Search Results (107)

This paper proposes a novel sound source localization system that combines parametric homomorphic deconvolution with neural network regression to estimate the angle of arrival from a single-channel signal. The system uses an analog adder to sum signals from three spatially arranged microphones, reducing system hardware complexity and requiring the estimation of time delays from a single-channel signal. Time delay features are extracted through parametric homomorphic deconvolution methods—Yule–Walker, Prony, and Steiglitz–McBride—and input to multilayer perceptrons configured with various structures. Simulations confirm that Steiglitz–McBride provides the sharpest and most accurate predictions with reduced model order, while Yule–Walker shows slightly better performance than Prony at higher orders. A hybrid learning strategy that combines synthetic and real-world data improves generalization and robustness across all angles. Experimental validations in an anechoic chamber support the simulation results, showing high correlation and low deviation values, especially with the Steiglitz–McBride method. The proposed sound source localization system demonstrates a compact and scalable design suitable for real-time and resource-constrained applications and provides a promising platform for future extensions in complex environments and broader signal interpretation domains. Full article

This study presents an approach based on data-driven methods for determining the parameters needed to model time-dependent material behaviour. The time-dependent behaviour of the thermoplastic polymer polybutylene terephthalate is investigated. The material was examined under two conditions, one with and one without the inclusion of reinforcing short fibres. Two modelling approaches are proposed to represent the time-dependent response. The first approach is the generalised Maxwell model formulated through the classical exponential Prony series, and the second approach is a model based on fractional calculus. In order to quantify the comparative capabilities of both models, experimental data from tensile creep tests on fibre-reinforced polybutylene terephthalate and unreinforced polybutylene terephthalate specimens are analysed. A central contribution of this work is the implementation of a machine-learning-ready parameter identification framework that enables the automated extraction of model parameters directly from time-series data. This framework enables the robust fitting of the Prony-based model, which requires multiple characteristic times and stiffness parameters, as well as the fractional model, which achieves high accuracy with significantly fewer parameters. The fractional model benefits from a novel neural solver for fractional differential equations, which not only reduces computational complexity but also permits the interpretation of the fractional order and stiffness coefficient in terms of physical creep resistance. The methodological framework is validated through a comparative assessment of predictive performance, parameter cheapness, and interpretability of each model, thereby providing a comprehensive understanding of their applicability to long-term material behaviour modelling in polymer-based composite materials. Full article

This article explores the viscoelastic properties of polyethylene terephthalate glycol samples created by fused filament fabrication, emphasising the anisotropy introduced during fabrication. The samples were fabricated with filament direction within samples aligned along the principal axis or perpendicular. A group of samples was loaded with constant stress for 5 h, and a recovery phase with no applied stress was observed. Another group of samples was loaded for 20 h without an additional deformation recovery phase. The continuous constant stress application results on the sample were analysed, and an overall effect of anisotropy on the samples was observed. Several models describing viscoelastic deformation were considered to adhere to experimental data, with the Prony series and general cubic theory models used in the final analysis. The models could describe experimental results up to 50% and 70% of sample strength, respectively. The analysis confirmed the nonlinear behaviour of printed samples under constant stress and the significant effect of anisotropy introduced by the 3D printing process on the material’s elastic properties. The viscoelastic properties in both directions were described using the same parameters. Full article

Polyurethane foam is widely used as a primary filling material in car seats. While it provides good damping and energy absorption, the mechanical properties are complex but play a vital role in vibration attenuation and vehicle ride comfort. This study proposes a comprehensive experimental and analytical method to characterize the visco-hyperelastic properties of seat-grade polyurethane foam. Quasi-static and dynamic compression tests were conducted on foam blocks to obtain load–deflection curves and dynamic stiffness. A visco-hyperelastic material model was developed, where the hyperelastic response was derived via the hereditary integral and difference-stress method, and viscoelastic behavior was captured using a Prony series fitted to dynamic stiffness data. The model was validated using finite element simulations, showing good agreement with experimental results in both static and dynamic conditions. The proposed method enables accurate characterization of the visco-hyperelastic material properties of seat-grade polyurethane foam. Full article

