电磁散射问题的两种反演方法研究

提出了电磁散射问题的两种反演方法:确定性梯度搜索方法和MonteCarlo随机搜索方法.给出了两种方法的基本原理,并对反演性能包括对散射体的定位能力、反演精度、反演时间进行了系统的分析与比较.数值计算结果表明,确定性方法定位精确,而随机性方法对散射体电磁参数的反演精度高且速度快,利用两者优点可以增强反演效果.     

Risk-sensitive finite-horizon piecewise deterministic Markov decision processes

This paper deals with risk-sensitive piecewise deterministic Markov decision processes, where the expected exponential utility of a finite-horizon reward is to be maximized. Both the transition rates and reward functions are allowed to be unbounded. Feynman–Kac’s formula is developed in our setup, using which along with an approximation technique, we establish the associated Hamilton–Jacobi–Bellman equation and the existence of risk-sensitive optimal policies under suitable conditions.     

基于可信性测度的模糊线性不等式组的确定性等价

在模糊优化决策问题中,约束条件包含模糊线性不等式组.如何把它们转化为确定性的等价形式,这在模糊控制和模糊决策中起着重要的作用.可信性测度是模糊数学实现公理化体系的一个基础.本文在可信性测度的基础上,探讨了模糊约束在一定的满意度水平之下转化为确定性的等价式,同时就三角形模糊变量给出了模糊线性不等式组的确定性等价的具体表达式.     

Hamilton-Jacobi-Bellman inequality for the average control of piecewise deterministic Markov processes

ABSTRACT

The main goal of this paper is to study the infinite-horizon long run average continuous-time optimal control problem of piecewise deterministic Markov processes (PDMPs) with the control acting continuously on the jump intensity λ and on the transition measure Q of the process. We provide conditions for the existence of a solution to an integro-differential optimality inequality, the so called Hamilton-Jacobi-Bellman (HJB) equation, and for the existence of a deterministic stationary optimal policy. These results are obtained by using the so-called vanishing discount approach, under some continuity and compactness assumptions on the parameters of the problem, as well as some non-explosive conditions for the process.     

Deterministic Secure Quantum Communication with Cluster State and Bell-Basis Measurements

We present a novel protocol for deterministic secure quantum communication by using the lour-qubit cluster state as quantum channel. It is shown that two legitimate users can directly transmit the secret messages based on Bellbasis measurements and classical communication. The present protocol makes use of the ideas of block transmission and decoy particle checking technique. It has a high capacity as each cluster state can carry two 5its of information, and has a high intrinsic efficieney 5ecause almost all the instances except the decoy checking particles （its numSer is negligible） are useful. Furthermore, this protocol is feasible with present-day technique.     

基于激光红外检测信号的表面裂纹定量重构

激光红外检测方法作为一种新型的远距离无损检测技术,对于表面裂纹的检测具有很高的效率。本文基于频域叠加法和数据库策略实现了激光激励下表面温度的快速数值模拟,研究了裂纹尺寸与温度分布间的关系,在时域与空域验证了快速数值方法的有效性。为基于红外检测信号实现裂纹尺寸的定量重构,选取合适的特征量并且建立了基于确定论方法的裂纹重构算法。最后,搭建了红外检测实验系统,进行了平板试件裂纹红外检测实验,基于实验信号实现了裂纹的定量重构,验证了重构算法的有效性。     

基于改进DDPG算法的中短期光伏发电功率预测

针对传统仿生智能算法处理异构光伏发电功率预测精确建模问题时存在的线路多阻抗参数约束下方差波动、线损分析易陷入局部极值等不足,提出了一种基于改进深度确定性策略梯度(DDPG)的中短期光伏发电功率预测模型。首先,通过引入多智能体机制,视发电系统涉及到的发电过程参数为独立活性的智能体,构建出具有社会属性的面向发电过程参数信息共享的全局最优协同控制体系。然后,通过改进的DDPG算法实现蓄电池储能功率自主精确调节和发电网输出功率的自动最优预测。最后,基于Tensorflow开源框架在Gym torcs环境下进行模型效能仿真并以某示范性异构光伏发电网为效能评价载体,对模型进行了工程应用合理性验证。     

Optimal impulsive control of piecewise deterministic Markov processes

In this paper, we study the infinite-horizon expected discounted continuous-time optimal control problem for Piecewise Deterministic Markov Processes with both impulsive and gradual (also called continuous) controls. The set of admissible control strategies is supposed to be formed by policies possibly randomized and depending on the past-history of the process. We assume that the gradual control acts on the jump intensity and on the transition measure, but not on the flow. The so-called Hamilton–Jacobi–Bellman (HJB) equation associated to this optimization problem is analyzed. We provide sufficient conditions for the existence of a solution to the HJB equation and show that the solution is in fact unique and coincides with the value function of the control problem. Moreover, the existence of an optimal control strategy is proven having the property to be stationary and non-randomized.     

Fast search algorithms for computational protein design

One of the main challenges in computational protein design (CPD) is the huge size of the protein sequence and conformational space that has to be computationally explored. Recently, we showed that state‐of‐the‐art combinatorial optimization technologies based on Cost Function Network (CFN) processing allow speeding up provable rigid backbone protein design methods by several orders of magnitudes. Building up on this, we improved and injected CFN technology into the well‐established CPD package Osprey to allow all Osprey CPD algorithms to benefit from associated speedups. Because Osprey fundamentally relies on the ability of to produce conformations in increasing order of energy, we defined new strategies combining CFN lower bounds, with new side‐chain positioning‐based branching scheme. Beyond the speedups obtained in the new ‐CFN combination, this novel branching scheme enables a much faster enumeration of suboptimal sequences, far beyond what is reachable without it. Together with the immediate and important speedups provided by CFN technology, these developments directly benefit to all the algorithms that previously relied on the DEE/ combination inside Osprey* and make it possible to solve larger CPD problems with provable algorithms. © 2016 Wiley Periodicals, Inc.