Low Frequency Domain Research Articles

Evaluation of Modified Asphalt Binders with Soybean Oil-Based Polymers: Preparation Temperature and Rheological Characterization

To investigate the feasibility of bio-based polymers in the modification of base asphalt binder, two soybean oil-based polymers (PAESO) were synthesized from bio-monomers with different functionalities via free radical polymerization. The characterization of both PAESOs was conducted using Hydrogen nuclear magnetic resonance and Gel Permeation Chromatography analyses, the results showed that the bio-monomer with a higher functionality exhibited a stronger tendency towards self-polymerization, resulting in the polymer with a high conversion rate (44.44 %) and a slightly lower molecular weight (14.76 kDa). The optimal mixing temperature for preparing PAESO-modified asphalt binders was determined through Molecular Dynamics (MD) simulation and Laser Scanning Confocal Microscopy. The MD simulation results indicated that PAESO-modified asphalt models had reached the most stable configurations at a mixing temperature of 160 °C, as evidenced by fewer and more homogeneous distributed small bright spots observed in fluorescence images. This suggested that the outstanding compatibility between PAESO and asphalt binders could be obtained at the optimal mixing temperature of 160 °C. Furthermore, the rheological characteristics of asphalt binders modified with PAESO were examined through Dynamic Shear Rheometer and Bending Beam Rheometer tests. The results revealed that PAESO mainly exhibited a softening effect by significantly enhancing the flexibility, stress relaxation, fatigue, and thermal cracking resistance of the base asphalt binders at intermediate and low temperatures. PAESO-L modified asphalt manifested elastic properties akin to those observed in polymer-modified asphalt within the low-frequency and high-temperature domain. Additionally, PAESO derived from monomers with a higher functionality presented notable benefits in enhancing thermal and fatigue cracking resistance than that of PAESO with a lower functionality. These findings provide valuable insights for the preparation and development of bio-polymer additives for use in asphalt materials.

EIS evaluation on the degradation behavior of rust-preventive oil coating exposure to NaCl electrolyte

The traditional evaluation on anti-corrosion performance of a rust-preventive oil is often time-consuming and of high cost. In this investigation, electrochemical impedance spectroscopy (EIS) has been employed to understand the degradation behavior of rust-preventive oil in contact with NaCl electrolyte, with a focus on analyzing the electrochemical responses that correspond to a different frequency range in the obtained EIS spectra. The results indicate that the obtained EIS spectra can be generally divided into four frequency ranges. The high-frequency range is influenced by the continuous seepage of NaCl electrolyte, and the sub-high frequency range can reflect the presence of reversed micelle formed by corrosion inhibitor molecules and water molecules. Besides, the mid-frequency range is associated with the adsorption process of corrosion inhibitor molecules at the oil-film/metal interface, and the low-frequency range is associated with the diffusion process of dissolved ferrous ions. An extended immersion will result in a continuous seepage of electrolyte into rust-preventive oil coating and degrade the adsorption of corrosion inhibitor molecules, leading to a poor corrosion protection performance of the oil coating. Therefore, it is believed that the proposed design can provide a method in evaluating the degradation behavior and corrosion protection performance of rust-preventive oil to underlying steel substrate by EIS measurement under a lower amplitude.

Spatial energy evolution of focused waves generated in numerical wave tank

This paper numerically investigates the energy evolution of the focused wave and its correlation with input parameters for the wave generation. The focused wave is numerically generated based on the fully nonlinear potential wave theory which is solved by the Quasi Arbitrary Lagrangian Eulerian-Finite Element Method. The investigation is performed by dividing the generated wave energy into three categories according to their frequency intervals, i.e., the initially assigned frequency interval [f1fN], the frequencies lower (f < f1) and higher (f > fN) than the initial interval. The amount of the generated energy falling into three energy categories and the energy distribution in the initial frequency interval are analysed against amplitude parameters and frequency bands under three amplitude spectra. Four indicators are proposed to indicate the actual energy variations comparing to the initial design. It is found that for all the three frequency intervals the energies increase when the amplitude parameters increases or the given frequency shifts towards the lower frequency domain. The choice of wave amplitude spectrum plays a significant role to maintain the distribution of the energy in the design frequency range and to minimise the energies out of the design frequency range. The results from the second-order wave group theory are used to assist analyses of the mechanism of the energy evolution alongside the focused wave generation. It is found that the lower-frequency energy in the vicinity of the focusing point is dominated by the second-order difference waves, whereas the higher-frequency energy is mainly produced by the wave flap. The waves which are higher than the second-order and the complex interactions not considered by the second-order wave theory are found significantly disturbing the energy distribution in the originally assigned wave-frequency interval.

