Low-frequency Characteristics Research Articles

Low-frequency broadband characteristics of stepwise radial acoustical black holes

In this paper, a stepped radial acoustic black hole (SR-ABH) structure formed via a stepwise arrangement of two-dimensional radial acoustic black holes(ABH) is proposed. The finite element method is used to calculate the energy band and frequency response curve of the SR-ABH structure. It is found that the low-frequency bandgap accounts for 83.4% of the whole energy band region (0-2000 Hz) , which is 7.6 times broader than that of a horizontally arranged radial acoustic black hole (HR-ABH). On the basis of modal analysis at special points, the introduction of gradient arrays into radial ABH is confirmed, which reduces the cut-off frequency of ABH and leads to a low-frequency broadband coupling of local resonance and acoustic black hole effects. In addition, the influence of geometric parameters on the bandgap is explored. The results indicate that as the step height increases, the coupling relationship between local resonance and acoustic black hole effects shows a trend of first strengthening and then weakening, thereby affecting the movement of the low-frequency bandgap. In addition, the proposed structure provides a way to reduce the high cutoff frequency of ABH.

SuperPCA and 2-D compact variational mode decomposition for feature extraction from hyperspectral images

ABSTRACT The 2-D compact variational mode decomposition (2-D-C-VMD) has potential research significance in data mining of hyperspectral images (HSIs). The paper proposes a method of concatenation of features. First, the 2-D-C-VMD technique is used to decompose the HSIs to obtain smooth and clear boundary sub-band images when the 2-D-C-VMD takes the most preferred a priori parameter K × . Second, the optimal low-frequency component features of the image and the optimal high-frequency component constructed features are concatenated first and last, and the structure of the concatenation is constructed into a new fused data. Finally, in order to avoid the redundancy of the fused data band information from affecting the classification result, combine a recently developed simple and practical superpixelwise principal component analysis (SuperPCA) dimensionality reduction method to effectively reduce the dimensionality of the fused data. By comparing the analysis with relying only on the optimal low-frequency image feature information and with the direct linear superposition fusion that has been employed, it is verified that the concatenation of features approach proposed in this paper can obtain better recognition results.

An efficient equalization filter design approach for the accurate sound reproduction in a room environment with wide-band content

In some sound reproduction applications, a desired sound field needs to be reproduced by remote loudspeakers in a room with sufficient accuracy over a wide frequency band. When low-frequency room modes exist and the low-frequency content is required to be reproduced accurately, an equalization filter with a long time-domain response may be necessary, resulting in a filter with a significantly high filter order, especially when the sampling rate is high for the high-frequency content. This imposes computational challenges on both filter design and implementation. In this article, a convex formulation is proposed for the wide-band sound reproduction filter design. To address issues due to the low-frequency room modes, a reduced-order technique is then proposed based on a sub-band filter structure where a low sampling rate is used to equalize the low-frequency room mode characteristics, and a high sampling rate is used for high-frequency features which usually has a short time span in response. The computational time can be reduced by several orders of magnitude and the numerical stability can be improved when the proposed order reduction method is used to eliminate redundant orders. Simulation and experimental results confirm the accuracy and efficiency advantages of the proposed method.

Channel Features and API Frequency-Based Transformer Model for Malware Identification.

Malicious software (malware), in various forms and variants, continues to pose significant threats to user information security. Researchers have identified the effectiveness of utilizing API call sequences to identify malware. However, the evasion techniques employed by malware, such as obfuscation and complex API call sequences, challenge existing detection methods. This research addresses this issue by introducing CAFTrans, a novel transformer-based model for malware detection. We enhance the traditional transformer encoder with a one-dimensional channel attention module (1D-CAM) to improve the correlation between API call vector features, thereby enhancing feature embedding. A word frequency reinforcement module is also implemented to refine API features by preserving low-frequency API features. To capture subtle relationships between APIs and achieve more accurate identification of features for different types of malware, we leverage convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. Experimental results demonstrate the effectiveness of CAFTrans, achieving state-of-the-art performance on the mal-api-2019 dataset with an F1 score of 0.65252 and an AUC of 0.8913. The findings suggest that CAFTrans improves accuracy in distinguishing between various types of malware and exhibits enhanced recognition capabilities for unknown samples and adversarial attacks.

Low-frequency human motion energy scavenging with wearable tumbler-inspired electromagnetic energy harvesters

Harvesting energy from human motion to power portable electronic devices has attracted widespread attention. However, human motion energy is primarily concentrated in low-frequency range below 5 Hz, which makes it hard to capture with traditional approaches. In this work, a tumbler-inspired electromagnetic energy harvester (T-EMH) is designed to harvest human motion energy. The magnet is placed on the top of the tumbler structure, allowing for easy conversion of irregular human motion into electricity through the tumbler mechanism. A theoretical model is established to predict the energy harvesting performance of T-EMH. Both simulations and experiments were conducted to verify the low-frequency characteristics of T-EMH. The harvested maximum peak voltage is 0.6 V and the maximum average output power can reach 0.55 mW by adjusting the external load. Additionally, human motion tests substantiate T-EMH's capacity to generate a peak power output of 0.86 mW at a running speed of 12 km/h. These results emphasize the promising potential of T-EMH as an effective approach for capturing energy from human motion.

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.

Image Super-Resolution via Efficient Transformer Embedding Frequency Decomposition With Restart.

Recently, transformer-based backbones show superior performance over the convolutional counterparts in computer vision. Due to quadratic complexity with respect to the token number in global attention, local attention is always adopted in low-level image processing with linear complexity. However, the limited receptive field is harmful to the performance. In this paper, motivated by Octave convolution, we propose a transformer-based single image super-resolution (SISR) model, which explicitly embeds dynamic frequency decomposition into the standard local transformer. All the frequency components are continuously updated and re-assigned via intra-scale attention and inter-scale interaction, respectively. Specifically, the attention in low resolution is enough for low-frequency features, which not only increases the receptive field, but also decreases the complexity. Compared with the standard local transformer, the proposed FDRTran layer simultaneously decreases FLOPs and parameters. By contrast, Octave convolution only decreases FLOPs of the standard convolution, but keeps the parameter number unchanged. In addition, the restart mechanism is proposed for every a few frequency updates, which first fuses the low and high frequency, then decomposes the features again. In this way, the features can be decomposed in multiple viewpoints by learnable parameters, which avoids the risk of early saturation for frequency representation. Furthermore, based on the FDRTran layer with restart mechanism, the proposed FDRNet is the first transformer backbone for SISR which discusses the Octave design. Sufficient experiments show our model reaches state-of-the-art performance on 6 synthetic and real datasets. The code and the models are available at https://github.com/catnip1029/FDRNet.

Effect of bell plate structure on high- and low-frequency characteristics of hydraulic mount

Effect of bell plate structure on high- and low-frequency characteristics of hydraulic mount

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.