During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resol...During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resolution seismic data processing technologies and methods tailored for drilling scenarios.The high-resolution processing of seismic data is divided into three stages:pre-drilling processing,post-drilling correction,and while-drilling updating.By integrating seismic data from different stages,spatial ranges,and frequencies,together with information from drilled wells and while-drilling data,and applying artificial intelligence modeling techniques,a progressive high-resolution processing technology of seismic data based on multi-source information fusion is developed,which performs simple and efficient seismic information updates during drilling.Case studies show that,with the gradual integration of multi-source information,the resolution and accuracy of seismic data are significantly improved,and thin-bed weak reflections are more clearly imaged.The updated seismic information while-drilling demonstrates high value in predicting geological bodies ahead of the drill bit.Validation using logging,mud logging,and drilling engineering data ensures the fidelity of the processing results of high-resolution seismic data.This provides clearer and more accurate stratigraphic information for drilling operations,enhancing both drilling safety and efficiency.展开更多
The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method f...The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.展开更多
Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells an...Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.展开更多
With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of...With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of multimodal approaches for fake news detection has gained significant attention.To solve the problems existing in previous multi-modal fake news detection algorithms,such as insufficient feature extraction and insufficient use of semantic relations between modes,this paper proposes the MFFFND-Co(Multimodal Feature Fusion Fake News Detection with Co-Attention Block)model.First,the model deeply explores the textual content,image content,and frequency domain features.Then,it employs a Co-Attention mechanism for cross-modal fusion.Additionally,a semantic consistency detectionmodule is designed to quantify semantic deviations,thereby enhancing the performance of fake news detection.Experimentally verified on two commonly used datasets,Twitter and Weibo,the model achieved F1 scores of 90.0% and 94.0%,respectively,significantly outperforming the pre-modified MFFFND(Multimodal Feature Fusion Fake News Detection with Attention Block)model and surpassing other baseline models.This improves the accuracy of detecting fake information in artificial intelligence detection and engineering software detection.展开更多
To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network mo...To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.展开更多
Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure p...Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment.Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment.However,traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level.On the other hand,models that focus on global semantic-level information might overlook critical,subtle local pathological features.To address this issue,we propose an adaptive multi-scale feature fusion network called(AMSFuse),which can adaptively combine multi-scale global and local features without compromising their individual representation.Specifically,our model incorporates global features for extracting high-level contextual information from retinal images.Concurrently,local features capture fine-grained details,such as microaneurysms,hemorrhages,and exudates,which are critical for DR diagnosis.These global and local features are adaptively fused using a fusion block,followed by an Integrated Attention Mechanism(IAM)that refines the fused features by emphasizing relevant regions,thereby enhancing classification accuracy for DR classification.Our model achieves 86.3%accuracy on the APTOS dataset and 96.6%RFMiD,both of which are comparable to state-of-the-art methods.展开更多
The capacity to diagnose faults in rolling bearings is of significant practical importance to ensure the normal operation of the equipment.Frequency-domain features can effectively enhance the identification of fault ...The capacity to diagnose faults in rolling bearings is of significant practical importance to ensure the normal operation of the equipment.Frequency-domain features can effectively enhance the identification of fault modes.However,existing methods often suffer from insufficient frequency-domain representation in practical applications,which greatly affects diagnostic performance.Therefore,this paper proposes a rolling bearing fault diagnosismethod based on aMulti-Scale FusionNetwork(MSFN)using the Time-Division Fourier Transform(TDFT).The method constructs multi-scale channels to extract time-domain and frequency-domain features of the signal in parallel.A multi-level,multi-scale filter-based approach is designed to extract frequency-domain features in a segmented manner.A cross-attention mechanism is introduced to facilitate the fusion of the extracted time-frequency domain features.The performance of the proposed method is validated using the CWRU and Ottawa datasets.The results show that the average accuracy of MSFN under complex noisy signals is 97.75%and 94.41%.The average accuracy under variable load conditions is 98.68%.This demonstrates its significant application potential compared to existing methods.展开更多
Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete v...Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.展开更多
