Search Results (440)

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

This paper presents a CMOS-based hybrid system capable of noninvasively quantifying the total hemoglobin (tHb), the oxygen saturation (SpO₂), and the heart rate (HR) by utilizing five-wavelength (670, 770, 810, 850, and 950 nm) photoplethysmography. Conventional pulse oximeters are limited to the measurements of SpO₂ and heart rate, therefore hindering the real-time estimation of tHb that is clinically essential for monitoring anemia, chronic diseases, and postoperative recovery. Therefore, the proposed hybrid system enables us to distinguish between the concentrations of oxygenated (HbO₂) and deoxygenated hemoglobin (Hb) by using the absorption characteristics of five wavelengths from the visible to near-infrared range. This CMOS hybrid mixed-signal architecture includes a light emitting diode (LED) driver as a transmitter and an optoelectronic receiver with on-chip avalanche photodiodes, followed by a field-programmable gate array (FPGA) for a real-time signal processing pipeline. The proposed hybrid system, validated through post-layout simulations and algorithmic verification, achieves high precision with ±0.3 g/dL accuracy for tHb and ±1.5% for SpO₂, while the heart rate is extracted via 1024-point Fast Fourier Transform (FFT) with an error below ±0.2%. These results demonstrate the potential of a CMOS-based hybrid system as a feasible solution to achieve real-time, low-power, and high-accuracy analysis of bio-signals for clinical and home-use applications. Full article

The extraction of ocean wave wavelengths from optical imagery via Fast Fourier Transform (FFT) exhibits significant potential for Wave-Derived Bathymetry (WDB). However, in practical applications, this method frequently produces anomalously large wavelength estimates. To date, there has been insufficient exploration into the mechanisms underlying image spectral leakage to low wavenumbers and its suppression strategies. This study investigates three plausible mechanisms contributing to spectral leakage in optical images and proposes a subimage-based preprocessing framework: prior to executing two-dimensional FFT, the remote sensing subimages employed for wavelength inversion undergo three sequential steps: (1) truncation of distorted pixel values using a Gaussian mixture model; (2) application of a polynomial detrending surface; (3) incorporation of a two-dimensional Hann window. Subsequently, the dominant wavenumber peak is localized in the power spectrum and converted to wavelength values. Water depth is then inverted using the linear dispersion equation, combined with wave periods derived from ERA5. Taking 2 m-resolution WorldView-2 imagery of Sanya Bay, China as a case study, 1024 m subimages are utilized, with validation conducted against chart-sounding data. Results demonstrate that the proportion of subimages with anomalous wavelengths is reduced from 18.9% to 3.3% (in contrast to 14.0%, 7.8%, and 16.6% when the three preprocessing steps are applied individually). Within the 0–20 m depth range, the water depth retrieval accuracy achieves a Mean Absolute Error (MAE) of 1.79 m; for the 20–40 m range, the MAE is 6.38 m. A sensitivity analysis of subimage sizes (512/1024/2048 m) reveals that the 1024 m subimage offers an optimal balance between accuracy and coverage. However, residual anomalous wavelengths persist in near-shore subimages, and errors still increase with increasing water depth. This method is both concise and effective, rendering it suitable for application in shallow-water WDB scenarios. Full article

To address the challenges posed by harmonic distortion and DC offset in the power grid, this paper proposes a novel Phase-Locked Loop (PLL) architecture tailored for single-phase grid-connected systems. The design integrates an Enhanced Adaptive Second-Order Generalized Integrator (EA-SOGI) with a Quasi-Proportional Resonant (QPR) controller. The proposed EA-SOGI extends the conventional SOGI by incorporating an all-pass filter and an additional integrator, which enhance the symmetry of the orthogonal signals and effectively suppress the estimation errors caused by DC offset. In addition, the conventional PI controller is replaced by a QPR controller, whose parameters are tuned using a hybrid Levy-AsyLnCPSO optimization algorithm to improve frequency locking performance and enhance system robustness under steady-state conditions. Simulation and experimental results demonstrate that the proposed PLL achieves a Total Harmonic Distortion (THD) as low as 2.8653% based on Fast Fourier Transform (FFT) analysis, indicating superior adaptability compared to conventional PLL structures and validating its effectiveness in DC offset suppression and harmonic mitigation. Full article

