辐射温度的数据处理方法

辐射温度是间接驱动惯性约束聚变实验中可以定量测量的重要物理量。引进了一种通过直接计算辐射通量来得到辐射温度的方法,该方法的主要优点是计算简单,避免了某些时刻不能计算辐射温度的情况。由该方法得到的辐射温度与传统方法计算结果符合较好,不确定度均为7%。通过与冲击波测量温度峰值的结果对比确认了数据处理方法的正确性。在此基础上建立了国内某大型激光装置上的新型15道软X光能谱仪的数据处理系统。     

基于自适应本征正交分解混合模型的跨音速流场分析

采用非线性模型替代线性回归模型响应本征正交分解(POD)基函数的系数,并采用自适应抽样方法确定快照集合,实现了基于自适应POD混合模型的跨音速叶片复杂流动分析及流场拟合.首先通过比较基于线性回归模型和非线性回归模型的基函数系数响应精度,验证非线性回归模型的收敛性和精确性;之后通过与静态抽样方法进行对比,研究分析自适应抽样技术的优越性;最后开展基于自适应POD混合模型的全三维跨音速流场分析及流动拟合.结果表明,采用自适应POD混合模型,不仅能够清晰地识别三维跨音速流场中的敏感流动特征,还能精确地拟合设计空间内任意状态的流场及出口气动参数.     

Wavefront sensorless adaptive optics for large aberrations

In some adaptive optics systems the aberration is determined not by using a wavefront sensor but by sequential optimization of the adaptive correction element. Efficient schemes for the control of such systems are essential if they are to be effective. A scheme is introduced that permits the efficient measurement of large amplitude wavefront aberrations that are represented by an appropriate series of modes. This scheme uses an optimization metric based on the root-mean-square spot radius (or focal spot second moment) and an aberration expansion using polynomials suited to the representation of lateral aberrations. Experimental correction of N aberration modes is demonstrated with a minimum of N+1 photodetector measurements. The geometrical optics basis means that the scheme can be extended to arbitrarily large aberrations.     

Optimum deformable mirror modes for sensorless adaptive optics

Wavefront sensorless adaptive optics schemes rely upon the accurate generation of aberration modes by the adaptive element, usually a deformable mirror. Analytic functions are often used for representation of the aberrations in these systems. Such functions cannot be perfectly reproduced by deformable mirrors and the approximation errors can affect the aberration correction procedure. We derive alternative modal basis sets directly from the actuator influence functions, thus avoiding the approximation errors. We investigate how the choice of aberration modes affects the performance of a sensorless adaptive imaging system. The new modes are found to be most advantageous for deformable mirrors with a small number of actuators.     

一种基于SCARS策略的近红外特征波长选择方法及其应用

针对近红外光谱数据的内在特点,提出了一种基于稳定性竞争自适应重加权采样(stability competitive adaptive reweighted sampling, SCARS)策略的近红外特征波长优选方法。该方法以PLS模型回归系数的稳定性作为变量选择的依据,其过程包含多次循环迭代,每次循环均首先计算相应变量的稳定性,而后通过强制变量筛选以及自适应重加权采样技术(ARS)进行变量筛选;最后对每次循环后所得变量子集建立PLS模型并计算交互验证均方根误差(RMSECV),将RMSECV值最小的集合作为最优变量子集。利用饲料蛋白固态发酵过程近红外光谱数据集对所提方法进行了验证,并与基于PLS的蒙特卡罗无信息变量消除法(MC-UVE)和竞争自适应重加权采样(CARS)方法所得结果进行了比较。试验结果显示： 建立在SCARS方法优选的21个特征波长变量基础上的PLS模型预测效果更好,其预测均方根误差(RMSEP)和相关系数(R_p)分别为0.054 3和0.990 8;该优选策略能有效地增强固态发酵光谱数据特征波长变量选择的准确性和稳定性,提高了模型的预测精度,具有一定的应用价值。     

An efficient adaptive arithmetic coding image compression technology

This paper proposes an efficient lossless image compression scheme for still images based on an adaptive arithmetic coding compression algorithm. The algorithm increases the image coding compression rate and ensures the quality of the decoded image combined with the adaptive probability model and predictive coding. The use of adaptive models for each encoded image block dynamically estimates the probability of the relevant image block. The decoded image block can accurately recover the encoded image according to the code book information. We adopt an adaptive arithmetic coding algorithm for image compression that greatly improves the image compression rate. The results show that it is an effective compression technology.     

