Search Results (919)

Based on the characteristics of offshore pile foundation engineering, this study proposes a novel interpretation method for pile settlement time history signals in Rapid Load Testing (RLT). The approach utilizes Variational Mode Decomposition (VMD) to decompose and reconstruct the originally acquired acceleration signals, effectively eliminating high-frequency noise and significantly enhancing signal quality. After obtaining a purified acceleration signal, the study further refines the velocity signal based on the velocity characteristics at the beginning and end of the loading process, aiming to mitigate the influence of initial and boundary conditions on the velocity data. This process yields a highly accurate displacement time history curve. To validate the superiority of VMD in acceleration signal processing, a signal model test was conducted. Comparative experimental results demonstrate that the displacement time history curve derived from VMD-processed signals not only exhibits smaller relative errors and higher precision but also shows significant waveform improvements compared to curves obtained through direct integration of filtered signals. The research indicates that for marine pile foundations, using VMD to decompose and reconstruct the signals, and applying the continuous mean square error theory to identify the critical components of noise and effective signals has significant advantages in the processing of displacement signals using RLT. Compared with traditional analysis methods, the study successfully achieved the effective removal of high-frequency noise in the signal by applying the VMD technique to the decomposition and reconstruction of acceleration signals, significantly improving the quality of the signal. The assumption of zero pile head velocity before and after loading enables accurate determination of the actual pile head displacement Full article

This paper investigates frequency regulation of an airport microgrid (AIM) through the application of an integral absolute error (IAE)-assisted control approach. The islanded AIM is initially captured using a linearized transfer function model to accurately reflect its dynamic characteristics. This model is then simplified using a first-order plus dead time (FOPDT) approximation derived via a reaction-curve-based method, which balances between model simplicity and accuracy. Two different proportional–integral–derivative (PID) controllers are designed to meet distinct objectives: one focuses on set-point tracking (SPT) to maintain the target frequency levels, while the other addresses load disturbance rejection (LDR) to reduce the effects of load fluctuations. A thorough comparison of these controllers demonstrates that the SPT-mode PID controller outperforms the LDR-mode controller by providing an improved transient response and notably lower error measures. The results underscore the effectiveness of combining IAE-based control with reaction curve modeling to tune PID controllers for islanded AIM systems, contributing to enhanced and reliable frequency regulation for microgrid operations. Full article

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article

By applying the Non-Perturbative Approach (NPA), the corresponding linear differential equation is obtained. Aimed at organizational investigation, the resulting linear equation is used. Strong agreement between numerical calculations and the precise frequency is demonstrated, and the reliability of the results acquired is established by the correlation with the numerical solution. Additionally, this study explores a new control process to affect the stability and behavior of dynamic motorcycle systems that vibrate nonlinearly. A multiple time-scale method (MTSM) is applied to examine the analytical solution of the nonlinear differential equations describing the aforementioned system. Every instance of resonance was taken out of the second-order approximations. The simultaneous primary and 1:1 internal resonance case ($\mathsf{\Omega }\approx {\omega }_{eq},$${\omega }_2\approx {\omega }_{eq}$) is recorded as the worst resonance case caused while working on the model. We investigated stability with frequency–response equations and bifurcation. Numerical solutions for the system are covered. The effects of the majority of the system parameters were examined. In order to mitigate harmful vibrations, the controller under investigation uses (PD) proportional derivatives with (PPF) positive position feedback as a new control technique. This creates a new active control technique called PDPPF. A comparison between the PD, PPF, and PDPPF controllers demonstrates the effectiveness of the PDPPF controller in reducing amplitude and suppressing vibrations. Unwanted consequences like chaotic dynamics, limit cycles, or loss of stability can result from bifurcation, which is the abrupt qualitative change in a system’s behavior as a parameter. The outcomes showed how effective the suggested controller is at reducing vibrations. According to the findings, bifurcation analysis and a control are crucial for designing vibrating dynamic motorcycle systems for a range of engineering applications. The MATLAB software is utilized to match the analytical and numerical solutions at time–history and frequency–response curves (FRCs) to confirm their comparability. Additionally, case studies and numerical simulations are presented to show how well these strategies work to control bifurcations and guarantee the desired system behaviors. An analytical and numerical solution comparison was prepared. Full article

