The dynamics of a power system with a significant presence of renewable energy resources are growing increasingly nonlinear. This nonlinearity is a result of the intermittent nature of these resources and the switchin...
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Solar photo voltaic (PV) energy system backbone of the renewable energy system. Energy system is depended on weather conditions such as temperature and radiation intensity. The role of machine learning (ML) for solar ...
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In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handling forces and torques, these systems...
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Millimeter-Wave(mmWave)Massive MIMO is one of the most effective technology for the fifth-generation(5G)wireless *** improves both the spectral and energy efficiency by utilizing the 30–300 GHz millimeter-wave bandwi...
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Millimeter-Wave(mmWave)Massive MIMO is one of the most effective technology for the fifth-generation(5G)wireless *** improves both the spectral and energy efficiency by utilizing the 30–300 GHz millimeter-wave bandwidth and a large number of antennas at the base ***,increasing the number of antennas requires a large number of radio frequency(RF)chains which results in high power *** order to reduce the RF chain’s energy,cost and provide desirable quality-ofservice(QoS)to the subscribers,this paper proposes an energy-efficient hybrid precoding algorithm formm Wave massive MIMO networks based on the idea of RF chains *** sparse digital precoding problem is generated by utilizing the analog precoding ***,it is jointly solved through iterative fractional programming and successive convex optimization(SCA)*** results show that the proposed scheme outperforms the existing schemes and effectively improves the system performance under different operating conditions.
The goal of this project is to use a servo motor to design and build a solar panel sun position tracking system. Because the solar panel is currently set in place, it is less effective at capturing solar energy. To so...
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Fermented foods in India are diverse and play a significant role in the country's culinary traditions. These foods are often categorized based on their base material and include a variety of items with reported me...
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Layered pathwidth is a new graph parameter studied by Bannister et al. (2015). In this paper we present two new results relating layered pathwidth to two types of linear layouts. Our first result shows that, for any g...
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This paper explores innovative machine learning technologies for the detection of Alzheimer's Disease (AD) from large and imbalanced clock drawing test (CDT) images. We present a deep ensemble learning framework, ...
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Mismatched filtering is a well-known approach of range sidelobe suppression in radar pulse compression;however, advances in radar waveform agility and spectrum sharing require the generation of filters to be efficient...
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Traffic signal control (TSC) has seen substantial advancements through the application of reinforcement learning (RL) algorithms, which have shown remarkable potential in enhancing traffic flow efficiency. These RL-ba...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
Traffic signal control (TSC) has seen substantial advancements through the application of reinforcement learning (RL) algorithms, which have shown remarkable potential in enhancing traffic flow efficiency. These RL-based approaches often surpass traditional rule-based methods, particularly in dynamic traffic environments. However, current RL solutions for TSC predominantly rely on model-free methods, necessitating extensive environmental interactions during training. This requirement can be prohibitively expensive or unfeasible in real-world implementations. Furthermore, existing methods have frequently neglected the issue of fairness in multi-intersection control, resulting in unbalanced congestion across different intersections. To address these challenges, we present FM2Light, a fairness-aware model-based multi-agent RL framework for TSC. Our approach leverages an ensemble of global world models for generating synthetic samples to enhance sample efficiency, thereby mitigating the data-intensive nature of the training process. Additionally, FM2Light incorporates a refined reward structure to promote fairness and improve coordination across multiple intersections. Extensive evaluations conducted in diverse real-world scenarios demonstrate that FM2Light achieves performance comparable to or exceeding that of model-free RL (MFRL) methods, while significantly reducing sample requirements and ensuring more equitable control among multiple agents.
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