Efficient Convolution Operator(ECO)algorithms have achieved impressive performances in visual tracking.However,its feature extraction network of ECO is unconducive for capturing the correlation features of occluded an...Efficient Convolution Operator(ECO)algorithms have achieved impressive performances in visual tracking.However,its feature extraction network of ECO is unconducive for capturing the correlation features of occluded and blurred targets between long-range complex scene frames.More so,its fixed weight fusion strategy does not use the complementary properties of deep and shallow features.In this paper,we propose a new target tracking method,namely ECO++,using deep feature adaptive fusion in a complex scene,in the following two aspects:First,we constructed a new temporal convolution mode and used it to replace the underlying convolution layer in Conformer network to obtain an improved Conformer network.Second,we adaptively fuse the deep features,which output through the improved Conformer network,by combining the Peak to Sidelobe Ratio(PSR),frame smoothness scores and adaptive adjustment weight.Extensive experiments on the OTB-2013,OTB-2015,UAV123,and VOT2019 benchmarks demonstrate that the proposed approach outperforms the state-of-the-art algorithms in tracking accuracy and robustness in complex scenes with occluded,blurred,and fast-moving targets.展开更多
Unmanned aerial vehicle(UAV)imagery poses significant challenges for object detection due to extreme scale variations,high-density small targets(68%in VisDrone dataset),and complex backgrounds.While YOLO-series models...Unmanned aerial vehicle(UAV)imagery poses significant challenges for object detection due to extreme scale variations,high-density small targets(68%in VisDrone dataset),and complex backgrounds.While YOLO-series models achieve speed-accuracy trade-offs via fixed convolution kernels and manual feature fusion,their rigid architectures struggle with multi-scale adaptability,as exemplified by YOLOv8n’s 36.4%mAP and 13.9%small-object AP on VisDrone2019.This paper presents YOLO-LE,a lightweight framework addressing these limitations through three novel designs:(1)We introduce the C2f-Dy and LDown modules to enhance the backbone’s sensitivity to small-object features while reducing backbone parameters,thereby improving model efficiency.(2)An adaptive feature fusion module is designed to dynamically integrate multi-scale feature maps,optimizing the neck structure,reducing neck complexity,and enhancing overall model performance.(3)We replace the original loss function with a distributed focal loss and incorporate a lightweight self-attention mechanism to improve small-object recognition and bounding box regression accuracy.Experimental results demonstrate that YOLO-LE achieves 39.9%mAP@0.5 on VisDrone2019,representing a 9.6%improvement over YOLOv8n,while maintaining 8.5 GFLOPs computational efficiency.This provides an efficient solution for UAV object detection in complex scenarios.展开更多
Landslide susceptibility evaluation plays an important role in disaster prevention and reduction.Feature-based transfer learning(TL)is an effective method for solving landslide susceptibility mapping(LSM)in target reg...Landslide susceptibility evaluation plays an important role in disaster prevention and reduction.Feature-based transfer learning(TL)is an effective method for solving landslide susceptibility mapping(LSM)in target regions with no available samples.However,as the study area expands,the distribution of land-slide types and triggering mechanisms becomes more diverse,leading to performance degradation in models relying on landslide evaluation knowledge from a single source domain due to domain feature shift.To address this,this study proposes a Multi-source Domain Adaptation Convolutional Neural Network(MDACNN),which combines the landslide prediction knowledge learned from two source domains to perform cross-regional LSM in complex large-scale areas.The method is validated through case studies in three regions located in southeastern coastal China and compared with single-source domain TL models(TCA-based models).The results demonstrate that MDACNN effectively integrates transfer knowledge from multiple source domains to learn diverse landslide-triggering mechanisms,thereby significantly reducing prediction bias inherent to single-source domain TL models,achieving an average improvement of 16.58%across all metrics.Moreover,the landslide susceptibility maps gener-ated by MDACNN accurately quantify the spatial distribution of landslide risks in the target area,provid-ing a powerful scientific and technological tool for landslide disaster management and prevention.展开更多
