Netessine and Rudi（2003） consider a consumer-driven substitution problem with an arbitrary number of products under both centralized management and competition. They obtain analytically tractable solutions, establis...Netessine and Rudi（2003） consider a consumer-driven substitution problem with an arbitrary number of products under both centralized management and competition. They obtain analytically tractable solutions, establish concavity of the objective function, i.e., the expected profit function generated by each product and uniqueness of the equilibrium for the decentralize case. For the centralized case, they indicate that the objective function, i.e., the expected profit function, might not be concave and not even quasiconcave. In this note we show, for the centralize case, that the objective function is submodular, and partially verify Netessine and Rudi＇s observation that the objective function be unimodal in each of the decision variables for some case.展开更多
In this paper, we give a method which aUows us to construct a class of Parseval frames for L2（R） from Fourier frame for L2（X）. The result shows that the function which generates a Oabor frame by translations and m...In this paper, we give a method which aUows us to construct a class of Parseval frames for L2（R） from Fourier frame for L2（X）. The result shows that the function which generates a Oabor frame by translations and modulations has ＂good＂ properties, i.e., it is suifficiently smooth and compactly supported.展开更多
Remote sensing image object detection is one of the core tasks of remote sensing image processing.In recent years,with the development of deep learning,great progress has been made in object detection in remote sensin...Remote sensing image object detection is one of the core tasks of remote sensing image processing.In recent years,with the development of deep learning,great progress has been made in object detection in remote sensing.However,the problems of dense small targets,complex backgrounds and poor target positioning accuracy in remote sensing images make the detection of remote sensing targets still difficult.In order to solve these problems,this research proposes a remote sensing image object detection algorithm based on improved YOLOX-S.Firstly,the Efficient Channel Attention(ECA)module is introduced to improve the network's ability to extract features in the image and suppress useless information such as background;Secondly,the loss function is optimized to improve the regression accuracy of the target bounding box.We evaluate the effectiveness of our algorithm on the NWPU VHR-10 remote sensing image dataset,the experimental results show that the detection accuracy of the algorithm can reach 95.5%,without increasing the amount of parameters.It is significantly improved compared with that of the original YOLOX-S network,and the detection performance is much better than that of some other mainstream remote sensing image detection methods.Besides,our method also shows good generalization detection performance in experiments on aircraft images in the RSOD dataset.展开更多
文摘Netessine and Rudi（2003） consider a consumer-driven substitution problem with an arbitrary number of products under both centralized management and competition. They obtain analytically tractable solutions, establish concavity of the objective function, i.e., the expected profit function generated by each product and uniqueness of the equilibrium for the decentralize case. For the centralized case, they indicate that the objective function, i.e., the expected profit function, might not be concave and not even quasiconcave. In this note we show, for the centralize case, that the objective function is submodular, and partially verify Netessine and Rudi＇s observation that the objective function be unimodal in each of the decision variables for some case.
基金Supported by Henan Province Education Department Natural Science Foundation of China（2008B510001）
文摘In this paper, we give a method which aUows us to construct a class of Parseval frames for L2（R） from Fourier frame for L2（X）. The result shows that the function which generates a Oabor frame by translations and modulations has ＂good＂ properties, i.e., it is suifficiently smooth and compactly supported.
基金Supported by the National Natural Science Foundation of China (72174172, 71774134)the Fundamental Research Funds for Central University,Southwest Minzu University (2022NYXXS094)。
文摘Remote sensing image object detection is one of the core tasks of remote sensing image processing.In recent years,with the development of deep learning,great progress has been made in object detection in remote sensing.However,the problems of dense small targets,complex backgrounds and poor target positioning accuracy in remote sensing images make the detection of remote sensing targets still difficult.In order to solve these problems,this research proposes a remote sensing image object detection algorithm based on improved YOLOX-S.Firstly,the Efficient Channel Attention(ECA)module is introduced to improve the network's ability to extract features in the image and suppress useless information such as background;Secondly,the loss function is optimized to improve the regression accuracy of the target bounding box.We evaluate the effectiveness of our algorithm on the NWPU VHR-10 remote sensing image dataset,the experimental results show that the detection accuracy of the algorithm can reach 95.5%,without increasing the amount of parameters.It is significantly improved compared with that of the original YOLOX-S network,and the detection performance is much better than that of some other mainstream remote sensing image detection methods.Besides,our method also shows good generalization detection performance in experiments on aircraft images in the RSOD dataset.
