Recently, the the support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecaster, the first step is feature extraction. This paper proposes the applications of principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA) to SVM for feature extraction. The PCA linearly transforms the original inputs into new uncorrelated features. The KPCA is a nonlinear PCA developed by using the kernel method. In ICA, the original inputs are linearly transformed into features which are mutually statistically independent. By examining the sunspot data, Santa Fe data set A and five real futures contracts, the experiment shows that SVM by feature extraction using PCA, KPCA or ICA can perform better than that without feature extraction. Furthermore, among the three methods, there is the best performance in the KPCA feature extraction, followed by the ICA feature extraction.
Recently, a support vector machine (SVM) has been receiving increasing attention in the field of regression estimation due to its remarkable characteristics such as good generalization performance, the absence of local minima and sparse representation of the solution. However, within the SVMs framework, there are very few established approaches for identifying important features. Selecting significant features from all candidate features is the first step in regression estimation, and this procedure can improve the network performance, reduce the network complexity, and speed up the training of the network.
This paper investigates the use of saliency analysis (SA) and genetic algorithm (GA) in SVMs for selecting important features in the context of regression estimation. The SA measures the importance of features by evaluating the sensitivity of the network output with respect to the feature input. The derivation of the sensitivity of the network output to the feature input in terms of the partial derivative in SVMs is presented, and a systematic approach to remove irrelevant features based on the sensitivity is developed. GA is an efficient search method based on the mechanics of natural selection and population genetics. A simple GA is used where all features are mapped into binary chromosomes with a bit "1" representing the inclusion of the feature and a bit of "0" representing the absence of the feature. The performances of SA and GA are tested using two simulated non-linear time series and five real financial time series. The experiments show that with the simulated data, GA and SA detect the same true feature set from the redundant feature set, and the method of SA is also insensitive to the kernel function selection. With the real financial data, GA and SA select different subsets of the features. Both selected feature sets achieve higher generation performance in SVMs than that of the full feature set. In addition, the generation performance between the selected feature sets of GA and SA is similar. All the results demonstrate that that both SA and GA are effective in the SVMs for identifying important features.
This paper proposes an endogenous human resources selection process by using linguistic information from a competency management perspective. We consider different sets of appraisers taking part in the evaluation process, having a different knowledge about the candidates that are being evaluated. Then, appraisers can express their assessments in different linguistic domains according to their knowledge. The proposed method converts each linguistic label into a fuzzy set on a common domain. Candidates are ranked by using different aggregation operators in order to allow the management team to make a final decision.
5-hydroxytryptamine (5-HT) is involved in the stress-induced alteration of colonic functions, specifically motility and secretion, but its precise mechanisms of regulation remain unclear. In the present study, we have investigated the effects of 5-HT on rat colonic mucosal secretion after acute water immersion restraint stress, as well as the underlying mechanism of this phenomenon, using short circuit current recording (ISC), real-time polymerase chain reaction, Western blot analysis, and enzyme-linked immunosorbance assays. After 2 h of water immersion restraint stress, the baseline ISC and 5-HT-induced ISC responses of the colonic mucosa were significantly increased. Pretreatment with selective 5-HT4 receptor antagonist, SB204070, inhibited the 5-HT-induced colonic ISC response by 96 % in normal rats and 91.2 % in acute-stress rats. However, pretreatment with the selective antagonist of 5-HT3 receptor, MDL72222 or Y-25130, had no obvious effect on 5-HT-induced ISC responses under either set of conditions. Total protein expression of both the mucosal 5-HT3 receptors and the 5-HT4 receptors underwent no significant changes following acute stress. Both colonic basal cAMP levels and foskolin-induced ISC responses were significantly enhanced in acute stress rats. 5-HT significantly enhanced the intracellular cAMP level via 5-HT4 receptors in the colonic mucosa from both control and stressed animals, and 5-HT-induced cAMP increase in stressed rats was not more than that in control rats. Taken together, the present results indicate that acute water immersion restraint stress enhances colonic secretory responses to 5-HT in rats, a process in which increased cellular cAMP accumulation is involved., Y. Li, L. S. Li, X. L. Zhang, Y. Zhang, J. D. Xu, J. X. Zhu., and Obsahuje bibliografii