For measurement of component content in the extraction and separation process of praseodymium/neodymium(Pr/Nd), a soft measurement method was proposed based on modeling of ion color features, which is suitable for fas...For measurement of component content in the extraction and separation process of praseodymium/neodymium(Pr/Nd), a soft measurement method was proposed based on modeling of ion color features, which is suitable for fast estimation of component content in production field. Feature analysis on images of the solution is conducted,which are captured from Pr/Nd extraction/separation field. H/S components in the HSI color space are selected as model inputs, so as to establish the least squares support vector machine(LSSVM) model for Nd(Pr) content,while the model parameters are determined with the GA algorithm. To improve the adaptability of the model,the adaptive iteration algorithm is used to correct parameters of the LSSVM model, on the basis of model correction strategy and new sample data. Using the field data collected from rare earth extraction production, predictive methods for component content and comparisons are given. The results indicate that the proposed method presents good adaptability and high prediction precision, so it is applicable to the fast detection of element content in the rare earth extraction.展开更多
The deployment of vehicle micro-motors has witnessed an expansion owing to the progression in electrification and intelligent technologies.However,some micro-motors may exhibit design deficiencies,component wear,assem...The deployment of vehicle micro-motors has witnessed an expansion owing to the progression in electrification and intelligent technologies.However,some micro-motors may exhibit design deficiencies,component wear,assembly errors,and other imperfections that may arise during the design or manufacturing phases.Conse-quently,these micro-motors might generate anomalous noises during their operation,consequently exerting a substantial adverse influence on the overall comfort of drivers and passengers.Automobile micro-motors exhibit a diverse array of structural variations,consequently leading to the manifestation of a multitude of distinctive auditory irregularities.To address the identification of diverse forms of abnormal noise,this research presents a novel approach rooted in the utilization of vibro-acoustic fusion-convolutional neural network(VAF-CNN).This method entails the deployment of distinct network branches,each serving to capture disparate features from the multi-sensor data,all the while considering the auditory perception traits inherent in the human auditory sys-tem.The intermediary layer integrates the concept of adaptive weighting of multi-sensor features,thus affording a calibration mechanism for the features hailing from multiple sensors,thereby enabling a further refinement of features within the branch network.For optimal model efficacy,a feature fusion mechanism is implemented in the concluding layer.To substantiate the efficacy of the proposed approach,this paper initially employs an augmented data methodology inspired by modified SpecAugment,applied to the dataset of abnormal noise sam-ples,encompassing scenarios both with and without in-vehicle interior noise.This serves to mitigate the issue of limited sample availability.Subsequent comparative evaluations are executed,contrasting the performance of the model founded upon single-sensor data against other feature fusion models reliant on multi-sensor data.The experimental results substantiate that the suggested methodology yields heightened recognition accuracy and greater resilience against interference.Moreover,it holds notable practical significance in the engineering domain,as it furnishes valuable support for the targeted management of noise emanating from vehicle micro-motors.展开更多
In order to provide a guidance to specify the element size dynamically during adaptive finite element mesh generation, adaptive criteria are firstly defined according to the relationships between the geometrical featu...In order to provide a guidance to specify the element size dynamically during adaptive finite element mesh generation, adaptive criteria are firstly defined according to the relationships between the geometrical features and the elements of 3D solid. Various modes based on different datum geometrical elements, such as vertex, curve, surface, and so on, are then designed for generating local refined mesh. With the guidance of the defmed criteria, different modes are automatically selected to apply on the appropriate datum objects to program the element size in the local special areas. As a result, the control information of element size is successfully programmed covering the entire domain based on the geometrical features of 3D solid. A new algorithm based on Delatmay triangulation is then developed for generating 3D adaptive finite element mesh, in which the element size is dynamically specified to catch the geometrical features and suitable tetrahedron facets are selected to locate interior nodes continuously. As a result, adaptive mesh with good-quality elements is generated. Examples show that the proposed method can be successfully applied to adaptive finite element mesh automatic generation based on the geometrical features of 3D solid.展开更多
Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia ar...Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia are analyzed and classified.Model of adaptive entropy weight for clustering is built.Wind power prediction model based on adaptive entropy fuzzy clustering feature weights is built.Simulation results show that the proposed method could distinguish the abnormal data and forecast more accurately and compute fastly.展开更多
