Qinghe Yao

Comparison of novel granulated pellet-containing tablets and traditional pellet-containing tablets by artificial neural networks.

Abstract Novel granulated pellets technique was adopted to prepare granulated pellet-containing tablets (GPCT). GPCT and traditional pellet-containing tablets (PCT) were prepared according to 29 formulations devised by the Design Expert 7.0, with doxycycline hydrochloride as model drug, blends of Eudragit FS 30D and Eudragit L 30D-55 as coating materials, for the comparison study to confirm the superiority of GPCT during compaction. Eudragit FS 30D content, coating weight gain, tablet hardness and pellet size were chosen as influential factors to investigate the properties and drug release behavior of tablets. The correlation coefficients between the experimental values and the predicted values by artificial neural networks (ANNs) for PCT and GPCT were 0.9474 and 0.9843, respectively, indicating the excellent prediction of ANNs. The similarity factors (f2) for release profiles of GPCT and the corresponding original pellets were higher than those of PCT, suggesting that the excipient layer of granulated pellets absorbed the compressing force and protected the integrity of coating films during compaction.

Numerical Simulation of the Hydrogen Dispersion Behavior by a Parallel Characteristic Curve Method

A parallel characteristic curve method is applied in domain decomposition system to simulate the dispersion behavior of hydrogen in this work. The characteristic curve method is employed to approximate the Navier-Stokes equations and the convection diffusion equation, and the feasibility of solving complex multicomponent flow problems is demonstrated by the numerical simulation of hydrogen dispersion in a partially open space. An analogy of the Boussinesq approximation is applied and numerical results are validated by comparing them with the experimental data. The dilution effect of ventilation is investigated. The transient behavior of hydrogen and the process of accumulation in the partially open space are discussed.

Feature Selection Tracking Algorithm Based on Sparse Representation

In order to enhance the robustness of visual tracking algorithm in complex environment, a novel visual tracking algorithm based on multifeature selection and sparse representation is proposed. In the framework of particles filter, particles with low target similarity are first filtered out by a fast algorithm; then, based on the principle of sparsely reconstructing the sample label, the features with high differentiation against the background are involved in the computation so as to reduce the disturbance of occlusions and noises. Finally, candidate targets are linearly reconstructed via sparse representation and the sparse equation is solved by using APG method to obtain the state of the target. Four comparative experiments demonstrate that the proposed algorithm in this paper has effectively improved the robustness of the target tracking algorithm.

Numerical simulation of hydrogen dispersion behaviour in a partially open space by a stabilized balancing domain decomposition method

The dispersion behaviour of leaking hydrogen in a partially open space is simulated numerically by a balancing domain decomposition method in this work. An analogy of the thermal convection problems is applied. The linear systems of Navier-Stokes equations and the convection diffusion equation are symmetrized by a pressure stabilized domain decomposition method. The interface problem after domain decomposition is solved by a balancing preconditioned conjugate gradient method. Numerical results are validated by comparing with the experimental data and available numerical results. The transient behaviour of hydrogen dispersion and accumulation in the partially open space is discussed; the mechanism of air influence is investigated and safe region inside this partially open space is classified.

Camera Calibration by Hybrid Hopfield Network and Self- Adaptive Genetic Algorithm

A new approach based on hybrid Hopfield neural network and self-adaptive genetic algorithm for camera calibration is proposed. First, a Hopfield network based on dynamics is structured according to the normal equation obtained from experiment data. The network has 11 neurons, its weights are elements of the symmetrical matrix of the normal equation and keep invariable, whose input vector is corresponding to the right term of normal equation, and its output signals are corresponding to the fitting coefficients of the camera’s projection matrix. At the same time an innovative genetic algorithm is presented to get the global optimization solution, where the cross-over probability and mutation probability are tuned self-adaptively according to the evolution speed factor in longitudinal direction and the aggregation degree factor in lateral direction, respectively. When the system comes to global equilibrium state, the camera’s projection matrix is estimated from the output vector of the Hopfield network, so the camera calibration is completed. Finally, the precision analysis is carried out, which demonstrates that, as opposed to the existing methods, such as Faugeras’s, the proposed approach has high precision, and provides a new scheme for machine vision system and precision manufacture.

A Parametric Study and Optimization of an Air Conditioning System for a Heat-Loaded Room

Optimization of an air conditioning system is critical in terms of the transient and steady state behavior of the air distribution along the room and the temperature of the equipment themselves. In this paper, three computational techniques, namely, the standard , RNG , and the model, are used to numerically simulate and determine the air distribution in an air-conditioned room. The simulation results for all three methods are verified via a comparison with an experiment involving a room that contains a computer server which generates up to 6 kW of heat. In doing so and by additionally performing an error analysis, it is determined that the model produces the most accurate results. The results also indicated that the direction of air supply from the air conditioners has a strong impact on the velocity field and temperature distribution along the room and on the computer server. Hence, many candidate directions of air supply options were selected for study and by conducting a performance evaluation in terms of air temperature around the server, the optimal solution was obtained.

Large-Scale Computations of Flow around Two Cylinders by a Domain Decomposition Method

A parallel computation is applied to study the flow past a pair of cylinders in tandem at Reynolds numbers of 1000 by Domain Decomposition Method. The computations were carried out for different sets of arrangements at large scale. The modeling by domain decomposition was validated by comparing available well-recognized results. Two cylinders with different diameters were further investigated; for different diameter ratios, the wake width ratio and some properties of the critical space ratio that dominates the flow regime were discovered. This result has important implications on future industrial application efforts as well as codes and standards related to the two-cylinder structure.

Numerical simulation of a backward-facing step flow in a microchannel with external electric field

A backward-facing step flow in the microchannel with external electric field was investigated numerically by a high-order accuracy upwind compact difference scheme in this work. The Poisson–Boltzmann and Navier–Stokes equations were computed by the high-order scheme, and the results confirmed the ability of the new solver in simulation of micro-scale electric double layer effects. The flow fields were displayed for different Reynolds numbers; the positions of the vortex saddle point of model with external electric field and model without external electric field were compared. The average velocity increases linearly with the electric field intensity; however, the Joule heating effects cannot be neglected when the electric field intensity increases to a certain level.

A preconditioner construction for domain decomposition analysis of large scale 3D magnetostatic problems

An iterative domain decomposition method is applied to numerical analysis of 3-dimensional (3D) linear magnetostatic problems taking the magnetic vector potential as an unknown function. The iterative domain decomposition method is combined with the Preconditioned Conjugate Gradient (PCG) procedure and the Hierarchical Domain Decomposition Method (HDDM) which is adopted in parallel computing. Our previously employed preconditioner was the Neumann-Neumann method. Numerical results showed that the method was only effective for smaller problems. In this paper, we consider its improvement with the Balancing Domain Decomposition (BDD) preconditioner.