Time Series Prediction Method of Clean Coal Ash Content in Dense Medium Separation Based on the Improved EMD-LSTM Model by Kai Cheng, Xiaokang Zhang, Keping Zhou, Chenao Zhou, Jielin Li, Chun Yang, Yurong Guo, Ranfeng Wang

“…High-frequency noise-containing IMFs are selectively removed, while LSTM predicts retained components. SSA optimizes LSTM parameters (learning rate, hidden layers, epochs) to minimize prediction errors. …”
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Unsupervised Learning for Distributed Downlink Power Allocation in Cell-Free mMIMO Networks by Mattia Fabiani, Asmaa Abdallah, Abdulkadir Celik, Omer Haliloglu, Ahmed M. Eltawil

“…The proposed unsupervised learning approach circumvents the arduous task of training data computations, typically required in supervised learning methods, bypassing the use of conventional complex optimization methods and heuristic methodologies. Furthermore, an LSF-based radio unit (RU) selection algorithm is employed to activate only the contributing RUs, allowing efficient utilization of network resources. …”
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Evaluation of Shelf Life Prediction for Broccoli Based on Multispectral Imaging and Multi-Feature Data Fusion by Xiaoshuo Cui, Xiaoxue Sun, Shuxin Xuan, Jinyu Liu, Dongfang Zhang, Jun Zhang, Xiaofei Fan, Xuesong Suo

“…Savitzky–Golay (SG) convolution smoothing and standard normal variate (SNV) and normalization (Norm) preprocessing methods were employed to preprocess the original spectral data and textural features, while a successive projection algorithm (SPA) was used to extract relevant feature bands. …”
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Capabilities of BIOMASS Three-Baseline PolInSAR Mode for the Characterization of Tropical Forests by Yanzhou Xie, Laurent Ferro-Famil, Yue Huang, Thuy Le Toan, Jianjun Zhu, Haiqiang Fu, Peng Shen

“…Second, the forest height was retrieved using a three-baseline algorithm based on polarimetric coherence optimization with multi-baseline joint diagonalization. …”
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Inherent Trade-Offs Between the Conflicting Aspects of Designing the Compact Global Navigation Satellite System (GNSS) Anti-Interference Array by Xiangjun Li, Xiaoyu Zhao, Xiaozhou Ye, Zukun Lu, Feixue Wang, Peiguo Liu

“…Building on these insights, we innovatively propose a multi-objective optimization framework based on the non-dominated sorting genetic algorithm-II (NSGA-II) model, integrating antenna electromagnetic characteristics and signal processing constraints. …”
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High-Quality Short-Range Radar Imaging with Coprime Sampling by Yaping Wang, Tianjiao Zeng, Xu Zhan, Xiangdong Ma, Mou Wang, Jun Shi, Shunjun Wei, Xiaoling Zhang

“…Our approach is built upon three core perspectives: (1) physical sampling: we adopt a coprime pattern to form an extended sampling aperture with a structured layout, enabling effective utilization of limited channels and minimizing aperture loss; (2) signal measurement: we utilize the second-order statistics of the measured data to generate additional equivalent measurements, thus enhancing the system’s capability to capture diverse spatial information; and (3) scene reconstruction: we establish a novel forward measurement model, linking these equivalent measurements to the scene, and then formulate a sparsity-regularized optimization problem. We design a background-texture-preserving, target-enhanced resolving method based on the first-order proximal gradient algorithm to achieve robust and high-quality imaging results. …”
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A Centralized Multi-Agent DRL-Based Trajectory Control Strategy for Unmanned Aerial Vehicle-Enabled Wireless Communications by Getaneh Berie Tarekegn, Rong-Terng Juang, Belayneh Abebe Tesfaw, Hsin-Piao Lin, Huan-Chia Hsu, Robel Berie Tarekegn, Li-Chia Tai

“…The trajectory of the aerial base stations is then continuously adjusted through a centralized multi-agent deep reinforcement learning algorithm to optimize communication performance. We evaluated our proposed system using real channel measurement data, i.e., amplitude and phase signal information. …”
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Complex Cloud-Sea Background Simulation for Space-Based Infrared Payload Digital Twin by Wen Sun, Yejin Li, Fenghong Li, Guangsen Liu, Peng Rao