Accurate and timely estimation of the root-mean-square (RMS) voltage is essential for grid-connected inverter systems, where it underpins reference generation, synchronization, and protection functions. Conventional RMS estimation methods, based on squaring, averaging, and taking the square root of values over full-cycle windows, achieve high accuracy but incur significant latency and computational overhead, thereby limiting their suitability for real-time control. Frequency-domain approaches, such as the FFT or wavelet analysis offer harmonic decomposition but are too complex for cost-sensitive embedded controllers. To address these challenges, this paper proposes an averaging-based RMS estimation method that exploits the proportionality between the mean absolute value of a sinusoidal waveform and its RMS. The method computes a moving average of the absolute voltage over a half-cycle window synchronized to the phase-locked loop (PLL) frequency, followed by a fixed scaling factor. This recursive implementation reduces the computational burden to a few arithmetic operations per sample while maintaining synchronization with off-nominal frequencies. Time-domain simulations under nominal (60 Hz) and deviated frequencies (57 Hz and 63 Hz) demonstrate that the proposed estimator achieves steady-state accuracy comparable to that of conventional and adaptive methods but with convergence within a half-cycle, thereby reducing latency by nearly 50%. These results confirm the method’s suitability for fast, reliable, and resource-efficient real-time inverter control in modern distribution grids. To provide a comprehensive evaluation, the paper first reviews conventional RMS estimation methods and their inherent limitations, followed by a detailed presentation of the proposed averaging-based approach. Simulation results under both nominal and off-nominal frequency conditions are then presented, along with a comparative analysis highlighting the advantages of the proposed method. Full article

The peregrine falcon, known as the fastest bird in the world, has been studied for its ability to stabilize during high-speed dives, a capability attributed to the configuration of its dorsal feathers. These feathers have inspired the design of vortex generators devices that promote controlled turbulence to delay boundary layer separation on aircraft wings and turbine blades. This study presents an experimental wind tunnel investigation of a bio-inspired peregrine falcon prototype, equipped with movable artificial feathers, a hot-wire anemometer, and a 3D accelerometer. Wake velocity profiles measured behind the prototype revealed fluctuations associated with feather motion. Spectral analysis of the velocity signals, recorded with oscillating feathers at a wind tunnel speed of 10 m/s, showed attenuation of specific frequency components, suggesting that feather dynamics may help mitigate wake fluctuations induced by structural vibrations. Three-dimensional acceleration measurements indicated that prototype vibrations remained below 1 g, with peak differences along the X and Z axes ranging from −0.06 g to 0.06 g, demonstrating the sensitivity of the vibration sensing system. Root Mean Square (RMS) values of velocity signals increased with wind tunnel speed but decreased as the feather inclination angle rose. When the mean value was subtracted from the signal, higher RMS variability was observed, reflecting increased flow disturbance from feather movement. Fast Fourier Transform (FFT) analysis revealed that, for fixed feather angles, spectral magnitudes increased uniformly with wind speed. In contrast, dynamic feather oscillation produced distinctive frequency peaks, highlighting the feather’s influence on the wake structure in the frequency domain. To complement the experimental findings, 3D CFD simulations were conducted on two HAWT-type wind turbines—one with bio-inspired vortex generators and one without. The simulations showed a significant reduction in turbulent kinetic energy contours in the wake of the modified turbine, particularly in the Y-Z plane, compared to the baseline configuration. Full article

Accurate water demand prediction is essential for optimizing the daily operations of water treatment plants and pumping stations. To achieve accurate prediction of water demand for university campuses, this study utilizes real hourly water consumption data collected over 380 observation days from a water treatment plant located on a university campus in Zhenjiang, Jiangsu Province. Based on periodicity analysis of the original data through Fast Fourier Transform (FFT) and autocorrelation coefficients, the data were preprocessed and aggregated into two-hour intervals. The processed water consumption data, along with temporal information (month, day of the week, date, and hour) and weather conditions (daily average wind speed, maximum and minimum temperature), were used as model inputs. The first 352 days of data were utilized to train the model, followed by 14 days serving as the validation set and the final two weeks as the test set. A hybrid forecasting model for campus water demand was developed by integrating a Back Propagation (BP) neural network with a Long Short-Term Memory (LSTM) neural network. The model’s performance was compared with standalone BP, LSTM, and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Simulation results demonstrate that, compared to other models, the proposed BP–LSTM hybrid model achieves a reduction in Mean Absolute Percentage Error (MAPE) ranging from 4.4% to 15.8%, and a decrease in Root Mean Squared Error (RMSE) between 2.5% and 16.8%. These findings indicate that the BP–LSTM model offers higher prediction accuracy and greater reliability compared to traditional single-model approaches. Full article

