Suchergebnisse

This paper explores why previous research failed to find any empirical evidence confirming the success of "Communist affirmative action" in reducing inequality in access to secondary and tertiary education in Bulgaria, the Czech republic, Hungary, Poland, and Slovakia between 1948- 1989. I argue that scholars have too narrowly focused on ultimate educational attainment of each cohort and have thus overlooked important life-course and historical dynamics of educational stratification in former socialist countries. In this paper I study detailed information on educational careers from the Social Stratification in Eastern Europe after 1989 survey, distinguish the stratification of early and delayed school transitions and compare the differential degree of stratification of early and delayed transitions across cohorts. I show that delayed school transitions were usually stratified less on socioeconomic background than delayed transitions, yet this life-course differential was by no means stable over time. It turns out that delayed school transitions were stratified more strongly in cohorts, in which early transitions were stratified less as a result of the "Communist Affirmative Action". These two offsetting tendencies were overlooked by previous research and combined to produce and overall stable effect of SES on school transitions. I conclude that delayed education careers worked against the success of the egalitarian policies and offered a highly selective second chance for socioeconomically advantaged and politically disadvantaged students. This finding is statistically robust and is identified even in models that control for unmeasured individual-level heterogeneity. I argue that scholars should pay more attention to detailed educational careers and should not only study highest degree completed as otherwise their results may be biased and/or incomplete.

This paper deals with ultrasonic signal de-noising by means of correlation. It is commonly known that the cross-correlation function shows the statistical dependence between two signals. In ultrasonic inspection, the measured signal is taken as the first signal. The most important aspect of this method is the choice of the second signal. Various types of the second signals can be tried.

Traditional techniques for reducing ultrasonic signal noise are based on the optimum frequency of an acoustic wave, ultrasonic probe construction and low-noise electronic circuits. This paper describes signal processing methods for noise suppression using a wavelet transform. Computer simulations of the proposed testing algorithms are presented.

Envelope analysis allows fast fault location of individual gearboxes and parts of bearings by repetition frequency determination of the mechanical catch of an amplitude-modulated signal. Systematic faults arise when using envelope analysis on a signal with strong changes. The source of these events is the range of function definition of used in convolution integral definition. This integral is used for Hilbert image calculation of analyzed signal. Overshoots (almost similar to Gibbs events on a synthetic signal using the Fourier series) are result from these faults. Overshoots are caused by parasitic spectral lines in the frequency domain, which can produce faulty diagnostic analysis.This paper describes systematic arising during faults rising by signal numerical calculation using envelope analysis with Hilbert transform. It goes on to offer a mathematical analysis of these systematic faults.

This paper evaluates methods used for separating vibrations produced by a gear transmission from the vibration signal acquired on the gearbox. The paper presents a novel method for evaluating the algorithms used for this separation. The evaluation method takes into account the statistical reliability of the results achieved on multiple sets of signals acquired on the same machine and conditions. The signal separation was applied in order to process data obtained during an experiment carried out with the aim of analyzing the influence of a torque load affecting a gearbox on the vibrations produced by the gear transmission. It is supposed that the vibration characteristics of the gear transmission are strongly affected by the value of the torque load influencing the gearbox shafts. This influence is analyzed using the vibration signal acquired on the gearbox housing. The vibration signal contains significant disturbances, and its interpretation is unclear. The vibration signal generated by the gear transmission can be separated using methods that make it possible to select the valid features included in the signal. Methods for feature selection which implement a systematic search in the state space and methods based on the genetic algorithm were applied. The genetic algorithm poses a robust stochastic global search in the state space that is well suited to deal with nonlinear problems and also shortens the necessary computing time. The evaluation and comparison of the results achieved during the separation process using different methods have to be taken into account. In the case of signal separation, it is important to evaluate differences between the results achieved during particular executions of the separation process performed by the same method on different datasets which were acquired in the case of the same experiment and conditions. Methods with results that vary, or that are different from the results given by other methods, are assumed not to be statistically reliable. It is also necessary to penalize methods leading to results that can vary greatly in some executions according to the scatter data. Conversely, methods that give results varying around the right set of features seem more acceptable. A novel method for rating the statistical reliability of the results has been proposed. This method is essential for methods using a stochastic search in the state space.

This paper presents a novel method for 3D visualization of flaws detected during Eddy Current (EC) inspection. The EC data was acquired using an automated scanning system equipped with precise eddy current probe positioning. The method was tested on a single frequency instrument with an absolute probe. The EC inspection procedure is implemented statically by registering the operating point of the instrument at each equidistant point on a tested object.The paper describes a data processing method based on the Fourier transform enabling 3D visualization of flaws. This three-dimensional image of the result of a scan enables the position of flaws to be determined, and the size and bevel (angle to the surface) of each detected flaw to be estimated. This research investigated flaws rising from the surface of the tested object, and flaw depth was not evaluated in this work. This method of visualization is simple to implement and is currently targeted for application in EC scanning devices.

New efficient and reliable methods for gearbox diagnostics are needed in automotive industry because of growing demand for production quality. This paper presents the application of two different classifiers for gearbox diagnostics – Kohonen Neural Networks and the Adaptive-Network-based Fuzzy Interface System (ANFIS). Two different practical applications are presented. In the first application, the tested gearboxes are separated into two classes according to their condition indicators. In the second example, ANFIS is applied to label the tested gearboxes with a Quality Index according to the condition indicators. In both applications, the condition indicators were computed from the vibration of the gearbox housing.

In ultrasonic non-destructive testing the signals characterizing the material structure are commonly evaluated. The sensitivity and resolution of ultrasonic systems is limited by the backscattering and electronic noise level commonly contained in the acquired ultrasonic signals. For this reason, it is very important to use appropriate advanced signal processing methods for noise reduction and signal separation. This paper compares algorithms used for efficient noise reduction in ultrasonic signals in A-scan. Algorithms based on the discrete wavelet transform and the Wiener filter are considered. Part of this paper analyses and applies blind source separation, which has never been used in practical ultrasonic non-destructive testing. All proposed methods are evaluated on both simulated and acquired ultrasonic signals.

Condition monitoring systems for manual transmissions based on vibration diagnostics are widely applied in industry. The systems deal with various condition indicators, most of which are focused on a specific type of gearbox fault. Frequently used condition indicators (CIs) are described in this paper. The ability of a selected condition indicator to describe the degree of gearing wear was tested using vibration signals acquired during durability testing of manual transmission with helical gears.

In ultrasonic testing it is very important to recognize the fault echoes buried in a noisy signal. The fault echo characterizes a flaw in the material. An important requirement on ultrasonic signal filtering is zero-time shift, because the position of ultrasonic echoes is essential. This requirement is accomplished using the discrete wavelet transform (DWT), which is used for reducing the signal-to-noise ratio. This paper evaluates the quality of filtering using the discrete wavelet transform. Additional computer simulations of the proposed algorithms are presented.