA) and continuous wavelet transform (CWT) are applied to study the blind source separation and noise source identification of gasoline engine. After the signal is decomposed with EEMD into a set of intrinsic mode function (IMFs), RobustICA has been applied to extract independent sources. The combined technique alleviates the problem of mode mixing in EMD and overcomes the problem that the number of sensors must be larger than or equal to the number of separated components. At the same time, RobustICA’s cost efficiency and robustness are particularly remarkable for short sample length in the absence of pre-whiten. CWT using the Complex Morlet Wavelet (CMW) is used for its better time–frequency localization features to analyze time–frequency characteristics of the ICA results. Combining the time–frequency results with different noise sources frequency spectrums, the corresponding relation of the different noise sources of gasoline engine and the independent components is determined. It turns out that these independent components correspond to the exhaust, combustion and piston slap noise of the gasoline engine respectively.     

Reconstruction of Fabry–Perot cavity interferometer nanometer micro-displacement based on Hilbert transform

A novel reconstruction method of nanometer micro-displacement of Fabry–Perot(F-P) interference is proposed in this study. Hilbert transforms are performed for F-P interference fringes, and the obtained signal performs tangent operation with the original signal. Finally, the validity of the proposed algorithm and the structure are verified by simulation and several experimental measurements for vibration. Results from the experiments show that the maximum relative error is 4.9%.     

A fusion method for infrared–visible image and infrared-polarization image based on multi-scale center-surround top-hat transform

This paper presents a fusion method for infrared–visible image and infrared-polarization image based on multi-scale center-surround top-hat transform which can effectively extract the feature information and detail information of source images. Firstly, the multi-scale bright (dark) feature regions of source images at different scale levels are respectively extracted by multi-scale center-surround top-hat transform. Secondly, the bright (dark) feature regions at different scale levels are refined for eliminating the redundancies by spatial scale. Thirdly, the refined bright (dark) feature regions from different scales are combined into the fused bright (dark) feature regions through adding. Then, a base image is calculated by performing dilation and erosion on the source images with the largest scale outer structure element. Finally, the fusion image is obtained by importing the fused bright and dark features into the base image with a reasonable strategy. Experimental results indicate that the proposed fusion method can obtain state-of-the-art performance in both aspects of objective assessment and subjective visual quality.     

Turbulence modulation model for gas–particle flow based on probability density function approach

The paper focuses on the turbulence modulation problem in gas–particle flow with the use of probability density function(PDF) approach. By means of the PDF method, a general statistical moment turbulence modulation model without considering the trajectory difference between two phases is derived from the Navier–Stokes equations. A new turbulence production term induced by the dispersed-phase is analyzed and considered. Furthermore, the trajectory difference between two media is taken into account. Subsequently, a new k–ε turbulence modulation model in dilute particle-laden flow is successfully set up. Then, the changes to several terms, including the turbulence production, dissipation, and diffusion terms, are well described consequently. The promoted model provides a more probable explanation for the modification of particles on the turbulence. Finally, we applied the model to simulate a gas–particle turbulence flow case in a wall jet, and found that the simulation results agree well with the experimental data.     

Empirical study of the scaling behavior of the amplitude–frequency distribution of the Hilbert–Huang transform and its application in sunspot time series analysis

Investigating long-range correlation by the Hurst exponent, H$H$, is crucial in the study of time series. Recently, empirical-mode-decomposition-based arbitrary-order Hilbert spectral analysis (EMD-HSA) has been proposed to numerically obtain without proof a scaling relationship, generated from the amplitude–frequency distribution, related to H$H$. We propose a formalism to empirically study EMD-HSA, to deduce its scaling exponent ξ(q)$\xi \left(q\right)$ from the perspective of EMD-based arbitrary-order Hilbert marginal spectrum (EMD-HMS), and to numerically compare the results with the expected H$H$. EMD-HSA and EMD-HMS experiments show that, by incompletely removing (quasi-)periodic trends, the sunspot series should have an H$H$ value around 0.12.     

An asymptotic-preserving method for highly anisotropic elliptic equations based on a Micro–Macro decomposition

The concern of the present work is the introduction of a very efficient asymptotic preserving scheme for the resolution of highly anisotropic diffusion equations. The characteristic features of this scheme are the uniform convergence with respect to the anisotropy parameter 0 < ε ? 1, the applicability (on cartesian grids) to cases of non-uniform and non-aligned anisotropy fields b and the simple extension to the case of a non-constant anisotropy intensity 1/ε. The mathematical approach and the numerical scheme are different from those presented in the previous work [P. Degond, F. Deluzet, A. Lozinski, J. Narski, C. Negulescu, Duality-based asymptotic-preserving method for highly anisotropic diffusion equations, Communications in Mathematical Sciences 10 (1) (2012) 1–31] and its considerable advantages are pointed out.     