Traffic accidents caused by black ice have become a serious public safety concern due to their high fatality rates and the limitations of conventional detection systems under low visibility. Millimeter-wave (mmWave) radar, capable of operating reliably in adverse weather and lighting conditions, offers a promising alternative for road surface monitoring. In this study, six representative road surface conditions—dry, wet, thin-ice, ice, snow, and sludge—were experimentally implemented on asphalt and concrete specimens using a temperature and humidity-controlled chamber. mmWave radar data were repeatedly collected to analyze the temporal variations in reflected signals. The acquired signals were transformed into range-based spectra using Range-Fast Fourier Transform (Range-FFT) and converted into statistical features and graphical representations. These features were used to train and evaluate classification models, including Extreme Gradient Boost (XGBoost), Light Gradient-Boosting Machine (LightGBM), Convolutional Neural Networks (CNN), and Vision Transformer (ViT). While machine learning models performed well under dry and wet conditions, their accuracy declined in hazardous states. Both CNN and ViT demonstrated superior performance across all conditions, with CNN showing consistent stability and ViT exhibiting competitive accuracy with enhanced global pattern-recognition capabilities. Comprehensive robustness evaluation under various noise and blur conditions revealed distinct characteristics of each model architecture. This study demonstrates the feasibility of mmWave radar for reliable road surface condition recognition and suggests potential for improvement through multimodal sensor fusion and time-series analysis. Full article

This study presents an advanced diagnostic methodology for assessing mechanical faults in high-performance jet engines using non-contact laser vibrometry and Fourier-based vi-bration analysis. Focusing on Pratt & Whitney F100-PW-229 engines used in F-16 aircraft, thise research identifies critical measurement locations, including the gearbox, turbine, and compressor supports. High-resolution vibration signals were collected under test bench conditions and processed using fFast Fourier tTransform (FFT) techniques to extract frequency-domain features indicative of rotor imbalances, bearing wear, and structural anomalies. Comparative analysis between nominal and degraded engines confirmed strong correlations between analytical predictions and empirical spectral patterns. Thise study introduces a signal processing framework combining time–frequency analysis with Relief-F-based feature selection, laying the groundwork for future integration with ma-chine learning algorithms. This non-intrusive, efficient diagnostic method supports early fault detection, enhances engine availability, and contributes to the development of a na-tional vibration reference database, especially vital in the absence of OEM-supplied tools. Full article

The aim of this study is to analyze the signal generated in the brain for a specific motor task and to identify the region where it occurs. For this purpose, electroencephalography (EEG) signals were divided into delta, theta, alpha, and beta frequency sub-bands, and feature extraction was performed by looking at the time-frequency characteristics of the signals belonging to the obtained sub-bands. The epoch corresponding to motor imagery or action and the signal source in the brain were determined by power spectral density features. This study focused on a hand open–close motor task as an example. A machine learning structure was used for signal recognition and classification. The highest accuracy of 92.9% was obtained with the neural network in relation to signal recognition and action realization. In addition to the classification framework, this study also incorporated advanced preprocessing and energy analysis techniques. Eye blink artifacts were automatically detected and removed using independent component analysis (ICA), enabling more reliable spectral estimation. Furthermore, a detailed channel-based and sub-band energy analysis was performed using fast Fourier transform (FFT) and power spectral density (PSD) estimation. The results revealed that frontal electrodes, particularly Fp1 and AF7, exhibited dominant energy patterns during both real and imagined motor tasks. Delta band activity was found to be most pronounced during rest with T1 and T2, while higher-frequency bands, especially beta, showed increased activity during motor imagery, indicating cognitive and motor planning processes. Although 30 s epochs were initially used, event-based selection was applied within each epoch to mark short task-related intervals, ensuring methodological consistency with the 2–4 s windows commonly emphasized in the literature. After artifact removal, motor activity typically associated with the C3 region was also observed with greater intensity over the frontal electrode sites Fp1, Fp2, AF7, and AF8, demonstrating hemispheric symmetry. The delta band power was found to be higher than that of other frequency bands across T0, T1, and T2 conditions. However, a marked decrease in delta power was observed from T0 to T1 and T2. In contrast, beta band power increased by approximately 20% from T0 to T2, with a similar pattern also evident in gamma band activity. These changes indicate cognitive and motor planning processes. The novelty of this study lies in identifying the electrode that exhibits the strongest signal characteristics for a specific motor activity among 64-channel EEG recordings and subsequently achieving high-performance classification of the corresponding motor activity. Full article

The compaction quality of soil–rock mixture (SRM) subgrades critically influences infrastructure stability, but conventional settlement difference methods exhibit high spatial sampling bias (error > 15% in heterogeneous zones) and fail to characterize the overall compaction quality. These limitations lead to under-compaction (porosity > 25%) or over-compaction (aggregate fragmentation rate > 40%), highlighting the need for real-time monitoring. This study develops an intelligent compaction system integrating (1) vibration acceleration sensors (PCB 356A16, ±50 g range) for compaction meter value (CMV) acquisition; (2) near-infrared (NIR) moisture meters (NDC CM710E, 1300–2500 nm wavelength) for real-time moisture monitoring (sampling rate 10 Hz); and (3) an embedded edge-computing module (NVIDIA Jetson Nano) for Python-based data fusion (FFT harmonic analysis + moisture correction) with 50 ms processing latency. Field validation on Linlin Expressway shows that the system meets JTG 3430-2020 standards, with the compaction qualification rate reaching 98% (vs. 82% for conventional methods) and 97.6% anomaly detection accuracy. This is the first system integrating NIR moisture correction (R² = 0.96 vs. oven-drying) with CMV harmonic analysis, reducing measurement error by 40% compared to conventional ICT (Bomag ECO Plus). It provides a digital solution for SRM subgrade quality control, enhancing construction efficiency and durability. Full article

