Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still st...Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.展开更多
A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5w...A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5wt%Er-1wt%TiB_(2)/Al-Mn-Mg-Sc-Zr nanocomposite were prepared using vacuum homogenization technique,and the density of samples prepared through the LPBF process reached 99.8%.The strengthening and toughening mechanisms of Er-TiB_(2)were investigated.The results show that Al_(3)Er diffraction peaks are detected by X-ray diffraction analysis,and texture strength decreases according to electron backscatter diffraction results.The added Er and TiB_(2)nano-reinforcing phases act as heterogeneous nucleation sites during the LPBF forming process,hindering grain growth and effectively refining the grains.After incorporating the Er-TiB_(2)dual-phase nano-reinforcing phases,the tensile strength and elongation at break of the LPBF-deposited samples reach 550 MPa and 18.7%,which are 13.4%and 26.4%higher than those of the matrix material,respectively.展开更多
The process of nuclear fusion in the presence of a laser field was theoretically analyzed.The analysis is applicable to most fusion reactions and different types of currently available intense lasers,from X-ray free-e...The process of nuclear fusion in the presence of a laser field was theoretically analyzed.The analysis is applicable to most fusion reactions and different types of currently available intense lasers,from X-ray free-electron lasers to solid-state near-infrared lasers.Laser fields were shown to enhance the fusion yields,and the mechanism of this enhancement was explained.Low-frequency lasers are more efficient in enhancing fusion than high-frequency lasers.The calculation results show enhancements of fusion yields by orders of magnitude with currently available intense low-frequency laser fields.The temperature requirement for controlled nuclear fusion may be reduced with the aid of intense laser fields.展开更多
Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collect...Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collected vibration signals,single-modal methods struggle to capture fault features fully.This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion.The method first employs the Hippopotamus Optimization Algorithm(HO)to optimize the number of modes in Variational Mode Decomposition(VMD)to achieve optimal modal decomposition performance.It combines Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)to extract temporal features from one-dimensional time-series signals.Meanwhile,the Markovian Transition Field(MTF)is used to transform one-dimensional signals into two-dimensional images for spatial feature mining.Through visualization techniques,the effectiveness of generated images from different parameter combinations is compared to determine the optimal parameter configuration.A multi-modal network(GSTCN)is constructed by integrating Swin-Transformer and the Convolutional Block Attention Module(CBAM),where the attention module is utilized to enhance fault features.Finally,the fault features extracted from different modalities are deeply fused and fed into a fully connected layer to complete fault classification.Experimental results show that the GSTCN model achieves an average diagnostic accuracy of 99.5%across three datasets,significantly outperforming existing comparison methods.This demonstrates that the proposed model has high diagnostic precision and good generalization ability,providing an efficient and reliable solution for rolling bearing fault diagnosis.展开更多
Parkinson’s disease remains a major clinical issue in terms of early detection,especially during its prodromal stage when symptoms are not evident or not distinct.To address this problem,we proposed a new deep learni...Parkinson’s disease remains a major clinical issue in terms of early detection,especially during its prodromal stage when symptoms are not evident or not distinct.To address this problem,we proposed a new deep learning 2-based approach for detecting Parkinson’s disease before any of the overt symptoms develop during their prodromal stage.We used 5 publicly accessible datasets,including UCI Parkinson’s Voice,Spiral Drawings,PaHaW,NewHandPD,and PPMI,and implemented a dual stream CNN–BiLSTM architecture with Fisher-weighted feature merging and SHAP-based explanation.The findings reveal that the model’s performance was superior and achieved 98.2%,a F1-score of 0.981,and AUC of 0.991 on the UCI Voice dataset.The model’s performance on the remaining datasets was also comparable,with up to a 2–7 percent betterment in accuracy compared to existing strong models such as CNN–RNN–MLP,ILN–GNet,and CASENet.Across the evidence,the findings back the diagnostic promise of micro-tremor assessment and demonstrate that combining temporal and spatial features with a scatter-based segment for a multi-modal approach can be an effective and scalable platform for an“early,”interpretable PD screening system.展开更多
