Accelerating hybrid stochastic algorithm for spin models

The problem of critical slowing down in simulations of statistical mechanics systems is discussed in the context of hybrid stochastic differential equations. By modifying the kinetic energy part of the system's molecular dynamics lagrangian, new algorithms are presented which evolve the system's infra-red modes as rapidly as their ultraviolet ones. These improved (“accelerated”) algorithms should yield accurate measurements of the critical behavior of models of magnets, glasses, etc. which have not been practical in the past.     

Angular distribution in exciton and hybrid models for preequilibrium reactions

Simple expressions for the calculation of angular distributions of nucleons emitted in nucleon-induced preequilibrium reactions are derived within the framework of both the exciton and hybrid model.     

A Monte-Carlo method for coulomb collisions in hybrid plasma models

A procedure for implementing Coulomb collisions into hybrid (particle-fluid) plasma models is outlined which is rapid in execution due to the use of approximate expressions for the collision integrals and conserves energy and momentum exactly. Particles undergo dynamic friction and diffusion in velocity-space at rates consistent with the velocity-dependent Fokker–Planck diffusion coefficients and there are no assumptions made about the shape or size of the particle distribution function. The method is tested against the analytical theory of test particle slowing in a background plasma and the thermal equilibration of a Maxwellian distribution.     

层叠式“产生/判别”混合模型的语音情感识别

提出了层叠式“产生/判别”混合模型的语音情感识别方法。首先,提取63维语句级特征,运用Fisher从中选择12个最佳的语句级特征,建立小波神经网络(WNN)的层叠式产生式模型进行语音情感识别;然后提取69维帧级特征,采用SFS选择出待使用的8维特征,将高斯混合模型(GMM)进行多维概率输出,建立层叠式“产生/判别”混合模型进行语音情感识别。实验结果显示:(1)层叠式“产生/判别”混合模型较单独WNN、GMM、HMM (隐马尔可夫模型)、SVM (支持向量机)的识别率要高;(2)层叠式“产生/判决式”混合模型识别率较基于WNN的层叠产生式模型高;(3) M=13,D维GMM-MAP/SVM (MAP,最大后验概率)串联融合模型为最优的层叠式“产生/判别”混合模型,能获得最高85.1%的识别率。     

Minimalistic hybrid models for the adsorption of polymers and peptides to solid substrates

We have performed chain-growth simulations of minimalistic hybrid lattice models for polymers interacting with interfaces of attractive solid substrates in order to gain insights into the conformational transitions of the polymers in the adsorption process. Primarily focusing on the dependence of the conformational behavior on temperature and solubility, we obtained pseudophase diagrams with a detailed structure of conformational subphases. In the study of hydrophobic-polar peptides in the vicinity of different types of substrates, we found a noticeable substrate specificity of the assembly of hydrophobic domains in the conformations dominating the adsorption subphases. The text was submitted by the authors in English.     

Identification of hybrid ARX-neural network models for three-dimensional simulation of a vibroacoustic system

Acoustic noise in industrial areas, typically generated by compressors and vacuum pumps, may be mitigated by the combined use of passive and active noise control strategies. Despite its widespread use, the traditional Active Noise Control (ANC) technique requires error feedback and has been proven to be effective only within a small spatial region. When the movement of human ears is required within a large region and error feedback is difficult to be accomplished, new cancelling strategies have to be devised to achieve acceptable levels of spatial coverage. In the pursuit of this goal, this paper proposes a vibroacustic model to predict noise radiated from machinery. The model output is the sound signal of the noise at a given point inside a closed room. The two model inputs are the vibration signal at the noise source and the spatial coordinates of the intended point. Experimental output data were measured at several points inside a region defined by a solid rectangle. A fixed-order ARX model was chosen (AutoRegressive with eXogenous input), and for each spatial point and its corresponding pair of input-output signals, a set of parameter values was estimated. To integrate all these models into a single one, a neural network was employed to associate or approximate each set of parameters to its spatial coordinates. With this approach, the total number of parameters is expected to be greatly reduced, when considering the original separated models. Experimental results are presented and comparisons with other models are established on the basis of least-square error metrics and parsimony of parameters. A qualitative perspective for employing the proposed model in the design of large-region ANC strategies is also offered.     