This article explores the design of a wireless fire alarm system supported by advanced data fusion technology.It includes discussions on the basic design ideas of the wireless fire alarm system,hardware design analysi...This article explores the design of a wireless fire alarm system supported by advanced data fusion technology.It includes discussions on the basic design ideas of the wireless fire alarm system,hardware design analysis,software design analysis,and simulation analysis,all supported by data fusion technology.Hopefully,this analysis can provide some reference for the rational application of data fusion technology to meet the actual design and application requirements of the system.展开更多
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
Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE...Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.展开更多
Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to ...Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.展开更多
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
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
Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional a...Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational efficiency. Key architectural components such as convolution operations, shallow and deep blocks, skip connections, and hybrid encoders are examined for their roles in enhancing spatial representation and semantic consistency. We further discuss the importance of hierarchical and instance-aware segmentation and annotation in interpreting complex biological scenes and multiplexed medical images. By bridging methodological developments with diverse application domains, this paper outlines current trends and future directions for semantic segmentation, emphasizing its critical role in facilitating annotation, diagnosis, and discovery in biomedical research.展开更多
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
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
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
This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was...This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was established for water and mud inrush in tunnels by analyzing advanced prediction data for specifi c tunnel segments.Additionally,the indicator weights were determined using the analytic hierarchy process combined with the Huber weighting method.Subsequently,a multisource data decision-layer fusion algorithm was utilized to generate fused imaging results for tunnel water and mud inrush risk predictions.Meanwhile,risk analysis was performed for different tunnel sections to achieve spatial and temporal complementarity within the indicator system and optimize redundant information.Finally,model feasibility was validated using the CZ Project Sejila Mountain Tunnel segment as a case study,yielding favorable risk prediction results and enabling effi cient information fusion and support for construction decision-making.展开更多
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
基金Supported by the National Natural Science Foundation of China(U24B2031)National Key Research and Development Project(2018YFA0702504)"14th Five-Year Plan"Science and Technology Project of CNOOC(KJGG2022-0201)。
文摘During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resolution seismic data processing technologies and methods tailored for drilling scenarios.The high-resolution processing of seismic data is divided into three stages:pre-drilling processing,post-drilling correction,and while-drilling updating.By integrating seismic data from different stages,spatial ranges,and frequencies,together with information from drilled wells and while-drilling data,and applying artificial intelligence modeling techniques,a progressive high-resolution processing technology of seismic data based on multi-source information fusion is developed,which performs simple and efficient seismic information updates during drilling.Case studies show that,with the gradual integration of multi-source information,the resolution and accuracy of seismic data are significantly improved,and thin-bed weak reflections are more clearly imaged.The updated seismic information while-drilling demonstrates high value in predicting geological bodies ahead of the drill bit.Validation using logging,mud logging,and drilling engineering data ensures the fidelity of the processing results of high-resolution seismic data.This provides clearer and more accurate stratigraphic information for drilling operations,enhancing both drilling safety and efficiency.
基金Supported by the Henan Province Key Research and Development Project(231111211300)the Central Government of Henan Province Guides Local Science and Technology Development Funds(Z20231811005)+2 种基金Henan Province Key Research and Development Project(231111110100)Henan Provincial Outstanding Foreign Scientist Studio(GZS2024006)Henan Provincial Joint Fund for Scientific and Technological Research and Development Plan(Application and Overcoming Technical Barriers)(242103810028)。
文摘The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.
基金funded by the China Chongqing Municipal Science and Technology Bureau,grant numbers 2024TIAD-CYKJCXX0121,2024NSCQ-LZX0135Chongqing Municipal Commission of Housing and Urban-Rural Development,grant number CKZ2024-87+3 种基金the Chongqing University of Technology graduate education high-quality development project,grant number gzlsz202401the Chongqing University of Technology-Chongqing LINGLUE Technology Co.,Ltd.,Electronic Information(Artificial Intelligence)graduate joint training basethe Postgraduate Education and Teaching Reform Research Project in Chongqing,grant number yjg213116the Chongqing University of Technology-CISDI Chongqing Information Technology Co.,Ltd.,Computer Technology graduate joint training base.
