Sinusoidal Components Research Articles

Gekko gecko as a model organism for understanding aspects of laryngeal vocal evolution.

The ability to communicate through vocalization plays a key role in the survival of animals across all vertebrate groups. Although avian reptiles have received much attention relating to their stunning sound repertoire, non-avian reptiles have been wrongfully assumed to have less elaborate vocalization types, and little is known about the biomechanics of sound production and their underlying neural pathways in this group. We investigated alarm calls of Gekko gecko using audio and cineradiographic recordings. Acoustic analysis revealed three distinct call types: a sinusoidal call type (type 1); a train-like call type, characterized by distinct pulse trains (type 3); and an intermediate type, which showed both sinusoidal and pulse train components (type 2). Kinematic analysis of cineradiographic recordings showed that laryngeal movements differ significantly between respiratory and vocal behavior. During respiration, animals repeatedly moved their jaws to partially open their mouths, which was accompanied by small glottal movements. During vocalization, the glottis was pulled back, contrasting with what has previously been reported. In vitro retrograde tracing of the nerve innervating the laryngeal constrictor and dilator muscles revealed round to fusiform motoneurons in the hindbrain-spinal cord transition ipsilateral to the labeled nerve. Taken together, our observations provide insight into the alarm calls generated by G. gecko, the biomechanics of this sound generation and the underlying organization of motoneurons involved in the generation of vocalizations. Our observations suggest that G. gecko may be an excellent non-avian reptile model organism for enhancing our understanding of the evolution of vertebrate vocalization.

Non-linear radiative second-grade nano fluid with sinusoidal magnetic force and arrhenius activation energy: A computational exploration

The goal of this work is to further improve our knowledge of the nonlinear radiative second-grade nano fluid flow boundary layer phenomena which is associated with an Arrhenius activation energy, a sinusoidal magnetic field, and a stretched peripheral with a heat source. The unsteady governing equations are transformed into a proper dimensionless arrangement, and then the explicit finite difference (EFD) method is applied to numerically calculate the equations. However, precise stability and convergence criteria have been developed to make the solution convergent. Along with the typical profile of other flow fields, the oscillatory forms of the velocity are shown. Tabular research has even demonstrated a relationship between the Nusselt number and other parameters, and graphical depiction has been used for regression and data prediction. The novel conclusions drawn from this research indicate that, in comparison to linear patterns, nonlinear radiative heat flux significantly raises (30.35 %) flow profiles with second-grade characteristics. Moreover, the heat transfer rates of second-grade Nano fluids are seen to be significantly influenced (35.14 %) by the sinusoidal magnetic component. When considering nonlinear thermal radiation, activation energy principles cause a major change (34.19 % more) in mass transmission, as high-temperature processes become an essential part of chemical reactions.

Investigation of ground movements induced by underground gas storages via unsupervised ML methodology applied to InSAR data

The research explores the definition, formalization, and validation of a sound and rigorous methodology for analyzing a vast amount of satellite-based measures to geo-localize and quantify the ground movements induced by the storage of natural gas in underground formations. Time series decomposition analysis and unsupervised machine learning algorithms (partitive and hierarchical clustering) are adopted for processing, categorizing, and interpreting ground vertical movements from InSAR acquisitions. At the surface level, storage operations induce characteristic seasonal and cyclical movements, showing uplift during the injection period and subsidence during the withdrawal one. Consequently, the analysis of the solely sinusoidal component of the vertical movements (obtained via the time-series decomposition) turns out to be the key aspect of the proposed approach for handling the superposition of different ground movement sources, and consequently for clearly and reliably identifying the effects of underground gas storage (UGS) only.The proposed methodology was validated using two independent case studies in the Po Plain (northern Italy), a highly urbanized area affected by ground movements induced by several natural and anthropogenic causes, including underground gas storage facilities. For each case study, the methodology localizes one well-defined and confined area as the most affected by storage operations: this area corresponds to a cluster characterized by a high cohesion and by a seasonality phase coherent with the storage injection/withdrawal periods. The other clusters group areally wide-spread measurement points; the phase of their sinusoidal curves shows no time-coherency (or even phase opposition) with the seasonal storage operations. The results were verified via available independent information about the storage locations and were compared with the findings of previous research.

