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Lamb waves occur in thin-walled structures in two wave modes—the symmetric and the antisymmetric mode. Their oscillation on the structures‘ surfaces is either in phase (symmetric) or shifted by a phase angle of π (antisymmetric). In this work, a method is developed by which to compare the surfaces’ oscillation phase relation to answer the question of whether fiber metal laminates show the same surface oscillation behavior as described for metals. The evaluation of time signals regarding the instantaneous phase angle is performed by using the continuous wavelet transformation and the short-time Fourier transformation. Numerical simulations utilizing the finite element method provide time signals from the top and bottom surface of different thin-walled structures of different material settings and configurations. The numerically obtained time signals are evaluated by the developed methods with respect to the oscillation phase. Subsequently, the oscillation phase is evaluated experimentally for the wave propagation in a fiber metal laminate. It is shown that the method based on the continuous wavelet transformation is suitable for the evaluation of oscillation phase relations in time signals. Additionally, it is proven that fiber metal laminates show only two phase relations, which indicates the occurrence of Lamb waves.

This study proposes a robust estimation technique for the single-phase grid voltage fundamental frequency under grid disturbances. The technique relies on a demodulation method and a finite-impulse-response-based differentiation filter (DF). A frequency domain analysis for designing the DF is presented and is used to estimate the time-varying fundamental frequency from the instantaneous phase angle obtained by the demodulation method. The technique can reject the negative effects caused by the presence of DC offset and harmonics. The proposed DF shows less sensitivity to the presence of oscillations caused by the demodulation method when compared to a similar finite-impulse-response-based DF. Simulation and experimental results are provided to verify the performance of the proposed technique.

This study proposes a phase-locked loop (PLL) algorithm employed for phase-angle detection of single-phase utility grid voltage. A detailed stability analysis is performed, as well as its performance is evaluated under several power quality problems. The proposed PLL structure is based on the instantaneous active power theory for three-phase power systems (pPLL), which is investigated into the fictitious two-phase stationary reference frame (αβ-pPLL). A non-autonomous adaptive filter (AF) operates in conjunction with the PLL, and its main function is to extract the fundamental component of the utility grid voltage allowing the rejection of voltage harmonics. The stability analysis of the proposed AF-αβ-pPLL scheme is carried out in order to provide adequate tuning procedures for choosing the parameters used in the proposed algorithm. In addition, the dynamic response and robustness of the AF-αβ-pPLL algorithm are evaluated by means of simulation and experimental tests, under utility grid disturbances, such as voltage harmonics, voltage sag, phase jumps and frequency variations. To emphasise the effectiveness of the algorithm, a comparative analysis with three other single-phase PLL schemes is carried out, such as the conventional power-based PLL (pPLL), the two-phase stationary reference frame pPLL (αβ-pPLL) and the well-known enhanced PLL (EPLL).

Locating active radars in real environmental conditions is a very important and complex task. The efficiency of the direction finding (DF) of ground-based radars and other microwave emitters using unmanned aerial vehicles (UAV) is dependent on the parameters of applied devices for angle location of microwave emitters, and on the construction and modes of operation of the observed transmitting antenna systems. An additional factor having the influence on DF of the radar, when are used systems installed on the UAV, is the rotation of the antenna of a radar. The accuracy of estimation of direction of any microwave transmitter is determined by the terrain properties that surround the transmitter and the objects reflecting microwave signals. The exemplary shapes of the radar antenna patterns and the associated relationships with the probability of remotely detecting the radar and determining its bearings are described. The simulated patterns of the signals received at an emitter-locating device mounted on a UAV and the expected results of a monopulse DF based on these signals are presented. The novelty of this work is the analysis of the DF efficiency of radars in conditions where intense multi-path phenomena appear, and for various amplitudes and phases of the direct signal and multi-path signals that reach the UAV when assuming that so-called simple signals and linear frequency modulation (LFM) signals are transmitted by the radar. The primary focus is on multi-path phenomenon, which can make it difficult, but not entirely impossible, to detect activity and location of radar with a low-flying small UAV and using only monopulse techniques, that is, when only a single pulse emitted by a radar must be sufficient to DF of this radar. Direction of arrival (DOA) algorithms of signals in dense signal environment were not presented in the work, but relevant suggestions were made for the design of such algorithms.

Seismic amplitude variation with offset (AVO) inversion is a cornerstone of oil and gas reservoir prediction, enabling the estimation of subsurface elastic parameters and characterization of stratigraphic interfaces. However, balancing inversion accuracy and computational efficiency remains a critical challenge. To address this, we propose a novel probabilistic AVO inversion framework integrating three key innovations. First, we derive a high-precision quadratic approximation for compressional (P-wave) reflectivity by retaining first- and second-order terms from the exact Zoeppritz equations through a perturbation strategy. This approach significantly enhances accuracy compared to conventional linear approximations, particularly in reflecting the true amplitude variation at large angles. Subsequently, to improve lateral continuity and stratigraphic resolution, we introduce an instantaneous phase constraint derived via the Hilbert transform. This constraint leverages phase sensitivity to seismic waveform coherence, ensuring geologically consistent interface characterization during stochastic inversion. Furthermore, we develop a hybrid Markov Chain Monte Carlo (MCMC) algorithm combining adaptive Gibbs sampling with the independent doubly adaptive rejection Metropolis sampling (IA2RMS) method. This framework efficiently samples high-dimensional posterior probability density functions (PDFs) of elastic parameters: Gibbs sampling generates adaptive proposal distributions, while IA2RMS accelerates Markov chain convergence through location- and scale-adjustable proposals. Numerical experiments and field seismic data demonstrate the robustness and feasibility of the proposed probabilistic AVO inversion method.

Stepping motors are often used for low-power open-loop positioning. In conventional stepping motor drives, a step rate or speed is imposed. To avoid step loss in open-loop, most stepping motors are driven at maximum current resulting in a poor energy efficiency. However, when position feedback is available, the drive current can be optimised. A position sensor adds costs and complexity to the system. Therefore, rotor position estimators are developed, often referred to as sensorless controllers. A drawback in some of these methods is the requirement of information on the mechanical load which is usually not available or varies over time. In this study, an alternative estimator is proposed, based on the load angle between the current excitation vector and the instantaneous rotor flux position. This load angle reflects the capability of the system to follow the speed setpoint and gives an indication of the robustness to torque disturbances. Therefore the load angle estimation is interesting to provide feedback to a controller which adapts the drive current. Here, an estimator is proposed solely based on electrical motor parameters and electrical measurements. The algorithm, based on a sliding discrete Fourier transformation, is applicable with the typical full-, half- and micro-stepping drive algorithms. Finally, measurement results validate the estimation algorithm.