This paper focuses on the initial testing, by using a DC variable load, of the laboratory scale Superconducting Magnetic Energy Storage (SMES) system developed in the University of Western Macedonia. The SMES is built...
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For small businesses, the integration of data analytics is fraught with a variety of obstacles that prevent these organizations from capitalizing on decision making support tools powered by their data. In this abstrac...
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The whole world is suffering from the wave of the novel coronavirus that causes the large-scale death of a population and is proclaimed a pandemic by WHO. As RT-PCR tests to detect Coronavirus are costly and time taki...
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In the era of ubiquitous data collection and analysis, preserving privacy while utilizing data, such as inner product evaluations, poses a significant challenge. One such method is inner product functional encryption ...
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In an age of information and digital communication, social media platforms are becoming essential spaces where individuals can voice their thoughts, exchange ideas, and take part in discussions on a variety of subject...
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ISBN:
(数字)9798350379587
ISBN:
(纸本)9798350379594
In an age of information and digital communication, social media platforms are becoming essential spaces where individuals can voice their thoughts, exchange ideas, and take part in discussions on a variety of subjects. High quality and high value datasets of such ideas and discussions in textual and formatted form are fundamental to the research aspects of Artificial Intelligence and data-science. In this paper, we present a dataset of indirect harassment. There are approximately 10,700 tweets with binary labels. The labels were assigned by a team of researchers collectively. The dataset contains approximately 19 percent positive indirect harassment labels and 81 percent negative indirect harassment labels. The data is useful for training and running machine and deep learning models to detect indirect and direct harassment. The ease in understanding the data for researchers and other peers is also very crucial. The corpus mentioned in this paper is easy to understand via the use of a binary label. The data is simplistic in nature because there are only two columns with one being the text and the other being the indirect label. This work was necessary because it was a component of a larger project that also needed this kind of dataset.
In recent days, the escalating need to seamlessly transfer data traffic without discontinuities across the Internet network has exerted immense pressure on the capacity of these networks. Consequently, this surge in d...
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During their studies, many students drop out of one specialty, then apply and enroll in another. The state subsidized several semesters of their studies, then again subsidized the student's studies in another spec...
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Buildings are significant contributors to global energy consumption. Maintaining comfortable indoor temperatures while reducing energy consumption are conflicting objectives. Deep Reinforcement Learning (DRL) is a pro...
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ISBN:
(数字)9798331521691
ISBN:
(纸本)9798331521707
Buildings are significant contributors to global energy consumption. Maintaining comfortable indoor temperatures while reducing energy consumption are conflicting objectives. Deep Reinforcement Learning (DRL) is a promising area of research for building Heating, Ventilation and Air Conditioning (HVAC) system optimization. In this study an open-source framework Building Optimization Testing Framework (BOPTEST), which is a virtual testbed that help comparison different control strategies for evaluation of DRL control methods is used. A Proportional-Integral (PI) controller is used to benchmark the DRL methods. A single zone residential building of 192 m 2 with a radial heating system and a heat pump in a climate zone with high heating requirement with dynamic electricity prices with prices varying every 15 min based on demand is chosen for implementing different control strategies. On comparing Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO) and Twin Delay DDPG (TD3) based DRL controllers and the baseline controller, the DDPG based controller reduced energy consumption by 97.3 % and operating cost by 17.7 % during the peak heating period with reference to baseline method. Then on analyzing the impact of inclusion of forecast parameters occupancy, solar irradiance, and electricity prices over the period 3, 6 and 12 hours in DDPG based controller. The prediction for 3 hours gave the greatest reduction in thermal discomfort of 99.7 % and prediction for 12 hours gave maximum reduction in cost by 30.4 % but resulted in only 82% reduction in thermal comfort when compared with baseline method indicating that longer prediction horizon is not necessarily results in better performance.
Processors have become increasingly overloaded with transistors in the last years. It causes heat dissipation and, as a result, error occurrence. One of the ways to solve those problems is to use reversible logic inst...
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With increasing public access to social media, many dubious and inaccurate content is being generated and shared for profitable targets. This content is generated to attract audiences, increase revenue, impact people&...
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