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Vision Mamba attention feature fusion UNet:an innovative state space model for accurate polyp segmentation 认领 引用
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作者 Bo YANG Biyuan LI +1 位作者 Gaowei SUN Jinying MA 《Optoelectronics Letters》 EI 2026年第3期187-192,共6页
Colorectal cancer(CRC)is a prevalent disease,with polyps serving as its precursors.Accurate polyp segmentation is crucial for early CRC prevention.However,due to different sizes of the polyps,the boundaries are not cl... Colorectal cancer(CRC)is a prevalent disease,with polyps serving as its precursors.Accurate polyp segmentation is crucial for early CRC prevention.However,due to different sizes of the polyps,the boundaries are not clear.Therefore,accurate segmentation of polyps is a challenging task.This paper proposes vision Mamba attention feature fusion UNet(VMA-UNet),a U-shaped asymmetric codec structure model grounded in the state space model(SSM).The VMA-UNet incorporates attention feature fusion(AFF)in order to enhance the feature representation of small polyps.A new IUD loss function,namely combining intersection over union(IoU)loss function and Dice loss function,is proposed to address both large polyps and small polyps,and to mitigate the issue of data imbalance.When applied to multiple datasets,VMA-UNet demonstrates robust performance,particularly in small polyp segmentation,showcasing its practical value.The network proposed in this paper overcomes the inherent shortcomings of convolutional neural network(CNN)and transformers,not only performing well in remote interaction modeling,but also maintaining linear computational complexity.Our study introduces a new method for polyp segmentation based on SSM and advances the field. 展开更多
关键词 unet state space model vision mamba polyp segmentation vision mamba attention feature fusion state space model ssm colorectal cancer crc attention feature fusion
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Fusion Correction for China's Domestic Remote Sensing Data of Sea Ice Concentration Using the TransUnet Model 认领 引用
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作者 ZHAO Chunxiao YANG Yanrui +1 位作者 ZHU Guocan ZHU Hongchun 《Journal of Ocean University of China》 SCIE CAS CSCD 2026年第1期106-122,共17页
The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navi... The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data. 展开更多
关键词 sea ice concentration quality assessment fusion correction Trans Unet model
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Empirical tropospheric zenith wet delay models with strong generalization capability based on a robust machine learning fusion algorithm 认领 引用
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作者 Jiahao Zhang Qin Liang Yunqing Huang 《Geodesy and Geodynamics》 EI CSCD 2026年第2期211-224,共14页
Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.H... Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.However,a single machine learning model has limited generalization capabilities.To address these limitations,this study introduces a novel machine learning fusion(MLF)algorithm with stronger generalization capabilities to enhance ZWD modeling and prediction accuracy.The MLF algorithm utilizes a two-layer structure integrating extra trees(ET),backpropagation neural network(BPNN),and linear regression models.By comparing the root mean square error(RMSE)of these models,we found that both ET-based and MLF-based models outperform RF-based and BPNN-based models in terms of internal and external accuracy,across both surface meteorological data-based and blind models.The improvement in exte rnal accuracy is particularly significant in the blind models.Our re sults show that the MLF(with an RMSE of 3.93 cm)and ET(3.99 cm)models outperform the traditional GPT3model(4.07 cm),while the RF(4.21 cm)and BPNN(4.14 cm)have worse external accuracies than the GPT3 model.It is worth noting that the BPNN suffered from overfitting during external accuracy tests,which was avoided by the MLF.In summary,regardless of the availability of surface meteorological data,the MLF-based empirical models demonstrate superior internal and external accuracy compared to the other tested models in this study. 展开更多
关键词 Tropospheric zenith wet delay Machine learning Extra trees Machine learning fusion algorithm Empirical models
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Construction of an Early Warning Model for Sepsis Complicating Severe Acute Pancreatitis Based on Multi-source Physiological Signal Fusion 认领 引用
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作者 HU Jin-jin DENG Xing +1 位作者 LU Zhi-ping QUE Gan-bo 《Chinese Journal of Biomedical Engineering(English Edition)》 CAS 2026年第1期16-25,共10页
