Low-frequency Characteristics Research Articles

Low frequency vibration isolation characteristics and intelligent design method of hull grillage metastructures

In this paper, the wave propagation mechanics theoretical model of hull grillage metastructures with local resonators is established, and the intelligent design method of hull grillage metastructures is proposed based on deep learning. The flexural vibration isolation characteristics of periodic hull grillage structures and the proposed hull grillage metastructures are theoretically studied using the spectral element method. Numerical calculation and experimental tests of the flexural wave vibration transmission of hull grillage metastructures are conducted to validate the proposed theoretical model. In addition, the data set is constructed by using the wave mechanics theoretical model of hull grillage metastructures, and the forward prediction neural network model of vibration transmission characteristics as well as the inverse design neural network model of hull grillage metastructures are established. Results show that compared with periodic hull grillage structures, the proposed hull grillage metastructures possess significant low-frequency flexural wave vibration isolation characteristics which almost completely covers the low-frequency range from 27 Hz to 169 Hz. Both forward prediction and inverse design network models have good performance, which verifies the correctness of the intelligent design method based on deep learning, and provides a new direction for the structural design on demand of low-frequency vibration reduction of ship and offshore structures.

Periodic WIB-trench wave barriers for seismic surface waves

Seismic surface waves possess detrimental characteristics of low frequency and significant amplitude, posing a threat to buildings and structures. This paper proposes a novel combined wave barrier, referred to as the periodic WIB-trench barrier, which is designed specifically for the purpose of isolating seismic surface waves. The periodic WIB-trench barrier consists of wave impeding blocks (WIB) and infilled trenches. The surface wave bandgaps of the periodic WIB, infilled trench, and the combined wave barrier have been compared, while the vibration modes of these three barriers have also been acquired to reveal the mechanism of the bandgaps of surface waves. Subsequently, a finite barrier transmission model has been established to analyze the vibration isolation performance of the periodic WIB-trench barrier in both the frequency domain and time domain. The results demonstrate that the proposed combined wave barrier exhibits larger bandgap widths and lower boundary frequencies when compared to the periodic WIB and periodic infilled trenches. Specifically, the bandgaps of the periodic WIB-trench barrier ranges from 4.5 to 6 Hz, 9.5 to 12.8 Hz, and 15 to 20 Hz. Through the confinement of surface wave energy within the barrier unit cells or the conversion of surface waves into body waves, an effective vibration mitigation approach proposed in this paper can be achieved. This research offers a novel perspective on the design of periodic barriers for seismic surface waves isolation purpose.

Harmonic and anharmonic studies on THz spectra of two vanillin polymorphs

Polymorphism commonly exists in organic molecular crystals. The fingerprint features in low-frequency vibrational range are important information reflecting different intermolecular interactions of polymorphs. Interpreting these features is very helpful to understand vibrational property of polymorphs and reveal the thermodynamic stability. In this work, the low-frequency vibrations of form I and II of vanillin are investigated using terahertz time-domain spectroscopy. Static DFT calculation and ab initio molecular dynamics (AIMD) are employed to interpret their low-frequency vibrations of both forms in harmonic and anharmonic ways, respectively. Their low-frequency vibration characteristics in harmonic calculations are discussed, and anharmonic mode couplings between OH bond stretch and the stretching and bending motion of hydrogen bonds are uncovered. Moreover, the thermodynamic energies including electronic potential energy and vibrational/kinetic energy arising from nuclear motions are calculated. The result reveals that the stability order of the two forms is mainly dependent on their electric potential energy difference.

Data-driven mathematical and visualization approaches for removing rare features for Compositional Data Analysis (CoDA).

Sparse feature tables, in which many features are present in very few samples, are common in big biological data (e.g.metagenomics). Ignoring issues of zero-laden datasets can result in biased statistical estimates and decreased power in downstream analyses. Zeros are also a particular issue for compositional data analysis using log-ratios since the log of zero is undefined. Researchers typically deal with this issue by removing low frequency features, but the thresholds for removal differ markedly between studies with little or no justification. Here, we present CurvCut, an unsupervised data-driven approach with human confirmation for rare-feature removal. CurvCut implements two distinct approaches for determining natural breaks in the feature distributions: a method based on curvature analysis borrowed from thermodynamics and the Fisher-Jenks statistical method. Our results show that CurvCut rapidly identifies data-specific breaks in these distributions that can be used as cutoff points for low-frequency feature removal that maximizes feature retention. We show that CurvCut works across different biological data types and rapidly generates clear visual results that allow researchers to confirm and apply feature removal cutoffs to individual datasets.

