Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high ...Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.展开更多
Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation.However,this model requires the number of clus...Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation.However,this model requires the number of clusters to be set manually,resulting in a low automation degree due to the complexity of the iterative clustering process.To address this problem,a segmentation method based on a self-learning super-pixel network(SLSP-Net)and modified automatic fuzzy clustering(MAFC)is proposed.SLSP-Net performs feature extraction,non-iterative clustering,and gradient reconstruction.A lightweight feature embedder is adopted for feature extraction,thus expanding the receiving range and generating multi-scale features.Automatic matching is used for non-iterative clustering,and the overfitting of the network model is overcome by adaptively adjusting the gradient weight parameters,providing a better irregular super-pixel neighborhood structure.An optimized density peak algorithm is adopted for MAFC.Based on the obtained super-pixel image,this maximizes the robust decision-making interval,which enhances the automation of regional clustering.Finally,prior entropy fuzzy C-means clustering is applied to optimize the robust decision-making and obtain the final segmentation result.Experimental results show that the proposed model offers reduced experimental complexity and achieves good performance,realizing not only automatic image segmentation,but also good segmentation results.展开更多
In this work,we demonstrate aπ-phase-shifted tilted fiber Bragg grating(π-PSTFBG)-based sensor for measuring the refractive index(RI)of NaCl solutions,achieving a real-time and online measurement system by employing...In this work,we demonstrate aπ-phase-shifted tilted fiber Bragg grating(π-PSTFBG)-based sensor for measuring the refractive index(RI)of NaCl solutions,achieving a real-time and online measurement system by employing a densely connected convolutional neural network(D-CNN)model to demodulate the full spectrum.The proposedπ-PSTFBG sensor is prepared by using the advanced fiber grating inscription system based on a two-beam interferometry method,which could introduce deeper features of dip-splitting for all the lossy dips in the spectrum,giving the possibility of fully measuring the change of RI.This enhanced feature gives relatively higher prediction accuracy(R^(2) of 99.67%)using the well-trained D-CNN model compared with the results achieved by pure TFBG or that with a gold coating.As a further demonstration from a practical view,a prototype integrated with the proposed D-CNN algorithm is developed to conduct RI measurement of NaCl solutions in real time using aπ-PSTFBG-based RI sensor.The results show that the proposed real-time demodulation system is capable of measuring RI with an average error of 1.6×10^(-4)RIU in a short response time of<1 s.The demonstrated spectral demodulation approach powered by deep learning shows great potential in real-time analysis for chemical solutions and point-of-care medical testing based on RI changes,especially for the portable requirements.展开更多
In this paper,a new bolt fault diagnosis method is developed to solve the fault diagnosis problem of wind turbine flange bolts using one-dimensional depthwise separable convolutions.The main idea is to use a one-dimen...In this paper,a new bolt fault diagnosis method is developed to solve the fault diagnosis problem of wind turbine flange bolts using one-dimensional depthwise separable convolutions.The main idea is to use a one-dimensional convolutional neural network model to classify and identify the acoustic vibration signals of bolts,which represent different bolt damage states.Through the methods of knock test and modal simulation,it is concluded that the damage state of wind turbine flange bolt is related to the natural frequency distribution of acoustic vibration signal.It is found that the bolt damage state affects the modal shape of the structure,and then affects the natural frequency distribution of the bolt vibration signal.Therefore,the damage state can be obtained by identifying the natural frequency distribution of the bolt acoustic vibration signal.In the present one-dimensional depth-detachable convolutional neural network model,the one-dimensional vector is first convolved into multiple channels,and then each channel is separately learned by depth-detachable convolution,which can effectively improve the feature quality and the effect of data classification.From the perspective of the realization mechanism of convolution operation,the depthwise separable convolution operation has fewer parameters and faster computing speed,making it easier to build lightweight models and deploy them to mobile devices.展开更多
Mechanical properties consisting of the bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,etc.,are key factors in determining the practical applications of MAX phases.These mechanical properties are mainly ...Mechanical properties consisting of the bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,etc.,are key factors in determining the practical applications of MAX phases.These mechanical properties are mainly dependent on the strength of M–X and M–A bonds.In this study,a novel strategy based on the crystal graph convolution neural network(CGCNN)model has been successfully employed to tune these mechanical properties of Ti_(3)AlC_(2)-based MAX phases via the A-site substitution(Ti_(3)(Al1-xAx)C_(2)).The structure–property correlation between the A-site substitution and mechanical properties of Ti_(3)(Al1-xAx)C_(2)is established.The results show that the thermodynamic stability of Ti_(3)(Al1-xAx)C_(2)is enhanced with substitutions A=Ga,Si,Sn,Ge,Te,As,or Sb.The stiffness of Ti_(3)AlC_(2)increases with the substitution concentration of Si or As increasing,and the higher thermal shock resistance is closely associated with the substitution of Sn or Te.In addition,the plasticity of Ti_(3)AlC_(2)can be greatly improved when As,Sn,or Ge is used as a substitution.The findings and understandings demonstrated herein can provide universal guidance for the individual synthesis of high-performance MAX phases for various applications.展开更多
