Direction of Arrival (DOA) Estimation Using a Deep Unfolded Learned Iterative Shrinkage Thresholding Algorithm (LISTA) Network in a Non-Uniform Metasurface by Xinyi Niu, Xiaolong Su, Lida He, Guanchao Chen

“…To address these issues, we optimize a non-uniform metasurface array to reduce hardware costs and mutual coupling effects while enhancing resolution. Additionally, a deep unfolded Learned Iterative Shrinkage Thresholding Algorithm (LISTA) network is constructed by transforming Iterative Shrinkage Thresholding Algorithm (ISTA) iterative steps into trainable neural network layers, combining model-driven logic with data-driven parameter optimization. …”
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Deep-learning Enhanced CT Reconstruction Algorithm for Multiphase-flow Measurement by Qian CHEN, Baodi YU, Yanwei QIN, Sunyang WANG, Xiaohui SU, Xin JIN, Fanyong MENG

“…Subsequently, based on the hardware design of a flowfield dynamic measurement system, which is a limited-angle dynamic X-ray CT system, a simulated gas–liquid two-phase flow dataset for training the deep-learning model is constructed from three-dimensional bubble structures obtained from hydrogel phantoms. …”
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Accuracy and robustness evaluation of deep learning algorithms in facial recognition systems by Jing Zhang, Ningyu Hu

“…To solve the high cost and low accuracy in facial recognition system, a facial recognition system based on deep learning algorithm is designed in this paper. …”
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Urban Tree Canopy Mapping and Analysis Using Iterative Annotation Method and Deep Learning: A Case Study in Beijing by Yulong Ding, Ximin Cui, Zhengchao Chen, Zeqing Wang, Debao Yuan, Xiang Meng, Xuan Yang, Yue Xu, Xiangyu Tian

“…To comprehensively analyze the tree canopy characteristics of urban trees across large areas, this study selects Beijing as the research area, and employs high-resolution remote sensing imagery with deep learning techniques to construct the UTC map of Beijing. …”
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Drowsiness Detection of Construction Workers: Accident Prevention Leveraging Yolov8 Deep Learning and Computer Vision Techniques by Adetayo Olugbenga Onososen, Innocent Musonda, Damilola Onatayo, Abdullahi Babatunde Saka, Samuel Adeniyi Adekunle, Eniola Onatayo

“…This study presents a vision-based approach using an improved version of the You Only Look Once (YOLOv8) algorithm for real-time drowsiness exposure among construction workers. …”
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Research on the construction of English vocabulary learning recommendation system based on multi-objective crow search algorithm by Mengli Li

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Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms? by Khabat Khosravi, Aitazaz A. Farooque, Sayed M. Bateni, Changhyun Jun, Dorsa Mohammadi, Zahra Kalantari, James R. Cooper

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A Deep Learning-Driven Black-Box Benchmark Generation Method via Exploratory Landscape Analysis by Haoming Liang, Fuqing Zhao, Tianpeng Xu, Jianlin Zhang

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Node selection method in federated learning based on deep reinforcement learning by Wenchen HE, Shaoyong GUO, Xuesong QIU, Liandong CHEN, Suxiang ZHANG

“…To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection based on deep reinforcement learning was proposed.It considered training quality and efficiency of heterogeneous terminal devices, and filtrate malicious nodes to guarantee higher model accuracy and shorter training delay of federated learning.Firstly, according to characteristics of model distributed training in federated learning, a node selection system model based on deep reinforcement learning was constructed.Secondly, considering such factors as device training delay, model transmission delay and accuracy, an optimization model of accuracy for node selection was proposed.Finally, the problem model was constructed as a Markov decision process and a node selection algorithm based on distributed proximal strategy optimization was designed to obtain a reasonable set of devices before each training iteration to complete model aggregation.Simulation results demonstrate that the proposed method significantly improves the accuracy and training speed of federated learning, and its convergence and robustness are also well.…”
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Node selection method in federated learning based on deep reinforcement learning by Wenchen HE, Shaoyong GUO, Xuesong QIU, Liandong CHEN, Suxiang ZHANG

“…To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection based on deep reinforcement learning was proposed.It considered training quality and efficiency of heterogeneous terminal devices, and filtrate malicious nodes to guarantee higher model accuracy and shorter training delay of federated learning.Firstly, according to characteristics of model distributed training in federated learning, a node selection system model based on deep reinforcement learning was constructed.Secondly, considering such factors as device training delay, model transmission delay and accuracy, an optimization model of accuracy for node selection was proposed.Finally, the problem model was constructed as a Markov decision process and a node selection algorithm based on distributed proximal strategy optimization was designed to obtain a reasonable set of devices before each training iteration to complete model aggregation.Simulation results demonstrate that the proposed method significantly improves the accuracy and training speed of federated learning, and its convergence and robustness are also well.…”
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Energy Minimization for Federated Learning Based Radio Map Construction by Fahui Wu, Yunfei Gao, Lin Xiao, Dingcheng Yang, Jiangbin Lyu

