Design and simulation of a novel metallic microgripper using vibration to release nano objects actively

In this study, we investigate a novel metallic microgripper which is able to grasp and transport nano particles (nano tubes/wires) and release them on desirable substrate by vibrating the gripper arms. This microgripper consists of a chevron actuator to grip nano object electrothermally and interdigited comb drive systems to generate vibration at the gripper arms electrostatically. Metallic (nickel) properties enable the chevron actuator to close the gap and pick the nano particle at low voltage and temperature. In order to reduce the out of plane bending during operation and also increase the gripping force, thickness of the nickel layer must be increased, hence electroplating process is proposed for deposition of nickel layer. To generate vibration at the end effectors, comb drive systems are stimulated by applying two voltage signals at desired resonant frequency to the stators. Practically, by sweeping the frequency of these signals around the resonant frequency the end effectors start vibrating. The vibration results in overcoming the adhesion forces due to inertial effects.     

A general insight into the effect of neuron structure on classification

This paper gives a general insight into how the neuron structure in a multilayer perceptron (MLP) can affect the ability of neurons to deal with classification. Most of the common neuron structures are based on monotonic activation functions and linear input mappings. In comparison, the proposed neuron structure utilizes a nonmonotonic activation function and/or a nonlinear input mapping to increase the power of a neuron. An MLP of these high power neurons usually requires a less number of hidden nodes than conventional MLP for solving classification problems. The fewer number of neurons is equivalent to the smaller number of network weights that must be optimally determined by a learning algorithm. The performance of learning algorithm is usually improved by reducing the number of weights, i.e., the dimension of the search space. This usually helps the learning algorithm to escape local optimums, and also, the convergence speed of the algorithm is increased regardless of which algorithm is used for learning. Several 2-dimensional examples are provided manually to visualize how the number of neurons can be reduced by choosing an appropriate neuron structure. Moreover, to show the efficiency of the proposed scheme in solving real-world classification problems, the Iris data classification problem is solved using an MLP whose neurons are equipped by nonmonotonic activation functions, and the result is compared with two well-known monotonic activation functions.     

EIS study of corrosion behavior of metallic materials in ethanol blended gasoline containing water as a contaminant

In the present paper, the effects of ethanol as a gasoline additive and water as a contaminant on the corrosion behavior of metallic components of a fuel delivery system were investigated. Electrochemical impedance spectroscopy (EIS) testing was performed in both water-free and water contaminated gasoline containing 0%, 5%, 10% and 15% ethanol without the addition of any supporting electrolyte. The surface of the specimens examined in 10% ethanol blended gasoline was observed by scanning electron microscope to understand what types of corrosion attack occurred. The results revealed that the addition of ethanol to gasoline fuel decreased the solution resistance and polarization resistance values of the specimens, resulting in an increase in the corrosion rates of these specimens in ethanol blended gasoline. Water contaminant caused a decrease in the polarization resistance of the ferrous specimens, whereas the observed behavior in others was reversed. Among the investigated metallic materials, the brazing alloy fared the best while Al 6061 alloy showed satisfactory corrosion resistance compared to the rest of the materials in both water-free and water-contaminated ethanol blended gasoline. Moreover, no localized attack was observed in corrosion products.     

Solving the Problem of Forbidden States in Discrete Event Systems: A Novel Systematic Method for Reducing the Number of Control Places

This paper deals with the problem of forbidden states in Discrete Event Systems modelled by non‐safe Petri Nets. To avoid these states, some Generalized Mutual Exclusion Constraints can be assigned to them. These constraints limit the weight sum of tokens in some places and can be enforced on the system using control places. When the number of these constraints is large, a large number of control places should be added to the system. In this paper, a method is presented to assign the small number of constraints to forbidden states using some states which cover the forbidden states. So, a small number of control places are added to the system leading to obtaining a maximally permissive controller.     

Epithelial Layer Estimation Using Curvatures and Textural Features for Dysplastic Tissue Detection

Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling process. The morphological pattern of an epithelial layer is greatly affected. Theoretically, the shape deformation model can normalise the distortions, but it needs a 2D image. Curvatures theory, on the other hand, is not yet tested on digital pathology images. Therefore, this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial layer. The boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being a straight line. The epithelial layer’s depth is estimated from the tissue edges and the connected nucleolus only. Then, the textural and spatial features along the estimated layer are used for dysplastic tissue detection. The proposed method achieved better performance compared to the whole tissue regions in terms of detecting dysplastic tissue. The result shows a leap of kappa points from fair to a substantial agreement with the expert’s ground truth classification. The improved results demonstrate that curvatures have been effective in reducing the boundary effects on the epithelial layer of tissue. Thus, quantifying and classifying the morphological patterns for dysplasia can be automated. The textural and spatial features on the detected epithelial layer can capture the changes in tissue.     