Research on improving low-frequency oscillation characteristics of wind power grid-connected power system with doubly-fed wind generator based on virtual impedance power system stabilizer

Research on improving low-frequency oscillation characteristics of wind power grid-connected power system with doubly-fed wind generator based on virtual impedance power system stabilizer

A cross-domain state monitoring method for high-speed train brake pads based on data generation under small sample conditions

The proper functioning of the high-speed train brake system is of utmost importance to ensure the train comes to a halt at the intended distance. Among the key components, brake pads are susceptible to abnormal wear over prolonged usage, posing a safety risk to train operations. However, limited real-world data on brake pad damage poses challenges for effective monitoring. This paper proposes an effective transfer learning model that utilizes a small amount of data at different speeds, which can solve the small sample data problem on brake pad damage. Initially, a deep convolutional generative adversarial network (DCGAN) is employed to simulate and expand a limited dataset of vibration signals. The generated signals accurately preserve both the high-frequency and low-frequency characteristics observed in the actual signals. Subsequently, a domain-adversarial neural network (DANN) is utilized to map features from the source and target domains onto a shared feature space, enabling multi-domain feature extraction and adaptation. Finally, an intelligent monitoring network is employed to identify the health status of unlabeled brake pads under fluctuating speeds. Experimental results demonstrate the proposed approach outperforms other commonly used algorithms, providing evidence of the feasibility to monitor the small sample conditions of high-speed train brake pads.

EM-Trans: Edge-Aware Multimodal Transformer for RGB-D Salient Object Detection.

RGB-D salient object detection (SOD) has gained tremendous attention in recent years. In particular, transformer has been employed and shown great potential. However, existing transformer models usually overlook the vital edge information, which is a major issue restricting the further improvement of SOD accuracy. To this end, we propose a novel edge-aware RGB-D SOD transformer, called, which explicitly models the edge information in a dual-band decomposition framework. Specifically, we employ two parallel decoder networks to learn the high-frequency edge and low-frequency body features from the low-and high-level features extracted from a two-steam multimodal backbone network, respectively. Next, we propose a cross-attention complementarity exploration module to enrich the edge/body features by exploiting the multimodal complementarity information. The refined features are then fed into our proposed color-hint guided fusion module for enhancing the depth feature and fusing the multimodal features. Finally, the resulting features are fused using our deeply supervised progressive fusion module, which progressively integrates edge and body features for predicting saliency maps. Our model explicitly considers the edge information for accurate RGB-D SOD, overcoming the limitations of existing methods and effectively improving the performance. Extensive experiments on benchmark datasets demonstrate that is an effective RGB-D SOD framework that outperforms the current state-of-the-art models, both quantitatively and qualitatively. A further extension to RGB-T SOD demonstrates the promising potential of our model in various kinds of multimodal SOD tasks.

A data-driven risk model for maritime casualty analysis: A global perspective

Maritime casualty analysis needs to be addressed given the increasing safety demand in the field due to the accidents’ low-frequency and high-consequence features. This paper aims to delve deeper into the factors that affect maritime accident casualties by establishing a new database and conducting an accident casualty evolution analysis. Based on the refined dataset, a pure data-driven Bayesian Network (BN) model is developed to conduct the casualty analysis of maritime accidents that occurred under different ship operational conditions. Methodologically, it introduces new risk factors to improve maritime casualty analysis accuracy through the enriched updated maritime accident database. Furthermore, the new database is categorised into five new datasets based on temporal development trends to better analyse the evolution of the casualty. Five risk analysis models are individually constructed based on different timeframes to illustrate the dynamics of the casualties and compared by seven evaluation indexes to demonstrate the effectiveness of the proposed data-driven BN model. It, for the first time, investigates the changing roles of different risk factors on maritime casualties with time. The insights gained from this model are invaluable, contributing to improved risk prediction and maritime safety strategies by acknowledging the changing patterns of maritime accidents.

Interference Response Prediction of Receiver Based on Wavelet Transform and a Temporal Convolution Network

In order to improve the prediction performance of existing methods amidst multi modulation coupling interference in complex electromagnetic environments, this paper introduces a novel approach that integrates wavelet transform with a temporal convolutional network. The model begins with a data preprocessing stage, where wavelet transform decomposes the original signal into various scales. This step generates scale coefficients across different frequency categories, effectively reducing the signal length. To enhance the model’s ability to capture long-term dependencies in time series data, temporal convolutional networks are employed for feature extraction. Moreover, the model’s performance is further refined by incorporating an attention mechanism-driven feature fusion strategy. This strategy methodically combines high and low frequency features along with local and global characteristics. The model’s efficacy is validated using a custom MATLAB dataset, with simulation results confirming a significant improvement in prediction accuracy.