A low-temperature-resistant and high-strength stainless-steel jacket is a key component in the superconducting magnet of a fusion reactor.The development of cryogenic structural materials with high strength and toughn...A low-temperature-resistant and high-strength stainless-steel jacket is a key component in the superconducting magnet of a fusion reactor.The development of cryogenic structural materials with high strength and toughness poses a challenge for the future development of high-field superconducting magnets in fusion reactors.The yield strength of the International Thermonuclear Experimental Reactor developed for low-temperature structural materials at 4.2K is below 1100MPa,which fails to meet the demand for structural components with yield strengths exceeding 1500MPa at 4.2K in the future fusion reactors.CHSN01(formerly N50H),which is a low-temperature structural material developed in China,exhibits exceptional strength and toughness,thereby making it highly promising for practical applications.Recently,a 30 t jacket measuring approximately 5000m in total length was produced.Its low-temperature mechanical properties were tested using a sampling method to ensure compliance with application requirements.This paper presents the experimental data of the CHSN01 jacket and tests of the physical properties of the material in the temperature range of 4–300 K.The physical properties were unaffected by magnetic field.Furthermore,this paper discusses the feasibility of employing CHSN01 as a cryogenic structural material capable of withstanding high magnetic fields in next-generation fusion reactors.展开更多
Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approa...Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.展开更多
AIM:To investigate the effects of binocular fusional C-optotypes(positive/negative)and 2D planar C-optotypes on the amplitude and stability of transient accommodation(TAC)in adults,and to provide a basis for non-conta...AIM:To investigate the effects of binocular fusional C-optotypes(positive/negative)and 2D planar C-optotypes on the amplitude and stability of transient accommodation(TAC)in adults,and to provide a basis for non-contact myopia intervention.METHODS:This was a self-controlled study.Using redblue 3D technology,four experimental stages were set up:Test A[fixating on the 1 m negative fusional C-optotypes,8△base-in(BI)],Test B(fixating on the 5 m planar C-optotypes),Test C(fixating on the 1 m planar C-optotypes),and Test D[fixating on the 1 m positive fusional C-optotypes,20△base-out(BO)].A WAM-5500 open-field autorefractor was used to measure TAC and accommodative microfluctuations[evaluated via interquartile range(IQR)and median-based coefficient of variation(CVmed)].Additionally,the convergence accommodation to convergence(CA/C)ratio was calculated,and a visual fatigue questionnaire was administered to assess participants’subjective visual comfort.RESULTS:A total of 21 subjects(7 males,14 females;aged 23-41y)with normal binocular visual function were enrolled.The results showed that the TAC increased gradually across the four stages,and these values were Test A(-0.35±0.26 D)<Test B(-0.46±0.24 D)<Test C(-0.77±0.32 D)<Test D(-1.38±0.31 D).There were significant overall differences(F=56.136,P<0.001).Compared with Test C,Test A reduced TAC by 0.42 D(P<0.05),while Test D increased it by 0.61 D(P<0.001).There was no significant intergroup difference in accommodative fluctuation amplitude(all P>0.05),but the fluctuation stability of Test D showed a significant difference between the first 20s and the second 20s(P=0.017).The CA/C ratio was significantly higher in Test D(0.05±0.02 D/△)than in Test A(0.03±0.02 D/△,P=0.007),indicating stronger accommodation-convergence linkage during positive fusional fixation.The visual fatigue scores of all stages were low(median 0-1),with Test D slightly higher than Test B and Test C(P<0.05).No linear correlation was found between TAC and age(all r<0.1,P>0.05).CONCLUSION:Negative fusional C-optotypes induce ciliary muscle relaxation to reduce TAC,while positive fusional C-optotypes enhance accommodation-convergence coordination to increase TAC.The red-blue 3D-based noncontact training mode exhibits good safety(median visual fatigue scores:0-1 across all tests)and provides a novel dual-directional(relaxation-activation)strategy for myopia prevention and control.展开更多
Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the backgroun...Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves F_(β)by 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in F_(β).OnCAMO and CHAMELEON,it obtains 4.5-point increases in F_(β),respectively.These consistent gains substantiate the superiority and robustness of EGMFNet.展开更多