“…This paper studies an unmanned aerial vehicle (UAV)-enabled communication network, in which the UAV acts as an air relay serving multiple ground users (GUs) to jointly construct an accurate radio map or channel knowledge maps (CKM) through a federated learning (FL) algorithm. …”
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Inverse Modeling for Subsurface Flow Based on Deep Learning Surrogates and Active Learning Strategies by Nanzhe Wang, Haibin Chang, Dongxiao Zhang

“…The retrained surrogate is further integrated with the iterative ensemble smoother (IES) algorithm for inversion. …”
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Adversarial sample generation algorithm for vertical federated learning by Xiaolin CHEN, Daoguang ZAN, Bingchao WU, Bei GUAN, Yongji WANG

“…To adapt to the scenario characteristics of vertical federated learning (VFL) applications regarding high communication cost, fast model iteration, and decentralized data storage, a generalized adversarial sample generation algorithm named VFL-GASG was proposed.Specifically, an adversarial sample generation framework was constructed for the VFL architecture.A white-box adversarial attack in the VFL was implemented by extending the centralized machine learning adversarial sample generation algorithm with different policies such as L-BFGS, FGSM, and C＆amp;W.By introducing deep convolutional generative adversarial network (DCGAN), an adversarial sample generation algorithm named VFL-GASG was designed to address the problem of universality in the generation of adversarial perturbations.Hidden layer vectors were utilized as local prior knowledge to train the adversarial perturbation generation model, and through a series of convolution-deconvolution network layers, finely crafted adversarial perturbations were produced.Experiments show that VFL-GASG can maintain a high attack success while achieving a higher generation efficiency, robustness, and generalization ability than the baseline algorithm, and further verify the impact of relevant settings for adversarial attacks.…”
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Routing Algorithm for Power Communication Networks Based on Serivce Differentiated Transmission Requirements by Songping XUE, Dequan GAO, Ziyan ZHAO, Yuqian LIN, Zejing GUANG, Dawei ZHANG

“…Addressing the intelligent routing challenge within the electric power communication network under multiple constraints, we propose an innovative routing algorithm that seamlessly integrates Message Passing Neural Network (MPNN) with deep reinforcement learning algorithms. …”
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AI-Driven predicting and optimizing lignocellulosic sisal fiber-reinforced lightweight foamed concrete: A machine learning and metaheuristic approach for sustainable construction by Mohamed Sahraoui, Aissa Laouissi, Yacine Karmi, Abderazek Hammoudi, Mostefa Hani, Yazid Chetbani, Ahmed Belaadi, Ibrahim M.H. Alshaikh, Djamel Ghernaout

“…The proposed approach minimizes dependence on labor-intensive trial-and-error testing and enhances the sustainability of construction materials by utilizing sisal fibers. The results emphasize the capabilities of metaheuristic-enhanced deep learning models in optimizing high-performance, environmentally friendly concrete mix design, thereby facilitating future advancements in intelligent material formulations for sustainable infrastructure.…”
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Research on leaf identification of table grape varieties based on deep learning by PAN Bowen, LIN Meiling, JU Yanlun, SU Baofeng, SUN Lei, FAN Xiucai, ZHANG Ying, ZHANG Yonghui, LIU Chonghuai, JIANG Jianfu, FANG Yulin

“…When ResNet-101 was used as the classification model, the optimized parameters were the learning rate of 0.005, the minimum batch of 32, and the number of iterations was 50. …”
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Pose measurement method for coal mine drilling robot based on deep learning by Jiangnan LUO, Jianping LI, Hongxiang JIANG, Deyi ZHANG

“…Then, the measured point cloud is registered using the Fast Point Feature Histogram (FPFH) and Iterative Closest Point (ICP) algorithms to obtain the transformation matrix from the measured point cloud to the source point cloud. …”
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Modeling and control strategy of small unmanned helicopter rotation based on deep learning by Hui Xia

“…To study the rotation phenomenon of small UAV landing process and propose control strategy, the study completes the rotation modeling of small unmanned helicopter based on deep learning algorithm and proposes the control strategy. …”
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THz UM-MIMO system channel estimation algorithm based on deep residual block fixed-point network by YU Shujuan, WEI Yuyao, CAI Lianglong, LU Hongyu, ZHANG Yun, ZHAO Shengmei

“…To mitigate the channel estimation challenges induced by hybrid near-far field and beam squint effects in THz ultra-massive MIMO systems, a deep learning-based FPN-OAMP-SRLG algorithm was proposed. …”
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Enhanced Occupational Safety in Agricultural Machinery Factories: Artificial Intelligence-Driven Helmet Detection Using Transfer Learning and Majority Voting by Simge Özüağ, Ömer Ertuğrul

“…Subsequently, the extracted features were subjected to iterative neighborhood component analysis (INCA) for feature selection, after which they were classified using the k-nearest neighbor (kNN) algorithm. …”
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