Characteristic analysis and data comparative of linear and nonlinear low-frequency sweep in vibroseis

Abstract Low-frequency seismic data plays a crucial role in seismic data processing and seismic wave inversion. At present, there are two methods to realize the low-frequency excitation of vibrators: one is exciting low-frequency vibrators by linear sweep signals, and the other is exciting conventional vibrators by nonlinear low-frequency sweep signals. The cost of exploration using low-frequency vibroseis is high, and it is challenging to obtain sufficient low-frequency information using traditional vibrators. To this end, this paper comparatively studies the low-frequency sweep signal characteristics and data effects of low-frequency and traditional vibrators. Therefore, three kinds of linear and nonlinear low-frequency sweep signal are designed. Theoretical analysis shows that there are certain differences between linear and nonlinear signal in design methods, signal shapes, etc., but after correlation calculations the signal spectra reflecting the seismic response and the related wavelet shapes are basically consistent. Besides, the actual force signal data shows that the linear and nonlinear harmonic distortion are basically equivalent. Finally, based on the forward simulation of three sweep signals and the comparative analysis of field test data, it can be considered that the linear and nonlinear low-frequency sweep signals of vibrators have almost the same denoising ability under the basic conditions of the spectrum and wavelet. Both can achieve low-frequency excitation and obtain rich low-frequency information, and the quality of seismic data is basically the same, so they can be applied in practical production.

Hand-held rolling magnetic-spring energy harvester: Design, analysis, and experimental verification

Aiming at the random and low-frequency characteristics of human motion, a hand-held rolling magnetic-spring energy harvester is proposed to scavenge energy from handshaking. The harvester applies the rolling movement of a magnetically suspended magnet and possesses the merit of low damping. The dynamic model is established and the open-circuit voltages are predicted by combining Faraday's law of electromagnetic induction and finite element analysis. By calculating magnetic flux through coils, the effect of the coil’s position on energy harvesting efficiency is studied through numerical and experimental approaches. Experimental results under harmonic excitations indicate that the coil positioned directly below the stable equilibrium position of the moving magnet has the best output capability, and a maximum open-circuit voltage of 1.33 V and a half-power bandwidth of 1.61 Hz is achieved at 0.3 g. Under handshaking excitation, an instantaneous peak power of 20.15 mW is achieved, along with a maximum average power of 2.22 mW. The corresponding volume and mass power densities are respectively 26.04 μW/cm3 and 23.28 μW/g. Furthermore, handshaking excitation in 5 s to charge a capacitor of 470 μF could enable the watch, time display, timer, and hygrothermograph to operate for 1100 s, 513 s, 86 s, and 35 s respectively, demonstrating the brilliant application foreground of the proposed harvester.

Dual contrast attention-guided multi-frequency fusion for multi-contrast MRI super-resolution

Objective. Multi-contrast magnetic resonance (MR) imaging super-resolution (SR) reconstruction is an effective solution for acquiring high-resolution MR images. It utilizes anatomical information from auxiliary contrast images to improve the quality of the target contrast images. However, existing studies have simply explored the relationships between auxiliary contrast and target contrast images but did not fully consider different anatomical information contained in multi-contrast images, resulting in texture details and artifacts unrelated to the target contrast images. Approach. To address these issues, we propose a dual contrast attention-guided multi-frequency fusion (DCAMF) network to reconstruct SR MR images from low-resolution MR images, which adaptively captures relevant anatomical information and processes the texture details and low-frequency information from multi-contrast images in parallel. Specifically, after the feature extraction, a feature selection module based on a dual contrast attention mechanism is proposed to focus on the texture details of the auxiliary contrast images and the low-frequency features of the target contrast images. Then, based on the characteristics of the selected features, a high- and low-frequency fusion decoder is constructed to fuse these features. In addition, a texture-enhancing module is embedded in the high-frequency fusion decoder, to highlight and refine the texture details of the auxiliary contrast and target contrast images. Finally, the high- and low-frequency fusion process is constrained by integrating a deeply-supervised mechanism into the DCAMF network. Main results. The experimental results show that the DCAMF outperforms other state-of-the-art methods. The peak signal-to-noise ratio and structural similarity of DCAMF are 39.02 dB and 0.9771 on the IXI dataset and 37.59 dB and 0.9770 on the BraTS2018 dataset, respectively. The image recovery is further validated in segmentation tasks. Significance. Our proposed SR model can enhance the quality of MR images. The results of the SR study provide a reliable basis for clinical diagnosis and subsequent image-guided treatment.

