Frequency Domain Research Articles

Local Fourier features for handwriting digit images classification

Multiple choice questions (MCQ) are effective in normative assessment and offline testing is still relevant due to the lack of efficient mass infrastructures and maintenance. For the automatic correction of MCQ paper form and reporting of the grade, it is generally necessary to read and recognize a handwriting digit in a box. This paper focuses on local feature extraction in the frequency domain using Fourier transform. The pre-process begins with the extraction of the fields from the entity map, followed by the application of 2D fast Fourier transform (2DFFT) and the reduction of computed coefficients to obtain the corresponding final local characteristic in the representation. The experimental results of the Modified National Institute of Standards and Technology (MNIST) handwriting digits dataset show that the local characteristics extracted in the frequency domain used as input to a support vector machine (SVM) classifier are efficient in terms of 99.51% accuracy. The proposed system successfully helped in the reporting of all the marks for seven subjects in a class of 98 students during the automatic correction of the MCQ exam papers.

Human movement detection and classification capabilities using passive Wi-Fi based radar

Human detection and classification via Wi-Fi transmission have received a lot of attention in recent years as crucial facilitators in security and human-computer interaction (HCI). The passive Wi-Fi radar (PWR) system used by previous researchers applied cross-ambiguity function (CAF) and CLEAN algorithms to process the detected signals. This paper explores the feasibility and viability of a PWR system in detecting and classifying human movements without utilizing CAF and CLEAN algorithms. The movements are performed by four participants but with comparable body sizes and heights. Three daily human movements are investigated namely walking, bending, and sitting, with each participant performing each movement 24 times, providing a total of 96 samples per activity. The system is evaluated based on the consistency of the signal pattern in a frequency domain and the percentage accuracy is assessed using an artificial neural network (ANN) classifier and trained using a leave-one-out cross-validation (LOOCV) method. The frequency domain results reveal that the signals are consistent, with no noticeable variations or changes in the voltage intensity or shape of the main lobe. The classification of the movements shows that the classifier has an overall accuracy of 97.6%.

Comparing sensitivities of groundwater head to variations in hydraulic parameters under boundary conditions of river stage rise and tidal variation

The sensitivity of hydraulic head responses to spatially distributed hydraulic parameters is essential for uncertainty analysis, inverse modeling, and parameter estimation and interpretation. This study formulates the Fréchet sensitivity kernel of hydraulic head responses to a suddenly rising boundary and a sinusoidal head fluctuation boundary to variation of spatially distributed hydraulic parameters in a semi-infinite, one-dimensional (1-D), confined aquifer, and it then derives analytical solutions. Different from previous studies that derived expressions for Fréchet kernels in the time domain for a 2-D pumping test, this study is the first to derive the closed-form Fréchet kernels in time and frequency domains for a semi-infinite, 1-D, confined aquifer. This study uses the Fréchet kernels to investigate the nature of singularities in the spatial sensitivity functions around the observation location and boundary. The information content revealed by observation of head change or head fluctuation amplitude at a given specified location and time (or frequency) under the above two boundary conditions is different. When comparing Fréchet sensitivity kernels across various times or periods, multi-frequency information, much like multi-time information, can be instrumental for hydrogeological parameter inversion. The explicit-form Fréchet sensitivity kernels also identify the optimal time or period for obtaining measurements.

Analysis of converter transformer pressboard insulation degradation under surge using mathematical morphology

Nowadays, with the significant expansion of industrial growth, the bulk power requirement can only be satisfied through high-voltage direct current HVDC transmission. The converter transformer is the utmost vital part of the HVDC transmission. Pressboard insulation is most commonly used as inter-disc insulation in converter transformers. During working conditions due to elevated temperature and different operational stresses, insulation material gets deteriorated. It may cause a risk to the life of the converter transformer. The effects of elevated temperatures as well as frequency on pressboard insulation of the converter transformer are examined in this study. The condition evaluation and morphological changes in pressboard insulation at elevated temperatures can evaluate with the help of frequency domain spectroscopy (FDS) and atomic force microscopy (AFM) techniques. The impact of elevated temperatures on insulation material can be analyzed based on surface roughness and dielectric parameters. In MATLAB Simulink environment, a dual winding single-phase converter transformers valve side star winding 60 discs model is constructed for impulse test. Based upon arrival time and velocity of traveling wave, insulation degradation location can be identified by using mathematical morphology. The simulation results demonstrate that the suggested method can notably located degradation across disc winding.