A Study of 2D Roughness Periodical Profiles on a Flat Surface Generated by Milling with a Ball Nose End Mill.

This paper presents a study of 2D roughness profiles on a flat surface generated on a steel workpiece by ball nose end milling with linear equidistant tool paths (pick-intervals). The exploration of the milled surface with a surface roughness tester (on the pick and feed directions) produces 2D roughness profiles that usually have periodic evolutions. These evolutions can be considered as time-dependent signals, which can be described as a sum of sinusoidal components (the wavelength of each component is considered as a period). In order to obtain a good approximate description of these sinusoidal components, two suitable signal processing techniques are used in this work: the first technique provides a direct mathematical (analytical) description and is based on computer-aided curve (signal) fitting (more accurate); the second technique (synthetic, less accurate, providing an indirect and incomplete description) is based on the spectrum generated by fast Fourier transform. This study can be seen as a way to better understand the interaction between the tool and the workpiece or to achieve a mathematical characterisation of the machined surface microgeometry in terms of roughness (e.g., its description as a collection of closely spaced 2D roughness profiles) and to characterise the workpiece material in terms of machinability by cutting.

Analytical Torque Ripple Reduction Strategy for Flux Modulated Doubly-Salient Reluctance Motor Drives Based on ZSC Harmonic Regulation

Compared to the conventional doubly-salient reluctance motor (DRM) drives, the novel flux modulated DRM (FMDRM) drives can achieve a better overall torque performance due to the dc-biased sinusoidal current excitation. However, previous investigations only focus on the dc-biased and fundamental current components, and the contributions of current harmonics to the torque production are not clear. This paper proposes an analytical torque ripple reduction strategy for open-winding FMDRM drives based on zero-sequence current (ZSC) harmonic regulation. Firstly, the contribution of third-order ZSC harmonic to the electromagnetic torque is investigated together with the dc-biased sinusoidal current components. Secondly, an alternative torque component cancellation method is put forward, where the additional third-order ZSC is utilized to counteract the dominating part of the alternative torque generated by the dc-biased sinusoidal current for torque ripple reduction. Both the amplitude and initial phase angle of the additional third-order ZSC are calculated by the analytical equation of electromagnetic torque. Moreover, the corresponding three-dimensional current distribution is investigated to achieve the desired ZSC regulation. With the proposed scheme, the torque ripple can be effectively suppressed while maintaining high output torque capability and efficiency. Furthermore, experimental results are presented on a three-phase FMDRM prototype to verify the proposed strategy. <fn fn-type="other" id="fn3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> </fn>

Investigation of multi-input multi-output sine on random mixed vibration control

This article investigates Multi-Input Multi-Output (MIMO) mixed vibration control testing for Sinusoid on Random (SoR). The challenge of this vibration test is the mixed signal separation. The response signals at the control channels are composed of the broadband random component and the sinusoidal components. Firstly, the extracted accuracy of the Improved Correlation Integral Method (ICIM) is analyzed. The influences of the parameters used in ICIM are discussed in detail. Secondly, a new method, named Variance Minimum Method (VMM), is proposed for signal separation. Extraction accuracies of the ICIM and the proposed VMM are compared under different parameters. Finally, the control methods are proposed to iteratively update the driving signals of each component in the light of the deviations between the response signals and specified references. A cantilever beam example and biaxial vibration experiment manifest that the VMM and control method can be applied for the multi-shaker SoR vibration control tests.

Does Sea Level Change in The Strait of Istanbul, Türkiye?

