一种新的知觉分割模型*

根据知觉的最新理论研究和实验结果,提出一种新的知觉模型,并用于考查计算机视觉中的分割问题.分割问题实质上是知觉表达问题,现在分割的困难性可能是知觉上局部到整体的还原论假设造成的;分割可能不是一次完成的,而是经过整体到局部的过程才最终完成.新的知觉模型将对建立更好的分割模型提供理论基础.     

基于时间序列模式表示的异常检测算法*

提出了一种基于时间序列的模式表示提取时间序列异常值的异常检测算法(PREOV).时间序列的模式表示本身就具有压缩数据、保持时间序列基本形态的功能,并且具有一定的除噪能力.在时间序列模式表示的基础上提取异常值,可以大大提高算法的效率和准确性,达到事半功倍的效果.在本算法中,还使用了一定的剪枝策略,使得算法的时间复杂度进一步降低.该算法计算简单、实现方便、无须训练,可以支持时间序列的动态增长.     

一种面向重构的工作流活动状态语义描述方法

工作流活动行为的精确描述是实现工作流过程控制的基础。基于状态的工作流活动行为描述方法在当前的工作流管理系统中被普遍采用。然而,非形式化的活动状态描述缺乏精确的语义并且不易扩充,导致系统适应性较差。针对业务过程管理需求的多样性和动态性,提出了一种支持快速重构的工作流活动状态语义的形式化描述方法。在分析工作流活动与工作项的状态关联基础上,定义了活动状态配置、活动状态配置全集等用于活动状态语义描述的基本概念。通过把活动状态映射为具有相同特征的活动状态配置的集合,建立了形式化的活动状态语义,研究了在此语义下活动状态的重构机制和基本实现步骤,并通过实例验证了该机制的有效性。应用本方法能够提高工作流管理系统适应企业业务过程管理需求快速变化的能力。     

机床故障诊断系统知识表示的研究

针对所研究的机床故障诊断专家系统，以故障树分析法为基础，采用面向对象的事件知识表示方法。并以面向对象编程技术给出了知识表示的示例。分析了这种知识表示方法的特点。实践证明，这种知识表示方法在故障诊断专家系统中是确实有效的，而且这种表示方法融合了多种传统知识表示方法的长处，是一种值得研究与应用的方法。     

压缩感知及其图像处理应用研究进展与展望

压缩感知理论（Compressed sensing,CS）通过少量的线性测量值感知信号的原始结构,并通过求解最优化问题精确地重构原信号.该理论减少了数字图像及视频 获取时的存储及传输代价,也为后续的图像处理及识别的研究提供了新的契机,促进了理论和工程应用的结合. 阐述了CS的基本原理,综述了其关键技术稀疏变换、观测矩阵 设计、重构算法的一系列最新理论成果和发展,深入分析和比较了CS理论应用到图像处理领域的研究和发展状况,总结了其中存在的问题,并对未来的应用前景进行了展望.     

FREQUENCY DOMAIN TESTS OF MULTIVARIATE GAUSSIANITY AND LINEARITY

A stationary multivariate time series { X _t } is defined as linear if it can be written in the form X _t = ∑^∞ _{j =−∞} A _j e _{t − j} where A _j are square matrices and e _t are independent and identically distributed random vectors. If the e _t } are normally distributed, then { X _t is a multivariate Gaussian linear process. This paper is concerned with the testing of departures of a vector stationary process from multivariate Gaussianity and linearity using the bispectral approach. First the definition and properties of cumulants of random matrices are used to obtain the expressions for the higher-order cumulant and spectral vectors of a linear vector process as defined above. Then it is shown that linearity of a vector process implies constancy of the modulus square of its normalized higher-order spectra whereas the component of such a vector process does not necessarily have a linear representation. Finally, statistics for the testing of multivariate Gaussianity and linearity are proposed.     

A low-cost real time virtual system for postural stability assessment at home

Background and objective

The degeneration of the balance control system in the elderly and in many pathologies requires measuring the equilibrium conditions very often. In clinical practice, equilibrium control is commonly evaluated by using a force platform (stabilometric platform) in a clinical environment.In this paper, we demonstrate how a simple movement analysis system, based on a 3D video camera and a 3D real time model reconstruction of the human body, can be used to collect information usually recorded by a physical stabilometric platform.

Methods

The algorithm used to reconstruct the human body model as a set of spheres is described and discussed. Moreover, experimental measurements and comparisons with data collected by a physical stabilometric platform are also reported. The measurements were collected on a set of 6 healthy subjects to whom a change in equilibrium condition was stimulated by performing an equilibrium task.

Results

The experimental results showed that more than 95% of data collected by the proposed method were not significantly different from those collected by the classic platform, thus confirming the usefulness of the proposed system.

Conclusions

The proposed virtual balance assessment system can be implemented at low cost (about 500$) and, for this reason, can be considered a home use medical device. On the contrary, astabilometric platform has a cost of about 10,000$ and requires periodical calibration. The proposed system does not require periodical calibration, as is necessary for stabilometric force platforms, and it is easy to use. In future, the proposed system with little integration can be used, besides being an emulator of a stabilometric platform, also to recognize and track, in real time, head, legs, arms and trunk, that is to collect information actually obtained by sophisticated optoelectronic systems.     

ASYMPTOTIC PROPERTIES OF SOME PRELIMINARY ESTIMATORS FOR AUTOREGRESSIVE MOVING AVERAGE TIME SERIES MODELS

Abstract. Some simple preliminary estimators for the coefficients of mixed autoregressive moving average time series models are considered. As the first step the estimators require the fitting of a long autoregression to the data. The first two methods of the paper are non-iterative and generally inefficient. The estimators are Yule-Walker type modifications of the least squares estimators of the coefficients in auxiliary linear regression models derived, respectively, for the coefficients of the long autoregression and for the coefficients of the corresponding long moving average approximation of the model. Both of these estimators are shown to be strongly consistent and their asymptotic distributions are derived. The asymptotic distributions are used in studying the loss in efficiency and in constructing the third estimator of the paper which is an asymptotically efficient two-step estimator. A numerical illustration of the third estimator with real data is given.     

智能化结构分析建模方法

建立正确的模型是进行机械结构分析的关键技术之一。提出了一种基于知识的结构分析建模方法,给出了基于知识结构分析建模方法的总体结构,讨论了知识的获取、分类与表达,提出了推理机制以及智能求解策略,提高了结构有限元分析建模方法的实用性和有效性。最后用实例说明了应用效果。