We apply the WKB approximation method,matrix method,and finite difference method to study the gravitational quasi-normal modes of charged spherically symmetric black holes surrounded by quintessence fluid in Rastall g...We apply the WKB approximation method,matrix method,and finite difference method to study the gravitational quasi-normal modes of charged spherically symmetric black holes surrounded by quintessence fluid in Rastall gravity.By comparing the spherically symmetric spacetime metric of charged black holes surrounded by quintessence fluid in Rastall gravity with that of general relativity,we can find that the modifications to general relativity in this modified gravity theory can be described by parameters such asλ,Q,and C_(a),etc.In four-dimensional spacetime,we investigate the impact of charge Q and parameter C_(a) on the gravitational quasi-normal modes of charged black holes surrounded by quintessence field in Rastall gravity.The aim is to search for observational evidence of such black holes in astrophysical observations and,consequently,test the validity of Rastall theory.In five-dimensional(5D)spacetime,we study the impact of the parameter C_(a) on the gravitational quasi-normal modes of Rastall black holes surrounded by quintessence field and summarize the corresponding variation patterns.展开更多
Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion s...Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the weighted feature data across different modalities, thereby reducing the interference of multi-modal features. Subsequently, lightweight modules with depthwise separable convolution are incorporated to reduce the model’s parameter count and computational load through channel-wise and point-wise convolutions. The network model algorithm proposed in this paper was experimentally validated on the publicly available KAIST dataset and compared with other existing methods. The experimental results demonstrate that our approach achieves favorable performance in various complex environments, affirming the effectiveness of the multispectral pedestrian detection technology proposed in this paper.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant No.42230207)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(Grant No.G1323523064)。
文摘We apply the WKB approximation method,matrix method,and finite difference method to study the gravitational quasi-normal modes of charged spherically symmetric black holes surrounded by quintessence fluid in Rastall gravity.By comparing the spherically symmetric spacetime metric of charged black holes surrounded by quintessence fluid in Rastall gravity with that of general relativity,we can find that the modifications to general relativity in this modified gravity theory can be described by parameters such asλ,Q,and C_(a),etc.In four-dimensional spacetime,we investigate the impact of charge Q and parameter C_(a) on the gravitational quasi-normal modes of charged black holes surrounded by quintessence field in Rastall gravity.The aim is to search for observational evidence of such black holes in astrophysical observations and,consequently,test the validity of Rastall theory.In five-dimensional(5D)spacetime,we study the impact of the parameter C_(a) on the gravitational quasi-normal modes of Rastall black holes surrounded by quintessence field and summarize the corresponding variation patterns.
基金supported by the Henan Provincial Science and Technology Research Project under Grants 232102211006,232102210044,232102211017,232102210055 and 222102210214the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205+1 种基金the Undergraduate Universities Smart Teaching Special Research Project of Henan Province under Grant Jiao Gao[2021]No.489-29the Doctor Natural Science Foundation of Zhengzhou University of Light Industry under Grants 2021BSJJ025 and 2022BSJJZK13.
文摘Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the weighted feature data across different modalities, thereby reducing the interference of multi-modal features. Subsequently, lightweight modules with depthwise separable convolution are incorporated to reduce the model’s parameter count and computational load through channel-wise and point-wise convolutions. The network model algorithm proposed in this paper was experimentally validated on the publicly available KAIST dataset and compared with other existing methods. The experimental results demonstrate that our approach achieves favorable performance in various complex environments, affirming the effectiveness of the multispectral pedestrian detection technology proposed in this paper.
