Modeling Reusable Web Business Processes from an Aspect Oriented Perspective

In this paper we present an aspect-oriented approach to define the navigational concern of Web business processes. Web business processes introduce important modifications into the navigational space and structure of e-commerce applications. From an aspect-oriented perspective, Web business processes are crosscutting concerns, whose elements are scattered and tangled with the rest of navigational elements of e-commerce systems. Web business process modeling lacks modularity, decreasing the system comprehensibility and adaptability. In this paper, we present an aspect-oriented approach to model reusable Web business process. This paper also states Web engineering approaches must be aware of the concepts and techniques introduced by the Aspect Oriented community.     

A Genetic Algorithm Convergence and Models for Eigenstructure Assignment via Linear Quadratic Regulator (LQR)

In this article, models and a genetic algorithm convergence analysis method for the LQR weight matrices determination are presented. The control goal is the eigenstructures allocation in multivariable dynamic systems imposed by the Optimal Control Law, and the analysis procedure goal is the speeding up of the convergence through metrics based upon first and second order statistical momentum. The proposal performance is evaluated under the initial populations building point of view and the populations search process, whereas the convergence analysis leads to the development of rules based upon fitness function metrics. Tests for the genetic search models performance evaluation and for the control law efficiency are conducted with a 6th order dynamic model representing an aircraft.     

Compact Models of Dynamic Thermal Networks with Many Heat Sources

A novel approach, based both on multipoint moment matching and principal component analysis, is proposed for generating passive compact models of dynamic thermal networks with many heat sources. As main result, the generated passive compact dynamic thermal networks both assure a prescribed accuracy and have a number of elements linearly related to the number of heat sources.     

一种多进程系统用例模型的逆向生成方法

用例模型是展现程序系统级行为的有效手段.文章针对具有并发特征的面向对象软件系统提出了一种多进程系统用例模型的逆向生成方法.该方法以进程为单位对目标系统的动态信息进行组织,使用单进程系统用例模型的逆向生成方法抽取出每个进程相应的用例模型,然后对具有相同进程名的不同进程的用例模型进行叠加,从而实现了多进程系统的用例模型的逆向生成.通过实验研究,表明该方法在恢复多进程系统的用例模型方面是有效的.     

基于过程结构树的流程间差别检测算法

流程模型差别检测是业务流程管理的关键技术之一.针对流程模型大多是由图结构建模,而流程图模型中有多种类型节点,因此经典的图编辑距离方法无法直接应用于流程差别检测的问题,提出了基于过程结构树的流程间差别检测算法.算法首先将流程模型转化为基于任务节点的过程结构树;然后采用分治思想快速获得流程间的最佳对等节点映射集合;最后基于节点映射集合生成一个近似最小代价编辑操作序列来表示两个流程的差别.实验结果表明,本文算法在准确率和效率两方面都能满足实际的应用需求.     

DCM模式Boost开关变换器的非线性全阶建模和仿真

提出了一种工作在断续电感电流模式下的DC-DC Boost(升压)变换器模型.在经典状态空间平均法基础上运用占空比限制和系数修正矩阵加以改进,并考虑寄生参数的影响.推导出状态方程,建立其非线性全阶模型.对瞬态响应和开环传递函数进行仿真,结果较已报道文献,直观、物理意义明确、精度更高.     

Generation of Dynamic Infrared Outer Space Scene

This paper proposes a new method to generate the dynamic infrared outer space scene in real time. In the paper, we first model the dynamic infrared background of star sky based on the infrared star catalogue and temporal and spacial coordinate transformations. We also present a new concept called infrared star magnitude for given infrared receiving wavebands to display the infrared star sky effectively. Then we put forward an infrared image synthesis model for outer space targets based on the method of divided elements and energy equilibrium. To generate the dynamic infrared outer space scene, several simplification and accelerating techniques such as Billboard, Levels of Details and Occlusion Culling are hired to accelerate the generating speed of the scene. Finally infrared images of targets, background of outer space scene at different infrared receiving wavebands are drawn and the dynamic infrared outer space scene is successfully carried out in real time.     

