近红外无创血糖测量中径向基准位置的模拟及验证

利用近红外光谱进行无创血糖检测时,人体自身状况的变化对测量结果的准确性造成非常大的影响.浮动基准法意在测量血糖的过程中寻找一个径向基准点,在这一位置处的漫反射光对葡萄糖浓度的变化不敏感,可以作为内部基准来消除各种背景变化的干扰.以intralipid溶液作为人体组织模拟液,用蒙特卡罗方法以及实验验证了径向基准位置的存在,并研究了不同光学参数对它的影响.模拟结果表明,1300nm波长下径向基准位置距光源1mm,且生物组织的散射特性对它的影响比吸收特性明显;设计的双光纤连续移动测量样品漫反射光能量的实验确认了模拟结果的正确性,证明了径向基准位置的存在.     

基于微扰蒙特卡罗的薄层状组织光学参数重构技术

针对层状组织体的光学参数（吸收系数和散射系数）快速重构问题展开讨论.发展了基于漫反射测量的适合于胃、宫颈等内脏薄层状组织光学参数重构的微扰蒙特卡罗模拟.数值模拟结果显示,该方法能够有效地利用漫反射光强重构临床应用范围内的光学参数,对单层模拟体的吸收系数和散射系数的重构误差均不大于±3%,对双层模拟体的重构误差小于10%,且两参数的重构之间几乎没有互扰.使用固体模型对上述算法进行的实验显示,重构出的散射参数与真值的误差小于±10%.进行一组光学参数重构耗时不超过1 min.结果表明,该方法可快速、准确地重构光学参数,为内脏器官近红外早期癌症诊断的临床应用提供保障.     

Two-terminal reliability analyses for a mobile ad hoc wireless network

Reliability is one of the most important performance measures for emerging technologies. For these systems, shortcomings are often overlooked in early releases as the cutting edge technology overshadows a fragile design. Currently, the proliferation of the mobile ad hoc wireless networks (MAWN) is moving from cutting edge to commodity and thus, reliable performance will be expected. Generally, ad hoc networking is applied for the flexibility and mobility it provides. As a result, military and first responders employ this network scheme and the reliability of the network becomes paramount. To ensure reliability is achieved, one must first be able to analyze and calculate the reliability of the MAWN. This work describes the unique attributes of the MAWN and how the classical analysis of network reliability, where the network configuration is known a priori, can be adjusted to model and analyze this type of network. The methods developed acknowledge the dynamic and scalable nature of the MAWN along with its absence of infrastructure. Thus, the methods rely on a modeling approach that considers the probabilistic formation of different network configurations in a MAWN. Hence, this paper proposes reliability analysis methods that consider the effect of node mobility and the continuous changes in the network's connectivity.     

Manufacturing process problem analysis using monte carlo simulation

The Monte Carlo simulation technique is already well known and used to analyse complex problems. In this paper we present its use to determine the impact of a manufacturing process problem on output quality. During production start-up on an automated manufacturing line a process problem escaped detection for a short period. There was the need to know its impact on output quality in order to provide an appropriate corrective action strategy for the products already in the warehouse and ready for shipment to selected users.     

Simulation and characterization of different setups for gamma ray detection using SiPMs and LYSO scintillators

Silicon photomultipliers (SiPMs) coupled to fast bright scintillators, like cerium doped silicate based crystals, allow the construction of compact gamma ray detectors. In this paper we discuss simulation results obtained from Monte Carlo ray tracing tools applied to SiPM and LYSO systems. We address the importance of three key factors in light propagation: the scintillator wrapping, the coupling medium, and the detector coating. We also propose a simple experiment to verify some of the findings related to the investigation of diffusive wrappings.     

Analysis of one-dimensional stochastic finite elements using neural networks

This paper explores the applicability of neural networks for analyzing the uncertainty spread of structural responses under the presence of one-dimensional random fields. Specifically, the neural network is intended to be a partial surrogate of the structural model needed in a Monte Carlo simulation, due to its associative memory properties. The network is trained with some pairs of input and output data obtained by some Monte Carlo simulations and then used in substitution of the finite element solver. In order to minimize the size of the networks, and hence the number of training pairs, the Karhunen–Loéve decomposition is applied as an optimal feature extraction tool. The Monte Carlo samples for training and validation are also generated using this decomposition. The Nyström technique is employed for the numerical solution of the Fredholm integral equation. The radial basis function (RBF) network was selected as the neural device for learning the input/output relationship due to its high accuracy and fast training speed. The analysis shows that this approach constitutes a promising method for stochastic finite element analysis inasmuch as the error with respect to the Monte Carlo simulation is negligible.     

