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Performance degradation and maintenance optimization strategy of rolling bearings based on data fusion and adaptive partition 认领 引用
1
作者 Hong-Chuan Cheng Xin-Hai Li +3 位作者 Guo-Hui Ma Yu Cui Zhi-Wu Shang Xia-Fei Shi 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期178-194,共17页
The traditional method of performance degradation prediction and maintenance of rolling bearings only considers a single sensor signal,which makes it difficult to automatically partition degradation stages and prone t... The traditional method of performance degradation prediction and maintenance of rolling bearings only considers a single sensor signal,which makes it difficult to automatically partition degradation stages and prone to over-detection.A new method of performance degradation evaluation and maintenance of rolling bearings based on data-level fusion,adaptive health state partitioning,and state maintenance is proposed.Firstly,considering the degradation and impact in the process of bearing deterioration,the multi-sensor signals are dynamically weighted to achieve data-level fusion.Secondly,a bearing health index was established based on fast spectral correlation,Wasserstein distance,and linear rectification techniques.On this basis,by combining the Bayesian information criterion and the elbow rule,the precise division of rolling bearing health state is realized through hidden Markov model regression.Then,random forest was used to classify and predict the data to verify the validity of the proposed data fusion method and health indicator.Finally,condition-based maintenance strategy based on the fourth moment,stress-strength interference model,and Gamma process is proposed to avoid excessive detection and reduce maintenance costs.Through accelerated degradation experiments and field validation tests on the rolling bearing test data set of Xi’an Jiaotong University and FEMTO(PRONOSTIA),the accuracy and superiority of the proposed method in the prediction and maintenance of bearing health state are verified. 展开更多
关键词 Rolling bearing Data fusion Gamma process Adaptive partition Maintenance strategy
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An NDVI-constrained multi-source remote sensing data fusion network for detecting the level of larch caterpillar(Dendrolimus superans)infestation 认领 引用
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作者 WU Linlin WANG Mingchang +1 位作者 WANG Fengyan MAO Dehua 《Journal of Mountain Science》 SCIE CSCD 2026年第7期3170-3191,共22页
Outbreaks of the larch caterpillar(Dendrolimus superans)cause severe ecological and economic damage to boreal forests,underscoring the urgent need for effective monitoring and early warning systems.However,the utility... Outbreaks of the larch caterpillar(Dendrolimus superans)cause severe ecological and economic damage to boreal forests,underscoring the urgent need for effective monitoring and early warning systems.However,the utility of space-borne multispectral imagery(MSI)for this purpose is often constrained by either coarse spatial resolution or insufficient spectral bands,limiting the accurate classification of pest occurrence levels.To address this challenge,we developed an NDVI-constrained Dynamic Ridge Polynomial Neural Network(NDRPNN)to fuse Sentinel-2 MSI data with Gaofen-2(GF-2)panchromatic imagery,thereby enhancing spatial detail while preserving spectral integrity.Timeseries spectral,textural,and polarimetric features derived from Sentinel-1/2 imagery were subsequently integrated,and correlation analysis was applied to identify the most sensitive indicators.Four classification models—Random Forest,Light Gradient Boosting Machine,Stacking Ensemble,and Soft Voting Ensemble(SVE)—were evaluated for detecting infestation levels,with Shapley(SHAP)analysis employed to interpret feature contributions.The NDRPNN exhibited robust fusion performance in forested landscapes.Ensemble methods outperformed single classifiers,with the SVE model achieving the highest accuracy(overall accuracy=87.6%,Kappa=0.83).SHAP analysis identified the mean and maximum Normalized Difference Vegetation Index(NDVI),minimum Anthocyanin Reflectance Index(ARI),minimum Normalized Burn Ratio(NBR),and seasonal amplitude of Enhanced Vegetation Index(EVI)as key contributing features,highlighting the critical role of time-series vegetation indices and textural metrics in early pest detection.This study demonstrates that the integration of high-quality Sentinel-2 and GF-2 imagery with ensemble learning enables rapid and precise assessment of pest occurrence,offering a robust foundation for the early warning and scientific management of forest pests in mountain regions. 展开更多
关键词 Larch caterpillar infestation Multisource data fusion Pansharpening Ensemble learning Early detecting
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Optimizing Energy Efficiency in Tunnel Boring Machine Rock Breaking via Multi-source Data Fusion 认领 引用 被引量：1
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作者 Xiaojun Yan Wencong Qi +4 位作者 Chuan Qu Minghui Ma Shanglin Liu Qian Zhang Xuesong Cheng 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2026年第4期481-493,共13页
