Objective To study the clinical features and surgical strategies of thoracic spinal stenosis caused by ossification of posterior longitudinal ligament(OPLL).Methods From January 2004 to March 2009,21 cases of tho-raci...Objective To study the clinical features and surgical strategies of thoracic spinal stenosis caused by ossification of posterior longitudinal ligament(OPLL).Methods From January 2004 to March 2009,21 cases of tho-racic spinal stenosis展开更多
Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely dis...Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases.Therefore,the proposed algorithm YOLOv2(“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection(referred to as YOLOv2PD)would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes.The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion(MLFF)strategy,which helps to improve the model’s feature extraction ability.In addition,one repeated convolution layer is removed from the final layer,which in turn reduces the computational complexity without losing any detection accuracy.The proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor boxes.Both the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller pedestrians.Experimental results show that,at 544×544 image resolution,the proposed model achieves 80.7%average precision(AP),which is 2.1%higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian dataset.Besides,based on the experimental results,the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on INRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results.展开更多
Fine-grained visual classification(FGVC)is a very challenging task due to distinguishing subcategories under the same super-category.Recent works mainly localize discriminative image regions and capture subtle inter-c...Fine-grained visual classification(FGVC)is a very challenging task due to distinguishing subcategories under the same super-category.Recent works mainly localize discriminative image regions and capture subtle inter-class differences by utilizing attention-based methods.However,at the same layer,most attention-based works only consider large-scale attention blocks with the same size as feature maps,and they ignore small-scale attention blocks that are smaller than feature maps.To distinguish subcategories,it is important to exploit small local regions.In this work,a novel multi-scale attention network(MSANet)is proposed to capture large and small regions at the same layer in fine-grained visual classification.Specifically,a novel multi-scale attention layer(MSAL)is proposed,which generates multiple groups in each feature maps to capture different-scale discriminative regions.The groups based on large-scale regions can exploit global features and the groups based on the small-scale regions can extract local subtle features.Then,a simple feature fusion strategy is utilized to fully integrate global features and local subtle features to mine information that are more conducive to FGVC.Comprehensive experiments in Caltech-UCSD Birds-200-2011(CUB),FGVC-Aircraft(AIR)and Stanford Cars(Cars)datasets show that our method achieves the competitive performances,which demonstrate its effectiveness.展开更多
Real-time voltage stability assessment(VSA)has long been an extensively research topic.In recent years,rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algor...Real-time voltage stability assessment(VSA)has long been an extensively research topic.In recent years,rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from the perspective of measurement data.Deep learning methods generally require a large dataset which contains measurements in both secure and insecure states,or even unstable state.However,in practice,the data of insecure or unstable state is very rare,as the power system should be guaranteed to operate far away from voltage collapse.Under this circumstance,this paper proposes an autoencoder based method which merely needs data of secure state to evaluate voltage stability of a power system.The principle of this method is that an autoencoder purely trained by secure data is expected to only create precise reconstruction for secure data,while it fails to rebuild data of insecure states.Thus,the residual of reconstruction is effective in indicating VSA.Besides,to develop a more accurate and robust algorithm,long short-term memory(LSTM)networks combined with fully-connected(FC)layers are used to build the autoencoder,and a moving strategy is introduced to bias the features of testing data toward the secure feature domain.Numerous experiments and comparison with traditional machine learning algorithms demonstrate the effectiveness and high accuracy of the proposed method.展开更多
Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern recognition.It has been widely used and studied in the multi-view clustering t...Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern recognition.It has been widely used and studied in the multi-view clustering tasks because of its effectiveness.This study proposes a general semi-supervised multi-view nonnegative matrix factorization algorithm.This algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different views.Two specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is presented.Experiments on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.展开更多
文摘Objective To study the clinical features and surgical strategies of thoracic spinal stenosis caused by ossification of posterior longitudinal ligament(OPLL).Methods From January 2004 to March 2009,21 cases of tho-racic spinal stenosis
基金The authors are grateful to the Deanship of Scientific Research,King Saud University,Riyadh,Saudi Arabia,for funding this work through the Vice Deanship of Scientific Research Chairs:Research Chair of Pervasive and Mobile Computing.
