Amplitude Characteristics Research Articles

Dependency of amplitude and phase characteristics of vasomotor oscillations on visual stimulation conditions and experiment duration

The intrinsic-signal optical imaging is widely used in experimental, theoretical and applied research of the mammal’s brain neocortex functional anatomy. However, a neural activity signal is hidden by the background activity, the amplitude of which is an order of magnitude larger than the mapping signal amplitude. Most of such background activity represents spontaneous oscillations in 0.01–0.15 Hz frequency range related to vasomotor oscillations. In this paper, we point out that such oscillations change their power and phase during the response time course. The most dramatic influence is intrinsic for 0.05–0.15 Hz oscillations. The power of vasomotor oscillations declines more quickly than the stability features of their phase characteristics. Departing from these data, we suggested approaches for minimization of role of vasomotor oscillations in functional maps resulting from intrinsic-signal optical imaging.

High-efficiency wide-angle anomalous refraction with acoustic metagrating

Abstract The emergent metagrating, with its unique and flexible beam shaping capabilities, offers new paths to efficient modulation of acoustic waves. In this work, an acoustic metagrating is demonstrated for high-efficiency and wide-angle anomalous refraction. It is shown that the normal reflection and transmission can be totally suppressed by properly modulating the amplitude and phase characteristics of the metagrating supercells for high-efficiency anomalous refraction. The anomalous refraction behavior is achieved in the wide range of incident angles from 28° to 78°, and the efficiency of -1st order diffraction is higher than 90% by finely designing the metagrating structure. The anomalous refraction behaviors are verified experimentally at incidence angle of 28°, 45° and 78°, respectively. The demonstrated metagrating is anticipated to possess efficient wide-angle composite wavefront engineering applications in such fields as communications.

A Versatile Image Encryption Scheme Based on Optical Hologram Technology and Chess Rules

A novel versatile image encryption scheme about hologram technology and chess rules is proposed in this paper. This scheme can encrypt images by transferring them from the spatial domain to the frequency domain. First, the theory of computer-generated holograms is applied to record phase and amplitude features by imitating the optical hologram. The original image is transformed to a hologram and the phase values of the points in the image are recorded. Second, the parameters associated with the plaintext image are obtained by a Hash function and used as the initial values of the chaotic system, and then the pawns on the board are regarded as pixel points of the image, where the movement direction of the pawns is the movement direction of the pixel points of the image. The hologram is confused by combining the pawns’ movement rules with the chaotic sequences. Finally, the encrypted image is gained through a combination of the chaotic sequence and the selective diffusion method. The test results show that the scheme is able to resist various attacks and has good security performance in encryption.

Dynamics of the Euler — Bernoully beam with distributed hysteresis properties

In this paper, we present a new mathematical approach to the analysis of a beam with distributed hysteresis properties. These hysteresis characteristics are described by two methods: phenomenological (Bouc — Wen model) and constructive (Prandtl — Ishlinskii model). The equations for beam are developed using the well-known Hamilton method. We investigate the dynamic response of a hysteresis beam under various external loads, including impulse, periodic and seismic loads. The results of numerical simulations show that the hysteresis beam exhibits differently to external influences as compared to the classical Euler-Bernoulli beam. In particular, under the same external loads, the vibration amplitude and energy characteristics of the hysteresis beam are lower than those of the classical one. These findings can be useful for buildings developers in the design of external load resistant buildings and structures

Geological inference of channel and polka-dot seismic anomalies in Yeongil bay, Pohang

The Engineering Ocean Seismic 3D system, which enables ultra-high-resolution seismic surveys on a smaller survey scale, was deployed in Yeongil Bay, Pohang, South Korea. The region is renowned for abundant shallow gas deposits and faults. Based on acoustic impedance contrast and abnormal behavior observations, two seismic anomalies termed channel and polka-dot anomalies have been identified in the seismic volume. Seismic attribute analysis based on the signal amplitude and structural characteristics reveals that these anomalies correspond to shallow biogenic gas deposits. Structural interpretation of the seismic volume revealed that the contractional deformation resulting from post-Miocene neotectonics has resulted in uplift and reverse faulting in the Pohang region, contributing to the formation of anomalies. The channel anomalies correspond to gas-saturated tidal channels that formed during eustatic sea-level changes in the post-Last Glacial Maximum period. The polka-dot anomalies are located in topographic lows and are overlain by neotectonic sediment. The different behaviors of these anomalies in a seismic volume can be attributed to the different thicknesses of overburden overlying each of the anomalies.