Bosphorus is a narrow water passage connecting the Marmara Sea and the Black Sea through a sea level balance current flow. In this study, change points in mean and in variance and trend analysis are performed to reveal whether sea level is changing or not. Moreover, the simple linear regression is calculated to explain the relationships among sea level data in the three stations located in the Black Sea, the Marmara Sea and Bosphorus. The Turkish National Sea Level Monitoring System (TUDES) measures sea water level at each 15-minute periods in three stations in order to minimize the effect of turbulence. The sea level in the coastal area of Istanbul city is most populous region with its highly economic importance. The most important reason for this is the serious decrease in the amount of water discharged into the Black Sea due to dams and excessive water usage. The three sea level stations have no change points on the average of the whole data. The change points in variance are depicted especially in gap data years and especially around year 2018. While the difference in the sea level of the Istanbul and Sile stations contain seasonality, the difference between the other stations has a sinusoidal component. While this study lights on understanding the sea level characteristics of the Bosphorus, it also emphasizes the importance of accuracy, completeness and long-term measurement data requirement.

AN APPROACH ON THE DESCRIPTION OF A FLAT DRIVING BELT BEHAVIOUR MIRRORED IN TRANSMITTED MECHANICAL POWER

For driving belt condition monitoring, the main interest is the certification of the capacity to keep their qualities unchanged over a long period of time and secondary to detect the imminence of the catastrophic failure. This paper presents a study on the behaviour detection and description of a flat driving belt health condition, used in a rotary machine electrically driven, particularly a lathe headstock gearbox running idle. It was discovered that in the mechanical power transmitted from electromotor to gearbox via a flat belt some specific sinusoidal components (a fundamental and some harmonics) of variable power are generated. The description of these power components (by values of amplitude, frequency and phase at origin of time) is indirectly detectable in the evolution of the active electrical power absorbed by the drive electromotor. Two arguments are available for this approach. Firstly, there is a reasonable assumption that between the mechanical power and the active electrical power there is an approximated proportionality relationship through the power efficiency. Secondly, the evolution of the active electrical power (or mechanical power as well) is a deterministic signal with a low level of noise. A simple computer assisted procedure of active electrical power signal acquisition and data processing was conceived, the detection was done by computer aided curve-fitting procedures in Matlab applied on active electrical power evolution absorbed by the driving motor in stationary working regimes (the electromotor playing the role of a mechanical power sensor). Mainly two ways of graphic representation have been proposed in order to describe the variable power generated by this flat belt (in time and frequency domains). The behaviour of many other types of belts involved in rotary machines driving can be similarly described.

A gear grinding allowance stochastic simulation model

The paper proposes a method and technique that allow, using a minimum sample (3 units) from a batch (30 units) of real gears, to obtain stochastic information about the actual distribution of the gear grinding allowance both on the left and right flanks of the gear gaps for all gears in the batch. The proposed gear grinding allowance stochastic model is based on the representation of the distribution of the gear grinding allowance along the gear periphery as a superposition of a sinusoidal component with a random amplitude and a random component of the white noise type. The conditions for the equivalence of the stochastic characteristics of real (measured) and virtual (not actually measured) gears are formulated, namely the ratio between the amplitude of the sinusoidal component and the average amplitude of the high-frequency harmonic components of the gear grinding allowance distribution along the gear periphery. The gear grinding allowance stochastic model adjusted according to the proposed method makes it possible to predict the distribution of the gear grinding allowance on the left and right flanks of the gear gaps for a batch of gears and, taking into account the information obtained (replacing the experimental data), both evaluate the existing technology and optimize the gear grinding allowance value. As a result, it becomes possible to assess the quality of the technological process of manufacturing the gear, taking into account the effect random deformations to gear grinding allowance at the stage of their heat treatment (hardening), which is performed before the gear grinding operation. In turn, optimization of the grinding allowance (based on its actual distribution along the periphery of the gear) makes it possible to reduce both defects in burns (with increased allowance) and defects in unground teeth (with insufficient allowance).