漫反射光谱测量压力作用下皮肤组织中水含量变化

在体漫反射光谱测量中常采用接触式测量方式,光纤测头压力对测量精度有很大影响.本文基于生物组织固液两相模型,研究了光纤测头压力作用下组织形变和成分变化机理.分别测量生物组织随压力大小和作用时间变化的漫反射光谱,并进行二阶导数处理,根据1 160 nm和1 220 nm两个波长吸收峰变化,得到生物组织中自由水和结合水含量改变,从而得到组织形变过程.实验结果表明,生物组织中的自由水和结合水体积变化与压力大小以及作用时间呈非线性关系,并与生物组织生理状态密切相关.在体光谱测量中应控制光纤测头压力小于200 kPa,以避免组织中结合水转化成自由水.本文的研究结果为漫反射光谱测量部位和测量策略选择提供了参考.     

随机结构数值模拟分析的神经网络法

在随机结构分析中,蒙特卡洛方法作为随机数值模拟方法,为问题提供了最为直观和精确的解答,但计算量大、效率低下的缺点大大降低了方法的实用性.研究在蒙特卡洛方法中引入人工神经网络,仅进行少量确定性分析,训练后即可模拟确定性有限元求解器,用神经网络的快速泛化映射取代蒙特卡洛法中的大量确定性有限元分析.算例结果显示,提出的蒙特卡洛-神经网络法可将蒙特卡洛法的计算效率提高几十至一百倍,计算精度令人满意,是一种有潜力的随机结构实用分析方法.     

Determination of the pore size distribution of correlated mesoporous networks

In the present work we study how the adsorption desorption hysteresis loop of a mesoporous disordered medium represented by a 3-dimensional Dual Site-Bond Model (DSBM) is affected by percolation. Site and bond distributions are assumed to be gaussians. The behavior of the threshold pressure for the evaporation processe suggests a method to determine the site and bond distributions from experimental adsorption-desorption hysteresis curves. Traditional methods developed for non-correlated networks are tested and evaluated against our simulation results showing the discrepancy mainly for highly correlated networks. Results of the prediction capability of our method are shown. Received: 30 March 2000     

SIMULATE Program: a gamma ray spectroscopy tool

A software package which simulates the virtual creation of gamma ray spectra emitted from a combination of radioactive sources, as seen by a semiconductor or scintillation detector, is presented . It partially utilizes Monte Carlo techniques based on the physics of gamma ray spectroscopy. In addition, certain algorithms are used to compensate for the premature termination of the fate of the detected photons of any particular energy.     

The Interpretation of Diffuse Reflectance Spectra

Numerous treatments of the diffuse reflecting properties of scattering media have been described. Many theories give an adequate account of the reflectance for a specific set of conditions for which the model was constructed and the solution tested experimentally. Only those models which are considered to be fairly general are considered here.It is convenient to divide the theories into those based upon continuum models and those based upon statistical models. The continuum models typically describe the scattering and absorbing properties of a given medium in terms of two phenomenological constants. These models may all be regarded as varying levels of approximate solution to the general equation of radiative transfer. This provides a convenient basis for comparison of the various theories.The statistical models are based upon a summation of transmittances and reflectances from individual layers or particles. Thus, some assumptions must be made about the nature of the fundamental units, and the validity of the ultimate result will depend upon how closely these assumptions correspond with reality. Only the statistical models lead to expressions from which absolute absorptivities and scattering coefficients can be calculated and related to the actual particle characteristics.The relationship between the various models will be discussed and the features which typify the absorptivity and scattering coefficient according to each will be compared and related to the available experimental data. This leads to a consideration of the characteristics of appropriate model systems and standards.     

用MC仿真计算不确定度

介绍MC仿真的基础,将它用于不确定度的分布传播,并作了扩展不确定度的算法验证.     

Optimal design approach for heating irregular-shaped objects in three-dimensional radiant furnaces using a hybrid genetic algorithm–artificial neural network method

In this article, a new hybrid method based on the combination of the genetic algorithm (GA) and artificial neural network (ANN) is developed to optimize the design of three-dimensional (3-D) radiant furnaces. A 3-D irregular shape design body (DB) heated inside a 3-D radiant furnace is considered as a case study. The uniform thermal conditions on the DB surfaces are obtained by minimizing an objective function. An ANN is developed to predict the objective function value which is trained through the data produced by applying the Monte Carlo method. The trained ANN is used in conjunction with the GA to find the optimal design variables. The results show that the computational time using the GA-ANN approach is significantly less than that of the conventional method. It is concluded that the integration of the ANN with GA is an efficient technique for optimization of the radiant furnaces.     

基于改进混沌粒子群的混合神经网络和蒙特卡洛法的结构随机灵敏度分析方法

针对不确定性参数对结构力学性能的随机影响,该文利用混合神经网络良好的小样本学习和泛化能力构建结构响应复杂的函数关系,采用改进的混沌粒子群算法优化网络寻址结构。结合蒙特卡洛法对结构进行随机性分析,并根据该文提出的新的灵敏度度量参数计算随机变量的全局灵敏度系数。通过数学算例和工程算例验证了所提方法的可行性,且结构响应的概率分布曲线也可以真实的反应实际情况。同时,利用该文所提出的随机灵敏度计算方法可以更好的反应各随机变量对结构响应的相关性和敏感性。