Nonlinear Loads Modelling and Harmonics Analysis: A Review

Abstract: In modern life, the applications of nonlinear loads are increasing, especially electronic loads, such as computers, monitors, and battery chargers. Moreover, the usage of semiconductors has increased in modern lighting systems, such as LED lamps and dimmers which are used to control the lighting level. Nonlinear loads may also be in the form of variable speed drive or arc furnace loads. Due to the spread of nonlinear loads, large amounts of harmonics are generated. To study the harmonics of nonlinear loads, it is essential to represent nonlinear loads by their equivalent models in the simulation programs. This paper presents a comprehensive review of modeling methods of nonlinear loads, including time domain methods, frequency domain methods, and state space methods. Equivalent circuits are presented for different types of nonlinear loads, including static loads, dynamic loads and arc furnace loads. The paper also presents the harmonic analysis performed by the Fourier transform of different types of nonlinear loads, including harmonic percentage and its angle. This paper is considered as a comprehensive reference for researchers and engineers to model nonlinear loads and is useful in power system planning for connecting nonlinear loads where mixing nonlinear loads of similar harmonic profiles causes worse situations. Moreover, knowing the nonlinear load profile will help in the suitable mixing of linear loads to achieve lower harmonics levels.

Energy Transition, fossil Fuels, and green Innovations: Paving the way to achieving sustainable development Goals in the United States

The United States, as the world's second-largest polluter, emitted a staggering 4.7 billion metric tonnes of carbon dioxide in 2020. During the Leaders’ Climate Summit, the US president articulated precise targets for combating climate change, notably a 50 to 52 percent reduction in net CO2 emissions from 2005 levels by 2030. Consequently, it becomes imperative to identify the pivotal factors that are driving this intensified dedication to achieving the Sustainable Development Goals (SDGs). Hence, it is vibrant to determine the basic causes propelling the intensified interest in meeting SDGs 7 and 13. From this perspective, this study explores the dynamic link between energy transition, fossil fuel energy, green innovation, and economic complexity index on CO2 emissions and ecological footprint in the United States from 1980 to 2020. The dynamic Autoregressive-Distributed Lag (ARDL) simulations and Frequency Domain Causality (FDC) techniques are applied for long-medium and short-run causal association among the variables. Our empirical evidence shows that energy transition, fossil fuel energy, and economic complexity increase CO2 emissions and ecological footprint. On the other hand, green innovation reduces CO2 emissions and ecological impact only in the long term. The pragmatic outcomes suggest that authorities should inhibit Fossil Fuel Energy, concentrate on energy transitions, and include more renewable sources in the US energy mix. The findings would assist the United States in meeting SDGs 7 and 13.

Time distribution of stimulated transition probabilities

In recent years, an increasing number of experimental results have been published for a wide variety of systems in which high-precision, ultra-fast, and ultra-short laser pulses techniques have been used to study time-resolved dynamical processes. In the absence of a theoretical prediction of the time distribution of the transition probabilities, the lifetimes τ are generally extracted by fitting the decay time with an exponential, bi-exponential or three-exponential function. In fitting the data, the short-time behavior is generally neglected. The purpose of this study is to show that an explicit formula for the time distribution of the transition probability can be determined rigorously using time-dependent perturbation theory. A formula that perfectly fits an important subset of the experimental results from t = 0 to t=∞ . We show that by following a route different from the usual procedure for deriving transition amplitudes and Einstein coefficients in time-dependent perturbation theory, one ends up with a time-dependent factor, that is, a temporal distribution function of the transition probabilities, and for the Einstein coefficients. The time distribution reported here looks in the time domain similar to the Planck distribution in the frequency domain. In fact, the behavior of the time distribution, with a maximum at 2τln⁡2 , and with τ playing the role of temperature, resembles the behavior of the Planck frequency distribution. We present several examples to demonstrate that the theoretical formula allows for easier fitting of the experimental results reported in the literature. We also show that the time distribution explains the difference in resonance heights between the experimental and theoretical blue laser spectra.