Objective:To construct an early warning model for sepsis complicating severe acute pancreatitis based on multi-source physiological signal fusion,improve the efficiency of early sepsis identification,and provide objec... Objective:To construct an early warning model for sepsis complicating severe acute pancreatitis based on multi-source physiological signal fusion,improve the efficiency of early sepsis identification,and provide objective decision-making evidence for clinical early intervention.Methods:A total of 66 patients diagnosed with severe acute pancreatitis admitted from February 2019 to February2024 were enrolled and divided into sepsis and non-sepsis groups.Multi-dimensional data were collected,including dynamic physiological signals(electrocardiography,respiration,blood pressure)and routine laboratory biomarkers(procalcitonin,white blood cell count).All signals underwent targeted preprocessing,and multi-domain features were extracted from time,frequency,and nonlinear dimensions.Weighted feature fusion and principal component analysis(PCA)were performed for dimensionality reduction,and 24 core principal components were finally retained.A hybrid deep learning model integrating convolutional neural network(CNN)and long shortterm memory(LSTM)was established.The overall dataset was randomly divided into a training set and a validation set at a ratio of 7∶3.Model performance was quantitatively evaluated using accuracy,sensitivity,specificity,and the area under the receiver operating characteristic curve(AUC),and was further compared with conventional single infection biomarkers.Results:Feature weight analysis demonstrated that procalcitonin,respiratory rate variability,and standard deviation of mean arterial pressure were the dominant core warning features.In the validation cohort,the proposed model achieved an accuracy of 85.0%,a sensitivity of 83.3%,a specificity of86.7%,and an AUC of 0.903(95%CI:0.786-0.969),which was significantly superior to traditional biomarkers including procalcitonin and white blood cell count(P<0.001).The model provided early sepsis warnings at an average time of(18.56±6.13)h before clinical confirmation,and 72.0%of patients acquired a clinical intervention window of no less than 12 h.Conclusion:The multi-source physiological signal fusion model integrates dynamic physiological time-series signals and static laboratory indicators to effectively capture subtle and complex early physiological perturbations during sepsis progression.It exhibits superior performance in both warning accuracy and timeliness compared with conventional detection methods,which serves as a reliable quantitative tool for early identification of sepsis in patients with severe acute pancreatitis. 展开更多
关键词 multi-source physiological signal fusion severe acute pancreatitis sepsis early warning model machine learning
Research on Real Scene 3D Modeling Method Based on Multi Source Point Cloud Fusion 认领 引用
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作者 ZHAO Kaijing 《外文科技期刊数据库（文摘版）工程技术》 2026年第7期022-025,共4页
Among numerous 3D data acquisition methods, oblique photogrammetry and 3D laser scanning stand out with their irreplaceable technological advantages and form highly complementary technical characteristics. Tilt photog... Among numerous 3D data acquisition methods, oblique photogrammetry and 3D laser scanning stand out with their irreplaceable technological advantages and form highly complementary technical characteristics. Tilt photogrammetry relies on unmanned aerial vehicle platforms equipped with multi angle cameras to reconstruct three-dimensional geometric information from optical images through motion recovery structure algorithms and automatically map real textures. It has significant advantages such as wide coverage, high collection efficiency, and strong texture realism. However, due to limitations in flight angles and lighting conditions, there are often geometric deficiencies or texture distortions in areas near the ground, under eaves, and in vegetation covered areas of buildings. 3D laser scanning directly obtains high-precision 3D point clouds of the target surface by actively emitting laser pulses, which is not affected by lighting conditions and can accurately record the 3D coordinates of objects with millimeter level accuracy. However, its point cloud data lacks color and texture information, resulting in relatively low acquisition efficiency, large data volume, and long processing cycle. 展开更多
关键词 multi-source point cloud fusion Realistic 3D modeling Point cloud registration Oblique photogrammetry 3D laser scanning
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Combined anisotropic and cyclic constitutive model for laser powder bed fusion fabricated aluminum alloy 认领 引用 被引量：1
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作者 Fei-Fan LI Jihong ZHU +4 位作者 Weihong ZHANG Shifeng WEN Jingwen SONG Jun MA Gang FANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第1期165-184,共20页
This study presents new methods to effectively model the anisotropic yielding and hardening behavior of laser powder bed fusion fabricated aluminum alloy under both monotonic and cyclic loading conditions.The proposed... This study presents new methods to effectively model the anisotropic yielding and hardening behavior of laser powder bed fusion fabricated aluminum alloy under both monotonic and cyclic loading conditions.The proposed model combines the yield surface-interpolation method to accurately describe the anisotropic hardening rates in various directions,with the Chaboche kinematic hardening rule to precisely reflect the cyclic characteristics.For numerical implementation of the combined anisotropic and cyclic constitutive model,a fully implicit stress integration algorithm based on return mapping method is provided.Moreover,the multiple parameters associated with the model are categorized and identified in an uncoupled manner.The isotropic and cyclic hardening parameters are determined by an inverse method,and the stability of the optimization outcomes is validated by applying different starting points for the parameters.Particularly,the back-stress effect on the identification of anisotropic parameters associated with the stress invariant-based Hill48 yield function is considered for the first time.This consideration leads to an improved prediction accuracy compared to the identification of anisotropic parameters without considering back-stress effect.The combined anisotropic and cyclic constitutive model,along with the calibrated parameters,are proven capable of accurately reproducing the intricate deformation behavior of laser powder bed fusion fabricated AlSi10Mg. 展开更多