Deep-well radar telemetry over ultra-long cables suffers from strong frequency-selective attenuation and impedance drift under high temperature and pressure. We have proposed a channel-adaptive “communication + acquisition” architecture for a 7500 m borehole radar system. The scheme integrates spread-spectrum time domain reflectometry (SSTDR; m-sequence with BPSK) to monitor the cable in situ, identify termination/cable impedance, and adaptively match the load, thereby reducing reflection-induced loss. On the receiving side, we combine time domain adaptive equalization—implemented as an LMS-driven FIR filter—with frequency domain OFDM equalization based on least-squares (LS) channel estimation, enabling constellation recovery and robust demodulation over the distorted channel. The full processing chain is realized in real time on a Xilinx Artix-7 (XC7A100T) FPGA with module-level reuse and pre-stored training sequences for efficient hardware scheduling. In a field deployment in the Shunbei area at 7500 m depth, radar results show high agreement with third-party geological logs: the GR-curve correlation reaches 0.92, the casing reflector at ~7250 m is clearly reproduced, and the key bottom depth error is 0.013%. These results verify that the proposed system maintains stable communication and accurate imaging in harsh deep-well environments while remaining compact and implementable on cost-effective hardware. Full article

CFRP is extensively utilized in the manufacturing of aerospace equipment owing to its distinctive properties, and hole-making processing continues to be the predominant processing method for this material. However, due to the anisotropy of CFRP, in its processing process, processing damage appears easily, such as stratification, fiber tearing, burrs, etc. These damages will seriously affect the performance of CFRP components in the service process. This work employs acoustic emission (AE) and infrared thermography (IT) techniques to analyze the characteristics of AE signals and temperature signals generated during the CFRP drilling process. Fast Fourier transform (FFT) and short-time Fourier transform (STFT) are used to process the collected AE signals. And in combination with the actual damage morphology, the material removal behavior during the drilling process and the AE signal characteristics corresponding to processing defects are studied. The results show that the time-frequency graph and root mean square (RMS) curve of the AE signal can accurately distinguish the different stages of the drilling process. Through the analysis of the frequency domain characteristics of the AE signal, the specific frequency range of the damage mode of the CFRP composite material during drilling is determined. This paper aims to demonstrate the feasibility of real-time monitoring of the drilling process. By analyzing the relationship between the RMS values of acoustic emission signals and hole surface topography under different drilling parameters, it provides a new approach for the research on online monitoring of CFRP drilling damage and improvement of CFRP machining quality. Full article

Vehicle dynamics play a crucial role in assessing vehicle performance, comfort, and safety. To precisely depict the dynamic behaviors of a vehicle, fractional damping is employed to substitute the conventional damping in suspensions and tires. Taking the fractional damping into account, a four-degrees-of-freedom vehicle model is developed, which encompasses the vertical vibration and pitch motion of the vehicle body, as well as the vertical motions of the front and rear axles. The vibration equations are solved in the Laplace domain using the transfer function method. The validity of the transfer function method is verified through comparison with a benchmark case. The vibrations of the vehicle are analyzed under the effects of suspension and tire properties, pavement excitation, and vehicle speed. The assessment methods employed include the time-domain vibration response, amplitude–frequency curves, phase diagrams, the frequency response function matrix, and weighted root mean square acceleration. The results show that the larger fractional order results in higher energy dissipation. Elevated values of the fractional order α, suspension stiffness, and the damping coefficient contribute to greater stable vibration amplitudes in vehicles and a consequent degradation in ride comfort. Higher tire stiffness reduces vehicle vibration amplitude, while the fractional order β and tire damping have a negligible effect. Moreover, increased vehicle speed and a greater pavement input amplitude adversely affect ride comfort. Full article

In response to the challenges of obstacle avoidance and terrain negotiation encountered by wheel-legged robots in static environments with complex obstacles, this study introduces an enhanced A* path planning algorithm that incorporates a jump-point search strategy, a dynamically weighted heuristic strategy, and a continuous jumping constraint mechanism to facilitate efficient obstacle traversal. The algorithm extends the traditional 8-neighborhood rule to support jumping in the horizontal, vertical, and diagonal directions. A dynamic, weighted heuristic is introduced to adaptively adjust heuristic weights, guide the search direction, improve efficiency, and reduce detours. Redundant point removal and Bézier curve smoothing were employed to enhance path smoothness, whereas the continuous jumping constraint limited the jump frequency and improved motion stability. The results validate that—relative to the standard A* algorithm, which achieves a 73.7% reduction in path nodes (from 54 to 16)—85% fewer search nodes (from 542 to 78) and a planning time of 0.0032 s were achieved while also enhancing performance in crossing complex structures. This enhances the capability of wheel-legged robots to perform real-time path planning in structurally complex yet static environments, thereby improving their autonomous navigation efficiency. Full article

