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.     

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.

     

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.     

Calculation of maximum surface settlement induced by EPB shield tunnelling and introducing most effective parameter

This study aims to predict ground surface settlement due to shallow tunneling and introduce the most affecting parameters on this phenomenon.Based on data collected from Shanghai LRT Line 2 project undertaken by TBM-EPB method,this research has considered the tunnel's geometric,strength,and operational factors as the dependent variables.At first,multiple regression(MR) method was used to propose equations based on various parameters.The results indicated the dependency of surface settlement on many parameters so that the interactions among different parameters make it impossible to use MR method as it leads to equations of poor accuracy.As such,adaptive neuro-fuzzy inference system(ANFIS),was used to evaluate its capabilities in terms of predicting surface settlement.Among generated ANFIS models,the model with all input parameters considered produced the best prediction,so as its associated R~2 in the test phase was obtained to be 0.957.The equations and models in which operational factors were taken into consideration gave better prediction results indicating larger relative effect of such factors.For sensitivity analysis of ANFIS model,cosine amplitude method(CAM) was employed; among other dependent variables,fill factor of grouting(n) and grouting pressure(P) were identified as the most affecting parameters.     

Application of MIKE SHE modelling system to set up a detailed water balance computation

Estimation of total water balance is a substantial issue for watershed modelling in order to simulate the major components of the hydrological cycle to determine the stress of different anthropogenic activities on the available water resources within a catchment. In this context, the fully distributed physically based MIKE SHE modelling system was used to simulate the individual hydrological components of the total water balance for the Paya Indah Wetlands (PIW) watershed in the west of Peninsular Malaysia. Results reveal that the overall water balance is predominantly controlled by climate variables. Application of the model to the PIW watershed provides detailed estimation of the total water balance for a first‐order catchment in which actual evapotranspiration (ET) represents approximately 65 and 58%, while overland flow (OL) to the PIW lake system represents 12.38 and 12.3% of the total rainfall during the calibration and validation periods, respectively. The difference of the inflow and outflow was taken as storage in depth. Overall, the model gives a reasonable output of total error of less than 1% of the total rainfall, which in turn indicates that the interaction among components is satisfactorily sustained.