单静止站纯方位目标航向估计

单静止站纯方位被动声纳系统是不完全可观测系统,关于该类系统的既往研究表明,对于匀速直线运动的目标,利用目标方位角信息可以估计出目标的航向,这具有重要的实际应用意义.通过对目标运动几何模型分析,推导得到目标方位角与目标航向的关系公式,并提出依据几何关系的目标航向估计算法.通过进行坐标变换引入最小二乘估计思想,提高了估计方法的有效性.使用Monte-Carlo计算机仿真测试了算法在不同目标方位角和不同数据长度下的性能,并与拟线性递推法和递推最小二乘法进行了比较,结果表明该算法具有更高的估计精度和更好的适应性.     

Parameter estimation for a morphochemical reaction-diffusion model of electrochemical pattern formation

The process of electrodeposition can be described in terms of a reaction-diffusion partial differential equation (PDE) system that models the dynamics of the morphology profile and the chemical composition. Here we fit such a model to the different patterns present in a range of electrodeposited and electrochemically modified alloys using PDE constrained optimization. Experiments with simulated data show how the parameter space of the model can be divided into zones corresponding to the different physical patterns by examining the structure of an appropriate cost function. We then use real data to demonstrate how numerical optimization of the cost function can allow the model to fit the rich variety of patterns arising in experiments. The computational technique developed provides a potential tool for tuning experimental parameters to produce desired patterns.     

Modelling the propagation of social response during a disease outbreak

Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed. When confronted with fatal or novel pathogens, people exhibit a variety of behaviours from anxiety to hoarding of medical supplies, overwhelming medical infrastructure and rioting. We developed a coupled network approach to understanding and predicting social response. We couple the disease spread and panic spread processes and model them through local interactions between agents. The social contagion process depends on the prevalence of the disease, its perceived risk and a global media signal. We verify the model by analysing the spread of disease and social response during the 2009 H1N1 outbreak in Mexico City and 2003 severe acute respiratory syndrome and 2009 H1N1 outbreaks in Hong Kong, accurately predicting population-level behaviour. This kind of empirically validated model is critical to exploring strategies for public health intervention, increasing our ability to anticipate the response to infectious disease outbreaks.     

Causes of delayed outbreak responses and their impacts on epidemic spread

Livestock diseases have devastating consequences economically, socially and politically across the globe. In certain systems, pathogens remain viable after host death, which enables residual transmissions from infected carcasses. Rapid culling and carcass disposal are well-established strategies for stamping out an outbreak and limiting its impact; however, wait-times for these procedures, i.e. response delays, are typically farm-specific and time-varying due to logistical constraints. Failing to incorporate variable response delays in epidemiological models may understate outbreak projections and mislead management decisions. We revisited the 2001 foot-and-mouth epidemic in the United Kingdom and sought to understand how misrepresented response delays can influence model predictions. Survival analysis identified farm size and control demand as key factors that impeded timely culling and disposal activities on individual farms. Using these factors in the context of an existing policy to predict local variation in response times significantly affected predictions at the national scale. Models that assumed fixed, timely responses grossly underestimated epidemic severity and its long-term consequences. As a result, this study demonstrates how general inclusion of response dynamics and recognition of partial controllability of interventions can help inform management priorities during epidemics of livestock diseases.     

Parameter estimation for the two-parameter weibull distribution using sample statistics and the Jack-Knife

Approximations are given relating the shape parameter of the two-parameter Weibull distribution to the coefficient of variation of a complete (uncensored) data set. The accuracy of the approximations over 0.4 < β < 5 is such as to give adequate estimates in most practical applications. For 0-25 < β < 0.4 and 5 < β < 10, the estimates from the approximation may provide initial values for iterative procedures using moments, maximum likelihood, minimum x², etc. The simplicity of the functions means that jack-knife methods may be used to obtain standard errors of estimates, including the scale parameter (9) where a suitable gamma function approximation is incorporated.     

Noninformative bayesian estimation for the optimum in a single factor quadratic response model

The estimation of the location and magnitude of the optimum has long been considered as an important problem in the realm of response surface methodology. In this paper, we consider the Bayes estimates in a single factor quadratic response function, after a reparametrization from the linear model, using noninformative priors. The usual constant noninformative prior for the reparametrized model does not yield a proper posterior, thus it is desirable to consider other noninformative priors such as the Jeffreys prior and reference priors. Comparisons will be made based on the resulting posterior means, variances and credible intervals by examples and simulations. This work has been supported by the National Science Council, Grants NSC88-2118-M008-009, in Taiwan.     

