Particle Swarm Optimization for Sorted Adapted Gaussian Mixture Models

Recently, we introduced the sorted Gaussian mixture models (SGMMs) algorithm providing the means to tradeoff performance for operational speed and thus permitting the speed-up of GMM-based classification schemes. The performance of the SGMM algorithm depends on the proper choice of the sorting function, and the proper adjustment of its parameters. In the present work, we employ particle swarm optimization (PSO) and an appropriate fitness function to find the most advantageous parameters of the sorting function. We evaluate the practical significance of our approach on the text-independent speaker verification task utilizing the NIST 2002 speaker recognition evaluation (SRE) database while following the NIST SRE experimental protocol. The experimental results demonstrate a superior performance of the SGMM algorithm using PSO when compared to the original SGMM. For comprehensiveness we also compared these results with those from a baseline Gaussian mixture model-universal background model (GMM-UBM) system. The experimental results suggest that the performance loss due to speed-up is partially mitigated using PSO-derived weights in a sorted GMM-based scheme.     

Integration of beamforming and uncertainty-of-observation techniques for robust ASR in multi-source environments

This paper presents a new approach for increasing the robustness of multi-channel automatic speech recognition in noisy and reverberant multi-source environments. The proposed method uses uncertainty propagation techniques to dynamically compensate the speech features and the acoustic models for the observation uncertainty determined at the beamforming stage. We present and analyze two methods that allow integrating classical multi-channel signal processing approaches like delay and sum beamformers or Zelinski-type Wiener filters, with uncertainty-of-observation techniques like uncertainty decoding or modified imputation. An analysis of the results on the PASCAL-CHiME task shows that this approach consistently outperforms conventional beamformers with a minimal increase in computational complexity. The use of dynamic compensation based on observation uncertainty also outperforms conventional static adaptation with no need of adaptation data.     

Calculation of Nano-charged Particles Pulsation Frequency in Tokamak

One of the unknown problems that cause to perturbation of tokamak function is charged particles pulsation. Applying the toroidal magnetic field to the charged motive particles implicate plasma in the system. This plasma has a frequency that depends on the toroidal magnetic field. Another frequency exists in this system that is caused to the plasma self magnetism. This lateral pulsation is principal origin of perturbation in the system. In this paper, nano-charged particles pulsation is investigated and its frequency is calculated by Buckingham method. Knowing this frequency is necessary to control perturbation. The result of calculations shows that temperature increasing of the system cause to more intense perturbation.     

Mechanical Properties and Microstructure of VPS and HVOF CoNiCrAlY Coatings

In this study, high velocity oxy-fuel (HVOF) and vacuum plasma spraying (VPS) coatings were sprayed using a Praxair (CO-210-24) CoNiCrAlY powder. Free-standing coatings underwent vacuum annealing at different temperatures for times of up to 840 h. Feedstock powder, and as-sprayed and annealed coatings, were characterized by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and x-ray diffraction (XRD). The hardness and Young’s modulus of the as-sprayed and the annealed HVOF and VPS coatings were measured, including the determination of Young’s moduli of the individual phases via nanoindentation and measurements of Young’s moduli of coatings at temperatures up to 500 °C. The Eshelby inclusion model was employed to investigate the effect of microstructure on the coatings’ mechanical properties. The sensitivity of the mechanical properties to microstructural details was confirmed. Young’s modulus was constant up to ~200 °C, and then decreased with increasing measurement temperature. The annealing process increased Young’s modulus because of a combination of decreased porosity and β volume fraction. Oxide stringers in the HVOF coating maintained its higher hardness than the VPS coating, even after annealing.     

Comparison of conventional and spherical reactor for the industrial auto-thermal reforming of methane to maximize synthesis gas and minimize CO2

Auto-thermal reforming (ATR), a combination of exothermic partial oxidation and endothermic steam reforming of methane, is an important process to produce syngas for petrochemical industries. In a commercial ATR unit, tubular fixed bed reactors are typically used. Pressure drop across the tube, high manufacturing costs, and low production capacity are some disadvantages of these reactors. The main propose of this study is to offer an optimized radial flow, spherical packed bed reactor as a promising alternative for overcoming the drawbacks of conventional tubular reactors. In the current research, a one dimensional pseudo-homogeneous model based on mass, energy, and momentum balances is applied to simulate the performance of packed-bed reactors for the production of syngas in both tubular and spherical reactors. In the optimization section, the proposed work explores optimal values of various decision variables that simultaneously maximize outlet molar flow rate of H₂, CO and minimize molar flow rate of CO₂ from novel spherical reactor. The multi-objective model is transformed to a single objective optimization problem by weighted sum method and the single optimum point is found by using genetic algorithm. The optimization results show that the pressure drop in the spherical reactor is negligible in comparison to that of the conventional tubular reactor. Therefore, it is inferred that the spherical reactor can operate with much higher feed flow rate, more catalyst loading, and smaller catalyst particles.     

Direct determination of a new mode-dependent cohesive zone model to simulate metal-to-metal adhesive joints

In this paper, a new mode-dependent cohesive zone model for the simulation of metal to metal adhesive joints is directly determined. Three consecutive steps have been taken into account for this end. First, double cantilever beam (DCB) and end-notched flexure (ENF) specimens are utilized for the direct experimental extraction of the traction-separation laws (TSLs) for adhesive bonded joints subjected to pure mode I and mode II, respectively. Next, the results are implemented to obtain the relative cohesive zone parameters for defining the simplified Park-Paulino-Roesler cohesive zone model (S-PPR CZM). Finally, mixed-mode characteristics parameters are derived for an arbitrary mode-mixity ratio based on pure mode TSLs. The model is further implemented in ABAQUS® commercial software to be verified against the experimental results of pure mode loadings which leads to the direct extraction of TSLs. The experiments conducted on the strength of single lap joint (SLJ) and scarf joint (SJ) specimens, commonly tested for mixed-mode loading, confirm the accuracy of the developed mixed-mode S-PPR model for different mode-mixity conditions.