Robustification of input redundant feedback systems using robust actuator weighting in the control allocation problem

In this article, a new methodology for robust actuator weighting in the control allocation (CA) problem of input redundant feedback systems is addressed. The methodology is based on the control structural properties of the plant which were previously used for control configuration selection. Robust performance (RP) measures including H _∞ norm and structured singular value of the closed-loop system are used in this article. The capability of the approach is proven with application to lateral dynamics control of the vehicle over-actuated with front and rear steering systems. Employing the RP measures, it is concluded that the vehicle feedback control with front steering angles gives superior RP properties in comparison with the feedback loop of the rear steering angles. Based on these results, the penalty weightings in the cost function of the CA unit are determined. Simulation results based on nonlinear seven degrees of freedom vehicle handling model show that the selection of penalty weightings in the CA unit based on the RP properties of the control inputs (front and rear steering angles) improves the RP of the closed-loop.     

Task-space control of robots using an adaptive Taylor series uncertainty estimator

An uncertainty estimation and compensation can improve the performance of control systems due to structured and unstructured uncertainty. This paper presents a robust task-space control approach using an adaptive Taylor series uncertainty estimator for electrically driven robot manipulators. It is worth noting that not only the lumped uncertainty is estimated and employed in the indirect form of robust controller, but also the upper bound of approximation error is estimated to form a robustifying term and the asymptotic convergence of tracking error and its time derivative are proven based on stability analysis. Finally, the effectiveness of the proposed controller is shown through simulation and comparison with two valuable control schemes applied on the Selective Compliance Assembly Robot Arm (SCARA) robot manipulator.     

Thermodynamic analysis of wide thrust bearings operating in laminar inertial flow regimes

Wide thrust bearings operating in laminar inertial flow regimes are studied. Isothermal and thermohydrodynamic performances of the bearing are analysed. Integro-differential equations of motion, continuity and energy equations are solved to yield pressure, mass-mean velocity and temperature distributions. The effects of contraction ratio and film Reynolds number on the pressure field, load bearing capacity, flow rate and frictional resistance force of the bearing are examined. The effect of film temperature variation on the performance of the bearing is studied and the result is compared with the isothermal case. Viscous heating is seen to reduce significantly the load bearing capacity. The effects of including the inertial terms are also considered. The results for inertial and non-inertial cases are plotted and compared. The results show that, with the inclusion of the inertial terms, the load bearing capacity does increase. Comparisons with some experimental data are made and reasonable agreement is observed.     

Semisupervised dimensionality reduction for hyperspectral images based on the combination of semisupervised learning and metric learning

This paper presents a semisupervised dimensionality reduction (DR) method based on the combination of semisupervised learning (SSL) and metric learning (ML) (CSSLML-DR) in order to overcome some existing limitations in HSIs analysis. Specifically, CSSML focuses on the difficulties of high dimensionality of hyperspectral images (HSIs) data, the insufficient number of labelled samples and inappropriate distance metric. CSSLML aims to learn a local metrics under which the similar samples are pushed as close as possible, and simultaneously, the different samples are pulled away as far as possible. CSSLML constructs two local-reweighted dynamic graphs in an iterative two-steps approach: L-step and V-step. In L-step, the local between-class and within-class graphs are updated. In V-step, the transformation matrix and the reduced space are updated. The algorithm is repeated until a stopping criterion is satisfied. Experimental results on two well-known hyperspectral image data sets demonstrate the superiority of CSSLML algorithm compared to some traditional DR methods.     

Estimation of water-hydrocarbon mutual solubility in gas processing operations using an intelligent model

An accurate prediction of the mutual solubilities of hydrocarbons and water is extremely useful in oil, gas, and chemical industries. Estimating the solubility of hydrocarbons in water is required to describe their phase distribution through the removal process and also in the design of separation equipment. The current study plays emphasis on applying the predictive model based on the least square support vector machine (LSSVM) to estimate mutual water-hydrocarbon solubility at a wide range of conditions. A genetic algorithm (GA) was employed to choose and optimize hyperparameters (γ and σ²), which are embedded in LSSVM model. Utilization of this model showed high competence of the applied model in terms of coefficient of determination (R²) of 0.9998 and 0.9994, Average absolute relative deviation (AARD) of 1.1378 and 1.12459 from experimental values for predicted water solubility in hydrocarbons and hydrocarbon solubility in water, correspondingly. Using this method is quite simple and accurate to determine the mutual water-hydrocarbon solubility with negligible uncertainty.     