A comparison of noise models in a hybrid reference spectrum and principal components analysis algorithm for Raman spectroscopy

Raman spectroscopy exploits the Raman scattering effect to analyze chemical compounds with the use of laser light. Raman spectra are most commonly analyzed using the ordinary least squares (LS) method. However, LS is known to be sensitive to variability in the spectra of the analyte and background materials. In a previous paper, we addressed this problem by proposing a novel algorithm that models expected variations in the analyte as well as background signals. The method was called the hybrid LS and principal component analysis (HLP) algorithm and used an unweighted Gaussian distribution to model the noise in the measured spectra. In this paper, we show that the noise in fact follows a Poisson distribution and improve the noise model of our hybrid algorithm accordingly. We also approximate the Poisson noise model by a weighted Gaussian noise model, which enables the use of a more efficient solver algorithm. To reflect the generalization of the noise model, we from hereon call the method the hybrid reference spectrum and principal components analysis (HRP) algorithm. We compare the performance of LS and HRP with the unweighted Gaussian (HRP‐G), Poisson (HRP‐P), and weighted Gaussian (HRP‐WG) noise models. Our experiments use both simulated data and experimental data acquired from a serial dilution of Raman‐enhanced gold‐silica nanoparticles placed on an excised pig colon. When the only signal variability was zero‐mean random noise (as examined using simulated data), HRP‐P consistently outperformed HRP‐G and HRP‐WG, with the latter coming in as a close second. Note that in this scenario, LS and HRP‐G were equivalent. In the presence of random noise as well as variations in the mean component spectra, the three HRP algorithms significantly outperformed LS, but performed similarly among themselves. This indicates that, in the presence of significant variations in the mean component spectra, modeling such variations is more important than optimizing the noise model. It also suggests that for real data, HRP‐WG provides a desirable trade‐off between noise model accuracy and computational speed. Copyright © 2013 John Wiley & Sons, Ltd.     

Reconstruction of object location for diffuse fluorescence tomography on the basis of hybrid models of light scattering in biotissues

We propose a new method for reconstructing the spatial distribution of fluorophore in a highly scattering object from its images obtained by the method of diffuse fluorescence tomography. This method is intended for diagnostics of the fluorophore-marked tumors and is based on the algebraic-reconstruction principle combined with a new theoretical model and simulation of light propagation in randomly scattering media, such as biotissues, by the Monte Carlo method. The model experiments show that for 18-mm thick objects, the developed method allows one to determine location of the geometric center of a fluorescent inhomogeneity and its transverse and longitudinal dimensions with accuracies of up to 0.5 and 1.5 mm, respectively.     

Numerical analysis of the hybrid transducer ultrasonic motor: comparison of characteristics calculated by transmission-line and lumped-element models

In this paper, a hybrid transducer ultrasonic motor is numerically analyzed by using two equivalent electrical circuit models. A transmission-line model for the torsional vibration in the stator, which can model any torsional vibration mode and their combinations, was introduced and compared with a lumped-element model, which modeled the fundamental torsional resonance mode in the stator. The calculation result by using the transmission-line model demonstrated that the second harmonic torsional vibration increased either with the static spring force by which the rotor was pressed to the stator or with the load torque placed on the rotor. The difference in the calculated motor performance between the two models appeared when the second harmonic torsional vibration became large at a sufficient static spring force.     

