基于隐式分段自回归模型的图像插值算法

利用自然图像信号的分段统计稳态性可以有效地对图像信号进行建模.其分段统计稳态区域往往具有非规则的形态.采用规则窗口对图像统计稳态区域内的统计量进行估计存在较大的误差.提出一种基于概率描述的隐式分段自回归模型来刻画分段统计稳态区域形态,并基于该模型提出了一种改进的图像插值算法.实验结果表明,该方法可以较好地改善插值图像中在边缘处的模糊、振铃和噪声等瑕疵现象.     

A bounded-error approach to piecewise affine system identification

This paper proposes a three-stage procedure for parametric identification of piecewise affine autoregressive exogenous (PWARX) models. The first stage simultaneously classifies the data points and estimates the number of submodels and the corresponding parameters by solving the partition into a minimum number of feasible subsystems (MIN PFS) problem for a suitable set of linear complementary inequalities derived from data. Second, a refinement procedure reduces misclassifications and improves parameter estimates. The third stage determines a polyhedral partition of the regressor set via two-class or multiclass linear separation techniques. As a main feature, the algorithm imposes that the identification error is bounded by a quantity /spl delta/. Such a bound is a useful tuning parameter to trade off between quality of fit and model complexity. The performance of the proposed PWA system identification procedure is demonstrated via numerical examples and on experimental data from an electronic component placement process in a pick-and-place machine.     

Identification of piecewise affine systems based on statistical clustering technique

This paper is concerned with the identification of a class of piecewise affine systems called a piecewise affine autoregressive exogenous (PWARX) model. The PWARX model is composed of ARX sub-models each of which corresponds to a polyhedral region of the regression space. Under the temporary assumption that the number of sub-models is known a priori, the input-output data are collected into several clusters by using a statistical clustering algorithm. We utilize support vector classifiers to estimate the boundary hyperplane between two adjacent regions in the regression space. In each cluster, the parameter vector of the sub-model is obtained by the least squares method. It turns out that the present statistical clustering approach enables us to estimate the number of sub-models based on the information criteria such as CAIC and MDL. The estimate of the number of sub-models is performed by applying the identification procedure several times to the same data set, after having fixed the number of sub-models to different values. Finally, we verify the applicability of the present identification method through a numerical example of a Hammerstein model.     

随机系数自回归模型变均值点在线监测与应用

对随机系数自回归模型的变均值点进行在线监测时, 如果变均值点的位置远离开始监测点, 则平均地说, 需要较长的运行时间方能检测到该变均值点. 为此, 笔者引进一个窗宽参数, 提出了一种改进的在线监测方法. 给出了监测统计量在原假设下的极限分布, 并证明了此方法的一致性. 模拟结果显示新方法明显优于已有的方法. 最后将该方法应用于两组股票价格均值点的监测问题中, 说明了方法的有效性.     

Recursive estimation for a noisy image represented by a hyperbolic PDE model

A recursive estimation technique is applied to the noisy image which is represented by a hyperbolic partial differential equation (PDE). Approximating a PDE model by a finite-difference approximation leads to an autoregressive (AR) model representation which Jain pointed out. In this paper, we use a new PDE model for the image representation. To apply a recursive estimation scheme to the image which is degraded by white noise, we propose the transformation of the.AR model into a state-space representation. To this representation, we apply a Kalman strip processor to reduce the order of the computation and storage, and the strip smoother (the optimal fixed-interval smoother) is also applied to obtain a better estimated imago. Three numerical examples are illustrated to show the effectiveness of the proposed algorithm.     

A novel binary image representation algorithm by using NAM and coordinate encoding procedure and its application to area calculation

We propose a novel binary image representation algorithm using the non-symmetry and anti-packing model and the coordinate encoding procedure (NAMCEP). By taking some idiomatic standard binary images in the field of image processing as typical test objects, and by comparing our proposed NAMCEP representation with linear quadtree (LQT), binary tree (Bintree), non-symmetry and anti-packing model (NAM) with K-lines (NAMK), and NAM representations, we show that NAMCEP can not only reduce the average node, but also simultaneously improve the average compression. We also present a novel NAMCEP-based algorithm for area calculation and show experimentally that our algorithm offers significant improvements.     

基于分片线性函数的图象压缩方法

使用了一种新分片线性逼近算法,算法首先对极大极小友谊赛一分片线性函数的紧凑表示形式做了改进,然后发挥了分片线性逼近的优势。在此基础上,提出了一种基于分片线性逼近的图象压缩编码方法。这种方法具有解压缩速度快的优点,与其它的图象压缩方法（例如DCT）相结合,能够提高图象的压缩效率。     

Data filtering based least squares iterative algorithm for parameter identification of output error autoregressive systems

This letter derives a data filtering based least squares iterative identification algorithm for output error autoregressive systems. The basic idea is to use the data filtering technique to transform the original identification model to an equation error model and to estimate the parameters of this model. The proposed algorithm is more efficient and can produce more accurate parameter estimation than the existing least squares iterative algorithm.     