The development of alginate/polyacrylamide hydrogels for various biomedical applications has attracted significant interest, particularly due to their potential use in wound healing and tissue engineering. This study explores the fabrication of these hydrogels via 3D bioprinting with ultraviolet light curing, focusing on how the alginate concentration and curing speed impact their mechanical properties. Rheological testing was employed to examine the viscoelastic behavior of alginate/polyacrylamide hydrogels manufactured using a 3D bioprinting technique. The relaxation behavior and dynamic response of these hydrogels were analyzed under torsional stress, with relaxation curves fitted using a two-term Prony series. Fourier Transform Infrared (FTIR) spectroscopy was also employed to assess biocompatibility and the conversion of acrylamide. This study successfully demonstrated the printability of alginate/polyacrylamide hydrogels with varying alginate contents. The rheological results indicated that 3D bioprinted hydrogels exhibited significantly high stiffness, viscoelasticity, and long relaxation times. The curing speed had a minimal impact on these properties. Additionally, the FTIR analysis confirmed the complete conversion of polyacrylamide, ensuring no harmful effects in biological applications. The study concludes that 3D bioprinting significantly enhances the mechanical properties of alginate/polyacrylamide hydrogels, with the alginate concentration playing a key role in the shear modulus. These hydrogels show promising potential for biocompatible applications such as wound healing dressings. Full article

This study aims to establish a methodology that integrates experimental measurements with finite element analysis (FEA) to investigate the mechanical behavior and dynamic characteristics of soft–hard laminated composites fabricated via additive manufacturing (AM) under dynamic excitation. A hybrid AM technique was employed, using the PolyJet process based on stereolithography (SLA) to fabricate composite beam structures composed of alternating soft and hard materials. Initially, impact tests using a steel ball on cantilever beams made of hard material were conducted to inversely calculate the first natural frequency via time–frequency analysis, thereby identifying Young’s modulus and Poisson’s ratio. For the viscoelastic soft material, tensile and stress relaxation tests were performed to construct a Generalized Maxwell Model, from which the Prony series parameters were derived. Subsequently, symmetric and asymmetric multilayer composite beams were fabricated and subjected to impact testing. The experimental results were compared with FEA simulations to evaluate the accuracy and validity of the identified material parameters of different structural configurations under vibration modes. The research focuses on the time- and frequency-dependent stiffness response of the composite by hard and soft materials and integrating this behavior into structural dynamic simulations. The specific objectives of the study include (1) establishing the Prony series parameters for the soft material integrated with hard material and implementing them in the FE model, (2) validating the accuracy of resonant frequencies and dynamic responses through combined experimental and simulation, (3) analyzing the influence of composite material symmetry and thickness ratio on dynamic modals, and (4) comparing simulation results with experimental measurements to assess the reliability and accuracy of the proposed modeling framework. Full article

Accurate measurement of broadband signals is fundamental to the broadband oscillation analysis of power grids. However, the measurement process of broadband signals generally suffers from noise interference and insufficient measurement accuracy. To address these issues, this study introduces a novel broadband measurement algorithm that integrates smooth linear segmented threshold (SLST) wavelet denoising with a fusion of the improved variational mode decomposition (VMD) and Prony methods. Initially, noise reduction preprocessing is designed for broadband signals based on the smooth linear segmented threshold wavelet denoising method to reduce the interference of noise on the measurement process, and two evaluation indices are established based on Pearson’s correlation coefficient and the signal-to-noise ratio (SNR) to assess the effectiveness of noise reduction. Subsequently, mutual information entropy and energy entropy are employed to optimize the parameters of VMD to enhance measurement precision. The denoised signal is decomposed into several modes with distinct center frequencies using the parameter-optimized VMD, thereby simplifying the signal processing complexity. Concurrently, the Prony algorithm is integrated to accurately identify the parameters of each mode, extracting frequency, amplitude, and phase information to achieve precise broadband signal measurement. The simulation results confirm that the proposed algorithm effectively reduces noise interference and enhances the measurement accuracy of broadband signals. Full article

The growing complexity and uncertainty in modern power systems—driven by increased integration of renewable energy sources and variable loads—underscore the need for robust tools to assess dynamic stability. This paper presents an enhanced methodology for modal analysis that combines Adaptive Variational Mode Decomposition (A-VMD) with Prony’s method. A novel energy-based selection mechanism is introduced to determine the optimal number of intrinsic mode functions (IMFs), improving the decomposition’s adaptability and precision. The resulting modes are analyzed to estimate modal frequencies and damping ratios. Validation is conducted using both synthetic datasets and real synchrophasor measurements from Ecuador’s national power grid under ambient and disturbed operating conditions. The proposed approach is benchmarked against established techniques, including a matrix pencil, conventional VMD-Prony, and commercial tools such as WAProtector and DIgSILENT PowerFactory. The results demonstrate that A-VMD consistently delivers more accurate and robust performance, especially for low signal-to-noise ratios and low-energy ambient conditions. These findings highlight the method’s potential for real-time oscillation mode identification and small-signal stability monitoring in wide-area power systems. Full article