The era of big data has profoundly transformed mechanics research,with data-driven approaches playing a vital role in modeling and optimization.This study focuses on tunnel boring machine(TBM),where the thrust-torque ... The era of big data has profoundly transformed mechanics research,with data-driven approaches playing a vital role in modeling and optimization.This study focuses on tunnel boring machine(TBM),where the thrust-torque ratio is a key determinant of their tunneling energy efficiency.However,due to the complexity of experiments and the testing requirements,obtaining sufficient high-quality data under varying geological conditions remains a major challenge in optimizing the tunneling energy efficiency of TBM.To address this,multi-cutter rotary cutting machine experiments and numerical simulations were conducted on 22 different rock types.Comprehensive datasets of normal and rolling forces were systematically collected.Using specific energy(SE)as the rock-breaking efficiency metric,we integrated physical and numerical data through a CatBoost-based fusion framework.The predictive model was initially trained on simulation data to capture the relationships among penetration,uniaxial compressive strength,tensile strength,and SE,and was subsequently fine-tuned with experimental data to develop the final fused model.Compared to models trained solely on experimental or simulated data,the fused model reduced RMSE by 37.1%and 58.6%,respectively,and improved R2by 19.0%and 44.6%,thereby enhancing both prediction accuracy and generalization capability.Furthermore,Bayesian optimization was employed to minimize SE and identify the optimal penetration.The results indicate that as rock strength increases,the optimal penetration decreases,while the corresponding minimal SE increases.These findings provide theoretical and engineering insights for improving TBM energy efficiency and parameter optimization,while establishing a robust data fusion framework for mechanical data analysis. 展开更多
关键词 Multi-source data fusion CatBoost Tunnel boring machine Multi-cutter rock-breaking Energy efficiency
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CCDNN:A Novel Deep Learning Architecture for Multi-Source Data Fusion 认领 引用
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作者 Zhiwen Chen Siwen Mo +4 位作者 Haobin Ke Steven X.Ding Zhaohui Jiang Chunhua Yang Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期555-567,共13页
With the rapid development of Industrial 4.0 and Industrial Internet of Things,the data collection with multisource has significantly improved.How to effectively fuse these data for various engineering applications is... With the rapid development of Industrial 4.0 and Industrial Internet of Things,the data collection with multisource has significantly improved.How to effectively fuse these data for various engineering applications is still an open and challenge issue.To this end,we propose the canonical correlation guided deep neural network(CCDNN),a novel deep learning architecture,to learn a correlated representation for multi-source data fusion.Unlike the linear canonical correlation analysis(CCA),kernel CCA and deep CCA,in the proposed method,the optimization formulation is not restricted to maximize correlation,instead we make canonical correlation as a constraint,which preserves the correlated representation learning ability and focuses more on the engineering tasks endowed by optimization formulation,such as reconstruction,classification and prediction.Furthermore,to reduce the redundancy induced by correlation,a redundancy filter is designed.We illustrate its data fusion ability via correlated representation learning and superior performance on various engineering tasks.In experiments on MNIST dataset,the results show that CCDNN has better reconstruction performance in terms of mean squared error and mean absolute error than deep CCA and deep canonically correlated autoencoders(DCCAE).Also,we present the application of the proposed network to industrial fault diagnosis and remaining useful life cases for the classification and prediction tasks accordingly.The proposed method demonstrates approving performance in both tasks when compared to existing methods.Extension of CCDNN to much more deeper with the aid of residual connection is also presented in Appendix. 展开更多
关键词 Canonical correlation analysis(CCA) correlated representation learning deep learning fault diagnosis multi-source data fusion remaining useful life
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Evapotranspiration inversion using a two-sources model coupling multiscale data fusion and interpolation methods 认领 引用
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作者 Shuo Lun TingXi Liu +6 位作者 LiNa Hao LiMin Duan Xin Tong YongZhi Bao YiXuan Wang SiMin Zhang YiRan Zhang 《Research in Cold and Arid Regions》 CAS CSCD 2026年第1期85-100,共16页