Construction of Nonlinear Dynamic MEMS Component Models Using Cosserat Theory

This paper presents a new modelling method for MEMS components based on the Cosserat theory. By constructing Cosserat rod elements, generating symbolic code and developing numeric code in VHDL-AMS for selected demonstrators, we are able to effectively simulate and predict the essential linear and nonlinear behaviour in estimating system performance and guiding the reliability verification process. The simplicity of the Cosserat approach follows from the possibility to formulate interconnected slender components in terms of a network of quasi-rigid bodies (Cosserat elements). This modularization fits well into the behavioural modelling where a relatively low degree of freedom is needed to capture salient features of the system. Furthermore, the manner in which Cosserat elements and their interactions are formulated makes this method ideally suited for nonlinear dynamic simulations in the presence of large deflections.     

Count Models for Software Quality Estimation

Identifying which software modules, during the software development process, are likely to be faulty is an effective technique for improving software quality. Such an approach allows a more focused software quality & reliability enhancement endeavor. The development team may also like to know the number of faults that are likely to exist in a given program module, i.e., a quantitative quality prediction. However, classification techniques such as the logistic regression model (lrm) cannot be used to predict the number of faults. In contrast, count models such as the Poisson regression model (prm), and the zero-inflated Poisson (zip) regression model can be used to obtain both a qualitative classification, and a quantitative prediction for software quality. In the case of the classification models, a classification rule based on our previously developed generalized classification rule is used. In the context of count models, this study is the first to propose a generalized classification rule. Case studies of two industrial software systems are examined, and for each we developed two count models, (prm, and zip), and a classification model (lrm). Evaluating the predictive capabilities of the models, we concluded that the prm, and the zip models have similar classification accuracies as the lrm. The count models are also used to predict the number of faults for the two case studies. The zip model yielded better fault prediction accuracy than the prm. As compared to other quantitative prediction models for software quality, such as multiple linear regression (mlr), the prm, and zip models have a unique property of yielding the probability that a given number of faults will occur in any module     

Numerical Simulation Models of Forming Systems of Intense Gyrotron Helical Electron Beams

Potentialities of different numerical models for calculation of helical electron beams parameters are analyzed. The most simple and economical with respect to the calculation time static mono-velocity model, as well as the model considering initial velocities of electrons and the dynamic model based on the PIC-method and on the quasi-static concept of the electric field are described. The main physical results obtained on the basis if each model mentioned above are given.     

Analytical Models of Passive Linear Structures in Printed Circuit Boards

Analytical models for passive linear structures, like metallic traces, vias, are proposed for simulations at the package and Printed circuit board (PCB) levels. In the proposed method, traces are modeled based on the transmission line theory, whereas the vias are described by the parallel-plate impedance and several equivalent circuits elements. The proposed models can be applied to efficiently simulate composed passive linear structures. Several scenarios are analyzed including traces with two or three width, traces routed into different layers and interconnects commonly used in PCBs. The results of the models are compared with those from the full-wave simulations and experiments. An improvement on the computation speed has been observed with respect to the full-wave simulations at the effective range of models. In our measurements, a compensation approach of impedance mismatch in parameter measurements is analyzed and calculated, which could significantly simplify the experimental process.     

Models and Tools for CMOS Integrated Inductors

In this paper we are reporting our research in the development of automatic tools to assist the designers in selecting and automatically laying-out integrated inductors. This task is accomplished by analyzing carefully the lumped equivalent circuit model for these passive components, and using different approaches and modifications depending on the required accuracy and application. As a result modified circuit models for integrated inductors based on the conventional lumped element model are proposed. Model development is based on measurements taken from more than 100 integrated spiral inductors designed and fabricated in a standard silicon process. We show the ability of the proposed models to accurately predict the integrated inductor behavior extending the frequency range where they can be applied as compared with the conventional model.     

Time-Frequency ARMA Models and Parameter Estimators for Underspread Nonstationary Random Processes

Parsimonious parametric models for nonstationary random processes are useful in many applications. Here, we consider a nonstationary extension of the classical autoregressive moving-average (ARMA) model that we term the time-frequency autoregressive moving-average (TFARMA) model. This model uses frequency shifts in addition to time shifts (delays) for modeling nonstationary process dynamics. The TFARMA model and its special cases, the TFAR and TFMA models, are shown to be specific types of time-varying ARMA (AR, MA) models. They are attractive because of their parsimony for underspread processes, that is, nonstationary processes with a limited time-frequency correlation structure. We develop computationally efficient order-recursive estimators for the TFARMA, TFAR, and TFMA model parameters which are based on linear time-frequency Yule-Walker equations or on a new time-frequency cepstrum. Simulation results demonstrate that the proposed parameter estimators outperform existing estimators for time-varying ARMA (AR, MA) models with respect to accuracy and/or numerical efficiency. An application to the time-varying spectral analysis of a natural signal is also discussed.     

