Support Vector Machine-based Denoising Technique for Removal of White Noise in Partial Discharge Signal

Abstract—Partial discharge (PD) measurement has emerged as a dominant investigative tool for condition monitoring of insulation in high voltage equipment. In general, PD signals are severely polluted by several noises like white noise, random noise, discrete spectral interferences (DSI). The challenge lies with removing these noises from PD signal effectively by preserving the signal features. In this article, support vector machine (SVM) based denoising technique has been proposed for the removal of white noise from PD signal. The proposed SVM technique retains the edge of the original signal efficiently and also pseudo Gibbs phenomenon does not exist with SVM technique. In order to evaluate the effectiveness of the proposed method, artificially simulated PD signal mixed with white noise and the measured PD readings are considered. For the purpose of comparison, other denoising techniques such as fast Fourier transform (FFT), discrete wavelet transform (DWT), and translation invariant wavelet transform (TIWT) are also considered. The results reveal that, SVM based denoising technique shows better performance in terms of higher signal to noise ratio, signal reconstruction error ratio, cross correlation coefficient and reduction in noise level, mean square error, and waveform distortion.     

Surface photovoltage measurement of PM10 atmospheric aerosols collected over SRMIST-Kattankulathur campus (12.81° N & 80.03° E): a step towards utilization of atmospheric aerosols in optoelectronic applications

Journal of Materials Science: Materials in Electronics - In this work, we have measured the surface photovoltage of airborne particulate matter of size up to 10 microns (PM10) through scanning...     

Direction of Arrival (DoA) Estimation Under Array Sensor Failures Using a Minimal Resource Allocation Neural Network

This paper presents the use of a minimal resource allocation network (MRAN) for the direction of arrival (DoA) estimation under array sensor failure in a noisy environment. MRAN is a sequential learning algorithm in which the number of hidden neurons are added or removed based on the input data and produces a compact network. The training for MRAN is done under no failure and no noise case and the trained network is then used when there is a failure. Thus, the need for knowing the element and the time of its failure, as required in other methods is eliminated. MRAN's performance is compared with the conventional MUSIC algorithm and also the radial basis function neural network scheme developed by A. H. El Zooghby under normal and failed cases. In normal case, different antenna effects like mutual coupling, nonuniform array and unequal source power have been studied under different signal to noise ratio (SNR) values. Results indicate the superior performance of MRAN based DoA estimation scheme under different antenna effects, failure conditions and noise levels     

Evaluation of two‐stage process (refining and homogenization) for nanofibrillation of cotton fibers

In this study, nonspinnable short staple cotton fibers were microfibrillated in disc refiner followed by nanofibrillation in high pressure homogenizer and their properties evaluated. The extent of fibrillation was improved by increased number of repeated passes. Refining as a pretreatment process helps to circumvent the fiber clogging problem in homogenizer nozzle. Microfibrillation by 30 passes in refining produced the fibrils of size 416 ± 177 nm and subsequent nanofibrillation by 15 passes in homogenization resulted in 112 ± 49 nm as revealed by scanning electron microscopy. While refining process does not significantly affect the crystalline index of fibers, homogenization reduced it by 10% due to very high shear and impact forces and hydrodynamic cavitation. The degree of polymerization of cotton fibers was reduced significantly (35.4%) during this two‐stage process. Fourier transform infrared spectroscopy proved that there is no change in the molecular structure of cotton fibrils during this two‐stage fibrillation process. Thus produced nanofibrils having potential application as fillers in composites which can be used for various application food packaging, much films membranes etc. add value to the cotton fibers that are not suitable for spinning in textile industries due to their very short length. POLYM. ENG. SCI., 2013. © 2012 Society of Plastics Engineers     

Nanocellulose-Polymer Composites for Applications in Food Packaging: Current Status,Future Prospects and Challenges

Nanocellulose has potential applications across the several industrial sectors and addresses a lot of issues related to environmental concern. As biodegradable filler in composite manufacturing, coating, and self-standing thin films, it offers novel and promising properties. Very few available reviews report on nanocellulose-impregnated composite materials for food packaging. Nanocellulose reinforcement is found to be promising for mechanical and barrier properties of composite for biopolymer and synthetic polymer. In this paper, we provide a thorough review of recent advances of nanocellulose synthesis and its application as a filler material for production of nanocomposites to be used for food packaging.     

Development of multi-functional cotton surface for sportswear using nano zinc oxide

ABSTRACT

This paper reports the development of multi-functional cotton knitted fabric by two-stage process. Cotton knitted fabric was finished with in situ generated nano zinc oxide (nano-ZnO) followed by electrospraying Arkophop FFR a non-fluorocarbon-based hydrophobic chemical. Electrospraying was carried out with different flow rates (0.01 and 0.02 mL/min) and durations (1– min) with the help of microprocessor controlled syringe pump. The fabric was analyzed for its antimicrobial, UV protective, and moisture management (MMT) properties. FTIR study confirmed the formation of hydrophobic chemical finish on one surface of cotton fabrics. Scanning electron microscope observation showed the presence of nano-ZnO and hydrophobic finish on the electrosprayed surface of the cotton fabric. One-way moisture transport rating of the treated fabric using MMT test was found to be very good for 0.01 mL/min flow rate of the hydrophobic chemical for duration of 3 min using an optimized voltage of 20 kV and the distance of 15 cm between the nozzle and the collector. In situ generated nano-ZnO positively influenced the MMT properties of the treated cotton fabric.     

On the Influence of the Functionalization of Graphene Nanoplatelets and Glass Fiber on the Mechanical Properties of GFRP Composites

Applied Composite Materials - This work presents the influence of functionalised graphene nanoplatelets (f-GnPs) and functionalised glass fibers (f-fiber) on the tensile strength and the fracture...     

Microfluidic electrochemical array for detection of reactive metabolites formed by cytochrome P450 enzymes

A novel, simple, rapid microfluidic array using bioelectronically driven cytochrome P450 enzyme catalysis for reactive metabolite screening is reported for the first time. The device incorporates an eight-electrode screen-printed carbon array coated with thin films of DNA, [Ru(bpy)(2)(PVP)(10)](ClO(4)) {RuPVP}, and rat liver microsomes (RLM) as enzyme sources. Catalysis features electron donation to cyt P450 reductase in the RLMs and subsequent cyt P450 reduction while flowing an oxygenated substrate solution past sensor electrodes. Metabolites react with DNA in the film if they are able, and damaged DNA is detected by catalytic square wave voltammetry (SWV) utilizing the RuPVP polymer. The microfluidic device was tested for a set of common pollutants known to form DNA-reactive metabolites. Logarithmic turnover rates based on SWV responses gave excellent correlation with the rodent liver TD(50) toxicity metric, supporting the utility of the device for toxicity screening. The microfluidic array gave much better S/N and reproducibility than single-electrode sensors based on similar principles.