Novel fusion method for visible light and infrared images based on NSST–SF–PCNN

The purpose of image fusion is to combine the source images of the same scene into a single composite image with more useful information and much better visual effects, which is undoubtedly suitable for further image processing tasks. This paper presented a novel fusion method for visible light and infrared images based on non-subsampled shearlet transform (NSST)–spatial frequency (SF)–pulse coupled neural network (PCNN). As a recently developed multi-resolution geometric analysis tool, NSST not only has remarked superiorities over other past conventional tools in terms of information capturing and computational costs saving, but also overcomes the lack of shift-invariance in shearlet transform (ST), so NSST applies to conducting the decompositions and reconstructions. Besides, traditional PCNN model is also upgraded to be an improved one called IPCNN in this paper to fuse the low-frequency and high-frequency subband coefficients. In the IPCNN structure, on the one hand, the value of the linking strength β is determined by the SF which represents the gradient features of the subband image; on the other hand, the time matrix is utilized to adaptively decide the iteration number of the IPCNN model, which is helpful to increase the function efficiency and save computational resources. Experimental results indicate that the proposed method performs well and has obvious superiorities over other current typical ones in both subjective visual performance and objective criteria.     

In-fiber Mach–Zehnder interferometer based on multi-mode fiber and up-taper for curvature sensing

A new type of curvature sensor comprises a stub of multi-mode fiber and an up-taper is proposed and demonstrated experimentally. The whole fabrication process is quite simple and the sensor head is cost effective. Measurement results show that it has a maximum curvature sensitivity of −61.877 nm/m⁻¹ at 1.1718 m⁻¹ (the highest value of reported papers among in-fiber Mach–Zehnder interferometers) and −9.2115 nm/m⁻¹ from 0.865 m⁻¹ to 1.1172 m⁻¹. Temperature sensitivity of 89.01 pm/°C within the range of 20–80 °C has also been achieved, which implies the possibility for measurement of temperature.     

On the dispersionless Davey–Stewartson system: Hamiltonian vector field Lax pair and relevant nonlinear Riemann–Hilbert problem for dDS-II system

Letters in Mathematical Physics - In this paper, the semiclassical limit of Davey–Stewartson system is studied. It shows that the dispersionless limited integrable system of hydrodynamic...     

A Mach–Zehnder interferometer based on peanut structure for temperature and refractive index measurement

Optical Review - A fiber Mach–Zehnder (M-Z) interferometer based on the peanut structure is proposed to measure the temperature and the refractive index (RI). This design includes the...     

Fabry–Pérot cavities based on chemical etching for high temperature and strain measurement

In this paper, two hybrid multimode/single mode fiber Fabry–Pérot (FP) cavities were compared. The cavities fabricated by chemical etching are presented as high temperature and strain sensors. In order to produce this FP cavity a single mode fiber was spliced to a graded index multimode fiber with 62.5 μm core diameter. The Fabry–Pérot cavities were tested as a high temperature sensor in the range between room temperature and 700 °C and as strain sensors. A reversible shift of the interferometric peaks with temperature allowed to estimate a sensitivity of 0.75 ± 0.03 pm/°C and 0.98 ± 0.04 pm/°C for the sensor A and B respectively. For strain measurement sensor A demonstrated a sensitivity of 1.85 ± 0.07 pm/μ? and sensor B showed a sensitivity of 3.14 ± 0.05 pm/μ?. The sensors demonstrated the feasibility of low cost fiber optic sensors for high temperature and strain.     

Understanding agent-based models of financial markets: A bottom–up approach based on order parameters and phase diagrams

We describe a bottom–up framework, based on the identification of appropriate order parameters and determination of phase diagrams, for understanding progressively refined agent-based models and simulations of financial markets. We illustrate this framework by starting with a deterministic toy model, whereby N$N$ independent traders buy and sell M$M$ stocks through an order book that acts as a clearing house. The price of a stock increases whenever it is bought and decreases whenever it is sold. Price changes are updated by the order book before the next transaction takes place. In this deterministic model, all traders based their buy decisions on a call utility function, and all their sell decisions on a put utility function. We then make the agent-based model more realistic, by either having a fraction _fb$f_b$ of traders buy a random stock on offer, or a fraction _fs$f_s$ of traders sell a random stock in their portfolio. Based on our simulations, we find that it is possible to identify useful order parameters from the steady-state price distributions of all three models. Using these order parameters as a guide, we find three phases: (i) the dead market; (ii) the boom market; and (iii) the jammed market in the phase diagram of the deterministic model. Comparing the phase diagrams of the stochastic models against that of the deterministic model, we realize that the primary effect of stochasticity is to eliminate the dead market phase.     

