Characterization of acoustic signals due to surface discharges on H.V. glass insulators using wavelet radial basis function neural networks

A hybrid model incorporating wavelet and radial basis function neural network is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on cleaned and polluted high voltage glass insulators by using surface tracking and erosion test procedure of international electrotechnical commission 60587. A laboratory experiment was conducted by preparing the prototypes of the discharges. This study suggests a feature extraction and classification algorithm for surface discharge classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, by “marrying” the wavelet to radial basis function neural network very high levels of classification are achieved. Wavelet signal treatment toolbox is used to recover the surface discharge acoustic signals by eliminating the noisy portion and to reduce the dimension of the feature input vector. A radial basis function neural network classifier was used to classify the surface discharge and assess the suitability of this feature vector in classification. This learning method is proved to be effective by applying the wavelet radial basis function neural network in the classification of surface discharge fault data set. The test results show that the proposed approach is efficient and reliable.     

STRUCTURAL DETERMINATION OF TITANIUM-OXIDE NANOPARTICLES BY X-RAY ABSORPTION SPECTROSCOPY

As a potential application of titanium-oxide nanoparticles, it is extremely important to investigate a detailed picture of the surface and interior structural properties of nanocrystalline materials, such as rutile and anatase with diameters 7.0 and 4.5nm, respectively. X-ray absorption spectroscopy has been used to identify the local Ti environment and related electronic structure. We combine the experimental results at the Ti edge in both bulk and nano-crystals to determine the lattice distortion in terms of differently characteristic preedge features and the variation in the multiple-scattering region of X-ray absorption near-edge structure (XANES) spectra. The relationship between the transition peaks and the surface-to volume ratio is also discussed.     

Synthesis and optimization of high surface area mesoporous date palm fiber-based nanostructured powder activated carbon for aluminum removal

Date palm fiber (DPF) derived from agrowaste was utilized as a new precursor for the optimized synthesis of a cost-effective, nanostructured, powder-activated carbon (nPAC) for aluminum (Al3+) removal from aqueous solutions using carbonization, KOH activation, response surface methodology (RSM) and central composite design (CCD). The optimum synthesis condition, activation temperature, time and impregna-tion ratio were found to be 650 ℃, 1.09 hour and 1:1, respectively. Furthermore, the optimum conditions for removal were 99.5％and 9.958 mg·g-1 in regard to uptake capacity. The optimum conditions of nPAC was analyzed and characterized using XRD, FTIR, FESEM, BET, TGA and Zeta potential. Moreover, the adsorption of the Al3+ conditions was optimized with an integrated RSM-CCD experimental design. Regression results revealed that the adsorption kinetics data was well fitted by the pseudo-second order model, whereas the adsorption isotherm data was best represented by the Freundlich isotherm model. Optimum activated carbon indicated that DPF can serve as a cost-effective precursor adsorbent for Al3+removal.     

Simultaneous removal of copper and zinc ions by low cost natural snail shell/hydroxyapatite/chitosan composite

In this work, the snail shell/hydroxyapatite/chitosan composite was prepared as adsorbent. The adsorption potential of the composite was studied for simultaneous sorption behavior of Zn(Ⅱ) and Cu(Ⅱ) ions in a batch system. Chitosan and hydroxyapatite(HAP) were extracted from shrimp shell and bone ash,respectively, so this is a low cost natural composite. To prepare the composite, chitosan was dissolved in acetic acid, then HAP and snail shell powders were added to the chitosan solution. The morphology and characterization of the composite was studied by SEM and EDX analysis. Atomic adsorption was used to measure the amount of the ions. Experimental parameters were optimized with Design Expert Software and five parameters such as the concentration of ions, p H, adsorbent amount and contact time were studied at room temperature. Optimized value for the parameters of Zn(Ⅱ) and Cu(Ⅱ) concentrations, p H, adsorbent dose, and contact time were 3.01 mg·L~(-1), 5.5, 0.02 g and 95 min, respectively. The adsorption isotherms for Zn(Ⅱ) and Cu(Ⅱ) showed Langmuir and Tempkin, respectively. Kinetic and equilibrium studies showed the experimental data of Zn(Ⅱ) and Cu(Ⅱ) ions were best described by the pseudo-second-order model. Studies on thermodynamic show the adsorption process were physical and spontaneous.     

Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.     

Rheological and extrusion characteristics of glass fiber-reinforced polycarbonate

An experimental investigation of the rheological properties of glass fiber-reinforced polycarbonate melts and the extrusion of such compounds through capillary and slit dies is presented. The viscosity–shear rate function seems independent of instrument for cone-plate and capillary investigations. The presence of fibers increases the level of the viscosity. Normal stresses at fixed shear stress are also increased by the presence of fibers. The extrudate swell is decreased by the presence of fibers and surface roughness is increased. Fiber orientation increases and surface roughness decreases with increasing extrusion rate.     

Rheological properties of poly(vinyl chloride) / epoxidized natural rubber blends. Part II: The effect of processing variables

The effect of processing variables on the rheological properties of PVC/ENR blends was investigated. The role of crosslinking in determining the flow behavior of blends was also examined by means of dynamically cured blends. It was found that PVC/ENR blends yield melts that are power law fluids. The flow of the melts improves with an increase in temperature and shear rate. However, the introduction of crosslinks reverses this trend, although under more rigorous conditions, the influence of crosslinks is superseded, and subsequently, flow becomes shear rate and temperature dependent. PVC/ENR systems also manifested elastic phenomena. The dependence of the elastic phenomena such as die swell and melt fracture on L/D ratio of the die was demonstrated.     

Investigations on the adhesion and interfacial properties of polyurethane foam/thermoplastic materials

The adhesion and interfacial properties of polyurethane (PU) foams with thermoplastic (TP) materials were investigated using different techniques. The adhesion performance of PU foam with TP materials was evaluated using the peel test method, and the adhesion durability was checked after different climate treatments. X‐ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), and contact angle measurements were used to study the surface and interface morphology of PU foam and TP material system. Three types of PU foam samples which differ in their composition and also five commercially available TP blends systems, based on poly(carbonate), poly(styrene‐co‐maleic anhydride), poly(acrylonitrile‐butadiene‐styrene), and silicone acrylate rubber have been used. The slow reacting foam shows the best adhesion properties with all the TP materials. The climate treatments strongly effected the PU foam adhesion durability with poly(carbonate) containing TP materials (70–80% loss in adhesion), but nearly no effect with poly(styrene‐co‐maleic anhydride). The samples with lowered adhesion could be separated by peeling without visible foam residues on the TP surface. AFM, XPS, and surface tension studies have shown that the surface properties of the TP material are still governed by the PU foam. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 479–488, 2007     

Fuzzy triangulation signature for detection of change in human emotion from face video image sequence

The present article proposes a geometry-based fuzzy relational technique for capturing gradual change in human emotion over time available from relevant face image sequences. As associated features, we make use of fuzzy membership arising out of five triangle signatures such as - (i) Fuzzy Isosceles Triangle Signature (FIS), (ii) Fuzzy Right Triangle Signature (FRS), (iii) Fuzzy Right Isosceles Triangle Signature (FIRS), (iv) Fuzzy Equilateral Triangle Signature (FES), and (v) Other Fuzzy Triangles Signature (OFS) to achieve the task of appropriate classification of facial transition from neutrality to one among the six expressions viz. anger (AN), disgust (DI), fear (FE), happiness (HA), sadness (SA) and surprise (SU). The effectiveness of the Multilayer Perceptron (MLP) classifier is tested and validated through 10 fold cross-validation method on three benchmark image sequence datasets namely Extended Cohn-Kanade (CK+), M&M Initiative (MMI), and Multimedia Understanding Group (MUG). Experimental outcomes are found to have achieved accuracy to the tune of 98.47%, 93.56%, and 99.25% on CK+, MMI, and MUG respectively vindicating the effectiveness by exhibiting the superiority of our proposed technique in comparison to other state-of-the-art methods in this regard.

     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.