Strain‐rate‐dependent mechanical properties of polypropylene separator for lithium‐ion batteries

The mechanical integrity of battery separators is critical for battery safety and durability. A comprehensive study of strain‐rate‐dependent tensile and puncture properties of a polypropylene lithium‐ion battery separator is presented here with a new model. Due to anisotropy of the polymeric membrane, tensile testing was conducted for different directions. Results showed that tensile strength and elastic modulus were increased 1000% and 500%, respectively, for different directions. It was also demonstrated that tensile strength changed 10 to 25% with strain rate (1.67 × 10^?4 to 1.67 × 10^?1 s^?1) for different directions. An equation was obtained for the first time for flow stress versus strain rate at varied tensile directions with respect to machine direction. Moreover, puncture testing was performed and it was shown that puncture strength was increased 140% with increasing strain rate from 0.25 to 250 mm min^?1. Two failure modes were also observed in puncture samples. Finally, Eyring's model was used to calculate activation enthalpy of the porous polypropylene separator. © 2020 Society of Chemical Industry     

Optimal production-inventory policy for make-to-order versus make-to-stock based on the M/Er/1 queuing model

This paper analyzes a multi-product production / inventory system where demands for each item arrive according to a Poisson process and the production time for each product has an Erlang distribution. The paper proposes an optimality condition that specifies whether each product should be produced make-to-stock or make-to-order. In the event a product should be produced make-to-stock, an approach for computing the optimal base-stock level is proposed. Numerical examples are given for illustrative purpose.     

Aggregation of Induction Motor Loads for Transient Stability Studies

This study investigates the dynamic representation of a group of induction motors by one or more equivalent motors. The unknown parameters of the equivalent are estimated through a weighted-least-squares procedure. Verification of simulated results with experimental ones suggests that a fifth order dynamic model of the aggregate with a ninth order parameter model is appropriate. The least squares technique was found to provide good convergence characteristics subject to satisfaction of the following conditions good initial estimate of the parameters, proper selection of acceleration factors and homogenity of the motor group.     

The Valve Metal Character of Zirconium as inferred from anode potential measurements in mineral acid solutions

A study on the value metal character of Zr in 0.1 M solutions of H₂SO₄, HNO₃, and H₃PO₄ has been performed using the anode potential as the primary variable in galvanostatic, potentiostatic, and capacity measurements. A method of surface pre-treatment, which suppresses both O₂ evolution and metal dissolution, has been described. Kinetic parameters of oxide growth have been calculated. The results indicate that:

• (i) the high field approximation is applicable following an exponential law, and
• (ii) the height and activation distance of the energy barrier for ion transport through the oxide phase (Verwey model) are the same three acids.

Measurements have been also made on the dielectric breakdown of oxide, and this occurs at potentials above 200 V. Direct capacity measurements give similar results as those based on reciprocal capacity calculated from galvanostatic experiments. It is concluded that the dominant anodic oxide species is ZrO₂ having a dielectric constant of 25. Open circuit potential measurements show that Zr is spontaneously oxidized in the three acids.     

Wave propagation in functionally graded nanocomposites reinforced with carbon nanotubes based on second-order shear deformation theory

In the present article, as a first endeavor, the wave propagation in functionally graded nanocomposites reinforced with carbon nanotubes is investigated on the basis of second-order shear deformation theory. Four different types of functionally graded nanocomposites are presented. An analytical method is used to find the circular frequencies and phase velocities. To show the accuracy of the present methodology, our results for the free vibration are compared with the results of functionally graded plates available in the literature. The influences of different parameters are also investigated on the circular frequencies and phase velocities.     

Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening

Regular eye screening is essential for the early detection and treatment of the diabetic retinopathy. This paper presents a novel automatic screening system for diabetic retinopathy that focuses on the detection of the earliest visible signs of retinopathy, which are microaneurysms. Microaneurysms are small dots on the retina, formed by ballooning out of a weak part of the capillary wall. The detection of the microaneurysms at an early stage is vital, and it is the first step in preventing the diabetic retinopathy. The paper first explores the existing systems and applications related to diabetic retinopathy screening, with a focus on the microaneurysm detection methods. The proposed decision support system consists of an automatic acquisition, screening and classification of diabetic retinopathy colour fundus images, which could assist in the detection and management of the diabetic retinopathy. Several feature extraction methods and the circular Hough transform have been employed in the proposed microaneurysm detection system, alongside the fuzzy histogram equalisation method. The latter method has been applied in the preprocessing stage of the diabetic retinopathy eye fundus images and provided improved results for detecting the microaneurysms.     

Structural,optical and electrical properties of Cu<Subscript>2</Subscript>Zn<Subscript>1−x</Subscript>Cd<Subscript>x</Subscript>SnS<Subscript>4</Subscript> quinternary alloys nanostructures deposited on porous silicon

The Cu₂Zn_1?xCd_xSnS₄ quinternary alloy nanostructures with different Cd contents were grown using spin coating technique on porous silicon (63.93 %) substrate. The structural properties of Cu₂Zn_1?xCd_xSnS₄/PS were investigated by X-ray diffraction and field emission-scanning electron microscope (FE-SEM). The optical properties studied through photoluminescence technique, indicated that the band gap is shifted as Cd content increases from 1.84 eV at x = 0 to 1.76 eV at x = 1. The electrical characterization of the Ag/n-PS/Cu₂Zn_1?xCd_xSnS₄/Ag diode through current to voltage (I–V) characterization shows the highest photo-response of (value if any) at Cu₂Zn_0.4Cd_0.6SnS₄ composition.     

Electroless copper-phosphorus coatings with the addition of silicon carbide （SiC） particles

Cu-P-silicon carbide(SiC)composite coatings were deposited by means of electroless plating.The effects ofpH values,temperature,and different concentrations of sodium hypophosphite(NaH2PO2·H2O),nickel sulfate(NiSO4·6H2O),sodium citrate(C6H5Na3O7·2H2O)and SiC on the deposition rate and coating compositions were evaluated,and the bath formulation for Cu-P-SiC composite coatings was optimised.The coating compositions were determined using energy-dispersive X-ray analysis(EDX).The corresponding optimal operating parameters for depositing Cu-P-SiC are as follows:pH 9; temperature,90℃; NaH2PO2·H2O concentration,125 g/L; NiSO4·6H2O concentration,3.125 g/L; SiC concentration,5 g/L; and C6H5Na3O7·2H2O concentration,50 g/L.The surface morphology of the coatings analysed by scanning electron microscopy(SEM)shows that Cu particles are uniformly distributed.The hardness and wear resistance of Cu-P composite coatings are improved with the addition of SiC particles and increase with the increase of SiC content.     

Deep features-based speech emotion recognition for smart affective services

Emotion recognition from speech signals is an interesting research with several applications like smart healthcare, autonomous voice response systems, assessing situational seriousness by caller affective state analysis in emergency centers, and other smart affective services. In this paper, we present a study of speech emotion recognition based on the features extracted from spectrograms using a deep convolutional neural network (CNN) with rectangular kernels. Typically, CNNs have square shaped kernels and pooling operators at various layers, which are suited for 2D image data. However, in case of spectrograms, the information is encoded in a slightly different manner. Time is represented along the x-axis and y-axis shows frequency of the speech signal, whereas, the amplitude is indicated by the intensity value in the spectrogram at a particular position. To analyze speech through spectrograms, we propose rectangular kernels of varying shapes and sizes, along with max pooling in rectangular neighborhoods, to extract discriminative features. The proposed scheme effectively learns discriminative features from speech spectrograms and performs better than many state-of-the-art techniques when evaluated its performance on Emo-DB and Korean speech dataset.

     

Selection of the optimum decomposition level using the discrete wavelet transform for automobile suspension system

Journal of Mechanical Science and Technology - This paper discusses the determination of the optimum decomposition level by using the discrete wavelet transform (DWT) method for an automobile...