To address the issues of strong subjectivity and difficulty in feature extraction that are inherent to traditional frequency response analysis methods used for diagnosing transformer winding ...
This study develops a unified framework for optimal portfolio selection in jump–uncertain stochastic markets, contributing both theoretical foundations and computational insights. We establish the ...
ABSTRACT: We make more specific initial contributions of prior work w.r.t. Tokamaks, relic black holes, and a relationship between a massive graviton particle count and quantum number n, and also add ...
The objective of the 3D-SCALO problem is to assign the given components to optimal mounting surfaces and position them at the best locations, while satisfying the requirements for (1) heat dissipation ...
Abstract: Stacking the mass block (MB) is a key process in maintaining the operation of a gravity energy storage system (GESS), and the energy consumption during this process directly affects the ...
ABSTRACT: Offline reinforcement learning (RL) focuses on learning policies using static datasets without further exploration. With the introduction of distributional reinforcement learning into ...
Abstract: In this article, we investigate the optimal control problem for an unknown linear time-invariant system. To solve this problem, a novel composite policy iteration algorithm based on adaptive ...
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