文摘Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.
基金supported by Communication University of China(HG23035)partly supported by the Fundamental Research Funds for the Central Universities(CUC230A013).
文摘With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of multimodal approaches for fake news detection has gained significant attention.To solve the problems existing in previous multi-modal fake news detection algorithms,such as insufficient feature extraction and insufficient use of semantic relations between modes,this paper proposes the MFFFND-Co(Multimodal Feature Fusion Fake News Detection with Co-Attention Block)model.First,the model deeply explores the textual content,image content,and frequency domain features.Then,it employs a Co-Attention mechanism for cross-modal fusion.Additionally,a semantic consistency detectionmodule is designed to quantify semantic deviations,thereby enhancing the performance of fake news detection.Experimentally verified on two commonly used datasets,Twitter and Weibo,the model achieved F1 scores of 90.0% and 94.0%,respectively,significantly outperforming the pre-modified MFFFND(Multimodal Feature Fusion Fake News Detection with Attention Block)model and surpassing other baseline models.This improves the accuracy of detecting fake information in artificial intelligence detection and engineering software detection.
基金supported by National Natural Science Foundation of China(No.61862037)Lanzhou Jiaotong University Tianyou Innovation Team Project(No.TY202002)。
文摘To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.
基金supported by the National Natural Science Foundation of China(No.62376287)the International Science and Technology Innovation Joint Base of Machine Vision and Medical Image Processing in Hunan Province(2021CB1013)the Natural Science Foundation of Hunan Province(Nos.2022JJ30762,2023JJ70016).
文摘Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment.Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment.However,traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level.On the other hand,models that focus on global semantic-level information might overlook critical,subtle local pathological features.To address this issue,we propose an adaptive multi-scale feature fusion network called(AMSFuse),which can adaptively combine multi-scale global and local features without compromising their individual representation.Specifically,our model incorporates global features for extracting high-level contextual information from retinal images.Concurrently,local features capture fine-grained details,such as microaneurysms,hemorrhages,and exudates,which are critical for DR diagnosis.These global and local features are adaptively fused using a fusion block,followed by an Integrated Attention Mechanism(IAM)that refines the fused features by emphasizing relevant regions,thereby enhancing classification accuracy for DR classification.Our model achieves 86.3%accuracy on the APTOS dataset and 96.6%RFMiD,both of which are comparable to state-of-the-art methods.
基金fully supported by the Frontier Exploration Projects of Longmen Laboratory(No.LMQYTSKT034)Key Research and Development and Promotion of Special(Science and Technology)Project of Henan Province,China(No.252102210158)。
文摘The capacity to diagnose faults in rolling bearings is of significant practical importance to ensure the normal operation of the equipment.Frequency-domain features can effectively enhance the identification of fault modes.However,existing methods often suffer from insufficient frequency-domain representation in practical applications,which greatly affects diagnostic performance.Therefore,this paper proposes a rolling bearing fault diagnosismethod based on aMulti-Scale FusionNetwork(MSFN)using the Time-Division Fourier Transform(TDFT).The method constructs multi-scale channels to extract time-domain and frequency-domain features of the signal in parallel.A multi-level,multi-scale filter-based approach is designed to extract frequency-domain features in a segmented manner.A cross-attention mechanism is introduced to facilitate the fusion of the extracted time-frequency domain features.The performance of the proposed method is validated using the CWRU and Ottawa datasets.The results show that the average accuracy of MSFN under complex noisy signals is 97.75%and 94.41%.The average accuracy under variable load conditions is 98.68%.This demonstrates its significant application potential compared to existing methods.
