一种考虑随机模型精化的单频单历元算法

研究了一种适合变形监测的考虑随机模型精化的单频单历元算法。分析了单频单历元相位双差观测方程法矩阵的特性;基于吉洪诺夫正则化方法,将秩亏的法方程由秩亏变为满秩,得到模糊度的浮动解及其相应的均方误差矩阵,结合LAMBDA方法可准确地固定模糊度,得到基线向量的单历元解;给出了实时权的计算公式,用实时权代替固定权,提高了解算模糊度的成功率。通过一个3km长的基线算例说明算法的效果。

考虑随机模型精化的精密GPS动态定位新方法(英文)

GPS动态定位要求建立函数模型和随机模型。函数模型描述的是观测值和待估参数之间的物理和几何关系,随机模型描述了GPS观测值的统计特征,并通过观测值的方差协方差给定了每个观测值对最后的定位结果的贡献。正确给定函数模型和随机模型对于GPS定位结果的估计和观测值的粗差探测均至关重要。由于有各种误差存在于伪距和载波相位观测值中,一般GPS动态定位模型均采用双差观测值来构建函数模型。有时候,仔细地使用单差观测值,较之双差观测值有更多的优点,给出了选用单差观测值的理由。但是单差观测值给函数模型带来了接收机钟差,如果直接使用单差观测方程,设计矩阵是奇异的。为了解决这个问题,将伪距观测值中接收机钟差项和接收机延迟项合并为一个新的未知参数。至于载波相位观测值,首先选定一个参考卫星,然后在观测方程的右端同时增加一正一负的参考卫星单差整周模糊度,将正项与接收机钟差项和接收机延迟项合并为一个新的未知参数,将负项和原观测方程中的单差整周模糊度项合并为双差整周模糊度,而参考卫星观测方程的模糊度项则为零,这样无须组建双差观测值,软件实现较容易,也可以直接使用LAMBDA法求整周模糊度,最终也解决了观测方程奇异的问题。准确理解观测值的统计特征是建立GPS随机模型的基础。

混凝土桥梁随机劣化过程的Monte-carlo模拟

混凝土桥梁由于本身抗力的衰减以及外界因素的影响,结构性能不断劣化。对桥梁结构劣化过程的分析与预测是桥梁管理优化的基础。由于桥梁劣化影响因素具有一定的随机性,该文利用随机模拟分析方法——Monte-carlo方法分别建立了无维修和考虑不同维修效果两种情况下的混凝土桥梁随机劣化模型,并通过实例分析验证了模型的可行性。

对流扩散传质滞后的电极过程中之非Poisson涨落与非Nernst浓度极化

根据对流扩散传质滞后的恒稳电极过程中边界层的物理图像,提出了该类电极过程的简化随机模型,建立了相应的浓度极化的随机热力学理论,揭示了非Nernst浓度极化来自于随电流密度增大电极化学反应体系涨落分布的非Poisson化与对中心极限律的偏离,进一步阐明了与滞后的扩散步骤共存的对流传质对非Nernst浓度极化的效应及其规律.同时,给出了对流引起的非Nernst浓度极化的随机热力学算例.

Enhancing rock fragmentation prediction in mining operations:A hybrid GWO-RF model with SHAP interpretability

In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.

TWO-DIMENSIONAL STOCHASTIC AIRFOIL OPTIMIZATION DESIGN METHOD BASED ON NEURAL NETWORKS

To avoid the aerodynamic performance loss of airfoil at non-design state which often appears in single point design optimization, and to improve the adaptability to the uncertain factors in actual flight environment, a two-dimensional stochastic airfoil optimization design method based on neural networks is presented. To provide highly efficient and credible analysis, four BP neural networks are built as surrogate models to predict the airfoil aerodynamic coefficients and geometry parameter. These networks are combined with the probability density function obeying normal distribution and the genetic algorithm, thus forming an optimization design method. Using the method, for GA（W）-2 airfoil, a stochastic optimization is implemented in a two-dimensional flight area about Mach number and angle of attack. Compared with original airfoil and single point optimization design airfoil, results show that the two-dimensional stochastic method can improve the performance in a specific flight area, and increase the airfoil adaptability to the stochastic changes of multiple flight parameters.

