Game and Information Theory Analysis of Electronic Countermeasures in Pursuit-Evasion Games

Two-player pursuit-evasion games in the literature typically either assume both players have perfect knowledge of the opponent's positions or use primitive sensing models. This unrealistically skews the problem in favor of the pursuer who needs only maintain a faster velocity at all turning radii. In real life, an evader usually escapes when the pursuer no longer knows the evader's position. In our previous work, we modeled pursuit evasion without perfect information as a two-player bimatrix game by using a realistic sensor model and information theory to compute game-theoretic payoff matrices. That game has a saddle point when the evader uses strategies that exploit sensor limitations, whereas the pursuer relies on strategies that ignore the sensing limitations. In this paper, we consider, for the first time, the effect of many types of electronic countermeasures (ECM) on pursuit-evasion games. The evader's decision to initiate its ECM is modeled as a function of the distance between the players. Simulations show how to find optimal strategies for ECM use when initial conditions are known. We also discuss the effectiveness of different ECM technologies in pursuit-evasion games.      

On the Length and Area Regularization for Multiphase Level Set Segmentation

In this paper we introduce novel regularization techniques for level set segmentation that target specifically the problem of multiphase segmentation. When the multiphase model is used to obtain a partitioning of the image in more than two regions, a new set of issues arise with respect to the single phase case in terms of regularization strategies. For example, if smoothing or shrinking each contour individually could be a good model in the single phase case, this is not necessarily true in the multiphase scenario.     

Associative learning on imbalanced environments: An empirical study

Associative memories have emerged as a powerful computational neural network model for several pattern classification problems. Like most traditional classifiers, these models assume that the classes share similar prior probabilities. However, in many real-life applications the ratios of prior probabilities between classes are extremely skewed. Although the literature has provided numerous studies that examine the performance degradation of renowned classifiers on different imbalanced scenarios, so far this effect has not been supported by a thorough empirical study in the context of associative memories. In this paper, we fix our attention on the applicability of the associative neural networks to the classification of imbalanced data. The key questions here addressed are whether these models perform better, the same or worse than other popular classifiers, how the level of imbalance affects their performance, and whether distinct resampling strategies produce a different impact on the associative memories. In order to answer these questions and gain further insight into the feasibility and efficiency of the associative memories, a large-scale experimental evaluation with 31 databases, seven classification models and four resampling algorithms is carried out here, along with a non-parametric statistical test to discover any significant differences between each pair of classifiers.     

Mapping lichen in a caribou habitat of Northern Quebec, Canada, using an enhancement_classification method and spectral mixture analysis

Studies of caribou herds in northern regions are important to better understand population dynamics and define wildlife management strategies. Lichen is a primary food source for caribou and is a good indicator of caribou herd activity because of its sensitivity to overgrazing and overtrampling, its widespread distribution over northern areas, and its influence on herd demography. In this paper, we used Landsat TM imagery for mapping lichen in the summer range of the George River caribou herd in northern Quebec, Canada. Results from the enhancement_classification method (ECM) and from spectral mixture analysis (SMA) were evaluated for their suitability to characterize lichen landcover and for their potential to be applied over large territories. ECM and SMA are assessed individually, and also for potential synergistic use. ECM is based on guided unsupervised classification of enhanced satellite images. Validation based on 3536 pixels from a relatively smaller number of field sites (20) showed an overall accuracy of 74.5% (kappa=0.70) for 10 classes and good discrimination between lichen and non_lichen classes, though we interpret these results with caution due to spatial autocorrelation and non_random sampling within field sites. However, discrimination amongst different lichen classes using ECM was more problematic. SMA derives the proportion of individual scene components at sub_pixel scales. This method provided good results in characterizing variations in lichen abundance validated against field observations, and provided additional and new information not provided by ECM which is important since the abundance of lichen as a primary food source is a key indicator of migration and demographic patterns essential for effective wildlife management. We concluded that the ECM and SMA methods are appropriate for different aspects of lichen mapping. ECM provided good discrimination between lichen and non_lichen classes whereas SMA provided additional lichen information not available by classification yet critical to the environmental application, which is also appropriate for application over much larger areas and in spatio_temporal studies. A synergistic use of SMA and ECM is therefore recommended for future research.     