In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free...In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.展开更多
In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although ...In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although intelligent rescue robots can enter hazardous environments in place of humans,smoke poses major challenges for human detection algorithms.These challenges include the attenuation of visible and infrared signals,complex thermal fields,and interference frombackground objects,all ofwhichmake it difficult to accurately identify trapped individuals.To address this problem,we propose VIF-YOLO,a visible–infrared fusion model for real-time human detection in dense smoke environments.The framework introduces a lightweight multimodal fusion(LMF)module based on learnable low-rank representation blocks to end-to-end integrate visible and infrared images,preserving fine details while enhancing salient features.In addition,an efficient multiscale attention(EMA)mechanism is incorporated into the YOLOv10n backbone to improve feature representation under low-light conditions.Extensive experiments on our newly constructedmultimodal smoke human detection(MSHD)dataset demonstrate thatVIF-YOLOachievesmAP50 of 99.5%,precision of 99.2%,and recall of 99.3%,outperforming YOLOv10n by a clear margin.Furthermore,when deployed on the NVIDIA Jetson Xavier NX,VIF-YOLO attains 40.6 FPS with an average inference latency of 24.6 ms,validating its real-time capability on edge-computing platforms.These results confirm that VIF-YOLO provides accurate,robust,and fast detection across complex backgrounds and diverse smoke conditions,ensuring reliable and rapid localization of individuals in need of rescue.展开更多
Fe-Ga-based alloys are considered promising magnetostrictive candidates because of their high permeability and favorable mechanical properties.However,currently developed Fe-Ga-based alloys often suffer from a limited...Fe-Ga-based alloys are considered promising magnetostrictive candidates because of their high permeability and favorable mechanical properties.However,currently developed Fe-Ga-based alloys often suffer from a limited capability for microstructure manipulation,which restricts their magnetostrictive performance.To address this limitation,this study proposes a novel strategy combining laser-beam powder bed fusion(LPBF)and aging treatment to modulate the microstructure and enhance magnetostrictive properties of Fe-Ga-B alloys.Considering the positive influence of B element on magnetostrictive property and ductility,B-doped magnetostrictive Fe-Ga alloys were prepared via the LPBF process and then aged at 600℃for varying times(1,2,and 3 h,respectively).The LPBF process,characterized by high thermal gradients and rapid solidification,produced a microstructure featuring<001>oriented grains and sparse m-D0_(3)nanoprecipitates embedded in an A2 matrix.After the aging treatment,sufficient nucleation and growth of nanoprecipitates were enhanced.Specifically,the sample aged for 2 h developed a high density of larger m-D0_(3)nanoprecipitates.This optimized microstructure yielded a high magnetostrictive strain of(109±12)ppm and a substantially reduced saturation field—decreased by~49.1%compared to the as-fabricated state—primarily due to the synergistic effect of the<001>texture and the dense nanoprecipitates.Moreover,all the prepared alloys exhibited good soft-magnetic characteristics and comparable mechanical properties.Therefore,the combination of LPBF and aging treatment offers a promising route for tailoring the macro/microstructure and performance of magnetostrictive Fe-Ga alloys for diverse applications.展开更多
Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operati...Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operation masks fault features,severely impairing the fault diagnosis performance of deep learning models.Second,current blade fault diagnosis often relies on single-sensor data,resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states.To address these issues,a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed.Specifically,a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions,while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information.Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input,enabling comprehensive and effective fault diagnosis.展开更多
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