Continuous monitoring of high spatiotemporal resolution evapotranspiration(ET)is crucial for accurately assessing water resource management and irrigation efficiency at both global and regional scales.However,constrai... Continuous monitoring of high spatiotemporal resolution evapotranspiration(ET)is crucial for accurately assessing water resource management and irrigation efficiency at both global and regional scales.However,constraints such as satellite image transit time and cloud contamination can inhibit a single satellite to provide fine spatial resolution and continuous daily sequences of remote sensing data.In this study,we employed a coupled multi-scale fusion and interpolation method evapotranspiration model(CFIEM)framework integrated the remote sensing ET models,data fusion models,and the HANTS-GEE interpolation method to generate highresolution surface feature parameters and hydrological variables.Landsat remote sensing parameters and MODIS-Landsat fusion data were adopted to simulate daily regional ET,and the simulated results were subsequently validated.The results revealed the strong consistency between the simulated CFIEM-ET and Landsat-ET values and measured data,with the R2 values of 0.83 and 0.73.Among the simulations across the sand dunes,grasslands,rice,and maize ecosystems,maize exhibited the most accurate simulation(R2?0.84),while the sand dunes demonstrated certain deviation(R2?0.72).The geographical detector analysis identified the net radiation(Rn)as the primary driver of multi-year ET variation,followed by the land surface temperature(LST)and leaf area index(LAI).This study sheds light on the interannual ET variation and its driving mechanisms in arid regions,laying a theoretical foundation and offering technical support for regional water resource management and desertification control. 展开更多
关键词 Evapotranspiration Data fusion methods Data interpolation methods Spatiotemporal variation Driving factors
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Seismic activity characteristics of the Sichuan-Yunnan region revealed by data fusion 认领 引用
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作者 Pei He Huai Zhang Yaolin Shi 《Earthquake Science》 CAS CSCD 2026年第4期370-386,共17页
Accurate characterization of regional seismic activity is crucial for assessing earthquake hazards.Seismic activity depends on multiple factors,including tectonic loading,fault geometry and distribution,crustal and ma... Accurate characterization of regional seismic activity is crucial for assessing earthquake hazards.Seismic activity depends on multiple factors,including tectonic loading,fault geometry and distribution,crustal and mantle structures,local topography,physical properties,and global environmental changes.Human influences,such as reservoir impoundment,enhanced geothermal systems,and shale gas extraction,further complicate these relationships.Establishing an integrated theoretical and methodological framework through data to analyze these natural and anthropogenic factors represents a frontier challenge in contemporary seismology and geodynamics.This study introduces three novel visualization methods for earthquake catalogs,efficiently capturing the complex relationships between magnitude,frequency,seismic origin time,and epicentral location.Utilizing these methods with comprehensive heterogeneous geophysical datasets from the Sichuan-Yunnan region,including over 420,000 earthquake records,160 three-dimensional active fault datasets,high-resolution topography,Moho depth,community velocity models,and crustal deformation data,the seismic characteristics of the region over the past 50 years were systematically analyzed.Results indicate that:(1)High seismic activity and hazard areas in the Sichuan-Yunnan region are primarily concentrated near deep major faults,block boundaries,and brittle transition zones with distinct low-and high-velocity anomalies,showing clear spatial banding,temporal clustering,and cyclicity;(2)Fault segments such as Longmenshan,Lijiang-Xiaojinhe,and Nujiang-Irrawaddy likely facilitate internal material exchange within the Qinghai-Xizang Plateau.Significant crustal thickening in their northwestern sections corresponds with lower seismic activity;(3)Crustal strain varies notably along the Xianshuihe-Anninghe-Zemuhe-Xiaojiang and Longmenshan fault zones,which delineate the boundary between regions of high-and low-velocity ratio anomalies.These zones host the majority of the regional earthquakes,requiring intensified monitoring due to frequent events despite moderate mainshock magnitudes.Overall,the proposed methodology provides a new reference for deepening our understanding of regional seismicity and developing an improved earthquake visualization technique. 展开更多
关键词 data fusion seismic activity Sichuan-Yunnan region magnitude-frequency plot earthquake catalog
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Tongue Image Analysis and Clinical Data Fusion:A Novel Approach for Non-invasive Diagnosis of Metabolic Dysfunction-associated Fatty Liver Disease 认领 引用
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作者 Chen-Xia Lu Chuan-Xi Tian +13 位作者 Yi-Bo Jiao Hui Zhu Hai-Yan Yu Zi-Xin Shu Ling-Han Zhang Jia Zhang Lan Wang Qi Hao Wen-Bin Zou Ming-Zhong Xiao Cheng-Hai Liu Qiu-Yang He Bee Luan Khoo Xiao-Dong Li 《Journal of Clinical and Translational Hepatology》 SCIE CSCD 2026年第4期416-429,共14页