关键词 Constitutive models Anisotropy Kinematic hardening Laser powder bed fusion Aluminum alloys
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A Prediction Method for Concrete Mixing Temperature Based on the Fusion of Physical Models and Neural Networks 认领 引用
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作者 Lei Zheng Hong Pan +6 位作者 Yuelei Ruan Guoxin Zhang Lei Zhang Jianda Xin Zhenyang Zhu Jianyao Zhang Wei Liu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第12期3217-3241,共25页
As a critical material in construction engineering,concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency.This study proposes a novel hybrid pr... As a critical material in construction engineering,concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency.This study proposes a novel hybrid prediction method that integrates a heat conduction physical model with a multilayer perceptron(MLP)neural network,dynamically fused via a weighted strategy to achieve high-precision temperature estimation.Experimental results on an independent test set demonstrated the superior performance of the fused model,with a root mean square error(RMSE)of 1.59℃ and a mean absolute error(MAE)of 1.23℃,representing a 25.3%RMSE reduction compared to conventional physical models.Ambient temperature and coarse aggregate temperature were identified as the most influential variables.Furthermore,the model-based temperature control strategy reduced costs by 0.81 CNY/m3,showing significant potential for improving resource efficiency and supporting sustainable construction practices. 展开更多
关键词 Concrete outlet temperature prediction physical model neural network dynamic weight fusion temperature control
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Improved selected soil properties predictions using MIR and pXRF sensor fusion 认领 引用
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作者 Junwei Wang Qi Zou Huimin Yuan 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第4期1687-1699,共13页
The timely and accurate assessment of soil nutrient information is essential for ensuring global food security and sustainable agricultural development.This study evaluated the individual and fusion performance of mid... The timely and accurate assessment of soil nutrient information is essential for ensuring global food security and sustainable agricultural development.This study evaluated the individual and fusion performance of mid-infrared(MIR)and portable X-ray fluorescence(p XRF)spectroscopy for predicting selected soil properties.Four sensor fusion strategies were implemented:direct concatenation(DC),feature-level fusion using stability competitive adaptive reweighted sampling(s CARS)and least absolute shrinkage and selection operator(LASSO)algorithms(s CARS-C and LASSO-C),multiblock fusion via sequential orthogonal partial least squares(SO-PLS),and Granger-Ramanathan model averaging(GRA)method to enhance prediction accuracy for 13 soil properties.The findings revealed that single sensor models using either MIR or p XRF provided accurate estimations for soil organic matter(SOM),total nitrogen(TN),available phosphorus(AP),calcium(Ca),iron(Fe),manganese(Mn),and p H,but showed limitations for total potassium(TK),magnesium(Mg),copper(Cu),zinc(Zn),available potassium(AK),and total phosphorus(TP).The DC model significantly improved predictions for Mg(Rp2=0.76,RMSEp=358.76 mg kg-1,RPDp=2.03)and TK(Rp2=0.75,RMSEp=775.96 mg kg-1,RPDp=2.00).The LASSO-C model demonstrated superior prediction accuracy compared to the DC model for AP,AK,TP,Zn,Mn,and Cu,achieving optimal results for AP(Rp2=0.89,RMSEp=21.37 mg kg-1,RPDp=3.01)and Zn(Rp2=0.80,RMSEp=9.88 mg kg-1,RPDp=2.32).This enhancement is attributed to LASSO's effective selection of feature information from the complete MIR and p XRF spectra.The GRA models achieved the highest prediction accuracy for TP,p H,AK,and Cu,with Rp2values of 0.80,0.82,0.82,and 0.65,RMSEp values of 129.21 mg kg-1,0.13,48.38 mg kg-1,and 3.87 mg kg-1,and RPDp values of 2.23,2.34,2.37,and 1.67,respectively.For single-sensor applications,MIR spectra are recommended for predicting SOM,TN,and Ca(Rp2≥0.88,RPDp≥2.87),while p XRF is more cost-effective for measuring Ca,Fe,and Mn(Rp2≥0.80,RPDp≥2.22).This research demonstrates the effectiveness of MIR and p XRF sensor fusion in enhancing soil nutrient assessment accuracy,particularly for available nutrients and micronutrients. 展开更多
关键词 soil available nutrients micronutrients multi-block fusion feature-level fusion model averaging
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Three-dimensional full-scale neutronics/thermal-hydraulics/mechanics coupling analysis-based structural assessment of helium-cooled ceramic breeder blanket for fusion reactor 认领 引用