Infrared neuroglial modulation of spinal locomotor networks

Infrared neural stimulation (INS) emerges as a promising tool for stimulating the nervous system by its high spatial precision and absence of the use of exogenous agents into the tissue, which led to the first successful proof of concept in human brain. While neural networks have been the focal point of INS research, this technique is also non cell type specific as it triggers activity in non electrically excitable cells. Despite increasing interest, there remains to demonstrate well defined simultaneous astrocytic and neuronal signals in response to INS. Using calcium imaging, we show that INS has the capacity to initiate calcium signaling in both astrocytes and neurons simultaneously from the rostral lumbar spinal cord, each exhibiting distinct temporal and amplitude characteristics. Importantly, the mechanism underlying infrared-induced neuronal and astrocytic calcium signaling differ, with neuronal activity relying on sodium channels, whereas induced astrocytic signaling is predominantly influenced by extracellular calcium and TRPV4 channels. Furthermore, our findings demonstrate the frequency shift of neuronal calcium oscillations through infrared stimulation. By deepening our understanding in INS fundamentals, this technique holds great promise for advancing neuroscience, deepening our understanding of pathologies, and potentially paving the way for future clinical applications.

An efficient pseudoelastic pure P‐mode wave equation and the implementation of the free surface boundary condition

AbstractBased on the elastic wave equation, a pseudoelastic pure P‐mode wave equation has been recently derived by projecting the wavefield along the wavefront normal direction. This pseudoelastic pure P‐mode wave equation offers an accurate simulation of P‐wave fields with accurate elastic phase and amplitude characteristics. Moreover, considering no S‐waves are involved, it is computationally more efficient than the elastic wave equation, making it an excellent choice as a forward simulation engine for P‐wave exploration. Here, we propose a new pseudoelastic pure P‐mode wave equation and apply the stress image method to it to implement the free surface boundary condition. The new pseudoelastic wave equation offers significantly improved computational efficiency compared to the previous pseudoelastic wave equation. Additionally, the wavefields simulated by this new pseudoelastic wave equation exhibit clear physical interpretations. We evaluate the accuracy of the new wave equation in simulating elastic P‐waves by employing a model with high‐velocity contrasts. We find that this new equation, which purely admits P‐waves, though having exact amplitude and phase behaviour as the elastic waves for transmission components, the amplitudes slightly suffer in the scattering scenario. The difference in amplitude between the elastic and our pseudoelastic increases as the contrast in velocity at the interface (interlayer velocity ratio) increases, especially the S‐wave velocities. This has negative implications on scattering from the free surface boundary condition or the sea bottom interface, especially if the shear wave velocity below the surface or the sea bottom is high. However, in cases where, like for land data in the Middle East, the transition to a free surface is smoother, the accuracy of the pseudoelastic equation is high. In all cases, regardless of the interlayer velocity ratio, the accuracy of the pseudoelastic wave equation in simulating the elastic case, for scattered waves, exceeds that of the acoustic wave equation in phase and amplitude.

Gust response alleviation of the aircraft wing aeroelastic systems in supersonic flows using nonlinear energy sink

In order to reduce the gust response level of the aircraft wing aeroelastic systems in supersonic flows, the nonlinear energy sink (NES) is introduced into the aeroelastic system, and the effect of the nonlinear energy sink on the aeroelastic gust response is studied. The gust response dynamic model without and with NES is established by Lagrange equation, considering the cubic nonlinear characteristics of both plunge and pitch stiffness. The piston theory model as well as the '1-cos' model are employed to describe the unsteady aerodynamic load and the gust velocity, respectively. Besides, the comparison study of the gust response amplitude and energy characteristics of the wing aeroelastic systems with and without NES are analyzed. For systems with NES, the amplitude of gust dynamic response decreases rapidly, while vibration can be transmitted to the NES mechanism and causing its vibration. From an energy perspective, about 72.15 % of the original vibration energy has been transferred into NES, and further consumed through its damping. The influence of NES parameters, such as frequency ratio, mass ratio, damping ratio, and installation position on gust response, is discussed. Numerical simulations demonstrate that: as the frequency ratio, damping ratio and mass ratio increase, the dynamic response amplitude of the aeroelastic system decays faster, and the energy transferred from the wing system also increases. In addition, an optimal relative distance for the installation of NES can be found with best gust alleviation effect. The influence of nonlinear energy sink on the targeted transfer of vibration energy in aeroelastic systems are analyzed under different working conditions. The NES shows great gust alleviation effect and good robustness under different flight speeds, gust intensities, and gust lengths.