Iterative image rain removal network using consecutive residual long short-term memory

Image rain removal is designed to effectively separate rain streaks from the background image layer. However, rain streaks in real-world scenarios vary in density, shape, and direction, making it difficult to decompose rainy images into clean backgrounds and rain layers. In this study, we introduce an iterative framework for image deraining to progressively enhance rainy images using a residual long short-term memory structure. The overall network comprises a multidomain residue channel, a fusion module, and a consecutive residual long short-term memory. We introduce multidomain residue channels by computing them in both the image and wavelet low-frequency domains. We propose a fusion module to combine the residue channel for guidance with wavelet domain features for rain removal. We also propose a feature extraction module based on successive residual long short-term memory to extract the main features in the wavelet domain. An iterative image restoration framework comprising three primary modules is introduced to progressively enhance rainy images. To evaluate the performance of the proposed approach, we conduct experiments using widely used benchmarks. The results demonstrate that our method outperforms state-of-the-art methods in image rain removal https://github.com/workofsu/.

Simplified V/f Control Algorithm for Reduction of Current Fluctuations in Variable-Speed Operation of Induction Motors

This paper introduces a straightforward control strategy aimed at the reduction of current fluctuations within the low-frequency domain of open-loop V/f control in induction motor drives. Traditional control techniques necessitate the addition of a current compensator based on motor parameters and the use of digital filters such as band-pass or high-pass filters. These methods, however, rely on precise motor parameters and involve complex filter design and implementation. The proposed control is capable of suppressing current fluctuations without controlling the slip of the induction motor. The proposed control strategy generates the forced rotation angle and command input voltage using the V/f block and outputs the d-axis voltage using a proportional integral controller to keep the d-axis current constant at zero. The difference between the command input voltage and the d-axis voltage is applied as the q-axis voltage and then applied through SVPWM. In order to verify the effectiveness of the proposed control, the proposed control is implemented and analyzed using power simulation based on the results of the analysis of the causes of current fluctuations in the induction motor. Finally, the effect of suppressing current fluctuations of the induction motor is verified through experimental results. In the 10~19 Hz range, where the conventional V/f control method resulted in current fluctuation rates exceeding 10% and peaking at 113.3% at 13 Hz, the proposed method suppressed the fluctuation rate to below 8.6% across all frequencies. This paper validates the effectiveness of the proposed control strategy through these results.

TraceEvader: Making DeepFakes More Untraceable via Evading the Forgery Model Attribution

In recent few years, DeepFakes are posing serve threats and concerns to both individuals and celebrities, as realistic DeepFakes facilitate the spread of disinformation. Model attribution techniques aim at attributing the adopted forgery models of DeepFakes for provenance purposes and providing explainable results to DeepFake forensics. However, the existing model attribution techniques rely on the trace left in the DeepFake creation, which can become futile if such traces were disrupted. Motivated by our observation that certain traces served for model attribution appeared in both the high-frequency and low-frequency domains and play a divergent role in model attribution. In this work, for the first time, we propose a novel training-free evasion attack, TraceEvader, in the most practical non-box setting. Specifically, TraceEvader injects a universal imitated traces learned from wild DeepFakes into the high-frequency component and introduces adversarial blur into the domain of the low-frequency component, where the added distortion confuses the extraction of certain traces for model attribution. The comprehensive evaluation on 4 state-of-the-art (SOTA) model attribution techniques and fake images generated by 8 generative models including generative adversarial networks (GANs) and diffusion models (DMs) demonstrates the effectiveness of our method. Overall, our TraceEvader achieves the highest average attack success rate of 79% and is robust against image transformations and dedicated denoising techniques as well where the average attack success rate is still around 75%. Our TraceEvader confirms the limitations of current model attribution techniques and calls the attention of DeepFake researchers and practitioners for more robust-purpose model attribution techniques.

