Blind system identification using precise and quantized observations

This paper studies the blind identification of multi-channel FIR systems using precise and quantized observations. First, a new deterministic blind identification (DBI) algorithm is presented for multi-channel FIR systems using precise observations, in which the system parameters can be consistently estimated and the common source signal can be stably recovered. When the observed samples are quantized by a static finite-level quantizer, an iterative deterministic blind identification (IDBI) method is then provided. The asymptotic characters of the proposed IDBI method are discussed and the quantization effect on the identification performance is analyzed. Numerical simulations are given to support the developed DBI method and IDBI method.     

Necessary and sufficient convergence conditions of the instrumental variable method for identification

The consistency of the instrumental variable method is considered for a system driven by independent identically distributed input signals with zero mean. Necessary and sufficient conditions for convergence are developed with respect to the given model structure. The sufficient and necessary conditions are established for cases with and without data prefiltering. In this paper we extend on existing results such that the requirements on the degree of the instrumental variables can be relaxed. Numerical examples supporting the theoretical results are provided.     

Errors-in-variables identification of dynamic systems excited by arbitrary non-white input

This work deals with the identification of dynamic systems from noisy input–output observations, where the noise-free input is not parameterized. The basic assumptions made are (1) the dynamic system can be modeled by a (discrete- or continuous-time) rational transfer function model, (2) the temporal input–output disturbances are mutually independent, identically distributed noises, and (3) the input power spectrum is non-white (not necessarily rational) and is modeled nonparametrically. The system identifiability is guaranteed by exploiting the non-white spectrum property of the noise-free input. A frequency domain identification strategy is developed to estimate consistently the plant model parameters and the input–output noise variances. The uncertainty bound of the estimates is calculated and compared to the Cramér–Rao lower bound. The efficiency of the proposed algorithm is illustrated on numerical examples.     

天文学在卫星技术中的应用

天文学在卫星技术中的应用越来越广泛．针对卫星目标识别技术,本文详细探讨了该项技术的最一般问题：坐标系的转换和星对的角距,并概述其实现方法以及试验结果分析,通过实验证明了该方法在卫星的空中定态技术中是很有效的。     

基于SolidWorks的自动焊缝标注的二次开发

在进行大型焊接零部件设计时,在现有的三维设计软件中设计人员通常采用多实体零件的设计方式进行设计,但是在工程图中焊缝的标注往往是十分费时和费力的,同时为了统计模型中的焊缝信息形成相应的焊缝清单,目前任是人工操作,大大增加了工作量和焊接件的设计周期。文章主要基于SolidWorks三维设计软件,采用C#编程语言,利用SolidWorks对外开放的API函数接口,结合实际的焊缝识别准则,实现了自动提取多实体三维设计模型中的焊缝信息,采用SQLServer数据库对焊缝信息进行存储与读取,并在工程图中进行自动焊缝标注,同时导出对应的焊缝清单。大大减少了人工进行焊缝识别和标注的过程,提高了焊接件的工程图的出图效率,对于减少焊接件的设计周期有着现实的意义。     

Stabilization of gas-lift oil wells by a nonlinear model predictive control scheme based on adaptive neural network models

Producing oil from gas-lift wells are often faced with severe producing oscillatory flow regimes. A major source of the oscillations is recognized as casing–heading instability which is caused by dynamic interaction between injection gas and multiphase fluid. This phenomenon poses strict production-related challenges in terms of lower average production and strain on downstream equipment. In this paper, an effective solution is proposed based on integration of an online interpretation dynamic model and a nonlinear model predictive control (NMPC) scheme. The paper uses adaptive growing and pruning radial basis function (GAP-RBF) neural networks (NNs) to recursively capture the essential dynamics of casing–heading instability in a nonlinear model structure. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) are comparatively investigated to adaptively train modified GAP-RBF NNs. NMPC methodology is developed on the basis of the identified nonlinear NN model for real-time stabilization of casing–heading instability in an oil reservoir equipped with a gas-lift production well. A set of test studies has been conducted to explore the superior performance of the proposed adaptive NMPC controller under different scenarios for an oil reservoir simulated in ECLIPSE and linked to a complementary gas-lifted oil well simulated in programming environment.     

Identification of nonlinear errors-in-variables models

The paper is about a generalization of a classical eigenvalue-decomposition method originally developed for errors-in-variables linear system identification to handle an important class of nonlinear problems. A number of examples are presented to call the attention to the most critical part of the procedure turning the identification problem to a generalized eigenvalue–eigenvector calculation problem with symmetrical matrices. The elaborated method generates consistent parameter estimation. Simulation results demonstrate the effectiveness of the proposed algorithm.     

局域网安全技术的实现

随着人们对Internet的依赖性越来越大,Internet上信息安全问题日益重要起来.运用各种技术手段来保护Internet上信息安全是非常必要的。本文将介绍目前几种主要的保护Internet上信息安全的技术,包括防火墙、数据加密、用户识别技术。     

Subspace morphing theory for appearance based object identification

Object identification techniques have wide applications ranging from industry, business, military, law enforcement, to people's daily life. This research is motivated to develop a new theory for appearance based object identification with its applications in different areas. Although many successful techniques have been proposed in certain specific applications, object identification, in general, still remains as a difficult and challenging problem. In appearance based approaches, almost all the proposed methods are based on a fundamental assumption, i.e., all the images (both in the model base and to be queried) are in the same dimensions, so that the feature vectors are all in the same feature space; if images are provided with different dimensions, a normalization in scale to a pre-determined image space must be conducted. In this research, a theory for appearance based object identification called subspace morphing is developed, which allows scale-invariant identification of images of objects, and therefore, does not require normalization. Theoretical analysis and experimental evaluation show that in the situation where images are provided in different dimensions, which is common in many applications, subspace morphing theory is superior to the existing, normalization-based techniques in performance.     

Can emotions be truly group level? Evidence regarding four conceptual criteria.

Recent advances in understanding prejudice and intergroup behavior have made clear that emotions help explain people's reactions to social groups and their members. Intergroup emotions theory (D. M. Mackie, T. Devos, & E. R. Smith, 2000; E. R. Smith, 1993) holds that intergroup emotions are experienced by individuals when they identify with a social group, making the group part of the psychological self. What differentiates such group-level emotions from emotions that occur purely at the individual level? The authors argue that 4 key criteria define group-level emotions: Group emotions are distinct from the same person's individual-level emotions, depend on the person's degree of group identification, are socially shared within a group, and contribute to regulating intragroup and intergroup attitudes and behavior. Evidence from 2 studies supports all 4 of these predictions and thus points to the meaningfulness, coherence, and functionality of group-level emotions. (PsycINFO Database Record (c) 2010 APA, all rights reserved)