文摘Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases.Therefore,the proposed algorithm YOLOv2(“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection(referred to as YOLOv2PD)would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes.The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion(MLFF)strategy,which helps to improve the model’s feature extraction ability.In addition,one repeated convolution layer is removed from the final layer,which in turn reduces the computational complexity without losing any detection accuracy.The proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor boxes.Both the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller pedestrians.Experimental results show that,at 544×544 image resolution,the proposed model achieves 80.7%average precision(AP),which is 2.1%higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian dataset.Besides,based on the experimental results,the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on INRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results.
基金jointly supported by the National Science and Technology Major Project(2022ZD0117103)the National Natural Science Foundations of China(62272364)+2 种基金the provincial Key Research and Development Program of Shaanxi(2024GH-ZDXM-47)the Research Project on Higher Education Teaching Reform of Shaanxi Province(23JG003)the Natural Science Basic Research Program of Shaanxi(2024JC-YBQN0639).
文摘Fine-grained visual classification(FGVC)is a very challenging task due to distinguishing subcategories under the same super-category.Recent works mainly localize discriminative image regions and capture subtle inter-class differences by utilizing attention-based methods.However,at the same layer,most attention-based works only consider large-scale attention blocks with the same size as feature maps,and they ignore small-scale attention blocks that are smaller than feature maps.To distinguish subcategories,it is important to exploit small local regions.In this work,a novel multi-scale attention network(MSANet)is proposed to capture large and small regions at the same layer in fine-grained visual classification.Specifically,a novel multi-scale attention layer(MSAL)is proposed,which generates multiple groups in each feature maps to capture different-scale discriminative regions.The groups based on large-scale regions can exploit global features and the groups based on the small-scale regions can extract local subtle features.Then,a simple feature fusion strategy is utilized to fully integrate global features and local subtle features to mine information that are more conducive to FGVC.Comprehensive experiments in Caltech-UCSD Birds-200-2011(CUB),FGVC-Aircraft(AIR)and Stanford Cars(Cars)datasets show that our method achieves the competitive performances,which demonstrate its effectiveness.
文摘Real-time voltage stability assessment(VSA)has long been an extensively research topic.In recent years,rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from the perspective of measurement data.Deep learning methods generally require a large dataset which contains measurements in both secure and insecure states,or even unstable state.However,in practice,the data of insecure or unstable state is very rare,as the power system should be guaranteed to operate far away from voltage collapse.Under this circumstance,this paper proposes an autoencoder based method which merely needs data of secure state to evaluate voltage stability of a power system.The principle of this method is that an autoencoder purely trained by secure data is expected to only create precise reconstruction for secure data,while it fails to rebuild data of insecure states.Thus,the residual of reconstruction is effective in indicating VSA.Besides,to develop a more accurate and robust algorithm,long short-term memory(LSTM)networks combined with fully-connected(FC)layers are used to build the autoencoder,and a moving strategy is introduced to bias the features of testing data toward the secure feature domain.Numerous experiments and comparison with traditional machine learning algorithms demonstrate the effectiveness and high accuracy of the proposed method.
基金This work was supported by the National Key Research and Development Project of China(No.2019YFB2102500)the Strategic Priority CAS Project(No.XDB38040200)+2 种基金the National Natural Science Foundation of China(Nos.62206269,U1913210)the Guangdong Provincial Science and Technology Projects(Nos.2022A1515011217,2022A1515011557)the Shenzhen Science and Technology Projects(No.JSGG20211029095546003)。
文摘Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern recognition.It has been widely used and studied in the multi-view clustering tasks because of its effectiveness.This study proposes a general semi-supervised multi-view nonnegative matrix factorization algorithm.This algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different views.Two specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is presented.Experiments on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.
