Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression

In this paper, we propose the application of the Kernel Principal Component Analysis (PCA) technique for feature selection in a high-dimensional feature space, where input variables are mapped by a Gaussian kernel. The extracted features are employed in the regression problems of chaotic Mackey–Glass time-series prediction in a noisy environment and estimating human signal detection performance from brain event-related potentials elicited by task relevant signals. We compared results obtained using either Kernel PCA or linear PCA as data preprocessing steps. On the human signal detection task, we report the superiority of Kernel PCA feature extraction over linear PCA. Similar to linear PCA, we demonstrate de-noising of the original data by the appropriate selection of various nonlinear principal components. The theoretical relation and experimental comparison of Kernel Principal Components Regression, Kernel Ridge Regression and ε-insensitive Support Vector Regression is also provided.     

On-line Algorithm for Blind Signal Extraction of Arbitrarily Distributed,but Temporally Correlated Sources Using Second Order Statistics

Most of the algorithms for blind separation/extraction and independent component analysis (ICA) can not separate mixtures of sources with extremely low kurtosis or colored Gaussian sources. Moreover, to separate mixtures of super- and sub-Gaussian signals, it is necessary to use adaptive (time-variable) or switching nonlinearities which are controlled via computationally intensive measures, such as estimation of the sign of kurtosis of extracted signals. In this paper, we develop a very simple neural network model and an efficient on-line adaptive algorithm that sequentially extract temporally correlated sources with arbitrary distributions, including colored Gaussian sources and sources with extremely low values (or even zero) of kurtosis. The validity and performance of the algorithm have been confirmed by extensive computer simulation experiments.     

Three tokens in Herman’s algorithm

Herman’s algorithm is a synchronous randomized protocol for achieving self-stabilization in a token ring consisting of N processes. The interaction of tokens makes the dynamics of the protocol very difficult to analyze. In this paper we study the distribution of the time to stabilization, assuming that there are three tokens in the initial configuration. We show for arbitrary N and for an arbitrary timeout t that the probability of stabilization within time t is minimized by choosing as the initial three-token configuration the configuration in which the tokens are placed equidistantly on the ring. Our result strengthens a corollary of a theorem of McIver and Morgan (Inf. Process Lett. 94(2): 79–84, 2005), which states that the expected stabilization time is minimized by the equidistant configuration.     

Analysis of fluid flow and energy transport in Czochralski's process

In this paper the conjugate fluid flow and energy transport problem (involving conduction-convection-radiation heat transfer) resulting from the Czochralski crystal growth process is analysed. The solidifying material is treated as a pure and semitransparent substance with material properties depending neither on temperature nor on the wavelength. The solution of the problem is obtained iteratively using two computer codes: FLUENT, a commercial CFD package, and BEM-based in-house code capable of analysing the radiative heat transfer in the entire computational domain.Obtained results not only show velocity field and temperature distribution within the bodies under consideration but also demonstrate the influence of thermal radiation on these quantities.     

A Spurious Equilibria-free Learning Algorithm for the Blind Separation of Non-zoer Skewness Signals

We present a new learning algorithm for the blind separation of independent source signals having non-zero skewness (the 3rd-order cumulant) (the source signals have non-symmetric probability distribution.), from their linear mixtures. It is shown that for a class of source signals whose probability distribution functions is not symmetric, a simple adaptive learning algorithm using quadratic function (f(x)=x²) is very efficient for blind source separation task. It is proved that all stable equilibria of the proposed learning algorithm are desirable solutions. Extensive computer simulation experiments confirmed the validity of the proposed algorithm.     

Methods of Image Compression by PHL Transform

In this paper an image data compression scheme based on Periodic Haar Piecewise-Linear (PHL) transform and quantization tables is proposed. Effectiveness of the compression for different classes of images is evaluated. The comparison of the compression quality using PHL and DCT transforms is given.     

Modification of Dugdale model to include the work hardening and in- and out-of-plane constraints

In the paper the classical Dugdale model has been generalized taking into account the influence of the specimen thickness, in-plane constraint as well as the effect of the strain hardening on the level of stress distribution within the strip yield zone (SYZ). Modification has been performed utilizing Huber, Mises, Hencky as well as Tresca yield hypotheses and Guo Wanlin Tz coefficient. Results are presented in a form useful for applications. As an example, the modified model has been applied to draw the failure assessment diagram (FAD). New FAD’s have been compared with others adopted from the SINTAP procedures.     

Formulation of Presliding Domain Non-local Memory Hysteretic Loops Based Upon Modified Maxwell Slip Model

In this paper, we investigate formulation of non-local memory effect hysteretic loops in presliding domain of friction by examining the dynamic responses of a single-degree-of-freedom friction oscillator. We modify the Maxwell slip model introducing characteristic features of asperities and perform detailed examination of the system from two perspectives. The first one, termed by us as microscale, presents contribution of each individual asperity to the total friction force, whereas the second one, termed by us as macroscale, concerns friction force behaviour of the whole model treated as the unity, with a special emphasis on non-local memory effect hysteretic loops formulation, complemented by time responses.     

Effect of the post-deposition annealing on electrical characteristics of MIS structures with HfO2/SiO2 gate dielectric stacks

In this work, we report on effects of post-deposition annealing on electrical characteristics of metal–insulator–semiconductor (MIS) structures with HfO₂/SiO₂ double gate dielectric stacks. Obtained results have shown the deterioration of electro-physical properties of MIS structures, e.g. higher interface traps density in the middle of silicon forbidden band (D_itmb), as well as non-uniform distribution and decrease of breakdown voltage (U_br) values, after annealing above 400 °C. Two potential hypothesis of such behavior were proposed: the formation of interfacial layer between hafnia and silicon dioxide and the increase of crystallinity of HfO₂ due to the high temperature treatment. Furthermore, the analysis of conduction mechanisms in investigated stacks revealed Poole–Frenkel (P–F) tunneling at broad range of electric field intensity.     

Anticipatory network models of multicriteria decision-making processes

In this article, we will investigate the properties of a compromise solution selection method based on modelling the consequences of a decision as factors influencing the decision making in subsequent problems. Specifically, we assume that the constraints and preference structures in the (k?+?1)st multicriteria optimisation problem depend on the values of criteria in the k-th problem. To make a decision in the initial problem, the decision maker should take into account the anticipated outcomes of each linked future decision problem. This model can be extended to a network of linked decision problems, such that causal relations are defined between the time-ordered nodes. Multiple edges starting from a decision node correspond to different future scenarios of consequences at this node. In addition, we will define the relation of anticipatory feedback, assuming that some decision makers take into account the anticipated future consequences of their decisions described by a network of optimisers ? a class of information processing units introduced in this article. Both relations (causal and anticipatory) form a feedback information model, which makes possible a selection of compromise solutions taking into account the anticipated consequences. We provide constructive algorithms to solve discrete multicriteria decision problems that admit the above preference information structure. An illustrative example is presented in Section 4. Various applications of the above model, including the construction of technology foresight scenarios, are discussed in the final section of this article.