A Sliding Mode Controller Based on Robust Model Reference Adaptive Proportional-integral Control for Stand-alone Three-phase Inverter

This paper proposes a sliding mode controller based on robust model reference adaptive proportional-integral (RMRA-PI) control for a stand-alone voltage source inverter (SA-VSI). The proposed controller has two control loops where the coefficients of PI controller are regulated by the adaptive sliding law. This method is used to regulate the output voltage of the inverter under different load conditions and uncertainty, and adapts the output to the reference model to reduce the total harmonic distortion (THD). In this paper, the stability of the proposed controller is proven by using Lyapunov’s theory and Barbalet’s lemma. The proposed controller performs well in voltage regulation such as low THD under sudden load change and uncertainty. Also, the results of the proposed controller are compared with PI controller to show the effectiveness of the presented control system.     

A high step‐up half‐bridge DC/DC converter with a special coupled inductor for input current ripple cancelation and extended voltage doubler circuit for power conditioning of fuel cell systems

This paper presents a high step‐up soft switched dc–dc converter having the feature of current ripple cancelation in the input stage that is specialized for power conditioning of fuel cell systems. The converter comprises a special half‐bridge converter and a rectifier stage based upon the voltage‐doubler circuit, in which the coupled‐inductor technology is amalgamated with switched‐capacitor circuit. The input current with no ripple is the principal characteristics of this topology that is achieved by utilizing a small coupled inductor. In addition, the low clamped voltage stress across both power switches and output diodes is another advantage of the proposed converter, which allows employing the metal–oxide–semiconductor field‐effect transistors with minuscule on‐state resistance and diodes with lower forward voltage‐drop, and thereby, the semiconductors' conduction losses diminish considerably. The inherent nature of this topology handles the switching scheme based on the asymmetrical pulse width modulation in order for switches to establish the zero voltage switching, leading to lower switching losses. Besides, because of the absence of the reverse‐recovery phenomenon, all diodes turn off with zero current switching. At last, a 250‐W laboratory prototype with the input voltage 24 V and output voltage 380 V is implemented to verify the especial features of the proposed converter. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.     

Control Optimization of a Slotted Switched Reluctance Generator for High-torque Applications

This article describes the control optimization of a slotted switched reluctance generator for low-speed applications. The machine is simulated with the finite-element method. The turn-off and turn-on angles are optimized by means of a genetic algorithm to maximize the output power and minimize the torque ripple. First, these two criteria are optimized separately with a mono-objective genetic algorithm, and then a multi-objective Pareto genetic algorithm is used. An analysis of optimized parameters and resulting current waveforms is also performed. Experimental results based on a 6/4 switched reluctance generator are also presented.     

Modeling industrial thickener using computational fluid dynamics (CFD), a case study: Tailing thickener in the Sarcheshmeh copper mine

Separation of particles from liquid in the large gravitational tanks is widely used in mining and industrial wastewater treatment process. Thickener is key unit in the operational processes of hydrometallurgy and is used to separate solid from liquid. In this study, population balance models were combined with computational fluid dynamics (CFD) for modeling the tailing thickener. Parameters such as feed flow rate, flocculant dosage, inlet solid percent and feedwell were investigated. CFD was used to simulate the industrial tailing thickener with settled bed of 120 m diameter which is located in the Sarcheshmeh copper mine. Important factor of drag force that defines the rake torque of rotating paddles on the bed was also determined. Two phases turbulence model of Eulerian/Eulerian in accordance with turbulence model of k-ε was used in the steady-state. Also population balance model consists of 15 groups of particle sizes with Luo and Lehr kernel was used for aggregation/breakage kernel. The simulation results showed good agreement with the operational data.     

Electromagnetic Design and Analysis of a Novel Axial-Transverse Flux Permanent Magnet Synchronous Machine

This paper analyzes a novel hybrid axial-transverse flux permanent magnet (PM) synchronous motor achieving high torque density. The stator pole pitch and the rotor pole pitch are the same. The motor is suitable for vehicle traction systems. The new axial-transverse machine has a single-sided or double-sided air gap. The machine has two adjacent quasi-U stator cores with ring-type winding, and the rotor has two PM groups, one outer and one inner. The shape of quasi-U stator core allows changing the flux path without changing power supply polarity. The analytical expressions of the no-load back-EMF and the torque are derived, after the development of 3-D magnetic equivalent circuit (MEC) model. The 3-D finite element method (FEM) is used to analyze the magnetic field, torque, and cogging torque for different skewings of the PMs.