In this paper,we establish an ShIhAhSvIvW model to investigate the impact of media communication on the transmission mechanism of dengue fever.Firstly,the basic reproduction number R0of the model is obtained by using ...In this paper,we establish an ShIhAhSvIvW model to investigate the impact of media communication on the transmission mechanism of dengue fever.Firstly,the basic reproduction number R0of the model is obtained by using the method of the next generation matrix.It shows that disease-free equilibrium is globally asymptotically stable when R0<1;the disease is uniformly persistent when R_(0)>1.Secondly,we select dengue fever case data from Guangdong Province from 2006 to 2019 for numerical simulations and predict its development trend.Finally,we conduct parameter sensitivity analysis,and the results show that increasing media publicity can to some extent reduce the number of patients.展开更多
针对合作靶标特征点成像在大跨度动态测量中的特征提取精度下降、实时性不足问题,提出了一种融合帧间运动预测与改进亚像素边缘检测的自适应光斑质心提取方法。基于合作靶标测量运动连贯性特性,构建了动态感兴趣区域(Region of Interest...针对合作靶标特征点成像在大跨度动态测量中的特征提取精度下降、实时性不足问题,提出了一种融合帧间运动预测与改进亚像素边缘检测的自适应光斑质心提取方法。基于合作靶标测量运动连贯性特性,构建了动态感兴趣区域(Region of Interest,ROI)特征参数模型,以帧间运动预测实现ROI的快速定位,结合大律法阈值优化策略实现自适应Canny边缘检测,在提升计算效率的同时有效解决了不同测量距离下的降噪问题。然后,采用多方向Sobel算子与强度斜坡改进的Zernike矩相结合改进了边缘点定位算法,并基于高斯牛顿迭代改进鲁棒最小二乘圆拟合法,实现质心坐标计算。实验结果表明:在仿真测试中,本方法在不同噪声水平下的质心定位误差为0.001~0.025像素;实际测试中,ROI预测算法可满足加速度8.75 m/s^(2)以内的测量场景需求,10~30 m测量距离内的光斑重复性定位误差稳定在0.016~0.040像素,优于传统方法;光斑提取速度提升约75.5%,显著增强了系统的实时处理能力。本研究可为合作靶标的测量应用提供有效技术保障。展开更多
Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering design.However,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes i...Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering design.However,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow field.In this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity data.Specifi-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored before.In our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder.This method significantly reduces the workload of data measure-ment in practical engineering applications.Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects.Furthermore,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field points.Compared with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields.This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios.The code is available at https://github.com/zhangzm0128/MSU.展开更多
基金Supported by the Natural Science Basic Research Plan in Shaanxi Province of China(2022JM-023)。
文摘In this paper,we establish an ShIhAhSvIvW model to investigate the impact of media communication on the transmission mechanism of dengue fever.Firstly,the basic reproduction number R0of the model is obtained by using the method of the next generation matrix.It shows that disease-free equilibrium is globally asymptotically stable when R0<1;the disease is uniformly persistent when R_(0)>1.Secondly,we select dengue fever case data from Guangdong Province from 2006 to 2019 for numerical simulations and predict its development trend.Finally,we conduct parameter sensitivity analysis,and the results show that increasing media publicity can to some extent reduce the number of patients.
文摘针对合作靶标特征点成像在大跨度动态测量中的特征提取精度下降、实时性不足问题,提出了一种融合帧间运动预测与改进亚像素边缘检测的自适应光斑质心提取方法。基于合作靶标测量运动连贯性特性,构建了动态感兴趣区域(Region of Interest,ROI)特征参数模型,以帧间运动预测实现ROI的快速定位,结合大律法阈值优化策略实现自适应Canny边缘检测,在提升计算效率的同时有效解决了不同测量距离下的降噪问题。然后,采用多方向Sobel算子与强度斜坡改进的Zernike矩相结合改进了边缘点定位算法,并基于高斯牛顿迭代改进鲁棒最小二乘圆拟合法,实现质心坐标计算。实验结果表明:在仿真测试中,本方法在不同噪声水平下的质心定位误差为0.001~0.025像素;实际测试中,ROI预测算法可满足加速度8.75 m/s^(2)以内的测量场景需求,10~30 m测量距离内的光斑重复性定位误差稳定在0.016~0.040像素,优于传统方法;光斑提取速度提升约75.5%,显著增强了系统的实时处理能力。本研究可为合作靶标的测量应用提供有效技术保障。
基金supported by the Japan Society for the Promotion of Science(JSPS)KAKENHI(JP22H03643)Japan Science and Technology Agency(JST)Support for Pioneering Research Initiated by the Next Generation(SPRING)(JPMJSP2145)+2 种基金JST Through the Establishment of University Fellowships Towards the Creation of Science Technology Innovation(JPMJFS2115)the National Natural Science Foundation of China(52078382)the State Key Laboratory of Disaster Reduction in Civil Engineering(CE19-A-01)。
文摘Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering design.However,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow field.In this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity data.Specifi-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored before.In our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder.This method significantly reduces the workload of data measure-ment in practical engineering applications.Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects.Furthermore,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field points.Compared with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields.This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios.The code is available at https://github.com/zhangzm0128/MSU.