自适应光学系统时域特性分析

分析了激光导星发出的波前通过湍流大气所造成的波前相位畸变的时间功率谱 ;根据湍流相位时间功率谱和自适应光学系统的传递函数 ,比较了使用自然导星和激光导星时自适应光学系统的时间响应误差 ;讨论了使用自然导星时自适应光学系统的时域优化问题。     

Free energy computations by minimization of Kullback–Leibler divergence: An efficient adaptive biasing potential method for sparse representations

The present paper proposes an adaptive biasing potential technique for the computation of free energy landscapes. It is motivated by statistical learning arguments and unifies the tasks of biasing the molecular dynamics to escape free energy wells and estimating the free energy function, under the same objective of minimizing the Kullback–Leibler divergence between appropriately selected densities. It offers rigorous convergence diagnostics even though history dependent, non-Markovian dynamics are employed. It makes use of a greedy optimization scheme in order to obtain sparse representations of the free energy function which can be particularly useful in multidimensional cases. It employs embarrassingly parallelizable sampling schemes that are based on adaptive Sequential Monte Carlo and can be readily coupled with legacy molecular dynamics simulators. The sequential nature of the learning and sampling scheme enables the efficient calculation of free energy functions parametrized by the temperature. The characteristics and capabilities of the proposed method are demonstrated in three numerical examples.     

A multi-resolution,non-parametric,Bayesian framework for identification of spatially-varying model parameters

This paper proposes a hierarchical, multi-resolution framework for the identification of model parameters and their spatially variability from noisy measurements of the response or output. Such parameters are frequently encountered in PDE-based models and correspond to quantities such as density or pressure fields, elasto-plastic moduli and internal variables in solid mechanics, conductivity fields in heat diffusion problems, permeability fields in fluid flow through porous media etc. The proposed model has all the advantages of traditional Bayesian formulations such as the ability to produce measures of confidence for the inferences made and providing not only predictive estimates but also quantitative measures of the predictive uncertainty. In contrast to existing approaches it utilizes a parsimonious, non-parametric formulation that favors sparse representations and whose complexity can be determined from the data. The proposed framework in non-intrusive and makes use of a sequence of forward solvers operating at various resolutions. As a result, inexpensive, coarse solvers are used to identify the most salient features of the unknown field(s) which are subsequently enriched by invoking solvers operating at finer resolutions. This leads to significant computational savings particularly in problems involving computationally demanding forward models but also improvements in accuracy. It is based on a novel, adaptive scheme based on Sequential Monte Carlo sampling which is embarrassingly parallelizable and circumvents issues with slow mixing encountered in Markov Chain Monte Carlo schemes. The capabilities of the proposed methodology are illustrated in problems from nonlinear solid mechanics with special attention to cases where the data is contaminated with random noise and the scale of variability of the unknown field is smaller than the scale of the grid where observations are collected.     

数据采集方案对神经扩散模型影响的评估

基于单次数据采集的多种扩散模型联合应用已逐渐成为临床研究的热点,本研究比较了三种采集方案对于神经扩散模型定量计算的影响,包括Q空间笛卡尔网格（QGrid）、多壳层异向（Free）和多壳层同向（MDDW）采集方案,涉及的扩散模型包含扩散张量成像（DTI）;扩散峰度成像（DKI）;神经突方向分散度和密度成像（NODDI）;平均表观传播（MAP）模型.结果表明DTI和DKI模型对采集方案相对不敏感,而NODDI和MAP对采集方案和最大b值的设置相对较敏感,并且QGrid和Free方案一致性较高,因此在大样本和多中心研究中需要考虑采集方案的选择.此外,考虑到QGrid和Free方案分别在结合更多扩散模型和神经纤维束成像应用上更具优势,因此推荐使用.     