Excitons in hybrid organic-inorganic nanostructures

In two-dimensional heterostructures made of semiconductor and organic layers, when resonance between the Wannier and Frenkel excitons is realized, the dipole-dipole interaction coupling them leads to novel effects. First, we discuss the pronounced nonlinear optical properties of the hybrid Frenkel-Wannier excitons appearing when the energy splitting of the excitonic spectrum is large compared to the exciton linewidths (the case of strong resonant coupling). Next, we consider the case of weak resonant coupling for which the Förster mechanism of energy transfer from an inorganic quantum well to an organic overlayer is of great interest: the electrical pumping of excitons in the semiconductor quantum well could be employed to turn on efficiently the organic material luminescence.     

混合算法中的耦合判据

混合算法是研究跨尺度流动问题的重要手段之一. 分界面位置以及分界面两侧的信息传递是混合算法需要解决的两个主要问题. 分析推导了现有耦合判据之间的关系,给出耦合判据的统一形式及参数的选取范围. 关键词： 混合算法 耦合判据     

Measure synchronization in hybrid quantum-classical systems

Measure synchronization in hybrid quantum-classical systems is investigated in this paper. The dynamics of the classical subsystem is described by the Hamiltonian equations, while the dynamics of the quantum subsystem is governed by the Schrödinger equation. By increasing the coupling strength in between the quantum and classical subsystems, we reveal the existence of measure synchronization in coupled quantum-classical dynamics under energy conservation for the hybrid systems.     

Remote-sensing image encryption in hybrid domains

Remote-sensing technology plays an important role in military and industrial fields. Remote-sensing image is the main means of acquiring information from satellites, which always contain some confidential information. To securely transmit and store remote-sensing images, we propose a new image encryption algorithm in hybrid domains. This algorithm makes full use of the advantages of image encryption in both spatial domain and transform domain. First, the low-pass subband coefficients of image DWT (discrete wavelet transform) decomposition are sorted by a PWLCM system in transform domain. Second, the image after IDWT (inverse discrete wavelet transform) reconstruction is diffused with 2D (two-dimensional) Logistic map and XOR operation in spatial domain. The experiment results and algorithm analyses show that the new algorithm possesses a large key space and can resist brute-force, statistical and differential attacks. Meanwhile, the proposed algorithm has the desirable encryption efficiency to satisfy requirements in practice.     

Rotons in a hybrid Bose-Fermi system

We calculate the spectrum of elementary excitations in a two-dimensional exciton condensate in the vicinity of a two-dimensional electron gas. We show that attraction of excitons due to their scattering with free electrons may lead to formation of a roton minimum. The energy of this minimum may go below the ground state energy which manifests breaking of the superfluidity. The Berezinsky-Kosterlitz-Thouless phase transition temperature decreases due to the exciton-exciton attraction mediated by electrons.     

Stabilized flames in hybrid aluminum-methane-air mixtures

A premixed methane–air bunsen-type flame is seeded with micron-sized (d₃₂ = 5.6 μm) atomized aluminum powder over a wide range of solid fuel concentrations. The burning velocities of the resulting two-phase hybrid flame are determined using the total surface area of the inner flame cone and the known volumetric flow rate, and spatially resolved flame spectra are obtained with a spectral scanning system. Flame temperatures are derived through polychromatic fitting of Planck’s law to the continuous part of the spectrum. It is found that an increase in the solid fuel concentration changes the aluminum combustion regime from low temperature oxidation to full-fledged flame front propagation. For stoichiometric methane–air mixtures, the transition occurs in the aluminum concentration range of 140–220 g/m³ and is manifested by the appearance of AlO sub-oxide bands and an increase in the flame temperature to 2500 K. The flame burning velocity is found to decrease only slightly with an increase in aluminum concentration, in contrast to the rapid decrease in flame speed, followed by quenching, that is observed for flames seeded with inert SiC particles. The observed behavior of the burning velocity and flame temperature leads to the conclusion that intense aluminum combustion in a hybrid flame only occurs when the flame front propagating through the aluminum suspension is coupled to the methane–air flame.