Frequency estimation of multiple complex sinusoids using noise suppressing predictive FIR filter

The paper presents a predictive filter based frequency estimation algorithm (PF-FE) for multiple complex sinusoids corrupted by additive white Gaussian noise. The proposed frequency estimator combines the noise suppression capability of an FIR filter with a least squares approach minimizing the sum of the squared errors of a prediction filter. The FIR filter coefficients are chosen such that the sinusoidal components are predicted with minimum distortion. Computer simulation results are given to contrast the performance of the proposed PF-FE method with six other state-of-the-art frequency-estimators and the Cramer-Rao lower bound. Furthermore, the paper provides a complexity analysis of the PF-FE method and compares it with those of the conventional MUltiple SIgnal Classification algorithm and some selected state-of-the-art methods from the literature. Performance of the proposed PF-FE method is validated with effective mean square error (EMSE) plots for model orders of three and five and under different observation lengths and signal-to-noise ratio values. Finally, the paper demonstrates how EMSE varies for the proposed method when the prediction filter length is increased from 20 to 75. After extensive simulations, it is observed that when filter length is larger than half the observation length the EMSE will start to degrade.

Analyzing the effect of Hilbert transform on diffraction of a plane wave by an oscillating half-plane

In this study, we are analyzing the effect of the Hilbert Transform (HT) on diffraction of a plane wave by an oscillating half-plane. Both the incident wave and oscillating half-plane have different oscillating frequencies. After the necessary mathematical computations for several types of time-dependent oscillating functions, it is verified graphically that the HT induces a shift of [Formula: see text], which can be observed from the flipping effect in the magnitude of the sinusoidal components occurring in their respective diffracted fields. Graphical illustrations presenting the effects of the HT are presented and discussed.

Varying Wave-Shape Component Decomposition: Algorithm and Applications

The decomposition problem for multiple sinusoidal component signals has been developed in the past decades. However, many complex time series are made up of wave-shape components, which invalidates signal decomposition methods based on sinusoidal components. Since wave-shape components are time-varying from one cycle to the other and often contaminated by strong noise, robustly decomposing a signal into wave-shape components is still a challenging task. In this paper, a varying wave-shape component decomposition (VWCD) method is proposed to extract time-varying and weak characteristics of wave-shape components from a multicomponent signal. Specifically, instantaneous frequencies (IFs) of varying wave-shape components are no longer inter multiples of a fundamental IF allowing the VWCD method more flexible in practical applications. We propose an instantaneous amplitudes and fundamental component phases estimation method based on an intrinsic chirp signal model and a regression coefficients initialization method by a fixed wave-shape signal model. The potential and effectiveness of the proposed VWCD method are verified by some simulated signals with different signal-to-noise ratios, a real-world electroencephalography seizure signal, and an experimental chest wall vibration signal from a microwave vital sign monitoring system.

Güney Afrika'da Ekonomik Büyüme ve Sabit Sermaye Oluşumu Arasındaki Granger Nedensel İlişkiler: Bir Frekans Alanı Analizi

The purpose of this study is to examine the Granger causal linkages between gross fixed capital formation (GFCF) growth and economic growth in South Africa. To provide a more comprehensive analysis of the Granger causality (GC) relationships between these macroeconomic indicators, this study investigates these causalities in the frequency domain. The methodology is based on decomposing time series into weighted sinusoidal components and performing separate GC tests for each component. Test results reveal that there is feedback between capital formation and economic growth in South Africa in the frequency domain, even at the 1% significance level. Furthermore, the test results indicate that the GC from fixed capital formation to economic growth is detected in lower frequencies compared to the GC from economic growth to capital formation. This means that the severity of the GC from economic growth to capital formation is stronger than the reverse direction GC in South Africa.

Development of tuned hybrid fuzzy and BiLSTM-based epileptic seizure classification model with stacked 1D-CNN-fisher discriminant feature selection

ABSTRACT Electroencephalogram (EEG) is a signal which consists of different sinusoidal components with a dense frequency spectrum. The existing methods are time-consumingand includes inconsistencies in judgment among seizure classification. Therefore, it is very difficult to accurately detect epileptic seizures from the recorded EEG. Therefore, a new epileptic seizure detection model is developed with hybrid deep-structured technology. The EEG signal data obtained from the standard online resources which are given into the pre-processing phase with the help of band pass filtering and smoothing techniques. Then, the 5-level Discrete Wavelet Transform (5-level DWT) is used for signal decomposition to get the decomposed signals. 1-D stacked Convolutional Neural Network (1-D stacked CNN) is utilized for the extraction of features. After, the feature selection process is executed by utilizing the Fisher Discriminant Analysis (FDA). The selected features are subjected to the classification phase by Tuned Hybrid Fuzzy Bi-directional Long Short-Term Memory (THFBi-LSTM). Here, the parameter optimization takes place with hybridized optimization algorithm of Probability-based Dingo Coyote Optimization (P-DCO). In the overall simulation estimation, the offered approach achieves a 97% accuracy rate and also a 92% F1-score rate. Thus, the experimental results are reveled that it is better than the other baseline approaches.