The current 3D CAD/CAM system, both research prototypes and commercial systems, based on traditional feature modeling are always hampered by the problems in their complicated modeling and difficult maintaining. This p...The current 3D CAD/CAM system, both research prototypes and commercial systems, based on traditional feature modeling are always hampered by the problems in their complicated modeling and difficult maintaining. This paper introduces a new method for modeling parts by using adaptability feature (AF), by which the consistent relationship among parts and assemblies can be maintained in whole design process. In addition, the design process, can be speeded, time-to-market shortened, and product quality improved. Some essential issues of the strategy are discussed. A system, KMCAD3D, by taking advantages of AF has been developed. It is shown that the method discussed is a feasible and effective way to improve current feature modeling technology.展开更多
Data collected in fields such as cybersecurity and biomedicine often encounter high dimensionality and class imbalance.To address the problem of low classification accuracy for minority class samples arising from nume...Data collected in fields such as cybersecurity and biomedicine often encounter high dimensionality and class imbalance.To address the problem of low classification accuracy for minority class samples arising from numerous irrelevant and redundant features in high-dimensional imbalanced data,we proposed a novel feature selection method named AMF-SGSK based on adaptive multi-filter and subspace-based gaining sharing knowledge.Firstly,the balanced dataset was obtained by random under-sampling.Secondly,combining the feature importance score with the AUC score for each filter method,we proposed a concept called feature hardness to judge the importance of feature,which could adaptively select the essential features.Finally,the optimal feature subset was obtained by gaining sharing knowledge in multiple subspaces.This approach effectively achieved dimensionality reduction for high-dimensional imbalanced data.The experiment results on 30 benchmark imbalanced datasets showed that AMF-SGSK performed better than other eight commonly used algorithms including BGWO and IG-SSO in terms of F1-score,AUC,and G-mean.The mean values of F1-score,AUC,and Gmean for AMF-SGSK are 0.950,0.967,and 0.965,respectively,achieving the highest among all algorithms.And the mean value of Gmean is higher than those of IG-PSO,ReliefF-GWO,and BGOA by 3.72%,11.12%,and 20.06%,respectively.Furthermore,the selected feature ratio is below 0.01 across the selected ten datasets,further demonstrating the proposed method’s overall superiority over competing approaches.AMF-SGSK could adaptively remove irrelevant and redundant features and effectively improve the classification accuracy of high-dimensional imbalanced data,providing scientific and technological references for practical applications.展开更多
A robust and eficient feature matching method is necessary for visual navigation in asteroid-landing missions.Based on the visual navigation framework and motion characteristics of asteroids,a robust and efficient tem...A robust and eficient feature matching method is necessary for visual navigation in asteroid-landing missions.Based on the visual navigation framework and motion characteristics of asteroids,a robust and efficient template feature matching method is proposed to adapt to feature distortion and scale change cases for visual navigation of asteroids.The proposed method is primarily based on a motion-constrained discriminative correlation filter(DCF).The prior information provided by the motion constraints between sequence images is used to provide a predicted search region for template feature matching.Additionally,some specific template feature samples are generated using the motion constraints for correlation filter learning,which is beneficial for training a scale and feature distortion adaptive correlation filter for accurate feature matching.Moreover,average peak-to-correlation energy(APCE)and jointly consistent measurements(JCMs)were used to eliminate false matching.Images captured by the Touch And Go Camera System(TAGCAMS)of the Bennu asteroid were used to evaluate the performance of the proposed method.In particular,both the robustness and accuracy of region matching and template center matching are evaluated.The qualitative and quantitative results illustrate the advancement of the proposed method in adapting to feature distortions and large-scale changes during spacecraft landing.展开更多
Entity linking(EL)is a fundamental task in natural language processing.Based on neural networks,existing systems pay more attention to the construction of the global model,but ignore latent semantic information in the...Entity linking(EL)is a fundamental task in natural language processing.Based on neural networks,existing systems pay more attention to the construction of the global model,but ignore latent semantic information in the local model and the acquisition of effective entity type information.In this paper,we propose two adaptive features,in which the first adaptive feature enables the local and global models to capture latent information,and the second adaptive feature describes effective information for entity type embeddings.These adaptive features can work together naturally to handle some uncertain entity type information for EL.Experimental results demonstrate that our EL system achieves the best performance on the AIDA-B and MSNBC datasets,and the best average performance on out-domain datasets.These results indicate that the proposed adaptive features,which are based on their own diverse contexts,can capture information that is conducive for EL.展开更多