[Objectives]This study was conducted to achieve rapid and accurate detection of protein content in rice with a particle size of 1.0 mm.[Methods]A multi-model fusion strategy was proposed on the basis of Stacking ensem...[Objectives]This study was conducted to achieve rapid and accurate detection of protein content in rice with a particle size of 1.0 mm.[Methods]A multi-model fusion strategy was proposed on the basis of Stacking ensemble learning.A base learner pool was constructed,containing Partial Least Squares(PLS),Support Vector Machine(SVM),Deep Extreme Learning Machine(DELM),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),and Multilayer Perceptron(MLP).PLS,DELM,and Linear Regression(LR)were used as meta-learner candidates.Employing integer coding technology,systematic dynamic combinations of base learners and meta-learners were generated,resulting in a total of 40 non-repetitive fusion models.The optimal combination was selected through a comprehensive evaluation based on multiple assessment indicators.[Results]The combination"PLS-DELM-MLP-LR"(code 1367)achieved coefficients of determination of 0.9732 and 0.9780 on the validation set and independent test set,respectively,with relative root mean square errors of 2.35%and 2.36%,and residual predictive deviations of 6.1075 and 6.7479,respectively.[Conclusions]The Stacking fusion model significantly enhances the predictive accuracy and robustness of spectral quantitative analysis,providing an efficient and feasible solution for modeling complex agricultural product spectral data.展开更多
To address the issue of incorrect fusion results caused by conflicting evidence due to inaccurate evidence and incomplete recognition frameworks in radar airborne target tactical intention recognition,a spatiotemporal...To address the issue of incorrect fusion results caused by conflicting evidence due to inaccurate evidence and incomplete recognition frameworks in radar airborne target tactical intention recognition,a spatiotemporal evidence fusion algorithm is proposed.To resolve the conflict evidence fusion problem caused by inaccurate evidence,the algorithm performs discounting of evidence from both spatial and temporal dimensions.Spatial discounting is influenced by both inter-evidence inconsistency and intra-evidence inconsistency,while temporal discounting is determined by time intervals and information entropy.For the problem of conflicting evidence fusion due to an incomplete recognition framework,an open recognition architecture based on dynamic composite focal elements is proposed.This approach allocates some conflicting information to temporary composite focal elements,avoiding excessive basic probability assignment(BPA)of the empty set after fusion,which can lead to deviations from the actual fusion results.Simulation experiments comparing various methods indicate that the proposed method can effectively improve target intention recognition accuracy and demonstrates good stability.展开更多
基金supported by the National Natural Science Foundation of China(No.62276204)the Fundamental Research Funds for the Central Universities,China(No.YJSJ24011)+1 种基金the Natural Science Basic Research Program of Shaanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)the China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470)。
文摘Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.
基金Shaanxi Province Qin Chuangyuan“Scientist+Engineer”Team Construction Project(2022KXJ-071)2022 Qin Chuangyuan Achievement Transformation Incubation Capacity Improvement Project(2022JH-ZHFHTS-0012)+8 种基金Shaanxi Province Key Research and Development Plan-“Two Chains”Integration Key Project-Qin Chuangyuan General Window Industrial Cluster Project(2023QCY-LL-02)Xixian New Area Science and Technology Plan(2022-YXYJ-003,2022-XXCY-010)2024 Scientific Research Project of Shaanxi National Defense Industry Vocational and Technical College(Gfy24-07)Shaanxi Vocational and Technical Education Association 2024 Vocational Education Teaching Reform Research Topic(2024SZX354)National Natural Science Foundation of China(U24A20115)2024 Shaanxi Provincial Education Department Service Local Special Scientific Research Program Project-Industrialization Cultivation Project(24JC005,24JC063)Shaanxi Province“14th Five-Year Plan”Education Science Plan,2024 Project(SGH24Y3181)National Key Research and Development Program of China(2023YFB4606400)Longmen Laboratory Frontier Exploration Topics Project(LMQYTSKT003)。
文摘A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5wt%Er-1wt%TiB_(2)/Al-Mn-Mg-Sc-Zr nanocomposite were prepared using vacuum homogenization technique,and the density of samples prepared through the LPBF process reached 99.8%.The strengthening and toughening mechanisms of Er-TiB_(2)were investigated.The results show that Al_(3)Er diffraction peaks are detected by X-ray diffraction analysis,and texture strength decreases according to electron backscatter diffraction results.The added Er and TiB_(2)nano-reinforcing phases act as heterogeneous nucleation sites during the LPBF forming process,hindering grain growth and effectively refining the grains.After incorporating the Er-TiB_(2)dual-phase nano-reinforcing phases,the tensile strength and elongation at break of the LPBF-deposited samples reach 550 MPa and 18.7%,which are 13.4%and 26.4%higher than those of the matrix material,respectively.
