Carbon nanotube field effect transistor (CNFET) performance is primarily influenced by a variety of characteristics, such as nanotube diameter, operating voltage, pitch, the number of tubes, dielectric constant, and c...
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Carbon nanotube field effect transistor (CNFET) performance is primarily influenced by a variety of characteristics, such as nanotube diameter, operating voltage, pitch, the number of tubes, dielectric constant, and contact materials. In this study, the dielectric constant and the number of carbon nanotubes (CNTs) were emphasized to analyze the propagation delay and average power dissipation of basic logic gates. Zirconium dioxide (ZrO 2 ), hafnium oxide (HfO 2 ), and silicon dioxide (SiO 2 ) were used as dielectric materials. In this experiment, the Stanford CNFET model was utilized, and variable factors such as the number of CNTs and the dielectric constant $(K_{ox})$ accumulated on the nanotube gate's surface were diversified. The correlation coefficients of the delay, the average power, and the CNT numbers were also presented. Furthermore, the multi-objective genetic algorithm and the Rmethod were utilized to find the optimal K ox and the number of CNTs for minimizing the average dissipation power and the propagation delay of some logic gates designed with CNFET. For all the gates considered, the optimal number of CNTs is 4 while the dielectric constant is within the ranges of 19 to 24.
This paper introduces ZETA, a new MATLAB library for Zonotope-based EsTimation and fAult diagnosis of discrete-time systems. It features user-friendly implementations of set representations based on zonotopes, namely ...
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Home security is a crucial aspect that requires careful attention, particularly when it comes to addressing theft concerns. Hence, implementing smart door technology equipped with facial recognition holds promising po...
Home security is a crucial aspect that requires careful attention, particularly when it comes to addressing theft concerns. Hence, implementing smart door technology equipped with facial recognition holds promising potential for enhancing home security. This study aims to develop a more secure and regulated home entry system by leveraging Internet of Things (IoT) technology and Machine Learning computer Vision for facial recognition. The system integrates IoT devices, such as cameras and automatic doors, wherein facial image data is captured by the camera and processed using the Convolutional Neural Network (CNN) algorithm to identify individuals. Once an individual is recognized, the system grants access to the home through an automated door. By relying on facial features, the system effectively restricts unauthorized access and safeguards homes against theft risks. Therefore, the advancement of a safer and more controlled home entry system utilizing IoT technology and Machine Learning computer Vision holds tremendous benefits for homeowners.
Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that ad...
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Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies.
This study presents an embedded system (ES) designed for fault detection and diagnosis in grid-connected photovoltaic (GCPV) systems using transient regime analysis. The primary aim of transient regime analysis is to ...
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ISBN:
(数字)9798350373974
ISBN:
(纸本)9798350373981
This study presents an embedded system (ES) designed for fault detection and diagnosis in grid-connected photovoltaic (GCPV) systems using transient regime analysis. The primary aim of transient regime analysis is to facilitate real-time decision-making, especially during critical faults. A neural network classifier, incorporating a Genetic Algorithm for automated hyperparameter optimization, is developed for GCPV fault classification. These classifiers are seamlessly integrated into a Raspberry Pi 4 platform for fault diagnosis in GCPV systems. Both simulation and experimental results substantiate the ES's viability for fault diagnosis in the examined GCPV system, achieving high accuracy and enabling prompt decision-making to enhance the reliability and safety of GCPV systems.
Sb2Se3 is used to switch between broadband transparency and enhanced index contrast in two device types leveraging Bragg gratings for tunable stop- and pass-band functionalities. Experimental results highlight fabrica...
Electric Vehicles (EV s) such as e-bikes, e-scooters, and e-skateboards become the most popular Eco-friendly personal modes of transportation in the United States. These EVs are mostly recharged via the power grid'...
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Electric Vehicles (EV s) such as e-bikes, e-scooters, and e-skateboards become the most popular Eco-friendly personal modes of transportation in the United States. These EVs are mostly recharged via the power grid's stations. Grid power is generated through multiple means including hydroelectric, thermal, solar, and wind. Even with using EVs to reduce the dangerous effects of fuel burning, there is a real need to take major steps towards green charging approaches. Charging EVs through renewable energy resources is maximizing the ecologically friendly potentials. The aim of this work is to provide a green and sustainable charging system for personal electric vehicles. Hence, a solar-powered charging dock has been built and controlled by an Arduino Mega 2560 adding to a Raspberry Pi 4. Subsequently, solar panel tracker linear actuator has been applied to be integrated with the installed dock solar panel tracking system for delivering maximum precision while operating. To get economic and reliable dock, it has been constructed from wood.
In this paper, the overall health index of underground cable system is determined using Fuzzy Logic and Scoring and Weighting Average methods. The relevant data of 73 feeders has been collected in the prepared evaluat...
In this paper, the overall health index of underground cable system is determined using Fuzzy Logic and Scoring and Weighting Average methods. The relevant data of 73 feeders has been collected in the prepared evaluation forms, divided into five major component groups: cable, joint, termination, manhole, and ductbank. In each component, various testing methods and diagnostic techniques are applied, and then the numerical score is determined according to the technical criteria for condition assessment. Subsequently, the obtained overall health index is multiplied with the conditional factor to incorporate the differences in installation type and configuration as well as the operating and environmental conditions. Finally, the overall health index based on these two methods is compared to verify the accuracy of the obtained results to properly plan the preventive and condition-based maintenance and to improve the reliability of the underground cable system.
This paper provides an examination of the impact of lightning strikes on a 220 kV double circuit (D/C) high voltage transmission line in Bhutan. The study employs the ATP-EMTP software to identify the leading causes o...
This paper provides an examination of the impact of lightning strikes on a 220 kV double circuit (D/C) high voltage transmission line in Bhutan. The study employs the ATP-EMTP software to identify the leading causes of transmission line faults, which are primarily attributed to lightning strikes. Since the transmission lines run through areas in Bhutan that are prone to severe weather conditions and lightning strikes, they are vulnerable to damage, leading to an unreliable power supply. To address this issue, the author proposes several solutions, such as enhancing the tower footing resistance and installing line surge arresters. The author also created models of different transmission towers and analyzed an 18 km stretch of the transmission line that is highly susceptible to lightning strikes.
Group IV GeSn double-heterostructure (DHS) lasers offer unique advantages of a direct bandgap and CMOS compatibility. However, further improvements in laser performance have been bottlenecked by limited junction prope...
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