In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This...In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This method combines two core modules:a simple parameter-free self-attention and cross-attention.By analyzing the self-correlation and cross-correlation between support images and query images,it achieves effective classification of infrared aircraft under few-shot conditions.The proposed cross-correlation network integrates these two modules and is trained in an end-to-end manner.The simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between images.Compared with existing few-shot infrared target classification models,this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set,thus better attending to the target objects.Experimental results show that this method outperforms existing infrared aircraft classification methods in various classification tasks,with the highest classification accuracy improvement exceeding 3%.In addition,ablation experiments and comparative experiments also prove the effectiveness of the method.展开更多
The label“posthumanism”identifies neither a doctrine,nor an intellectual or analytical approach with a defining set of protocols.Rather,we may take“posthumanism”as a term loosely applied to a range of contribution...The label“posthumanism”identifies neither a doctrine,nor an intellectual or analytical approach with a defining set of protocols.Rather,we may take“posthumanism”as a term loosely applied to a range of contributions and approaches,on the basis of a generally under-specified collection of sympathies and commitments.Approximately stated,these sympathies and commitments would entail the claim that—with due allowance for historical variation—the category of“the human”has long played a key role at the centre of Western thought;that this role has included serving to justify the promotion of human beings above other forms of being,and indeed within this,over its history,the violent oppression of the great majority of human beings themselves;and that,in league with the effects of various recent technological developments,it is therefore important to decentre,relativize,critique,and perhaps even move beyond“the human”.In this article,I will accordingly not look to define the term“posthumanism”:rather,I will present some of the background to and influences on the range of contributions and approaches that have come to be assembled under this label;delineate two principles which may be discerned within these contributions and approaches;and consider some of the critiques which these“posthumanist”interventions have attracted.Ultimately,I will argue that the label itself matters less than the impulses behind the contributions it has come to identify.展开更多
The sharp increase of the amount of Internet Chinese text data has significantly prolonged the processing time of classification on these data.In order to solve this problem,this paper proposes and implements a parall...The sharp increase of the amount of Internet Chinese text data has significantly prolonged the processing time of classification on these data.In order to solve this problem,this paper proposes and implements a parallel naive Bayes algorithm(PNBA)for Chinese text classification based on Spark,a parallel memory computing platform for big data.This algorithm has implemented parallel operation throughout the entire training and prediction process of naive Bayes classifier mainly by adopting the programming model of resilient distributed datasets(RDD).For comparison,a PNBA based on Hadoop is also implemented.The test results show that in the same computing environment and for the same text sets,the Spark PNBA is obviously superior to the Hadoop PNBA in terms of key indicators such as speedup ratio and scalability.Therefore,Spark-based parallel algorithms can better meet the requirement of large-scale Chinese text data mining.展开更多
Focusing on data imbalance and intraclass variation,an improved pedestrian detection with a cascade of complex peer AdaBoost classifiers is proposed.The series of the AdaBoost classifiers are learned greedily,along wi...Focusing on data imbalance and intraclass variation,an improved pedestrian detection with a cascade of complex peer AdaBoost classifiers is proposed.The series of the AdaBoost classifiers are learned greedily,along with negative example mining.The complexity of classifiers in the cascade is not limited,so more negative examples are used for training.Furthermore,the cascade becomes an ensemble of strong peer classifiers,which treats intraclass variation.To locally train the AdaBoost classifiers with a high detection rate,a refining strategy is used to discard the hardest negative training examples rather than decreasing their thresholds.Using the aggregate channel feature(ACF),the method achieves miss rates of 35%and 14%on the Caltech pedestrian benchmark and Inria pedestrian dataset,respectively,which are lower than that of increasingly complex AdaBoost classifiers,i.e.,44%and 17%,respectively.Using deep features extracted by the region proposal network(RPN),the method achieves a miss rate of 10.06%on the Caltech pedestrian benchmark,which is also lower than 10.53%from the increasingly complex cascade.This study shows that the proposed method can use more negative examples to train the pedestrian detector.It outperforms the existing cascade of increasingly complex classifiers.展开更多
Seeking for innovative structures with higher mechanical performance is a continuous target in railway vehicle crashworthiness design.In the present study,three types of hexagonal reinforced honeycomb-like structures ...Seeking for innovative structures with higher mechanical performance is a continuous target in railway vehicle crashworthiness design.In the present study,three types of hexagonal reinforced honeycomb-like structures were developed and analyzed subjected to out-of-plane compression,namely triangular honeycomb(TH),double honeycomb(DH)and full inside honeycomb(FH).Theoretical formulas of average force and specific energy absorption(SEA)were constructed based on the energy minimization principle.To validate,corresponding numerical simulations were carried out by explicit finite element method.Good agreement has been observed between them.The results show that all these honeycomb-like structures maintain the same collapsed stages as conventional honeycomb;cell reinforcement can significantly promote the performance,both in the average force and SEA;full inside honeycomb performs better than the general,triangular and double schemes in average force;meanwhile,its SEA is close to that of double scheme;toroidal surface can dissipate higher plastic energy,so more toroidal surfaces should be considered in design of thin-walled structure.These achievements pave a way for designing high-performance cellular energy absorption devices.展开更多
In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel pr...In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.展开更多
基金Supported by the National Pre-research Program during the 14th Five-Year Plan(514010405)。
文摘In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This method combines two core modules:a simple parameter-free self-attention and cross-attention.By analyzing the self-correlation and cross-correlation between support images and query images,it achieves effective classification of infrared aircraft under few-shot conditions.The proposed cross-correlation network integrates these two modules and is trained in an end-to-end manner.The simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between images.Compared with existing few-shot infrared target classification models,this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set,thus better attending to the target objects.Experimental results show that this method outperforms existing infrared aircraft classification methods in various classification tasks,with the highest classification accuracy improvement exceeding 3%.In addition,ablation experiments and comparative experiments also prove the effectiveness of the method.