Backcalculation of elastic moduli of elements of layered halfspace on the basis of dynamic deformation analysis (by the example of highways)

The paper is devoted to the improvement of the method of inverse calculation of elasticity moduli of layers of motorway pavements in the dynamic setting, which involves the analysis of deformation characteristics in the time domain. To solve this problem, the mathematical model of a layered half-space is adapted to the calculation of amplitude-time characteristics of deformation on the surface of the layered medium, and the construction of the corresponding bowls of maximum values of vertical displacements. Adjustment of the calculated values of vertical displacements with respect to the registered experimental displacements in the field was performed. The correspondence between the final values of maximum vertical displacements, amplitude and time characteristics on the surface of the layered medium, and the shapes and areas of dynamic hysteresis loops on the surface of the investigated medium, achieved by adjusting the calculated characteristics relative to the experimental ones, has been demonstrated. For the first time in solving the problem of determining the mechanical parameters of a layered medium, it was proposed to consider dynamic hysteresis loops as one of the parameters characterising them and the correspondence of their calculated and experimental areas as a criterion of the adequacy of the achieved result.

Simultaneous identification of propeller fluctuating forces and unknown parameters based on a Bayesian inversion approach

Accurate quantification of propeller excitation is essential for effective vibro-acoustic analysis and mitigation in marine vessels. This study introduces a Bayesian statistical framework that indirectly reconstruct uncertain forces and system parameters by fusing available prior information, forward modeling and measured vibration responses through Bayesian theorem. The Markov Chain Monte Carlo (MCMC) algorithm, enhanced with Polynomial Chaos Kriging (PC-Kriging) surrogates, is employed to facilitate efficient estimation of likelihood functions and robust uncertainty propagation, providing posterior probabilistic estimates and credible intervals for the identified forces. Additionally, the framework incorporates model class selection based on model evidence to determine the most plausible empirical spectrum model for the propeller broadband thrust spectrum. Specifically, this study proposes and evaluates two candidate empirical spectrum models, the Ornstein–Uhlenbeck (OU)-Gaussian and OU-Weibull models, which effectively capture the frequency–amplitude characteristics of unsteady thrust. Numerical results demonstrate substantial parameter uncertainty reduction, accurate thrust spectrum reconstruction, and effective quantification of statistical properties. Moreover, the OU-Gaussian model is revealed to be more plausible for characterizing the propeller thrust spectrum and response prediction. This framework may offer a practical solution for propeller excitation identification, enabling rapid vibro-acoustic performance assessments of marine vessels.

Fatigue Overview of Ship Structures under Induced Wave Loads

Fatigue damage represents a key failure mode in ship structures. Such damage typically begins at vulnerable points in the structure, like welded joints, stress concentration areas, and cracks. Cyclic loading, particularly from waves, encountered by ships during their operational life is a major cause of fatigue damage, which is the main focus of this study. There are various methods to address different sea state conditions, though they can sometimes be approximate. This paper aims to review the most commonly used methods to highlight their strengths and weaknesses and to provide essential background knowledge for developing reliable theoretical and numerical models for predicting the fatigue life of ship structures exposed to various sea states over their lifetime. The primary theoretical approaches discussed include energy spectral methods in both time and frequency domains, which are used to quantify wave-related energy and amplitude characteristics and evaluate wave loads for predicting the fatigue life of structures and welded joints. The discussion also covers the determination of cyclic stress in specific structural details of the hull girder and welded joints to identify the relevant maximum stress range for subsequent fatigue studies conducted using finite element analysis.

