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Corrosion was induced by the electrochemical accelerated corrosion method to reinforcement at various locations in the beams.
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This article presents a new method to obtain local reinforcement in near-surface layers of channels in castings made of a particle-reinforced metal matrix composite in the alternating electromagnetic field generated by an inductor placed inside the channel.
In the present study we developed a new method to analyze the reinforcement dynamics analytically.
Among the reinforcement methods in the present study, the best reinforcement method to improve anti-explosion performances of the beams is the beam strengthened with steel plates on the top and bottom of it.
To solve this problem, we introduce a reinforcement learning method to virtual network embedding.
This paper presents an improved reinforcement learning method to minimize electricity costs on the premise of satisfying the power balance and generation limit of units in a microgrid with grid-connected mode.
Several design guidelines adopt limit state methods (e.g., earth pressure or limit equilibrium slope stability methods) to estimate the reinforcement loads for geosynthetic-reinforced soil structures (GRSSs).
Methods to quantify root reinforcement have been developed over the last 35 years.
We explore the relevance of this notion of control to reinforcement learning methods of optimising behavior in such environments and consider how apparently maladaptive beliefs can result from normative inference processes.
Our method uses reinforcement learning to correct such errors and fill in gaps in the knowledge of a context-based tactical agent.
One of the difficulties encountered in the application of reinforcement learning methods to real-world problems is their limited ability to cope with large-scale or continuous spaces.
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