Optimization research on UAV semantic communication system based on SVD-MADRL by Yibo Yang, Yahe Tan, Liu Liu

“…In addition, compared with traditional algorithms, the semantic communication optimization model constructed in this study performs the best in terms of total reward, data collection efficiency, and accuracy. It has strong stability and convergence ability, with an accuracy close to 1, significantly improving the accuracy and efficiency of unmanned aerial vehicle communication transmission. …”
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Maximizing theoretical and practical storage capacity in single-layer feedforward neural networks by Zane Z. Chou, Jean-Marie C. Bouteiller, Jean-Marie C. Bouteiller, Jean-Marie C. Bouteiller, Jean-Marie C. Bouteiller

“…This work offers a foundational framework for maximizing storage efficiency in neural network systems and supports the development of data-efficient, sustainable AI.…”
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Some research results by risk-inform approaches for npp safety and operational efficiency by Yu. А. Komarov

“…In the article the per-spective problems of further development risk-oriented approach (ROA) for the grounding and realization of measures on increase of safety and operational efficiency of NPP are considered. Unlike the traditional approach for the ROA, mean due the definition of probabilistic and/or deterministic methods of risk parameters, as criterion functions essence and the measure of the estimation are defined by the solution of specific problem in nuclear field. …”
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Research of efficiency of the algorithms for finding solutions based on the use of functional semantic networks by Mironova M.N.

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Evaluating the Efficiency of Nature-Inspired Algorithms for Finite Element Optimization in the ANSYS Environment by Antonino Cirello, Tommaso Ingrassia, Antonio Mancuso, Giuseppe Marannano, Agostino Igor Mirulla, Vito Ricotta

“…Nature-inspired metaheuristics have proven effective for addressing complex structural optimization challenges where traditional deterministic or gradient-based methods often fall short. …”
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Efficient Software Development Effort Estimation Approaches for Improving Scalability in the Training Phase by Ho Le Thi Kim Nhung, Petr Silhavy, Radek Silhavy

“…While clustering can address this complexity, standard methods often rely on random initial centers, leading to inconsistent and less precise results. …”
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An Efficient Procedure for Inserting Buffers to Generate Robust Berth Plans in Container Terminals by Yangcan Wu, Lixin Miao

“…Such a method is highly versatile and compatible with various solutions to berth allocation problem with different objectives. …”
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A Scheduling Model in Capturing Methane Gas from Methane Clathrates Deposits by Anyanwu U. O., Okafor O. C., Nkwor C. A.

“…The execution of any project type, especially engineering-based projects, is usually time-based, efficiency-driven, and cost-effective. These factors are the deterministic parameters that engineer successful project completion. …”
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Efficient planning and optimization of integrated energy system considering double uncertainty of source and load by LI Aiwu, DI Liang, DONG Jie, TIAN Zhe, WANG Yi, NIU Jide

“…The design process of integrated energy system will face the uncertainty of renewable energy generation and energy demand, and the risk of sub-optimal decision will be introduced when the deterministic method is used for design. In this paper, an efficient planning and optimization model for integrated energy systems considering dual source-load uncertainty was proposed. …”
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Novel efficient deep reinforcement learning-based load frequency control for isolated microgrid by Xin Shen, Yijing Zhang, Jiahao Li, Yitao Zhao, Jianlin Tang, Bin Qian, Xiaoming Lin

“…In addition, a novel sort replay actor critic technique is proposed, leveraging the deep deterministic policy gradient algorithm and sort experience replay to enhance control efficiency and robustness. …”
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Energy efficient control of indoor environments under time-varying multi-parameter uncertainty by Jianhao ZHAO, Hua SONG, Xinyuan NAN, Xin CAI

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Efficient Robot Manipulation via Reinforcement Learning with Dynamic Movement Primitives-Based Policy by Shangde Li, Wenjun Huang, Chenyang Miao, Kun Xu, Yidong Chen, Tianfu Sun, Yunduan Cui

“…The proposed method naturally integrates a DMP-based policy into the actor–critic framework of the traditional RL approach Deep Deterministic Policy Gradient (DDPG) and derives the corresponding update formulas to learn the networks that properly decide the parameters of DMPs. …”
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Digital Twin and TD3-Enabled Optimization of xEV Energy Management in Vehicle-to-Grid Networks by Irum Saba, Abdulraheem H. Alobaidi, Sultan Alghamdi, Muhammad Tariq

“…This paper addresses these challenges by proposing an advanced ESS framework that integrates digital twin (DT) technology with the twin-delayed deep deterministic policy gradient (TD3) algorithm, a state-of-the-art reinforcement learning method derived from the deep deterministic policy gradient (DDPG). …”
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Mode Coresets for Efficient, Interpretable Tensor Decompositions: An Application to Feature Selection in fMRI Analysis by Ben Gabrielson, Hanlu Yang, Trung Vu, Vince Calhoun, Tulay Adali

“…These methods’ efficiencies are often due to their randomized natures; however, deterministic methods can provide better approximations, and can perform feature selection, highlighting a meaningful subset that well-represents the entire tensor. …”
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Energy-efficient control of thermal comfort in multi-zone residential HVAC via reinforcement learning by Zheng-Kai Ding, Qi-Ming Fu, Jian-Ping Chen, Hong-Jie Wu, You Lu, Fu-Yuan Hu

“…Energy efficient control of thermal comfort has been already an important part of residential heating, ventilation, and air conditioning (HVAC) systems. …”
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A novel approach to pseudorandom number generation using Hamiltonian conservative chaotic systems by Vinod Patidar, Tanu Singh

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OPERATIONAL ANALYSIS OF COMPLEX MEDICAL STATES BY PHOTONICS METHODS by A. I. Larkin, K. A. Trukhanov

“…Such an analysis is carried out by the means of vector-matrix multiplication using laser photonics methods. It is significant that with the widening of the range of probability algorithms, it is possible to preserve certain advantages of the holographic method: multidimensionality, efficiency, high information capacity and speed, visibility and flexibility of the result presentation. …”
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Decentralized Voltage and Var Control of Active Distribution Network Based on Parameter-Sharing Deep Reinforcement Learning by Binqiao Zhang, Changlin He

“…This work introduces a Parameter Sharing - twin-delay deep deterministic policy gradient (PS-TD3) method for carrying out decentralized voltage and var control in active distribution networks. …”
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Tracking Control of CSTRs Based on Improved OU Noise and the TD3 Algorithm by Hongyan Shi, Xiaofei Wu, Guogang Wang

“…To address this challenge, this study proposes a novel deep reinforcement learning (DRL) approach that integrates an improved Ornstein-Uhlenbeck (IOU) noise into the twin delayed deep deterministic policy gradient (TD3) algorithm. This method is applied to the tracking control of continuous stirred tank reactors (CSTRs). …”
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A DRL-based optimization method for microgrid operation by ZENG Lei, DING Quan, CHEN Xiaoyu, YUE Xianya

“…Third， a penalty term for high-proportion erroneous actions is incorporated into the reward function to constrain the output of each device within a reasonable range， mitigating the risk of insufficient safety guarantees inherent in reinforcement learning methods. Finally， simulation results demonstrate that， compared to the deep deterministic policy gradient （DDPG） algorithm， the proposed method achieves superior economic efficiency and stability， with economic costs closer to those of ideal deterministic optimization methods.…”
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