Devignet: High-Resolution Vignetting Removal via a Dual Aggregated Fusion Transformer with Adaptive Channel Expansion

Vignetting commonly occurs as a degradation in images resulting from factors such as lens design, improper lens hood usage, and limitations in camera sensors. This degradation affects image details, color accuracy, and presents challenges in computational photography. Existing vignetting removal algorithms predominantly rely on ideal physics assumptions and hand-crafted parameters, resulting in the ineffective removal of irregular vignetting and suboptimal results. Moreover, the substantial lack of real-world vignetting datasets hinders the objective and comprehensive evaluation of vignetting removal. To address these challenges, we present VigSet, a pioneering dataset for vignetting removal. VigSet includes 983 pairs of both vignetting and vignetting-free high-resolution (over 4k) real-world images under various conditions. In addition, We introduce DeVigNet, a novel frequency-aware Transformer architecture designed for vignetting removal. Through the Laplacian Pyramid decomposition, we propose the Dual Aggregated Fusion Transformer to handle global features and remove vignetting in the low-frequency domain. Additionally, we propose the Adaptive Channel Expansion Module to enhance details in the high-frequency domain. The experiments demonstrate that the proposed model outperforms existing state-of-the-art methods. The code, models, and dataset are available at https://github.com/CXH-Research/DeVigNet.

Electrochemical dielectric response of steel corrosion induced by chloride in simulated concrete pore solution

Electrochemical and dielectric properties of steel oxide films can have an impact on the corrosion process but are still poorly understood due to the unavailability of measurement methods. In this study, electrochemical impedance spectroscopy (EIS) was adopted to interpret steel corrosion induced by chloride in a simulated concrete pore solution. Particularly, validation of EIS data by Kramers–Kronig transforms was performed prior to further EIS interpretation. Relative dielectric permittivity of corroded steel/electrolyte interface was derived through theoretical induction and corrosion products were analyzed by X-ray photoelectron spectroscopy. Results indicated that measured EIS data in the present study depicted good causality and stability. Corrosion at the steel/electrolyte interface induced by increasing chloride led to high relative permittivity within the low frequency domain while marginal difference could be observed at the high frequency range. AC conductivity of corrosion products at steel/solution interfaces revealed a notable frequency dependence, based on which three distinct frequency regions were subdivided.

Active low frequency band expansion mechanism of electromagnetic wave absorber composite structure

Bandwidth expansion of modern antennas and communication systems raises growing demand for electromagnetic wave absorber in compatibility equipment. Single absorption mechanism is hard to reach well-balanced characteristics including wide bandwidth, low frequency absorption and small thickness. Based on the composite structure concept, composite structure for electromagnetic wave absorber’s active low frequency band expansion is proposed, by combining circuit-analog absorber and active absorber to form a superimposed structure. The characteristic of composite structure is better than the single absorber structure, which improves enhancement mechanism brought by the composite structure. Sample fabrication and experiments indicate reflectivity curve envelope reaches −10 dB in 2.15–18 GHz frequency range (relative bandwidth 157.3%), and the tunable absorption frequency range in low-frequency domain is greater than 1.3 GHz. The structure has good adaptation on TE and TM polarizations, as well as good angle insensitivity. This composite structure has application potential in intelligent response system related with electromagnetic compatibility, energy absorption and object security management.

