Adaptive anti-spam filtering for agglutinative languages: a special case for Turkish

We propose anti-spam filtering methods for agglutinative languages in general and for Turkish in particular. The methods are dynamic and are based on Artificial Neural Networks (ANN) and Bayesian Networks. The developed algorithms are user-specific and adapt themselves with the characteristics of the incoming e-mails. The algorithms have two main components. The first one deals with the morphology of the words and the second one classifies the e-mails by using the roots of the words extracted by the morphological analysis. Two ANN structures, single layer perceptron and multi-layer perceptron, are considered and the inputs to the networks are determined using binary model and probabilistic model. Similarly, for Bayesian classification, three different approaches are employed: binary model, probabilistic model, and advanced probabilistic model. In the experiments, a total of 750 e-mails (410 spam and 340 normal) were used and a success rate of about 90% was achieved.     

自适应模型更新相关滤波目标跟踪方法

为了解决目标因遮挡、跟踪框发生漂移后相关滤波跟踪算法仍持续更新目标模型和滤波器模型,导致背景信息被更新到目标模型和滤波器模型中的情况,提出一种自适应模型更新策略。采用相关滤波方法得到新的目标位置;提取新目标位置的统计协方差特征并计算其与协方差模板的相似性;根据相似性判断是否更新目标模型和滤波器模型。实验结果表明:所提方法有效解决了因目标形变、遮挡等情况导致目标模型和滤波器模型的更新问题,提高了相关滤波目标跟踪的精度。     

Adaptive smoothing: a general tool for early vision

A method to smooth a signal while preserving discontinuities is presented. This is achieved by repeatedly convolving the signal with a very small averaging mask weighted by a measure of the signal continuity at each point. Edge detection can be performed after a few iterations, and features extracted from the smoothed signal are correctly localized (hence, no tracking is needed). This last property allows the derivation of a scale-space representation of a signal using the adaptive smoothing parameter k as the scale dimension. The relation of this process to anisotropic diffusion is shown. A scheme to preserve higher-order discontinuities and results on range images is proposed. Different implementations of adaptive smoothing are presented, first on a serial machine, for which a multigrid algorithm is proposed to speed up the smoothing effect, then on a single instruction multiple data (SIMD) parallel machine such as the Connection Machine. Various applications of adaptive smoothing such as edge detection, range image feature extraction, corner detection, and stereo matching are discussed     

Adaptive nonlocal filtering: a fast alternative to anisotropicdiffusion for image enhancement

Nonlinear anisotropic diffusion algorithms provide significant improvement in image enhancement as compared to linear filters. However, the excessive computational cost of solving nonlinear PDEs precludes their use in real-time vision applications. We show that two orders of magnitude speed improvement is provided by a new image filtering paradigm in which an adaptively determined vector field specifies nonlocal application points for an image filter     

Filter shape dependence and effective scale separation in large-eddy simulations based on relaxation filtering

The influence of the filter shape on the effective scale separation and the numerical accuracy of large-eddy simulations based on relaxation filtering (LES-RF) is investigated. The simulation of the turbulent flow development of a high-Reynolds number low-subsonic compressible mixing layer is performed using the LES-RF procedure, for discrete filters of order 2–10. A reference solution is first obtained using high-order numerical algorithms and shows a good agreement with experimental data found in the literature. Discrete filters of order 2, 4, 6, 8 and 10 are then considered to study the influence of the filter shape on numerical results. The 2nd-order scheme turns out to be too dissipative and prevents the emergence of unsteady motions within the mixing layer. For higher order schemes, from 4th- to 10th-order, the flow solutions are turbulent but exhibit mean flows and turbulent intensities depending on the filter. The investigation of the one-dimensional kinetic energy spectra then shows that the 4th-order filter may still be too dissipative whereas large scales remain unaffected using the 6th-, 8th- and 10th-order filters. A further study of the kinetic energy spectra nonetheless demonstrates that the effective spatial bandwidth of the LES increases with the order of the filtering scheme. Simulations using the 6th-, 8th- and 10th-order filters, with mesh sizes chosen to provide the same effective LES cut-off wavenumber, are performed and yield similar results. It is hence found that the value of the effective LES cut-off wavenumber, rather than to the filter shape itself, is mainly responsible for the discrepancies between the flow statistics obtained using different filters. One may conclude that filter shape independence is consequently achieved in the present LES of a mixing layer.     