Background and Aims:Metabolic dysfunction-associated fatty liver disease(MAFLD)represents a predominant cause of chronic liver disease,underscoring the demand for accessible,non-invasive diagnostic tools.Tongue diagno... Background and Aims:Metabolic dysfunction-associated fatty liver disease(MAFLD)represents a predominant cause of chronic liver disease,underscoring the demand for accessible,non-invasive diagnostic tools.Tongue diagnosis in Traditional Chinese Medicine provides a distinctive perspective on systemic health,though it remains largely subjective.This study aimed to develop an interpretable multimodal deep learning model for MAFLD screening by integrating quantitative tongue image features with routine clinical data.Methods:From 904 screened candidates,477 subjects(157 healthy,320 MAFLD)were included and randomly allocated to training,validation,and test sets in an 8:1:1 ratio.All participants underwent standardized tongue imaging(International Commission on Illumination L*a*b color features)and comprehensive clinical evaluation.We constructed a dual-stream deep learning model,combining a ConvNeXt-Tiny network for tongue images and a multilayer perceptron for clinical variables.Feature fusion was achieved via a Dynamic Affine Feature Transformation module,and the model was trained using weighted cross-entropy loss.Results:MAFLD patients showed significant metabolic abnormalities compared to healthy controls.A progressive decrease in tongue yellowness(b* value)was observed with advancing fibrosis.On an independent test set(n=48),the multimodal model achieved 97.92%accuracy,Quadratic Weighted Kappa of 0.9538,and 96.88%sensitivity,and 100%specificity,outperforming single-modality and serological models.Interpretability analyses confirmed the model’s focus on clinically relevant tongue regions and key metabolic drivers.Conclusions:We developed an accurate and interpretable multimodal model that synergizes tongue image features with metabolic indicators for MAFLD screening.This approach presents a promising,low-cost tool potentially well-suited for resource-limited settings. 展开更多
关键词 Metabolic dysfunction-associated fatty liver disease Tongue image analysis Non-invasive prediction Multi-modal data fusion Deep learning ConvNeXt-Tiny network Non-invasive Diagnosis.
Computing the Planet:Integrating Machine Learning,Remote Sensing,and Sensor Data Fusion for Environmental Insights 认领 引用
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作者 Kai Mao 《Journal of Environmental & Earth Sciences》 CAS 2026年第1期277-297,共21页
Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable meas... Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable measures of the Earth system across scales.This review summarizes how the realization of the Compute the Planet is underway in the form of machine learning,remote sensing,and sensor data fusion to generate decision-ready environmental insights.We use the application-first approach,which considers remote sensing,in situ and Internet of Things(IoT)sensing,and physics-based models as complementary streams of evidence with similar strengths and failures.We look critically at how an integrated system can convert heterogeneous observations to action products across three high impact application areas:atmosphere and air quality,water–land–ecosystem dynamics,and hazards.Rapid-response situational awareness,ecosystem condition metrics,drought and flood indicators,exposure maps,and hazard/extreme indicators are key products.The integrated systems to environment interface in three high impact application areas:atmosphere and air quality,water-land-ecosystem dynamics,and hazard Examine Our operational requirements can often determine real-life value such as latency,time stability,smooth degradation in the presence of missing or degraded inputs,and calibrated uncertainty usable in thresholdbased decisions.These pitfalls are common across fields:mismatch in the scale between a point sensor and a gridded product,objectives on proxies in remotely sensed measurements,domain shift in the extremes and changing baselines,and evaluation aspects,which overestimate generalization because of spatiotemporal autocorrelation.Based on these lessons,we present cross-domain proposals for strong validation,uncertainty quantification,provenance,and versioning,as well as fair performance evaluation.We conclude that the next era of environmental intelligence will see a reduction in average accuracy improvement and an increase in terms of robustness,transparency,and operational responsibility,thus allowing the integrated environmental intelligence system to be deployed,which may be relied on to monitor human health,resource allocation,and survival in a more climate-adapted world. 展开更多
关键词 Machine Learning Remote Sensing Sensor Data Fusion Environmental Monitoring Uncertainty Quantification
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Research on Rapid Update and Accuracy Improvement of DOM Based on Multi-source Aerial Data Fusion 认领 引用
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作者 GAN Yuting 《外文科技期刊数据库（文摘版）自然科学》 2026年第1期042-045,共4页
As a core geospatial data product, Digital Orthophoto Map (DOM) plays a key role in many fields such as urban planning, disaster assessment, engineering surveying and mapping, and topographic mapping. With the increas... As a core geospatial data product, Digital Orthophoto Map (DOM) plays a key role in many fields such as urban planning, disaster assessment, engineering surveying and mapping, and topographic mapping. With the increasing requirements of various industries for the timeliness and accuracy of DOM, the traditional single-data-source update method can hardly meet the actual needs. Aiming at this problem, this paper deeply studies the rapid update and accuracy improvement methods of DOM driven by multi-source aerial data fusion. The research shows that multi-source aerial data fusion technology can effectively improve the efficiency and accuracy of DOM update, providing reliable support for the efficient application of geospatial data. 展开更多
