Robust design optimization of electro-thermal microactuator using probabilistic methods

Micromachining of microelectromechanical systems which is similar to other fabrication processes has inherent variation that leads to uncertain dimensional and material properties. Methods for optimization under uncertainty analysis can be used to reduce microdevice sensitivity to these uncertainties in order to create a more robust design, thereby increasing reliability and yield. In this paper, approaches for uncertainty and sensitivity analysis, and robust optimization of an electro-thermal microactuator are applied to take into account the influence of dimensional and material property uncertainties on microactuator tip deflection. These uncertainties include variation of thickness, length and width of cold and hot arms, gap, Young modulus and thermal expansion coefficient. A simple and efficient uncertainty analysis method is performed by creating second-order metamodel through Box-Behnken design and Monte Carlo simulation. Also, the influence of uncertainties has been examined using direct Monte Carlo Simulation method. The results show that the standard deviations of tip deflection generated by these uncertainty analysis methods are very close to each other. Simulation results of tip deflection have been validated by a comparison with experimental results in literature. The analysis is performed at multiple input voltages to estimate uncertainty bands around the deflection curve. Experimental data fall within 95 % confidence boundary obtained by simulation results. Also, the sensitivity analysis results demonstrate that microactuator performance has been affected more by thermal expansion coefficient and microactuator gap uncertainties. Finally, approaches for robust optimization to achieve the optimal designs for microactuator are used. The proposed robust microactuators are less sensitive to uncertainties. For this goal, two methods including Genetic Algorithm and Non-dominated Sorting Genetic Algorithm are employed to find the robust designs for microactuator.     

Quality Management in Pakistan's Bedwear Industry

Pakistan's bedwear industry contributes significantly to Pakistan's industry in general and to its textile industrial output and exports in particular. The exports of bedwear have increased steadily. However, its key basis of competitive advantage has been low cost. After greater liberalization from 2005 onwards and due to tariffication of quotas, the bedwear industry will not only have to scale higher tariff walls but will be more exposed to the threat of anti-dumping duties as it relies primarily on low prices to penetrate the export markets. To enhance its competitiveness, it will have to combine better quality with low costs and quick response. Research in quality management in Pakistan's bedwear industry was, therefore, undertaken. A postal self-completion questionnaire was mailed to 38 members of Pakistan Bedwear Manufacturers' and Exporters' Association (PBMEA). The response rate was almost 79%. It was found that this industrial segment was in various stages of development. This study demonstrates both the effectiveness and the limits of quality assurance in improving the levels of quality which remain a moving target in a dynamic liberalizing international trade environment that is more competitive since 2005. The bedwear industry, therefore, needs to graduate to advanced practices in quality management in order to remain competitive in rapidly changing times.     

Adaptive neural dynamic surface control of MIMO nonlinear time delay systems with time‐varying actuator failures

In this paper, an adaptive dynamic surface control approach is developed for a class of multi‐input multi‐output nonlinear systems with unknown nonlinearities, bounded time‐varying state delays, and in the presence of time‐varying actuator failures. The type of the considered actuator failure is that some unknown inputs may be stuck at some time‐varying values where the values, times, and patterns of the failures are unknown. The considered actuator failure can cover most failures that may occur in actuators of the systems. With the help of neural networks to approximate the unknown nonlinear functions and combining the dynamic surface control approach with the backstepping design method, a novel control approach is constructed. The proposed design method does not require a priori knowledge of the bounds of the unknown time delays and actuator failures. The boundedness of all the closed‐loop signals is guaranteed, and the tracking errors are proved to converge to a small neighborhood of the origin. The proposed approach is employed for a double inverted pendulums benchmark as well as a chemical reactor system. The simulation results show the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.     

CuIns2/Cus Nanocomposite: Synthesis via Simple Microwave Approach and Investigation Its Behavior in Solar Cell

CuInS₂/CuS nanocomposite were synthesized by a copper complex, [bis(ethylenediamine)copper(ΙΙ)] sulfate. Eight sulfur sources were used for this experiment. The products were characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, energy dispersive X-ray analysis spectroscopy, and room temperature photoluminescence spectroscopy. Thin film of nanocomposite powder was fabricated and its feature (V_oc, J_sc and FF) was calculated by current–voltage (I–V) curve.     

