Application Of Neural Network For Transformer Protection

The demand for a reliable supply of electrical energy for the exigency of modern world in each and every field has increased considerably requiring nearly a no-fault operation of power systems. The crucial objective is to mitigate the frequency and duration of unwanted outages related to power transformer puts a high pointed demand on power transformer protective relays to operate immaculately and capriciously. The high pointed demand includes the requirements of dependability associated with no false tripping, and operating speed with short fault detection and clearing time. The second harmonic restrain principle is widely used in industrial application for many years, which uses discrete Fourier transform (DFT) often encounters some problems such as long restrain time and inability to discriminate internal fault from magnetizing inrush condition. Hence, artificial neural network (ANN), a powerful tool for artificial intelligence (AI), which has the ability to mimic and automate the knowledge, has been proposed for detection and classification of faults from normal and inrush condition. The wavelet transform(WT) which has the ability to extract information from transient signals in both time and frequency domain simultaneously is used for the analysis of power transformer transient phenomena in various conditions. All the above mentioned conditions of power transformer to be analysed in a power system are modelled in MATLAB/SIMULINK environment. Secondly the WT is applied to decompose the different current signals of the power transformer into a series of detailed wavelet components. The statistical features of the wavelet components are calculated and are used to train a multilayer feed forward neural network designed using back propagation algorithm to discriminate various conditions. The best suitable architecture of ANN is selected having least mean square error during training. The ANN model is implemented in LabVIEW environment

Noise Impact Assessment and Prediction in Mines Using Soft Computing Techniques

Mining of minerals necessitates use of heavy energy intensive machineries and equipment leading to miners to be exposed to high noise levels. Prolonged exposure of miners to the high levels of noise can cause noise induced hearing loss besides several non-auditory health effects. Hence, in order to improve the environmental condition in work place, it is of utmost importance to develop appropriate noise prediction model for ensuring the accurate status of noise levels from various surface mining machineries. The measurement of sound pressure level (SPL) using sound measuring devices is not accurate due to instrumental error, attenuation due to geometrical aberration, atmospheric attenuation etc. Some of the popular frequency dependent noise prediction models e.g. ISO 9613- 2, ENM, CONCAWE and non-frequency based noise prediction model e.g. VDI-2714 have been applied in mining and allied industries. These models are used to predict the machineries noise by considering all the attenuation factors. Amongst above mathematical models, VDI-2714 is simplest noise prediction model as it is independent from frequency domain. From literature review, it was found that VDI-2714 gives noise prediction in dB (A) not in 1/1 or 1/3 octave bands as compared to other prediction models e.g. ISO-9613-2, CONCAWE, OCMA, and ENM etc. Compared to VDI-2714 noise prediction model, frequency dependent models are mathematically complex to use. All the noise prediction models treat noise as a function of distance, sound power level (SWL), different forms of attenuations such as geometrical absorptions, barrier effects, ground topography, etc. Generally, these parameters are measured in the mines and best fitting models are applied to predict noise. Mathematical models are generally complex and cannot be implemented in real time systems. Additionally, they fail to predict the future parameters from current and past measurements. To overcome these limitations, in this work, soft-computing models have been used. It has been seen that noise prediction is a non-stationary process and soft-computing techniques have been tested for non-stationary time-series prediction for nearly two decades. Considering successful application of soft-computing models in complex engineering problems, in this thesis work, soft-computing system based noise prediction models were developed for predicting far field noise levels due to operation of specific set of mining machinery. Soft Computing models: Fuzzy Inference System (Mamdani and Takagi Sugeno Kang (T-S-K) fuzzy inference systems), MLP (multi layer perceptron or back propagation neural network), RBF (radial basis function) and Adaptive network-based fuzzy inference systems (ANFIS) were used to predict the machinery noise in two opencast mines. The proposed soft-computing based noise prediction models were designed for both frequency and non-frequency based noise prediction models. After successful application of all proposed soft-computing models, comparitive studies were made considering Root Mean Square Error (RMSE) as the performance parameter. It was observed that proposed soft-computing models give good prediction results with accuracy. However, ANFIS model gives better noise prediction with better accuracy than other proposed soft-computing models

