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3rd International Conference on Advanced Engineering Materials and Architecture Science (ICAEMAS)

作者： Li Shu-ying Tian Mu-qin Xue Lei Taiyuan Univ Technol Shanxi Prov Key Lab Coal Mine Equipment & Safety Taiyuan 030024 Peoples R China Econ & Tech Inst Shanxi Elect Power Co Taiyuan 030001 Peoples R China

ISBN: (纸本)9783038352679

For the conclusions of single parameter fault feature diagnosis has some uncertainty, in induction motor early fault, we proposed the use of multi-sensor data fusion technology, acted signal processing to the collected current, vibration and temperature, extracted feature information failure, fused the evidence independent with each other using D-S evidence fusion rules. According to the final combination results of all the evidence, combined with intermediate results of the evidence combination, we achieved the accurate identification of induction motor rotor early failures and composite fault. The diagnosis examples show that the use of multi-sensor data fusion technology can significantly improve the accuracy and reliability of early fault diagnosis.

关键词： multi-sensor data fusion fault diagnosis D-S evidential theory

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An Application Case Study on multi-sensor data fusion System for Intelligent Process Monitoring

An Application Case Study on Multi-sensor Data fusion System...

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47th CIRP Conference on Manufacturing Systems

作者： Lu, Zhi-Jun Xiang, Qian Xu, Lan Engineering Research Center of Advanced Textile Machinery Ministry of Education China College of Mechanical Engineering Donghua University China

multi-sensor data fusion is a technology to enable combining information from several sources in order to form a unified picture. Focusing on the indirect method, an attempt was made to build up a multi-sensor data fusion system to monitor the condition of grinding wheels with force signals and the acoustic emission (AE) signals. An artificial immune algorithm based multi-signals processing method was presented in this paper. The intelligent monitoring system is capable of incremental supervised learning of grinding conditions and quickly pattern recognition, and can continually improve the monitoring precision. The application case indicates that the accuracy of condition identification is about 87%, and able to meet the industrial need on the whole. (C) 2014 Elsevier B.V.

关键词： multi-sensor data fusion Negative-Selection Algorithm Artificial immune Process Monitoring Grinding

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Research on the Bearing Load Measurement Method Based on the fusion of Redundant Strain data and Jacking data

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APPLIED SCIENCES-BASEL 2025年第1期15卷

作者： Fang, Chengwei Liu, Aihua Zhu, Hanhua Deng, Yibin Wuhan Univ Technol Sch Naval Architecture Ocean & Energy Power Engn Wuhan 430063 Peoples R China

To improve the accuracy of bearing load measurement in propulsion shafting, a bearing load measurement method based on redundant strain and jacking data fusion is proposed. Firstly, to address the issue of low measurement accuracy in strain gauge methods (SGM), a redundant strain gauge method (RSGM) is introduced to enhance the precision of strain-based measurements. Secondly, synchronize the measurement of strain data during the jacking phase and fuse the measurement data from the jacking phase based on the Bayesian estimation method to enhance the measurement accuracy of the jack-up method (JM). Lastly, the pre-decision results of the two types of measurement data are integrated at the decision level to obtain the final measured load. Simulation experiments have verified the correctness and applicability of the proposed measurement method. The research results indicate that, compared to the SGM, the RSGM offers higher measurement precision;the fusion measurement method can achieve more accurate results than the JM and RSGM under different bearing support conditions and jack-up scenarios.

关键词： bearing load jack-up method strain gauge method multi-sensor data fusion

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Adaptive fusion of multi-modal remote sensing data for optimal sub-field crop yield prediction

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REMOTE SENSING OF ENVIRONMENT 2025年 318卷

作者： Mena, Francisco Pathak, Deepak Najjar, Hiba Sanchez, Cristhian Helber, Patrick Bischke, Benjamin Habelitz, Peter Miranda, Miro Siddamsetty, Jayanth Nuske, Marlon Charfuelan, Marcela Arenas, Diego Vollmer, Michaela Dengel, Andreas Univ Kaiserslautern Landau RPTU Dept Comp Sci Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI SDS Kaiserslautern Germany Vis Impulse GmbH Kaiserslautern Germany

