The quality of the supplied power by electricity utilities is regulated and of concern to the end user. Power quality disturbances include interruptions, sags, swells, transients and harmonic distortion. The instruments used to measure these disturbances have to satisfy minimum requirements set by international standards. In this paper, an analysis of multi-harmonic least-squares fitting algorithms applied to total harmonic distortion (THD) estimation is presented. The results from the different least-squares algorithms are compared with the results from the discrete Fourier transform (DFT) algorithm. The algorithms are assessed in the different testing states required by the standards.

Additional motor vibrations are the result of a faulty bearing. They are reflected in the harmonic content of stator currents. The object of the investigation presented in the paper are measurements related to diagnostics of induction motors, especially damages caused to bearings. Due to the fact that the amplitude of the network voltage basic harmonic in the current spectrum is high in comparison with components responsible for damages of bearings, preliminary elimination of this component from the analog current signal has been proposed.

The problem with interpretation of diagnostic measurements in present systems is the difference between measurement results of characteristic frequencies and theoretical calculations.

In the proposed measurement system this problem was solved in such a way that the value of the angular speed and of the supply frequency is calculated on the basis of appropriate components in the very same current spectrum that is further used in the search for diagnostic components.

The paper presents also the measuring system and provides results of the investigations carried out on a motor encumbered with a specially prepared defect.

The paper presents a spectral formulation of surface profile irregularity in a wideband frequency range for roughness, waviness and shape components along the measured length. A unique distribution of roughness and waviness components is proposed, according to the nature of their origination in the course of machining with tools of defined cutting edge, as distinct from standard filtration in measurements of surface irregularities. Differences resulting from both formulations are outlined as well as the method of determining the frequency of component separation for surface roughness and waviness.

Position time series from permanent Global Navigation Satellite System (GNSS) stations are commonly used for estimating secular velocities of discrete points on the Earth’s surface. An understanding of background noise in the GNSS position time series is essential to obtain realistic estimates of velocity uncertainties. The current study focuses on the investigation of background noise in position time series obtained from thirteen permanent GNSS stations located in Nepal Himalaya using the spectral analysis method. The power spectrum of the GNSS position time series has been estimated using the Lomb–Scargle method. The iterative nonlinear Levenberg–Marquardt (LM) algorithm has been applied to estimate the spectral index of the power spectrum. The power spectrum can be described by white noise in the high frequency zone and power law noise in the lower frequency zone. The mean and the standard deviation of the estimated spectral indices are −1.46±0.14,−1.39±0.16 and −1.53±0.07 for north, east and vertical components, respectively. On average, the power law noise extends up to a period of ca. 21 days. For a shorter period, i.e. less than ca. 21 days, the spectra are white. The spectral index corresponding to random walk noise (ca. –2) is obtained for a site located above the base of a seismogenic zone which can be due to the combined effect of tectonic and nontectonic factors rather than a spurious monumental motion. Overall, the usefulness of investigating the background noise in the GNSS position time series is discussed.

We discuss the notion of the financial cycle making a clear indication that the thorough study of its empirical properties in case of developing economies is still missing. We focus on the observed series of credit and equity and make formal statistical inference about the properties of the cycles in case of Polish economy. The non-standard subsampling procedure and discrete spectral characteristics of almost periodically correlated time series are applied to make formal statistical inference about the cycle. We compare the results with those obtained for UK and USA. We extract the cyclical component and confront empirical properties of the financial cycle for small open economy with those established so far in case of developed economies.

The aim of the article is to construct an asymptotically consistent test, based on a subsampling approach, to verify hypothesis about existence of the individual or common deterministic cycle in coordinates of multivariate macroeconomic time series. By the deterministic cycle we mean the periodic or almost periodic fluctuations in the mean function in cyclical fluctuations. To construct test we formulate a multivariate non-parametric model containing the business cycle component in the unconditional mean function. The construction relies on the Fourier representation of the unconditional expectation of the multivariate Almost Periodically Correlated time series and is related to fixed deterministic cycle presented in the literature. The analysis of the existence of common deterministic business cycles for selected European countries is presented based on monthly industrial production indexes. Our main findings from the empirical part is that the deterministic cycle can be strongly supported by the data and therefore should not be automatically neglected during analysis without justification.

The classic relationships concerning the harmonic content in the air gap field of three-phase machines are presented in form of series of rotating waves. The same approach is applied to modeling of permanent magnet motors with fractional phase windings. All main reasons of non-sinusoidal shape of flux density distribution, namely, magnets’ shape and their placement, slotting, magnetic saturation and eccentricity are also related to their counterparts in modal-frequency spectrum. The Fourier 2D spectrum of time-stepping finite element solution is confronted with results of measurements, with special attention paid to accuracy of both methods.

