Fluorescence molecular tomography(FMT)can non-invasively monitor glioblastomas in small animals.Both handcrafted prior regularization and deep learning algorithms have made remarkable achievements in this field.But ha...Fluorescence molecular tomography(FMT)can non-invasively monitor glioblastomas in small animals.Both handcrafted prior regularization and deep learning algorithms have made remarkable achievements in this field.But handcrafted priors often cannot deal well with different kinds of tumors.Also,some deep learning methods still rely on handcrafted priors.In this paper,we introduce a Chebyshev non-explicit prior regularizer network(CNPRN).It replaces the handcrafted prior with a non-explicit prior and combines it with an optimization-inspired network.The CNPRN has two main parts:First,because of the long-range spatial correlation of light transmission in the finite element mesh,we create a non-explicit prior regularizer using high-order Chebyshev graph convolution.We also add inter-stage information pathways to combine useful data from the reconstructed outputs of each phase's regularizer;Second,to solve the problem of heavy computation in iterative optimization and make the network more flexible,we introduce a dynamic gradient descent module.This module allows parameters to be adjusted adaptively.As a deep unrolling method,CNPRN naturally obtains the solution constraints of the half-quadratic splitting method.This improves the network's generalizability and stability.Both simulations and in vivo experiments indicate that CNPRN has superior reconstruction performance.展开更多
受低惯量系统中高维不确定性与多类型跨时段约束的影响,忽略调度非预期性的频率安全调度方案无法保证其鲁棒性,更不足以充分发挥光热电站平抑不确定性出力、为系统提供频率支撑的潜能。首先,基于“箱式”边界下考虑光热电站(concentrati...受低惯量系统中高维不确定性与多类型跨时段约束的影响,忽略调度非预期性的频率安全调度方案无法保证其鲁棒性,更不足以充分发挥光热电站平抑不确定性出力、为系统提供频率支撑的潜能。首先,基于“箱式”边界下考虑光热电站(concentrating solar power,CSP)多工况运行状态的跨时段约束解耦方法,构建隐式决策方法下考虑动态频率安全的多时期优化模型;然后,基于无限维优化问题降维优化思想,提出适配于该模型结构的非确定列与约束生成算法;最后,结合显式决策方法下考虑动态频率安全的优化调度建模与求解方法,依托改进IEEE-30节点与IEEE RTS-118节点系统,讨论光热电站在低惯量系统中的频率支撑特性,同时验证两种模型在保证调度方案非预期性和鲁棒性方面的联系与适用性。展开更多
基金supported by the National Natural Science Foundation of China(Nos.12271434,62271394,and 62201459)the Natural Science Basic Research Plan in Shaanxi Province of China(No.2023-JC-JQ-57)the Scientific and Technology New Star in Shaanxi Province of China(No.2023KJXX-035)。
文摘Fluorescence molecular tomography(FMT)can non-invasively monitor glioblastomas in small animals.Both handcrafted prior regularization and deep learning algorithms have made remarkable achievements in this field.But handcrafted priors often cannot deal well with different kinds of tumors.Also,some deep learning methods still rely on handcrafted priors.In this paper,we introduce a Chebyshev non-explicit prior regularizer network(CNPRN).It replaces the handcrafted prior with a non-explicit prior and combines it with an optimization-inspired network.The CNPRN has two main parts:First,because of the long-range spatial correlation of light transmission in the finite element mesh,we create a non-explicit prior regularizer using high-order Chebyshev graph convolution.We also add inter-stage information pathways to combine useful data from the reconstructed outputs of each phase's regularizer;Second,to solve the problem of heavy computation in iterative optimization and make the network more flexible,we introduce a dynamic gradient descent module.This module allows parameters to be adjusted adaptively.As a deep unrolling method,CNPRN naturally obtains the solution constraints of the half-quadratic splitting method.This improves the network's generalizability and stability.Both simulations and in vivo experiments indicate that CNPRN has superior reconstruction performance.
文摘受低惯量系统中高维不确定性与多类型跨时段约束的影响,忽略调度非预期性的频率安全调度方案无法保证其鲁棒性,更不足以充分发挥光热电站平抑不确定性出力、为系统提供频率支撑的潜能。首先,基于“箱式”边界下考虑光热电站(concentrating solar power,CSP)多工况运行状态的跨时段约束解耦方法,构建隐式决策方法下考虑动态频率安全的多时期优化模型;然后,基于无限维优化问题降维优化思想,提出适配于该模型结构的非确定列与约束生成算法;最后,结合显式决策方法下考虑动态频率安全的优化调度建模与求解方法,依托改进IEEE-30节点与IEEE RTS-118节点系统,讨论光热电站在低惯量系统中的频率支撑特性,同时验证两种模型在保证调度方案非预期性和鲁棒性方面的联系与适用性。
