Showing papers by "National Institute of Technology, Karnataka published in 2021"
102 citations
TL;DR: The application of multi attributes decision making (MADM) tools is demonstrated for selection of fiber and matrix materials which can serve as a benchmark study for the researchers in future.
72 citations
TL;DR: The potential of Mg implant in repairing fractures at upper and lower limb of large, small animal models and humans is compared and discussed in detail and the possible future Mg implants that might treat problems concerning to urology and gynecology are reviewed.
70 citations
TL;DR: In this article, a multilayer artificial neural network model is developed to forecast the GDP for the April-June quarter of 2020 for eight countries, namely, the United States, Mexico, Germany, Italy, Spain, France, India, and Japan.
69 citations
TL;DR: The proposed deep learning architecture for nuclei segmentation was applied to a set of H&E stained histopathology images from two datasets, and the comprehensive results show that the proposed architecture outperforms state-of-the-art methods.
65 citations
TL;DR: Wang et al. as discussed by the authors used deep learning architectures to develop a Coronavirus diagnostic system using X-ray and CT images, and achieved the accuracy of 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively.
Abstract: In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients' X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible.
64 citations
TL;DR: In this article, the limitations of scaling and ways to resolve them are discussed and detailed study of silicon nanowire and other distinctive nano field effect transistors are presented, which are helpful in directing the current advancements in MOSFET technology and gave a brief sketch of possible future technologies.
58 citations
TL;DR: A new risk management approach known as the Fuzzy rule base interface system was proposed in this research in order to mitigate the failures of underground mining machinery such as Load-Haul-Dumper.
57 citations
TL;DR: In this article, the authors examined the impacts of COVID-19 on seaport transportation and the maritime supply chain field and its related issues in India and found that a negative growth in the cargo traffic and a decrease in the number of vessel traffic compared to pre-COVID19.
54 citations
TL;DR: In this paper, a facile synthesis of porous graphene-NiO (PGNO) nanocomposites via a unique mixed solvent system through a solvothermal approach was reported.
53 citations
TL;DR: In this article, the ionic/electronic conductivity of various pyrochlore structure materials (titanates, zirconates, hafnates, stannates, niobates, ruthenates, and tantalite-based) as electrolyte and electrode materials for solid oxide fuel cells (SOFCs) are reported.
TL;DR: COPYGO showed very high efficient removal rate for the pollutants in simulated effluents which guaranteed its benefits and efficacy in industrial wastewater treatment.
TL;DR: In this paper, superparamagnetic based silica nanocomposites modified with aminosilane were characterized for their physicochemical properties and also the purity of the nanocomposition obtained was determined.
TL;DR: In this paper, the experimental, finite element and analytical assessment of low ballistic impact response of proposed flexible composite make use of naturally available jute and rubber as the constituents of the composite with stacking sequences.
TL;DR: In this article, the frequency response of carbon nanotube reinforced magneto-electro-elastic (FG-CNTMEE) plates subjected to open and closed electro-magnetic circuit conditions was derived.
TL;DR: In this article, the authors systematically summarized the recent progress on the 2D/2D heterojunction constructed between BiOX/BixOyXz with graphitic carbon nitride (g-C3N4).
Abstract: Semiconductor-based photocatalysis has been identified as an encouraging approach for solving the two main challenging problems, viz., remedying our polluted environment and the generation of sustainable chemical energy. Stoichiometric and non-stoichiometric bismuth oxyhalides (BiOX and BixOyXz where X = Cl, Br, and I) are a relatively new class of semiconductors that have attracted considerable interest for photocatalysis applications due to attributes, viz., high stability, suitable band structure, modifiable energy bandgap and two-dimensional layered structure capable of generating an internal electric field. Recently, the construction of heterojunction photocatalysts, especially 2D/2D systems, has convincingly drawn momentous attention practicably owing to the productive influence of having two dissimilar layered semiconductors in face-to-face contact with each other. This review has systematically summarized the recent progress on the 2D/2D heterojunction constructed between BiOX/BixOyXz with graphitic carbon nitride (g-C3N4). The band structure of individual components, various fabrication methods, different strategies developed for improving the photocatalytic performance and their applications in the degradation of various organic contaminants, hydrogen (H2) evolution, carbon dioxide (CO2) reduction, nitrogen (N2) fixation and the organic synthesis of clean chemicals are summarized. The perspectives and plausible opportunities for developing high performance BiOX/BixOyXz-g-C3N4 heterojunction photocatalysts are also discussed.