考虑决策规则异质性的地铁应急疏散选择行为研究

为深入研究乘客在地铁应急疏散选择中的决策规则,考虑决策者的个体选择偏好与所面临决策环境的差异性,从相同决策者在不同情景下和不同决策者在相同情景下遵循的决策规则两个角度出发,分别建立混合Logit模型、广义随机后悔最小化模型和前景理论模型。通过将模型应用于上海世纪大道地铁站问卷调查数据,结果表明地铁应急疏散选择行为中存在决策规则异质性,即相同决策者在不同情景中遵循的决策规则不同,相同的决策者在到出口距离较近的情景中遵循后悔最小,在到出口距离较远的情景中遵循前景最大,在到出口距离更远的情景中遵循效用最大的决策规则;不同决策者在同一情景中遵循的决策规则不同,到该站的目的为通勤、上/下学但并非几乎每天都来的决策者在相同情景中遵循效用最大,到该站的目的为购物餐饮娱乐的决策者遵循前景最大,换乘或其他的决策者遵循后悔最小。决策规则的异质性在地铁应急疏散选择行为研究中不可忽视,将为制定合理的应急疏散策略提供理论依据。

Key parameter optimization and analysis of stochastic seismic inversion

Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density function （PDF） of subsurface models. The Markov chain Monte Carlo （MCMC） method is used to sample the posterior PDF and the subsurface model characteristics can be inferred by analyzing a set of the posterior PDF samples. In this paper, we first introduce the stochastic seismic inversion theory, discuss and analyze the four key parameters： seismic data signal-to-noise ratio （S/N）, variogram, the posterior PDF sample number, and well density, and propose the optimum selection of these parameters. The analysis results show that seismic data S/N adjusts the compromise between the influence of the seismic data and geostatistics on the inversion results, the variogram controls the smoothness of the inversion results, the posterior PDF sample number determines the reliability of the statistical characteristics derived from the samples, and well density influences the inversion uncertainty. Finally, the comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more reliable information of the subsurface character.

The quantified analysis of China's GM cotton yield capacity by C-D function and stochastic frontier model

Using a modified C D function and stochastic frontier model, the paper analyzed China's cotton yield capacity and found that the yield and technical efficiency of China's cotton planting system can be increased by the use of genetically modified (GM) varieties.

后悔理论及其在出行中的应用研究综述

由于后悔理论关注决策者心理体验且能够很好的捕捉决策者心理偏好,因此迅速成为研究决策行为的新兴理论,并广泛应用于出行行为研究.首先阐述后悔理论的提出及其模型发展历程,然后通过对国内外研究现状进行归纳和梳理,从城际出行方式选择、出行线路选择及应急疏散选择等方面,分析了后悔理论在出行行为研究中的应用,最后提出后悔理论在出行行为研究领域的可能发展趋势及研究方向.

在役混凝土桥梁非平稳抗力劣化模型建立与更新

为建立在役混凝土桥梁结构构件非平稳随机抗力劣化模型,并通过后验更新解决劣化模型与实际结构劣化特征的匹配问题,首先,联系在役混凝土桥梁结构实际劣化特征,基于Gamma随机过程推导并建立了初始抗力劣化模型,对初始模型所存在的问题进行探讨;其次,以初始模型吸收融合结构近期实际劣化状态为原则,以结构特定时刻劣化状态的确定为基础,构建了初始抗力劣化模型的后验更新流程;再次,联合非确定性层次分析法(NAHP)与实数遗传算法(RGA)建立了基于定期外观检测数据的特定时刻抗力劣化系数评定方法,为确保该评定方法为初始抗力劣化模型更新提供准确可靠的劣化数据,采用室内模型梁加载试验对所建立评定方法的准确性、适用性进行验证;最后,以一座在役25年的混凝土桥梁为例,基于所建立的分析方法框架,阐述了其一片内梁抗弯承载力劣化模型的建立与后验更新过程。结果发现:基于特定时刻抗力劣化系数评定方法所得到的劣化系数分析结果与试验值的误差介于2.83%~6.24%之间,由该方法所得到的特定时刻抗力劣化系数可应用于初始劣化模型的后验与更新,经过初始劣化模型的后验与更新,所得到的后验模型由于吸收了结构近期的实际劣化状态,在抗力劣化进程描述方面较初始模型具有更高的准确性和更好的匹配性。

OUTLIER TEST IN RANDOMIZED LINEAR MODEL

In this papert we give an approach for detecting one or more outliers inrandomized linear model.The likelihood ratio test statistic and its distributions underthe null hypothesis and the alternative hypothesis are given. Furthermore,the robustnessof the test statistic in a certain sense is proved. Finally,the optimality properties of thetest are derived.