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作者 Qiang Lian Kui Zhang +6 位作者 Shan-Shan Bu Liang-Ming Pan Wen-Xi Tian Sui-Zheng Qiu Guang-Hui Su Xing-Hua Wu Xiao-Yu Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第6期178-195,共18页
To accelerate the development and utilization of fusion energy,the China Fusion Engineering Test Reactor(CFETR)has been proposed as a bridge between the International Thermonuclear Experimental Reactor and demonstrati... To accelerate the development and utilization of fusion energy,the China Fusion Engineering Test Reactor(CFETR)has been proposed as a bridge between the International Thermonuclear Experimental Reactor and demonstration fusion reactors.The primary objective of the CFETR is to achieve fusion energy transformation and tritium self-sufficiency,which is realized through the function of the blanket.In this study,a neutronicshermal-hydraulics/mechanics coupling method is developed and applied to a helium-cooled ceramic breeder(HCCB)blanket,which is one of the two blanket candidates for the CFETR.A three-dimensional full-scale model is utilized in the coupling analysis to obtain the distributions of the neutronic,thermal-hydraulic,and mechanical parameters.A structural assessment of the CFETR HCCB blanket is then conducted considering steady-state conditions and two transient scenarios.The results demonstrate that following optimization of the blanket structure,the maximum temperatures of the different components remain below the safety limit of the corresponding materials.The structural assessment indicates that the blanket maintains its structural integrity under steady-state conditions.However,immediately after an in-box loss-of-coolant accident,structural failure owing to stress concentration may occur.Additionally,in the early stage of a loss-of-flow accident,the stress at the joint point between the cooling plate and cap exceeds the allowable stress of the material,potentially leading to structural failure within 17 s if no protective response is implemented.These findings provide comprehensive insights into the performance and safety of the CFETR HCCB blanket design. 展开更多
关键词 Structural assessment Fusion blanket Three-dimensional full-scale model Coupling analysis CFETR
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A machine learning-based depression recognition model integrating spiritexpression features from traditional Chinese medicine 认领 引用
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作者 Minghui Yao Rongrong Zhu +4 位作者 Peng Qian Huilin Liu Xirong Sun Limin Gao Fufeng Li 《Digital Chinese Medicine》 CAS CSCD 2026年第1期68-79,共12页
Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish ... Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish a TCM-informed tool for early depression screening,thereby bridging traditional diagnostic principles with modern computational approaches.Methods The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1,2022 to October 1,2023,as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group.Videos of 3–10 s were captured using a Xiaomi Pad 5,and the TCM spirit and expressions were determined by TCM experts(at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions).Basic information,facial images,and interview information were collected through a portable TCM intelligent analysis and diagnosis device,and facial diagnosis features were extracted using the Open CV computer vision library technology.Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data,TCM spirit and expression features,and facial diagnosis feature parameters of the two groups,to compare the differences in TCM spirit and expression and facial features.Five machine learning algorithms,including extreme gradient boosting(XGBoost),decision tree(DT),Bernoulli naive Bayes(BernoulliNB),support vector machine(SVM),and k-nearest neighbor(KNN)classification,were used to construct a depression recognition model based on the fusion of TCM spirit and expression features.The performance of the model was evaluated using metrics such as accuracy,precision,and the area under the receiver operating characteristic(ROC)curve(AUC).The model results were explained using the Shapley Additive exPlanations(SHAP).Results A total of 93 depression patients and 87 healthy individuals were ultimately included in this study.There was no statistically significant difference in the baseline characteristics between the two groups(P>0.05).The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows.(i)Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls(P<0.05),with characteristic features such as sad expressions,facial erythema,and changes in the lip color ranging from erythematous to cyanotic.(ii)Depressed patients exhibited significantly lower values in facial complexion L,lip L,and a values,and gloss index,but higher values in facial complexion a and b,lip b,low gloss index,and matte index(all P<0.05).(iii)The results of multiple models show that the XGBoost-based depression recognition model,integrating the TCM“spirit-expression”diagnostic framework,achieved an accuracy of 98.61%and significantly outperformed four benchmark algorithms—DT,BernoulliNB,SVM,and KNN(P<0.01).(iv)The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm,the complexion b value,categories of facial spirit,high gloss index,low gloss index,categories of facial expression and texture features have significant contribution to the model.Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model,offering a novel paradigm for objective depression diagnosis. 展开更多