This study investigates self-pulsation phenomena in a liquid-centered swirl coaxial injector with a recess length of 4 mm, under varying liquid flow conditions, using numerical simulations. The simulations focused on analyzing spray patterns, pressure oscillations, and dominant frequency characteristics, and the results were compared with previous experimental data. Self-pulsation, observed at liquid flow rates of 60%, 90%, and 100% of nominal values, generated distinctive periodic oscillations in the spray pattern, forming “neck” and “shoulder” breakup structures that resemble a Christmas tree. Surface waves induced by Kelvin-Helmholtz and Rayleigh-Taylor instabilities were identified at the gas-liquid interface, contributing to enhanced atomization and reduced spray breakup length. FFT analysis of the pressure oscillations highlighted a match in trends between simulation and experimental data, although variations in dominant frequency magnitudes arose due to the absence of manifold space in simulations, confining oscillations and slightly elevating dominant frequencies. Regional analysis revealed that interactions between the high-speed gas and liquid film in the recess region drive self-pulsation, leading to amplified pressure oscillations throughout the injector’s internal regions, including the gas annular passage, tangential hole, and gas core. These findings provide insights into the internal flow dynamics of swirl coaxial injectors and inform design optimizations to control instabilities in liquid rocket engines. Full article

This study investigates the electromagnetic and NVH characteristics of an outer-rotor surface-mounted permanent magnet synchronous generator (SPMSG) for wave energy applications, focusing on the effect of rotor eccentricity. To reflect potential fault due to manufacturing or assembly defects, a 0.5 mm rotor eccentricity was introduced in finite element method (FEM) simulations. The torque ripple waveform was analyzed using fast Fourier transform (FFT) to identify dominant harmonic components that generate unbalanced electromagnetic forces and induce structural vibration. These harmonic components were further examined under variable marine operating conditions to evaluate their impact on acoustic radiation and vibration responses. Based on the simulation and analysis results, a design-stage methodology is proposed for predicting vibration and noise by targeting critical harmonic excitations, providing practical insights for marine generator design and improving long-term operational reliability in wave energy systems. Full article

In autonomous railway systems, where there is no driver acting as the primary fault detector, annoying interior noise caused by track defects can go unnoticed for long periods. One of the main contributors to this phenomenon is rail corrugation, a recurring defect that generates vibrations and acoustic emissions, directly affecting passenger comfort and accelerating infrastructure deterioration. This work presents a methodology for the automatic detection of corrugated track sections, based on the mathematical modeling of the spectral content of onboard-recorded acoustic signals. The hypothesis is that these defects produce characteristic peaks in the frequency domain, whose position depends on speed but whose wavelength remains constant. The novelty of the proposed approach lies in the formulation of two functional spectral indices—IIAPD (permissive) and EWISI (restrictive)—that combine power spectral density (PSD) and fast Fourier transform (FFT) analysis over spatial windows, incorporating adaptive frequency bands and dynamic prominence thresholds according to train speed. This enables robust detection without manual intervention or subjective interpretation. The methodology was validated under real operating conditions on a commercially operated metro line and compared with two reference techniques. The results show that the proposed approach achieved up to 19% higher diagnostic accuracy compared to the best-performing reference method, maintaining consistent detection performance across all evaluated speeds. These results demonstrate the robustness and applicability of the method for integration into autonomous trains as an onboard diagnostic system, enabling reliable, continuous monitoring of rail corrugation severity using reproducible mathematical metrics. Full article

This article addresses the monitoring and diagnosis of high-speed spindles (HSM) used in CNC grinding machines, emphasizing the importance of the real-time evaluation of their dynamic behavior during commissioning. Due to the complexity of these dynamic phenomena, especially at high speeds (up to 150,000 RPM), common faults such as bearing wear, imbalance, or misalignment can lead to catastrophic failures and high repair costs. An original method is proposed, based on synchronous envelope vibration analysis (SEVA) using the Hilbert transform, to detect mechanical defects in both low-frequency domains (imbalance, mechanical looseness) and high-frequency domains (bearing faults). The system includes vibration, temperature, and speed sensors, and the experimental protocol involves step-by-step monitoring from 10,000 to 90,000 RPM. Through synchronous FFT analysis and IFFT, critical frequencies and their impacts on machining quality are identified. The method enables the accurate fault diagnosis of new or refurbished spindles under real industrial conditions, reducing downtime and production losses. The method involves both local and remote real-time monitoring and diagnosis using a remote data center protocol. Full article