Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS/PROBA observations

Neural networks trained over radiative transfer simulations constitute the basis of several operational algorithms to estimate canopy biophysical variables from satellite reflectance measurements. However, only little attention was paid to the training process which has a major impact on retrieval performances. This study focused on the several modalities of the training process within neural network estimation of LAI, FCOVER and FAPAR biophysical variables. Performances were evaluated over both actual experimental observations and model simulations. The SAIL and PROSPECT radiative transfer models were used here to simulate the training and the synthetic test datasets. Measurements of LAI, FCOVER and FAPAR were achieved over the Barrax (Spain) agricultural site for a range of crop types concurrently to CHRIS/PROBA satellite image acquisition. Results showed that the spectral band selection was specific to LAI, FCOVER and FAPAR variables. The optimal band set provided significantly improved performances for LAI, while only small differences were observed for the other variables. Gaussian distributions of the radiative transfer model input variables performed better than uniform distributions for which no prior information was exploited. Including moderate uncertainties in the reflectance simulations used in the training process improved the flexibility of the neural network in cases where simulations departed slightly from observations. Simple neural network architecture with a single hidden layer of five tangent sigmoid transfer functions was performing as good as more complex architectures if the training dataset was larger than ten times the number of coefficients to tune. Small sensitivity of performances was observed depending on the way the solution was selected when several networks were trained in parallel. Finally, comparison with a NDVI based approach showed the generally better retrieval accuracy of neural networks.     

Towards inverse modeling of turbidity currents: The inverse lock-exchange problem

A new approach is introduced for turbidite modeling, leveraging the potential of computational fluid dynamics methods to simulate the flow processes that led to turbidite formation. The practical use of numerical flow simulation for the purpose of turbidite modeling so far is hindered by the need to specify parameters and initial flow conditions that are a priori unknown. The present study proposes a method to determine optimal simulation parameters via an automated optimization process. An iterative procedure matches deposit predictions from successive flow simulations against available localized reference data, as in practice may be obtained from well logs, and aims at convergence towards the best-fit scenario. The final result is a prediction of the entire deposit thickness and local grain size distribution. The optimization strategy is based on a derivative-free, surrogate-based technique. Direct numerical simulations are performed to compute the flow dynamics. A proof of concept is successfully conducted for the simple test case of a two-dimensional lock-exchange turbidity current. The optimization approach is demonstrated to accurately retrieve the initial conditions used in a reference calculation.     

3D porosity prediction from seismic inversion and neural networks

In this work, we address the problem of transforming seismic reflection data into an intrinsic rock property model. Specifically, we present an application of a methodology that allows interpreters to obtain effective porosity 3D maps from post-stack 3D seismic amplitude data, using measured density and sonic well log data as constraints. In this methodology, a 3D acoustic impedance model is calculated from seismic reflection amplitudes by applying an L₁-norm sparse-spike inversion algorithm in the time domain, followed by a recursive inversion performed in the frequency domain. A 3D low-frequency impedance model is estimated by kriging interpolation of impedance values calculated from well log data. This low-frequency model is added to the inversion result which otherwise provides only a relative numerical scale. To convert acoustic impedance into a single reservoir property, a feed-forward Neural Network (NN) is trained, validated and tested using gamma-ray and acoustic impedance values observed at the well log positions as input and effective porosity values as target. The trained NN is then applied for the whole reservoir volume in order to obtain a 3D effective porosity model. While the particular conclusions drawn from the results obtained in this work cannot be generalized, such results suggest that this workflow can be applied successfully as an aid in reservoir characterization, especially when there is a strong non-linear relationship between effective porosity and acoustic impedance.     

基于IOC的GUI框架设计与实现

传统的图形用户界面GUI(Graphics User Interface)设计中,存在过度耦合、组件与事件之间的映射关系混乱等问题.对此,提出了基于控制反转( IOC)的GUI框架,该框架采用Java反射机制,解析xml配置文件完成组件实例化、组件添加事件监听.实验表明,利用该框架建立的GUI实现了业务对象的松散耦合,组件和事件处理方法分离,缩短开发周期,具有较高的可扩展性.     

基于遗传算法的多处理器系统任务调度

用一种遗传算法的调度策略,以大维度矩阵求逆为实验对象,探索在多核中如何完成任务的均衡分配问题,以达到加速效果.算法利用系统资源的弹性,自动搜寻可以并行的子任务并将其合理地分配到相应计算节点中,提高了多核系统资源调度性能,实现了对用户提交的任务的优化调度,达到了均衡系统各处理器计算负载和提高多核系统的总体性能的目标.     

一种复杂非线性系统的智能变结构控制方法

采用一种智能结构方法实现复杂非线性系统的控制,给出了设计原则,并用第四代歼击机飞行控制系统设计一个复杂实例来说明方法的有效性,实例系统中包含了两种控制律：增益调参和非线性动态逆控制律,两种控制律都包括了对发动机推力矢量喷管的控制。文中采用了一种基于状态的控制律切换算法进行两类控制律的切换,考虑了变结构系统切换瞬态问题和强迫振荡问题,并给出了简便的解决方法。     

应用于Template框架的SVG Tiny创作工具的设计与实现

通过对Template框架的研究,对在该框架中引入SVG Tiny技术进行了研究,并为该框架设计和实现了SVG Tiny创作工具。     

Efficient Solution of A, x ( k )?= b ( k ) Using A ?1

In this work, we consider the problem of solving , , where b ^(k+1) = f(x ^(k)). We show that when A is a full matrix and , where depends on the specific software and hardware setup, it is faster to solve for by explicitly evaluating the inverse matrix A ⁻¹ rather than through the LU decomposition of A. We also show that the forward error is comparable in both methods, regardless of the condition number of A.     

湿法相分离不对称超滤膜形成机理

本文选择了憎水的极怀非普质子性聚砜和亲水的极性质子怀聚砜酰胺两种膜材料，从铸膜液组成和凝胶过程两方面，用气相色谱和高效液相色谱研究了表征膜材料－溶剂－添加剂－水作用力的混合自由焓，平衡分配常数，及其与膜性能，不同溶剂特性粘度，雾点值和溶解度参数的关系，提出了铸膜液中有机溶剂的作用机理，及不对称超滤膜形成的一些规律。     

复杂滑带条件下的岩体 参数反演研究

结合1967年稚碧江唐古栋滑坡1 -1剖面模型,综合比较分析了相关岩体各工况下的稳定 性,确定了最危险工况,以极限平衡原理确定了最危险滑面下强度参数,并取其满足规范要求的最低 稳定性标准为反演目标。由于滑带可能穿越不同的岩层,反演过程中需要分别对不同岩层进行危险滑 带搜索,以确定未知岩层的力学参数。结果显示岩体参数反演在工程上具有较高的可靠性,可为实际 边坡工程提供重要的依据。