Design of artificial genetic regulatory networks with multiple delayed adaptive responses*

Genetic regulatory networks with adaptive responses are widely studied in biology. Usually, models consisting only of a few nodes have been considered. They present one input receptor for activation and one output node where the adaptive response is computed. In this work, we design genetic regulatory networks with many receptors and many output nodes able to produce delayed adaptive responses. This design is performed by using an evolutionary algorithm of mutations and selections that minimizes an error function defined by the adaptive response in signal shapes. We present several examples of network constructions with a predefined required set of adaptive delayed responses. We show that an output node can have different kinds of responses as a function of the activated receptor. Additionally, complex network structures are presented since processing nodes can be involved in several input-output pathways.     

Cooperative adaptive bidirectional control of a train platoon for efficient utility and string stability

This paper proposes cooperative adaptive control schemes for a train platoon to improve efficient utility and guarantee string stability. The control schemes are developed based on a bidirectional strategy, i.e., the information of proximal(preceding and following) trains is used in the controller design. Based on available proximal information(prox-info) of location, speed, and acceleration, a direct adaptive control is designed to maintain the tracking interval at the minimum safe distance. Based on available prox-info of location, an observer-based adaptive control is designed to achieve the same target, which alleviates the requirements of equipped sensors to measure prox-info of speed and acceleration. The developed schemes are capable of on-line estimating of the unknown system parameters and stabilizing the closed-loop system, the string stability of train platoon is guaranteed on the basis of Lyapunov stability theorem. Numerical simulation results are presented to verify the effectiveness of the proposed control laws.     

An approach to estimating and extrapolating model error based on inverse problem methods:towards accurate numerical weather prediction

Model error is one of the key factors restricting the accuracy of numerical weather prediction(NWP). Considering the continuous evolution of the atmosphere, the observed data(ignoring the measurement error) can be viewed as a series of solutions of an accurate model governing the actual atmosphere. Model error is represented as an unknown term in the accurate model, thus NWP can be considered as an inverse problem to uncover the unknown error term. The inverse problem models can absorb long periods of observed data to generate model error correction procedures. They thus resolve the deficiency and faultiness of the NWP schemes employing only the initial-time data. In this study we construct two inverse problem models to estimate and extrapolate the time-varying and spatial-varying model errors in both the historical and forecast periods by using recent observations and analogue phenomena of the atmosphere. Numerical experiment on Burgers’ equation has illustrated the substantial forecast improvement using inverse problem algorithms. The proposed inverse problem methods of suppressing NWP errors will be useful in future high accuracy applications of NWP.     

A simplified adaptive feedback active noise control system

In the adaptive feedback active noise control system based on the internal model control (IMC) structure, the reference signal is regenerated by synthesizing the error signal and the secondary signal filtered with the estimation of the secondary path, hence more computation load and extra programming are required. Motivated by the engineering truth that the primary noise cannot be completely cancelled in most practical active noise control applications and the error signal still contains some portions of the primary noise, a simplified adaptive feedback active noise control system is proposed in this paper, which adopts the error signal directly as the reference signal in an adaptive feedforward control system and utilizes the leaky filtered-x LMS algorithm to update the controller. The convergence properties of the proposed system are investigated and its advantages are discussed by comparing with other feedback control systems as well as the weakness. Finally, simulations and experiments are carried out to demonstrate the effectiveness of the proposed system.     

New communication schemes based on adaptive synchronization

In this paper, adaptive synchronization with unknown parameters is discussed for a unified chaotic system by using the Lyapunov method and the adaptive control approach. Some communication schemes, including chaotic masking, chaotic modulation, and chaotic shift key strategies, are then proposed based on the modified adaptive method. The transmitted signal is masked by chaotic signal or modulated into the system, which effectively blurs the constructed return map and can resist this return map attack. The driving system with unknown parameters and functions is almost completely unknown to the attackers, so it is more secure to apply this method into the communication. Finally, some simulation examples based on the proposed communication schemes and some cryptanalysis works are also given to verify the theoretical analysis in this paper.     