Evaluation of the performance of technologies for reducing ships' machinery noise using a small-scale ship-like structure in a Water Basin

Ocean ambient levels have increased in the last decades, especially in the low-frequency domain (under 500 Hz). This increase is partly due to underwater radiated noise (URN) from commercial ships. Excessive URN harms marine life and is, therefore, considered a pollution that needs to be reduced. At low speeds, machinery is the primary noise source on ships. Mitigation technologies exist to limit machinery’s contribution to URN. While implementing these technologies is costly, a lack of quantitative data regarding their exact performances usually results in limited concrete ship applications since the cost-to-benefit ratio is imprecise. This study aims to quantify better the performance of standard noise mitigation technologies using a small-scale ship-like structure in a water basin. The basin’s acoustic field is first characterized with and without the structure. The structure is then equipped with different mitigation technologies. A loudspeaker and a vibration shaker are fed with pink noise or measured signals on actual machinery. Hydrophones, microphones, accelerometers, and force sensors measure the response in the basin and on the structure. The performance of each tested technology is evaluated and ranked in terms of URN reduction. The relative contributions of airborne and structure-borne transmission paths on URN are also examined.

Mitigating low-frequency broadband aircraft noise through a structured assembly of acoustic material and devices

Addressing the challenge of attenuating low-frequency broadband noise emerges as a critical concern within the fields of aeronautics, ground transportation, and construction industries, requires innovative solutions for enhanced acoustic control. Over the last few decades, the literature has seen an increase in low-frequency noise control solutions centered around acoustic metamaterial designs. These proposed technologies exhibit promising acoustic performance, especially proving superior to conventional sound insulation materials in constrained spaces, such as in aerospace applications. Despite the efficacy of typical metamaterials in attenuating tonal noise through narrow resonant frequency maxima, practical applications reveal some challenges, as even slight variations in tonal noise frequencies can compromise the overall effectiveness of such solutions. In response to this, the present paper introduces a novel thin acoustic metamaterial design aimed at improving broadband noise attenuation at low frequencies. This design uses carefully arranged structured metamaterials within a fiberglass layer to create optimal resonance frequency bands for maximum low-frequency noise attenuation. Performance assessment in the low-frequency domain employed COMSOL Multiphysics finite element methods, predicting sound absorption coefficient and transmission loss. Results confirm the effectiveness of the proposed metamaterial design, showcasing broad noise attenuation at low frequencies.

Study on Dynamic Characteristics of Vibration System with Two Degrees of Freedom and Multiple Rigid Constraints

A dynamic model of a vibration system with two degrees of freedom and multiple rigid constraints is constructed, the dynamics constraints of the segmented smooth vibration system under the multi-body impact coupling condition are given. Based on the cell mapping principle, a two-parameter co-simulation is carried out with two important parameters of excitation frequency and constraint gap as control variables. The model types and distribution rules of the periodic motion of the system response on the [Formula: see text]-whole two-parameter plane are obtained. The correlation between the dynamic response of the system and the model parameters is analyzed, and the evolution and bifurcation characteristics of fundamental periodic vibrations in the low-frequency small-gap parameter domain of the two-degree-of-freedom multi-rigid constrained vibration system are discussed. The diversity and regularity of subharmonic shock vibrations in the translational domain of the lingual domain are found.

Techniques for Accelerometer Reading Processing on Railway Transport Using Wavelet Transform

Introduction. Safety issues of railway transport are inevitably connected with the condition of railway tracks and railway wheels. Various defects, such as irregularities of the railway track, may lead to emergencies and incidents. Therefore, it is important to measure and calculate short and impulse irregularities. A joint analysis of vibration acceleration signals is needed in order to study the types and size of railway track irregularities.Aim. Development of an algorithm for irregularity search based on accelerometer readings with the vertical measurement axis.Materials and methods. The research encompassed wavelet transformation and wavelet-based signal processing including discrete-time signal processing and continuous wavelet processing. In addition, time-frequency analysis based on Fourier transform and continuous wavelet scalogram was used. These methods provide for time-frequency localization for irregularity detection and measurement.Results. Algorithms for vibrational signal processing using the continuous and discrete-time wavelet transform are proposed. The results show that the discrete wavelet transform is effective for multiresolution and multiband analysis, and continuous wavelet transform and wavelet scalogram allows extraction of irregularities and determination of their parameters. The relative error for irregularity depth was improved by 18 %, and the absolute error for irregularity length determinations was reduced by 7 times.Conclusion. Application of the discrete Fourier transform and Fourier spectrogram provides for fine resolution in the frequency domain. However, separation of signal components in the time-frequency domain is impeded. The continuous-time wavelet transform ensures sufficient resolution in the low-frequency domain for component localization and visualization of irregularities.