The knowledge of snowpack dynamics is of critical importance to several real-time applications especially in mountain basins, such as agricultural production, water resource management, flood prevention, hydropower generation. Since simulations are affected by model biases and forcing data uncertainty, an increasing interest focuses on the assimilation of snow-related observations with the purpose of enhancing predictions on snowpack state. The study aims at investigating the effectiveness of snow multivariable data assimilation (DA) at an Alpine site. The system consists of a snow energy-balance model strengthened by a multivariable DA system. An Ensemble Kalman Filter (EnKF) scheme allows assimilating ground-based and remotely sensed snow observations in order to improve the model simulations. This research aims to investigate and discuss: (1) the limitations and constraints in implementing a multivariate EnKF scheme in the framework of snow modelling, and (2) its performance in consistently updating the snowpack state. The performance of the multivariable DA is shown for the study case of Torgnon station (Aosta Valley, Italy) in the period June 2012 – December 2013. The results of several experiments are discussed with the aim of analyzing system sensitivity to the DA frequency, the ensemble size, and the impact of assimilating different observations.
The portable chlorophyll (Chl) meter (CL-01, Hansatech) has been successfully used for a rapid and direct estimation of total Chl content in the leaves of some crops. We compared CL-01 meter readings (Chl value) and Chl contents in leaves of Zea mays, Cucumis sativus, Raphanus sativus, and Ceiba speciosa. Chl index was linearly and positively correlated to Chl content in all the species. and D. Cassol ... [et al.].
A major cause of a surface damage of the contact loading machine components (bearings, gears, cams etc.) is the rolling contact fatigue, Various methods of condition monitoring are used to detect damage of these components or specimens in the industry or during testing in laboratories. In the past decades, the acoustic emission technique has been developed into useful condition monitoring method. This paper is focused on the testing of rolling contact fatigue of the metallic materials using acoustic emission method. The methodology of testing, experimenal test-rig and preliminary results, are presented in this paper. It can be concluded, that acoustic emission technique can be applied for more accurate rolling contact fatique evaluation of material. and Obsahuje seznam literatury
The velocities of the Global Positioning System (GPS) stations are widely employed for numerous geodynamical studies. The aim of this paper is to investigate the reliability of station velocities and to draw reader’s attention that for proper estimates of velocity, we need to consider the optimal character of noise. We focus on a set of 115 European GPS stations which contributed to the newest release of the International Terrestrial Reference Frame (ITRF), i.e. ITRF2014. Based on stacked Power Spectral Densities (PSDs), we show that amplitudes o f seasonal signals are significant for nine harmonics of tropical year (365.25 days) and two harmonics of draconitic year (351.60 days). The amplitudes of tropical annual signal fall between 0.1-8.4 mm and are much higher for vertical component than for horizontal. Draconitic annual signal reaches the maximum amplitudes of 1.2 and 0.9 mm for North and East, respectively, whereas is slightly higher for the Up component with a maximum of 3.1 mm. We performed a noise analysis with Maximum Like lihood Estimation (MLE) and found that stations in Central and Northern Europe are characterized by spectral index between flicker and random-walk noise, while stations in Southern and Western Europe: between white and flicker noise. Both amplitudes and spectral indices of power-law noise show a spatial correlation for Up component. We compared the uncertainties of velocities derived in this study with a combination of power-law and white noises to the ones offici ally released in the ITRF2014 with a pure white noise. A ratio of the two estimates is larger than 10 for 13 % and 30 % of stations in horizontal and vertical direction, respectively with medians of 6 and 7. The large differences support the fact that at the velocity determination the proper noise characteristic should be taken into account to avoid any mislead interpretation., Anna Klos and Janusz Bogusz., and Obsahuje bibliografické odkazy