文摘The label“posthumanism”identifies neither a doctrine,nor an intellectual or analytical approach with a defining set of protocols.Rather,we may take“posthumanism”as a term loosely applied to a range of contributions and approaches,on the basis of a generally under-specified collection of sympathies and commitments.Approximately stated,these sympathies and commitments would entail the claim that—with due allowance for historical variation—the category of“the human”has long played a key role at the centre of Western thought;that this role has included serving to justify the promotion of human beings above other forms of being,and indeed within this,over its history,the violent oppression of the great majority of human beings themselves;and that,in league with the effects of various recent technological developments,it is therefore important to decentre,relativize,critique,and perhaps even move beyond“the human”.In this article,I will accordingly not look to define the term“posthumanism”:rather,I will present some of the background to and influences on the range of contributions and approaches that have come to be assembled under this label;delineate two principles which may be discerned within these contributions and approaches;and consider some of the critiques which these“posthumanist”interventions have attracted.Ultimately,I will argue that the label itself matters less than the impulses behind the contributions it has come to identify.
基金Project(KC18071)supported by the Application Foundation Research Program of Xuzhou,ChinaProjects(2017YFC0804401,2017YFC0804409)supported by the National Key R&D Program of China
文摘The sharp increase of the amount of Internet Chinese text data has significantly prolonged the processing time of classification on these data.In order to solve this problem,this paper proposes and implements a parallel naive Bayes algorithm(PNBA)for Chinese text classification based on Spark,a parallel memory computing platform for big data.This algorithm has implemented parallel operation throughout the entire training and prediction process of naive Bayes classifier mainly by adopting the programming model of resilient distributed datasets(RDD).For comparison,a PNBA based on Hadoop is also implemented.The test results show that in the same computing environment and for the same text sets,the Spark PNBA is obviously superior to the Hadoop PNBA in terms of key indicators such as speedup ratio and scalability.Therefore,Spark-based parallel algorithms can better meet the requirement of large-scale Chinese text data mining.
基金Project(2018AAA0102102)supported by the National Science and Technology Major Project,ChinaProject(2017WK2074)supported by the Planned Science and Technology Project of Hunan Province,China+1 种基金Project(B18059)supported by the National 111 Project,ChinaProject(61702559)supported by the National Natural Science Foundation of China。
文摘Focusing on data imbalance and intraclass variation,an improved pedestrian detection with a cascade of complex peer AdaBoost classifiers is proposed.The series of the AdaBoost classifiers are learned greedily,along with negative example mining.The complexity of classifiers in the cascade is not limited,so more negative examples are used for training.Furthermore,the cascade becomes an ensemble of strong peer classifiers,which treats intraclass variation.To locally train the AdaBoost classifiers with a high detection rate,a refining strategy is used to discard the hardest negative training examples rather than decreasing their thresholds.Using the aggregate channel feature(ACF),the method achieves miss rates of 35%and 14%on the Caltech pedestrian benchmark and Inria pedestrian dataset,respectively,which are lower than that of increasingly complex AdaBoost classifiers,i.e.,44%and 17%,respectively.Using deep features extracted by the region proposal network(RPN),the method achieves a miss rate of 10.06%on the Caltech pedestrian benchmark,which is also lower than 10.53%from the increasingly complex cascade.This study shows that the proposed method can use more negative examples to train the pedestrian detector.It outperforms the existing cascade of increasingly complex classifiers.
基金Projects(51875581,51505502)supported by the National Natural Science Foundation of ChinaProjects(2017M620358,2018T110707)supported by China Postdoctoral Science FoundationProject(kq1905057)supported by the Training Program for Excellent Young Innovators of Changsha,China
文摘Seeking for innovative structures with higher mechanical performance is a continuous target in railway vehicle crashworthiness design.In the present study,three types of hexagonal reinforced honeycomb-like structures were developed and analyzed subjected to out-of-plane compression,namely triangular honeycomb(TH),double honeycomb(DH)and full inside honeycomb(FH).Theoretical formulas of average force and specific energy absorption(SEA)were constructed based on the energy minimization principle.To validate,corresponding numerical simulations were carried out by explicit finite element method.Good agreement has been observed between them.The results show that all these honeycomb-like structures maintain the same collapsed stages as conventional honeycomb;cell reinforcement can significantly promote the performance,both in the average force and SEA;full inside honeycomb performs better than the general,triangular and double schemes in average force;meanwhile,its SEA is close to that of double scheme;toroidal surface can dissipate higher plastic energy,so more toroidal surfaces should be considered in design of thin-walled structure.These achievements pave a way for designing high-performance cellular energy absorption devices.
基金Project(51209167) supported by Youth Project of the National Natural Science Foundation of ChinaProject(2012JM8026) supported by Shaanxi Provincial Natural Science Foundation, China
文摘In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.
