动画柔性可变形物体的弹簧模型研究

连续变形模型通常用控制点网格表示，用这些点间的相互作用模糊弹性属性。该文给出了一个可变形弹簧模型和动画布料的弹力公式，在公式中将布料属性当作外部弹力考虑，可适当适当地设置布料的弹性属性，简单地考虑模型点间的相互作用，较精确地描述布料的行为。     

Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones

We study Markov models whose state spaces arise from the Cartesian product of two or more discrete random variables. We show how to parameterize the transition matrices of these models as a convex combination—or mixture—of simpler dynamical models. The parameters in these models admit a simple probabilistic interpretation and can be fitted iteratively by an Expectation-Maximization (EM) procedure. We derive a set of generalized Baum-Welch updates for factorial hidden Markov models that make use of this parameterization. We also describe a simple iterative procedure for approximately computing the statistics of the hidden states. Throughout, we give examples where mixed memory models provide a useful representation of complex stochastic processes.     

A Calibration Procedure of Dynamic CGE Models for Non-Steady State Situations Using GEMPACK

This paper is concerned with the development of a calibration procedure for dynamic CGE models for non-steady state situations. While the literature on calibration of dynamic models mainly concentrates on the process of calibration in the context of a steady state, this essay centers upon the calibration procedure for the case that the model's state variables are not initially at their steady state values. Though this is the more realistic interpretation of a base year's data set it raises a number of additional difficulties including intertemporal feedback and the requirement that the basic data are consistent with the intertemporal equations of the model. These difficulties are discussed and a procedure is presented which can cope with them. Finally, the procedure is applied to an overlapping generations growth model with foresight and the steps involved in calibration are illustrated in detail.     

基于反例确认的CPS不确定性模型校准

信息物理系统被广泛应用于众多关键领域,例如工业控制与智能制造.作为部署在这些关键领域中的系统,其系统质量尤为重要.然而,由于信息物理系统自身的复杂性以及系统中存在的不确定性（例如系统通过传感器感知环境时的偏差）,信息物理系统的质量保障面临巨大挑战.验证是保障系统质量的有效途径之一,基于系统模型与规约它可以证明系统是否满足要求的性质.现有一些信息物理系统的验证工作也取得了显著进展,例如模型检验技术就被已用工作用于验证系统在不确定性影响下的行为是否满足性质规约,并在性质违反的情况给出具体的反例.这些验证工作的一个重要输入就是不确定性模型,它描述了系统中不确定性的具体情况.而实际中要对系统中不确定性精确建模却并非易事,因此验证中使用的不确定性模型很可能与实际不完全相符,这将导致验证结果不准确并与现实偏离.针对这一问题,本文提出了一种基于反例确认的不确定性模型校准方法,来进一步精化验证结果以提高其准确度.首先通过确认反例在系统的执行中能否被触发来判断验证使用的不确定性模型是否精确.对于不精确的模型再利用遗传算法进行校准,并根据反例确认的结果来构造遗传算法的适应度函数以指导搜索,最后结合假设检验来帮助决定是否接受校准后的结果.在代表案例上的实验结果表明了我们提出的不确定性模型校准方法的有效性.     

基于anchor模型的说话人检索技术

本文研究了基于anchor模型的说话人检索技术,提出了基于SCV分量方差和基于广义似然比聚类的模型剪枝方法,对基于序数比较的相似测度进行了改进,使SCV各分量的数值和比值都参与到识别中来,提高了检索性能.通过实验印证了将传统的说话人检索中的模型训练过程转变为计算语音相对于anchor说话人模型距离的映射过程,所带来的计算量和存储量的优势,为说话人检索在大型语音库和嵌入式系统的应用提出了新的思路.     

基于星凸RHM的扩展目标SMC-PHD滤波

针对扩展目标跟踪过程中存在的外形拟合和非线性的问题,提出一种基于星凸随机超曲面模型(RHM)的扩展目标序贯蒙特卡洛概率假设密度滤波(SMC-PHD)算法。该算法运用星凸RHM对扩展目标量测源建模,在SMC-PHD的框架下,推导出非线性滤波算法的量测似然表达式和更新方程,实现扩展目标跟踪。仿真结果证明,文中所提算法的跟踪性能较其他滤波对于目标扩散程度和质心估计均有提高。     

卷烟生产线柔性优化调度模型研究

Through analyzing technological process of cigarette producing line and utilizing the basic idea of modern graph theory, the connected network system based on continuous material flow is drawn from cigarette producing line. The thought of flexible optimization schedule is proposed and the optimization schedule models of cigarette producing line are created. Then their visualized example is given. These optimization schedule models have already been used for actual cigarette producing schedule and they have direction meaning for instructing manager at producing ground to carry on optimization schedule.     

