一维控制系统的最大生存域及人口模型

研究了控制系统的生存性问题.给出了一维微分包含的生存域求法,用优化方法给出了系统的最大生存域,并将相应结果推广到一维混杂微分包含的生存域情形.基于混杂微分包含建立了一种混杂人口模型,此模型的优点在于可以描述人口的突增或突减,然后针对此模型给出了控制人口数量的方法.最后以北京市人口增长为例说明了模型的有效性.     

差分方程在人口增长预测中的应用研究

在人口增长预测的研究中,关于人口总数、性别、年龄结构等预测,一般预测模型准确性较低,预测结果缺乏指导意义;而双曲模型和微分方程模型计算复杂,很难求解,不便于科技水平较低地区使用.为解决上述问题,基于灰色预测模型,提出差分方程构建人口增长预测模型.模型能仿真出不同年龄、不同类型人口变化情况,能准确预测结构变化、性别比例变化、城镇化情况等.应用模型对中国人口进行仿真预测,得到结果与国家人口发展战略研究人口发展预测相符合,为人口增长提供了较好的预测模型.     

知行结合--利用数学模型预测SARS流行规律

费尔哈斯模型 英国经济学家马尔萨斯(Malthus)认为,人口会以几何级的速度无限制增长.而德国生物学家费尔哈斯(Verhulst)经过草履虫实验,建立了一个新的数学模型,即著名的费尔哈斯模型.其核心观点是:人口增长与生物繁殖受环境的制约,不可能按指数规律无限制增长.     

一种基于MAS和GIS平台的城市人口变迁模拟仿真方法

中国的城市化进程非常迅速,人口增长和迁移是重要特征之一．以地理信息系统、Multi—agent系统和随机过程为基础,建立一个研究人口增长的仿真模型．首先,利用GIS建立一个数字环境,用效能函数的概念在GIS中表示各类资源的效能．其次,详细阐述了一个Multi—agent系统设计和实现,重点是用agent代表某个地块及其属性和行为设计．由于发展的不确定性,设计了一个基于随机过程的行为模型,这也和agent宏观水平上的特性一致．最后,以上海市浦东新区为对象,进行了实验模拟和分析．结果表明这个基于Multi—agent系统和GIS的人口变迁仿真模型,具有模块性和可扩展性方面的优势,可应用到其他的城市规划研究中去．     

关于人口系统的稳定性

对于人口系统的连续模型,本文通过其解的递推表达式,用比较简洁的办法,证明了系统 的稳定性与临界生育率之间的关系;并给出了人口系统的更为精细的模型,求得了相应的临界 生育率.     

模型驱动架构中模型构造与集成策略

模型驱动式开发是以模型构造、模型转换和精化为核心的.对象管理组织OMG选择将统一建模语言UML作为标准建模语言,并将其作为模型驱动架构MDA的支持平台.但UML模型缺乏严格的语义,不同视角和不同开发阶段的模型很难集成,所以目前MDA中各种模型之间是脱节的.基于统一程序设计理论UTP,将对象精化演算系统rCOS与UML结合起来,提高了UML模型的精确性与模型转换能力.建立模型驱动式开发中不同层次、不同视角的相关模型并将它们集成起来,建立了一个统一的模型系统.     

人口发展系统的最优控制

本文对连续人口发展模型讨论人口发展系统关于二次性能指标的最优控制,得到了相应的最大值原理。并由此得出时间最优控制所满足的必要条件。作为方法的应用。我们也对离散人口发展系统的最优控制进行了讨论,得到了与[1]同样的结果。     

人口控制

这是一篇有关人口控制理论的综述性文章.人口问题是人类,特别是发展中国家面临的 问题.本文的英文稿曾收集在1987年出版的"系统与控制百科全书"中,文中扼要地叙述了有 关人口控制的一些最近的研究结果.理论的主要部分--人口指数,稳定性理论,人口增长的 最优控制等,取材于作者所进行的研究.     

人口发展系统的最优控制*

本文对连续人口发展模型讨论人口发展系统关于二次性能指标的最优控制，得到了相应的最大值原理。并由此得出时间最优控制所满足的必要条件。作为方法的应用。我们也对离散人口发展系统的最优控制进行了讨论，得到了与[1]同样的结果。     

随机离散人口系统建模及稳定性分析

建立了随机人口系统生育、死亡、迁移过程的差分方程模型,对随机离散人口系统按龄均 值和二阶中心矩的Lyapunov稳定性进行了研究,给出了根据我国1987年1％人口抽查资料 进行仿真的结果.     