Study on the Unsteady Flow Characteristics of a Pump Turbine in Pump Mode

Extensive research has been conducted on the performance of pump turbines, particularly focused on understanding the generation mechanism of S-shaped characteristics. However, there has been a lack of research on unsteady flow characteristics in hump characteristics with small guide vane openings. This study focuses on the hump characteristics of a pump turbine in pump mode. The unsteady numerical simulation method is used along with experimental testing to examine the internal flow characteristics and induced pressure fluctuations under pump operating conditions. The results indicate that flow separation occurs in the impeller when the flow rate decreases to the valley operating condition, and recirculation flow occurs near the impeller inlet at the partial flow rate. Moreover, the unstable flow on the positive slope exhibits a low-frequency characteristic of 0.15fn. The pressure fluctuation from the hub to shroud areas of the guide vane region diminishes sequentially. Notably, distinct vortex structures emerge at the draft tube cone section under the valley operating condition. These structures extend toward the elbow section of the draft tube as the flow rate decreases. This phenomenon generates low-frequency pressure fluctuation originating from the primary frequency of the vortex and dean vortex on the surface, located at 0.4 D of the draft tube under conditions of low flow rate.

Low-frequency background estimation and noise separation from high-frequency for background and noise subtraction.

In fluorescence microscopy, background blur and noise are two main factors preventing the achievement of high-signal-to-noise ratio (SNR) imaging. Background blur primarily emanates from inherent factors including the spontaneous fluorescence of biological samples and out-of-focus backgrounds, while noise encompasses Gaussian and Poisson noise components. To achieve background blur subtraction and denoising simultaneously, a pioneering algorithm based on low-frequency background estimation and noise separation from high-frequency (LBNH-BNS) is presented, which effectively disentangles noise from the desired signal. Furthermore, it seamlessly integrates low-frequency features derived from background blur estimation, leading to the effective elimination of noise and background blur in wide-field fluorescence images. In comparisons with other state-of-the-art background removal algorithms, LBNH-BNS demonstrates significant advantages in key quantitative metrics such as peak signal-to-noise ratio (PSNR) and manifests substantial visual enhancements. LBNH-BNS holds immense potential for advancing the overall performance and quality of wide-field fluorescence imaging techniques.

NCGLF[formula omitted]: Network combining global and local features for fusion of multisource remote sensing data

The fusion of multisource remote sensing (RS) data has demonstrated significant potential in target recognition and classification tasks. However, there is limited emphasis on capturing both high- and low-frequency information from these data sources. Additionally, effectively integrating multisource data remains a challenging task, as the absence of redundancy and discriminant information hampers the applications of RS data. In this paper, we propose a fusion network called network combining global and local features (NCGLF2) that integrates global and local features (GLF) extracted from multisource RS data. This approach effectively leverages the capabilities of convolutional neural networks (CNNs) to extract high frequency features while utilizing transformer architecture to replicate low frequency information and remote correlations. Firstly, a scale information aggregation (SIA) module extracts multiscale shallow layer features from the input data sources. Secondly, a structural information learning transformer (SIL-Trans) module captures low frequency features, while an invertible neural network (INN) module learns high frequency information. Finally, a GLF fusion module maximizes the complementary characteristics of multisource RS data and GLF to effectively fuse high- and low-frequency information. Our experimental results with three benchmark datasets indicate that NCGLF2 outperforms existing state-of-the-art approaches in terms of feature representation and compatibility with diverse data types. The code is available at https://github.com/renqi1998/NCGLF2.

Low-frequency variability in theta activity modulates the attention-fluctuation across task and resting states

Sustained attention is not constant but fluctuates influencing our task performance. Albeit intensive investigations, it remains unclear whether the attention-fluctuation during tasks is derived from its spontaneous fluctuation in the resting state. Here, we addressed this issue by investigating the attention-fluctuation in both task and resting states, through the EEG measurement of theta-variability. We found significant rest-task modulation of theta-variability, i.e., reduced theta-variability in the task state compared to the resting state. This task and rest modulation was manifested in the low-frequency of theta-variability (<0.1 Hz). Furthermore, the low-frequency theta-variability exhibited a significant rest-task correlation, however, only the low-frequency theta-variability in the task state but not in the resting state was correlated with the behavioral performance. These findings shed light on the low-frequency feature of attention-fluctuation, and advanced our understanding of sustained attention by suggesting that the theta-variability in low-frequencies was relevant to attention level in task state.