The paper describes the influence of the machining operation on a surface, which disturbs the projection of the tool profile in the form of its relative movements with respect to the object. The elements of the machine tool undergo constant wear during the machining process, it is therefore important to recognize the effects of their influence on the surface's irregularities. Amplitude-frequency analysis of lateral profiles has been used to evaluate and changes of turned lateral profiles. The results of simulation of radial and axial effects of the machine tool on surface and their spectral components were analyzed. Surfaces obtained in similar machining conditions on lathes operated in various time periods were analyzed spectrally. From the analysis of surface irregularity changes caused by disturbances in movements of the tool against the object, testifying the wear of main machine elements during its operation, the modulated, amplitude-frequency character of changes in surface irregularities of workpiece can be noticed.

In this study, the temperature influence on the spectral responsivity of a Light Emitting Diode (LED) used as a photoreceptor, combined to light source spectrum is correlated to electrical characteristics in order to propose an alternative method to estimate LED junction temperature, regardless of the absolute illumination intensity and based on the direct correlation between the integral of the product of two optical spectra and the photo-generated currents. A laboratory test bench for experimental optical measurements has been set in order to enable any characterizing of photoelectric devices in terms of spectral behaviour, in a wavelength range placed between 400–1000 nm, and of current-voltage characteristics as function of temperature by using two different illumination sources. The temperature is analysed in a range from 5°C up to 85°C, so as to evaluate thermal variation effects on the sensor performance. The photo-generated current of two LEDs with different peak wavelengths has been studied. Research has observed and mathematically analysed what follows: since the photo-generated current strictly depends on the combination between the spectral response of the photoreceptor and the lighting source response, it becomes possible to estimate indirectly the junction temperature of the LEDs by considering the ratio between the photogenerated currents obtained by using two different illumination sources. Such results may for one thing increase knowledge in the fields where LEDs are used as photo-detectors for many applications and for another, they could be extended to generic photodetectors, thus providing useful information in photovoltaic field, for instance.

The article is devoted to the problem of voice signals recognition means introduction in the system of distance learning. The results of the conducted research determine the prospects of neural network means of phoneme recognition. It is also shown that the main difficulties of creation of the neural network model, intended for recognition of phonemes in the system of distance learning, are connected with the uncertain duration of a phoneme-like element. Due to this reason for recognition of phonemes, it is impossible to use the most effective type of neural network model on the basis of a multilayered perceptron, at which the number of input parameters is a fixed value. To mitigate this shortcoming, the procedure, allowing to transform the non-stationary digitized voice signal to the fixed quantity of mel-cepstral coefficients, which are the basis for calculation of input parameters of the neural network model, is developed. In contrast to the known ones, the possibility of linear scaling of phoneme-like elements is available in the procedure. The number of computer experiments confirmed expediency of the fact that the use of the offered coding procedure of input parameters provides the acceptable accuracy of neural network recognition of phonemes under near-natural conditions of the distance learning system. Moreover, the prospects of further research in the field of development of neural network means of phoneme recognition of a voice signal in the system of distance learning is connected with an increase in admissible noise level. Besides, the adaptation of the offered procedure to various natural languages, as well as to other applied tasks, for instance, a problem of biometric authentication in the banking sector, is also of great interest.

Automated motion reduction in dynamic infrared imaging is on demand in clinical applications, since movement disarranges time−temperature series of each pixel, thus originating thermal artifacts that might bias the clinical decision. All previously proposed registration methods are feature based algorithms requiring manual intervention. The aim of this work is to optimize the registration strategy specifically for Breast Dynamic Infrared Imaging and to make it user−independent. We implemented and evaluated 3 different 3D time−series registration methods: 1. Linear affine, 2. Non−linear Bspline, 3. Demons applied to 12 datasets of healthy breast thermal images. The results are evaluated through normalized mutual information with average values of 0.70 ±0.03, 0.74 ±0.03 and 0.81 ±0.09 (out of 1) for Affine, Bspline and Demons registration, respectively, as well as breast boundary overlap and Jacobian determinant of the deformation field. The statistical analysis of the results showed that symmetric diffeomorphic Demons’ registration method outperforms also with the best breast alignment and non−negative Jacobian values which guarantee image similarity and anatomical consistency of the transformation, due to homologous forces enforcing the pixel geometric disparities to be shortened on all the frames. We propose Demons’ registration as an effective technique for time−series dynamic infrared registration, to stabilize the local temperature oscillation.