In this study an innovative approach to adaptable voltage regulation, employing machinelearning to optimize the control of power electronics systems has been proposed. The crucial goal of efficient voltage regulation...
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Nowadays the surveillance systems are widely used to find out the suspicious events that have occurred. In conventional systems, there are a lot of limitations such as storage, bandwidth, cost, the short lifespan of h...
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The most significant creature in the ecology is the butterfly. The relationship between plants and butterflies is essential for supporting many ecosystem processes. They are occasionally referred to as flying flowers ...
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The proceedings contain 138 papers. The topics discussed include: implementation of sorted stair-case modulation and sorted phase disposition PWM for grid-tied multi-level inverter;bi-directional converter for hybrid ...
ISBN:
(纸本)9798350305005
The proceedings contain 138 papers. The topics discussed include: implementation of sorted stair-case modulation and sorted phase disposition PWM for grid-tied multi-level inverter;bi-directional converter for hybrid energy storage system;experimental investigation of the effectiveness and robustness of active disturbance rejection control using a DC motor-driven piston;instantaneous power based effective power balance operation in all-electric ship microgrid;modelling of EV penetration for an Indian state using machinelearning approach;finite element-based dynamic eccentricity fault analysis in switched reluctance motor using machinelearning approach;setting-free smart recloser for three terminal mixed lines;elevator based car parking system;optimal sizing and operation of PV based battery swapping station considering EV uncertainties for an Indian residential community;and sensitivity of threshold value ‘R’ in approximate entropy based brain-computer interface.
This study investigates the potential of machinelearning to predict signal power fluctuations in free-space optical communications, impacted by atmospheric fluctuations. Conducted over a 600-meter terrestrial link, o...
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Industries across the world are witnessing profound technological paradigm changes, necessitating a new set of engineering skills and capabilities for the future workforce. This paper investigates the needed engineeri...
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Industries across the world are witnessing profound technological paradigm changes, necessitating a new set of engineering skills and capabilities for the future workforce. This paper investigates the needed engineering competencies required to bridge the skill gap related to Industry 5.0. Our work analyses current educational frameworks on the topic and proposes a new concept based on the exploitation of learning factories. The paper showcases the conception, execution, and evaluation of a summer school program dedicated to such an objective. It discusses the structure, methodologies, and outcomes of this course, demonstrating the effectiveness of the proposed didactic framework. The findings offer valuable insights for educators and industry professionals alike in preparing the future workforce for the challenges and opportunities of Industry 5.0.
The proceedings contain 289 papers. The topics discussed include: employing multifaceted bioinformatics strategies for the discovery of novel ASK1 inhibitors targeting neurodegenerative disorders;sailfish optimizer al...
ISBN:
(纸本)9798350359688
The proceedings contain 289 papers. The topics discussed include: employing multifaceted bioinformatics strategies for the discovery of novel ASK1 inhibitors targeting neurodegenerative disorders;sailfish optimizer algorithm for effective toxic gas detection sensor placement in IioT;a comprehensive study on satellite-based data communication for big earth observation systems;APIs insight for phenotype classification and hive health forecasting using IoT and deep learning;the future of teaching: exploring the integration of machinelearning in higher education;person recognition using ear images based on fractional gannet sparrow optimization enabled deep learning;restaurant recommendation system using machinelearning algorithms;and AI-driven remote Parkinson's diagnosis with BPNN framework and cloud-based data security.
Switched Reluctance Motor (SRM) is becoming popular in several fields, such as electric vehicles, renewable energy-dependent machines, domestic appliances, etc. Among the faults in SRM, bearing and eccentricity-relate...
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Popularity of Social media sites has been on the rise ever since its invention. Due to the anonymity the social media sites provide to the users, an influx of divergent behavior has also been increasing. Abusive langu...
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We propose a masked pretraining method for Graph Neural Networks (GNNs) to improve their performance on fitting potential energy surfaces, particularly in water and small organic molecule systems. GNNs are pretrained ...
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We propose a masked pretraining method for Graph Neural Networks (GNNs) to improve their performance on fitting potential energy surfaces, particularly in water and small organic molecule systems. GNNs are pretrained by recovering the spatial information of masked-out atoms from molecules selected with certain ratios and then transferred and fine-tuned on atomic force fields. Through such pretraining, GNNs learn meaningful prior about the structural and underlying physical information of molecule systems that are useful for downstream tasks. With comprehensive experiments and ablation studies, we show that the proposed method improves both the accuracy and convergence speed of GNNs compared to their counterparts trained from scratch or with other pretraining techniques. This approach showcases its potential to enhance the performance and data efficiency of GNNs in fitting molecular force fields. (c) 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial 4.0international (CC BY-NC) license (https://***/licenses/by-nc/4.0/).
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