基金supported by the National Natural Science Foundation of China(Nos.12405288,12374241,12474484,U2330401,12088101)the Natural Science Foundation of Top Talent of SZTU(No.GDRC202526)。
文摘The process of nuclear fusion in the presence of a laser field was theoretically analyzed.The analysis is applicable to most fusion reactions and different types of currently available intense lasers,from X-ray free-electron lasers to solid-state near-infrared lasers.Laser fields were shown to enhance the fusion yields,and the mechanism of this enhancement was explained.Low-frequency lasers are more efficient in enhancing fusion than high-frequency lasers.The calculation results show enhancements of fusion yields by orders of magnitude with currently available intense low-frequency laser fields.The temperature requirement for controlled nuclear fusion may be reduced with the aid of intense laser fields.
基金funded by the Jilin Provincial Department of Science and Technology,grant number 20230101208JC.
文摘Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collected vibration signals,single-modal methods struggle to capture fault features fully.This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion.The method first employs the Hippopotamus Optimization Algorithm(HO)to optimize the number of modes in Variational Mode Decomposition(VMD)to achieve optimal modal decomposition performance.It combines Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)to extract temporal features from one-dimensional time-series signals.Meanwhile,the Markovian Transition Field(MTF)is used to transform one-dimensional signals into two-dimensional images for spatial feature mining.Through visualization techniques,the effectiveness of generated images from different parameter combinations is compared to determine the optimal parameter configuration.A multi-modal network(GSTCN)is constructed by integrating Swin-Transformer and the Convolutional Block Attention Module(CBAM),where the attention module is utilized to enhance fault features.Finally,the fault features extracted from different modalities are deeply fused and fed into a fully connected layer to complete fault classification.Experimental results show that the GSTCN model achieves an average diagnostic accuracy of 99.5%across three datasets,significantly outperforming existing comparison methods.This demonstrates that the proposed model has high diagnostic precision and good generalization ability,providing an efficient and reliable solution for rolling bearing fault diagnosis.
基金supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2025/03/32440).
文摘Parkinson’s disease remains a major clinical issue in terms of early detection,especially during its prodromal stage when symptoms are not evident or not distinct.To address this problem,we proposed a new deep learning 2-based approach for detecting Parkinson’s disease before any of the overt symptoms develop during their prodromal stage.We used 5 publicly accessible datasets,including UCI Parkinson’s Voice,Spiral Drawings,PaHaW,NewHandPD,and PPMI,and implemented a dual stream CNN–BiLSTM architecture with Fisher-weighted feature merging and SHAP-based explanation.The findings reveal that the model’s performance was superior and achieved 98.2%,a F1-score of 0.981,and AUC of 0.991 on the UCI Voice dataset.The model’s performance on the remaining datasets was also comparable,with up to a 2–7 percent betterment in accuracy compared to existing strong models such as CNN–RNN–MLP,ILN–GNet,and CASENet.Across the evidence,the findings back the diagnostic promise of micro-tremor assessment and demonstrate that combining temporal and spatial features with a scatter-based segment for a multi-modal approach can be an effective and scalable platform for an“early,”interpretable PD screening system.
