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Proceedings ArticleDOI
Barbaros Kirisken, H. Fatih Ugurdag 
17 Apr 2014
22 Citations
This cost-benefit approximation for capacitor selection process gives good opportunity to design engineers to do the selection based on reliability and overall cost.
The designed 3.0-V class NiCo 2 O 4 /activated carbon (AC) hybrid capacitor exhibits a much superior performance to the theoretical 4.3-V class capacitor, owing to the effective control of Na-ion insertion/extraction reactions in the NiCo 2 O 4 electrode.
This paper proposes a switched-capacitor multilevel inverter for high-frequency ac power distribution systems.
The technique of ac impedance combined with equivalent circuit clearly showed that oxidized CNT/CP capacitor imparts not only enhanced capacitance but also a low equivalent series resistance.
Open accessJournal ArticleDOI
Chun Li, Jason Li, Jieming Li 
18 Citations
This study presents a new ac circuit element – the mutual capacitor, being a dual of the mutual inductor, which is also a new ac transformer.
This paper proposes a new bridge modular switched-capacitor-based ac–ac converter, which is intended to replace conventional transformers in commercial and residential applications, especially for microgrids.
Therefore, PDHA/AC composite electrodes were more promising for application in capacitor.
In addition, replacing the ac capacitor with one rated for dc can lead to a system reduction, in addition to a considerable reduction in the size of the circuit due to the use of integrated circuits.

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What is the main topic of the paper by Selvapandian and Santhosh (2021)?
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To create an RRL (Resnet as representation for Reinforcement Learning), a method proposed in the research papers, one should fuse features extracted from pre-trained Resnet into the standard reinforcement learning pipeline, enabling the learning of complex behaviors directly from proprioceptive inputs. This approach proves effective in scenarios where traditional methods struggle, such as in uninstrumented environments with high dimensionality and partial observability challenges. The appeal of RRL lies in its simplicity, combining advancements from Representation Learning, Imitation Learning, and Reinforcement Learning fields to achieve comparable results to learning directly from the state, even in complex, high-dimensional domains. By incorporating RRL into the learning process, robots can autonomously acquire behaviors crucial for tasks in unstructured settings like homes or productivity-enhancing roles.
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4 answers
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