基于ADS的脉冲压缩雷达系统仿真与性能分析

在高级设计系统ADS仿真平台上,开发了一套具有良好通用性和灵活性的脉冲压缩雷达系统模型库,为今后开发大规模雷达系统模型库和进行复杂雷达系统仿真打下了基础;在此工作基础上,以巴克码脉冲压缩信号为例,建立了相应的雷达系统模型,并结合系统性能分析的需求,在该系统仿真设计过程中引入蒙特.卡罗模型,较全面地得到了系统基本性能指标值;由这些指标值可以看出,该巴克码脉冲压缩雷达系统性能良好;仿真结果与理论值和真实系统值相符,从而正确地完成了系统的方案论证和性能评估。

Growth Mechanism of Microcrystalline Silicon Films by Scaling Theory and Monte Carlo Simulation

Hydrogenated microcrystalline silicon （~c-Si：H） films with a high deposition rate of 1.2nm/s were prepared by hot-wire chemical vapor deposition （HWCVD）. The growth-front roughening processes of the μc-Si..H films were investi- gated by atomic force microscopy. According to the scaling theory, the growth exponent β≈0.67, the roughness exponent α≈0.80,and the dynamic exponent 1/z = 0.40 are obtained. These scaling exponents cannot be explained well by the known growth models. An attempt at Monte Carlo simulation has been made to describe the growth process of μc-Si： H film using a particle reemission model where the incident flux distribution,the type and concentration of growth radical, and sticking,reemission,shadowing mechanisms all contributed to the growing morphology.

Critical Spreading of Active Region in a Ladder Model Possessing Infinite AbsorbingStates

The spreading of active region of the ladder model is simulated. Finite-time scaling behaviors are observedin the vicinity of the critical point.

Regional Clustering and Synchronization of Provincial Business Fluctuations in China

In this article, we propose a novel, multilevel, dynamic factor model, to determine endogenously clustered regions for the investigation of regional clustering and synchronization of provincial business fluctuations in China. The parameter identification and model estimation was conducted using the Markov Chain Monte Carlo method. We then conducted an empirical study of the provincial business fluctuations in China(31 Chinese provinces are considered except Hong Kong, Macao, and Taiwan due to the data unavailability), which were sampled from January 2000 to December 2015. Our results indicated that these provinces could be clustered into four regions: leading, coincident, lagging, and overshooting. In comparison with traditional geographical divisions, this novel clustering into four regions enabled the regional business cycle synchronization to be more accurately captured. Within the four regional clusters it was possible to identify substantial heterogeneities among regional business cycle fluctuations, especially during the periods of the 2008 financial crisis and the ‘four-trillion economic stimulus plan'.

Joint analysis of urban shopping destination and travel mode choice accounting for potential spatial correlation between alternatives

In recent years,there have been important developments in the joint analysis of the travel behavior based on discrete choice models as well as in the formulation of increasingly flexible closed-form models belonging to the generalized extreme value class.The objective of this work is to describe the simultaneous choice of shopping destination and travel-to-shop mode in downtown area by making use of the cross-nested logit(CNL) structure that allows for potential spatial correlation.The analysis uses data collected in the downtown areas of Maryland-Washington,D.C.region for shopping trips,considering household,individual,land use,and travel-related characteristics.The estimation results show that the dissimilarity parameter in the CNL model is 0.37 and significant at the 95% level,indicating that the alternatives have high spatial correlation for the short shopping distance.The results of analysis reveal detailed significant influences on travel behavior of joint choice shopping destination and travel mode.Moreover,a Monte Carlo simulation for a group of scenarios arising from transportation policies and parking fees in downtown area,was undertaken to examine the impact of a change in car travel cost on the shopping destination and travel mode switching.These findings have important implications for transportation demand management and urban planning.

Natural ventilation performance of single room building with fluctuating wind speed and thermal mass

Natural ventilation is driven by either buoyancy forces or wind pressure forces or their combinations that inherit stochastic variation into ventilation rates. Since the ventilation rate is a nonlinear function of multiple variable factors including wind speed, wind direction, internal heat source and building structural thermal mass, the conventional methods for quantifying ventilation rate simply using dominant wind direction and average wind speed may not accurately describe the characteristic performance of natural ventilation. From a new point of view, the natural ventilation performance of a single room building under fluctuating wind speed condition using the Monte-Carlo simulation approach was investigated by incorporating building facade thermal mass effect. Given a same hourly turbulence intensity distribution, the wind speeds with 1 rain frequency fluctuations were generated using a stochastic model, the modified GARCH model. Comparisons of natural ventilation profiles, effective ventilation rates, and air conditioning electricity use for a three-month period show statistically significant differences （for 80% confidence interval） between the new calculations and the traditional methods based on hourly average wind speed.