Voice conversion using General Regression Neural Network

The objective of voice conversion system is to formulate the mapping function which can transform the source speaker characteristics to that of the target speaker. In this paper, we propose the General Regression Neural Network (GRNN) based model for voice conversion. It is a single pass learning network that makes the training procedure fast and comparatively less time consuming. The proposed system uses the shape of the vocal tract, the shape of the glottal pulse (excitation signal) and long term prosodic features to carry out the voice conversion task. In this paper, the shape of the vocal tract and the shape of source excitation of a particular speaker are represented using Line Spectral Frequencies (LSFs) and Linear Prediction (LP) residual respectively. GRNN is used to obtain the mapping function between the source and target speakers. The direct transformation of the time domain residual using Artificial Neural Network (ANN) causes phase change and generates artifacts in consecutive frames. In order to alleviate it, wavelet packet decomposed coefficients are used to characterize the excitation of the speech signal. The long term prosodic parameters namely, pitch contour (intonation) and the energy profile of the test signal are also modified in relation to that of the target (desired) speaker using the baseline method. The relative performances of the proposed model are compared to voice conversion system based on the state of the art RBF and GMM models using objective and subjective evaluation measures. The evaluation measures show that the proposed GRNN based voice conversion system performs slightly better than the state of the art models.     

Calculation of hydrodynamic forces acting on a submerged moving object using immersed boundary method

General formulae are derived to calculate the hydrodynamic force acting on a solid object, either stationary or in motion, when an immersed boundary (IB) method is used to simulate the flow around the object. These formulae explore the fact that the imposed force term in the IB method contributes to the force applied by the object on the external fluid as well as the unsteady flow inside the virtual domain which is occupied by the object. These formulae are particularly important when the object in unsteady motion is solved in an inertial coordinate system. The formulae are adopted in the present two-dimensional (2D) numerical model, in which a SIMPLEC-type two-step computational scheme is introduced to solve the Navier-Stokes (N-S) equations. Several case studies, including the simulation of the vortex-induced vibration (VIV) of a circular cylinder, are carried out in this work. The agreement of the predicted results with the experimental and numerical data reported by other researchers proves the significance of these formulae.     

Estimating oxygen consumption from heart rate using adaptive neuro-fuzzy inference system and analytical approaches

In new approaches based on adaptive neuro-fuzzy systems (ANFIS) and analytical method, heart rate (HR) measurements were used to estimate oxygen consumption (VO₂). Thirty-five participants performed Meyer and Flenghi's step-test (eight of which performed regeneration release work), during which heart rate and oxygen consumption were measured. Two individualized models and a General ANFIS model that does not require individual calibration were developed. Results indicated the superior precision achieved with individualized ANFIS modelling (RMSE = 1.0 and 2.8 ml/kg min in laboratory and field, respectively). The analytical model outperformed the traditional linear calibration and Flex-HR methods with field data. The General ANFIS model's estimates of VO₂ were not significantly different from actual field VO₂ measurements (RMSE = 3.5 ml/kg min). With its ease of use and low implementation cost, the General ANFIS model shows potential to replace any of the traditional individualized methods for VO₂ estimation from HR data collected in the field.     

Joint sparsity and fidelity regularization for segmentation-driven CT image preprocessing基于稀疏性和保真性正则化模型的分割驱动的CT图像预处理研究

In this paper, we propose a novel segmentation-driven computed tomography (CT) image preprocessing approach. The proposed approach, namely, joint sparsity and fidelity regularization (JSFR) model can be regarded as a generalized total variation (TV) denoising model or a generalized sparse representation denoising model by adding an additional gradient fidelity regularizer and a stronger gradient sparsity regularizer. Thus, JSFR model consists of three terms: intensity fidelity term, gradient fidelity term, and gradient sparsity term. The interactions and counterbalance of these terms make JSFR model has the ability to reduce intensity inhomogeneities and improve edge ambiguities of a given image. Experimental results carried out on the real dental cone-beam CT data demonstrate the effectiveness and usefulness of JSFR model for CT image intensity homogenization, edge enhancement, as well as tissue segmentation.     