Deadbeat-Direct Torque and Flux Control of a Brushless Axial-Flux Magnetic-Geared Double-Rotor Machine for Power-Splitting HEVs

A deadbeat-direct torque and flux control (DB-DTFC) for brushless axial-flux magnetic-geared double-rotor machines (AMGDRMs) is presented in this paper. The AMGDRM is employed as a power-splitting component in hybrid electric vehicles (HEVs) to enable speed decoupling between the internal combustion engine (ICE) and load. Since the rigid connection between the ICE and the drive train is replaced by the AMGDRM, fast torque control of the AMGDRM is required for the ICE speed regulation. Furthermore, the ICE torque contains abundant harmonics, inevitable sinusoidal components are introduced to the modulating rotor torque for torque balance, and further to the PM rotor torque owing to the magnetic-gear effect. Since both the PM rotor of the AMGDRM and the traction motor contribute to the hybrid electric system total torque output (i.e., torque coupling), the cascaded traction motor should compensate the PM rotor torque ripple actively to guarantee smooth total output torque. However, proportional-integral (PI) regulator suffers from limited bandwidth and thus could not realize accurate torque decoupling. Therefore, DB-DTFC, where torque and flux linkage are decoupled and respectively achieve their reference commands within two sampling periods, i.e., deadbeat responses, is proposed to enable a faster and more robust ICE speed control and accurate torque decoupling. The DB-DTFC law is derived and discrete-time close-loop current and flux linkage observers of the AMGDRM are developed. The proposed scheme and its superiorities are experimentally validated on an AMGDRM prototype test bench.

Instantaneous speed estimation of induction motor by time-varying sinusoidal mode extraction from stator current

When an induction motor operates at variable speed, the fundamental wave and harmonics in the stator current are presented in the form of nonstationary sinusoidal components with time-varying parameters. To estimate the instantaneous speed of the motor utilizing the stator current signal, a time-varying sinusoidal mode extraction (TVSME) method is firstly proposed. In TVSME, a variational model, which compacts the frequency bandwidth of the desired time-varying sinusoidal mode (TVSM) to the narrowest bandwidth and excludes other harmonics as much as possible, is constructed to optimally extract the desired TVSM and to estimate its instantaneous frequency (IF). Then, the fundamental wave and the principal slot harmonics (PSH) are extracted by TVSME, and their IFs are used to calculate the instantaneous rotational speed of the motor. As a result, a highly accurate and smooth rotational speed curve, which is valuable for performance evaluation and fault diagnosis of induction motors, can be obtained. The proposed method is evaluated by both an artificial signal and two experimental signals collected from actual motors. Results show that the instantaneous speed of induction motor can be accurately estimated under the condition of speed variation and multi-harmonic interference.

Optical fiber sensor with a lateral-offset structure assisted by virtual vernier effect for stretching strain measurement

This paper proposes an optical fiber sensor based on multimode interference (MMI) with a lateral offset structure to measure tensile strain. The coupling ratio of each higher-order mode in the coreless fiber (NCF) can be controlled by the dislocation distance. The experimental results show that the best interference fringes are obtained when the dislocation is 10μm and the sensitivity in the range of 0–500 μɛ is 28 pm/μɛ. To improve the sensitivity of the sensor further, the measured data were filtered by fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) to obtain a sinusoidal component, superimposed with a simulated signal to form a vernier effect. With the method of virtual vernier effect, the sensitivity is increased to 452.64 pm/μɛ, which is 15.6 times the original sensitivity. Compared with the traditional vernier effect, the virtual vernier effect method does not require cascade or series method, the reference part is not affected by the environment, and the method can be widely used in many types of interferometric fiber optic sensor devices, especially to overcome the problem that MMI is not suitable for vernier effect.