TL;DR: A hybrid nine-level inverter topology (HNIT) for DC-AC conversion is proposed in this brief, where each phase of the HNIT is designed with only eight semiconductor switches, one diode, and two electrolytic capacitors.
Abstract: Nowadays, output voltage boosting gain property along with curtailment in the circuit voltage stress, and component count are considered as the essential topological features for the new multilevel inverter (MLI) circuits. Recognizing the above, a hybrid nine-level inverter topology (HNIT) for DC-AC conversion is proposed in this brief. Each phase of the HNIT is designed with only eight semiconductor switches, one diode, and two electrolytic capacitors. Herein, series-parallel and conventional-series techniques are utilized effectively to balance the capacitor voltages. Further, cost and quantitative comparisons are carried among the state-of-art circuits to highlight the supremacy of proposed circuit. Subsequently, the performance of HNIT is verified experimentally with the fundamental switching PWM technique at different load conditions.
TL;DR: The proposed scheme utilizes the switching-frequency-based harmonic component for fault detection and localization, and a postfault restoration and control strategy is also proposed to ensure equal current sharing among the remaining healthy modules within their maximum current rating and minimize the input current ripple in the PV panel.
Abstract: To utilize the solar photovoltaic (PV) energy efficiently, dc–dc converters are widely used in both grid-connected and stand-alone systems. Among the various topologies, interleaved dc–dc boost converter offers the benefit of modularity, high power density, and high efficiency along with reduced input current ripple to the PV panel, thereby improving its power extraction efficiency. Open-circuit faults in any of the semiconductor switches of interleaved boost converter could lead to unequal loading on the healthy phases and increase in ripple current that reduces the extraction efficiency of the PV system. To address this issue, a new fault detection and localization scheme is proposed in this article. The proposed scheme utilizes the switching-frequency-based harmonic component for fault detection and localization. Once the fault is localized, a postfault restoration and control strategy is also proposed to ensure equal current sharing among the remaining healthy modules within their maximum current rating and minimize the input current ripple in the PV panel. Detailed simulations are carried out to show the effectiveness of the proposed approach. A laboratory prototype of the interleaved converter is built to validate the proposed approach and experimental test results are provided.
TL;DR: In this paper, a review describes recent advances in the synthesis of MXene/TMD heterostructures and the nature of the synergistic interactions between TMDs and MXenes in energy-related applications.
Abstract: Various two-dimensional (2D) materials have demonstrated unique structure-dependent characteristics that are conducive to energy-harvesting applications. Among them, the family of layered MXenes has found a wide range of applications in batteries, supercapacitors, photo- and electrocatalysis, water purification, biosensors, electromagnetic interference shielding, structural composites, etc., owing to their well-defined structure, large surface area, large interlayer distance, and excellent thermal and electrical conductivity. However, layer restacking due to hydrogen bonding or van der Waals forces between the layers considerably impedes the utility of MXenes. To tackle the restacking issues, transition metal dichalcogenides (TMDs) such as MoS2, WS2, and MoSe2 nanosheets have been uniformly dispersed on the surface of MXenes, which not only mitigates the restacking of the MXenes but also improves the electrochemical performance due to the synergistic interaction between MXenes and TMDs. This review describes recent advances in the synthesis of MXene/TMD heterostructures and the nature of the synergistic interactions between TMDs and MXenes in energy-related applications. We further highlight future research directions for MXene/TMD-based materials for energy storage applications.
TL;DR: In this article, the authors developed earth-abundant-electrocatalysts for water-splitting reactions to curb contemporary energy demands and to address the important issues such as global warming, polluting, and pollution.
Abstract: Developing earth-abundant-electrocatalysts for water-splitting reactions is of great importance to curb contemporary energy demands and to address the important issues such as global warming, pollu...