Eduction of coherent structures from schlieren images of twin jets using SPOD informed with momentum potential theory in the spectral domain

Abstract This work presents a methodology to extract coherent structures from high-speed schlieren images of turbulent twin jets which are more physically interpretable than those obtained with currently existing techniques. Recently, Prasad and Gaitonde (J Fluid Mech 940:1–11, 2022) introduced an approach which employs the momentum potential theory of Doak (J Sound Vib 131(1):67–90, 1989) to compute potential (acoustic and thermal) energy fluctuations from the schlieren images by solving a Poisson equation, and combines it with spectral proper orthogonal decomposition (SPOD) to educe coherent structures from the momentum potential field instead of the original schlieren field. While the latter field is dominated by a broad range of vortical fluctuations in the turbulent mixing region of unheated high-speed jets, the momentum potential field is governed by fluctuations which are intimately related to acoustic emission, and its spatial structure in the frequency domain is very organized. The proposed methodology in this paper improves the technique of Prasad and Gaitonde (J Fluid Mech 940:1–11, 2022) in three new ways. First, the solution of the Poisson equation is carried out in the frequency-wavenumber domain instead of the time-space domain, which simplifies and integrates the solution of the Poisson equation within the SPOD framework based on momentum potential fluctuations. Second, the issue of solving the Poisson equation on a finite domain with ad hoc boundary conditions is explicitly addressed, identifying and removing those unphysical harmonic components introduced in the solution process. Third, the solution of the SPOD problem in terms of momentum potential fluctuations is used to reconstruct schlieren SPOD fields associated with each mode, allowing the visualization of the obtained coherent structures also in terms of the density gradient. The method is applied here to schlieren images of a twin-jet configuration with a small jet separation at two supersonic operation conditions: a perfectly-expanded and an overexpanded one. The SPOD modes based on momentum potential fluctuations retain the wavepacket structure including the direct Mach-wave radiation, together with upstream- and downstream-traveling acoustic waves, similar to SPOD modes based on the schlieren images. However, for the same dataset, they result in a lower-rank decomposition than schlieren-based SPOD and provide an effective separation of twin-jet fluctuations into independent toroidal and flapping oscillations that are recovered as different SPOD modes. These coherent structures are more consistent with twin-jet wavepacket models available in the literature than those originally obtained with direct schlieren-based SPOD, facilitating their interpretation and comparison against theoretical analyses. Graphical abstract

Analytically separating the source of the Teukolsky equation

Recent gravitational wave detections from black hole mergers have underscored the critical role black hole perturbation theory and the Teukolsky equation play in understanding the behavior of black holes. The separable nature of the Teukolsky equation has long been leveraged to study the vacuum linear Teukolsky equation; however, as theory and measurements advance, solving the sourced Teukolsky equation is becoming a frontier of research. In particular, second-order calculations, such as in quasinormal mode and self-force problems, have extended sources. This paper presents a novel method for analytically separating the Teukolsky equation’s source, aimed to improve efficiency. Separating the source is a nontrivial problem due to the angular and radial mixing of generic quantities in Kerr spacetime. We provide a proof-of-concept demonstration of our method and show that it is accurate, separating the Teukolsky source produced by the stress-energy tensor of an ideal gas cloud surrounding a Kerr black hole. The detailed application of our method is provided in an accompanying notebook. Our approach opens up a new avenue for accurate black hole perturbation theory calculations with sources in both the time and frequency domain. Published by the American Physical Society 2024

Integrating gated recurrent unit in graph neural network to improve infectious disease prediction: an attempt

ObjectiveThis study focuses on enhancing the precision of epidemic time series data prediction by integrating Gated Recurrent Unit (GRU) into a Graph Neural Network (GNN), forming the GRGNN. The accuracy of the GNN (Graph Neural Network) network with introduced GRU (Gated Recurrent Units) is validated by comparing it with seven commonly used prediction methods.MethodThe GRGNN methodology involves multivariate time series prediction using a GNN (Graph Neural Network) network improved by the integration of GRU (Gated Recurrent Units). Additionally, Graphical Fourier Transform (GFT) and Discrete Fourier Transform (DFT) are introduced. GFT captures inter-sequence correlations in the spectral domain, while DFT transforms data from the time domain to the frequency domain, revealing temporal node correlations. Following GFT and DFT, outbreak data are predicted through one-dimensional convolution and gated linear regression in the frequency domain, graph convolution in the spectral domain, and GRU (Gated Recurrent Units) in the time domain. The inverse transformation of GFT and DFT is employed, and final predictions are obtained after passing through a fully connected layer. Evaluation is conducted on three datasets: the COVID-19 datasets of 38 African countries and 42 European countries from worldometers, and the chickenpox dataset of 20 Hungarian regions from Kaggle. Metrics include Average Root Mean Square Error (ARMSE) and Average Mean Absolute Error (AMAE).ResultFor African COVID-19 dataset and Hungarian Chickenpox dataset, GRGNN consistently outperforms other methods in ARMSE and AMAE across various prediction step lengths. Optimal results are achieved even at extended prediction steps, highlighting the model’s robustness.ConclusionGRGNN proves effective in predicting epidemic time series data with high accuracy, demonstrating its potential in epidemic surveillance and early warning applications. However, further discussions and studies are warranted to refine its application and judgment methods, emphasizing the ongoing need for exploration and research in this domain.