关键词 Traditional Chinese medicine Spirit Expression Feature fusion Depression Recognition model
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Seasonal machine learning fusion for improved satellite precipitation estimates:A case study in the upper Ganjiang River,China 认领 引用
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作者 CHEN Yunyao LI Binquan +4 位作者 XIAO Yang ZHANG Huiming XU Dong ZHANG Taotao WU Zhijun 《Journal of Mountain Science》 SCIE CSCD 2026年第3期1062-1078,共17页
Rainfall input errors are a major source of uncertainty in flood forecasting,and merging multi-source precipitation data is essential for improving accuracy.Traditional merging methods often prioritize precipitation m... Rainfall input errors are a major source of uncertainty in flood forecasting,and merging multi-source precipitation data is essential for improving accuracy.Traditional merging methods often prioritize precipitation magnitude enhancements while overlooking event detection and false alarms.To address these limitations,this study developed a precipitation integration framework that combines machine learning classification-plus-regression models with Bayesian model averaging(BMA).Three machine learning algorithms-categorical boosting(CatBoost),light gradient boosting machine(LightGBM),and random forest(RF)-were used to improve precipitation event detection.The framework includes spatial unification of raw satellite products using bilinear interpolation,bias correction through classification-plus-regression models,and final merging via a seasonal-scale BMA model.The method integrated GSMaP,IMERG,and PERSIANN satellite precipitation products,with ground observations used for model training(2001-2014)and independent validation(2015-2020)in the Upper Ganjiang River Basin,China.Results showed that the framework significantly enhanced precipitation estimation accuracy and detection capability.LightGBM-based integration exhibited superior detection performance(FAR=0.08,CSI=0.86),while RF-based integration achieved the highest overall accuracy(RMSE=4.67,CC=0.92).Seasonal variations in BMA weights underscored the need to account for seasonal characteristics of precipitation products.Additionally,accuracy improvements were observed across all rainfall categories,especially for heavy rainstorms.The seasonal-scale BMA fusion has combined the strengths of individual corrections and further enhanced precipitation estimation.This research offers a robust method for generating accurate rainfall inputs,providing valuable support for hydrological modeling and flood forecasting applications. 展开更多
关键词 Multi-source precipitation fusion Rain classification Machine learning Bayesian model averaging Upper Ganjiang River
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Online porosity monitoring in laser metal deposition via MFST-Net:A multimodal feature-level spatiotemporal fusion approach 认领 引用
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作者 Peng Rong Fenglei Zheng +2 位作者 Xinyu Ding Xin Fang Ming Yin 《Additive Manufacturing Frontiers》 CAS CSCD 2026年第2期119-131,共13页
In recent years,data-driven approaches for online defect monitoring in metal laser additive manufacturing(LAM)have achieved remarkable progress.However,most existing studies primarily rely on spatial features extracte... In recent years,data-driven approaches for online defect monitoring in metal laser additive manufacturing(LAM)have achieved remarkable progress.However,most existing studies primarily rely on spatial features extracted from single-modal transient images,which are insufficient to capture the temporal evolution characteristics of the melt pool and the associated variations in local thermal history during the laser metal deposition(LMD)process.Moreover,the complementary information provided by multi-sensor data has often been overlooked.To address these limitations,this study proposes a multimodal feature-level spatiotemporal network(MFST-Net),which enables joint modeling and deep fusion of melt pool image sequences and in-situ process temperature signals.Specifically,a spatiotemporal feature fusion neural network(STFNN)is constructed to extract spatial distribution patterns from melt pool images while capturing multi-scale temporal dependencies at both the intra-layer and inter-layer levels.In parallel,a self-attention convolutional long short-term memory(SAConvLSTM)network is employed to model the dynamic evolution of thermal signals.Finally,cross-modal feature fusion is performed at the feature level to characterize the relationship between thermal–morphological evolution and pore formation mechanisms.Experimental results demonstrate the effectiveness and superiority of MFST-Net in online monitoring of local porosity,achieving an accuracy of 95.8%.These findings provide a promising reference for the integration of multimodal spatiotemporal feature fusion in complex manufacturing process monitoring. 展开更多
关键词 Laser directed energy deposition Multimodal fusion Spatiotemporal feature modeling Online defect monitoring
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Reentry glide vehicle intent inference method via multidimensional intention fusion 认领 引用