Feature Selection Using a Piecewise Linear Network

We present an efficient feature selection algorithm for the general regression problem, which utilizes a piecewise linear orthonormal least squares (OLS) procedure. The algorithm 1) determines an appropriate piecewise linear network (PLN) model for the given data set, 2) applies the OLS procedure to the PLN model, and 3) searches for useful feature subsets using a floating search algorithm. The floating search prevents the “nesting effect.” The proposed algorithm is computationally very efficient because only one data pass is required. Several examples are given to demonstrate the effectiveness of the proposed algorithm.     

A Wiener-type recurrent neural network and its control strategy for nonlinear dynamic applications

This paper presents a Wiener-type recurrent neural network with a systematic identification algorithm and a control strategy for the identification and control of unknown dynamic nonlinear systems. The proposed Wiener-type recurrent network resembles the conventional Wiener model that consists of a dynamic linear subsystem cascaded with a static nonlinear subsystem. The novelties of our network include: (1) the two subsystems are integrated into a single network whose output is expressed by a nonlinear transformation of a linear state-space equation; (2) the characteristics of the trained network can be analyzed by its associated state-space equation using the well-developed theory of linear systems; and (3) the size of the network structure is determined by the number of state variables (or the system order) of the unknown systems to be identified. To effectively identify a given unknown system from its input–output data, we have developed a systematic identification algorithm that consists of an order determination procedure, a parameterization procedure, and an online learning procedure. The false nearest neighbors algorithm was adopted to acquire a minimal embedding dimension from the input–output data as the system order, and then the eigensystem realization algorithm (ERA) was used to initialize a best-fit state-space representation according to the acquired system order. To improve the overall identification performance, we have derived an online parameter learning algorithm based on an ordered derivatives and momentum terms. Subsequently, a simple feedback linear controller was designed to control the unknown dynamic nonlinear systems without much complexity. Computer simulations and comparisons with some existing recurrent networks have conducted to confirm the effectiveness and superiority of the proposed Wiener-type network, identification algorithm and control strategy.     

Adaptive hinging hyperplanes and its applications in dynamic system identification

The model of adaptive hinging hyperplanes (AHH) is proposed in this paper. It is based on multivariate adaptive regression splines (MARS) and generalized hinging hyperplanes (GHH) and shares attractive properties of the two. By making a modification to the basis function of MARS, AHH shows linear property in each subregion. The AHH model is actually a special case of the GHH model, which has a universal representation capability for continuous piecewise linear functions. The approximation ability of the AHH model is proved. The AHH algorithm is developed similar to the MARS algorithm. It is adaptive and can be executed efficiently, hence has power and flexibility to model unknown relationships. The AHH procedure is applied to identifying two dynamic systems and its potential is illustrated.     

Dynamic slicing of concurrent AspectJ programs: An explicit context‐sensitive approach

This paper presents a context‐sensitive dynamic slicing technique for the concurrent and aspectized programs. To effectively represent the concurrent aspect‐oriented programs, we propose an intermediate graph called the multithreaded aspect‐oriented dependence graph (MAODG). The MAODG is a dynamic graph generated from the execution trace of a given program with respect to a particular set of values given as an input. Interference dependencies between the statements are shown by a distinguished edge called the interference dependence edge in the MAODG. Based on this intermediate representation, we propose a precise and accurate dynamic slicing algorithm for the concurrent aspect‐oriented programs implemented using AspectJ. The proposed dynamic slicing algorithm is implemented in a slicing tool developed using the ASM framework. Several open source programs are studied and evaluated using the proposed technique along with some existing techniques. The experimentation shows that our proposed slicing algorithm generates slices of the same or smaller size, as compared with the existing algorithms. Furthermore, we found that the slice computation time is significantly less in our proposed algorithm, as compared with the existing algorithms.     

Input–output data filtering based recursive least squares identification for CARARMA systems

This paper uses an estimated noise transfer function to filter the input–output data and presents filtering based recursive least squares algorithms (F-RLS) for controlled autoregressive autoregressive moving average (CARARMA) systems. Through the data filtering, we obtain two identification models, one including the parameters of the system model, and the other including the parameters of the noise model. Thus, the recursive least squares method can be used to estimate the parameters of these two identification models, respectively, by replacing the unmeasurable variables in the information vectors with their estimates. The proposed F-RLS algorithm has a high computational efficiency because the dimensions of its covariance matrices become small and can generate more accurate parameter estimation compared with other existing algorithms.     