Link prediction is used to complete the knowledge graph.Convolu-tional neural network models are commonly used for link prediction tasks,but they only consider the direct relations between entity pairs,ignoring the se...Link prediction is used to complete the knowledge graph.Convolu-tional neural network models are commonly used for link prediction tasks,but they only consider the direct relations between entity pairs,ignoring the semantic information contained in the relation paths.In addition,the embedding dimension of the relation is generally larger than that of the entity in the ConvR model,which blocks the progress of downstream tasks.If we reduce the embedding dimension of the relation,the performance will be greatly degraded.This paper proposes a convolutional model PITri-R-ConvR based on triangular structure relational infer-ence.The model uses relational path inference to capture semantic information,while using a triangular structure to ensure the reliability and computational effi-ciency of relational inference.In addition,the decoder R-ConvR improves the initial embedding of the ConvR model,which solves the problems of the ConvR model and significantly improves the prediction performance.Finally,this paper conducts sufficient experiments in multiple datasets to verify the superiority of the model and the rationality of each module.展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.U1564201,61573171,61403172,51305167)China Postdoctoral Science Foundation(Grant Nos.2015T80511,2014M561592)+3 种基金Jiangsu Provincial Natural Science Foundation of China(Grant No.BK20140555)Six Talent Peaks Project of Jiangsu Province,China(Grant Nos.2015-JXQC-012,2014-DZXX-040)Jiangsu Postdoctoral Science Foundation,China(Grant No.1402097C)Jiangsu University Scientific Research Foundation for Senior Professionals,China(Grant No.14JDG028)
文摘Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.
基金funded by Scientific and Technological Innovation Team of Universities in Henan Province,grant number 22IRTSTHN008Innovative Research Team(in Philosophy and Social Science)in University of Henan Province grant number 2022-CXTD-02the National Natural Science Foundation of China,grant number 41371524.
文摘Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation.However,this model requires the number of clusters to be set manually,resulting in a low automation degree due to the complexity of the iterative clustering process.To address this problem,a segmentation method based on a self-learning super-pixel network(SLSP-Net)and modified automatic fuzzy clustering(MAFC)is proposed.SLSP-Net performs feature extraction,non-iterative clustering,and gradient reconstruction.A lightweight feature embedder is adopted for feature extraction,thus expanding the receiving range and generating multi-scale features.Automatic matching is used for non-iterative clustering,and the overfitting of the network model is overcome by adaptively adjusting the gradient weight parameters,providing a better irregular super-pixel neighborhood structure.An optimized density peak algorithm is adopted for MAFC.Based on the obtained super-pixel image,this maximizes the robust decision-making interval,which enhances the automation of regional clustering.Finally,prior entropy fuzzy C-means clustering is applied to optimize the robust decision-making and obtain the final segmentation result.Experimental results show that the proposed model offers reduced experimental complexity and achieves good performance,realizing not only automatic image segmentation,but also good segmentation results.
基金National Natural Science Foundation of China(62275277,U2001601)Guangdong Project(2021QN02X055)+3 种基金Guangdong ST Programme(2024B0101030001)Fundamental Research Funds for the Central Universities,Sun Yat-sen University(23lgbj007)Science and Technology Planning Project of Guangzhou(2024A04J9891)Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(SML2023SP231)。
文摘In this work,we demonstrate aπ-phase-shifted tilted fiber Bragg grating(π-PSTFBG)-based sensor for measuring the refractive index(RI)of NaCl solutions,achieving a real-time and online measurement system by employing a densely connected convolutional neural network(D-CNN)model to demodulate the full spectrum.The proposedπ-PSTFBG sensor is prepared by using the advanced fiber grating inscription system based on a two-beam interferometry method,which could introduce deeper features of dip-splitting for all the lossy dips in the spectrum,giving the possibility of fully measuring the change of RI.This enhanced feature gives relatively higher prediction accuracy(R^(2) of 99.67%)using the well-trained D-CNN model compared with the results achieved by pure TFBG or that with a gold coating.As a further demonstration from a practical view,a prototype integrated with the proposed D-CNN algorithm is developed to conduct RI measurement of NaCl solutions in real time using aπ-PSTFBG-based RI sensor.The results show that the proposed real-time demodulation system is capable of measuring RI with an average error of 1.6×10^(-4)RIU in a short response time of<1 s.The demonstrated spectral demodulation approach powered by deep learning shows great potential in real-time analysis for chemical solutions and point-of-care medical testing based on RI changes,especially for the portable requirements.
