Research on multi-sensor fusion detection and tracking technology for autonomous-rail rapid transit tram by PAN Wenbo, YUAN Xiwen, LIN Jun, XIE Guotao, LONG Teng, HUANG Wenyu

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ARIMA-Kriging and GWO-BiLSTM Multi-Model Coupling in Greenhouse Temperature Prediction by Wei Zhou, Shuo Liu, Junxian Guo, Na Liu, Zhenglin Li, Chang Xie

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Multi-Model Gait-Based KAM Prediction System Using LSTM-RNN and Wearable Devices by Doyun Jung, Cheolwon Lee, Heung Seok Jeon

“…This study proposes two systems for predicting Knee Adduction Moment based on wearable IMU sensor data and gait patterns: the Multi-model Gait-based KAM Prediction System and the Single-model KAM Prediction System. …”
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Malicious Traffic Detection Method for Power Monitoring Systems Based on Multi-Model Fusion Stacking Ensemble Learning by Hao Zhang, Ye Liang, Yuanzhuo Li, Sihan Wang, Huimin Gong, Junkai Zhai, Hua Zhang

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Roadside Perception System Based on LiDAR and Millimeter-wave Radar Fusion in Mining Area by JIANG Liangyu

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A Predictive Method for Greenhouse Soil Pore Water Electrical Conductivity Based on Multi-Model Fusion and Variable Weight Combination by Jiawei Zhao, Peng Tian, Jihong Sun, Xinrui Wang, Changjun Deng, Yunlei Yang, Haokai Zhang, Ye Qian

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Wearable sensors-based assistive technologies for patient health monitoring by Nouf Abdullah Almujally, Danyal Khan, Danyal Khan, Naif Al Mudawi, Mohammed Alonazi, Haifa F. Alhasson, Ahmad Jalal, Ahmad Jalal, Hui Liu, Hui Liu, Hui Liu

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Machine learning-guided integration of fixed and mobile sensors for high resolution urban PM2.5 mapping by Tianshuai Li, Xin Huang, Qingzhu Zhang, Xinfeng Wang, Xianfeng Wang, Anbao Zhu, Zhaolin Wei, Xinyan Wang, Haolin Wang, Jiaqi Chen, Min Li, Qiao Wang, Wenxing Wang

“…Here, we utilized machine learning and integrated air quality sensor monitoring networks consisting of 200 mobile cruising vehicles and 614 fixed micro–stations to reconstruct PM2.5 pollution maps for Jinan’s urban area with a high spatiotemporal resolution of 500 m and 1 h. …”
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Multi-sensor Fault Detection for Natural Gas Combined Cycle Power Plants Based on Multiple Robust Input Training Neural Network Models by Zheng HUANG, Hongxing WANG, Haiquan YU, Dou LI, Fengqi SI

“…In order to enhance the accuracy and reliability of multiple sensor measurements in natural gas combined cycle (NGCC) power plants, a multi-sensor fault detection method based on multi-model robust input training neural network is proposed in this paper, in which multiple robust input training neural network (RITNN) models are built and prioritized for the purpose of sensor faults reconstruction and monitoring. …”
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An Elastic Filtering Algorithm with Visual Perception for Vehicle GNSS Navigation and Positioning by Wenzhuo Ma, Zhe Yue, Zengzeng Lian, Kezhao Li, Chenchen Sun, Mengshuo Zhang

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Multi-Maneuvering Target Tracking Based on a Gaussian Process by Ziwen Zhao, Hui Chen

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Cross-Modal Data Fusion via Vision-Language Model for Crop Disease Recognition by Wenjie Liu, Guoqing Wu, Han Wang, Fuji Ren

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An Adaptive Spatio-Temporal Traffic Flow Prediction Using Self-Attention and Multi-Graph Networks by Basma Alsehaimi, Ohoud Alzamzami, Nahed Alowidi, Manar Ali

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Anomaly Detection Method for Hydropower Units Based on KSQDC-ADEAD Under Complex Operating Conditions by Tongqiang Yi, Xiaowu Zhao, Yongjie Shi, Xiangnan Jing, Wenyang Lei, Jiang Guo, Yang Meng, Zhengyu Zhang

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A seamless LiDAR/IMU/RTK fused localization method for UAV-Based bridge inspection by A. Liang, Y. Pan, Y. Huo, Q. Li, B. Zhou, Z. Chen

“…In cross-scene situations (when the UAV passes through a bridge arch), the quality of sensor data is evaluated based on an interactive multi-model (IMM), and an adaptive switching mechanism is employed between two localization modes—IMU/RTK mode and IMU/LiDAR mode—ensuring smooth and seamless multi-source fusion localization even in the presence of sensor signal fluctuations. …”
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Dynamic monitoring and warning system for unstable rock collapse and its engineering application by Chen CHEN, Mowen XIE, Yan DU, Shuangquan LI, Zhengjun HUANG

“…Future efforts must prioritize theoretical robustness, sensor innovation, and artificial intelligence (AI) integration to enable proactive disaster prevention in infrastructure, mining, and reservoir projects.…”
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Research progress in globular fruit picking recognition algorithm based on deep learning by LI Hui, ZHANG Jun, YU Shuochen, LI Zhixin

“…When changing new environments and adding new categories, the fruit detection algorithm based on deep learning can improve the recognition ability and recognition accuracy of the model through transfer learning, data enhancement, multi-model combination, feature fusion and multi-modal data. …”
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