Research on secure transport strategy of mobile edge computing based on deep reinforcement learning by WANG Yijun, LI Jiaxin, YAN Zhiying, LYU Jingying, QIAN Zhihong

“…Firstly, the mobile edge computing system based on unmanned aerial vehicle (UAV) was studied, which was composed of<italic> I</italic> user devices, <italic>M</italic> legal UAV (L-UAV) and <italic>N</italic> eavesdropping UAV (E-UAV). Secondly, while ensuring the unloading of L-UAV within a specified period, the multi-agent depth deterministic policy gradient (Attention-MADDPG) algorithm with the addition of attention mechanism was adopted to solve and optimize the problem with the aim of maximizing the safety unloading efficiency of the communication system. …”
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Multimodal Control by Variable-Structure Neural Network Modeling for Coagulant Dosing in Water Purification Process by Jun Zhang, Da-Yong Luo

“…This method rectifies coagulant dosage effectively while keeping valid coagulation performance. By the way, the MM-P-VSRVNN algorithm can decrease computation time and avoid overfitting learning ability. …”
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A bayesian network model for neurocognitive disorders digital screening in Chinese population: development and validation study by Yifan Yu, Shuaijie Zhang, Hongkai Li, Fuzhong Xue

“…Gender and the top 30 variables with the highest coefficient of determination () in explaining the variance in NCD status were retained for model construction. Subsequently, the optimal network structure was identified using the Tabu search algorithm guided by Bayesian Information Criterion, with parameters estimated by maximum likelihood estimation. …”
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Research on Unmanned Aerial Vehicle Path Planning for Carbon Emission Monitoring of Land-Side Heavy Vehicles in Ports by Xincong Wu, Zhanzhu Li, Xiaohua Cao

“…In the simulation experiment results, the proposed algorithm outperforms A*, ACO, and IEA algorithms in optimizing path length, reducing collision points to ensure path safety, and robustness. …”
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Learning-Based Multi-Stage Formation Scheduling with a Hybrid Controller by Zhichao Zhang, Yao Li

“…The comparison with other scheduling strategies demonstrates the optimality and effectiveness of our proposed framework and algorithms.…”
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Hybrid beamforming for ISAC based on mmWave cell-free MIMO by DUO Lin, YANG Xin, REN Yong, XU Boyu

“…Two kinds of power constraints are considered in the study, and the improved orthogonal matching pursuit algorithm and the Riemann conjugate gradient algorithm are used for optimization. …”
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Observer-Based Sliding Mode Control for Vehicle Way-Point Tracking with Unknown Disturbances and Obstacles by Jiacheng Song, Mingjie Shen, Yanan Zhang

“…An observer compensation technique is utilized to monitor and mitigate disturbances, while sliding mode control, enhanced by a HyperSpiral algorithm, ensures accurate and stable tracking performance. …”
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A distributed energy-efficient opportunistic routing accompanied by timeslot allocation in wireless sensor networks by Man Gun Ri, Ye Song Han, Jin Pak

“…In our new algorithm, clustering and routing tree construction is performed while introducing the optimal relay transmission distance achieved by using opportunistic routing principle, and at the same time, interference-free wake up time is scheduled, which may optimize energy consumption and decrease the number of various control messages as possible to prolong the network lifetime. …”
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Adaptive Anomaly Detection in Network Flows With Low-Rank Tensor Decompositions and Deep Unrolling by Lukas Schynol, Marius Pesavento

“…To optimize the deep network weights for detection performance, we employ a homotopy optimization approach based on an efficient approximation of the area under the receiver operating characteristic curve. …”
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Adaptive Localization in Wireless Sensor Network through Bayesian Compressive Sensing by Zuoxin Xiahou, Xiaotong Zhang

“…Furthermore, we provide adaptive iteration BCS localization (AIBCSL) algorithm, which is based on BCS and will choose measurement sensors according to the environment adaptively with only an initial value, while other frameworks require prior knowledge such as target numbers to choose measurements. …”
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Corrosion rate prediction for long-distance submarine pipelines based on MWIWOA-SVM by Zhengshan LUO, Haipeng LYU, Jihao LUO