一种最差情况下性能最优化的特征分析自适应波束形成方法

自适应波束形成在弱目标检测和空域滤波中应用广泛。然而在实际海洋环境中,失配情况比较普遍,往往会导致传统自适应波束形成方法性能明显下降。针对此问题,本文基于特征分析重构观测信号协方差矩阵来抑制干扰对弱目标检测的影响,在此基础上进一步利用凸优化算法来提高波束形成方法的鲁棒性。数值仿真和海试数据处理结果表明,本文所提方法在抑制干扰对弱目标影响的基础上,在阵元位置平均误差不超过入射信号中心频率对应波长的40%的情况下,仍可以鲁棒地检测目标信号。同时,该方法显著提高了自适应波束形成方法的输出信干噪比,且受对角加载量取值的影响较小。      

基于在线误差修正自适应SVR的非线性不确定分数阶混沌系统滑模控制

提出了一种基于在线误差修正自适应SVR的滑模控制方法, 用于解决一类非线性不确定分数阶混沌系统的控制问题. 分别通过对混沌系统非线性函数的离线SVR估计和基于增量学习的状态跟踪误差在线SVR预测, 解决了不确定分数阶混沌系统模型难以预测的问题. 同时根据Lyapunov稳定性理论设计出SVR权值自适应调整律. 本文以分数阶Arneodo 系统为例进行仿真, 仿真结果表明了, 对于带有外界噪声扰动的非线性不确定分数阶混沌系统, 该方法可以在有限时间内将系统稳定至期望状态, 提高对非线性函数的预测精度, 改善控制性能.     

基于高光谱技术的厌氧发酵液固形物含量检测的研究

实时监测发酵液中固形物含量的变化,对控制厌氧发酵过程的稳定性具有重要作用。研究中采用近红外高光谱技术结合化学计量学方法,对水葫芦和稻草秸秆混合厌氧发酵过程中的固形物含量进行定量检测研究。与传统2540G(APHA,1990)标准方法相比,近红外高光谱技术具有无损、快速的优点。实验过程中,首先获取发酵液样本的高光谱信息,应用移动平均平滑法(MAS)进行光谱预处理,并采用竞争自适应重加权采样算法(CARS)、连续投影算法(SPA)和Random frog算法提取光谱特征信息,然后基于全谱和所选特征波长下的光谱信息分别建立总固形物含量(TS)和挥发性固形物含量(VS)的校正模型,建模方法包括偏最小二乘回归(PLSR)和最小二乘-支持向量机(LS-SVM)。研究表明,SPA-LS-SVM模型的预测结果最好,其中TS的预测均方根误差(RMSEP)及相关系数(R_p)分别为0.005 8和0.841;而VS的RMSEP和R_p分别为0.004 1和0.874。结果表明,利用近红外高光谱结合化学计量学方法可以实现厌氧发酵液中的固形物含量的检测,为布置光谱传感器以便定量检测厌氧发酵过程中的固形物含量奠定了理论依据。     

Implicit subgrid-scale modeling by adaptive deconvolution

A new approach for the construction of implicit subgrid-scale models for large-eddy simulation based on adaptive local deconvolution is proposed. An approximation of the unfiltered solution is obtained from a quasi-linear combination of local interpolation polynomials. The physical flux function is modeled by a suitable numerical flux function. The effective subgrid-scale model can be determined by a modified-differential equation analysis. Discretization parameters which determine the behavior of the implicit model in regions of developed turbulence can be adjusted so that a given explicit subgrid-scale model is recovered to leading order in filter width. Alternatively, improved discretization parameters can be found directly by evolutionary optimization. Computational results for stochastically forced and decaying Burgers turbulence are provided. An assessment of the computational experiments shows that results for a given explicit subgrid-scale model can be matched by computations with an implicit representation. A considerable improvement can be achieved if instead of the parameters matching an explicit model discretization parameters determined by evolutionary optimization are used.