The overall response of asphalt concrete under a subjected load is governed not only by the properties of its constituents but also by the interactions among them. In this paper, we focus on the numerical analysis of aggregate debonding, which is typically a phenomenon that precedes crack initiation. The interfacial transition zone plays a crucial role in the macroscopic performance of this material. Using image processing to reconstruct a specific sample microstructure, we carried out several finite element analyses to assess the impact of the debonding phenomenon on the general performance of asphalt concrete. Image segmentation algorithms were employed to accurately detect aggregate boundaries, followed by vectorization to describe their geometries. After applying a series of error-controlled geometry simplification procedures, the final microstructure was exported to the ABAQUS/Standard 2023 environment. A linear elastic solution for the reconstructed asphalt concrete sample was used as the reference solution. It was compared with linear viscoelastic solutions with a perfect bonding between constituents and, in the next step, with debonding allowed at aggregate–matrix interfaces. The latter phenomenon was analyzed by enforcing respective contact conditions between the aggregate and the bituminous matrix. It was found that introducing the viscoelastic material model for mastic resulted in a 142.72% increase in the vertical extreme displacement relative to the purely elastic solution. When debonding effects were additionally considered, this increase rose to 188.44%. The results confirm the necessity of debonding conditions to be introduced in reliable finite element analyses of asphalt concrete. Full article

This study systematically investigates continuous and discrete spectra methodologies for determining time-domain viscoelastic response functions (creep compliance and relaxation modulus) in asphalt mixtures. Through complex modulus testing of three asphalt mixtures (base asphalt mixture, SBS-modified asphalt mixture, and crumb rubber-modified asphalt mixture), we established unified master curves using a Generalized Sigmoidal model with approximated Kramers–Kronig (K-K) relations. Discrete spectra can be obtained by Prony series of Maxwell/Kelvin modeling, while continuous spectra derived through integral transformation produced complementary response functions by numerical integration. Comparative analysis demonstrated that discrete and continuous spectra methods yield highly consistent predictions of the relaxation modulus and creep compliance within conventional time scales (10⁻⁷–10⁵ s), with significant deviations emerging only at extreme temporal extremities. Compared to discrete spectra results, material parameters (relaxation modulus and creep compliance) derived from continuous spectra methods invariably asymptotically approach upper and lower plateaus. Notably, the maximum equilibrium values derived from continuous spectra methods consistently surpassed those obtained through discrete approaches, whereas the corresponding minimum values were consistently lower. This comparative analysis highlights the inherent limitations in the extrapolation reliability of computational methodologies, particularly regarding spectra method implementation. Furthermore, within the linear viscoelastic range, the crumb rubber-modified asphalt mixtures exhibited superior low-temperature cracking resistance, whereas the SBS-modified asphalt mixtures demonstrated enhanced high-temperature deformation resistance. This systematic comparative study not only establishes a critical theoretical foundation for the precise characterization of asphalt mixture viscoelasticity across practical engineering time scales through optimal spectral method selection, but also provides actionable guidance for region-specific material selection strategies. Full article

Analysing power system oscillations is essential for maintaining electrical grid stability and reliability. To assess power system oscillations and demonstrate the actual application in a real grid system, this research compares two popular signal processing methods: Prony’s approach and the Fast Fourier Transform from Phasor Measurement Unit data in the Java Bali (Indonesia) power system interconnection. FFT gives information about the prominent frequency components by representing system oscillations in the frequency domain. Nevertheless, windowing effects and resolution limitations limit it. By fitting exponential functions to time-domain signals, Prony’s approach, on the other hand, excels at precisely estimating the frequency and damping characteristics of oscillatory modes. The accuracy, computational effectiveness, and applicability for the real-time monitoring of both approaches are assessed in this study. Simulation results on both simulated and actual power system data illustrate the benefits and drawbacks of each strategy. The results show that although FFT is helpful for rapid spectral analysis, Prony’s approach offers more thorough mode identification, which makes it especially advantageous for damping evaluations. This study ends with suggestions for choosing the best method for power system stability analysis based on application requirements. Full article