Accurate crop yield prediction is of utmost importance for informed decision-making in agriculture, aiding farmers, industry stakeholders, and policymakers in optimizing agricultural practices. However, this task is complex and depends on multiple factors, such as environmental conditions, soil properties, and management practices. Leveraging Remote Sensing (RS) technologies, multi-modal data from diverse global data sources can be collected to enhance predictive model accuracy. However, combining heterogeneous RS data poses a fusion challenge, like identifying the specific contribution of each modality in the predictive task. In this paper, we present a novel multi-modal learning approach to predict crop yield for different crops (soybean, wheat, rapeseed) and regions (Argentina, Uruguay, and Germany). Our multi-modal input data includes multi-spectral optical images from Sentinel-2 satellites and weather data as dynamic features during the crop growing season, complemented by static features like soil properties and topographic information. To effectively fuse the multi- modal data, we introduce a multi-modal Gated fusion (MMGF) model, comprising dedicated modality-encoders and a Gated Unit (GU) module. The modality-encoders handle the heterogeneity of data sources with varying temporal resolutions by learning a modality-specific representation. These representations are adaptively fused via a weighted sum. The fusion weights are computed for each sample by the GU using a concatenation of the multi-modal representations. The MMGF model is trained at sub-field level with 10 m resolution pixels. Our evaluations show that the MMGF outperforms conventional models on the same task, achieving the best results by incorporating all the data sources, unlike the usual fusion results in the literature. For Argentina, the MMGF model achieves an R 2 value of 0.68 at sub-field yield prediction, while at the field level evaluation (comparing field averages), it reaches around 0.

关键词： multi-modal learning multi-sensor data fusion Remote sensing Deep neural networks Crop yield prediction

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An efficient end-to-end EKF-SLAM architecture based on LiDAR, GNSS, and IMU data sensor fusion for autonomous ground vehicles

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multiMEDIA TOOLS AND APPLICATIONS 2023年第18期83卷 56183页

作者： Mailka, Hamza Abouzahir, Mohamed Ramzi, Mustapha Mohammed V Univ Rabat High Sch Technol Sale Lab Syst Anal Informat Proc & Ind Management Rabat Morocco

The autonomous ground vehicle's successful navigation with a high level of performance is dependent on accurate state estimation, which may help in providing excellent decision-making, planning, and control tasks. Outside factors like air bias and multipath effects have an impact on the GPS data, obtaining accurate pose estimation remains challenging. To obtain a highly precise pose estimation, the authors propose using an end-to-end simultaneous localization and mapping architecture based on scan matching and an extended Kalman filter to perform a successful prediction using lidar, GNSS and IMU data sensor fusion. In terms of pose estimation efficiency, the obtained results from EKF are compared to the UKF filter in different sequences on the Kitti dataset to more thoroughly evaluate our designed approach. The EKF filter has a good improvement, with approximately 0.29, 0.31, 0.24, 0.34, and 0.27 of the mean RMSE error of the trajectories 09_30_drive_0018, 09_30_drive_0020, 09_30_drive_0027, 10_03_drive_0033, and 10_03_drive_0034, respectively. The proposed approach was evaluated not only on KITTI dataset but also using our data recording platform called KONA Platform.

关键词： State estimation Simultaneous localisation and mapping (SLAM) Extended kalman filter (EKF) multi-sensor data fusion Scan correlation Autonomous driving

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An Application Case Study on multi-sensor data fusion System for Intelligent Process Monitoring

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Procedia CIRP 2014年 17卷 721-725页

作者： Zhi-Jun Lu Qian Xiang Lan Xu Engineering Research Center of Advanced Textile Machinery Ministry of Education College of Mechanical Engineering Donghua University

关键词： multi-sensor data fusion Negative-Selection Algorithm Artificial immune Process Monitoring Grinding

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Prediction of environment variables in precision agriculture using a sparse model as data fusion strategy

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Information Processing in Agriculture 2022年第2期9卷 171-183页

作者： L.Mancipe-Castro R.E.Gutierrez-Carvajal Multimedia Engineering Program/Universidad Militar Nueva Granada Bogota 110111Colombia

Precision agriculture seeks to optimize production processes by monitoring and analyzingenvironmental variables. For example, establishing farming actions on the crop requiresanalyzing variables such as temperature, ambient humidity, soil moisture, solar irradiance,and Rainfall. Although these signals might contain valuable information, it is vital to mixup the monitored signals and analyze them as a whole to provide more accurate information than analyzing the signals separately. Unfortunately, monitoring all these variablesresults in high costs. Hence it is necessary to establish an appropriate method that allowsthe infer variables behavior without the direct measurement of all of *** paper introduces a multi-sensor data fusion technique, based on a sparse representation, to find the most straightforward and complete linear equation to predict and understand a particular variable behavior based on other monitored environmental variablesmeasurements. Moreover, this approach aims to provide an interpretable model that allowsunderstanding how these variables are combined to achieve such results. The fusion strategy explained in this manuscript follows a four-step process that includes 1. data cleaning,2. redundant variable detection, 3. dictionary generation, and 4. sparse regression. Thealgorithm requires a target variable and two highly correlated signals. It is essential to pointout that the developed method has no restrictions to specific variables. Consequently, it ispossible to replicate this method for the semiautomatic prediction of multiple critical environmental *** a case study, this work used the SML2010 data set of the UCI machine learning repository to predicted the humidity’s derivative trend function with an error rate lower than 17%and a mean absolute error lower than 6%. The experiment results show that even thoughsparse model predictions might not be the most accurate compared to those of linearregression (LR), support vector m