Efficient and precise localization is a prerequisite for the intelligent navigation of mobile robots. Traditional visual localization systems, such as visual odometry (VO) and simultaneous localization and mapping (SL...Efficient and precise localization is a prerequisite for the intelligent navigation of mobile robots. Traditional visual localization systems, such as visual odometry (VO) and simultaneous localization and mapping (SLAM), suffer from two shortcomings: a drift problem caused by accumulated localization error, and erroneous motion estimation due to illumination variation and moving objects. In this paper, we propose an enhanced VO by introducing a panoramic camera into the traditional stereo-only VO system. Benefiting from the 360° field of view, the panoramic camera is responsible for three tasks: (1) detect- ing road junctions and building a landmark library online; (2) correcting the robot's position when the landmarks are revisited with any orientation; (3) working as a panoramic compass when the stereo VO cannot provide reliable positioning results. To use the large-sized panoramic images efficiently, the concept of compressed sensing is introduced into the solution and an adap- tive compressive feature is presented. Combined with our previous two-stage local binocular bundle adjustment (TLBBA) stereo VO, the new system can obtain reliable positioning results in quasi-real time. Experimental results of challenging long-range tests show that our enhanced VO is much more accurate and robust than the traditional VO, thanks to the compressive panoramic landmarks built online.展开更多
基金supported by the National Key R&D Plan"Intelligent Robots"Key Project of P.R.China(Grant No.2018YFB1308602)the National Natural Science Foundation of P.R.China(Grant No.61173184)+3 种基金the Chongqing Natural Science Foundation of P.R.China(Grant No.cstc2018jcyj AX0694)Research Project of Chongqing Big Data Application and Development Administration Bureau(No.22-30)Basic and Advanced Research Projects of CSTC(No.cstc2019jcyj-zdxmX0008)the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJZD-K201900605)。
文摘Efficient Convolution Operator(ECO)algorithms have achieved impressive performances in visual tracking.However,its feature extraction network of ECO is unconducive for capturing the correlation features of occluded and blurred targets between long-range complex scene frames.More so,its fixed weight fusion strategy does not use the complementary properties of deep and shallow features.In this paper,we propose a new target tracking method,namely ECO++,using deep feature adaptive fusion in a complex scene,in the following two aspects:First,we constructed a new temporal convolution mode and used it to replace the underlying convolution layer in Conformer network to obtain an improved Conformer network.Second,we adaptively fuse the deep features,which output through the improved Conformer network,by combining the Peak to Sidelobe Ratio(PSR),frame smoothness scores and adaptive adjustment weight.Extensive experiments on the OTB-2013,OTB-2015,UAV123,and VOT2019 benchmarks demonstrate that the proposed approach outperforms the state-of-the-art algorithms in tracking accuracy and robustness in complex scenes with occluded,blurred,and fast-moving targets.
文摘Unmanned aerial vehicle(UAV)imagery poses significant challenges for object detection due to extreme scale variations,high-density small targets(68%in VisDrone dataset),and complex backgrounds.While YOLO-series models achieve speed-accuracy trade-offs via fixed convolution kernels and manual feature fusion,their rigid architectures struggle with multi-scale adaptability,as exemplified by YOLOv8n’s 36.4%mAP and 13.9%small-object AP on VisDrone2019.This paper presents YOLO-LE,a lightweight framework addressing these limitations through three novel designs:(1)We introduce the C2f-Dy and LDown modules to enhance the backbone’s sensitivity to small-object features while reducing backbone parameters,thereby improving model efficiency.(2)An adaptive feature fusion module is designed to dynamically integrate multi-scale feature maps,optimizing the neck structure,reducing neck complexity,and enhancing overall model performance.(3)We replace the original loss function with a distributed focal loss and incorporate a lightweight self-attention mechanism to improve small-object recognition and bounding box regression accuracy.Experimental results demonstrate that YOLO-LE achieves 39.9%mAP@0.5 on VisDrone2019,representing a 9.6%improvement over YOLOv8n,while maintaining 8.5 GFLOPs computational efficiency.This provides an efficient solution for UAV object detection in complex scenarios.
