Search Results (8,290)

During the reentry terminal flight of lifting-body Reusable Launch Vehicles (RLVs) propelled by liquid fuel, the sloshing of liquid propellent presents new features that, if neglected, could lead to adverse flight oscillations or even worse. This paper focuses on liquid sloshing coupled flight dynamics, sloshing effect prediction, and the suppression of adverse flight oscillations. First, a transfer function model for unsteady aerodynamics is improved and applied to describe the sloshing force effect, being included in the rigid–liquid control coupled flight dynamics model. The frequency domain analysis results show that liquid sloshing tends to degrade the closed-loop stability margin of the vehicle and even induce less damped oscillations, which can be predicted through the frequency characteristics with the sloshing force effect included. Furthermore, three suppression control measures to mitigate adverse oscillation are addressed, which include enhancing the trajectory-tracking loop damping, separating the frequencies of the rigid body motion and the liquid sloshing, and especially introducing a compensation loop to counteract the sloshing effect. Simulations demonstrate that all the provided approaches help mitigate the sloshing effect, while the compensation control with sloshing frequency characteristics included works best. Full article

The emergence of radio frequency machine learning has significantly propelled the application of deep learning (DL) methods in automatic modulation classification (AMC). However, under non-cooperative scenarios, the performance of DL-based AMC suffers severe performance degradation due to scarce labeled samples and noise interference. To enhance noise robustness in few-shot AMC, this paper proposes a complex-domain autoencoder-based method where a complex-valued noise reduction network (CNRN) is embedded into the AMC framework, jointly extracting complex-valued and temporal features from noisy signals to achieve signal–noise separation. Our framework executes four sequential operations: high-signal-to-noise-ratio (high-SNR) samples are first isolated from limited raw data via unsupervised classification; rotation and cyclic time-shifting operations then augment the sample space; the CNRN is subsequently trained on augmented data; and final AMC classification is implemented through DL-based classifiers. Experimental validation on RML 2016.10a dataset demonstrates: (1) for −20 dB signals, denoising achieves 20.18 dB SNR improvement with 87.74% mean squared error reduction; (2) across the −20 dB to 18 dB range, denoised signals exhibit accuracy improvements of 21.57% under DL-based classifiers. Physical validation further confirms that the proposed method exhibits enhanced noise robustness, demonstrating its practical utility in real-world scenarios. Full article

Spatial resolution is a critical parameter for optical frequency domain reflectometry (OFDR). Phase-sensitive OFDR (Φ-OFDR) measures strain by detecting phase variations between adjacent sampling points, having the potential to achieve the theoretical limitation of spatial resolution. However, the results of Φ-OFDR suffer from large fluctuations due to multiple types of noise, including coherent fading and system noise. This work presents an OFDR-based strain sensing method that combines phase demodulation with cross-correlation analysis to achieve high spatial resolution. In the phase demodulation, the frequency-shift averaging (FSAV) and rotating vector summation (RVS) algorithms are first employed to suppress coherent fading noise and achieve accurate strain localization. Then the cross-correlation approach with an adaptive window is proposed. Guided by the accurate strain boundary obtained from phase demodulation, the length and position of the cross-correlation window are automatically adjusted to fit for continuous and uniform strain regions. As a result, an accurate and complete strain distribution along the entire fiber is finally obtained. The experimental results show that, within a strain range of 100–700 με, the method achieves a spatial resolution of 0.27 mm for the strain boundary, with a root-mean-square error approaching 0.94%. The processing time reaches approximately 0.035 s, with a demodulation length of 1.6 m. The proposed approach offers precise spatial localization of the strain boundary and stable strain measurement, demonstrating its potential for high-resolution OFDR-based sensing applications. Full article