关键词 multi-source aerial data data fusion Digital Orthophoto Map (DOM) rapid update accuracy optimization
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Dynamic UAV data fusion and deep learning for improved maize phenological-stage tracking 认领 引用 被引量：2
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作者 Ziheng Feng Jiliang Zhao +8 位作者 Liunan Suo Heguang Sun Huiling Long Hao Yang Xiaoyu Song Haikuan Feng Bo Xu Guijun Yang Chunjiang Zhao 《The Crop Journal》 SCIE CAS CSCD 2025年第3期961-974,共14页
Near real-time maize phenology monitoring is crucial for field management,cropping system adjustments,and yield estimation.Most phenological monitoring methods are post-seasonal and heavily rely on high-frequency time... Near real-time maize phenology monitoring is crucial for field management,cropping system adjustments,and yield estimation.Most phenological monitoring methods are post-seasonal and heavily rely on high-frequency time-series data.These methods are not applicable on the unmanned aerial vehicle(UAV)platform due to the high cost of acquiring time-series UAV images and the shortage of UAV-based phenological monitoring methods.To address these challenges,we employed the Synthetic Minority Oversampling Technique(SMOTE)for sample augmentation,aiming to resolve the small sample modelling problem.Moreover,we utilized enhanced"separation"and"compactness"feature selection methods to identify input features from multiple data sources.In this process,we incorporated dynamic multi-source data fusion strategies,involving Vegetation index(VI),Color index(CI),and Texture features(TF).A two-stage neural network that combines Convolutional Neural Network(CNN)and Long Short-Term Memory Network(LSTM)is proposed to identify maize phenological stages(including sowing,seedling,jointing,trumpet,tasseling,maturity,and harvesting)on UAV platforms.The results indicate that the dataset generated by SMOTE closely resembles the measured dataset.Among dynamic data fusion strategies,the VI-TF combination proves to be most effective,with CI-TF and VI-CI combinations following behind.Notably,as more data sources are integrated,the model's demand for input features experiences a significant decline.In particular,the CNN-LSTM model,based on the fusion of three data sources,exhibited remarkable reliability when validating the three datasets.For Dataset 1(Beijing Xiaotangshan,2023:Data from 12 UAV Flight Missions),the model achieved an overall accuracy(OA)of 86.53%.Additionally,its precision(Pre),recall(Rec),F1 score(F1),false acceptance rate(FAR),and false rejection rate(FRR)were 0.89,0.89,0.87,0.11,and 0.11,respectively.The model also showed strong generalizability in Dataset 2(Beijing Xiaotangshan,2023:Data from 6 UAV Flight Missions)and Dataset 3(Beijing Xiaotangshan,2022:Data from 4 UAV Flight Missions),with OAs of 89.4%and 85%,respectively.Meanwhile,the model has a low demand for input featu res,requiring only 54.55%(99 of all featu res).The findings of this study not only offer novel insights into near real-time crop phenology monitoring,but also provide technical support for agricultural field management and cropping system adaptation. 展开更多
关键词 Near real-time Maize phenology Deep learning UAV Multi-source data fusion
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Prediction Method for Carbon Emission of Hobbing Based on Cross-Process Data Fusion 认领 引用
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作者 Qian Yi Yusong Luo +2 位作者 Chunhui Hu Congbo Li Shuping Yi 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2025年第2期120-137,共18页
Accurate prediction of manufacturing carbon emissions is of great significance for subsequent low-carbon optimization.To improve the accuracy of carbon emission prediction with insufficient hobbing data,combining the ... Accurate prediction of manufacturing carbon emissions is of great significance for subsequent low-carbon optimization.To improve the accuracy of carbon emission prediction with insufficient hobbing data,combining the advantages of improved algorithm and supplementary data,a method of carbon emission prediction of hobbing based on cross-process data fusion was proposed.Firstly,we analyzed the similarity of machining process and manufacturing characteristics and selected milling data as the fusion material for hobbing data.Then,the adversarial learning was used to reduce the difference between data from the two processes,so as to realize the data fusion at the characteristic level.After that,based on Meta-Transfer Learning method,the carbon emission prediction model of hobbing was established.The effectiveness and superiority of the proposed method were verified by case analysis and comparison.The prediction accuracy of the proposed method is better than other methods across different data sizes. 展开更多
关键词 Gear hobbing Carbon emission prediction Data fusion Meta-transfer learning
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Class-Imbalanced Machinery Fault Diagnosis using Heterogeneous Data Fusion Support Tensor Machine 认领 引用
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作者 Zhishan Min Minghui Shao +1 位作者 Haidong Shao Bin Liu 《Journal of Dynamics, Monitoring and Diagnostics》 2025年第1期11-21,共11页