Image fusion research based on the Haar-like multi-scale analysis

In view of the serious color and definition distortion in the process of the traditional image fusion, this study proposes a Haar-like multi-scale analysis model, in which Haar wavelet has been modified and used for the medical image fusion to obtain even better results. Firstly, when the improved Haar wavelet basis function is translated, inner product and down-sampled with each band of the original image, the band is decomposed into four sub-images containing one low-frequency subdomain and three high-frequency subdomains. Secondly, the different fusion rules are applied in the low-frequency domain and the high-frequency domains to get the low-frequency sub-image and the high-frequency sub-images in each band. The four new sub-frequency domains are inverse-decomposed to reconstruct each new band. The study configures and synthesizes these new bands to produce a fusion image. Lastly, the two groups of the medical images are used for experimental simulation. The Experimental results are analyzed and compared with those of other fusion methods. It can be found the fusion method proposed in the study obtain the superior effects in the spatial definition and the color depth feature, especially in color criteria such as OP, SpD, CR and SSIM, comparing with the other methods.

Dark channel prior in low frequency domain for time-efficient single image dehazing

ABSTRACT Dehazing plays a vital role in enhancing the quality of outdoor images for computer-based applications. While dehazing algorithms significantly improve outdoor image quality for computer vision tasks, their effectiveness hinges on two crucial factors: 1. Speed of dehazing operations 2. Platform-agnostic nature of the chosen algorithm. This study introduces a novel Dark Channel Prior-Low Frequency Domain (DCP-LFD) method designed to effectively and efficiently eliminate haze from single hazy images. The method employs a hybrid strategy, combining DCP for haze reduction in the low-frequency domain and Soft-thresholding for enhancing high-frequency coefficients. This dual-pronged approach enables improvement of image quality in low and high-frequency domains, contributing to better dehazing outcomes taking less computation time. The proposed method’s effectiveness is accessed using metrics like PSNR and SSIM, comparing its performance against state-of-the-art dehazing methods across five benchmark datasets: SOTS, RTTS and HSTS subsets from RESIDE, HazeRD, and Middlebury datasets. No-Reference Image Quality Assessments viz. BRISQUE and FADE are performed on RTTS and private dataset to objectively evaluate dehazed image quality. A comparison of dehazing time is executed, highlighting the method’s proficiency in achieving faster results. This analysis accentuates the method’s capacity to efficiently remove haze from outdoor images while emphasising its time-saving efficiency.

Molecular Junctions for Terahertz Switches and Detectors.

Molecular electronics targets tiny devices exploiting the electronic properties of the molecular orbitals, which can be tailored and controlled by the chemical structure and configuration of the molecules. Many functional devices have been experimentally demonstrated; however, these devices were operated in the low-frequency domain (mainly dc to MHz). This represents a serious limitation for electronic applications, although molecular devices working in the THz regime have been theoretically predicted. Here, we experimentally demonstrate molecular THz switches at room temperature. The devices consist of self-assembled monolayers of molecules bearing two conjugated moieties coupled through a nonconjugated linker. These devices exhibit clear negative differential conductance behaviors (peaks in the current-voltage curves), as confirmed by ab initio simulations, which were reversibly suppressed under illumination with a 30 THz wave. We analyze how the THz switching behavior depends on the THz wave properties (power and frequency), and we benchmark that these molecular devices would outperform actual THz detectors.

Dual-polarization defogging method based on frequency division and blind separation of polarization information.