基金the National Natural Science Foundation of China(Grant No.42301002,and 52109118)Fujian Provincial Water Resources Science and Technology Project(Grant No.MSK202524)Guidance fund for Science and Technology Program,Fujian province(Grant No.2024Y0002).
文摘Landslide susceptibility evaluation plays an important role in disaster prevention and reduction.Feature-based transfer learning(TL)is an effective method for solving landslide susceptibility mapping(LSM)in target regions with no available samples.However,as the study area expands,the distribution of land-slide types and triggering mechanisms becomes more diverse,leading to performance degradation in models relying on landslide evaluation knowledge from a single source domain due to domain feature shift.To address this,this study proposes a Multi-source Domain Adaptation Convolutional Neural Network(MDACNN),which combines the landslide prediction knowledge learned from two source domains to perform cross-regional LSM in complex large-scale areas.The method is validated through case studies in three regions located in southeastern coastal China and compared with single-source domain TL models(TCA-based models).The results demonstrate that MDACNN effectively integrates transfer knowledge from multiple source domains to learn diverse landslide-triggering mechanisms,thereby significantly reducing prediction bias inherent to single-source domain TL models,achieving an average improvement of 16.58%across all metrics.Moreover,the landslide susceptibility maps gener-ated by MDACNN accurately quantify the spatial distribution of landslide risks in the target area,provid-ing a powerful scientific and technological tool for landslide disaster management and prevention.
基金Supported by the National Natural Science Foundation of China(51174091,61364013,61164013)Earlier Research Project of the State Key Development Program for Basic Research of China(2014CB360502)
文摘For measurement of component content in the extraction and separation process of praseodymium/neodymium(Pr/Nd), a soft measurement method was proposed based on modeling of ion color features, which is suitable for fast estimation of component content in production field. Feature analysis on images of the solution is conducted,which are captured from Pr/Nd extraction/separation field. H/S components in the HSI color space are selected as model inputs, so as to establish the least squares support vector machine(LSSVM) model for Nd(Pr) content,while the model parameters are determined with the GA algorithm. To improve the adaptability of the model,the adaptive iteration algorithm is used to correct parameters of the LSSVM model, on the basis of model correction strategy and new sample data. Using the field data collected from rare earth extraction production, predictive methods for component content and comparisons are given. The results indicate that the proposed method presents good adaptability and high prediction precision, so it is applicable to the fast detection of element content in the rare earth extraction.
基金The author received the funding from Sichuan Natural Science Foundation(2022NSFSC1892).
文摘The deployment of vehicle micro-motors has witnessed an expansion owing to the progression in electrification and intelligent technologies.However,some micro-motors may exhibit design deficiencies,component wear,assembly errors,and other imperfections that may arise during the design or manufacturing phases.Conse-quently,these micro-motors might generate anomalous noises during their operation,consequently exerting a substantial adverse influence on the overall comfort of drivers and passengers.Automobile micro-motors exhibit a diverse array of structural variations,consequently leading to the manifestation of a multitude of distinctive auditory irregularities.To address the identification of diverse forms of abnormal noise,this research presents a novel approach rooted in the utilization of vibro-acoustic fusion-convolutional neural network(VAF-CNN).This method entails the deployment of distinct network branches,each serving to capture disparate features from the multi-sensor data,all the while considering the auditory perception traits inherent in the human auditory sys-tem.The intermediary layer integrates the concept of adaptive weighting of multi-sensor features,thus affording a calibration mechanism for the features hailing from multiple sensors,thereby enabling a further refinement of features within the branch network.For optimal model efficacy,a feature fusion mechanism is implemented in the concluding layer.To substantiate the efficacy of the proposed approach,this paper initially employs an augmented data methodology inspired by modified SpecAugment,applied to the dataset of abnormal noise sam-ples,encompassing scenarios both with and without in-vehicle interior noise.This serves to mitigate the issue of limited sample availability.Subsequent comparative evaluations are executed,contrasting the performance of the model founded upon single-sensor data against other feature fusion models reliant on multi-sensor data.The experimental results substantiate that the suggested methodology yields heightened recognition accuracy and greater resilience against interference.Moreover,it holds notable practical significance in the engineering domain,as it furnishes valuable support for the targeted management of noise emanating from vehicle micro-motors.