“…Static cloud-sea scenes are generated using Qilu-2 and New Technology satellite images, while dynamic scenarios are simulated with our iterative fractal dimension optimization algorithm. …”
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Using Graph Neural Networks in Reinforcement Learning With Application to Monte Carlo Simulations in Power System Reliability Analysis by Oystein Rognes Solheim, Boye Annfelt Hoverstad, Magnus Korpas

“…Recent efforts from the authors indicate that optimal power flow solvers could potentially be replaced with the policies of deep reinforcement learning agents, to obtain significant speedups of Monte Carlo simulations while retaining close to optimal accuracies. …”
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A Hybrid Deep Learning Model for UAV Path Planning in Dynamic Environments by Junchi Zhang, Yanning Xian, Xun Zhu, Hongtao Deng

“…While its variants can converge to the optimal solution, they suffer from more memory and computation consumption. …”
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The Role of Artificial Intelligence (AI) in the Future of Forestry Sector Logistics by Leonel J. R. Nunes

“…Results: The results demonstrated that optimization algorithms reduced transportation costs and carbon emissions. …”
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Projecting Forest Fire Probability in South Korea Under Climate Change, Population, and Forest Management Scenarios Using AI & Process-Based Hybrid Model (FLAM-Net) by Hyun-Woo Jo, Myoungsoo Won, Florian Kraxner, Seong Woo Jeon, Yowhan Son, Andrey Krasovskiy, Woo-Kyun Lee

“…Enhancements included improving backpropagation for optimization and introducing algorithms for national-specific fire ignition dynamics. …”
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Nitrous oxide prediction through machine learning and field-based experimentation: A novel strategy for data-driven insights by Muhammad Hassan, Khabat Khosravi, Travis J. Esau, Gurjit S. Randhawa, Aitazaz A. Farooque, Seyyed Ebrahim Hashemi Garmdareh, Yulin Hu, Nauman Yaqoob, Asad T. Jappa

“…Feature selection and optimization of input scenarios were achieved using a combination of particle swarm optimization (PSO) and manual methods. …”
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RL based transmission power control for energy harvesting sensor by LIU Nanjun, JIA Riheng, XU Wentao, LI Minglu

“…Experimental results demonstrate that the proposed algorithm outperformes baseline strategies and approaches the theoretical optimal in certain scenarios.…”
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Modeling Analysis of the Relationship between Adolescent Aerobic Exercise and Obesity Reduction Based on Deep Learning by Peng Mu

“…It also explains the constraint conditions that aerobic exercise has to meet in the reduction of adolescent obesity and introduces particle algorithm to establish a model of optimal aerobic exercise to reduce adolescent obesity. …”
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Application of collaborative innovation between the logical brain and the associative brain in oil and gas gathering and transportation systems by Jing GONG, Siheng SHEN, Daqian LIU, Qi KANG, Shangfei SONG, Haihao WU, Bohui SHI

“…Traditional simplified analytical methods struggle to cope with dynamic uncertainties, while existing data-driven algorithms are confronted with the dual challenges of lacking interpretability and insufficient robustness. …”
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SV-SAE: Layer-Wise Pruning for Autoencoder Based on Link Contributions by Joohong Rheey, Hyunggon Park

“…This paper introduces a layer-wise pruning algorithm specifically for multilayer perceptron-based autoencoders. …”
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YOLO-APDM: Improved YOLOv8 for Road Target Detection in Infrared Images by Song Ling, Xianggong Hong, Yongchao Liu

“…A new algorithm called YOLO-APDM is proposed to address low quality and multi-scale target detection issues in infrared road scenes. …”
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Deep Q-Learning-Based Resource Management in IRS-Assisted VLC Systems by Ahmed Al Hammadi, Lina Bariah, Sami Muhaidat, Mahmoud Al-Qutayri, Paschalis C. Sofotasios, Merouane Debbah

“…Moreover, the synergy between the MPF and the DQL algorithms is investigated. To this end, we demonstrate that the MPF algorithm maximizes the fairness amongst the users while the DQL algorithm maximizes the overall SE and improves the robustness against the noise. …”
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Multi-Vehicle Object Recognition Method Based on YOLOv7-W by Xin Liu, Jingbin Zhao, Zhi Zhang, Rongxing Wu, Xun Li

“…To improve the matching accuracy of prior frames, the k-means++ clustering algorithm is employed to optimize the prior frame parameters. …”
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