基金supported in part by the National Natural Science Foundation of China(No.12305196)Anhui Provincial Natural Science Foundation(No.2308085QA23)+1 种基金Open Fund of Magnetic confinement Fusion Laboratory of Anhui Province(No.2023AMF03003)Science Foundation of Institute of Plasma Physics,Chinese Academy of Sciences(No.DSJJ-2024-10).
文摘A low-temperature-resistant and high-strength stainless-steel jacket is a key component in the superconducting magnet of a fusion reactor.The development of cryogenic structural materials with high strength and toughness poses a challenge for the future development of high-field superconducting magnets in fusion reactors.The yield strength of the International Thermonuclear Experimental Reactor developed for low-temperature structural materials at 4.2K is below 1100MPa,which fails to meet the demand for structural components with yield strengths exceeding 1500MPa at 4.2K in the future fusion reactors.CHSN01(formerly N50H),which is a low-temperature structural material developed in China,exhibits exceptional strength and toughness,thereby making it highly promising for practical applications.Recently,a 30 t jacket measuring approximately 5000m in total length was produced.Its low-temperature mechanical properties were tested using a sampling method to ensure compliance with application requirements.This paper presents the experimental data of the CHSN01 jacket and tests of the physical properties of the material in the temperature range of 4–300 K.The physical properties were unaffected by magnetic field.Furthermore,this paper discusses the feasibility of employing CHSN01 as a cryogenic structural material capable of withstanding high magnetic fields in next-generation fusion reactors.
基金supported by the National Natural Science Foundation of China(Grant Nos.62572057,62272049,U24A20331)Beijing Natural Science Foundation(Grant Nos.4232026,4242020)Academic Research Projects of Beijing Union University(Grant No.ZK10202404).
文摘Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.
文摘AIM:To investigate the effects of binocular fusional C-optotypes(positive/negative)and 2D planar C-optotypes on the amplitude and stability of transient accommodation(TAC)in adults,and to provide a basis for non-contact myopia intervention.METHODS:This was a self-controlled study.Using redblue 3D technology,four experimental stages were set up:Test A[fixating on the 1 m negative fusional C-optotypes,8△base-in(BI)],Test B(fixating on the 5 m planar C-optotypes),Test C(fixating on the 1 m planar C-optotypes),and Test D[fixating on the 1 m positive fusional C-optotypes,20△base-out(BO)].A WAM-5500 open-field autorefractor was used to measure TAC and accommodative microfluctuations[evaluated via interquartile range(IQR)and median-based coefficient of variation(CVmed)].Additionally,the convergence accommodation to convergence(CA/C)ratio was calculated,and a visual fatigue questionnaire was administered to assess participants’subjective visual comfort.RESULTS:A total of 21 subjects(7 males,14 females;aged 23-41y)with normal binocular visual function were enrolled.The results showed that the TAC increased gradually across the four stages,and these values were Test A(-0.35±0.26 D)<Test B(-0.46±0.24 D)<Test C(-0.77±0.32 D)<Test D(-1.38±0.31 D).There were significant overall differences(F=56.136,P<0.001).Compared with Test C,Test A reduced TAC by 0.42 D(P<0.05),while Test D increased it by 0.61 D(P<0.001).There was no significant intergroup difference in accommodative fluctuation amplitude(all P>0.05),but the fluctuation stability of Test D showed a significant difference between the first 20s and the second 20s(P=0.017).The CA/C ratio was significantly higher in Test D(0.05±0.02 D/△)than in Test A(0.03±0.02 D/△,P=0.007),indicating stronger accommodation-convergence linkage during positive fusional fixation.The visual fatigue scores of all stages were low(median 0-1),with Test D slightly higher than Test B and Test C(P<0.05).No linear correlation was found between TAC and age(all r<0.1,P>0.05).CONCLUSION:Negative fusional C-optotypes induce ciliary muscle relaxation to reduce TAC,while positive fusional C-optotypes enhance accommodation-convergence coordination to increase TAC.The red-blue 3D-based noncontact training mode exhibits good safety(median visual fatigue scores:0-1 across all tests)and provides a novel dual-directional(relaxation-activation)strategy for myopia prevention and control.