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作者 HE Yangchao LI Jiong +2 位作者 SHAO Lei ZHOU Chijun LEI Humin 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第2期712-724,共13页
To address the challenge of predicting reentry glide vehicle attack intention in no-fly zone scenarios,this paper proposes a multidimensional intention fusion-based inference method.Firstly,the recursive formula for t... To address the challenge of predicting reentry glide vehicle attack intention in no-fly zone scenarios,this paper proposes a multidimensional intention fusion-based inference method.Firstly,the recursive formula for the posterior probability of the vehicle’s intention is derived using Bayes’theorem.Secondly,the concepts of pseudo heading deviation angle and endpoint relative energy are introduced to formulate an intention cost function that incorporates both angular and energetic dimensions,and the corresponding likelihood probability is obtained by quantifying the cost of different intentions,which solves the problem that the traditional cost function cannot characterize the real intention of the vehicle in scenarios involving nofly zones.Finally,a dynamically weighted multidimensional intention fusion model is proposed to deduce the vehicle’s attack intent in the footprints.The simulation results show that the proposed method has a higher accuracy rate of intent inference compared to the existing methods. 展开更多
关键词 reentry glide vehicle intent inference intention cost function multidimensional intention fusion model Bayes’theorem
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Construction of the clinical diagnosis and treatment model during the“pre-disease to disease”window period in traditional Chinese medicine:integration of objective multimodal data from the perspective of traditional Chinese medicine stateology 认领 引用
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作者 Danyang Li Min Ai +3 位作者 Pai Zhou Ying Deng Chaoyang Yang Qinghua Peng 《Digital Chinese Medicine》 CAS CSCD 2026年第2期173-183,共11页
The philosophy of“treating disease before its onset”is a fundamental concept of traditional Chinese medicine(TCM),permeating its diagnostic and therapeutic framework,and is central to clinical practice.However,curre... The philosophy of“treating disease before its onset”is a fundamental concept of traditional Chinese medicine(TCM),permeating its diagnostic and therapeutic framework,and is central to clinical practice.However,current TCM diagnostic and treatment models for the“predisease to disease”window period face several limitations,including the lack of comprehensive clinical parameters,difficulties in characterizing and integrating heterogeneous multimodal data,and insufficient dynamic precision in interventions and efficacy evaluations.To address these issues,guided by Professor Candong Li’s theory of TCM stateology,this study focuses on integrating objective multimodal data.It proposes a new model for personalized TCM diagnosis and treatment targeting the“pre-disease to disease”window period.This approach first proposes the idea of restructuring the conceptual framework of“symptom”and integrating multi-source heterogeneous data at macroscopic,mesoscopic,and microscopic levels to form a three-dimensional assessment indicator system.By integrating graph neural networks,convolutional neural networks,attention mechanisms,and knowledge graph-guided weight allocation,this approach enables collaborative representation,alignment,and fusion of multi-source data.Subsequently,it plans to construct a multimodal fusion model at both feature and decision levels,in order to establish mappings between indicators and TCM state elements,and to screen key indicators characterizing pathological evolution during the window period.Furthermore,it proposes a technical path for enhancing model interpretability using methods such as SHapley Additive exPlanations(SHAP)and Ablation-CAM++.Finally,with state assessment as the core,it proposes the concept of constructing a dynamic evaluation method for individualized diagnosis and treatment based on time-series data analysis using algorithms such as long short-term memory(LSTM)networks and gated recurrent units(GRUs).Moreover,a causal inference framework and semi-supervised learning strategies are introduced to enable quantitative evaluation of individual intervention effects and to provide interpretable therapeutic feedback,forming a complete technical path from data representation and fusion,weight adjustment,and interpretability analysis,to dynamic diagnosis feedback.This study aims to address deficiencies in the current TCM diagnosis and treatment model during the“pre-disease to disease”window period and to provide an operational framework for the clinical practice of TCM’s“treating disease before its onset”. 展开更多
关键词 Traditional Chinese medicine stateology Window period Clinical diagnostic and therapeutic model Multimodal data fusion Deep learning
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SYMPHONIA-Enhanced Multimodal Emotion Recognition with Dual-Branch Dynamic Attention and Hierarchical Adaptive Fusion 认领 引用
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作者 Akmalbek Abdusalomov Mukhriddin Mukhiddinov +4 位作者 Kamola Abdurashidova Alpamis Kutlimuratov Avazjon Marakhimov Kuanishbay Seytnazarov Young-Im Cho 《Computers, Materials & Continua》 SCIE EI 2026年第7期1786-1807,共22页