Performance comparison of Fabry–Perot and Mach–Zehnder interferometers for molecular Doppler lidar based on fringe-imaging technique

Fringe-imaging Fabry–Perot interferometer (FIFPI) and fringe-imaging Mach–Zehnder interferometer (FIMZI) used as frequency discriminator for incoherent molecular Doppler lidar were analyzed, respectively. For a pure molecular backscattered signal, performances (wind measurement sensitivity and signal-to-noise ratio) of both FIFPI and FIMZI systems were simulated based on the U.S. standard atmospheric model. Comparisons of two systems were made under the same emitting and receiving parameters with certain wind speed dynamic range. Simulated results show that, though relatively lower sensitivity to Doppler shift, the single-channel FIMZI system provides a factor of 1.3 times smaller error in the horizontal wind velocity than that of FIFPI at a range of 20 km. We expect that the FIMZI frequency discriminator would provide an effective technique to improve the measurement accuracy for incoherent molecular Doppler lidar.     

Joint precoding and equalization design for reduced-CP OTFS based on the compact input–output model

This work studies the system design for the reduced cycle-prefix (CP) orthogonal time frequency space (OTFS) modulation in which only one CP is inserted in each frame. First, a compact input–output model is established which does not need to assume that the delay and Doppler shifts are on the delay-Doppler sampling grids. Second, based on the proposed compact model, a joint precoding and equalization method is proposed for the reduced-CP OTFS modulation, which can decomposes the whole reduced-CP OTFS communication system into parallel multiple-input multiple-output (MIMO) subsystems. Hence, the computational complexity can be greatly reduced. Simulation results of the bit error rate (BER) performance of the proposed method are reported and compared with the standard method under different system parameters.     

Influence of turbulence–chemistry interaction for n-heptane spray combustion under diesel engine conditions with emphasis on soot formation and oxidation

The influence of the turbulence–chemistry interaction (TCI) for n-heptane sprays under diesel engine conditions has been investigated by means of computational fluid dynamics (CFD) simulations. The conditional moment closure approach, which has been previously validated thoroughly for such flows, and the homogeneous reactor (i.e. no turbulent combustion model) approach have been compared, in view of the recent resurgence of the latter approaches for diesel engine CFD. Experimental data available from a constant-volume combustion chamber have been used for model validation purposes for a broad range of conditions including variations in ambient oxygen (8?21% by vol.), ambient temperature (900 and 1000 K) and ambient density (14.8 and 30 kg/m³). The results from both numerical approaches have been compared to the experimental values of ignition delay (ID), flame lift-off length (LOL), and soot volume fraction distributions. TCI was found to have a weak influence on ignition delay for the conditions simulated, attributed to the low values of the scalar dissipation relative to the critical value above which auto-ignition does not occur. In contrast, the flame LOL was considerably affected, in particular at low oxygen concentrations. Quasi-steady soot formation was similar; however, pronounced differences in soot oxidation behaviour are reported. The differences were further emphasised for a case with short injection duration: in such conditions, TCI was found to play a major role concerning the soot oxidation behaviour because of the importance of soot-oxidiser structure in mixture fraction space. Neglecting TCI leads to a strong over-estimation of soot oxidation after the end of injection. The results suggest that for some engines, and for some phenomena, the neglect of turbulent fluctuations may lead to predictions of acceptable engineering accuracy, but that a proper turbulent combustion model is needed for more reliable results.     

An adaptive implicit–explicit scheme for the DNS and LES of compressible flows on unstructured grids

An adaptive implicit–explicit scheme for Direct Numerical Simulation (DNS) and Large-Eddy Simulation (LES) of compressible turbulent flows on unstructured grids is developed. The method uses a node-based finite-volume discretization with Summation-by-Parts (SBP) property, which, in conjunction with Simultaneous Approximation Terms (SAT) for imposing boundary conditions, leads to a linearly stable semi-discrete scheme. The solution is marched in time using an Implicit–Explicit Runge–Kutta (IMEX-RK) time-advancement scheme. A novel adaptive algorithm for splitting the system into implicit and explicit sets is developed. The method is validated using several canonical laminar and turbulent flows. Load balance for the new scheme is achieved by a dual-constraint, domain decomposition algorithm. The scalability and computational efficiency of the method is investigated, and memory savings compared with a fully implicit method is demonstrated. A notable reduction of computational costs compared to both fully implicit and fully explicit schemes is observed.