This paper presents the results of a rheological and spectral study characterising the change in the microstructure of lubricants depending on the type of vegetable oil base. The three lubricating compositions were prepared based on vegetable oils (rapeseed, sunflower and abyssinian), where amorphous silica of a specific particle size was used as a thickener. These three lubricating compositions were then modified by introducing the AW/EP additive (BCH 351) into their structure. Rheological tests were performed for the prepared lubricating compositions on a DWS diffusion spectrometer. Based on the tests, the dependence of ICF function values on time, MSD function values on time and G’ and G” modulus values on frequency were determined. From the collected data, rheological parameters such as the elasticity coefficient, MSD curve slope factor, diffusion coefficients and the value at which the G’ and G” curves intersect were determined, which characterise the microstructure of the tested lubricants. Raman and FTIR spectra were also performed to characterise the chemical structure of the compositions studied, and the intensity of integration of characteristic bands of vegetable greases was calculated. For vegetable greases made from different vegetable oils, a change in the value of the MSD function was observed, and the calculated value of the elasticity index indicates better viscoelastic properties for the grease made from rapeseed oil. Modification of vegetable greases with a multifunctional additive leads to a change in rheological parameters, indicating a change in the structure of the greases studied. The results of tests of diffusion coefficients for vegetable greases show a change in microstructure for greases made with different vegetable oils. Such results testify to moderately strong viscoelastic properties, leading to the conclusion that the produced greases are substances stable to changes in chemical structure depending on the base oil and modifying additive used. Raman and FTIR spectroscopy is a technique that enables changes in the chemical composition of vegetable oils to be assessed by analysing the degree of unsaturation of fatty acids in vegetable oils, making it a very good diagnostic method for quality control of lubricants based on vegetable oils. The results obtained make it possible to differentiate lubricants prepared with different vegetable oils and allow the chemical structure of the vegetable lubricants studied to be assessed on the basis of the intensity of integration of characteristic bands. Full article

Extended reality (XR) technologies are gaining traction in technical education due to their potential for creating immersive and interactive training environments. This study presents the development and empirical evaluation of X-RAPT, a collaborative VR-based platform designed to train students in industrial robotics programming. The system enables multi-user interaction, cross-platform compatibility (VR and PC), and real-time data logging through a modular simulation framework. A pilot evaluation was conducted in a vocational training institute with 15 students performing progressively complex tasks in alternating roles using both VR and PC interfaces. Performance metrics were captured automatically from system logs, while post-task questionnaires assessed usability, comfort, and interaction quality. The findings indicate high user engagement and a distinct learning curve, evidenced by progressively shorter task completion times across levels of increasing complexity. Role-based differences were observed, with main users showing greater interaction frequency but both roles contributing meaningfully. Although hardware demands and institutional constraints limited the scale of the pilot, the findings support the platform’s potential for enhancing robotics education. Full article

Currently, deepfake detection has garnered widespread attention as a key defense mechanism against the misuse of deepfake technology. However, existing deepfake detection networks still face challenges such as insufficient robustness, limited generalization capabilities, and a single feature extraction domain (e.g., using only spatial domain features) when confronted with evolving algorithms or diverse datasets, which severely limits their application capabilities. To address these issues, this study proposes a deepfake detection network named EFIMD-Net, which enhances performance by strengthening feature interaction and integrating spatial and frequency domain features. The proposed network integrates a Cross-feature Interaction Enhancement module (CFIE) based on cosine similarity, which achieves adaptive interaction between spatial domain features (RGB stream) and frequency domain features (SRM, Spatial Rich Model stream) through a channel attention mechanism, effectively fusing macro-semantic information with high-frequency artifact information. Additionally, an Enhanced Multi-scale Feature Fusion (EMFF) module is proposed, which effectively integrates multi-scale feature information from various layers of the network through adaptive feature enhancement and reorganization techniques. Experimental results show that compared to the baseline network Xception, EFIMD-Net achieves comparable or even better Area Under the Curve (AUC) on multiple datasets. Ablation experiments also validate the effectiveness of the proposed modules. Furthermore, compared to the baseline traditional two-stream network Locate and Verify, EFIMD-Net significantly improves forgery detection performance, with a 9-percentage-point increase in Area Under the Curve on the CelebDF-v1 dataset and a 7-percentage-point increase on the CelebDF-v2 dataset. These results fully demonstrate the effectiveness and generalization of EFIMD-Net in forgery detection. Potential limitations regarding real-time processing efficiency are acknowledged. Full article