Mutual information-based optimization of sparse spatio-spectral filters in brain–computer interface

Recently, neuro-rehabilitation based on brain–computer interface (BCI) has been considered one of the important applications for BCI. A key challenge in this system is the accurate and reliable detection of motor imagery. In motor imagery-based BCIs, the common spatial patterns (CSP) algorithm is widely used to extract discriminative patterns from electroencephalography signals. However, the CSP algorithm is sensitive to noise and artifacts, and its performance depends on the operational frequency band. To address these issues, this paper proposes a novel optimized sparse spatio-spectral filtering (OSSSF) algorithm. The proposed OSSSF algorithm combines a filter bank framework with sparse CSP filters to automatically select subject-specific discriminative frequency bands as well as to robustify against noise and artifacts. The proposed algorithm directly selects the optimal regularization parameters using a novel mutual information-based approach, instead of the cross-validation approach that is computationally intractable in a filter bank framework. The performance of the proposed OSSSF algorithm is evaluated on a dataset from 11 stroke patients performing neuro-rehabilitation, as well as on the publicly available BCI competition III dataset IVa. The results show that the proposed OSSSF algorithm outperforms the existing algorithms based on CSP, stationary CSP, sparse CSP and filter bank CSP in terms of the classification accuracy, and substantially reduce the computational time of selecting the regularization parameters compared with the cross-validation approach.     

Parkinson’s Disease Detection Using Biogeography-Based Optimization

In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0-disease status and 1- good control status. The performance of proposed methods compared with PSO, GA, ACO and ES method. The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection. The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms, and consequently, it served as a promising new robust tool with excellent PD diagnosis performance.     

Low Temperature Hall Effect Investigation of Conducting Polymer-Carbon Nanotubes Composite Network

Polypyrrole (PPy) and polypyrrole-carboxylic functionalized multi wall carbon nanotube composites (PPy/f-MWCNT) were synthesized by in situ chemical oxidative polymerization of pyrrole on the carbon nanotubes (CNTs). The structure of the resulting complex nanotubes was characterized by transmission electron microscopy (TEM) and X-ray diffraction (XRD). The effects of f-MWCNT concentration on the electrical properties of the resulting composites were studied at temperatures between 100 K and 300 K. The Hall mobility and Hall coefficient of PPy and PPy/f-MWCNT composite samples with different concentrations of f-MWCNT were measured using the van der Pauw technique. The mobility decreased slightly with increasing temperature, while the conductivity was dominated by the gradually increasing carrier density.     

Investigation of β phase formation in piezoelectric response of electrospun polyvinylidene fluoride nanofibers: LiCl additive and increasing fibers tension

As a piezoelectric polymer, poly (vinylidene fluoride) (PVDF) is attractive in energy conversion applications between electrical and mechanical forms because of its low cost, high flexibility, and biocompatibility. The piezoelectricity of electrospun PVDF polymer is due to changes in the crystalline structure (e.g., creating the β‐phase) during the electrospinning process. This research focuses on two approaches for investigation of β Phase formation: (1) addition of LiCl in different concentrations (0.001, 0.00133, 0.002, 0.004 wt%) as inorganic salt to the polymer solution, (2) increasing tension along the fiber axis by increasing the collector drum speed during the aligning process. Performances of these structures were evaluated by using X‐ray diffraction (XRD), Fourier Transform Infrared (FTIR), differential scanning calorimetry (DSC), and scanning electron microscopy (SEM). A one‐step nano‐generator and cost‐effective package based on electrospun nanofibers was presented to measure output voltages as a performance factor. Results show that the addition of LiCl leads to β Phase formation in the crystalline structure, decreasing fiber diameter to 65 nm, and increment in the work of rupture and piezoelectric output. Moreover, the results show that increasing collector drum speed causes the alignment of β‐crystallites along the fiber axis and subsequently no considerable effect on the formation of β‐phases and output voltage. POLYM. ENG. SCI., 56:61–70, 2016. © 2015 Society of Plastics Engineers     

Recrystallization of Cold Spray-Fabricated CP Titanium Structures

Cold gas dynamic spray (cold spray) is a promising rapid deposition technology in which particles deposit at supersonic velocities. The effect of isothermal annealing on recrystallization and mechanical properties of commercial purity (CP) titanium structures that were directly fabricated through cold spray deposition is studied. The optimized cold spray parameters led to a dense cold spray structure. Results show that annealing improves ductility of the cold-sprayed CP titanium structure. The mechanism for softening is the nucleation and growth of equiaxed grains, which include an ultrafine grain structure. A physical-based model for recrystallization of the cold spray CP titanium is proposed. The results show that recrystallization does not eliminate preferred orientation inherited from the cold spray material.