Performance evaluation of multilayer perceptron,radial basis function,fuzzy inference system,and an adaptively tuned fuzzy wavelet neural network in parameter prediction of multiphase flow measurement instrumentation

Application of predictive models in industrial multiphase flow metering has attracted an increasing attention recently. Void fraction (VF), water–liquid ratio (WLR), and flow regime are key parameters, measured by oil/water/gas multiphase flow metres (MPFM) in petroleum industry. Artificial neural networks and fuzzy inference systems (FIS) are reliable and efficient computational models, which can be simply implemented on microprocessors of MPFMs, having the advantages of trainability, adaptability, and capability to model non‐linear functions. In this paper, a wavelet‐based adaptive neuro‐FIS (WANFIS) is introduced and validated by the prediction of multiphase flow measurement critical parameters including flow regime, VF, and WLR. The performance of the proposed WANFIS model is then compared with multilayer perceptron (MLP), radial basis function (RBF) network, and an FIS trained by fuzzy c‐means and a subtractive clustering method in the prediction of flow parameters in a customized designed structure of oil/water/gas MPFM. Structural parameters of all predictive models are first optimized to yield the most efficient structure for the available dataset. Comparison is then made between the optimized predictive models, in terms of mean squared error of parameter prediction, computation time, and repeatability of the prediction process. According to the obtained results, MLP model using Levenberg–Marquardt training algorithm and WANFIS model using gradient‐based back propagation dynamical iterative learning algorithm are the most efficient models, which give the best performance compared with other used models. All predictive models can predict the flow regime with 100% accuracy, whereas the highest inaccuracy is related to the prediction of WLR. The results of this study can be used to select and develop the most appropriate predictive model applicable in predicting and identifying flow measurement parameters in industrial MPFMs.     

New Perspectives on Continuum Traffic Flow Models

This paper discusses the mathematical properties of two classes of continuum models that extend the basic kinematic wave model of Lighthill and Whitham, and Richards (LWR)—lower-order and higher-order continuum models. While the differences among the discussed models are pointed out and contrasted, the emphasis is on their commonality. In the latter we found that 1) both classes of models, including the basic kinematic wave model, can violate the anisotropic property of traffic flow, 2) both types of models produce waves non-existent in the LWR model, and 3) both lower and higher order models can be reduced to a kinematic wave model with an effective fundamental diagram. It can therefore be said that the two classes of models are much more closely related than their appearances had led people to believe. The paper concludes with a discussion on the treatment of inhomogeneities and a proposal of a proper procedure for experimentally validating any continuum traffic flow models.     

基于预计算核函数的PBF流体模拟

为加速基于物理模型的流体模拟过程,提升PBF模型解算的实时性,引入一种预计算核函数方法。方法在模型初始化阶段构造并存储函数值表,在解算过程中,通过直接查表得到近似的核函数值及梯度函数值,避免了复杂的高次运算,在误差允许范围内,模型解算速度提升显著。此外还提出了一种PBF框架下的多相流模拟方法,能够模拟密度差别较大的两种不相溶流体之间的交互过程。为图形学中基于物理的多相流场景的模拟提供了一种稳定且高效的解决方案。     

Towards integrating GIS and catchment models

Modeling the impact of non-point source pollution in catchments is a complex problem, and one that has troubled natural resource managers for many years. The development of spatially distributed hydrologic models has led to improved model forecasting at the cost of requiring more detailed spatial information. In addition, the analysis is much more sensitive to errors in the data. Incorporation of catchment models into a Geographical Information System (GIS) has improved matters by streamlining data input and providing better interpretation of model outputs. This paper reviews different strategies for linking a catchment model with GIS. It examines data issues related to the performance of models and how well they match physical landscape conditions. Integration with GIS is shown to be necessary for the efficient and proper operation of models in resource management situations. The paper concludes that tighter integration between generic sub-models for physical landscape processes and GIS is still required.     

Bottleneck analysis for network flow model

It is very important to develop effective strategies for process industry to implement feasible scheduling while the process bottlenecks work optimally. However, the bottlenecks adopted by the existed scheduling strategies are often partial bottlenecks, local bottlenecks, or even deceptive bottlenecks, which are basically not key constraints to achieve optimal objective, so that the corresponding scheduling strategies are not perfect, and the further improvement and evaluation can hardly be proposed. So, it is a valuable issue to analyze the bottlenecks both in academic and engineering fields. This paper aims at the minimum cost problem of generalized network flow model to define and analyse three classes of bottlenecks based on generalized network simplex algorithm, and the corresponding search algorithms are proposed in this paper. The obtained bottlenecks cannot only be used to determine whether the object cost will be increased or decreased, but also be used to propose the corresponding strategy to evaluate the improvement on the network flow model for scheduling. Finally, a typical example is discussed.     