“…The extended search mode was refined by integrating the simplex method, while the optimization capability of the Whale Optimization Algorithm (WOA) was enhanced with Levy flight to improve step length. …”
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A Vision-Based Method for Detecting the Position of Stacked Goods in Automated Storage and Retrieval Systems by Chuanjun Chen, Junjie Liu, Haonan Yin, Biqing Huang

“…This paper proposes a novel machine vision-based detection algorithm that integrates a pallet surface object detection network (STEGNet) with a box edge detection algorithm. …”
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Efficient black-box attack with surrogate models and multiple universal adversarial perturbations by Tao Ma, Hong Zhao, Ling Tang, Mingsheng Xue, Jing Liu

“…By combining MUAPs with surrogate models, SMPack effectively overcomes the black-box constraints and improves the efficiency of generating adversarial examples. Additionally, we optimize this process using a Genetic Algorithm (GA), allowing for efficient search of the perturbation space while conserving query budget. …”
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TA-RRT*: Adaptive Sampling-Based Path Planning Using Terrain Analysis by Taegeun Oh, Yun-Jae Won, Sungjin Lee

“…While existing RRT*-based algorithms perform node sampling and tree expansion in various ways to optimize path planning, they may still generate inefficient paths in complex terrain environments. …”
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Causal Unit Selection using Tractable Arithmetic Circuits by Haiying Huang, Adnan Darwiche

“…While early studies focused mainly on bounding a specific class of counterfactual objective functions using data, more recent work allows one to find optimal units exactly by reducing the causal objective to a classical objective on a meta-model, and then applying a variant of the classical Variable Elimination (VE) algorithm to the meta-model---assuming a fully specified causal model is available. …”
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Short-Term Traffic Flow Forecasting Model Based on GA-TCN by Rongji Zhang, Feng Sun, Ziwen Song, Xiaolin Wang, Yingcui Du, Shulong Dong

“…Therefore, a short-term traffic flow forecasting model GA-TCN based on genetic algorithm (GA) optimized time convolutional neural network (TCN) is proposed in this paper. …”
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Resource Allocation and Interference Coordination Strategies in Heterogeneous Dual-Layer Satellite Networks by Jinhong Li, Rong Chai, Tianyi Zhou, Chengchao Liang

“…We formulate a mathematical optimization problem to optimize resource allocation while minimizing co-channel interference and develop an ADMM-based distributed algorithm for efficient problem-solving. …”
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Real-time torque distribution simulation of parallel hybrid vehicle engine by Jing Wang

“…This study aims to develop a high-precision, robust torque distribution model to enhance energy utilization while addressing interference from environmental noise and extreme temperatures.MethodsA real-time torque distribution model integrates three core components: a Markov Decision Process framework transforms torque allocation into a mathematical optimization problem; the Proximal Policy Optimization algorithm enhanced with Prioritized Experience Replay dynamically generates control strategies; and Fiber Bragg Grating sensors achieve millisecond-level torque measurement by correlating shaft strain forces with wavelength shifts. …”
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Rethinking Exploration and Experience Exploitation in Value-Based Multi-Agent Reinforcement Learning by Anatolii Borzilov, Alexey Skrynnik, Aleksandr Panov

“…We aim to optimize a deep MARL algorithm with minimal modifications to the well-known QMIX approach. …”
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Lightweight CNN-Based Visual Perception Method for Assessing Local Environment Complexity of Unmanned Surface Vehicle by Tulin Li, Xiufeng Zhang, Yingbo Huang, Chunxi Yang

“…Subsequently, this identification result can be used as feedback for the heuristic algorithm to optimize and plan the USV’s path. The simulation results indicate that the developed model achieves an 80% reduction in model size while maintaining an accuracy exceeding 90%. …”
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