Study on incentive mechanism of reward and punishment on work efficiency of PCB welder based on recurrence quantification analysis and electroencephalogram signals by Zhang Qian, Mingyue Guo, Fuwang Wang

“…To address this issue, this study innovatively combines recurrence quantification analysis (RQA) with electroencephalogram (EEG) signals, proposing a dynamic incentive evaluation model based on the analysis of brain chaos characteristics. …”
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Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis by Fernanda Pereira Leite Aguiar, Irenilza de Alencar Nääs, Marcelo Tsuguio Okano

“…Future work will explore dynamic parameter optimization and algorithm adaptability across diverse datasets to further refine its capabilities. …”
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Path planning algorithm for logistics autonomous vehicles at Cainiao stations based on multi-sensor data fusion. by Yan Chen

“…The model is able to provide an optimal path from the starting point to the target point in a complex environment, avoiding obstacles and realizing the smoothness and dynamic adjustment ability of the path. The model consists of three modules: the sensor data fusion module uses Convolutional Neural Networks (CNN) and Lidar-Inertial Odometry and Simultaneous Localization and Mapping (LIO-SAM) technology to build a high-precision dynamic environment map; the path planning module combines Artificial Potential Field (APF) and Deep Deterministic Policy Gradient (DDPG) algorithms to balance path length, smoothness, and obstacle avoidance capabilities; the decision and control module uses Model Predictive Control (MPC) and Long Short-Term Memory (LSTM) to achieve real-time path tracking and dynamic adjustment. …”
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ST-GAT Resident OD Prediction Model Based on Mobile Signaling Data by Xianguang Jia, Weijie Fang, Yingying Lyu, Jinke Zhang, Mengyi Guo, Dong Li, Jie Qu, Fengxiang Guo

“…This model innovatively introduces the graph attention mechanism into the spatio-temporal graph network (ST-GNN), in the spatial dimension, the attention layer (GAL) dynamically learns the attention weights among nodes to adaptively capture the dynamic spatial dependencies in the transportation network, and in the temporal dimension, the temporal convolutional layer extracts the multiscale temporal patterns, which efficiently captures the complex spatiotemporal dependencies in the OD data. …”
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Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection by Sunghyuk Lee, Donghwan Roh, Jaehak Yu, Daesung Moon, Jonghyuk Lee, Ji-Hoon Bae

“…These findings underscore the effectiveness of the proposed feature fusion strategy via transfer learning in improving detection capabilities within dynamic and evolving cyberthreat environments.…”
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Real-Time Fault Diagnosis of Mooring Chain Jack Hydraulic System Based on Multi-Scale Feature Fusion Under Diverse Operating Conditions by Yujia Liu, Wenhua Li, Haoran Ye, Shanying Lin, Lei Hong

“…Subsequently, the bidirectional long short-term memory (BiLSTM) layer is introduced to construct a dynamic temporal model to comprehensively capture the evolution of the fault severity. …”
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HSF-DETR: A Special Vehicle Detection Algorithm Based on Hypergraph Spatial Features and Bipolar Attention by Kaipeng Wang, Guanglin He, Xinmin Li

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SWMD-YOLO: A Lightweight Model for Tomato Detection in Greenhouse Environments by Quan Wang, Ye Hua, Qiongdan Lou, Xi Kan

“…The model integrates switchable atrous convolution (SAConv) and wavelet transform convolution (WTConv) for the dynamic adjustment of receptive fields for occlusion-adaptive feature extraction and to decompose features into multi-frequency sub-bands, respectively, thus preserving critical edge details of obscured targets. …”
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Enhanced multiscale plant disease detection with the PYOLO model innovations by Yirong Wang, Yuhao Wang, Jiong Mu, Ghulam Raza Mustafa, Qianqian Wu, Ying Wang, Bi Zhao, Siyue Zhao

