Guaranteed Dynamic-System State Estimation by the Method of Bidirectional Dynamic Programming

The problem of guaranteed estimation (smoothing, filtration, prediction) of a dynamic process observed on a finite discrete time interval is solved, based on generalization of the dynamic programming procedure for the case with sequential optmization in direct and inverse time.     

Estimation of the Bivariate Stable Spectral Representation by theProjection Method

A method of estimating the spectral representation of a generalized bivariatestable distribution is presented, based on a series of maximum likelihood (ML)estimates of the stable parameters of univariate projections of the data. Thecorresponding stable spectral density is obtained by solving a quadraticprogram. The proposed method avoids the often arduous task of computing themultivariate stable density, relying instead on the standard univariate stabledensity. The paper applies this projection procedure, under the simplifyingassumption of symmetry, to simulated data as well as to foreign exchangereturn data, with favorable results. Kanter projection coefficients governingconditional expectations are computed from the estimated spectral density. For the simulated data these compare well to their known true values.     

An Artificial Neural Network Classifier Design Based-on Variable Kernel and Non-Parametric Density Estimation

This paper proposes a probabilistic variant of the SOM-kMER (Self Organising Map-kernel-based Maximum Entropy learning Rule) model for data classification. The classifier, known as pSOM-kMER (probabilistic SOM-kMER), is able to operate in a probabilistic environment and to implement the principles of statistical decision theory in undertaking classification problems. A distinctive feature of pSOM-kMER is its ability in revealing the underlying structure of data. In addition, the Receptive Field (RF) regions generated can be used for variable kernel and non-parametric density estimation. Empirical evaluation using benchmark datasets shows that pSOM-kMER is able to achieve good performance as compared with those from a number of machine learning systems. The applicability of the proposed model as a useful data classifier is also demonstrated with a real-world medical data classification problem.     

Some Statistical-Estimation Methods for Stochastic Finite-State Transducers

Formal translations constitute a suitable framework for dealing with many problems in pattern recognition and computational linguistics. The application of formal transducers to these areas requires a stochastic extension for dealing with noisy, distorted patterns with high variability. In this paper, some estimation criteria are proposed and developed for the parameter estimation of regular syntax-directed translation schemata. These criteria are: maximum likelihood estimation, minimum conditional entropy estimation and conditional maximum likelihood estimation. The last two criteria were proposed in order to deal with situations when training data is sparse. These criteria take into account the possibility of ambiguity in the translations: i.e., there can be different output strings for a single input string. In this case, the final goal of the stochastic framework is to find the highest probability translation of a given input string. These criteria were tested on a translation task which has a high degree of ambiguity.     

矿井中频感应通信系统研究

分析与研究矿井中频感应通信系统 ,并提出了一种新型的矿井中频数字感应通信系统。这种系统采用连续可变斜率增量调制 (CVSD)来实现语音信号的编码、GFM调制解调方式、FDMA、PLL频率合成器技术。本系统具有通话质量好、设备简单、抗衰减能力强等特点 ,适于矿井通信。     

ON THE PARTIAL SUMS OF RESIDUALS IN AUTOREGRESSIVE AND MOVING AVERAGE MODELS

Abstract. The limiting process of partial sums of residuals in stationary and invertible autoregressive moving-average models is studied. It is shown that the partial sums converge to a standard Brownian motion under the assumptions that estimators of unknown parameters are root- n consistent and that innovations are independent and identically distributed random variables with zero mean and finite variance or, more generally, are martingale differences with moment restrictions specified in Theorem 1. Applications for goodness-of-fit and change-point problems are considered. The use of residuals for constructing nonparametric density estimation is discussed.     

Stability analysis in a second-order differential equation with delays

A second-order differential equation with finite discrete delays is considered. Local stability of the zero equilibrium is investigated, and we obtain some sufficient conditions for the zero equilibrium is stable or unstable. Moreover, it is found that there exist the local Hopf bifurcations of the system when the delay varies.     

烧结与冷镦过程中铝硅共晶合金显微结构与密度的演变

采用烧结及后续的镦粗工艺制备了铝硅共晶合金块体材料,研究了烧结温度对烧结体显微结构、抗压强度及相对密度的影响。结果表明：烧结温度显著影响烧结体的显微结构和抗压强度,以临近铝硅共晶合金液相线的温度烧结时,发生了局部熔化,产生的熔融液体破坏了颗粒表面的氧化膜,颗粒之间相互黏结,形成了烧结骨架。以优化的555℃烧结,Si颗粒呈球状,抗压强度达到最佳,但相对密度未发生变化。在后续的冷镦过程中,烧结骨架及粉末颗粒均产生变形,孔隙减小,颗粒呈扁平状,相对密度达到了98%。     

Accurate mirror-based camera pose estimation with explicit geometric meanings

An accurate closed form solution is proposed to estimate camera pose by several mirrored reference object images acquired via a planar mirror under different unknown poses. Compared with state-of-the-art methods, our method is more accurate when there are more than three images and has explicit geometric meanings. This method also properly handles cases in which some of the mirror poses are parallel. The central idea is to minimize an error metric based on all reflections of rotation, which enables the camera rotation to be estimated directly by SVD of sum of mirrored camera rotations. After that, the camera translation is computed by solving a large system of linear equations to minimize object space collinearity error. Both synthesized data and real data experiments show the advantages of our approach.     

Tracking of moving interfaces in sedimentation processes using electrical impedance tomography

Sedimentation monitoring is widely used to control and optimize industrial processes. In this paper we propose a novel computational method for sedimentation monitoring using electrical impedance tomography (EIT). EIT measurements consist of electric current and voltage measurements that are made on the surface of the sedimentation tank and therefore they do not interfere with the sedimentation process. The proposed computational method is based on shape estimation and state estimation formulation of the EIT problem. The sedimentation is parameterized by the locations of the phase interfaces and conductivities of the phase layers. Three different evolution models for the state parameters are considered and the state estimates are computed using the extended Kalman filter algorithm. The performance of the method and the models are evaluated using simulated data from a six electrode EIT measurement configuration. From the results a promising performance of the method can be seen.