TL;DR: In this article, the authors designed a two-level resource provisioning fog framework using Docker and containers and formulated the service placement problem in fog computing environment as a multi-objective optimization problem for minimizing the service time, cost, energy consumption and thus ensuring the QoS of IoT applications.
TL;DR: In this article, the electronic properties of tin telluride (SnTe) were improved by co-doping Mn and Bi below their individual solubility limit, achieving a very high power factor of ∼24.3 μW/cmK2 at 773 K when compared to other high performance SnTe based materials.
08 Apr 2021
TL;DR: A detailed review of single-stage and multi-stage WPS consisting of renewable source powered AC motors is presented in this article, where the critical review is performed based on the following figure of merits, including the type of motor, power electronics interface and associated control strategies.
Abstract: In India, the demand for water is continuously increasing due to the growing population. Approximately 16.5% of all country's electricity used to pump this water is from fossil fuels leading to increased pump Life Cycle Cost (LCC) and Green House Gas (GHG) emissions. With the recent advancement in power electronics and drives, renewables like solar photovoltaic and wind energy are becoming readily available for water pumping applications resulting in the reduction of GHG emissions. Recently, research towards AC motor based Water Pumping Systems (WPS) has received a great emphasis owing to its numerous merits. Further, considering the tremendous acceptance of renewable sources, especially solar and wind, this paper provides a detailed review of single-stage and multi-stage WPS consisting of renewable source powered AC motors. The critical review is performed based on the following figure of merits, including the type of motor, power electronics interface and associated control strategies. Also, to add to the reliability of solar PV WPS, hybrid Wind-PV WPS will be discussed in detail. Readers will be presented with the state-of-the- art technology and research directions in Renewable Energy-based WPS (REWPS) to improve the overall system efficiency and hence reduce the payback period.
TL;DR: This article considers machines fault diagnosis based on their operating sound using the fog computing architecture in the industrial environment and shows the performance of ML models for the machines sound recorded with different signal-to-noise ratios for normal and abnormal operations.
Abstract: There is an exponential increase in the use of Industrial Internet of Things (IIoT) devices for controlling and monitoring the machines in an automated manufacturing industry. Different temperature sensors, pressure sensors, audio sensors, and camera devices are used as IIoT devices for pipeline monitoring and machine operation control in the industrial environment. But, monitoring and identifying the machine malfunction in an industrial environment is a challenging task. In this article, we consider machines fault diagnosis based on their operating sound using the fog computing architecture in the industrial environment. The different computing units, such as industrial controller units or micro data center are used as the fog server in the industrial environment to analyze and classify the machine sounds as normal and abnormal. The linear prediction coefficients and Mel-frequency cepstral coefficients are extracted from the machine sound to develop and deploy supervised machine learning (ML) models on the fog server to monitor and identify the malfunctioning machines based on the operating sound. The experimental results show the performance of ML models for the machines sound recorded with different signal-to-noise ratio levels for normal and abnormal operations.
TL;DR: A fault diagnosis model that includes a multi-scale deep residual learning with a stacked long short-term memory (MDRL-SLSTM) to address sequence data in a gearbox health prediction task in an internal combustion (IC) engine is introduced.
TL;DR: In this paper, the chemical catalytic synthesis of various derivatives of Levulinic acid (LA) by focusing on its functionalities and reactivity patterns has been discussed, and a critical assessment of the existing synthetic strategies for various derivatives has been presented to enkindle fresh ideas.
Abstract: Levulinic acid (LA) is one of the most prominent biomass‐derived chemical building blocks that can be transformed into specialty chemicals like fuels, solvents, monomers for polymers, plasticizers, surfactants, agrochemicals, and pharmaceuticals. Over the past three decades, an enormous amount of research data have been acquired on the preparation and downstream value addition of LA, and these works have been reviewed. However, considering the astonishing number of publications appearing every year on LA derivatives, the periodical review of recent works focusing on unique aspects of chemistry must be undertaken to critically evaluate the achievements to date, reassess the challenges, and recognize new opportunities. This review discusses the chemical‐catalytic synthesis of various derivatives of LA by focusing on its functionalities and reactivity patterns. Recent literature on some crucial derivatives such as γ‐valerolactone, 4,4’‐diphenolic acid, and ethyl levulinate have been tabulated and discussed. The synthetic interconversion between various derivatives, mechanistic insights, critical analysis of the reaction parameters toward selective preparation of various derivatives, and their potential commercial applications have been elaborated using predominantly heterogeneous catalysts. A critical assessment of the relative advantages and shortcomings of the existing synthetic strategies for various derivatives of LA has been presented to enkindle fresh ideas.