To detect delamination and internal void defects within sandwich composite materials, such as those used in wind turbine blades, this study employs a Remote Impact Test (RIT), analyzing the dispersion characteristics of the generated stress waves. RITs were conducted on specimens that varied in both thickness and defect type. Time–frequency spectrograms and dispersion curves were then obtained using two time–frequency analysis techniques: wavelet analysis and reassigned spectrograms (derived from Short–Time Fourier Transformation). The accuracy of defect identification is demonstrably improved through the cross–examination of the findings from these methods. Full article

Curved non-motorized treadmills (cNMTs) have been demonstrated to reduce impact accelerations in comparison with motorized treadmills (MTs). Most studies have analyzed impacts in the time domain, but analysis in the frequency domain can provide useful information associated with the increase in the running risk of injury. The purpose of this study was to analyze the frequency components (low- and high-frequency bands) of impact accelerations, countermovement jump (CMJ) height, and perceived comfort during a prolonged run on different surfaces: MT, cNMT, and overground (OVG). Twenty-one recreational runners completed three randomized prolonged running tests on cNMT, MT, and OVG for 30 min (80% of the individual maximal aerobic speed). Impact accelerations were registered at minutes 5 and 30 of the test, the countermovement jump test (CMJ) was performed before (PreTest) and after (PostTest) the test, and perceived comfort was determined at the end of each test. A two-way repeated-measures analysis of variance (significance at p < 0.05) showed a reduction on cNMT in both low- and high-frequency bands of impact accelerations, such as head power (p < 0.001, ESd = 3.0) on the cNMT vs. the MT and tibia peak power (p = 0.001, ESd = 2.2) on the cNMT vs. OVG. However, cNMT was perceived as the least comfortable surface by runners. The prolonged running effect decreased impact accelerations during the treadmill running test (MT and cNMT) in the low-frequency band, while CMJ height decreased (p = 0.024, ESd = 1.4) during the PostTest vs. PreTest with the cNMT. Using a cNMT could be an interesting strategy for load reduction in long-distance runners or in return-to-play rehabilitation protocols. Full article

Background and Objectives: Neonates with moderate-to-severe hypoxic–ischemic encephalopathy (HIE) in low- and middle-income countries (LMICs) remain at high risk of neurological sequelae despite access to therapeutic hypothermia (TH). Real-time accessible biomarkers are required to improve risk stratification and guide neuroprotective care in these settings. This study evaluated the predictive capacity of heart rate variability (HRV) metrics for brain injury detected using magnetic resonance imaging (MRI) in neonates with HIE who underwent TH at an LMIC. Materials and Methods: We conducted a prospective observational study of 87 neonates treated with TH in a tertiary neonatal intensive care unit in Colombia. HRV was recorded during the first 24 h of TH, during rewarming, and 24 h after rewarming. Brain MRI was performed within the first week of life and scored using the Rutherford system. Associations between HRV metrics and global and regional brain injuries were analyzed using receiver operating characteristic (ROC) curves and multivariable logistic regression models. Results: Low-frequency (LF) and high-frequency (HF) powers were significantly lower in neonates with MRI abnormalities. LF power during rewarming demonstrated the highest predictive accuracy (AUC = 0.90), followed by HF power during the first 24 h (AUC = 0.80). Region-specific analyses showed that LF power reduction was significantly associated with white matter and basal ganglia injury. Conclusions: HRV, particularly LF power during rewarming, is a promising and accessible biomarker for regional brain injury in neonates with perinatal asphyxia treated with TH. Full article

In response to the technical challenges of traditional reservoir inversion techniques in determining inter-well wavelets and estimating geological statistical parameters, this study proposes an artificial intelligence high-resolution reservoir inversion technique based on seismic waveform constraints. This technology integrates multi-source heterogeneous data such as lithology characteristics, logging curves, and seismic waveforms through a deep learning neural network framework, and constructs an intelligent reservoir prediction model with geological and physical constraints. Results demonstrate that the proposed technique significantly enhances prediction accuracy for thin sand layers by effectively extracting high-frequency seismic information and establishing robust nonlinear mapping relationships. Inversion errors of reservoir parameters were reduced by more than 25%, while a vertical resolution of 0.5 m was achieved. Predictions agreed with actual drilling data with an accuracy of 86%, representing an 18% improvement over traditional methods. In practical applications, the technique successfully supported new well placement, contributing to a 22% increase in initial oil production in the pilot area. Furthermore, this study establishes a standardized technical procedure: “Time–Depth Modeling-Phase-Controlled Interpolation-Intelligent Inversion”. This workflow provides an innovative solution for high-precision reservoir characterization in regions with limited well control and complex terrestrial depositional systems, offering both theoretical significance and practical value for advancing reservoir prediction technology. Full article