The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelli... The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelligent fault diagnosis.On the other hand,the vectorization of multi-source sensor signals may not only generate high-dimensional vectors,leading to increasing computational complexity and overfitting problems,but also lose the structural information and the coupling information.This paper proposes a new method for class-imbalanced fault diagnosis of bearing using support tensor machine(STM)driven by heterogeneous data fusion.The collected sound and vibration signals of bearings are successively decomposed into multiple frequency band components to extract various time-domain and frequency-domain statistical parameters.A third-order hetero-geneous feature tensor is designed based on multisensors,frequency band components,and statistical parameters.STM-based intelligent model is constructed to preserve the structural information of the third-order heterogeneous feature tensor for bearing fault diagnosis.A series of comparative experiments verify the advantages of the proposed method. 展开更多
关键词 class-imbalanced fault diagnosis feature tensor heterogeneous data fusion support tensor machine
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Enhanced Multi-Object Dwarf Mongoose Algorithm for Optimization Stochastic Data Fusion Wireless Sensor Network Deployment 认领 引用 被引量：3
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作者 Shumin Li Qifang Luo Yongquan Zhou 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第2期1955-1994,共40页
Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic ... Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained. 展开更多
关键词 Stochastic data fusion wireless sensor networks network deployment spatiotemporal coverage dwarf mongoose optimization algorithm multi-objective optimization
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Failure rate analysis and maintenance plan optimization method for civil aircraft parts based on data fusion 认领 引用 被引量：2
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作者 Kang CAO Yongjie ZHANG Jianfei FENG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第1期306-324,共19页
In the face of data scarcity in the optimization of maintenance strategies for civil aircraft,traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft design.... In the face of data scarcity in the optimization of maintenance strategies for civil aircraft,traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft design.This study addresses this issue by presenting a novel combined data fusion algorithm,which serves to enhance the accuracy and reliability of failure rate analysis for a specific aircraft model by integrating historical failure data from similar models as supplementary information.Through a comprehensive analysis of two different maintenance projects,this study illustrates the application process of the algorithm.Building upon the analysis results,this paper introduces the innovative equal integral value method as a replacement for the conventional equal interval method in the context of maintenance schedule optimization.The Monte Carlo simulation example validates that the equivalent essential value method surpasses the traditional method by over 20%in terms of inspection efficiency ratio.This discovery indicates that the equal critical value method not only upholds maintenance efficiency but also substantially decreases workload and maintenance costs.The findings of this study open up novel perspectives for airlines grappling with data scarcity,offer fresh strategies for the optimization of aviation maintenance practices,and chart a new course toward achieving more efficient and cost-effective maintenance schedule optimization through refined data analysis. 展开更多
关键词 Small sample data Data fusion Failure rate Maintenance planning Aircraft parts
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Multisource Data Fusion Using MLP for Human Activity Recognition 认领 引用 被引量：1
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作者 Sujittra Sarakon Wansuree Massagram Kreangsak Tamee 《Computers, Materials & Continua》 SCIE EI 2025年第2期2109-2136,共28页
This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, ... This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, MotionSense, and PAMAP2—to develop a generalized MLP model for classifying six human activities. Performance analysis of the fused model for each dataset reveals accuracy rates of 95.83 for WISDM, 97 for DaLiAc, 94.65 for MotionSense, and 98.54 for PAMAP2. A comparative evaluation was conducted between the fused MLP model and the individual dataset models, with the latter tested on separate validation sets. The results indicate that the MLP model, trained on the fused dataset, exhibits superior performance relative to the models trained on individual datasets. This finding suggests that multisource data fusion significantly enhances the generalization and accuracy of HAR systems. The improved performance underscores the potential of integrating diverse data sources to create more robust and comprehensive models for activity recognition. 展开更多
关键词 Multisource data fusion human activity recognition multi-layer perceptron(MLP) artificial intelligent
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Evaluating Urban Housing Contradictions Through Multisource Data Fusion:a Case Study of Spatiotemporal Mismatch Analysis in Shenzhen with the HCEWI Model 认领 引用 被引量：1