基金This project is supported by Provincial Project Foundation of Science and Technology of Guangdong, China(No.2002104040101).
文摘In order to provide a guidance to specify the element size dynamically during adaptive finite element mesh generation, adaptive criteria are firstly defined according to the relationships between the geometrical features and the elements of 3D solid. Various modes based on different datum geometrical elements, such as vertex, curve, surface, and so on, are then designed for generating local refined mesh. With the guidance of the defmed criteria, different modes are automatically selected to apply on the appropriate datum objects to program the element size in the local special areas. As a result, the control information of element size is successfully programmed covering the entire domain based on the geometrical features of 3D solid. A new algorithm based on Delatmay triangulation is then developed for generating 3D adaptive finite element mesh, in which the element size is dynamically specified to catch the geometrical features and suitable tetrahedron facets are selected to locate interior nodes continuously. As a result, adaptive mesh with good-quality elements is generated. Examples show that the proposed method can be successfully applied to adaptive finite element mesh automatic generation based on the geometrical features of 3D solid.
基金supported by the Natural Science Foundation of China under contact(61233007)
文摘Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia are analyzed and classified.Model of adaptive entropy weight for clustering is built.Wind power prediction model based on adaptive entropy fuzzy clustering feature weights is built.Simulation results show that the proposed method could distinguish the abnormal data and forecast more accurately and compute fastly.
文摘The current 3D CAD/CAM system, both research prototypes and commercial systems, based on traditional feature modeling are always hampered by the problems in their complicated modeling and difficult maintaining. This paper introduces a new method for modeling parts by using adaptability feature (AF), by which the consistent relationship among parts and assemblies can be maintained in whole design process. In addition, the design process, can be speeded, time-to-market shortened, and product quality improved. Some essential issues of the strategy are discussed. A system, KMCAD3D, by taking advantages of AF has been developed. It is shown that the method discussed is a feasible and effective way to improve current feature modeling technology.
基金supported by Fundamental Research Program of Shanxi Province(Nos.202203021211088,202403021212254,202403021221109)Graduate Research Innovation Project in Shanxi Province(No.2024KY616).
文摘Data collected in fields such as cybersecurity and biomedicine often encounter high dimensionality and class imbalance.To address the problem of low classification accuracy for minority class samples arising from numerous irrelevant and redundant features in high-dimensional imbalanced data,we proposed a novel feature selection method named AMF-SGSK based on adaptive multi-filter and subspace-based gaining sharing knowledge.Firstly,the balanced dataset was obtained by random under-sampling.Secondly,combining the feature importance score with the AUC score for each filter method,we proposed a concept called feature hardness to judge the importance of feature,which could adaptively select the essential features.Finally,the optimal feature subset was obtained by gaining sharing knowledge in multiple subspaces.This approach effectively achieved dimensionality reduction for high-dimensional imbalanced data.The experiment results on 30 benchmark imbalanced datasets showed that AMF-SGSK performed better than other eight commonly used algorithms including BGWO and IG-SSO in terms of F1-score,AUC,and G-mean.The mean values of F1-score,AUC,and Gmean for AMF-SGSK are 0.950,0.967,and 0.965,respectively,achieving the highest among all algorithms.And the mean value of Gmean is higher than those of IG-PSO,ReliefF-GWO,and BGOA by 3.72%,11.12%,and 20.06%,respectively.Furthermore,the selected feature ratio is below 0.01 across the selected ten datasets,further demonstrating the proposed method’s overall superiority over competing approaches.AMF-SGSK could adaptively remove irrelevant and redundant features and effectively improve the classification accuracy of high-dimensional imbalanced data,providing scientific and technological references for practical applications.