To address the challenges of strong electromechanical coupling, nonlinear friction, and poor disturbance rejection in semi-direct-drive scraper conveyor systems under complex coal mining conditions, this paper aims to propose a high-performance drive control strategy that balances dynamic response speed with steady-state operational smoothness. First, an integrated electromechanical coupling dynamic model incorporating Permanent Magnet Synchronous Motor (PMSM) vector control and the time-varying meshing stiffness of a two-stage planetary gear train is established. Subsequently, a Sliding Mode Control (SMC) strategy optimized with a saturation boundary layer is designed and compared with traditional Proportional-Integral (PI) control under multiple operating conditions. Time-frequency domain analysis indicates that SMC significantly enhances the dynamic stiffness of the drive system. Under sudden load change conditions, the speed recovery time is shortened by approximately 76%, and the steady-state error is reduced by 37% compared to PI control. Microscopic characteristic evaluation based on FFT and Total Variation (TV) metrics reveals that SMC achieves active disturbance rejection through spectral broadening of the electromagnetic torque. Crucially, the steady-state cumulative control effort of SMC is equivalent to that of PI, implying no additional mechanical stress burden, while the equivalent dynamic transmission force fluctuation in the mechanical chain is reduced by about 3%. The study confirms that the proposed strategy successfully achieves a synergistic optimization of “macroscopic rapid response” and “microscopic smooth operation,” providing a theoretical basis for the high-precision control of heavy-duty underground transmission equipment. Full article

Understanding how the brain adapts to repeated food-related cues provides insight into attentional and motivational mechanisms that influence eating behaviour. Previous studies using event-related potentials (ERPs) have shown that food cues, particularly high-calorie stimuli, elicit sustained neural responses with repeated exposure. The present study extends this line of inquiry by examining the oscillatory dynamics of within-session habituation using time-frequency analysis of electroencephalographic (EEG) data from 24 healthy adult participants. Repeated presentations of the same high-calorie, low-calorie, and non-food images were shown, and changes in power across the delta, theta, alpha, beta, and gamma bands were analysed using cluster-based permutation testing. The results revealed a significant habituation effect for the non-food image within the theta band at frontal scalp electrode clusters between 110–330 ms, characterised by a progressive reduction in power over time. In contrast, both high and low-calorie food cues maintained more stable oscillatory activity, indicating sustained attentional engagement. Participant-level analyses further suggested that changes in attentional engagement followed a graded pattern rather than clear categorical differences across stimulus types. These findings suggest that neural habituation is modulated by stimulus salience, with high-calorie food images resisting adaptation through persistent theta-band synchronisation at frontal scalp electrodes. Integrating these oscillatory results with prior time-domain evidence highlights a multi-stage attentional process: an early sensory filtering phase reflected in parietal ERPs and a sustained regulatory phase indexed by theta-band activity recorded at frontal scalp electrodes. This study provides novel evidence that time-frequency analysis captures complementary aspects of attentional adaptation that are not visible in traditional ERP measures, offering a richer understanding of how the brain maintains attention to appetitive visual stimuli. Full article

The present study extends the previous work which was concerned with the identification of damage in GFRP composite plates by damage detection algorithms such as the Normalized Curvature Damage Factor (NCDF), Strain Energy Difference (SED), and Damage Index (DI), using a novel damage (crack) modeling technique called the ‘Node-Releasing Technique’ (NRT) in Finite Element Analysis (FEA) for modeling and detecting perpendicular and slant partial-depth cracks in GFRP composite beams. This study explores the sensitivity of the damage modeling technique NRT in damage detection for composite beams using the NCDF algorithm, since it was concluded in the previous work that the NCDF performs better compared to the other methods when detecting both perpendicular and slant partial-depth cracks. This study also examines the variations in the Frequency Response Function (FRF) as another novel tool for identifying even small-scale damage. Most prior research in this domain has focused on variations in natural frequency, displacement mode shape, and damping as indicators for detecting and localizing structural damage through various experimental, theoretical, and computational approaches. However, these conventional parameters often lack the sensitivity required to detect small-scale damage and, still, there exists a gap in the use of the node-releasing technique in FEA to model the partial-depth perpendicular and slant crack damage in laminated composite structures, such as beam-like structures. To fill this gap, the present study attempts to use Curvature Mode Shapes (CMS)-based NCDF, obtained from numerical modal analysis, and variations in the Frequency Response Function (FRF), obtained through harmonic analysis, as more sensitive indicators for damage detection in laminated composite beams. FEA simulations are performed using the commercial FEA software package ANSYS 2021 R1 to obtain the first five flexural natural frequencies and the corresponding displacement mode shapes of both the intact and damaged composite beams. The curvature mode shapes are obtained from the displacement mode shapes data using the central difference approximation method to compute the NCDF. Simultaneously, GFRP composite beams were fabricated by the hand lay-up method, and Experimental Modal Analysis (EMA) was employed to substantiate the FE model and the validity of the numerical results. By combining both numerical and experimental methods, we proved that NCDF and FRF are reliable tools to determine and locate structural damage, even at a comparatively small scale. In general, the results indicate that NCDF is a stable and practically applicable parameter to locate cracks in laminated composite beams and provide meaningful information to be used as guidelines in applications of vibration-based structural health monitoring. Full article