基于系统动力学机制的电子对抗装备管理基模研究

基模是分析系统中动态性复杂问题的重要技术，也是进行系统思考的有力工具。文章在系统因果关系图和动态性复杂分析的基础上，建立了电子对抗装备管理的五个基模，并给出了一个具体应用实例——电子对抗装备规模优化管理模型。     

Improved Gaussian Mixture Models for Adaptive Foreground Segmentation

Adaptive foreground segmentation is traditionally performed using Stauffer and Grimson’s algorithm that models every pixel of the frame by a mixture of Gaussian distributions with continuously adapted parameters. In this paper we provide an enhancement of the algorithm by adding two important dynamic elements to the baseline algorithm: The learning rate can change across space and time, while the Gaussian distributions can be merged together if they become similar due to their adaptation process. We quantify the importance of our enhancements and the effect of parameter tuning using an annotated outdoors sequence.     

Generalized Shock Models Based on a Cluster Point Process

We review the principal progress of shock models during the last three decades. Our new model, the$delta$-shock model, and its latest developments are also introduced. Furthermore, for adapting shock models to a wider reliability field, we put forward a generalized framework for studying shock models, based on cluster point processes with cluster marks. In addition, we provide a noteworthy case under this framework as a concrete example in reliability with an insurance background, and give the asymptotic distribution of the risk process.     

Automated Generation of Compact Models for Fluidic Microsystems

Simulation and design of microfluidic systems requires various level models: high-fidelity models (usually 3D) for design and optimization of particular elements and devices, as well as system-level models allowing for VLSI-scale simulation of such systems. For the latter purpose, reduced or compact models are necessary to make such system simulations computationally feasible. In this paper, we present a design methodology and practical approach for generation of compact models of microfluidic elements. In this procedure we use high-fidelity 3D simulations of the microfluidic devices to extract their characteristics for compact models, and subsequently, to validate the compact model behavior in various regimes of operation. The compact models are generated automatically in the formats that can be directly used in SPICE or SABER. As an example of a nonlinear fluidic device, the generation of compact model for Tesla valve is described in detail. Tesla valve is one of the no-moving-parts (NMP) valves used in micropumps in MEMS. Its principle of operation is based on the rectification of the fluid, so it may be considered as a fluidic diode.     

超高阶的RLGC互连线时域状态空间模型及其研究

在互连线的建模方面,无论是考虑频变或工艺参数变化还是考虑降解,都需要一个精确的原始模型。它不仅使各种模型简化方法有个精确的起点,而且也是评估各种模型简化方法近似性能的基础。该文用直接闭合式的方法给出了一个计算复杂度为O(N)的精确的超高阶互连线RLGC时域状态空间模型,有3种形式,它是一般互连线树的基本构造元素,也是一种特殊的互连线树。该模型在形式上比文献中给出的更简洁。低阶模型与2000阶模型进行了比较,结果表明阶数较低的RLGC/RLC电路模型与分布参数电路模型相比在电路的振荡特性以及上升时间的描述上存在相当大的误差,因此在互连线树的建模中支路(包括根和叶的互连线)采用低阶原始模型值得商榷。     

Models and Algorithms for Tracking of Maneuvering Objects Using Variable Rate Particle Filters

Standard algorithms in tracking and other state-space models assume identical and synchronous sampling rates for the state and measurement processes. However, real trajectories of objects are typically characterized by prolonged smooth sections, with sharp, but infrequent, changes. Thus, a more parsimonious representation of a target trajectory may be obtained by direct modeling of maneuver times in the state process, independently from the observation times. This is achieved by assuming the state arrival times to follow a random process, typically specified as Markovian, so that state points may be allocated along the trajectory according to the degree of variation observed. The resulting variable dimension state inference problem is solved by developing an efficient variable rate particle filtering algorithm to recursively update the posterior distribution of the state sequence as new data becomes available. The methodology is quite general and can be applied across many models where dynamic model uncertainty occurs on-line. Specific models are proposed for the dynamics of a moving object under internal forcing, expressed in terms of the intrinsic dynamics of the object. The performance of the algorithms with these dynamical models is demonstrated on several challenging maneuvering target tracking problems in clutter.