Modelling of the inventory replenishment problem with a two-segment piecewise linear demand

The classical inventory replenishment problem with a linear function in demand uses a ‘single-segment’ linear function as its demand and can be modelled by a simple algorithm. Moreover, this article extends the algorithm to provide a heuristic solution for the inventory replenishment model with a two-segment linear function in demand called the ‘two-segment piecewise linear demand model’. In addition, this article proposes a general procedure for solving both models. Meanwhile, several examples taken from the literature illustrate our algorithm for these two models with convincing results. Furthermore, this study shows that when the demand is a two-segment piecewise linear function over time, it is better to use the proposed algorithm rather than devising a decoupled solution approach by treating segments separately. Finally, a sensitivity analysis of two factors, demand and cost, is performed. The model is highly extensible and applicable, so it can serve as an inventory planning tool to solve the replenishment problem.     

基于时态边缘算子的时间序列分段线性表示

时间序列的分段线性表示算法通常基于单一的启发式规则,难以适用于不同数据特征的时间序列。借鉴了边缘算子的思想来提取时间序列的边缘点,提出了一种基于时态边缘算子的时间序列分段线性表示算法。在来自不同领域的公开数据集上进行的实验结果表明：与两种主要的分段线性表示算法相比,该算法具有更好的拟合性能,并且更为稳定,能够适用于各类不同数据特征的时间序列。     

一种基于遗传算法的自动排课系统设计

该文提出并实现了一种高校自动排课算法,利用遗传算法建立数据模型,定义了时间片、授课单元、切片算子、不完全两点交叉和适应度函数。通过使用遗传算法,对课程进行编排和对课表进行优化;并用VC 进行编程,Matlab进行仿真,用文件输出结果;实验结果表明,遗传算法对课表的编排和优化有着比较显著的作用。     

基于分段加权的反向稀疏跟踪算法研究

为提高稀疏表示跟踪模型性能，提出一种分段加权的反向稀疏跟踪算法，将跟踪问题转化为在贝叶斯框架下寻找概率最高的候选对象问题，构造不同的分段权重函数来分别度量候选目标与正负模板的判别特征系数。通过池化来降低跟踪结果的不确定性干扰，选择正负模板加权系数差值最大的候选表示作为跟踪结果。实验表明，在光照变化、遮挡、快速运动、运动模糊情况下，所提出的算法可以确保跟踪结果的准确性和鲁棒性。     

On the Input-Output Representation of Piecewise Affine State Space Models

This paper addresses the conversion of discrete-time piecewise affine (PWA) state space models into input-output form. Necessary and sufficient conditions for the existence of equivalent input-output representations of a given PWA state space model are derived. Connections to the observability properties of PWA models are investigated. Under a technical assumption, it is shown that every finite-time observable PWA model admits an equivalent input-output representation. When an equivalent input-output model exists, a constructive procedure is presented to derive its equations. Several examples illustrate the proposed results.      

A new noise-compensated estimation scheme for multichannel autoregressive signals from noisy observations

In many engineering applications concerning the recovery of signals from noisy observations, a common approach consists in adopting autoregressive (AR) models. This paper is concerned with not only the estimation of multichannel autoregressive (MAR) model parameters but also the recovery of signals. A new noise compensated parameter estimation scheme is introduced in this paper. It contains an advanced least square vector (ALSV) algorithm which not only keeps the advantage of blindly estimating the MAR parameters and the variance-covariance matrix of observation noises, but also aims at ensuring the variance-covariance matrix to be symmetric in each iterative procedure. Moreover, the estimation of variance-covariance matrix of input noise is proposed, and then we form an optimal filtering to recover the signals. In the numerical simulations, the estimation performance of the ALSV estimation algorithm significantly outperforms that of other existed methods. Moreover, the optimal filtering based on the ALSV algorithm leads to more accurate recovery of the true signals.     

基于NSGAⅡ的高精铜板带配料优化问题

为实现高精铜板带配料过程的优化,建立了考虑降低成本、减少烧损、循环利用旧料且满足多种旧料可代用性等配料原则的配料优化模型,并利用拉格朗日松弛法对模型进行了变换.针对所建模型,设计了基于第2代非支配排序遗传算法（NSGAⅡ）的求解方法,重点研究了染色体表示、比较算子和基于模糊集合理论的Pareto选优方法.研究结果表明,与现有配料算法相比,此种求解方法可获得更优的结果且一次运行便可提供多种可行的配料方案.