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作者 JIANG Aiyi CHEN Guanzhou CAO Jinzhou 《Journal of Geodesy and Geoinformation Science》 CSCD 2025年第3期1-16,共16页
The rapid urbanization and structural imbalances in Chinese megacities have exacerbated the housing supplydemand mismatch,creating an urgent need for fine-scale diagnostic tools.This study addresses this critical gap ... The rapid urbanization and structural imbalances in Chinese megacities have exacerbated the housing supplydemand mismatch,creating an urgent need for fine-scale diagnostic tools.This study addresses this critical gap by developing the Housing Contradiction Evaluation Weighted Index(HCEWI)model,making three key contributions to high-resolution housing monitoring.First,we establish a tripartite theoretical framework integrating dynamic population pressure(PPI),housing supply potential(HSI),and functional diversity(HHI).The PPI innovatively combines mobile signaling data with principal component analysis to capture real-time commuting patterns,while the HSI introduces a novel dual-criteria system based on Local Climate Zones(LCZ),weighted by building density and residential function ratio.Second,we develop a spatiotemporal coupling architecture featuring an entropy-weighted dynamic integration mechanism with self-correcting modules,demonstrating robust performance against data noise.Third,our 25-month longitudinal analysis in Shenzhen reveals significant findings,including persistent bipolar clustering patterns,contrasting volatility between peripheral and core areas,and seasonal policy responsiveness.Methodologically,we advance urban diagnostics through 500-meter grid monthly monitoring and process-oriented temporal operators that reveal“tentacle-like”spatial restructuring along transit corridors.Our findings provide a replicable framework for precision housing governance and demonstrate the transformative potential of mobile signaling data in implementing China’s“city-specific policy”approach.We further propose targeted intervention strategies,including balance regulation for high-contradiction zones,Transit-Oriented Development(TOD)activation for low-contradiction clusters,and dynamic land conversion mechanisms for transitional areas. 展开更多
关键词 index terms-housing contradiction assessment multi-source data fusion spatiotemporal heterogeneity job-housing spatial mismatch high-resolution urban diagnostics
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Deep learning-based multimodal data fusion in bone tumor management:Advances in clinical decision support 认领 引用 被引量：1
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作者 Tongtong Huo Wei Wu +12 位作者 Xiaoliang Chen Mingdi Xue Pengran Liu Jiayao Zhang Yi Xie Honglin Wang Hong Zhou Zineng Yan Songxiang Liu Lin Lu Jiaming Yang Jin Liu Zhewei Ye 《Intelligent Oncology》 2025年第3期204-215,共12页
Bone tumors(BTs)-including osteosarcoma,Ewing sarcoma,and chondrosarcoma-are rare but biologically complex malignancies characterized by pronounced heterogeneity in anatomical location,histological subtype,and molecul... Bone tumors(BTs)-including osteosarcoma,Ewing sarcoma,and chondrosarcoma-are rare but biologically complex malignancies characterized by pronounced heterogeneity in anatomical location,histological subtype,and molecular alterations.Recent advances in artificial intelligence(AI),particularly deep learning,have enabled the integration of diverse clinical data modalities to support diagnosis,treatment planning,and prognostication in bone oncology.This review provides a comprehensive synthesis of AI-driven multimodal fusion strategies that incorporate radiological imaging,digital pathology,multi-omics profiling,and electronic health records.We conducted a structured review of peer-reviewed literature published between 2015 and early 2025,focusing on the development,validation,and clinical applicability of AI models for BT diagnosis,subtyping,treatment response prediction,and recurrence monitoring.Although multimodal models have demonstrated advantages over unimodal approaches,especially in handling missing data and improving generalizability,most remain constrained by single-center study designs,small sample sizes,and limited prospective or external validation.Persistent technical and translational challenges include semantic misalignment across modalities,incomplete datasets,limited model interpretability,and regulatory and infrastructural barriers to clinical integration.To address these limitations,we highlight emerging directions such as contrastive representation learning,generative data augmentation,transformer-based fusion architectures,and privacy-preserving federated learning.We also discuss the evolving role of foundation models and workflow-integrated AI agents in enhancing scalability and clinical usability.In summary,multimodal AI represents a promising paradigm for advancing precision care in BTs.Realizing its full clinical potential will require methodologically rigorous,biologically informed,and system-level approaches that bridge algorithmic innovation with real-world healthcare delivery. 展开更多
关键词 Bone tumors Multimodal data fusion Artificial intelligence Clinical decision support systems Deep learning
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Comparison of two data fusion methods from Sentinel-3 and Himawari-9 data for snow cover monitoring in mountainous areas 认领 引用