Automatic Music Transcription (AMT) plays a fundamental role in Music Information Retrieval (MIR) by converting raw audio signals into symbolic representations such as MIDI or musical scores. Despite advances in deep learning, accurately transcribing piano performances remains challenging due to dense polyphony, wide dynamic range, sustain pedal effects, and harmonic interactions between simultaneous notes. Existing approaches using convolutional and recurrent architectures, or autoregressive models, often fail to capture long-range temporal dependencies and global harmonic structures, while conventional Vision Transformers overlook the anisotropic characteristics of audio spectrograms, leading to harmonic neglect. In this work, we propose SpectTrans, a novel piano transcription framework that integrates a Spectral Gating Network with a multi-head self-attention Transformer to jointly model spectral and temporal dependencies. Latent CNN features are projected into the frequency domain via a Real Fast Fourier Transform, enabling adaptive filtering of overlapping harmonics and suppression of non-stationary noise, while deeper layers capture long-term melodic and chordal relationships. Experimental evaluation on polyphonic piano datasets demonstrates that this architecture produces acoustically coherent representations, improving the robustness and precision of transcription under complex performance conditions. These results suggest that combining frequency-domain refinement with global temporal modeling provides an effective strategy for high-fidelity AMT. Full article

Laser Doppler vibrometer (LDV) has the characteristics of long-distance, non-contact, and high sensitivity, and plays an increasingly important role in industrial, military, and security fields. Remote speech acquisition technology based on LDV has progressed significantly in recent years. However, unlike microphone receivers, LDV-captured signals have severe signal distortion, which affects the quality of the LDV-captured speech. This paper proposes a nested U-network with gated temporal convolution (TCNUNet) to enhance monaural speech based on LDV. Specifically, the network is based on an encoder-decoder structure with skip connections and introduces nested U-Net (NUNet) in the encoder to better reconstruct speech signals. In addition, a temporal convolutional network with a gating mechanism is inserted between the encoder and decoder. The gating mechanism helps to control the information flow, while temporal convolution helps to model the long-range temporal dependencies. In a real-world environment, we designed an LDV monitoring system to collect and enhance voice signals remotely. Different datasets were collected from various target objects to fully validate the performance of the proposed network. Compared with baseline models, the proposed model achieves state-of-the-art performance. Finally, the results of the generalization experiment also indicate that the proposed model has a certain degree of generalization ability for different languages. Full article

A three-dimensional frequency-domain ship-motion solver based on the Rankine source method is extended to predict added resistance in waves. Although middle-field formulations have been used mainly in time-domain Rankine panel methods, a middle-field evaluation is implemented here within a frequency-domain Rankine source framework and its validity is examined, including low-speed conditions where the enforcement of radiation conditions is challenging. To enhance robustness at low forward speeds, a hybrid radiation technique is incorporated. Convergence studies are carried out for the free-surface and radiation-boundary discretization, as well as for the control-surface resolution and the clearance distance, and practical numerical settings for added-resistance computations are established. The approach is first verified for Wigley III hulls by comparing motion RAOs and added resistance with published experimental and numerical results. It is then validated for the blunt KVLCC2 hull at the design speed and at low speeds (0 and 4 knots) against published measurements and calculations. Further validations are conducted for additional hull forms (Wigley I, KCS, S-175, and Series 60). The results indicate that the proposed frequency-domain Rankine source method with middle-field evaluation and hybrid radiation yields consistent predictions of motion responses and added resistance over a range of speeds and hull forms, while retaining computational efficiency. Full article