Outlier Detection for Permanent Magnet Synchronous Motor (PMSM) Fault Detection and Severity Estimation

Today, Permanent Magnet Synchronous Motors (PMSMs) are a dominant choice in industry applications. During operation, different possible faults in the system can occur, so early and automated fault detection and severity estimation are required to ensure smooth operation and optimal maintenance planning. In this direction, outlier detection methods are employed in this paper. The motor’s current signals are used to extract useful indicators of the fault, along with d-q transform. Statistical indicators in both time and frequency domains are selected to describe fault-related patterns. Based on the extracted features, three outlier detection methods are investigated: the Isolation Forest, the One Class Support Vector Machine, and the Robust Covariance Ellipse. Each method is investigated through different model parameters to evaluate fault detection and severity estimation capabilities. Finally, an ensemble approach is proposed based on decisions and outlier score ensemble. The proposed methodology is verified through different operating conditions in a PMSM test bench.

Fractional differentiation-based variational level set model for noisy image segmentation without contour initialization

Abstract Noisy image segmentation is a hot topic in image analysis. In this paper, we present a novel methodology for tackling this issue through the integration of fractional differentiation in the frequency domain with a variational level set model, which eliminates user-selected initial contours by incorporating the convex energy function. Additionally, the fractional differentiation reduces noises while preserving more detail information. Experiments on synthetic and real noisy images demonstrate that our proposed model surpasses other denoising variational level set models in terms of noise reduction, segmentation accuracy and efficiency.

Res2Net-based multi-scale and multi-attention model for traffic scene image classification.

With the increasing applications of traffic scene image classification in intelligent transportation systems, there is a growing demand for improved accuracy and robustness in this classification task. However, due to weather conditions, time, lighting variations, and annotation costs, traditional deep learning methods still have limitations in extracting complex traffic scene features and achieving higher recognition accuracy. The previous classification methods for traffic scene images had gaps in multi-scale feature extraction and the combination of frequency domain, spatial, and channel attention. To address these issues, this paper proposes a multi-scale and multi-attention model based on Res2Net. Our proposed framework introduces an Adaptive Feature Refinement Pyramid Module (AFRPM) to enhance multi-scale feature extraction, thus improving the accuracy of traffic scene image classification. Additionally, we integrate frequency domain and spatial-channel attention mechanisms to develop recognition capabilities for complex backgrounds, objects of different scales, and local details in traffic scene images. The paper conducts the task of classifying traffic scene images using the Traffic-Net dataset. The experimental results demonstrate that our model achieves an accuracy of 96.88% on this dataset, which is an improvement of approximately 2% compared to the baseline Res2Net network. Furthermore, we validate the effectiveness of the proposed modules through ablation experiments.

Research on the Attenuation Characteristics of Frequency Domain Parameters in Different Rock Medium

Abstract This study examines the frequency attenuation characteristics of elastic waves in marble, granite, and red sandstone through laboratory tests and numerical simulations based on ABAQUS software on eight rock samples. The correlation between the distance of the decay of the peak frequency and the petrophysical and mechanical parameters is also analyzed. The results show that elastic waves undergo stepwise attenuation of their peak frequency during laboratory attenuation tests. This finding was confirmed by numerical simulations. As elastic waves propagate through a rock medium, the amplitude of the high-frequency peak area near the excitation frequency decreases rapidly, while the proportion of low-frequency signals increases. As the propagation continues, the signal spectrum is dominated by low-frequency components, resulting in a stepwise attenuation of the peak frequency. It has been observed that the step decay distance of the peak frequency of elastic waves varies with the degree of particle bonding, which can be used to characterize the attenuation properties of elastic waves in a medium.