Parameter estimation of the generalized extreme value distribution for structural health monitoring

Structural health monitoring (SHM) can be defined as a statistical pattern recognition problem which necessitates establishing a decision boundary for damage identification. In general, data points associated with damage manifest themselves near the tail of a baseline data distribution, which is obtained from a healthy state of a structure. Because damage diagnosis is concerned with outliers potentially associated with damage, improper modeling of the tail distribution may impair the performance of SHM by misclassifying a condition state of the structure. This paper attempts to address the issue of establishing a decision boundary based on extreme value statistics (EVS) so that the extreme values associated with the tail distribution can be properly modeled. The generalized extreme value distribution (GEV) is adopted to model the extreme values. A theoretical framework and a parameter estimation technique are developed to automatically estimate model parameters of the GEV. The validity of the proposed method is demonstrated through numerically simulated data, previously published real sample data sets, and experimental data obtained from the damage detection study in a composite plate.     

Bulk wind estimation and prediction for adaptive optics control systems

We present a wind-predictive controller for astronomical adaptive optics (AO) systems that is able to predict the motion of a single windblown layer in the presence of other, more slowly varying phase aberrations. This controller relies on fast, gradient-based optical flow estimation to identify the velocity of the translating layer and a recursive mean estimator to account for turbulence that varies on a time scale much slower than the operating speed of the AO loop. We derive the Cramer-Rao lower bound for the wind estimation problem and show that the proposed estimator is very close to achieving theoretical minimum-variance performance. We also present simulations using on-sky data that show significant Strehl increases from using this controller in realistic atmospheric conditions.     

Range and severity of a plant disease increased by global warming.

Climate change affects plants in natural and agricultural ecosystems throughout the world but little work has been done on the effects of climate change on plant disease epidemics. To illustrate such effects, a weather-based disease forecasting model was combined with a climate change model predicting UK temperature and rainfall under high- and low-carbon emissions for the 2020s and 2050s. Multi-site data collected over a 15-year period were used to develop and validate a weather-based model forecasting severity of phoma stem canker epidemics on oilseed rape across the UK. This was combined with climate change scenarios to predict that epidemics will not only increase in severity but also spread northwards by the 2020s. These results provide a stimulus to develop models to predict the effects of climate change on other plant diseases, especially in delicately balanced agricultural or natural ecosystems. Such predictions can be used to guide policy and practice in adapting to effects of climate change on food security and wildlife.     

Parameter estimation and thermodynamic model fitting for components in mixtures for bio-diesel production

In the search of clean, sustainable and renewable energy sources, at present the use of bio-diesel from vegetable oils is one of the possibilities being considered. The knowledge of phase equilibrium in mixtures found in the bio-diesel production is essential for the correct design and operation of the process. However, there is still a lack of information about the parameter values for the models used to describe the phase equilibrium, which could lead to incorrect design of the reactors and the separation processes, and to low efficiency caused by low mass transfer due to the presence of heterogeneous mixtures. In this study, two algorithms are described for the model fitting and parameter estimation, which were used to estimate the values of the parameters in the UNIQUAC and NRTL model for a mixture of vegetable oils and bio-diesel. One of the algorithms uses the Simulated Annealing (SA) method, to find good initial estimates, without any previous knowledge of the parameters. Another algorithm uses Successive Quadratic Programming, using the estimates calculated in the SA algorithm to refine these estimates. In both algorithms, the criterion for fitting is the minimization of the square of the difference between calculated and experimental values. In both algorithms, the equilibrium is calculated using the direct global minimization of the Gibbs free energy with respect to the number of moles of each component, at constant T and P, for a given set of model parameters. The results obtained were able to correctly reproduce the experimental values of phase equilibrium composition.     

The ideal reporting interval for an epidemic to objectively interpret the epidemiological time course

The reporting interval of infectious diseases is often determined as a time unit in the calendar regardless of the epidemiological characteristics of the disease. No guidelines have been proposed to choose the reporting interval of infectious diseases. The present study aims at translating coarsely reported epidemic data into the reproduction number and clarifying the ideal reporting interval to offer detailed insights into the time course of an epidemic. We briefly revisit the dispersibility ratio, i.e. ratio of cases in successive reporting intervals, proposed by Clare Oswald Stallybrass, detecting technical flaws in the historical studies. We derive a corrected expression for this quantity and propose simple algorithms to estimate the effective reproduction number as a function of time, adjusting the reporting interval to the generation time of a disease and demonstrating a clear relationship among the generation-time distribution, reporting interval and growth rate of an epidemic. Our exercise suggests that an ideal reporting interval is the mean generation time, so that the ratio of cases in successive intervals can yield the reproduction number. When it is impractical to report observations every mean generation time, we also present an alternative method that enables us to obtain straightforward estimates of the reproduction number for any reporting interval that suits the practical purpose of infection control.     