Role of seismogeological modeling in the attribute analysis of seismic data using the example of the forecast of the thickness of the Yu3t formation of the southwestern Kazakhatsan oil field

Relevance. The need to introduce the results of two-dimensional seismogeological modeling into seismic data attribute interpretation to identify signs of manifestation of the target object of the forecast in the wave field of seismic recording and assess the influence of interference of the host strata on it. Object. Highly productive terrigenous Upper Jurassic Yu3t formation of the Nizhnekumkol formation of the oil field of southwestern Kazakhstan, formed under conditions of a cone of liquid proluvial fan. Aim. To assess the impact of the interaction of the enclosing strata of the Upper Jurassic Yu3t formation on the seismogeological forecast of its capacities within the considered oil field. Methods. Two-dimensional seismogeological modeling based on the analysis of the frequency response and determination of the shape of the elementary pulse of seismic recording in the interval of the productive formation. Analysis of the thicknesses of geological inhomogeneities overlying the Yu3t formation in order to assess their effect on the wave characteristic of the time interval of research. Results. The performed two-dimensional seismogeological modeling showed that the amplitude characteristic of the negative phase of the wave packet of the target object, characterizing the power of the Yu3t formation, is affected to varying degrees by the overlapping thicknesses of the Yu3a formation and the inter-reservoir seal P1. Based on the modeling results, it was concluded that the basis for predicting the thickness of the Yu3t formation should be considered an attribute map of the difference in amplitudes of the upper positive (A) and average negative (B) phases of the considered wave packet A, B, C. At the same time, the correlation dependences of the obtained attribute with the values of the thickness of the Yu3t formation in drilled wells should be considered within the areas outside the zone the influence of other elements complicating the wave pattern of the target object (tectonic disturbances and erosive protrusions of the foundation).

Application of Convolutional Neural Networks to time domain astrophysics. 2D image analysis of OGLE light curves

In recent years the amount of publicly available astronomical data has increased exponentially, with a remarkable example being large-scale multiepoch photometric surveys. This wealth of data poses challenges to the classical methodologies commonly employed in the study of variable objects. As a response, deep learning techniques are increasingly being explored to effectively classify, analyze, and interpret these large datasets. In this paper we use two-dimensional histograms to represent Optical Gravitational Lensing Experiment (OGLE) phasefolded light curves as images. We use a Convolutional Neural Network (CNN) to classify variable objects within eight different categories (from now on labels): Classical Cepheid (CEP), RR Lyrae (RR), Long Period Variable (LPV), Miras (M), Ellipsoidal Binary (ELL), Delta Scuti (DST), Eclipsing Binary (E), and spurious class with Incorrect Periods (Rndm). We set up different training sets to train the same CNN architecture in order to characterize the impact of the training. The training sets were built from the same source of labels but different filters and balancing techniques were applied. Namely: Undersampling (U), Data Augmentation (DA), and Batch Balancing (BB). The best performance was achieved with the BB approach and a training sample size of sim 370000 stars. Regarding computational performance, the image representation production rate is of sim 76 images per core per second, and the time to predict is sim 60$\ s $ per star. The accuracy of the classification improves from sim 92<!PCT!>, when based only on the CNN, to sim 98<!PCT!> when the results of the CNN are combined with the period and amplitude features in a two step approach. This methodology achieves comparable results with previous studies but with two main advantages: the identification of miscalculated periods and the improvement in computational time cost.

Characterization of ultrasonic vibration and polishing force in sapphire ultrasonic vibration-assisted flexible polishing: Insights from in-situ monitoring systems

Sapphire ultrasonic vibration-assisted flexible polishing (UVAFP) is a promising technique for comprehensively improving the surface integrity of machined parts. The technique was performed on an ultra-precision machine tool with the in-situ monitoring systems in this paper, which aims to provide a new perspective for understanding the material removal mechanisms in the sapphire UVAFP process. A Taguchi L9 (43) orthogonal experiment was conducted to investigate the effects of feed distance, spindle speed, ultrasonic vibration (UV), and polishing time on the surface finish and material removal in the process. In addition, the effect of a polyurethane ball tool is not trivial. A single-factor experiment was conducted for exploring it. Based on a laser displacement measurement system and an acoustic emission sensor system, the characteristics of time-dependent ultrasonic amplitude and ultrasonic frequency for the sapphire UVAFP system were analyzed, with the effectiveness of UV demonstrated. Based on a three-component force measurement system, the characteristics of normal force and its relationship with process parameters and tool deformation were analyzed, with macro- and micro-level examined. In conclusion, this paper presents the characterization of UV and polishing force in the sapphire UVAFP process, providing novel insights into understanding the material removal mechanisms of sapphire and even more manufacturing problems.