Thermoelectric active cooling for transient hot spots in microprocessors

Modern microprocessor performance is limited by local hot spots induced at high frequency by busy integrated circuit elements such as the clock generator. Locally embedded thermoelectric devices (TEDs) are proposed to perform active cooling whereby thermoelectric effects enhance passive cooling by the Fourier law in removing heat from the hot spot to colder regions. To mitigate transient heating events and improve temperature stability, we propose a novel analytical solution that describes the temperature response of a periodically heated hot spot that is actively cooled by a TED driven electrically at the same frequency. The analytical solution that we present is validated by experimental data from frequency domain thermal reflectance (FDTR) measurements made directly on an actively cooled Si thermoelectric device where the pump laser replicates the transient hot spot. We herein demonstrate a practical method to actively cancel the transient temperature variations on circuit elements with TEDs. This result opens a new path to optimize the design of cooling systems for transient localized hot spots in integrated circuits.

Novel Waveform Design with a Reduced Cyclic Prefix in MIMO Systems

For well-known orthogonal frequency division multiplexing (OFDM), the cyclic prefix (CP) is essential for coping with multipath channels. Nevertheless, CP is a pure redundant signal, which wastes valuable time–frequency resources. We propose a novel waveform based on symbol repetition, which is presented to cut down the CP overhead in OFDM. In the presented OFDM with symbol repetition (SR-OFDM), one CP is inserted in the front of several transmitted symbols, instead of only one symbol, as in the conventional way. As a result, it can save the overhead created by CP. Furthermore, due to the existence of the remaining CP, the multipath channel can still be converted into the frequency domain, and single-tap equalization can still be used to equalize information free from interference. In addition, we also extend the proposed SR-OFDM into multiple input–multiple output (MIMO) systems. Finally, the proposed schemes are validated by computer simulations under the various channels.

Tunable temporal dynamics of dipole response in graphene-wrapped core–shell nanoparticles

The investigation on the temporal dynamics of graphene-wrapped core–shell nanoparticles under the illumination of a Gaussian impulse have been carried out. By altering the graphene layers and the aspect ratio of the core–shell structure, we can adjust the resonant modes into typical cases in regime of terahertz. Accordingly, different scenarios for the temporal evolution are detected, which include two kinds of ultrafast oscillation with exponential decay tendency, pure exponential decay, and Gaussian shape, when the pulse duration of the incident pulse is much shorter than, similar to, and much longer than the localized surface plasmon lifetime. To one's interest, when the coupling between two resonant modes exists, one predicts the long-periodic oscillation, whose period is just the difference between the frequencies of the resonant modes. Hence, the intrinsic properties of the ultrafast oscillation can be hardly influenced by the input signals. Further quantitative calculation demonstrate that the periods of the ultrafast oscillations can be tuned by different physical mechanisms, which are, respectively, based on the self-interacting correction of a single resonance and the strong coupling between the resonant modes in frequency domain. Our results may be applicable in the fields of optical sensors, optical information processing, and other nanophotonic devices.

Long term follow-up of heart rate variability in healthcare workers with mild COVID-19