Towards realistic theories of learning

In computational learning theory continuous efforts are made to formulate models of machine learning that are more realistic than previously available models. Two of the most popular models that have been recently proposed, Valiant’s PAC learning model and Angluin’s query learning model, can be thought of as refinements of preceding models such as Gold’s classic paradigm of identification in the limit, in which the question ofhow fast the learning can take place is emphasized. A considerable amount of results have been obtained within these two frameworks, resolving the learnability questions of many important classes of functions and languages. These two particular learning models are by no means comprehensive, and many important aspects of learning are not directly addressed in these models. Aiming towards more realistic theories of learning, many new models and extensions of existing learning models that attempt to formalize such aspects have been developed recently. In this paper, we will review some of these new extensions and models in computational learning theory, concentrating in particular on those proposed and studied by researchers at Theory NEC Laboratory RWCP, and their colleagues at other institutions.     

Comparing classical and neural regression techniques in modeling crude oil viscosity

The importance of crude oil viscosity makes its accurate determination necessary for reservoir performance calculations, evaluation of hydrocarbon reserves, planning thermal methods of enhanced oil recovery, and designing production equipment and pipelines. Viscosity data are also involved in several dimensionless parameters to calculate flow regimes, friction factors and pressure gradients in multiphase flow problems. Numerous research efforts have been directed towards the development of viscosity models that are capable of accurately predicting crude oil viscosity as a function of production data, and/or composition of well stream fluids, if available, using equation of State. Since fluid compositions are not always available, most of the efforts were focused on developing viscosity correlations using classical regression techniques.The study presents, for the first time, a comparison among several models developed using both classical regression techniques (CRT) and neural regression techniques (NRT). These models are developed in this study from viscosity data collected from different oil fields. The models have also been tested using another collection of viscosity data that was not used before in the development phase. Results show that viscosity models developed using NRT were more accurate than viscosity models developed using CRT. Based on this comparison, a viscosity model is therefore presented, which uses stock-tank oil API gravity, gas gravity, pressure(s), and temperature(s) to predict crude oil viscosity. The model was developed using General Regression Neural Network algorithm.     

Numerical simulation of the collision of two microdroplets with a pseudopotential multiple-relaxation-time lattice Boltzmann model

Collisions of two equally sized liquid microdroplets in gaseous phase are numerically studied by the lattice Boltzmann method (LBM). The multiphase formulation adopted is a pseudopotential model with improved treatment of the equation of state and force incorporation which is then coupled with the multiple-relaxation-time scheme. That allows a detailed investigation into microdroplet collisions characterized by high-density ratios as well as by relevant inertial effects. Simulations related to a wide range of flow parameters (e.g. Weber and Reynolds numbers) are reported, in order to embrace all the collisional regimes presented in previous experimental studies. From surface tension-driven coalescence (both inertial and viscous coalescence have been examined) to catastrophic break-up with the formation of children microdroplets, the simulations demonstrate that the LBM correctly reproduces the collision dynamics and the final outcomes in almost all the regimes. Different break-up mechanisms like end-pinching and capillary wave-induced break-up have been observed. Finally, the initial stages of the inertia-dominated head-on collision process have been studied, showing once more the effectiveness and reliability of this multiphase LBM implementation.     

A Divergence-free Mixture Model for Multiphase Fluids

We present a novel divergence free mixture model for multiphase flows and the related fluid-solid coupling. The new mixture model is built upon a volume-weighted mixture velocity so that the divergence free condition is satisfied for miscible and immiscible multiphase fluids. The proposed mixture velocity can be solved efficiently by adapted single phase incompressible solvers, allowing for larger time steps and smaller volume deviations. Besides, the drift velocity formulation is corrected to ensure mass conservation during the simulation. The new approach increases the accuracy of multiphase fluid simulation by several orders. The capability of the new divergence-free mixture model is demonstrated by simulating different multiphase flow phenomena including mixing and unmixing of multiple fluids, fluid-solid coupling involving deformable solids and granular materials.     

液-固-固三相流搅拌槽内流动特性研究

采用计算流体动力学方法,通过欧拉多相流模型对螺杆-挡板搅拌槽内的某液-固-固三相流进行速度场、密度场和各相体积分数分布进行研究。结果表明,在原始的螺杆-挡板式搅拌桨作用下,多相流整体流动性和湍流特性均不太明显,近壁区易形成高密度聚集现象。增设辅助螺杆桨后,湍流现象明显增强,促进了各区域流体的混合对流;近壁区各相体积分数偏离设定值较小,偏差为±5%;中间区域各相体积分数相对稳定,且与设定数值更接近,偏差为±2%,整体而言,混合均匀化效果更好,有效避免了搅拌死角。研究结果对多相流流动特性分析和搅拌装置优化设计具有一定的参考意义和理论价值。     