A general method for the shape finding of lightweight tension structures

A computer based procedure for finding the shape of tension roof structures is presented. The method is quicker, more accurate and cheaper than that using models. A number of examples taken from existing and proposed large scale structures are given.     

A spatio-frequency trade-off scale for scale-space filtering

We study implementation issues for spatial convolution filters and their Fourier alternative, with the aim to optimize the accuracy of filter output. We focus on Gaussian scale-space filters and show that there exists a trade-off scale that subdivides the available scale range into two subintervals of equal length. Below this trade-off scale Fourier filtering yields more accurate results than spatial filtering; above it is the other way around. This should be contrasted with demands of computational speed, which show the opposite tenet     

An efficient method for obtaining the general solution for theforce balance equations with hard point contacts

A compact formulation which is more efficient than the traditional pseudoinverse formulation for obtaining the general solution for the force balance equations has been presented (Cheng and Orin, 1991). With hard point contacts considered, the force balance equations can be decomposed into two sets of rank 3, smaller linear equations if proper coordinate frames of the reference member and at the contact points are chosen. This decomposition, together with the compact formulation, can reduce the steps of the Gaussian elimination process, increase parallelism of the algorithm, and therefore, keep the computation time for obtaining the general solution for the force balance equations to a minimum     

交互式训练样本获取方法

有监督学习算法是机器学习中的一类重要算法,该类算法要求外界提供含监督信号的样本作为训练数据。虽然机器学习领域提供了许多基准测试数据,但很多情况下需要自己生成训练样本。给出了一种交互式训练样本获取方法：通过对原始图像进行一种或多种混合的随机变换,用户挑选那些能被人眼识别的样本作为有效样本加以保存。实验结果表明,所提方法产生的图片能模拟摄像头在不同角度、姿态、光照、遮挡等各种复杂场景下拍摄的图像的效果。用系统生成的训练样本训练朴素贝叶斯(NB)分类器,能达到95.042%的识别精度,结果优于UCI人工字符集训练同样的NB分类器时88.4875%的识别精度。     

Adaptive control for a class of large scale non-linear systems

The design of stabilizing controllers for multi-input multi-output (MIMO) non-linear plants with unknown non-linearities is a challenging problem. The inability to identify the non-linearities on-line or off-line accurately motivates the design of stabilizing controllers based on approximations or on approximate estimates of the plant non-linearities. However, the design of a centralized controller for large scale non-linear systems is often complex due to the high dimensionality and difficulty to implement in real time. In this paper a decentralized-like non-linear adaptive control algorithm is designed and analysed for a class of large scale non-linear systems with unknown non-linearities. The controller guarantees closed loop semi-global stability and convergence of the tracking error to a small residual set whose size can be specified a priori provided the neglected in the control design non-linear interconnections are small relative to the modelled non-linear parts. A procedure for choosing the various design parameters to guarantee that the tracking error bound will converge to within the specified a priori bound is presented. Even though the proposed controller is not purely decentralized it does reduce computations and makes the control design easier than a corresponding centralized approach.     

自适应极化滤波的水中目标线谱提取方法

利用水中目标具有极化的特点,采用奇异值分解方法,对水中目标线谱信号在频域进行极化分析,通过构造极化滤波函数,对线谱信号进行极化滤波处理.仿真和实验结果表明:采用提出的方法可以自适应地提取出线谱,当水中目标信号的信噪比大于-7dB时,提出的方法具有较高的线谱提取性能.     