Hybrid LEAP modeling method for long-term energy demand forecasting of regions with limited statistical data

An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.

A Wave Propagation Model Based on Monte-Carlo Particle-Tracing

This article describes a new wave propagation model based on Monte-Carlo particle-tracing. This model relies on Monte-Carlo integration and Huygens currents radiating. The particles used to compute the field permit to consider the interferences. This model includes the diffraction of the surface without edge computation. The implementation of this propagation model is based on a image synthesis renderer. The results of this model are studied in far field situation with perfectly conducting shapes, by comparing results with a classical MoM method.

Calculation model of military aircraft suryiyability to a missile

Since missiles are main threat against aircrafts in air war,a model is proposed for calculating the aircraft survivability to a missile.The hit characteristic of aircraft to a missile is analyzed,and then Monte Carlo method is applied to generate missile detonation location according to its distribution rule.In addition,based on the analysis of fragment trajectory and critical components,the intersection point of these two is determined.Then the kill probability of critical component to a fragment can be calculated,and the aircraft survivability to a missile is obtained accordingly.Finally,the feasibility of the proposed method is demonstrated.Simulation results show that this method captures the basic effects of missile detonation locations on aircraft survivability,which may provide an effective reference to aircraft survivability research.

Evaluation of Deposit Insurance Fund Adequacy Using Credit Risk Model--An Indian Experience

There are two methods widely used for evaluating the adequacy of Deposit Insurance Fund： （i） Target Reserve Ratio and （ii） Credit Risk Model. Target Reserve Ratio is one of the macro level indicators more often set by Regulatory act on the basis of minimum Deposit Insurance Fund margin safety, Target Reserve Ratio is calculated as the ratio of Deposit Insurance Fund to the value of insured deposits. However, TRR does not take into consideration the level of Deposit Insurance potential liability, the Loss at Given Default （LGD） and the historical trend of default rate prevailing among the insured banks. It does not also consider the present condition of the economy and current scenario of the banking sector. This paper discusses primarily about development of Credit Risk Model for evaluating the Deposit Insurance Fund Adequacy. For this purpose, Econometric Credit Risk Model was developed based on the historical data of bank failures and the associated losses of the last 25 years from 1990-91 to 2014-15. The model assesses various possible factors impacting the Deposit Insurance Fund： Default rate, Deposit growth, Exposures, impact of macro-economic factors like GDP, GDS, Inflation and Interest rate changes, etc. on the Deposit Insurance Fund through econometric modeling. The model evaluates the adequacy of Deposit Insurance Fund under both （i） Normal scenarios where there is no （economic） systemic risk assumed and （ii） Worst case scenario at 1% level of significance using Monte Carlo Simulation. Since the model empirically validates all the critical factors impacting the assets and liabilities associated with Loss at Given Default, the model output can also be used to determine a suitable Target Reserve Ratio and such models are being used in countries like USA, Canada, Hong Kong, and Singapore, etc. （IADI, 2009）. More importantly, the model outputs are quite useful in determining the adequacy of deposit insurance fund which is an effective risk control measure that organization like Deposit Insurance Credit Guarantee Corporation （DICGC） can adopt both under normal economic scenario as well as worst case scenario, ensuring a strong financial safety net for the banking sector in India. The model also assesses the default probability and the Loss at Given Default of different types of banks： commercial banks, rural banks, cooperative banks, foreign banks, etc. A risk based on premium can possibly be determined for each type of banks in India.

Phase Diagram of XY Model with Nematic Coupling on the Simple Cubic Lattice

Using cluster Monte Carlo method, we numerically investigate the coupling on the simple cubic lattice. We determine critical lines belong to the criticality in the XY model with nematic three-dimensional XY universality class in variable of θ （2θ） between the XY-ferromagnetic （nematic） and disordered states. Fhrthermore, the phase transition between the XY-ferromagnetic and the nematie states is found to be in the three-dimensional Ising universality class. The critical points are determined from the intersections of Binder ratios for various system sizes. With two sets of critical points obtained, we finally construct the phase diagram on the A-J plane.