The current advancements in image processing have led to significant progress in polarization defogging methods. However, most existing approaches are not suitable for scenes with targets exhibiting a high degree of polarization (DOP), as they rely on the assumption that the detected polarization information solely originates from the airlight. In this paper, a dual-polarization defogging method connecting frequency division and blind separation of polarization information is proposed. To extract the polarization component of direct transmission light from the detected polarized signal, blind separation of overlapped polarized information is performed in the low-frequency domain based on visual perception. Subsequently, after estimating airlight, a high-quality defogging image can be restored. Extensive experiments conducted on real-world scenes and comparative tests confirm the superior performance of our proposed method compared to other competitive methods, particularly in reconstructing objects with high DOP. This work provides a quantitative approach for estimating the contributions of polarization light from different sources and further expands the application range of polarimetric defogging imaging.

Enhanced sound transmission loss of a truss-like cellular structure in broad-band low frequency domains

Enhanced sound transmission loss of a truss-like cellular structure in broad-band low frequency domains

Probabilistic Model-Based Reinforcement Learning Unmanned Surface Vehicles Using Local Update Sparse Spectrum Approximation

This article focuses on the computational efficiency of probabilistic model-based reinforcement learning (MBRL) in unmanned surface vehicles (USV) under unforeseeable and unobservable external disturbances. A novel MBRL approach, local update spectrum probabilistic model predictive control (LUSPMPC) is proposed to fully release the superiority of the probabilistic model approximated in the frequency domain in computational efficiency while mitigating its risk of overfitting during the learning procedure. It employs a local update strategy to relieve the violation of Bochner's theory, and a frequency clipping trick to encourage the approximated model to focus on the features in the low-frequency domain. Evaluated by the position-keeping task in a real USV data-driven simulation, LUSPMPC shows its significant advantages in computational efficiency while achieving better learning capability, generalization capability and control performances in a wide range of sparse scales compared with the baseline MBRL approaches that approximate their models in sample space and frequency domain, and therefore becomes an appealing solution for MBRL USV system defending against rapidly changing ocean disturbances.

Risk and causality Co-movement of Malaysia's stock market with its emerging and OECD trading partners. Evidence from the wavelet approach.

The growing trend of interdependence between the international stock markets indicated the amalgamation of risk across borders that plays a significant role in portfolio diversification by selecting different assets from the financial markets and is also helpful for making extensive economic policy for the economies. By applying different methodologies, this study undertakes the volatility analysis of the emerging and OECD economies and analyzes the co-movement pattern between them. Moreover, with that motive, using the wavelet approach, we provide strong evidence of the short and long-run risk transfer over different time domains from Malaysia to its trading partners. Our findings show that during the Asian financial crisis (1997-98), Malaysia had short- and long-term relationships with China, Germany, Japan, Singapore, the UK, and Indonesia due to both high and low-frequency domains. Meanwhile, after the Global financial crisis (2008-09), it is being observed that Malaysia has long-term and short-term synchronization with emerging (China, India, Indonesia), OECD (Germany, France, USA, UK, Japan, Singapore) stock markets but Pakistan has the low level of co-movement with Malaysian stock market during the global financial crisis (2008-09). Moreover, it is being seen that Malaysia has short-term at both high and low-frequency co-movement with all the emerging and OECD economies except Japan, Singapore, and Indonesia during the COVID-19 period (2020-21). Japan, Singapore, and Indonesia have long-term synchronization relationships with the Malaysian stock market at high and low frequencies during COVID-19. While in a leading-lagging relationship, Malaysia's stock market risk has both leading and lagging behavior with its trading partners' stock market risk in the selected period; this behavior changes based on the different trade and investment flow factors. Moreover, DCC-GARCH findings shows that Malaysian market has both short term and long-term synchronization with trading partners except USA. Conspicuously, the integration pattern seems that the cooperation development between stock markets matters rather than the regional proximity in driving the cointegration. The study findings have significant implications for investors, governments, and policymakers around the globe.