Revealing Hidden Features of Chaotic Systems Using High-Performance Bifurcation Analysis Tools Based on CUDA Technology

Bifurcation analysis is an essential tool in nonlinear dynamics. Bifurcation diagrams help to discover subtle features of investigated dynamics such as chaotic and periodic regimes, hidden attractors and fixed points. However, plotting high-resolution bifurcation diagrams can be a computationally challenging task, especially in multiparametric evaluation. It should be noted that the bifurcation analysis is a task with natural parallelism and thus can be efficiently solved using hybrid central-graphics processing architectures. In this paper, we propose an advanced algorithm and special software for plotting bifurcation diagrams using calculations accelerated by graphics processing unit in combination with a highly efficient semi-implicit ordinary differential equation solver. Time series processing is based on the extraction of amplitude and phase features for the density-based spatial clustering of applications with noise to determine oscillation periodicity. We showcase the features of the application of proposed solutions on a set of test chaotic systems. The performance of the analysis algorithms is investigated in comparison with conventional solutions based on central processing unit and several approaches known from the literature. We explicitly show that the proposed algorithm outperforms known solutions in both calculation speed and precision.

Deep-learning-based natural fracture identification method through seismic multi-attribute data: a case study from the Bozi-Dabei area of the Kuqa Basin, China

Fractures play a crucial role in tight sandstone gas reservoirs with low permeability and low effective porosity. If open, they not only significantly increase the permeability of the reservoir but also serve as channels connecting the storage space. Among numerous fracture identification methods, seismic data provide unique advantages for fracture identification owing to the provision of three-dimensional information between wells. How to accurately identify the development of fractures in geological bodies between wells using seismic data is a major challenge. In this study, a tight sandstone gas reservoir in the Kuqa Basin (China) was used as an example for identifying reservoir fractures using deep-learning-based method. First, a feasibility analysis is necessary. Intersection analysis between the fracture density and seismic attributes (the characteristics of frequency, amplitude, phase, and other aspects of seismic signals) indicates that there is a correlation between the two when the fracture density exceeds a certain degree. The development of fractures is closely related to the lithology and structure, indirectly affecting differences in seismic attributes. This indicates that the use of seismic attributes for fracture identification is feasible and reasonable. Subsequently, the effective fracture density data obtained from imaging logging were used as label data, and the optimized seismic attribute near the well data were used as feature data to construct a fracture identification sample dataset. Based on a feed-forward neural network algorithm combined with natural fracture density and effectiveness control factor constraints, a trained identification model was obtained. The identification model was applied to seismic multi-attribute data for the entire work area. Finally, the accuracy of the results from the training, testing, and validation datasets were used to determine the effectiveness of the method. The relationship between the fracture identification results and the location of the fractures in the target reservoir was used to determine the reasonableness of the results. The results indicate that there is a certain relationship between multiple seismic attributes and fracture development, which can be established using deep learning models. Furthermore, the deep-learning-based seismic data fracture identification method can effectively identify fractures in the three-dimensional space of reservoirs.

Investigation into NSAW excitation and modulation utilizing the grating mask technique

Narrowband surface acoustic wave (NSAW) method is a promising ultrasonic detection technique, with its laser-induced excitation technology offering the advantages of non-contact and flexible regulation. This paper proposes an NSAW excitation and modulation system based on the grating mask method to detect the nonlinear characteristics of the spectra caused by the variation in material surface properties. According to Doyer’s sharp line excitation theory, a strip line source array excitation model formed by superposition principle is established, and the effects of duty ratios of the masks on NSAW spectra amplitude characteristics are interpreted. An NSAW excitation and B-scan experimental system that can realize line source spacing changes is developed, and the effects of cylindrical lens height and masks with different duty ratios on NSAW spectra modulation are studied. The experimental results show that the amplitude ratios (the ratio of the double frequency amplitude to the second harmonic amplitude) are consistent with the results of the Doyer superimposed strip line source array excitation model in which the excitation light source energy is evenly distributed. The second-order nonlinear coefficients extracted from the experimental spectra can effectively characterize the surface properties of 6061 aluminum alloy at different annealing temperatures.