Human emotions are intricate and difficult to decipher through various modalities.Current methodologies frequently employ inflexible fusion strategies that do not consider the dynamic and context-sensitive characteris... Human emotions are intricate and difficult to decipher through various modalities.Current methodologies frequently employ inflexible fusion strategies that do not consider the dynamic and context-sensitive characteristics of emotional expressions in both visual and textual mediums.This paper presents SYMPHONIA(Synchronizing Facial and Textual Modalities for Emotion Understanding),an innovative architecture engineered to capture and amalgamate emotional signals from facial expressions and language,attuned to contextual and modality interactions.There are two parts to SYMPHONIA:a Facial Emotion Branch that uses Vision Transformers and facial landmarks,and a Textual Emotion Branch that uses RoBERTa embeddings and graph-based reasoning.A Dual-Branch Dynamic Attention Mechanism and a Hierarchical Adaptive Fusion Module are used to connect these branches.SYMPHONIA beat the best models on four datasets:IEMOCAP,MELD,CMU-MOSI,and CMU-MOSEI.It got 80.9%accuracy and 80.1%F1-score on IEMOCAP,which was better than Dualgats(74.8%)and EmoCLIP(75.3%).SYMPHONIA got 74.2%accuracy and 73.5%F1-score for MELD.It beat its competitors by getting a 0.86 Pearson correlation on MOSI and a 0.83 on MOSEI for predicting sentiment.Cross-dataset tests showed that SYMPHONIA could generalize,with 66.9%accuracy when trained on IEMOCAP and tested on MELD.This was better than all the baselines.These results show that SYMPHONIA is good at recognizing emotions and analyzing sentiment in different situations,which shows that it can adapt and do well in different settings. 展开更多
关键词 Multimodal emotion recognition RoBERTa cross-modal attention graph neural networks contrastive learning adaptive fusion temporal modeling affective computing context-aware representation
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A Review of Applications and Challenges of Large Language Models for Foundry Intelligence in the Casting Industry 认领 引用
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作者 Yutong Guo Jianying Yang Chao Yang 《Computers, Materials & Continua》 SCIE EI 2026年第7期207-233,共27页
Large language models(LLMs)and related foundation-model workflows are emerging as promising tools for advancing foundry intelligence across the casting value chain.This review examines their applications in material d... Large language models(LLMs)and related foundation-model workflows are emerging as promising tools for advancing foundry intelligence across the casting value chain.This review examines their applications in material design and property prediction,process parameter optimization and intelligent control,and defect detection and quality tracing in casting environments.The surveyed studies indicate that LLM-enabled systems can help integrate unstructured technical knowledge with multimodal industrial data.This integration supports composition design,simulation-assisted process optimization,diagnostic reasoning,and knowledge-grounded decision support.However,current evidence shows that the transition from pilot demonstrations to robust industrial deployment remains constrained by several practical barriers,including heterogeneous data integration,insufficient traceability across process stages,reliability under physical and safety constraints,and the latency and resource limitations of shop-floor environments.We further highlight key research directions for real-world foundry applications,including multimodal cognitive systems,lightweight domain-adapted models,trustworthy retrieval-augmented and physics-aware reasoning,and human-in-the-loop validation frameworks.Overall,the review suggests that the future of foundry intelligence will depend not only on model capability,but also on data governance,deployable system design,and reliable integration with metallurgical knowledge and industrial workflows. 展开更多
关键词 Large scale language models foundry industry material design process optimization defect detection multimodal data fusion
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Solar flare forecasting based on a Fusion Model 认领 引用
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作者 YiYang Li ShiYong Huang +4 位作者 SiBo Xu ZhiGang Yuan Kui Jiang QiYang Xiong RenTong Lin 《Earth and Planetary Physics》 EI CSCD 2025年第1期171-181,共11页
Solar flare prediction is an important subject in the field of space weather.Deep learning technology has greatly promoted the development of this subject.In this study,we propose a novel solar flare forecasting model... Solar flare prediction is an important subject in the field of space weather.Deep learning technology has greatly promoted the development of this subject.In this study,we propose a novel solar flare forecasting model integrating Deep Residual Network(ResNet)and Support Vector Machine(SVM)for both≥C-class(C,M,and X classes)and≥M-class(M and X classes)flares.We collected samples of magnetograms from May 1,2010 to September 13,2018 from Space-weather Helioseismic and Magnetic Imager(HMI)Active Region Patches and then used a cross-validation method to obtain seven independent data sets.We then utilized five metrics to evaluate our fusion model,based on intermediate-output extracted by ResNet and SVM using the Gaussian kernel function.Our results show that the primary metric true skill statistics(TSS)achieves a value of 0.708±0.027 for≥C-class prediction,and of 0.758±0.042 for≥M-class prediction;these values indicate that our approach performs significantly better than those of previous studies.The metrics of our fusion model’s performance on the seven datasets indicate that the model is quite stable and robust,suggesting that fusion models that integrate an excellent baseline network with SVM can achieve improved performance in solar flare prediction.Besides,we also discuss the performance impact of architectural innovation in our fusion model. 展开更多