Parameter estimation via stochastic variants of the ECM algorithm with applications to plant growth modeling

Mathematical modeling of plant growth has gained increasing interest in recent years due to its potential applications. A general family of models, known as functional–structural plant models (FSPMs) and formalized as dynamic systems, serves as the basis for the current study. Modeling, parameterization and estimation are very challenging problems due to the complicated mechanisms involved in plant evolution. A specific type of a non-homogeneous hidden Markov model has been proposed as an extension of the GreenLab FSPM to study a certain class of plants with known organogenesis. In such a model, the maximum likelihood estimator cannot be derived explicitly. Thus, a stochastic version of an expectation conditional maximization (ECM) algorithm was adopted, where the E-step was approximated by sequential importance sampling with resampling (SISR). The complexity of the E-step creates the need for the design and the comparison of different simulation methods for its approximation. In this direction, three variants of SISR and a Markov Chain Monte Carlo (MCMC) approach are compared for their efficiency in parameter estimation on simulated and real sugar beet data, where observations are taken by censoring plant’s evolution (destructive measurements). The MCMC approach seems to be more efficient for this particular application context and also for a large variety of crop plants. Moreover, a data-driven automated MCMC–ECM algorithm for finding an appropriate sample size in each ECM step and also an appropriate number of ECM steps is proposed. Based on the available real dataset, some competing models are compared via model selection techniques.     

An expert system for flow routing in a river network

Flow routing in a river network is one of the most important parts in the domain of water resources and hydrology. There are different characteristics in the different river networks. The mathematical models adopted to deal with the flow routing have their own advantages respectively. Thus a suitable model should be selected to solve the problem of flow routing on a specially appointed river network. But this selection is very difficult and requires highly specialized knowledge and expertise about the models. It is not easy and not appropriate for a civil engineer to spend much time in learning how to select a suitable model and how to manipulate it. Recent developments in mathematical model of flow routing and artificial intelligence have rendered it possible to construct an expert system to help civil engineers make this kind of decision. An expert system is addressed to handle this decision-making in this paper. The way for manipulation of the selected model and whether or not the interbasin rainfall-runoff should be considered are integrated into the system too. VP-Expert is employed as the shell of this expert system. The expert system has been tested on the Changtan watershed, north Guangdong province of China.     

A level set method based on the Bayesian risk for medical image segmentation

This paper proposes an alternative criterion derived from the Bayesian risk classification error for image segmentation. The proposed model introduces a region-based force determined through the difference of the posterior image densities for the different classes, a term based on the prior probability derived from Kullback-Leibler information number, and a regularity term adopted to avoid the generation of excessively irregular and small segmented regions. Compared with other level set methods, the proposed approach relies on the optimum decision of pixel classification and the estimates of prior probabilities; thus the approach has more reliability in theory and practice. Experiments show that the proposed approach is able to extract the complicated shapes of targets and robust for various types of medical images. Moreover, the algorithm can be easily extendable for multiphase segmentation.     

基于USB CDC的虚拟以太网接口研究及设计

分析USB通讯设备类(CDC)协议的机制与流程,讨论CDC协议中的以太网控制模型(ECM)的框架与接口.在CDC的ECM基础上实现USB虚拟以太网接口,对USB虚拟以太网接口与物理以太网接口的性能进行测试与比较,指出USB虚拟以太网接口相对物理以太网接口的特点,为进一步研究USBCDC协议提供参考.     

Selection and fusion of color models for image feature detection

The choice of a color model is of great importance for many computer vision algorithms (e.g., feature detection, object recognition, and tracking) as the chosen color model induces the equivalence classes to the actual algorithms. As there are many color models available, the inherent difficulty is how to automatically select a single color model or, alternatively, a weighted subset of color models producing the best result for a particular task. The subsequent hurdle is how to obtain a proper fusion scheme for the algorithms so that the results are combined in an optimal setting. To achieve proper color model selection and fusion of feature detection algorithms, in this paper, we propose a method that exploits nonperfect correlation between color models or feature detection algorithms derived from the principles of diversification. As a consequence, a proper balance is obtained between repeatability and distinctiveness. The result is a weighting scheme which yields maximal feature discrimination. The method is verified experimentally for three different image feature detectors. The experimental results show that the fusion method provides feature detection results having a higher discriminative power than the standard weighting scheme. Further, it is experimentally shown that the color model selection scheme provides a proper balance between color invariance (repeatability) and discriminative power (distinctiveness)