“…Additionally, the model’s ability to focus on different parts of the image is improved by redesigning the EC2f structure and dynamically adjusting the convolutional kernel size to better capture features at various scales. …”
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Exploration of genetic algorithms to build a balanced neutron spectra dataset useful to train unfolding techniques based on artificial neural networks by Bouhadida Maha, Hmede Rodayna, Brovchenko Mariya, Monange Wilfried, Vinchon Thibaut, Ducasse Quentin, Trompier François

“…The resulting dataset is then used to train a new convolutional neural network architecture for unfolding neutron spectra. …”
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Multi-anchor adaptive fusion and bi-focus attention for enhanced gait-based emotion recognition by Jincheng Li, Xuejing Dai, Ruiao Yan, Chengqing Tang, Yunpeng Li

“…However, existing methods often suffer from excessive globalization, feature redundancy, and lack of dynamic time dependence. To address these issues, we propose a novel temporal graph convolutional network (MDT-GCN) that integrates multi-anchor (MAAF) and bi-focus attention (BFA) mechanisms. …”
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Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfall by Kalpesh R. Patil, Takeshi Doi, J.V. Ratnam, Swadhin K. Behera

“…The state-of-the-art dynamical models often fail to capture the sign and amplitude of the rainfall anomalies in the extreme rainfall seasons, limiting the overall skill of the models. …”
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Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks by Zahra Roozbehi, Ajit Narayanan, Mahsa Mohaghegh, Samaneh-Alsadat Saeedinia

“…The architecture processes spatiotemporal data to dynamically update synaptic weights, enabling precise identification of sound event categories and their occurrences. …”
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Infrared object detection for robot vision based on multiple focus diffusion and task interaction alignment by Jixu Zhang, Li Wang, Hung-Wei Li, Meng-Yen Hsieh, Shunxiang Zhang, Hua Wen, Meng Chen

“…The feature extraction module adopts a dual-stream fusion structure in the backbone network, which combines the local feature extraction of CNN with the global feature modeling of transformer. The task-aligned dynamic detection header enhances inter-task interaction by sharing convolutional and task-aligned structures, exploiting task interaction features to optimize detection accuracy while reducing the number of parameters to accommodate resource-constrained devices. …”
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Motion blur aware multiscale adaptive cascade framework for ear tag dropout detection in reserve breeding pigs by Weijun Duan, Fang Wang, Xueliang Fu, Honghui Li, Buyu Wang

“…Second, Density-Aware Dilated Convolution (DA-DC) dynamically adjusts the convolutional receptive field to improve small ear tag detection. …”
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EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning by Waqas Ali, Wesam Alsabban, Muhammad Shahbaz, Ali Al-Laith, Bassam Almogadwy

“…Performance evaluation on the EchoNet-Dynamic dataset yielded a mean absolute error (MAE) of 3.7 and an R2 score of 0.82. …”
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A computational framework for IoT security integrating deep learning-based semantic algorithms for real-time threat response by Ripal Ranpara, Shobhit K. Patel, Om Prakash Kumar, Fahad Ahmed Al-Zahrani

“…The proposed research framework integrates Convolutional Neural Networks for spatial anomaly detection and Recurrent Neural Networks for sequential pattern recognition. …”
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BGLE-YOLO: A Lightweight Model for Underwater Bio-Detection by Hua Zhao, Chao Xu, Jiaxing Chen, Zhexian Zhang, Xiang Wang

“…First, an efficient multi-scale convolutional EMC module is introduced to enhance the backbone network and capture the dynamic changes in targets in the underwater environment. …”
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Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities by Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas, Adrianna Piszcz

“…Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) facilitate detailed analysis of spatial and temporal data to better predict energy usage. …”
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Detection of failures in HV surge arrester using chaos pattern with deep learning neural network by Chun‐Chun Hung, Meng‐Hui Wang, Shiue‐Der Lu, Cheng‐Chien Kuo

“…This study integrates chaotic systems with Convolutional Neural Networks (CNN) to diagnose faults in HV surge arresters. …”
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