基金supported in part by the National Key R&D Program of China(Nos.2021YFE0206100 and 2018YFB1702300)the National Natural Science Foundation of China(No.62073321)+1 种基金the National Defense Basic Scientific Research Program(No.JCKY2019203C029)the Science and Technology Development Fund,Macao SAR(No.0015/2020/AMJ).
文摘In this paper,a new bolt fault diagnosis method is developed to solve the fault diagnosis problem of wind turbine flange bolts using one-dimensional depthwise separable convolutions.The main idea is to use a one-dimensional convolutional neural network model to classify and identify the acoustic vibration signals of bolts,which represent different bolt damage states.Through the methods of knock test and modal simulation,it is concluded that the damage state of wind turbine flange bolt is related to the natural frequency distribution of acoustic vibration signal.It is found that the bolt damage state affects the modal shape of the structure,and then affects the natural frequency distribution of the bolt vibration signal.Therefore,the damage state can be obtained by identifying the natural frequency distribution of the bolt acoustic vibration signal.In the present one-dimensional depth-detachable convolutional neural network model,the one-dimensional vector is first convolved into multiple channels,and then each channel is separately learned by depth-detachable convolution,which can effectively improve the feature quality and the effect of data classification.From the perspective of the realization mechanism of convolution operation,the depthwise separable convolution operation has fewer parameters and faster computing speed,making it easier to build lightweight models and deploy them to mobile devices.
基金This work was supported by the National Science Fund for Distinguished Young Scholars(No.52025041)the National Natural Science Foundation of China(Nos.51904021,51974021,and 52174294)the National Key R&D Program of China(No.2021YFB3700400).
文摘Mechanical properties consisting of the bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,etc.,are key factors in determining the practical applications of MAX phases.These mechanical properties are mainly dependent on the strength of M–X and M–A bonds.In this study,a novel strategy based on the crystal graph convolution neural network(CGCNN)model has been successfully employed to tune these mechanical properties of Ti_(3)AlC_(2)-based MAX phases via the A-site substitution(Ti_(3)(Al1-xAx)C_(2)).The structure–property correlation between the A-site substitution and mechanical properties of Ti_(3)(Al1-xAx)C_(2)is established.The results show that the thermodynamic stability of Ti_(3)(Al1-xAx)C_(2)is enhanced with substitutions A=Ga,Si,Sn,Ge,Te,As,or Sb.The stiffness of Ti_(3)AlC_(2)increases with the substitution concentration of Si or As increasing,and the higher thermal shock resistance is closely associated with the substitution of Sn or Te.In addition,the plasticity of Ti_(3)AlC_(2)can be greatly improved when As,Sn,or Ge is used as a substitution.The findings and understandings demonstrated herein can provide universal guidance for the individual synthesis of high-performance MAX phases for various applications.
基金This work was supported by the National Key R&D Program of China under Grant No.20201710200.
文摘Link prediction is used to complete the knowledge graph.Convolu-tional neural network models are commonly used for link prediction tasks,but they only consider the direct relations between entity pairs,ignoring the semantic information contained in the relation paths.In addition,the embedding dimension of the relation is generally larger than that of the entity in the ConvR model,which blocks the progress of downstream tasks.If we reduce the embedding dimension of the relation,the performance will be greatly degraded.This paper proposes a convolutional model PITri-R-ConvR based on triangular structure relational infer-ence.The model uses relational path inference to capture semantic information,while using a triangular structure to ensure the reliability and computational effi-ciency of relational inference.In addition,the decoder R-ConvR improves the initial embedding of the ConvR model,which solves the problems of the ConvR model and significantly improves the prediction performance.Finally,this paper conducts sufficient experiments in multiple datasets to verify the superiority of the model and the rationality of each module.