Digital signal processing plays a central role in intelligent railway communications under high-mobility, strong-multipath, and time-varying-channel conditions. This review surveys representative techniques for multi-carrier modulation, precoding, index modulation, and chaos-inspired physical layer security and highlights their mathematical foundations. Core themes include transform-domain representations typified by time–frequency analysis, linear-algebraic formulations of precoding and equalization, combinatorial structures underlying index mapping and spectral efficiency gains, and nonlinear dynamical systems theory of chaotic encryption. The methods are compared in terms of bit error performance, peak-to-average power ratio, spectral efficiency, computational complexity, and information security, with emphasis on railway-specific deployment constraints. The synergistic application of these methods with intelligent railway transportation systems is expected to enhance the overall performance of railway transportation systems in terms of transmission efficiency, reliability, and security. It provides critical technological support for the efficient and secure operation of next-generation intelligent transportation systems. Full article

This paper examines precise timestamping of LoRaWAN messages (particularly beacons) to enable wide-area synchronization for end devices without GNSS. The need for accuracy demands hardware-level timestamping architectures, possibly using time-domain cross-correlation (matched filtering) against internally generated chirp references. Focusing on Time-of-Arrival (TOA) estimation from raw IQ samples, the authors analyze effects of non-idealities—additive white Gaussian noise (AWGN), Carrier Frequency Offset (CFO), Sampling Phase and Frequency Offset (SPO and SFO, respectively), and radio parameters such as spreading factor (SF) and sampling rate of the baseband signals. A MATLAB (R2020) simulation mimics preamble detection and Start-of-Frame Delimiter (SFD) timestamping while sweeping SF (7, 9, 12), sampling rates (0.25–10 MSa/s), SNR (−20 to +20 dB), and CFO/SFO offsets (−10–10 ppm frequency deviation). Errors are evaluated in terms of mean and dispersion, the latter represented by the P95–P5 range metric. Results show that oversampling not only improves temporal resolution, but sub-microsecond error dispersion can be achieved with high sampling rates in favorable SNR and SF cases. Indeed, SPO and SNR greatly contribute to error dispersion. On the other hand, higher SF values increase correlation robustness at the cost of longer chirps, making SFO a dominant error source; ±10 ppm SFO can induce roughly ±3 μs SFD bias for SF12. CFO largely cancels after up-/down-chirp averaging. As a concluding remark, matched-filter hardware timestamping can ensure sub-μs errors thanks to oversampling but requires SFO compensation for accurate real-world synchronization in practice. Full article

A new lumped-parameter matrix method is proposed to model the decoupled, in-plane longitudinal and transverse free undamped vibrations of a collinear system with fixed ends and formed of two end flexible and prismatic members linked by a middle rigid connector. The method calculates the natural frequencies associated with the system’s three degrees of freedom by solving a linear algebraic characteristic equation related to the dynamic matrix, which is obtained from the system compliance and mass matrices. The linear, small-displacement model characterizes either long or short beams by adequately formulating the compliance and mass matrices. The lumped-parameter model is comprehensively validated by two separate distributed-parameter models, which determine the system’s longitudinal-vibration and long-beam, bending-vibration natural frequencies. Numerical simulations are performed with the lumped-parameter model to identify the sensitivity of the natural frequencies to system parameters variations and model variants. The system’s matrices are also utilized to perform frequency-domain analysis of the three-member system in a displacement/acceleration sensing application. The method can be adapted and expanded to describe more complex configurations with multiple, non-collinear, and non-prismatic members. Full article

Despite the widespread adoption of UAV-based object detection, traditional YOLO architectures are bottlenecked by their reliance on NMS, which complicates deployment on edge devices due to limited support across hardware acceleration platforms. While end-to-end models such as RT-DETR eliminate this bottleneck, they suffer from severe feature degradation for small targets caused by the inherent conflict between deep downsampling and detail preservation. To bridge this gap, we propose a Frequency-Enhanced Real-Time Detection framework specifically designed for UAV perspectives. Unlike standard backbones, our design incorporates a Frequency-Enhanced Multi-Scale Fusion module, which transforms features into the frequency domain to explicitly amplify high-frequency components essential for small object localization. Additionally, a Grouped Multi-Kernel Interaction module is introduced to dynamically capture multi-scale contextual information. Furthermore, we integrate Shape-NWD into the loss computation by introducing shape weight coefficients and scale correlation factors, directing focus toward the intrinsic attributes of bounding boxes to enhance regression accuracy for tiny targets. Experimental results on the VisDrone dataset demonstrate that our method improves the Average Precision by 0.9% and AP₅₀ by 1.1% compared to the baseline, with consistent gains observed on the UAVVaste dataset. Full article