Diagnosis of Interturn Short-Circuit of SRM Based on Ratio of Current Components

Inter-turn short-circuit is a common fault mode of switched reluctance motors (SRM) that causes changes in the parameters of the motor that can cause vibrations and noise. In this paper a diagnosis method is proposed to detect short circuit fault. Firstly, the model of a phase winding with inter-turn short-circuit is built and the fault characteristics are analyzed. Under normal condition, the sinusoidal components of phase currents are mostly a positive sequence with minimum distortion. The frequency signature of the motor is extracted by Fourier analysis when operating in normal conditions. When a fault condition occurs, symmetry of the phase currents is broken and both negative and zero sequence component of the phase currents appear. According to the asymmetry, an index, the ratio of negative to positive sequence component of the phase currents, is defined to identify whether the motor is working normally. The proposal is verified by simulations and experiments on a three-phase 12/8 structure SRM under different mechanical operating conditions. In normal condition, the fault index is about 0.02; in 12.5% fault condition, the fault index rises to about 0.07; in 25% fault condition, the fault index rises to about 0.08. According to the experimental results, fault index is robust because it is not influenced by the following parameters: turn on angle, turn off angle, rotor speed, load torque, etc.

Submodule Capacitor Voltage Ripple Reduction of Full-Bridge Submodule-Based MMC (FBSM-MMC) with Non-Sinusoidal Voltage Injection

The full-bridge submodule (FBSM)-based modular multilevel converter (FBSM-MMC) offers promising performance due to its ability to output negative FBSM voltage. This paper studies this ability of the FBSM-MMC under zero-sequence voltage injection (ZSVI) and second-order harmonic current injection (SHCI). The focus of the research is to redistribute the FBSM powers by injecting an additional power degree of freedom, resulting in a smaller FBSM capacitor voltage ripple, while keeping the maximum AC output voltage for a given fundamental frequency component of the arm voltage reference. Accordingly, a control strategy was developed, based on non-sinusoidal ZSVI, and SHCI is proposed for further improving the performance of the FBSM-MMC. The proposed non-sinusoidal ZSVI contains a higher sinusoidal third-order harmonic component than that of pure sinusoidal third-order voltage injection (THVI) when operating under the same maximum AC output voltage. By implementing this solution, a smaller amplitude of the injected second-order harmonic current can be achieved, producing a lower power loss in the FBSM-MMC. Considering the proposed solution, the relationship of the arm powers and FBSM capacitor voltages are also discussed. Finally, the simulation results and experimental results are presented to verify the effectiveness of the theoretical analysis of the proposed method.

Phase-Derived Ranging Based Fiber Transfer Delay Measurement Using a Composite Signal for Distributed Radars with Fiber Networks

Fiber transfer delay (FTD) variations influence the coherence of distributed radars with fiber networks, resulting in a performance degradation in target detecting and imaging. To measure and compensate for the variation, a phase-derived ranging based FTD measurement using a composite signal is proposed. The composite signal comprises a sinusoidal component and a linear frequency modulation (LFM) component. As the composite signal passes through a fiber under test (FUT), the sinusoidal component generates a phase shift that corresponds to the FTD. The phase shift can be represented by two parameters: the number of complete periods of 2π that can be estimated by using the LFM component, and a phase shift less than 2π that be measured employing the sinusoidal component. When using the proposed measurement system to measure FTD variations in a distributed radar, only an additional sinusoidal component is needed, which minimizes interference with radar signals. Moreover, the proposed measurement system can share core function modules such as signal generation and process modules with distributed radars, which enhances the compatibility and reduces the overall complexity. Experiments are carried out to measure a variable optical delay line and a long optical fiber. The experiment results verify the feasibility of the measurement system and show that a measurement range of more than 15 km, an accuracy of ±0.1 ps and a measurement time of 105 ms can be achieved.