Application of Functional Link Artificial Neural Network for Prediction of Machinery Noise in Opencast Mines

Functional link-based neural network models were applied to predict opencast mining machineries noise. The paper analyzes the prediction capabilities of functional link neural network based noise prediction models vis-à-vis existing statistical models. In order to find the actual noise status in opencast mines, some of the popular noise prediction models, for example, ISO-9613-2, CONCAWE, VDI, and ENM, have been applied in mining and allied industries to predict the machineries noise by considering various attenuation factors. Functional link artificial neural network (FLANN), polynomial perceptron network (PPN), and Legendre neural network (LeNN) were used to predict the machinery noise in opencast mines. The case study is based on data collected from an opencast coal mine of Orissa, India. From the present investigations, it could be concluded that the FLANN model give better noise prediction than the PPN and LeNN model

Extensional collapse of the Gondwana orogen: evidence from Cambrian mafic magmatism in the Trivandrum Block, southern India

The assembly of Late Neoproterozoic–Cambrian supercontinent Gondwana involved prolonged subduction and accretion generating arc magmatic and accretionary complexes, culminating in collision and formation of high grade metamorphic orogens. Here we report evidence for mafic magmatism associated with post-collisional extension from a suite of gabbroic rocks in the Trivandrum Block of southern Indian Gondwana fragment. Our petrological and geochemical data on these gabbroic suite show that they are analogous to high Fe tholeiitic basalts with evolution of the parental melts dominantly controlled by fractional crystallization. They display enrichment of LILE and LREE and depletion of HFSE with negative anomalies at Zr–Hf and Ti corresponding to subduction zone magmatic regime. The tectonic affinity of the gabbros coupled with their geochemical features endorse a heterogeneous mantle source with collective melt contributions from sub-slab asthenospheric mantle upwelling through slab break-off and arc-related metasomatized mantle wedge, with magma emplacement in subduction to post-collisional intraplate settings. The high Nb contents and positive Nb–Ta anomalies of the rocks are attributed to inflow of asthenospheric melts containing ancient recycled subducted slab components and/or fusion of subducted slab materials owing to upwelling of hot asthenosphere. Zircon grains from the gabbros show magmatic crystallization texture with low U and Pb content. The LA-ICPMS analyses show 206Pb/238U mean ages in the range of 507–494 Ma suggesting Cambrian mafic magmatism. The post-collisional mafic magmatism identified in our study provides new insights into mantle dynamics during the waning stage of the birth of a supercontinent.Qiong-Yan Yang, Sohini Ganguly, E.Shaji, Yunpeng Dong, V. Nanda-Kuma

Pattern of Tobacco Use and Perceived Risk of COVID-19 Following Tobacco Use among the COVID-19 Patients of a Tertiary Health Care Institution in Eastern India

Background: COVID-19 presented an unprecedented situation in which behavioural factors including tobacco use were believed to increase the risk of morbidity and mortality. The objective of the present study was to find the tobacco use pattern among the COVID-19 patients and the perceived risk of developing severe COVID-19 following tobacco use.Methods: This hospital-based, cross-sectional, analytical study was conducted among 300 COVID-19 patients at the All India Institute of Medical Sciences (AIIMS), Patna, India, during November and December 2020 using a semi-structured, pretested questionnaire. Descriptive and univariate analyses were performed using statistical software and the results were presented as proportion and percentage.Findings: About 27% and 16% of the COVID-19 patients were ever and current tobacco users, respectively. Quit attempts were found to have increased during the COVID-19 pandemic. A majority (65%) of current tobacco users had reduced their amount of tobacco use. Nearly 2 in every 3 patients perceived high risk of developing severe COVID-19 following tobacco use. Perceived risk was significantly higher among tobacco non-users, patients who were aware of the ill health effects of tobacco use, and patients who had noticed anti-tobacco messages or had been advised to quit tobacco. Among the current tobacco users, a significantly higher proportion of patients who perceived high risk of developing severe COVID-19 following tobacco use had made quit attempts or had reduced tobacco consumption during the pandemic (76.7% vs. 40%; P = 0.032).Conclusion: A high proportion of COVID-19 patients believed that tobacco use aggravated the COVID-19 condition. Increased quit attempts and reduction in tobacco consumption during this pandemic is a positive sign for tobacco contro