Dispersion and Deposition of Spherical Particles from Point Sources in a Turbulent Channel Flow

The dispersion and deposition of particles from a point source in a turbulent channel flow are studied. An empirical mean velocity profile and the experimental data for turbulent intensities are used in the analysis. The instantaneous turbulence fluctuation is simulated as a continuous Gaussian random field, and an ensemble of particle trajectories is generated and statistically analyzed. A series of digital simulations for dispersion and deposition of aerosol particles of various sizes from point sources at different positions from the wall is performed. Effects of Brownian diffusion on particle dispersion are studied. The effects of variation in particle density and particle-surface interaction are also discussed.     

Thermodynamic and kinetic studies for the adsorption of Hg(II) by nano-TiO2 from aqueous solution

Titanium dioxide nanocrystals were employed, for the first time, for the sorption of Hg(II) ions from aqueous solutions. The effects of varying parameters such as pH, temperature, initial metal concentration, and contact time on the adsorption process were examined. Adsorption equilibrium was established in 420 min and the maximum adsorption of Hg(II) on the TiO₂ was observed to occur at pH 8.0. The adsorption data correlated with Freundlich, Langmuir, Dubinin–Radushkevich (D–R), and Temkin isotherms. The Freundlich isotherm showed the best fit to the equilibrium data. The Pseudo-first order and pseudo-second-order kinetic models were studied to analyze the kinetic data. A second-order kinetic model fit the data with the (k₂ = 2.8126 × 10^?3 g mg^?1min^?1, 303 K). The intraparticle diffusion models were applied to ascertain the rate-controlling step. The thermodynamic parameters (ΔG°, ΔH°, and ΔS°) were calculated which showed an endothermic adsorption process. The equilibrium parameter (R_L) indicated that TiO₂ nanocrystals are useful for Hg(II) removal from aqueous solutions.     

Synthesizing and Characterizing of Gelatin-Chitosan-Bioactive Glass (58s) Scaffolds for Bone Tissue Engineering

Nowadays repairing and regenerating of lost or damaged tissue still remain an important challenge in clinical techniques. Due to the variety of available bone grafts, different types of biodegradable materials are being utilized as a scaffold implant. The basic structure of the bone is an excellent natural composite which contains varieties of polymers and ceramics; therefore, it is important to manufacture a bone scaffold featuring sufficient mechanical strength, a good degree of biocompatibility, biodegradation and an increased rate of formation of new tissue. Bioactive glass has an appealing characteristic which can be utilized for repairing purposes as well as to cause a rapid response from the bone graft. In this study, a composite scaffold based on polymer matrix (gelatin-chitosan) and bioactive glass 58s was synthesized in the laboratory. Five samples of polymer scaffold with different proportions of bioactive glass were designed and investigated. The scaffolds were dried with freeze dryer, and a spongy structure was generated. The composite survey was carried out through FTIR technique to examine the crystallization of the structure, XRD to examine the morphology of the porosities, and SEM to examine the size of porosities and formation of apatite. This study reveals that the size of porosities is about 170–320 μm, which is suitable for angiogenesis and cell growth in the bone. The combination of enhanced properties and the formation of apatite on the surface of the scaffolds make them an ideal option as a bone substitute.     

Interaction of α-Cyano-4-hydroxycinnamic Acid Drug with Inorganic BN Nanocluster: A Density Functional Study

We inspected the possibility of using a synthesized boron nitride nanocluster (B₁₂N₁₂) as a potential chemical sensor for anticancer α-cyano-4-hydroxycinnamic acid (CHC) drug by performing density functional theory calculations. It was found that CHC drug is mainly adsorbed via its acidic group on the BN nanocluster with adsorption energy about ??23.7 kcal/mol. In view of the high decrease of HOMO–LUMO energy gap (E_g) of the BN nanocluster after the adsorption process, it is expected that this process induces a significant increase in its electrical conductivity. Thus, the BN nanocluster is suggested as a potential sensor for CHC drug detection. We predicted that the BN cluster benefits from a short recovery time (~?22.7 s) and high sensitivity (55.2% decrease in the E_g). Also, it is interesting that the BN nanocluster can be used as a sensor in the pristine form without needing to the functionalization, doping, decoration, etc. Finally, we showed that by increasing the percentage of Hartree–Fock exchange of the functional, the adsorption energy, and sensitivity are increased and decreased, respectively. Also, the sensitivity of the BN nanocluster is predicted to decrease in water solution.     

Hydrodynamics of pulsed spouted beds: Effects of pulsation waveform,amplitude, and frequency

The effect of flow pulsations is studied via a discrete element model on hydrodynamics of spouted bed, which is being used in many important industries like drying processes. Decreased horizontal air percolation and preserving upward momentum, increased particle circulation, increased particle traverse distance, and better homogeneity are resulted from flow pulsations in spouted beds. Among the waveforms studied, square and triangular waveforms differ most and least from steady spouting, respectively. The predictions indicate that the pulsed spouted bed has the potential to enhance both gas and particle motion, hence being useful in drying and other operations.