随机二元序列等概性的一种检验方法

等概性是随机密钥品质的一个重要特性,分析了频数检验在检测等概性上存在的不足,并提出了一种随机二元序列等概性的检验方法。     

基于云模型的自适应网络安全通信系统

针对传统安全通信系统采用静态安全策略,从而缺乏可生存性与高效性的问题,提出了一种由系统安全威胁和系统性能双重驱动的白适应网络安全通信模型。使用冗余和多样性技术,该模型能够根据系统当前安全态势以及系统性能状态动态地针对每个会话调整安全策略,具有很强的可生存性和高效性。     

点度精确的随机网络模型

文章针对Waxman模型提出了两种使用方便、平均点度精确的网络模型:PD1和PD2。并对几种主要模型通过实验进行了对比。结果表明,新方法产生的随机网络几乎在任何尺寸下都很接近实际的网络。同时,文中方法可使网络节点的平均点度非常精确,几乎等于预设的度。     

采用随机移动模型的无线自组织仿真网络连通性分析

以无线自组织网络的连通性作为研究对象,根据几何随机图中最小度原理,结合均匀分布、泊松分布、随机点模型网络、随机方向模型网络等几种网络的节点概率分布,计算网络中节点的最小度,得到网络1-连通度与节点数和通信半径的定量关系,证明网络连通性按照均匀网络、随机方向模型网络和随机点模型网络的次序减弱。     

Archaeological Data Models and Web Publication Using XML

An appropriate standardized data model is necessary tofacilitate electronic publication and analysis ofarchaeological data on the World Wide Web. Ahierarchical ``item-based' model is proposed which canbe readily implemented as an Extensible MarkupLanguage (XML) tagging scheme that can represent anykind of archaeological data and deliver it in across-platform, standardized fashion to any Webbrowser. This tagging scheme and the data model itimplements permit seamless integration and jointquerying of archaeological datasets derived from manydifferent sources.     

Multi-Modal Dialog Scene Detection Using Hidden Markov Models for Content-Based Multimedia Indexing

A class of audio-visual data (fiction entertainment: movies, TV series) is segmented into scenes, which contain dialogs, using a novel hidden Markov model-based (HMM) method. Each shot is classified using both audio track (via classification of speech, silence and music) and visual content (face and location information). The result of this shot-based classification is an audio-visual token to be used by the HMM state diagram to achieve scene analysis. After simulations with circular and left-to-right HMM topologies, it is observed that both are performing very good with multi-modal inputs. Moreover, for circular topology, the comparisons between different training and observation sets show that audio and face information together gives the most consistent results among different observation sets.     

基于双模预测的精确码率控制

精确的码率控制是进行高效视频编码与传输的关键。针对现有码率控制技术的不足,提出了在视频编码中采用两个率失真模型进行码率预测的思想以提高码率控制的精度。对H.264的G012码率控制框架进行改进,给出一种基于双模预测的自适应码率控制方法。实验仿真结果显示,与传统的单模码率控制方法相比,基于双模预测的码率控制算法能保证在不影响编码质量的前提下有效地提高码率控制的精度。     

Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels

Many process model analysis techniques rely on the accurate analysis of the natural language contents captured in the models’ activity labels. Since these labels are typically short and diverse in terms of their grammatical style, standard natural language processing tools are not suitable to analyze them. While a dedicated technique for the analysis of process model activity labels was proposed in the past, it suffers from considerable limitations. First of all, its performance varies greatly among data sets with different characteristics and it cannot handle uncommon grammatical styles. What is more, adapting the technique requires in-depth domain knowledge. We use this paper to propose a machine learning-based technique for activity label analysis that overcomes the issues associated with this rule-based state of the art. Our technique conceptualizes activity label analysis as a tagging task based on a Hidden Markov Model. By doing so, the analysis of activity labels no longer requires the manual specification of rules. An evaluation using a collection of 15,000 activity labels demonstrates that our machine learning-based technique outperforms the state of the art in all aspects.     

基于多模型切换的多变量直接自适应极点配置控制器

提出一种基于多模型切换的多变量直接自适应控制器,控制器参数模型集可由系统参数模型集直接得到,不但仍为一紧集并且被其模型子集完全覆盖.该多模型控制器不仅改善了控制系统的暂态响应,而且可以实现极点配置,最后给出了全局收敛性分析.     

Flexible latent variable models for multi-task learning

Given multiple prediction problems such as regression or classification, we are interested in a joint inference framework that can effectively share information between tasks to improve the prediction accuracy, especially when the number of training examples per problem is small. In this paper we propose a probabilistic framework which can support a set of latent variable models for different multi-task learning scenarios. We show that the framework is a generalization of standard learning methods for single prediction problems and it can effectively model the shared structure among different prediction tasks. Furthermore, we present efficient algorithms for the empirical Bayes method as well as point estimation. Our experiments on both simulated datasets and real world classification datasets show the effectiveness of the proposed models in two evaluation settings: a standard multi-task learning setting and a transfer learning setting.     

A Hybrid Framework for Mobile Robot Localization: Formulation Using Switching State-Space Models

In this paper we address one of the most important issues for autonomous mobile robots, namely their ability to localize themselves safely and reliably within their environments. We propose a probabilistic framework for modelling the robot's state and sensory information based on a Switching State-Space Model. The proposed framework generalizes two of the most successful probabilistic model families currently used for this purpose: the Kalman filter Linear models and the Hidden Markov Models. The proposed model combines the advantages of both models, relaxing at the same time inherent assumptions made individually in each of these existing models.