基金funded by the National Natural Science Foundation of China under Grant Nos.41822106 and 42101447the Dawn Scholar of Shanghai Program under Grant No.18SG22+2 种基金the Science and Technology on Aerospace Flight Dynamics Laboratory,China,under Grant No.KGJ6142210110305State Key Laboratory of Disaster Reduction in Civil Engineering under Grant No.SLDRCE19-B-35Fundamental Research Funds for the Central Universities of China.
文摘A robust and eficient feature matching method is necessary for visual navigation in asteroid-landing missions.Based on the visual navigation framework and motion characteristics of asteroids,a robust and efficient template feature matching method is proposed to adapt to feature distortion and scale change cases for visual navigation of asteroids.The proposed method is primarily based on a motion-constrained discriminative correlation filter(DCF).The prior information provided by the motion constraints between sequence images is used to provide a predicted search region for template feature matching.Additionally,some specific template feature samples are generated using the motion constraints for correlation filter learning,which is beneficial for training a scale and feature distortion adaptive correlation filter for accurate feature matching.Moreover,average peak-to-correlation energy(APCE)and jointly consistent measurements(JCMs)were used to eliminate false matching.Images captured by the Touch And Go Camera System(TAGCAMS)of the Bennu asteroid were used to evaluate the performance of the proposed method.In particular,both the robustness and accuracy of region matching and template center matching are evaluated.The qualitative and quantitative results illustrate the advancement of the proposed method in adapting to feature distortions and large-scale changes during spacecraft landing.
基金Project supported by the Key-Area Research and Development Program of Guangdong Province,China(No.2019B010153002)the Program of Marine Economy Development(Six Marine Industries)Special Foundation of Department of Natural Resources of Guangdong Province,China(No.GDNRC[2020]056)+2 种基金the National Natural Science Foundation of China(No.62002071)the Top Youth Talent Project of Zhujiang Talent Program,China(No.2019QN01X516)the Guangdong Provincial Key Laboratory of Cyber-Physical System,China(No.2020B1212060069)。
文摘Entity linking(EL)is a fundamental task in natural language processing.Based on neural networks,existing systems pay more attention to the construction of the global model,but ignore latent semantic information in the local model and the acquisition of effective entity type information.In this paper,we propose two adaptive features,in which the first adaptive feature enables the local and global models to capture latent information,and the second adaptive feature describes effective information for entity type embeddings.These adaptive features can work together naturally to handle some uncertain entity type information for EL.Experimental results demonstrate that our EL system achieves the best performance on the AIDA-B and MSNBC datasets,and the best average performance on out-domain datasets.These results indicate that the proposed adaptive features,which are based on their own diverse contexts,can capture information that is conducive for EL.
基金Project supported by the National Natural Science Foundation of China (Nos. 61071219 and 90820306) and the Fundamental Research Funds for the Central Universities, China
文摘Efficient and precise localization is a prerequisite for the intelligent navigation of mobile robots. Traditional visual localization systems, such as visual odometry (VO) and simultaneous localization and mapping (SLAM), suffer from two shortcomings: a drift problem caused by accumulated localization error, and erroneous motion estimation due to illumination variation and moving objects. In this paper, we propose an enhanced VO by introducing a panoramic camera into the traditional stereo-only VO system. Benefiting from the 360° field of view, the panoramic camera is responsible for three tasks: (1) detect- ing road junctions and building a landmark library online; (2) correcting the robot's position when the landmarks are revisited with any orientation; (3) working as a panoramic compass when the stereo VO cannot provide reliable positioning results. To use the large-sized panoramic images efficiently, the concept of compressed sensing is introduced into the solution and an adap- tive compressive feature is presented. Combined with our previous two-stage local binocular bundle adjustment (TLBBA) stereo VO, the new system can obtain reliable positioning results in quasi-real time. Experimental results of challenging long-range tests show that our enhanced VO is much more accurate and robust than the traditional VO, thanks to the compressive panoramic landmarks built online.