Research on Surface Processing Method of Pulse Transmission Signal of Amplitude-Modulated Drilling Fluid in 10,000-m Deep Wells

Conventional Measurement-While-Drilling (MWD) technology is unable to function statically at the predicted temperatures of deep formations exceeding 200 °C in wells reaching depths of 10,000 m. It is limited to measuring downhole engineering parameters through purely mechanical means, such as inclination. However, the accurate long-distance transmission of drilling fluid pulse signals poses a significant bottleneck, restricting the application of these mechanical measurement methods. To address these issues, this paper develops and designs an algorithm to identify and analyze the amplitude characteristics of deep well mud signals. By employing a signal coding algorithm, a signal processing analysis method, and a signal feature recognition algorithm based on grey correlation degree, we construct a signal recognition method capable of decoding mud amplitude encoded signals. Key techniques such as filtering, smoothing, and feature extraction are utilized in the signal processing, and the proposed method’s effectiveness is verified through the analysis of collected signals. Furthermore, long-distance simulation analysis software is developed to evaluate waveform distortion during extended transmission, confirming the feasibility of the recognition algorithm. Laboratory experiments demonstrate that this algorithm can accurately recognize and demodulate signals generated by mechanical inclinometer structures, providing a novel decoding method for signal transmission in deep and ultra-deep wells.

ICG signal denoising based on ICEEMDAN and PSO-VMD methods.

Impedance cardiography (ICG) plays a crucial role in clinically evaluating cardiac systolic and diastolic functions, along with various other cardiac parameters. However, its accuracy heavily depends on precisely identifying feature points reflecting cardiac function. Moreover, traditional signal processing techniques used to mitigate random noise and breathing artifacts may inadvertently distort the amplitude and temporal characteristics of ICG signals. To address this issue, this study investigates a noise and artifact elimination method based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Particle Swarm Optimization-based Variational Mode Decomposition Algorithm (PSO-VMD). The goal is to preserve the amplitude and temporal features of ICG signals to ensure accurate feature point extraction and computation of associated cardiac parameters. Comparative analysis with signal processing methods employing various wavelet families and Ensemble Empirical Mode Decomposition (EEMD) in ICG signal processing applications reveals that the proposed method achieves superior signal-to-noise ratio (SNR) and lower root-mean-square error (RMSE), while demonstrating enhanced correlation and waveform consistency with the original signal.

The digital signature of emergent tremor in Parkinson’s disease

Emergent tremor in Parkinson’s disease (PD) can occur during sustained postures or movements that are different from action tremor. Tremor can contaminate the clinical rating of bradykinesia during finger tapping. Currently, there is no reliable way of isolating emergent tremor and measuring the cardinal motor symptoms based on voluntary movements only. In this study, we investigated whether emergent tremor during repetitive alternating finger tapping (RAFT) on a quantitative digitography (QDG) device could be reliably identified and distinguished from voluntary tapping. Ninety-six individuals with PD and forty-two healthy controls performed a thirty-second QDG-RAFT task and the Movement Disorders Society – Unified Parkinson’s Disease Rating Scale Part III (MDS-UPDRS III). Visual identification of tremor during QDG-RAFT was labeled by an experienced movement disorders specialist. Two methods of identifying tremor were investigated: 1) physiologically informed temporal thresholds 2) XGBoost model using temporal and amplitude features of tapping. The XGBoost model showed high accuracy for identifying tremor (area under the precision-recall curve of 0.981) and outperformed temporal-based thresholds. Percent time duration of classifier-identified tremor showed significant correlations with MDS-UPDRS III tremor subscores (r = 0.50, p < 0.0001). There was a significant change in QDG metrics for bradykinesia, rigidity, and arrhythmicity after tremor strikes were excluded (p < 0.01). The results demonstrate that emergent tremor during QDG-RAFT has a unique digital signature and the duration of tremor correlated with the MDS-UPDRS III tremor items. When involuntary tremor strikes were excluded, the QDG metrics of bradykinesia and rigidity were significantly worse, demonstrating the importance of distinguishing tremor from voluntary movement when rating bradykinesia.