Identification of ground water flow patterns using particle models

Particle models are often used to simulate transport processes in ground water. The ground water flow pattern is one of the driving parameters of the transport model. In this paper a parameter identification algorithm is developed for particle type models to identify the underlying flow pattern from concentration measurements. The estimation problem is solved with a gradient based algorithm. For each generated particle track, the adjoint track is determined to efficiently compute gradient of the criterion.     

Adaptive finite element methods for the identification of distributed parameters in elliptic equation

In this paper, adaptive finite element method is developed for the estimation of distributed parameter in elliptic equation. Both upper and lower error bound are derived and used to improve the accuracy by appropriate mesh refinement. An efficient preconditioned project gradient algorithm is employed to solve the nonlinear least-squares problem arising in the context of parameter identification problem. The efficiency of our error estimators is demonstrated by some numerical experiments.      

基于弹性波散射对二维障碍物边界的识别

给出了二维障碍物弹性波散射场的一种近似积分形式 ,基于 Fourier变换法建立了均匀障碍物特征函数与其远场散射振幅中形状因子的关系式 ,最后就基体为锌 ,对圆形和椭圆形截面铁夹杂进行了计算机模拟 ,结果表明该反演方法对定量无损检测技术具有应用价值。     

Synchronization of neural networks based on parameter identification and via output or state coupling

For neural networks with all the parameters unknown, we focus on the global robust synchronization between two coupled neural networks with time-varying delay that are linearly and unidirectionally coupled. First, we use Lyapunov functionals to establish general theoretical conditions for designing the coupling matrix. Neither symmetry nor negative (positive) definiteness of the coupling matrix are required; under less restrictive conditions, the two coupled chaotic neural networks can achieve global robust synchronization regardless of their initial states. Second, by employing the invariance principle of functional differential equations, a simple, analytical, and rigorous adaptive feedback scheme is proposed for the robust synchronization of almost all kinds of coupled neural networks with time-varying delay based on the parameter identification of uncertain delayed neural networks. Finally, numerical simulations validate the effectiveness and feasibility of the proposed technique.     

孔洞受压的统一极限分析

采用俞茂宏统一强度理论分别就平面应变和平面应力总是对受压孔洞进行了极限分析，得到了各自的统一解形式。以往的基于Ｔｒｅｓｃａ、Ｍｉｓｅｓ、双剪屈服准则以及Ｍｏｈｒ－Ｃｏｕｌｏｍｂ强度理论的极限解均为本文统一解的特例。此解可以适应于广泛的不同性质的各灯材料，合理也得出不同材料的相应解。     

Application of nanodiamonds in human body fluid analysis by matrix-assisted laser desorption/ionization mass spectrometry

Direct mass spectrometric analysis of complex biological samples is very important and challenging. In this paper, nanodiamonds have been successfully used in matrix-assisted laser desorption/ionization mass spectrometric analysis of human serum and urine. As a practical tool and platform, it can be widely used in the field of humoral proteomics, and it plays a very promising role in clinical diagnosis, including identification of novel disease-associated biomarkers.     

2种参数对预应力混凝土梁性能的影响分析

分析了跨高比、加载方式对预应力混凝土梁抗弯强度和结构性能的影响．利用平面梁弯曲理论导出单元切线刚度矩阵，提出了新的分析模型可用于预测预应力混凝土梁从开始加载直至失效的非线性全过程响应，并利用建立的分析模型详细评估了跨高比及加载方式等参数对预应力混凝土梁的弯曲强度和受力性能的影响．     

断裂过程区D—B模型的叠加原理和截荷修正法

提出分析中心裂纹板剪切断裂的断裂过程区D－B模型的叠加原理，考虑了压剪断裂的摩擦阻力和体现有限权宽影响的载荷修正，以非常简便的方法推导出计算断裂过程区长度和位移的公式。     

规模化蛋白质生物质谱鉴定中肽段氨基端环化修饰现象

对蛋白质样品制备中引入的氨基酸残基的一种现象--蛋白质酶切肽段氨基端的环化修饰现象的初步研究结果显示,很多以谷氨酰胺(Q)或氨乙酰化修饰的半胱氨酸(CAM_C)残基起始的肽段会发生氨基端的环化修饰,且修饰反应不完全,在同一样本中修饰与非修饰两种状态常同时存在,并且环化修饰后的肽段的反相色谱保留时间发生延迟.在数据库检索时添加环化修饰,可以提高蛋白质的鉴定成功率.本研究结果为大规模的蛋白质质谱数据解析提供了有价值的参考.     

Optimal Conditional Estimation: Average Case Setting

We consider an estimation problem which appears in the identification of systems by means of restricted complexity models: find the optimal approximation to an element of a linear normed space (a system) based on noisy information, subject to the restriction that approximations (models) can be selected from a prescribed subspace M of the problem element space. In contrast to the worst-case optimization criterion, which may be pessimistic, in this paper the quality of an identification algorithm is measured by its local average performance. Two types of local average errors are considered: for a given information (measurement) y and for a given unknown element x, the latter in two versions. For a wide spectrum of norms in the measurement space, we define an optimal algorithm and give expressions for its average errors which show the dependence on information, information errors, unmodelled dynamics, and norm in the measurement space.