基金financially supported byChongqingUniversity of Technology Graduate Innovation Foundation(Grant No.gzlcx20253267).
文摘Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves F_(β)by 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in F_(β).OnCAMO and CHAMELEON,it obtains 4.5-point increases in F_(β),respectively.These consistent gains substantiate the superiority and robustness of EGMFNet.
文摘In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.
基金funded by the National Natural Science Foundation of China under Grant 62306128the Leading Innovation Project of Changzhou Science and Technology Bureau underGrant CQ20230072+2 种基金the Basic Science Research Project of Jiangsu Provincial Department of Education under Grant 23KJD520003the Science and Technology Development Plan Project of Jilin Provinceunder Grant 20240101382JCthe National KeyR esearch and Development Program of China under Grant 2023YFF1105102.
文摘In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although intelligent rescue robots can enter hazardous environments in place of humans,smoke poses major challenges for human detection algorithms.These challenges include the attenuation of visible and infrared signals,complex thermal fields,and interference frombackground objects,all ofwhichmake it difficult to accurately identify trapped individuals.To address this problem,we propose VIF-YOLO,a visible–infrared fusion model for real-time human detection in dense smoke environments.The framework introduces a lightweight multimodal fusion(LMF)module based on learnable low-rank representation blocks to end-to-end integrate visible and infrared images,preserving fine details while enhancing salient features.In addition,an efficient multiscale attention(EMA)mechanism is incorporated into the YOLOv10n backbone to improve feature representation under low-light conditions.Extensive experiments on our newly constructedmultimodal smoke human detection(MSHD)dataset demonstrate thatVIF-YOLOachievesmAP50 of 99.5%,precision of 99.2%,and recall of 99.3%,outperforming YOLOv10n by a clear margin.Furthermore,when deployed on the NVIDIA Jetson Xavier NX,VIF-YOLO attains 40.6 FPS with an average inference latency of 24.6 ms,validating its real-time capability on edge-computing platforms.These results confirm that VIF-YOLO provides accurate,robust,and fast detection across complex backgrounds and diverse smoke conditions,ensuring reliable and rapid localization of individuals in need of rescue.
基金supported by the Hunan Provincial Natural Science Foundation of China(Grant No.2025JJ30015)the National Natural Science Foundation of China(Grant Nos.52571276,52275395,U24A20120,52475362)+4 种基金the Science and Technology Innovation Program of Hunan Province(Grant No.2023RC3046)the National Key Research and Development Program of China(Grant No.2023YFB4605800)the Central South University Innovation-driven Research Programme(Grant No.2023CXQD023)the Jiangxi Provincial Natural Science Foundation of China(Grant No.20224ACB204013)the Project of State Key Laboratory of Precision Manufacturing for Extreme Service Performance of Central South University and the Fundamental Research Funds for the Central Universities of Central South University(Grant No.1053320230182)。
文摘Fe-Ga-based alloys are considered promising magnetostrictive candidates because of their high permeability and favorable mechanical properties.However,currently developed Fe-Ga-based alloys often suffer from a limited capability for microstructure manipulation,which restricts their magnetostrictive performance.To address this limitation,this study proposes a novel strategy combining laser-beam powder bed fusion(LPBF)and aging treatment to modulate the microstructure and enhance magnetostrictive properties of Fe-Ga-B alloys.Considering the positive influence of B element on magnetostrictive property and ductility,B-doped magnetostrictive Fe-Ga alloys were prepared via the LPBF process and then aged at 600℃for varying times(1,2,and 3 h,respectively).The LPBF process,characterized by high thermal gradients and rapid solidification,produced a microstructure featuring<001>oriented grains and sparse m-D0_(3)nanoprecipitates embedded in an A2 matrix.After the aging treatment,sufficient nucleation and growth of nanoprecipitates were enhanced.Specifically,the sample aged for 2 h developed a high density of larger m-D0_(3)nanoprecipitates.This optimized microstructure yielded a high magnetostrictive strain of(109±12)ppm and a substantially reduced saturation field—decreased by~49.1%compared to the as-fabricated state—primarily due to the synergistic effect of the<001>texture and the dense nanoprecipitates.Moreover,all the prepared alloys exhibited good soft-magnetic characteristics and comparable mechanical properties.Therefore,the combination of LPBF and aging treatment offers a promising route for tailoring the macro/microstructure and performance of magnetostrictive Fe-Ga alloys for diverse applications.
