Power quality diagnosis using time frequency analysis and rule based techniques

Diagnosing a power quality disturbance means identifying the type and cause of the disturbance. Fast diagnosis of power quality disturbances is important so as to assist network operators in performing counter measures and implementing suitable power quality mitigation actions. In this study a novel method for performing power quality diagnosis is presented by using the S-transform and rule based classification techniques. The proposed power quality diagnosis method was evaluated for its functionality in detecting the type of short duration voltage disturbances and identifying the cause of the disturbances which may be due to permanent or non permanent faults. Based on the results, this new method has the potential to be used in the existing real time power quality monitoring system in Malaysia to expedite the diagnosis on the recorded voltage disturbances.     

High Thermal Conductivity Polymer Composites Fabrication through Conventional and 3D Printing Processes: State-of-the-Art and Future Trends

The lifespan and the performance of flexible electronic devices and components are affected by the large accumulation of heat, and this problem must be addressed by thermally conductive polymer composite films. Therefore, the need for the development of high thermal conductivity nanocomposites has a strong role in various applications. In this article, the effect of different particle reinforcements such as single and hybrid form, coated and uncoated particles, and chemically treated particles on the thermal conductivity of various polymers are reviewed and the mechanism behind the improvement of the required properties are discussed. Furthermore, the role of manufacturing processes such as injection molding, compression molding, and 3D printing techniques in the production of high thermal conductivity polymer composites is detailed. Finally, the potential for future research is discussed, which can help researchers to work on the thermal properties enhancement for polymeric materials.     

Review: Heart Diseases Detection by Machine Learning Classification Algorithms

Most human deaths are caused by heart diseases. Such diseases cannot be efficiently detected for the lack of specialized knowledge and experience. Data science is important in healthcare sector for the role it plays in bulk data processing. Machine learning (ML) also plays a significant part in disease prediction and decision-making in medical care industry. This study reviews and evaluates the ML approaches applied in heart disease detection. The primary goal is to find mathematically effective ML algorithm to predict heart diseases more accurately. Various ML approaches including Logistic Regression, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), t-Distributed Stochastic Neighbor Embedding (t-SNE), Nave Bayes, and Random Forest were utilized to process heart disease dataset and extract the unknown patterns of heart disease detection. An analysis was conducted on their performance to examine the effecacy and efficiency. The results show that Random Forest out-performed other ML algorithms with an accuracy of 97%.     

An investigation over the effect of the reducing agent on the properties of the ZnO-reinforced Ni–P coatings

Journal of Materials Science: Materials in Electronics - Electroless ZnO-reinforced Ni–P coatings are developed on mild steel substrates in the Electroless bath, which contains an optimum...     

Classification of power system disturbances using linear Kalman filter and fuzzy-expert system

Identification and classification of voltage and current disturbances in power systems is an important task in power system monitoring and protection. This paper presents a new approach for power system disturbances identification and classification. The concept of linear Kalman filter together with discrete wavelet transform (DWT) is used to extract two parameters; the amplitude and the slope from the captured voltage or current waveform. DWT is used to help Kalman filter to give a good performance; the captured distorted waveform is passed through the DWT to determine the noise inside it and the covariance of this noise is fed together with the captured voltage waveform to the Kalman filter. The two parameters are the inputs to fuzzy-expert system that uses some rules on these inputs to identify the class to which the waveform belongs. To prove the ability of the new approach for classifying power system disturbances, detailed digital simulation and experimental results involving various types of power quality events are presented. The results depict that the proposed technique has the ability to accurately identify and classify PQ disturbances.     

Fusion of voice signal information for detection of mild laryngeal pathology

Detection of mild laryngeal disorders using acoustic parameters of human voice is the main objective in this study. Observations of sustained phonation (audio recordings of vocalized /a/) are labeled by clinical diagnosis and rated by severity (from 0 to 3). Research is exclusively constrained to healthy (severity 0) and mildly pathological (severity 1) cases – two the most difficult classes to distinguish between.Comprehensive voice signal characterization and information fusion constitute the approach adopted here. Characterization is obtained through diverse feature set, containing 26 feature subsets of varying size, extracted from the voice signal. Usefulness of feature-level and decision-level fusion is explored using support vector machine (SVM) and random forest (RF) as basic classifiers. For both types of fusion we also investigate the influence of feature selection on model accuracy. To improve the decision-level fusion we introduce a simple unsupervised technique for ensemble design, which is based on partitioning the feature set by k-means clustering, where the parameter k controls the size and diversity of the prospective ensemble.All types of the fusion resulted in an evident improvement over the best individual feature subset. However, none of the types, including fusion setups comprising feature selection, proved to be significantly superior over the rest. The proposed ensemble design by feature set decomposition discernibly enhanced decision-level and significantly outperformed feature-level fusion. Ensemble of RF classifiers, induced from a cluster-based partitioning of the feature set, achieved equal error rate of 13.1 ± 1.8% in the detection of mildly pathological larynx. This is a very encouraging result, considering that detection of mild laryngeal disorder is a more challenging task than a common discrimination between healthy and a wide spectrum of pathological cases.     

A GRNN based frame work to test the influence of nano zinc additive biodiesel blends on CI engine performance and emissions

Environmental friendly fuels have increasing demand in the field of automotive industry. Hybridizing with biodiesel and addition of nanoparticles are two evident techniques to improve the performance and to control the hazardous emissions. Combination of appropriate weight fractions of nanoparticles, biodiesel, and diesel further optimize the engine outcomes. The present work aims at investigating the effect of zinc oxide (ZnO) nanoparticles suspensions in diesel and Mahua biodiesel blended fuel on single cylinder diesel engine performance characteristics. Experimental tests are performed with neat diesel fuel, biodiesel blends and ZnO added biodiesel blends. The results indicate that ZnO particulate addition yields favorable performance and emission control of the engine. A generalized regression neural network (GRNN) is implemented for predicting the performance and emissions of the engine at various operating conditions based on the experimental results. The neural network predictions are corroborated with the experimental results and are found in good agreement. A classical differential evolution algorithm (DEA) is further used on the network model to find out optimal combination of nanoparticles, biodiesel and diesel and proven through experimental validation.     

Visible photoluminescense of the (112¯0), (101¯1) and (0001) surfaces of ZnO nanofilms

Thin ZnO nanofilms 25 nm thick with (0001), $\left(11\overline{2}0\right)$ and $\left(10\overline{1}1\right)$ as surfaces were grown epitaxially on the NaCl (111) and (001) surfaces. The room temperature photoluminescence (PL) spectrum from the $\left(11\overline{2}0\right)$ nanofilm has a sharp UV emission but negligible green emission, indicating that it has good quality and low defect density. However, the PL spectra from the $\left(10\overline{1}1\right)$ and (0001) surface nanofilms have a broad green emission, and that of the $\left(10\overline{1}1\right)$ surface is stronger than the (0001) surface. The result supports that the surface oxygen vacancies are the probable origins of the green emission.