Potential-Decomposition Strategy in Markov Chain Monte Carlo Sampling Algorithms

We introduce the potential-decomposition strategy （PDS）, which can be used in Markov chain Monte Carlo sampling algorithms. PDS can be designed to make particles move in a modified potential that favors diffusion in phase space, then, by rejecting some trial samples, the target distributions can be sampled in an unbiased manner. Furthermore, if the accepted trial samples are insumcient, they can be recycled as initial states to form more unbiased samples. This strategy can greatly improve efficiency when the original potential has multiple metastable states separated by large barriers. We apply PDS to the 2d Ising model and a double-well potential model with a large barrier, demonstrating in these two representative examples that convergence is accelerated by orders of magnitude.

Validation of the Monte Carlo Model Designed to Simulate the Neutronic Characteristics of Advanced Boiling Water Reactor Assembly

In the last few years, interest in burnup calculations using Monte Carlo methods has increased. Previous burnup codes have used diffusion theory for the neutronic portion of the codes. Diffusion theory works well for most reactors. However, diffusion theory does not produce accurate results in burnup problems that include strong absorbers or large voids. MCNPX code based on Mont Carlo Method, is used to design a three dimensional model for a BWR fuel assembly in a typical operating temperature and pressure conditions. A test case was compared with a benchmark problem and good agreement was found. The model is used to calculate the distribution of pin by pin power and flux inside the assembly. The effect of axial variation of water （coolant） density, and of control rods motion on the neutron flux and power distribution is analyzed. The effect of addition of Gd2O3 to natural uranium （0.711%） on both the thermal neutron flux and normalized power are analyzed. The concentration of U^235, U^238, Pu^239, and its isotopes is also calculated at burn-up 50 GWD/T.

The Testing Intelligence System Based on Factor Models and Self-Organizing Feature Maps

Presented is a new testing system based on using the factor models and self-organizing feature maps as well as the method of filtering undesirable environment influence. Testing process is described by the factor model with simplex structure, which represents the influences of genetics and environmental factors on the observed parameters - the answers to the questions of the test subjects in one case and for the time, which is spent on responding to each test question to another. The Monte Carlo method is applied to get sufficient samples for training self-organizing feature maps, which are used to estimate model goodness-of-fit measures and, consequently, ability level. A prototype of the system is implemented using the Raven＇s Progressive Matrices （Advanced Progressive Matrices） - an intelligence test of abstract reasoning. Elimination of environment influence results is performed by comparing the observed and predicted answers to the test tasks using the Kalman filter, which is adapted to solve the problem. The testing procedure is optimized by reducing the number of tasks using the distribution of measures to belong to different ability levels after performing each test task provided the required level of conclusion reliability is obtained.

Simulating Particle Swarm Optimization Algorithm to Estimate Likelihood Function of ARMA（1, 1） Model

This paper present a simulation study of an evolutionary algorithms, Particle Swarm Optimization PSO algorithm to optimize likelihood function of ARMA（1, 1） model, where maximizing likelihood function is equivalent to maximizing its logarithm, so the objective function ＇obj.fun＇ is maximizing log-likelihood function. Monte Carlo method adapted for implementing and designing the experiments of this simulation. This study including a comparison among three versions of PSO algorithm “Constriction coefficient CCPSO, Inertia weight IWPSO, and Fully Informed FIPSO”, the experiments designed by setting different values of model parameters al, bs sample size n, moreover the parameters of PSO algorithms. MSE used as test statistic to measure the efficiency PSO to estimate model. The results show the ability of PSO to estimate ARMA＇ s parameters, and the minimum values of MSE getting for COPSO.

Pore scale simulation of liquid and gas two-phase flow based on digital core technology

Two-phase flow in two digital cores is simulated by the color-gradient lattice Boltzmann method.This model can be applied totwo-phase flow with high-density ratio(on order of 1000).The first digital core is an artificial sandstone core,and itsthree-dimensional gray model is obtained by Micro-CT scanning.The gray scale images are segmented into discrete phases(solid particles and pore space) by the Otsu algorithm.The second one is a digital core of shale,which is reconstructed usingMarkov Chain Monte Carlo method with segmented SEM scanning image as input.The wettability of solid wall and relativepermeability of a cylindrical tube are simulated to verify the model.In the simulations of liquid and gas two phase flow in digital cores,density ratios of 100,200,500 and 1000 between liquid and gas are chosen.Based on the gas distribution in the digital core at different times,it is found that the fingering phenomenon is more salient at high density ratio.With the density ratioincreasing,the displacement efficiency decreases.Besides,due to numerous small pores in the shale,the displacement efficiency is over 20% less than that in the artificial sandstone and the difference is even about 30% when density ratio is greaterthan 500.As the density ratio increases,the gas saturation decreases in big pores,and even reaches zero in some small pores orbig pores with small throats.Residual liquid mainly distributes in the small pores and the edge of big pores due to the wettability of liquid.Liquid recovery can be enhanced effectively by decreasing its viscosity.