TL;DR: In this paper, acid assisted hydrothermal method was employed to synthesize zirconium based metal-organic framework (MOF) and utilized for the adsorptive removal of methylene blue (MB) dye and heavy metals (lead and cadmium) from aqueous solution.
Abstract: Industrial effluents contain multiple pollutants, which affect the quality of water remediation operations. Hence, it is important to understand the outcome of the multicomponent adsorption system to develop efficient decontamination process. In this work, acid assisted hydrothermal method (reflux temperature at atmospheric pressure) was employed to synthesize zirconium based metal-organic framework (MOF) and utilized for the adsorptive removal of methylene blue (MB) dye and heavy metals (lead and cadmium) from aqueous solution. The adsorbent was characterized by powder X-ray diffraction (PXRD), which confirms the face centered cubic (FCC) crystal structure similar to previously reported UiO-66. Brunauer–Emmett–Teller (BET) and surface area analysis shows that, the MOF has surface area of 505 m2.g−1 and micropores ranging from 0.4 to 0.7 nm. Fourier transform infrared spectroscopy (FTIR) analysis corroborate the presence of free carboxylic groups at 1710 cm−1, apart from this FTIR confirms the presence of regular bands of metal-carboxylic bonds. X-ray photoelectron spectroscopy (XPS) analysis of the adsorbent was conducted to understand the nature of adsorbate-adsorbent interaction and to confirm the loading of metal ions on adsorbent after adsorption. The morphological nature of the MOF was analyzed by scanning electron microscope (SEM) and energy dispersive X-ray spectroscopy (EDX). Effects of various parameters such as pH, adsorbent dosage, adsorption time and effect of initial concentration of adsorbates on adsorption were evaluated. Kinetics and isotherm studies were conducted to understand the nature and extent of adsorption. Langmuir monolayer adsorption capacity of the adsorbent for cadmium, lead and methylene blue were found to be 37 mg.g−1, 100 mg.g−1 and 169 mg.g−1 respectively.
Indian Institutes of Information Technology1, Indian Institute of Technology Guwahati2, Nitte Meenakshi Institute of Technology3, Indian Institute of Engineering Science and Technology, Shibpur4, International Institute of Information Technology5, Bennett University6, National Institute of Technology, Karnataka7, Panjab University, Chandigarh8, Indian Institute of Science9, National Institute of Technology, Rourkela10, National Institute of Science Education and Research11, Indian Institute of Technology Kanpur12, Indian Institutes of Technology13, Central University of Karnataka14, Indian Institute of Science Education and Research, Kolkata15
TL;DR: The previous literature on quantum machine learning is reviewed and the current status of it is provided, postulating that quantum computers may overtake classical computers on machine learning tasks.
Abstract: Quantum machine learning is at the intersection of two of the most sought after research areas—quantum computing and classical machine learning. Quantum machine learning investigates how results from the quantum world can be used to solve problems from machine learning. The amount of data needed to reliably train a classical computation model is evergrowing and reaching the limits which normal computing devices can handle. In such a scenario, quantum computation can aid in continuing training with huge data. Quantum machine learning looks to devise learning algorithms faster than their classical counterparts. Classical machine learning is about trying to find patterns in data and using those patterns to predict further events. Quantum systems, on the other hand, produce atypical patterns which are not producible by classical systems, thereby postulating that quantum computers may overtake classical computers on machine learning tasks. Here, we review the previous literature on quantum machine learning and provide the current status of it.
TL;DR: In this paper, a numerical analysis of a lumped thermal model coupled with fluid flow equations is employed to investigate the novel air-cooled battery thermal management system (BTMS), which offers a 25% reduction in peak temperature when compared to the standard one.