Adaptive weighted sum method for multiobjective optimization: a new method for Pareto front generation

This paper presents an adaptive weighted sum (AWS) method for multiobjective optimization problems. The method extends the previously developed biobjective AWS method to problems with more than two objective functions. In the first phase, the usual weighted sum method is performed to approximate the Pareto surface quickly, and a mesh of Pareto front patches is identified. Each Pareto front patch is then refined by imposing additional equality constraints that connect the pseudonadir point and the expected Pareto optimal solutions on a piecewise planar hypersurface in the -dimensional objective space. It is demonstrated that the method produces a well-distributed Pareto front mesh for effective visualization, and that it finds solutions in nonconvex regions. Two numerical examples and a simple structural optimization problem are solved as case studies. Presented as paper AIAA-2004-4322 at the 10th AIAA-ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, New York, August 30–September 1, 2004     

Adaptive filtering of a random signal in Gaussian white noise

We consider the problem of estimating an infinite-dimensional vector θ observed in Gaussian white noise. Under the condition that components of the vector have a Gaussian prior distribution that depends on an unknown parameter β, we construct an adaptive estimator with respect to β. The proposed method of estimation is based on the empirical Bayes approach.     

针对PRT的大规模场景的自适应网格细分

针对Pre-computed Radiance Transfer(PRT)全局光照算法的特点,提出了一种针对大规模场景的模型细分方法.基于PRT的全局光照算法的渲染结果和模型的精细程度密切相关,对于小规模的场景,可以使用比较精细的模型以得到很好的绘制结果,但对于大规模场景,对整个模型都使用精细的网格是十分浪费资源和不可接受的.通过加入PRT算法中计算的一些权重值,对模型中影响全局光照效果很大的地方进行精细的细分,对全局光照效果影响很小的地方进行较粗的细分或者不细分,从而实现既能提升全局光照效果,又不会过量增加模型规模的自适应网格细分.     

Adaptive divided difference filtering for simultaneous state and parameter estimation

A novel adaptive version of the divided difference filter (DDF) applicable to non-linear systems with a linear output equation is presented in this work. In order to make the filter robust to modeling errors, upper bounds on the state covariance matrix are derived. The parameters of this upper bound are then estimated using a combination of offline tuning and online optimization with a linear matrix inequality (LMI) constraint, which ensures that the predicted output error covariance is larger than the observed output error covariance. The resulting sub-optimal, high-gain filter is applied to the problem of joint state and parameter estimation. Simulation results demonstrate the superior performance of the proposed filter as compared to the standard DDF.     

Adaptive observers for unknown general nonlinear systems

Several neural network (NN) models have been applied successfully for modeling complex nonlinear dynamical systems. However, the stable adaptive state estimation of an unknown general nonlinear system from its input and output measurements is an unresolved problem. This paper addresses the nonlinear adaptive observer design for unknown general nonlinear systems. Only mild assumptions on the system are imposed: output equation is at least C(1) and existence and uniqueness of solution for the state equation. The proposed observer uses linearly parameterized neural networks (LPNNs) whose weights are adaptively adjusted, and Lyapunov theory is used in order to guarantee stability for state estimation and NN weight errors. No strictly positive real (SPR) assumption on the output error equation is required for the construction of the proposed observer.     

The development of a general Internet attitudes scale

The popularity of Internet use has generated a need for reliable and valid Internet attitude assessments. Current practices in the development and validation of Internet attitude scales have raised several methodological and practical issues. The purpose of this study was to develop general Internet attitudes measure as well as to explore the psychometric and methodological concerns surrounding the construct validity of existing Internet attitude scales. A three-study sequence was conducted that included the development and refinement of the Attitudes Towards the Internet Scale (ATIS), an investigation of differences between Internet and paper-and-pencil administrations, and differential item and person functioning. The ATIS demonstrated appropriate psychometric qualities and is suitable for general use. Suggestions for future researchers and practitioners interested in the assessment of Internet attitudes are provided.