关键词： Precision agriculture multi-sensor data fusion Sparse representation Inference of variables Humidity

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multi-attentive hierarchical dense fusion net for fusion classification of hyperspectral and LiDAR data

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INFORMATION fusion 2022年 82卷 1-18页

作者： Wang, Xianghai Feng, Yining Song, Ruoxi Mu, Zhenhua Song, Chuanming Liaoning Normal Univ Sch Geog Dalian 116029 Peoples R China Liaoning Normal Univ Sch Comp & Informat Technol Dalian 116029 Peoples R China

With recent advance in Earth Observation techniques, the availability of multi-sensor data acquired in the same geographical area has been increasing greatly, which makes it possible to jointly depict the underlying landcover phenomenon using different sensor data. In this paper, a novel multi-attentive hierarchical fusion net (MAHiDFNet) is proposed to realize the feature-level fusion and classification of hyperspectral image (HSI) with Light Detection and Ranging (LiDAR) data. More specifically, a triple branch HSI-LiDAR Convolutional Neural Network (CNN) backbone is first developed to simultaneously extract the spatial features, spectral features and elevation features of the land-cover objects. On this basis, hierarchical fusion strategy is adopted to fuse the oriented feature embeddings. In the shallow feature fusion stage, we propose a novel modality attention (MA) module to generate the modality integrated features. By fully considering the correlation and heterogeneity between different sensor data, feature interaction and integration is released by the proposed MA module. At the same time, self-attention modules are also adopted to highlight the modality specific features. In the deep feature fusion stage, the obtained modality specific features and modality integrated features are fused to construct the hierarchical feature fusion framework. Experiments on three real HSI-LiDAR datasets demonstrate the effectiveness of the proposed framework. The code will be public on https://***/SYFYN0317/-MAHiDFNet.

关键词： multi-sensor data fusion Hierarchical dense fusion strategy Modality attention HSI-LiDAR classification self-attention

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Research for multi-sensor data fusion Based on Huffman Tree Clustering Algorithm in Greenhouse

Research for Multi-sensor Data Fusion Based on Huffman Tree ...

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第八届中国可信计算与信息安全学术会议

Information monitoring system in the greenhouse will often deploy multiple redundant collection nodes in the same region in order to gather environmental *** will improve data collection accuracy and enhance system stability through data *** paper proposes a multi-sensor data fusion algorithm for greenhouse environment ***,triple exponential smoothing applied to a variety of greenhouse environmental *** then the smoothed data are sent to a router or gateway,removing invalid data,then the introduction of the idea of the Huffman tree to determine the order of the integration of each sensor,so as to realize multi-sensor data *** the Institute for Farmland Irrigation Chinese Academy of Agricultural Sciences,The tests indicated that three exponential smoothing fluctuations significantly reduce the data to improve the accuracy of the data fusion,meanwhile,running time of data fusion algorithm was significantly shorter,and better than weighted average method.

关键词： multi-sensor data fusion Distributed Huffman Tree Greenhouse Monitor

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3D multi-sensor data fusion for Object Localization in Industrial Applications

3D Multi-Sensor Data Fusion for Object Localization in Indus...

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ISR/Robotik 2014;41st International Symposium on Robotics

作者： C. Pfitzner W. Antal P. Hess S. May C. Merkl P. Koch R. Koch M. Wagner Nuremberg Institute of Technology Georg Simon Ohm

This paper describes a data fusion approach for 3D sensors exploiting assets of the signed distance function. The objectoriented model is described as well as the algorithm design. We developed a framework respecting different modalities for multi-sensor fusion, 3D mapping and object localization. This approach is suitable for industrial applications having need for contact-less object localization like bin picking. In experiments we demonstrate 3D mapping as well as sensor fusion of a structured light sensor with a Time-of-Flight (ToF) camera.

关键词： Industrial applications multi-sensor data fusion sensor data fusion Object oriented modeling data fusion sensor fusion Three Dimensions

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