Progress in mesoscopic modeling of microstructure evolution in steels

The mesoscopic modeling developed rapidly in the past three decades is a promising tool for predicting and understanding the microstructure evolution at grain scale.In this paper,the recent development of mesoscopic modeling and its application to microstructure evolution in steels is reviewed.Firstly,some representative computational models are briefly introduced,e.g.,the phase field model,the cellular automaton model and the Monte Carlo model.Then,the emphasis is put on the application of mesoscopic modeling of the complex features of microstructure evolution,including solidification,solid-state phase transformation,recrystallization and grain growth.Finally,some issues in the present mesoscopic modeling and its perspective are discussed.

Classification of estuaries in China based on eutrophication susceptibility to nutrient load

Recently, environmental pressures along coasts have increased substantially. Classification of estuaries according to their sus- ceptibility to eutrophication nutrient load is a useful method to determine priority management objects and to enforce control measures. Using historical monitoring data from 2007 to 2012, from 65 estuaries, including 101 estuarine monitoring sections and 260 coastal monitoring stations, a nutrient-driven phytoplankton dynamic model was developed based on the relationship among phytoplankton biomass, Total Nitrogen （TN） load and physical features of estuaries. The ecological filter effect of es- tuaries was quantified by introducing conversion efficiency parameter values into the model. Markov Chain Monte Carlo algo- rithm of Bayesian inference was then employed to estimate parameters in the mode/. The developed model fitted well to the observed chlorophyll, primary production, grazing, and sinking rates. The analysis suggests that an estuary with Q/V （the ratio of river flow to estuarine volume） greater than 2.0 per year and e （conversion efficiency ratio） less than 1.0 g C/g N can be classified as less susceptible to TN load, Q/V between 0.7 to 2.0 per year and e between 1.0 to 3.0 g C/g N as moderately sus- ceptible, and e greater than 3.0 g C/g N as very susceptible. The estuaries with Q/V less than 0.7 per year vary greatly in their susceptibility. The estuaries with high and moderate susceptibility accounted for 67% of all the analyzed estuaries. They have relatively high eutrophication risks and should be the focus of environmental supervision and pollution prevention.

The skinner automaton: A psychological model formalizing the theory of operant conditioning

Operant conditioning is one of the fundamental mechanisms of animal learning, which suggests that the behavior of all animals, from protists to humans, is guided by its consequences. We present a new stochastic learning automaton called a Skinner au- tomaton that is a psychological model for formalizing the theory of operant conditioning. We identify animal operant learning with a thermodynamic process, and derive a so-called Skinner algorithm from Monte Carlo method as well as Metropolis algo- rithm and simulated annealing. Under certain conditions, we prove that the Skinner automaton is expedient, 6-optimal, optimal, and that the operant probabilities converge to the set of stable roots with probability of 1. The Skinner automaton enables ma- chines to autonomously learn in an animal-like way.

Molecule-based kinetic modeling by Monte Carlo methods for heavy petroleum conversion

A methodology for kinetic modeling of conversion processes is presented.The proposed approach allows to overcome the lack of molecular detail of the petroleum fractions and to simulate the reactions of the process by means of a two-step procedure.In the first step,a synthetic mixture of molecules representing the feedstock is generated via a molecular reconstruction method,termed SR-REM molecular reconstruction.In the second step,a kinetic Monte Carlo method,termed stochastic simulation algorithm(SSA),is used to simulate the effect of the conversion reactions on the mixture of molecules.The resulting methodology is applied to the Athabasca vacuum residue hydrocracking.An adequate molecular representation of the vacuum residue is obtained using the SR-REM algorithm.The reaction simulations present a good agreement with the laboratory data for Athabasca vacuum residue conversion.In addition,the proposed methodology provides the molecular detail of the vacuum residue conversion throughout the reactions simulations.