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作者 RuiRui Yang YanLi Zhang +2 位作者 Qi Wei FengYang Liu KeGong Li 《Research in Cold and Arid Regions》 CAS CSCD 2025年第3期159-171,共13页
Snow cover in mountainous areas is characterized by high reflectivity,strong spatial heterogeneity,rapid changes,and susceptibility to cloud interference.However,due to the limitations of a single sensor,it is challen... Snow cover in mountainous areas is characterized by high reflectivity,strong spatial heterogeneity,rapid changes,and susceptibility to cloud interference.However,due to the limitations of a single sensor,it is challenging to obtain high-resolution satellite remote sensing data for monitoring the dynamic changes of snow cover within a day.This study focuses on two typical data fusion methods for polar-orbiting satellites(Sentinel-3 SLSTR)and geostationary satellites(Himawari-9 AHI),and explores the snow cover detection accuracy of a multitemporal cloud-gap snow cover identification model(Loose data fusion)and the ESTARFM(Spatiotemporal data fusion).Taking the Qilian Mountains as the research area,the accuracy of two data fusion results was verified using the snow cover extracted from Landsat-8 SR products.The results showed that both data fusion models could effectively capture the spatiotemporal variations of snow cover,but the ESTARFM demonstrated superior performance.It not only obtained fusion images at any target time,but also extracted snow cover that was closer to the spatial distribution of real satellite images.Therefore,the ESTARFM was utilized to fuse images for hourly reconstruction of the snow cover on February 14–15,2023.It was found that the maximum snow cover area of this snowfall reached 83.84%of the Qilian Mountains area,and the melting rate of the snow was extremely rapid,with a change of up to 4.30%per hour of the study area.This study offers reliable high spatiotemporal resolution satellite remote sensing data for monitoring snow cover changes in mountainous areas,contributing to more accurate and timely assessments. 展开更多
关键词 Snow cover Data fusion Sentinel-3 Himawari-9
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A Residual Convolutional Autoencoder-Based Structural Damage Detection Approach for Deep-Sea Mining Riser Considering Data Fusion 认领 引用
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作者 JIANG Yufeng ZHENG Zepeng +4 位作者 LIU Yu WANG Shuqing LIU Yuchi YANG Zeyun YANG Yuan 《Journal of Ocean University of China》 SCIE CAS CSCD 2025年第6期1657-1669,共13页
A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safe... A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures. 展开更多
关键词 deep-sea mining riser structural damage detection residual convolutional auto-encoder data fusion principal component analysis
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Dynamic Characteristic Testing of Wind Turbine Structure Based on Visual Monitoring Data Fusion 认领 引用
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作者 Wenhai Zhao Wanrun Li +2 位作者 Ximei Li Shoutu Li Yongfeng Du 《Structural Durability & Health Monitoring》 EI 2025年第3期593-611,共19页
Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies,as well as the inability to achieve precise full-field monitoring,this paper proposes a... Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies,as well as the inability to achieve precise full-field monitoring,this paper proposes a method for identifying the structural dynamic characteristics of wind turbines based on visual monitoring data fusion.Firstly,the Lucas-Kanade Tomasi(LKT)optical flow method and a multi-region of interest(ROI)monitoring structure are employed to track pixel displacements,which are subsequently subjected to band pass filtering and resampling operations.Secondly,the actual displacement time history is derived through double integration of the acquired acceleration data and subsequent band pass filtering.The scale factor is obtained by applying the least squares method to compare the visual displacement with the displacement derived from double integration of the acceleration data.Based on this,the multi-point displacement time histories under physical coordinates are obtained using the vision data and the scale factor.Subsequently,when visual monitoring of displacements becomes impossible due to issues such as image blurring or lens occlusion,the structural vibration equation and boundary condition constraints,among other key parameters,are employed to predict the displacements at unknown monitoring points,thereby enabling full-field displacement monitoring and dynamic characteristic testing of the structure.Finally,a small-scale shaking table test was conducted on a simulated wind turbine structure undergoing shutdown to validate the dynamic characteristics of the proposed method through test verification.The research results indicate that the proposed method achieves a time-domain error within the submillimeter range and a frequency-domain accuracy of over 99%,effectively monitoring the full-field structural dynamic characteristics of wind turbines and providing a basis for the condition assessment of wind turbine structures. 展开更多
关键词 Structural health monitoring dynamic characteristics computer vision vibration monitoring data fusion
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