Nanoparticle-formulated curcumin prevents posttherapeutic disease reactivation and reinfection with Mycobacterium tuberculosis following isoniazid therapy

Curcumin, the bioactive component of turmeric also known as “Indian Yellow Gold,” exhibits therapeutic efficacy against several chronic inflammatory and infectious diseases. Even though considered as a wonder drug pertaining to a myriad of reported benefits, the translational potential of curcumin is limited by its low systemic bioavailability due to its poor intestinal absorption, rapid metabolism, and rapid systemic elimination. Therefore, the translational potential of this compound is specifically challenged by bioavailability issues, and several laboratories are making efforts to improve its bioavailability. We developed a simple one-step process to generate curcumin nanoparticles of ~200 nm in size, which yielded a fivefold enhanced bioavailability in mice over regular curcumin. Curcumin nanoparticles drastically reduced hepatotoxicity induced by antitubercular antibiotics during treatment in mice. Most interestingly, co-treatment of nanoparticle-formulated curcumin along with antitubercular antibiotics dramatically reduced the risk for disease reactivation and reinfection, which is the major shortfall of current antibiotic treatment adopted by Directly Observed Treatment Short-course. Furthermore, nanoparticle-formulated curcumin significantly reduced the time needed for antibiotic therapy to obtain sterile immunity, thereby reducing the possibility of generating drug-resistant variants of the organisms. Therefore, adjunct therapy of nano-formulated curcumin with enhanced bioavailability may be beneficial to treatment of tuberculosis and possibly other diseases

Defective Hepatic Response to Interferon and Activation of Suppressor of Cytokine Signaling 3 in Chronic Hepatitis C

Approximately half of hepatitis C virus (HCV)-infected patients do not respond to current interferon (IFN)-α combination therapy. To understand IFN-α resistance in vivo, we examined the dynamic responses to both type I and type II IFNs, human IFN (hIFN)- α, -γ, and consensus IFN, in the chimpanzee model

Hepatic gene expression during treatment with peginterferon and ribavirin: Identifying molecular pathways for treatment response

The reasons for hepatitis C treatment failure remain unknown but may be related to different host responses to therapy. In this study, we compared hepatic gene expression in patients prior to and during peginterferon and ribavirin therapy. In the on-treatment group, patients received either ribavirin for 72 hours prior to peginterferon alpha-2a injection or peginterferon alpha-2a for 24 hours, prior to biopsy. The patients were grouped into rapid responders (RRs) with a greater than 2-log drop and slow responders (SRs) with a less than 2-log drop in hepatitis C virus RNA by week 4. Pretreatment biopsy specimens were obtained from a matched control group. The pretreatment patients were grouped as RRs or SRs on the basis of the subsequent treatment response. Gene expression profiling was performed with Affymetrix microarray technology. Known interferon-stimulated genes (ISGs) were induced in treated patients. In the pretreatment group, future SRs had higher pretreatment ISG expression than RRs. On treatment, RRs and SRs had similar absolute ISG expression, but when it was corrected for the baseline expression with the pretreatment group, RRs showed a greater fold change in ISGs, whereas SRs showed a greater change in interferon (IFN)-inhibitory pathways. The patients pretreated with ribavirin had heightened induction of IFN-related genes and down-regulation of genes involved in IFN inhibition and hepatic stellate cell activation