Earthworm‐Inspired Triboelectric Nanogenerator with O‐Shaped Multilayer Structure for Marine Ranching

In view of the characteristics of small wave amplitude, stable direction, and low frequency near marine ranching, an energy‐harvesting device is designed and produced through an O‐shaped multilayer earthworm‐like triboelectric nanogenerator (OME–TENG) to provide a self‐sufficient energy supply for sensors without relying on batteries. Under the same conditions, O‐shaped multilayer TENG structure can carry out a fuller contact‐separation movement due to its unique O‐shaped structure, thus the peak power of the O‐shaped multilayer TENG structure can reach 10.4 mW, which is more than twice that of the Z‐shaped multilayer TENG structure. When the water wave frequency is 0.5 Hz and the wave amplitude is about 7.5 cm, the open‐circuit voltage, short‐circuit current, and transferred charge of the left units of the OME–TENG device can reach 300 V, 25 μA, and 900 nC, respectively, and can directly light up 107 light emitting diode. After rectification and parallel connection, the energy collected by the OME–TENG can easily power a digital hygrograph to monitor marine temperature and humidity. Therefore, the OME–TENG shows great application prospects in the energy supply of marine ranching.

Pemanfaatan Gelombang Elektromagnet Extremely Low Frequency (Elf) Dalam Ketahanan Pangan

Physics is considered an important subject that is taught as a subject in itself with various considerations. Physics is seen as both a process and a product, so successful physics learning requires attention to effective and efficient learning. The utilization of Extremely Low Frequency (ELF) electromagnetic waves has great potential in the field of food security. ELF waves have low-frequency characteristics that create a unique potential to influence microbial activity and nutrient availability in improving food security productivity. ELF waves can be used to increase the productivity and quality of food crops, as well as extend the shelf life of food products. Thus, the utilization of ELF waves can help in achieving sustainable food security. In an effort to improve food security, it is necessary to innovate and develop technology that can support agricultural productivity. More research is still needed to fully understand the mechanism of action of ELF waves and how best to use them in agricultural and food processing practices.

A comparative study of accuracy in major adaptive filters for motion artifact removal in sleep apnea tests.

Sleep apnea is probably the most common respiratory disorder; respiration and blood oxygen saturation (SpO2) are major concerns in sleep apnea and are also the two main parameters checked by polysomnography (PSG, the gold standard for diagnosing sleep apnea). In this study, we used a simple, non-invasive monitoring system based on photoplethysmography (PPG) to continuously monitor SpO2 and heart rate (HR) for individuals at home. Various breathing experiments were conducted to investigate the relationship between SpO2, HR, and apnea under different conditions, where two techniques (empirical formula and customized formula) for calculating SpO2 and two methods (resting HR and instantaneous HR) for assessing HR were compared. Various adaptive filters were implemented to compare the effectiveness in removing motion artifacts (MAs) during the tests. This study fills the gap in the literature by comparing the performance of different adaptive filters on estimating SpO2 and HR during apnea. The results showed that up-down finger motion introduced more MA than left-right motion, and the errors in SpO2 estimation were increased as the frequency of movement was increased; due to the low sampling frequency features of these tests, the insertion of adaptive filter increased the noise in the data instead of eliminating the MA for SpO2 estimation; the normal least mean squares (NLMS) filter is more effective in removing MA in HR estimation than other filters.

Time domain response analysis of multi-flow channel hydraulic mount

In order to analyze the effect of the combination of long and short inertia channels and orifice flow channels on the time domain response of hydraulic mounts. Firstly, six hydraulic mounts with different combinations of inertia channels and orifice flow channels are proposed. And then, the transfer functions of dynamic stiffness and upper chamber pressure for six structures of hydraulic mounts are derived using the lumped parameter method. Next, the time domain analytic formulas for the transfer force and upper chamber pressure for six structural hydraulic mounts under steady-state excitation and step excitation are obtained using the convolution method. Finally, the analytical formula is compared with the hydraulic mount’s model built by AMEsim; Meanwhile, the effects of inertia terms of inertia channels, damping, and damping of orifice flow channels on hydraulic mounts transfer forces are analyzed; Analyze the effect of transfer force variation and excitation amplitude on hydraulic mounts damping for different configurations of structures. Research shows that inertia channels and orifice flow channels directly affect the low-frequency dynamic characteristics of hydraulic mounts. At the same time, the effective damping height of the hydraulic mounts depends on the excitation amplitude.