Whole-body vibration (WBV) remains a critical factor influencing ride comfort, driver performance and occupational health in vehicle applications. Despite the widespread use of standardized indicators, assessing WBV exposure and its perceptual implications remains challenging due to the complex interaction between road excitation, vehicle dynamics, seat transmissibility and human biodynamic response. This review provides a comprehensive synthesis of contemporary methods for WBV assessment, emphasizing their theoretical foundations, practical implementation and inherent limitations. The paper examines classical evaluation metrics, including frequency-weighted root mean square acceleration and vibration dose value, alongside complementary approaches such as overall vibration total value, absorbed power and motion sickness indicators. Biodynamic modeling strategies for the human–seat–vehicle system are critically reviewed, highlighting trade-offs between model simplicity and physiological realism. Particular attention is given to road surface representation and excitation modeling, discussing the implications of ISO 8608-based stochastic profiles versus measured, time-domain inputs on WBV assessment outcomes. Simulation frameworks, experimental platforms and driving simulators are reviewed as complementary tools for evaluating vibration exposure and validating predictive models. Emerging methods, including time–frequency analysis and data-driven approaches, are discussed with a focus on interpretability, validation and integration with established standards such as ISO 2631. The review consolidates recent advances in integrated evaluation approaches, including the role of driving simulators and simulation-, hardware- and driver-in-the-loop (SiL/HiL/DiL) frameworks as enabling tools for repeatable testing, objective–subjective comfort correlation and early-stage vibration-control development. By critically examining both established and emerging methodologies, this review aims to support informed selection and interpretation of WBV assessment tools in vehicle design and evaluation. The findings underscore the need for integrated, transparent and application-oriented approaches to advance vibration comfort assessment and guide future research and standardization efforts. Full article

Neutral atomic hydrogen (H I) regulates galaxy growth and quenching, but direct 21 cm measurements remain observationally expensive and affected by selection biases. We develop Bayesian neural networks (BNNs)—a type of neural model that returns both a prediction and an associated uncertainty—to infer the H I mass, ${log}_{10}\left(M_{\mathrm{HI}}\right)$, from widely available optical properties (e.g., stellar mass, apparent magnitudes, and diagnostic colors) and simple structural parameters. For continuity with the photometric gas fraction (PGF) literature, we also report the gas-to-stellar-mass ratio, ${log}_{10}(G/S)$, where explicitly noted. Our dataset is a reproducible cross-match of SDSS DR12, the MPA–JHU value-added catalogs, and the 100% ALFALFA release, resulting in 31,501 galaxies after quality controls. To ensure fair evaluation, we adopt fixed train/validation/test partitions and an additional sky-holdout region to probe domain shift, i.e., how well the model extrapolates to sky regions that were not used for training. We also audit features to avoid information leakage and benchmark the BNNs against deterministic models, including a feed-forward neural network baseline and gradient-boosted trees (GBTs, a standard tree-based ensemble method in machine learning). Performance is assessed using mean absolute error (MAE), root-mean-square error (RMSE), and probabilistic diagnostics such as the negative log-likelihood (NLL, a loss that rewards models that assign high probability to the observed H I masses), reliability diagrams (plots comparing predicted probabilities to observed frequencies), and empirical 68%/95% coverage. The Bayesian models achieve point accuracy comparable to the deterministic baselines while additionally providing calibrated prediction intervals that adapt to stellar mass, surface density, and color. This enables galaxy-by-galaxy uncertainty estimation and prioritization for 21 cm follow-up that explicitly accounts for predicted uncertainties (“risk-aware” target selection). Overall, the results demonstrate that uncertainty-aware machine-learning methods offer a scalable and reproducible route to inferring galactic H I content from widely available optical data. Full article