Fatigue life prediction of motor-generator rotor for pumped-storage plant

To predict the fatigue life of motor-generator rotor in a pumped-storage plant, the most dangerous position of the rotor under the highest applied load was found to be at the dovetail of magnetic pole and yoke after failure analysis and stress condition analysis. In this work, two types of steel sheets used to manufacture those parts were chosen to study the microstructures and mechanical properties, especially the fatigue properties. Based on those results, some methods including widely applied Forschungskuratorium Maschinenbau (FKM) method and modified one were used to predict fatigue life of the key parts. Considering possible influencing factors, based on the fatigue theories or methods to modify mean stress and predict fatigue life, a novel method, named key part life (KPL) method, was proposed briefly. The KPL method not only suits well for the motor-generator rotor, but also provides a new idea for life prediction of parts in engineering field.     

Parameter estimation in a thermoelastic composite problem via adjoint formulation and model reduction

Advances in nondestructive material characterization are providing a wealth of information that could be exploited to gain insight into general aspects of material performance and, in particular, discover relationships between microstructure and thermo‐mechanical properties in polycrystalline and other complex composite materials. In order to facilitate the integration of such measurements into existing models, as well as inform new physics‐based predictions, we developed a C++/MPI computational framework for sensitivity analysis and parameter estimation. The framework utilizes a micro‐mechanical modeling based on fast Fourier transforms, direct and adjoint formulations, and Markov chain Monte Carlo sampling techniques. We illustrate the characteristics of this framework and demonstrate its utility by computing the residual stresses arising from thermal expansion of an elastic composite and using data from simulated experiments. We show that the availability of nondestructive 3‐D measurements is crucial to reduce the uncertainty in predictions, emphasizing the importance of an integrated experimental/modeling/data analysis approach for improved material characterization and design. Copyright © 2017 John Wiley & Sons, Ltd.     

State estimation of discrete event systems for RUL prediction issue

This paper concerns Remaining Useful Life (RUL) estimation of discrete event systems. For that purpose, physics-based models with partially observed stochastic Petri nets are used to represent the system and its sensors. The advantage of the proposed modelling approach is to provide a realistic representation of the system, including the interaction between the normal behaviours and the failure processes. From the proposed modelling and collected measurements, timed trajectories, which are consistent with the observations, are obtained. Based on the event dates, our approach consists in evaluating the probabilities of the consistent behaviours using probabilistic models. State estimation is obtained as a consequence. The most probable future degradations, from the current state, are then considered and a method for fault prognosis is presented. Finally, the prognosis result is used to estimate the RUL as a time interval. A case study is proposed to show the applicability of the proposed method.     

Variational estimation of inhomogeneous specular reflectance and illumination from a single view

Estimating the illumination and the reflectance properties of an object surface from a few images is an important but challenging problem. The problem becomes even more challenging if we wish to deal with real-world objects that naturally have spatially inhomogeneous reflectance. In this paper, we derive a novel method for estimating the spatially varying specular reflectance properties of a surface of known geometry as well as the illumination distribution of a scene from a specular-only image, for instance, recovered from two images captured with a polarizer to separate reflection components. Unlike previous work, we do not assume the illumination to be a single point light source. We model specular reflection with a spherical statistical distribution and encode its spatial variation with a radial basis function (RBF) network of their parameter values, which allows us to formulate the simultaneous estimation of spatially varying specular reflectance and illumination as a constrained optimization based on the I-divergence measure. To solve it, we derive a variational algorithm based on the expectation maximization principle. At the same time, we estimate optimal encoding of the specular reflectance properties by learning the number, centers, and widths of the RBF hidden units. We demonstrate the effectiveness of the method on images of synthetic and real-world objects.     

Contact geometry estimation and precise radial force prediction for the radial-axial ring rolling process

In the present research work, the modular parametric design plug-in Grasshopper, available in Rhinoceros 5, is utilized as a pre-processor for the estimation of the projection of the contact length between ring and tools in the radial-axial ring rolling process. The estimated lengths, for each round of the process, are then used in a slip line based force model for the precise estimation of the radial forming force. The proposed method allows reducing the inaccuracies of the traditional approaches since it supersedes the concept of common thickness draft on both mandrel and main roll side, allowing a more precise estimation of the projection of the contact arc between ring and tools, considered to have a unique value on both mandrel side and main roll side. The fulfillment of this last assumption ensures the forming force to have the same value regardless it is calculated on the mandrel side or on the main roll side. The model has been validated by cross-comparing the analytical results with those of laboratory experiment and finite element simulation. The developed analytical model has been also applied to three different study cases where the previous literature models for the calculation of the projection of the contact arc have shown inaccuracies, demonstrating that the proposed approach can overcome these limitations. The positive cross comparisons among laboratory experiment, FEM simulations, and analytical estimations prove the reliability of the proposed approach, as well as its good integration with authors’ previous analytical algorithms.