基于前向后向算子分裂的稀疏性正则化图像超分辨率算法

提出了一种新的基于稀疏表示正则化的多帧图像超分辨凸变分模型, 模型中的正则项刻画了理想图 像在框架系统下的稀疏性先验, 保真项度量其在退化模型下与观测信号的一致性, 同时分析了最优解条件. 进一步, 基于前向后向算子分裂法提出了求解该模型的不动点迭代数值算法, 每一次迭代分解为仅对保真项的前向(显式)步与仅对正则项的后向(隐式)步, 从而大幅度降低了计算复杂性; 分析了算法的收敛性, 并采取序贯策略提高收敛速度. 针对可见光与红外图像序列进行了数值仿真, 实验结果验证了本文模型与数值算法的有效性.     

改进人工蜂群算法优化RBF神经网络的短时交通流预测

为了提高径向基函数RBF神经网络预测模型对短时交通流的预测准确性,提出了一种基于改进人工蜂群算法优化RBF神经网络的短时交通流预测模型。利用改进人工蜂群算法确定RBF网络隐含层的中心值以及隐含层单元数,然后训练改进的人工蜂群算法RBF神经网络预测模型,并将其应用到某城市4天的短时交通流量数据的验证。将实验结果与传统RBF神经网络预测模型、BP神经网络预测模型和小波神经网络预测模型进行了比较。对比结果表明,该方法对短时交通流具有更高的预测准确性。     

General模型辅助变量辨识方法的研究

对于存在相关噪声干扰的General系统,研究了一种新的辨识方法。首先系统模型用一个有限的脉冲响应（FIR）模型逼近,得到一个Box Jenkins模型,再使用辅助变量法辨识系统参数,最后根据模型等价原理确定原系统的参数估计。仿真结果表明：在这种近似下递推辅助变量法(RIV)比递推广义增广最小二乘法(RGELS)可以得到更好的参数估计。     

Metaphor-based meaning excavation

Excavating the meaning of a target term is a challenge facing information sciences and various applications of Web 3.0. In this paper, we present a novel method for excavating the meaning of a target term by analyzing metaphors in which the term is embedded. More specifically, we (1) review some of the basic theoretical difficulties associated with the dominant cognitive theories of metaphor; (2) outline a mathematical model of metaphor that addresses some of these difficulties; (3) introduce a novel method for excavating metaphor-based meaning; and (4) illustrate the relevance of the methodology and the potential applications of the model with a concrete example.     

GPRS小区流量预测中时序模型的比较研究

针对通用无线分组业务(GPRS)小区流量预测问题,对几种典型时序预测模型的性能进行了综合分析。在总结时序预测模型使用步骤的基础上,分析了自回归（AR）、自回归移动平均（ARIMA）和乘积季节自回归求和移动平均（ARIMA）模型的性能。首先,对GPRS小区流量的变化情况进行分析;再根据流量的自相关系数和偏相关系数,从不同的角度进行分析,分别得到了流量变化的AR模型和ARMA模型;进而利用小区流量以天为周期变化的特点,得到了流量变化的乘积季节ARIMA模型。最后根据GPRS小区历史流量数据,应用这三种模型预测将来某一时间的流量,并对模型性能进行比较研究。     

语音识别中广义模型及其算法收敛性分析

语音识别中,动态时间规整（Ｄｙｎａｍｉｃ Ｔｉｍｅ Ｗａｒｐｉｎｇ,简称ＤＴＷ）和隐马尔可夫模型（Ｈｉｄｄｅｎ Ｍａｒｋｏｖ Ｍｏｄｅｌ,简称 ＨＭＭ）是最有效的两种识别算法,并且 ＤＴＷ和 ＨＭＭ在本质上是一致的~［１］。根据 ＤＴＷ和 ＨＭＭ的本质联系和各自所对应的声学模型,在前期工作中建立了一种广义声学模型 ~［２］［３］（Ｇｅｎｅｒａｌ Ｍｏｄｅｌ,简称 ＧＭ）,并指出 ＤＴＷ和ＨＭＭ 只是 ＧＭ的特例,且 ＤＴＷ和 ＨＭＭ都可以转化为 ＧＭ。并在此基础上,首次将 Ｆｉｓｈｅｒ算法~［４］引进ＧＭ的学习算法,确保了ＧＭ状态分割的收敛性,并且这种分割在最小离差意义上是全局精确最优的。最后,从大数定理的角度出发,对 ＧＭ算法的收敛性进行了分析, 从理论上论证了该算法的依概率收敛性,并为实际应用中 ＧＭ算法的有效性提供了理论依据。