A mixed stochastic user equilibrium model considering influence of advanced traveller information systems in degradable transport network

Advanced traveler information systems （ATIS） can not only improve drivers＇ accessibility to the more accurate route travel time information, but also can improve drivers＇ adaptability to the stochastic network capacity degradations. In this paper, a mixed stochastic user equilibrium model was proposed to describe the interactive route choice behaviors between ATIS equipped and unequipped drivers on a degradable transport network. In the proposed model the information accessibility of equipped drivers was reflected by lower degree of uncertainty in their stochastic equilibrium flow distributions, and their behavioral adaptability was captured by multiple equilibrium behaviors over the stochastic network state set. The mixed equilibrium model was formulated as a fixed point problem defined in the mixed route flows, and its solution was achieved by executing an iterative algorithm. Numerical experiments were provided to verify the properties of the mixed network equilibrium model and the efficiency of the iterative algorithm.

An efficient latent variable optimization approach with stochastic constraints for complex industrial process

For complex chemical processes,process optimization is usually performed on causal models from first principle models.When the mechanism models cannot be obtained easily,restricted model built by process data is used for dynamic process optimization.A new strategy is proposed for complex process optimization,in which latent variables are used as decision variables and statistics is used to describe constraints.As the constraint condition will be more complex by projecting the original variable to latent space,Hotelling T^2 statistics is introduced for constraint formulation in latent space.In this way,the constraint is simplified when the optimization is solved in low-dimensional space of latent variable.The validity of the methodology is illustrated in pH-level optimal control process and practical polypropylene grade transition process.

Phase Transitions Induced by Top-Priority of Randomization

We study the characteristics of phase transition to take the top-priority of randomization in the rules of NaSch model （i.e. noise-first model） into account via computing the relaxation time and the order parameter. The scaling exponents of the relaxation time and the scaling relation of order parameter, respectively, are obtained.

Regression Model to Analyze Differential Response to Treatment in Randomized Controlled Clinical Trial

The primary aim of clinical trials is to investigate whether a treatment is effective for a particular disease or condition. Randomized controlled clinical trials are considered to be the gold standard for evaluating the effect of a certain intervention. However, in clinical trials, even after randomization, there are situations where the patients differ substantially with respect to the baseline value of the outcome variable. Many a times the response to interventions depends on the baseline values of the outcome variable. When there are baseline-dependent treatment effects, differences among treatments vary as a function of baseline level. Although variation in outcome associated with baseline value is accounted for in ANCOVA, analysis of individual differences in treatment effect is precluded by the homogeneity of regression assumption. This assumption requires that expected differences in outcome among treatments be constant across all baseline levels. To overcome this difficulty, Weigel and Narvaez [7] proposed a regression model for two treatment groups to analyze individual response to treatments in randomized controlled clinical trials. The authors reviewed the model suggested by Weigel and Narvaez and extended further for three or more treatment groups. The utility of the model was demonstrated with real life data from a randomized controlled clinical trial of bronchial asthma.

Scaling in Rate-Changeable Birth and Death Processes with Random Removals

We propose a monomer birth-death model with random removals, in which an aggregate of size k can produce a new monomer at a time-dependent rate I（t）k or lose one monomer at a rate J（t）k, and with a probability P（t） an aggregate of any size is randomly removed. We then anedytically investigate the kinetic evolution of the model by means of the rate equation. The results show that the scaling behavior of the aggregate size distribution is dependent crucially on the net birth rate I（t） - J（t） as well as the birth rate I（t）. The aggregate size distribution can approach a standard or modified scaling form in some cases, but it may take a scale-free form in other cases. Moreover, the species can survive finally only if either I（t） - J（t） ≥ P（t） or [J（t） ＋ P（t） - I（t）]t ≈ 0 at t ≥ 1; otherwise, it will become extinct.

A Stochastic Model for Optimizing the Patching Time of Software Bugs

New bugs and vulnerabilities are discovered and reported from time to time even after software products are released. One of the common ways to handle these bugs is to patch the software. In this paper, the authors propose a stochastic model for optimizing the patching time for software bugs and vulnerabilities. The optimal patching time can be computed in the patching script development and operational costs in fix. The authors present two case studies using the Nimda worm vulnerability in Microsoft Internet Information Services web server and the bug report of the Debian project. These studies indicate that the patch applications are later than their optimal fix time.

A Novel Method for Banks to Monitor the Cumulative Loss Due to Defaults

Banking institutions all over the world face significant challenge due to the cumulative loss due to defaults of borrowers of different types of loans. The cumulative default loss built up over a period of time could wipe out the capital cushion of the banks. The aim of this paper is to help the banks to forecast the cumulative loss and its volatility. Defaulting amounts are random and defaults occur at random instants of time. A non Markovian time dependent random point process is used to model the cumulative loss. The expected loss and volatility are evaluated analytically. They are functions of probability of default, probability of loss amount, recovery rate and time. Probability of default being the important contributor is evaluated using Hidden Markov modeling. Numerical results obtained validate the model.