Inspired by Scylla serrata, a novel thermoplastic polyurethane (TPU) negative Poisson’s ratio sign-switching metamaterial is proposed, and the corresponding original and gradient structures (i.e., OPSM and GPSM) are created. Numerical simulation is utilized to simulate the quasi-static and dynamic compression behavior of the proposed structures considering the rate-dependent properties, elastoplastic response, and nonlinear contact. The neo-Hookean hyperelastic constitutive model and the Prony series are adopted to model the target structures. Finite element results are validated through experimental results. Parametric studies are conducted to study the effects of gradient characteristics and loading velocities on the mechanical behavior and Poisson’s ratio of the structures. Testing results indicate that the proposed novel bioinspired structure patterns exhibit fascinating mechanical behavior and interesting negative Poisson’s ratio sign-switching characteristics, which would provide the design guidance for the development and application of bioinspired structural materials. Full article

Microbial-induced carbonate precipitation (MICP) is gaining attention as an eco-friendly and sustainable method for concrete crack repair. However, key challenges related to its large-scale implementation, regulatory approval, and integration into existing construction standards remain underexplored. This review examines recent advances in MICP, emphasizing its role in circular economy practices and sustainable building solutions. Traditional synthetic sealants contribute to environmental pollution and have limited long-term durability, highlighting the need for greener alternatives. Global research trends reveal an increasing focus on self-healing materials, biomineralization, and durability enhancement, alongside emerging innovations such as encapsulation technologies, marine applications, and bio-based composites. Unlike previous reviews, this study integrates bibliometric analysis to systematically assess research trends, identify key collaboration networks, and evaluate regulatory challenges that impact MICP adoption. While MICP offers significant advantages, including self-healing capabilities and compatibility with industrial by-products, barriers related to cost, scalability, and policy integration persist. This review identifies critical thematic clusters which include microbial action, sustainability, and engineering applications. This helps to provide actionable insights for researchers, engineers, and policymakers. By fostering interdisciplinary collaboration, MICP has the potential to become a transformative solution for resilient and environmentally sustainable infrastructure. Full article

The interaction between antibiotics and antibiotic resistance genes (ARGs) in freshwater ecosystems has become a critical environmental concern. This study investigates seasonal variations of sulfonamide and tetracycline antibiotics and their relationship with ARGs in three urban reservoirs in Nanjing, China: Pingshan Forest Park, Shanhu Lake Wetland Park, and Zhaoqiao Reservoir. Sampling was conducted in May and September 2023 to assess water quality, antibiotic concentrations, and ARG abundance. A total of 30 water samples were analyzed in regard to their physicochemical parameters, heavy metals, and antibiotics. A quantitative PCR assay was used to measure the ARG abundance relative to the 16S rRNA gene. Sulfonamide concentrations ranged from 30 to 120 ng/L, while the concentrations of tetracyclines were 50–160 ng/L. Notably, sulfamethazine decreased significantly in two reservoirs (Shanhu and Zhaoqiao, p < 0.05), while other antibiotics showed minimal variation, indicating persistent contamination from agricultural runoff and wastewater discharge. ARG abundance was lower in May than in September, with sulfonamide resistance genes being lower cumulatively than tetracycline resistance genes. Strong correlations (r > 0.7) were observed between ARGs and parameters like dissolved oxygen and pH. High antibiotic levels were observed in areas without nearby hospitals or pharmaceutical companies, implicating agriculture as a major pollution source. By analyzing sulfonamide and tetracycline antibiotics and their resistance genes across three eutrophic reservoirs in Nanjing, China, we highlight critical environmental drivers of ARG proliferation and propose targeted mitigation strategies. Full article

This research focuses on the development of a chassis dynamometer for light electric vehicles (LEV), utilizing the Prony Brake method for torque measurement. The primary goal was to create a robust testing platform that accurately assesses the performance characteristics of LEVs under controlled conditions. The dynamometer’s performance evaluation revealed an average error of 0.55 for RPM readings, indicating a moderate level of variability in the sensor’s accuracy. In contrast, the torque measurement yielded a significantly lower average error of 0.03, demonstrating high precision in capturing torque data. Additionally, a standard deviation of 0.34 was observed during the torque versus RPM assessments, reflecting the consistency of the collected data. These findings validate the effectiveness of the chassis dynamometer in delivering reliable performance metrics for LEVs, providing essential insights for future advancements in electric vehicle technology and performance evaluation methodologies. Full article