基金the National Natural Science Foundation of China(No.62266025)。
文摘Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.
基金Chongqing Engineering University Undergraduate Innovation and Entrepreneurship Training Program Project:Wireless Fire Automatic Alarm System(Project No.:CXCY2024017)Chongqing Municipal Education Commission Science and Technology Research Project:Development and Research of Chongqing Wireless Fire Automatic Alarm System(Project No.:KJQN202401906)。
文摘This article explores the design of a wireless fire alarm system supported by advanced data fusion technology.It includes discussions on the basic design ideas of the wireless fire alarm system,hardware design analysis,software design analysis,and simulation analysis,all supported by data fusion technology.Hopefully,this analysis can provide some reference for the rational application of data fusion technology to meet the actual design and application requirements of the system.
文摘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.
基金supported by the National Key Research and Development Program of China(2023YFB3307800)National Natural Science Foundation of China(62394343,62373155)+2 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024A26)Fundamental Research Funds for the Central Universities.
文摘Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.
基金supported by the Natural Science Foundation of the Anhui Higher Education Institutions of China(Grant Nos.2023AH040149 and 2024AH051915)the Anhui Provincial Natural Science Foundation(Grant No.2208085MF168)+1 种基金the Science and Technology Innovation Tackle Plan Project of Maanshan(Grant No.2024RGZN001)the Scientific Research Fund Project of Anhui Medical University(Grant No.2023xkj122).
文摘Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.
基金funded by the National Natural Science Foundation of China(42361058)supported by the Science and Technology Program of Gansu Province(22YF7FA074)。
文摘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.
基金supported by grants from the National Key Research and Development Program of China(2022YFD2001103)the National Natural Science Foundation of China(42371373)。
文摘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.
基金Open Access funding provided by the National Institutes of Health(NIH)The funding for this project was provided by NCATS Intramural Fund.
文摘Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational efficiency. Key architectural components such as convolution operations, shallow and deep blocks, skip connections, and hybrid encoders are examined for their roles in enhancing spatial representation and semantic consistency. We further discuss the importance of hierarchical and instance-aware segmentation and annotation in interpreting complex biological scenes and multiplexed medical images. By bridging methodological developments with diverse application domains, this paper outlines current trends and future directions for semantic segmentation, emphasizing its critical role in facilitating annotation, diagnosis, and discovery in biomedical research.
基金Supported by National Natural Science Foundation of China(Grant No.52005062)Chongqing Municipal Natural Science Foundation of China(Grant No.CSTB2023NSCQ-MSX0390)。
文摘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.
基金supported by the National Science Foundation of China(Grant Nos.52068049 and 51908266)the Science Fund for Distinguished Young Scholars of Gansu Province(No.21JR7RA267)Hongliu Outstanding Young Talents Program of Lanzhou University of Technology.
文摘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.
基金supported by the National Natural Science Foundation of China(No.52275104)the Science and Technology Innovation Program of Hunan Province(No.2023RC3097).
文摘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.
基金supported by the National Natural Science Foundation of China (grant numbers 42293351, and U2468221)。
文摘This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was established for water and mud inrush in tunnels by analyzing advanced prediction data for specifi c tunnel segments.Additionally,the indicator weights were determined using the analytic hierarchy process combined with the Huber weighting method.Subsequently,a multisource data decision-layer fusion algorithm was utilized to generate fused imaging results for tunnel water and mud inrush risk predictions.Meanwhile,risk analysis was performed for different tunnel sections to achieve spatial and temporal complementarity within the indicator system and optimize redundant information.Finally,model feasibility was validated using the CZ Project Sejila Mountain Tunnel segment as a case study,yielding favorable risk prediction results and enabling effi cient information fusion and support for construction decision-making.
基金supported by the Royal Golden Jubilee(RGJ)Ph.D.Programme(Grant No.PHD/0079/2561)through the National Research Council of Thailand(NRCT)and Thailand Research Fund(TRF).
文摘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.