关键词 solar flare pace weather deep learning Fusion Model
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Improved Thermal Resolution and Macroscale Phase Transformation Modeling of the Semi-Crystalline Polymer Polyamide-12 during Laser Powder Bed Fusion 认领 引用
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作者 Zhongfeng Xu Lionel Freire +2 位作者 Noelle Billon Jean-Luc Bouvard Yancheng Zhang 《Additive Manufacturing Frontiers》 CSCD 2025年第1期197-212,共16页
Semi-crystalline polymer laser powder bed fusion(L-PBF)has recently attracted increasing interest due to its potential for fabricating complex geometry.However,a more comprehensive understanding of the underlying phys... Semi-crystalline polymer laser powder bed fusion(L-PBF)has recently attracted increasing interest due to its potential for fabricating complex geometry.However,a more comprehensive understanding of the underlying physics during L-PBF is required to better control the properties of the final part.This work proposed a multi-layer numerical model to study the temperature and phase evolution during the polyamide-12(PA12)L-PBF process.The Descend and Parallel Chord methods were introduced to improve the convergence of the non-linear thermal solver.The level-set-based mesh adaptation strategy,governed by multi-physical fields,was applied to alleviate the calculation and accurately track the phase evolution.The processing simulation on the dog-bone model revealed that preheating temperature significantly influences the crystallization behavior.Finally,the multi-layer simulation demonstrated that such a developed numerical model can be used to study the phase transformation during powder layer updating and the cyclic laser sintering phenomena.Moreover,the numerical study suggested that crystallization occurs slowly during the L-PBF process. 展开更多
关键词 Laser powder bed fusion Crystallization Numerical modeling Mesh adaptation Enhanced resolution method
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A multi-scale temporal fusion network for accurate and uncertainty-aware battery remaining useful life prediction 认领 引用
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作者 Chenguang You Chenzhuang Dong +6 位作者 Jiachen Lu Siyuan Xie Yijie Duan Lili Duan Hao Yan Wei Luo Shuangqiang Chen 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第5期454-469,I0011,共16页
Accurate prediction of the remaining useful life(RUL)of lithium-ion batteries requires the concurrent satisfaction of three technical demands:efficient modeling of long sequences,fusion of multi-scale features,and rel... Accurate prediction of the remaining useful life(RUL)of lithium-ion batteries requires the concurrent satisfaction of three technical demands:efficient modeling of long sequences,fusion of multi-scale features,and reliable quantification of prediction uncertainty.This study proposes MSTFNet,a multi-scale temporal fusion network that integrates a spatial convolutional neural network(SCNN)for local patterns,a Mamba state-space module for linear-time modeling of global dependencies,and an Informer module for sparse temporal focusing,all under a Bayesian inference head optimized via a combined negative loglikelihood and mean-squared-error objective.The Bayesian head not only calibrates uncertainty but also regulates multi-module feature fusion,achieving a cooperative synergy unavailable to single modules alone.Across three benchmark datasets(NASA,CALCE,and HUST),MSTFNet attains a superior balance between prediction accuracy and model efficiency,achieving up to 29.4%lower RMSE than state-ofthe-art baselines.Further analyses confirm well-calibrated predictive intervals,robust cross-dataset generalization under train-on-Aest-on-B and few-shot settings,and deployment feasibility in terms of latency,memory,and INT8/pruning performance.Ablation results substantiate that BNN-regulated joint optimization effectively enhances Informer collaboration,validating the proposed regulatory mechanism. 展开更多
关键词 Battery remaining useful life Multi-scale fusion State-space modeling Bayesian deep learning Uncertainty calibration Deployment
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YOLO-SPDNet:Multi-Scale Sequence and Attention-Based Tomato Leaf Disease Detection Model 认领 引用
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作者 Meng Wang Jinghan Cai +6 位作者 Wenzheng Liu Xue Yang Jingjing Zhang Qiangmin Zhou Fanzhen Wang Hang Zhang Tonghai Liu 《Phyton-International Journal of Experimental Botany》 SCIE 2026年第1期290-308,共19页
Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet th... Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes. 展开更多
关键词 Tomato disease detection YOLO multi-scale feature fusion attention mechanism lightweight model
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