reinforcement learning

This comprehensive dataset comprises multiple files, encompassing essential information on various aspects of power systems. It includes the active and reactive power consumption data for both the 33- and 136-bus test systems, along with the resistance and reactance values of the distribution lines, and the network structure.

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6 Views

This study presents a benchmark for evaluating action-constrained reinforcement learning (RL) algorithms. In action-constrained RL, each action taken by the learning system must comply with certain constraints. These constraints are crucial for ensuring the feasibility and safety of actions in real-world systems. We evaluate existing algorithms and their novel variants across multiple robotics control environments, encompassing multiple action constraint types.

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79 Views

Abstract—A novel approach is proposed in this article to boost the energy efficiency (EE) of an AoI-aware IoT network. In particular, we propose a new approach that is based a combination of simultaneous wireless information and power

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543 Views
These datasets contain the database of the local television channels in the greater manila 
area (GMA) in the Philippines as of 2020. There are 8 databases corresponding to 8 eight channels 
namely Channel4.xlsx, Channel5.xlsx, Channel7.xlsx, Channel9.xlsx, Channel11.xlsx, 
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161 Views

Safety assessment of Cyber-Physical Systems (CPS) requires a tremendous amount of effort as the complexity of cyber-physical systems is increasing. A well-known approach for the safety assessment of CPS is Fault Injection (FI). The goal of fault injection is to find a catastrophic fault that can fail the system by injecting faults into it. These catastrophic faults are less likely to happen, and finding it requires tremendous labor and cost.

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148 Views

Biomechanics has predominantly relied upon the trajectory optimization method for the analysis and prediction of the movement of the limbs. Such approaches have paved the way for the motion planning of biped and quadruped robots as well. Most of these methods are deterministic, utilizing first-order iterative gradient-based algorithms incorporating the constrained differentiable objective functions.

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137 Views

the measurement data  simulated data of Hd-TCP and its comparisons' performance on the real high-speed railways scenario

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511 Views

This dataset contains in-silico results of insulin treatment using a fully automated artificial pancreas algorithm based on reinforcement learning for FDA-approved virtual patients (C. D. Man et al., 2014) with type 1 diabetes (10 adults and 10 adolescents). 

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1230 Views

Supplementary Material for IEEE-TII Transaction Article "Controller Design for Electrical Drives by Deep Reinforcement Learning - a Proof of Concept"

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512 Views