基金supported by the China Three Gorges Corporation(No.NBZZ202300860)the National Natural Science Foundation of China(No.52275104)the Science and Technology Innovation Program of Hunan Province(No.2023RC3097).
文摘Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operation masks fault features,severely impairing the fault diagnosis performance of deep learning models.Second,current blade fault diagnosis often relies on single-sensor data,resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states.To address these issues,a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed.Specifically,a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions,while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information.Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input,enabling comprehensive and effective fault diagnosis.
基金supported by the National Natural Science Foundation of China(No.41971339)the SDUST Research Fund(No.2019TDJH103)。
文摘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.
文摘[Objectives]This study was conducted to achieve rapid and accurate detection of protein content in rice with a particle size of 1.0 mm.[Methods]A multi-model fusion strategy was proposed on the basis of Stacking ensemble learning.A base learner pool was constructed,containing Partial Least Squares(PLS),Support Vector Machine(SVM),Deep Extreme Learning Machine(DELM),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),and Multilayer Perceptron(MLP).PLS,DELM,and Linear Regression(LR)were used as meta-learner candidates.Employing integer coding technology,systematic dynamic combinations of base learners and meta-learners were generated,resulting in a total of 40 non-repetitive fusion models.The optimal combination was selected through a comprehensive evaluation based on multiple assessment indicators.[Results]The combination"PLS-DELM-MLP-LR"(code 1367)achieved coefficients of determination of 0.9732 and 0.9780 on the validation set and independent test set,respectively,with relative root mean square errors of 2.35%and 2.36%,and residual predictive deviations of 6.1075 and 6.7479,respectively.[Conclusions]The Stacking fusion model significantly enhances the predictive accuracy and robustness of spectral quantitative analysis,providing an efficient and feasible solution for modeling complex agricultural product spectral data.
基金supported by the Key Research and Development Program of Shaanxi Province(2023-GHZD-33)the Open Project of the State Key Laboratory of Intelligent Game(ZBKF-23-05)the National Nature Science Foundation of China(62003267)。
文摘To address the issue of incorrect fusion results caused by conflicting evidence due to inaccurate evidence and incomplete recognition frameworks in radar airborne target tactical intention recognition,a spatiotemporal evidence fusion algorithm is proposed.To resolve the conflict evidence fusion problem caused by inaccurate evidence,the algorithm performs discounting of evidence from both spatial and temporal dimensions.Spatial discounting is influenced by both inter-evidence inconsistency and intra-evidence inconsistency,while temporal discounting is determined by time intervals and information entropy.For the problem of conflicting evidence fusion due to an incomplete recognition framework,an open recognition architecture based on dynamic composite focal elements is proposed.This approach allocates some conflicting information to temporary composite focal elements,avoiding excessive basic probability assignment(BPA)of the empty set after fusion,which can lead to deviations from the actual fusion results.Simulation experiments comparing various methods indicate that the proposed method can effectively improve target intention recognition accuracy and demonstrates good stability.