IntroductionPrior investigations into post-COVID dysautonomia often lacked control groups or compared affected individuals solely to healthy volunteers. In addition, no data on the follow-up of patients with SARS-CoV-2-related autonomic imbalance are available.MethodsIn this study, we conducted a comprehensive clinical and functional follow-up on healthcare workers (HCWs) with former mild COVID-19 (group 1, n = 67), to delineate the trajectory of post-acute autonomic imbalance, we previously detected in a case–control study. Additionally, we assessed HCWs for which a test before SARS-CoV-2 infection was available (group 2, n = 29), who later contracted SARS-CoV-2, aiming to validate findings from our prior case–control investigation. We evaluated autonomic nervous system heart modulation by means of time and frequency domain heart rate variability analysis (HRV) in HCWs during health surveillance visits. Short-term electrocardiogram (ECG) recordings, were obtained at about 6, 13 months and both at 6 and 13 months from the negative SARS-CoV-2 naso-pharyngeal swab (NPS) for group 1 and at about 1-month from the negative NPS for group 2. HCWs who used drugs, had comorbidities that affected HRV, or were hospitalized with severe COVID-19 were excluded.ResultsGroup 1 was split into three subgroups clinically and functionally followed at, about 6 months (subgroup-A, n = 17), 13 months (subgroup-B, n = 37) and both at 6 and 13 months (subgroup-C, n = 13) from the negative SARS-CoV-2 NPS. In subgroup-A, at 6-month follow-up compared with baseline, the spectral components in the frequency domain HRV parameters, showed an increase in normalized high frequency power (nHF) (t = 2.99, p = 0.009), a decrease in the normalized low frequency power (nLF) (t = 2.98, p = 0.009) and in the LF/HF ratio (t = 3.13, p = 0.006). In subgroup B, the comparison of the spectral components in the frequency domain HRV parameters, at 13-month follow-up compared with baseline, showed an increase in nHF (t = 2.54, p = 0.02); a decrease in nLF (t = 2.62, p = 0.01) and in the LF/HF ratio (t = 4.00, p = 0.0003). In subgroup-C, at both 6 and 13-month follow-ups, the spectral components in the frequency domain HRV parameters were higher than baseline in nHF (t = 2.64, p = 0.02 and (t = 2.13, p = 0.05, respectively); lower in nLF (t = 2.64, p = 0.02 and (t = 2.13, p = 0.05, respectively), and in LF/HF (t = 1.92, p = 0.08 and (t = 2.43, p = 0.03, respectively). A significant proportion of HCWs reported persistent COVID-19 symptoms at both the 6 and 13-month follow-ups, seemingly unrelated to cardiac autonomic balance. In group 2 HCWs, at 1-month follow-up compared with baseline, the spectral components in the frequency domain HRV parameters, showed a decrease in nHF (t = 2.19, p = 0.04); an increase in nLF (t = 2.15, p = 0.04) and in LF/HF (t = 3.49, p = 0.002).ConclusionThese results are consistent with epidemiological data suggesting a higher risk of acute cardiovascular complications during the first 30 days after COVID-19. The SARS-CoV-2 associated autonomic imbalance in the post-acute phase after recovery of mild COVID-19 resolved 6 months after the first negative SARS-CoV-2 NPS. However, a significant proportion of HCWs reported long-term COVID-19 symptoms, which dot not seems to be related to cardiac autonomic balance. Future research should certainly further test whether autonomic imbalance has a role in the mechanisms of long-COVID syndrome.

Subtraction of liposome signals in cryo-EM structural determination of protein-liposome complexes

Abstract Reconstituting membrane proteins in liposomes and determining their structure is a common method for determining membrane protein structures using single-particle cryo-electron microscopy (cryo-EM). However, the strong signal of liposomes under cryo-EM imaging conditions often interferes with the structural determination of the embedded membrane proteins. Here, we propose a liposome signal subtraction method based on single-particle two-dimensional (2D) classification average images, aimed at enhancing the reconstruction resolution of membrane proteins. We analyzed the signal distribution characteristics of liposomes and proteins within the 2D classification average images of protein-liposome complexes in the frequency domain. Based on this analysis, we designed a method to subtract the liposome signals from the original particle images. After the subtraction, the accuracy of single-particle three-dimensional (3D) alignment was improved, enhancing the resolution of the final 3D reconstruction. We demonstrated this method using a PIEZO1-proteoliposome dataset by improving the resolution of the PIEZO1 protein.

Research on Efficient Suspension Vibration Reduction Configuration for Effectively Reducing Energy Consumption

Reducing vehicle energy consumption is crucial for sustainable development, especially in the context of energy crises and environmental pollution. Energy regenerative suspension offers a promising solution, yet its practical implementation faces challenges like inertial mass issues, cost, and reliability concerns. This study introduces a novel suspension configuration, optimizing shock absorber technology with energy regenerative principles. The objective is to drastically cut energy consumption. Through a frequency domain analysis, this study identifies the root causes of increased energy consumption and worsened vibration in traditional suspensions. This study presents a comparative analysis of the frequency-domain characteristics between the novel suspension configuration and the traditional one. This study reveals that the new configuration exhibits a low-pass filtering effect on the shock absorber’s velocity, effectively minimizing vibrations in the low-frequency range, while mitigating their impact in the high-frequency range. This approach mitigates the trade-off between increased energy consumption and worsened vibration in the high-frequency range, making it a promising solution. Simulations show that this configuration significantly reduces acceleration by 7.04% and suspension power consumption by 10.47% at 60 km/h on the D-level road, while maintaining handling stability. This makes it a promising candidate for future energy-efficient suspension systems.