Search Results (949)

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

A high-speed magnetic pump rated at 7800 r/min was studied. A numerical model was established, and a hydraulic, vibration, and noise testing system was set up to conduct flow simulations, noise, and vibration experiments at different speeds. The results show that increasing speed leads to a higher pressure difference between the pump chamber and the cooling circuit. Meanwhile, the turbulent kinetic energy at the impeller outlet increases. Despite an increase in energy loss, the loss ratio decreases, and overall efficiency improves. The internal flow noise collected by the outlet hydrophone mainly comes from Rotor–Stator Interference (RSI), and it can sensitively capture changes in rotational speed. The dominant frequency of the outlet noise agrees well with the blade frequency calculated from the set speed, with a maximum deviation of 0.26%. As the speed increases, the overall sound pressure level (OASPL) at the inlet and outlet and the Root Mean Square (RMS) acceleration values at the outlet and pump body generally increase, while the acceleration at the motor base shows a decreasing trend. The conclusions are helpful for the design and optimization of rotary machinery such as high-speed magnetic pumps. Full article

With rising global energy demand and the gradual depletion of petroleum-based resources, interest in alternative fuels for internal combustion diesel engines (ICDEs) has increased. In ICDEs, firing-related and mechanical excitations may result in adverse vibration and noise responses. This study examines whether incorporating sour cherry pit pyrolysis oil (SCPO) with n-butanol and 2-ethylhexyl nitrate (2-EHN) may reduce vibration and noise under constant-load, steady-state operating conditions compared with neat diesel (D100). For the experimental tests, five fuel types were prepared: one neat diesel fuel and four blended fuels with a constant diesel fraction of 40% and a fixed 2-ethylhexyl nitrate (2-EHN) content of 5%, while the SCPO and n-butanol fractions were varied (D40/SCPO0/B55/2-EHN5, D40/SCPO5/B50/2-EHN5, D40/SCPO10/B45/2-EHN5, and D40/SCPO15/B40/2-EHN5). Experiments were performed using a single-cylinder ICDE at a fixed load of 10 Nm under steady-state conditions at engine speeds of 1500, 1800, 2400, 3000, and 3600 rpm. For each operating condition, vibration and noise data were recorded over a 10.4 s window. Experimental findings indicate that D40/SCPO10/B45/2-EHN5 yielded the lowest mean overall RMS vibration, with a 37.5% reduction relative to neat diesel (D100), and the lowest equivalent sound level (LAeq) among the tested fuels. Under the investigated steady-state constant-load conditions, the D40/SCPO10/B45/2-EHN5 fuel blend demonstrates the potential to achieve lower measured vibration and noise levels than neat diesel. Full article

Unlike terrestrial environments, the inhomogeneity distribution of underwater sound speed poses significant challenges for underwater ranging and target localization. In the presence of sound ray bending and sensor node position errors in underwater acoustic sensor networks (UASNs), this paper proposes a joint TOA-AOA deep-sea target localization framework based on sound ray bending correction. From the perspective of information theory and time series analysis, the TOA measurements are time series signals carrying target position information, and the entropy-based analysis quantifies the fundamental limit on localization uncertainty. First, based on the TOA time series measurements and combined with the acoustic propagation characteristics of the deep sea, a sound ray bending correction method is adopted to improve the accuracy of slant range measurement. To enhance target localization accuracy, this paper proposes a two-step WLS closed-form solution based on TOA-AOA. To further reduce localization bias, a maximum likelihood estimation (MLE) method based on the Gauss-Newton is also derived. Subsequently, the paper derives and analyzes the Cramér-Rao lower bound (CRLB) for target localization, proving theoretically that jointly using TOA-AOA can improve localization accuracy. Simulations verify the performance of the proposed methods. The slant range estimation method based on sound ray bending correction effectively improves range measurement accuracy. The proposed closed-form solution enhances target localization accuracy, achieving the CRLB accuracy. The Gauss-Newton MLE solution can attain the CRLB accuracy under certain localization geometries and further reduces localization bias. Full article

This study proposes a feature-enhanced diffusion model based on wavelet transform and Mamba to address the issues of low audio realism, inadequate text relevance, and slow inference speed in text-guided sound effect generation. A wavelet transform-based downsampling module is designed to mitigate the loss of high-frequency feature information during the downsampling process of the diffusion model, thereby enhancing the realism of the generated audio. A multi-scale feature extraction and fusion method is employed to capture both local and global acoustic information, while the channel attention mechanism further strengthens the model’s focus on text-relevant key features. Additionally, an optimization method based on Mamba and adaptive weight adjustment is proposed, which takes advantage of Mamba’s efficient information processing mechanism and learnable parameters to optimize skip connections, improving model training and inference efficiency without adding substantial computational cost. Experiments show that the model achieves FAD and KL scores of 1.608 and 1.609, respectively, reflecting improvements of 33.8% and 26.1% compared to the baseline model. Full article

A proposed study based on an artificial neural network (ANN) model will be used to predict microhardness (VHN) and tensile strength (TS) of Friction Stir Additive Manufacturing (FSAM) of AA8090 alloy. The process parameters taken into consideration were rotational speed (1000, 1500, 2000 rpm), traverse speed (45, 65, 85 mm/min) and tilt angle (0°, 1°, 2°). We performed 90 physical experiments (74 + 7 + 6 + 3), in which 74 experiments were generated with the help of the Central Composite Design of ANN modeling, seven independent experiments were used to validate the results, six repeat experiments were taken, and three mid-level interpolation experiments were performed. Out of 74 modeling runs, 60 samples were trained, 14 were internally tested, and seven separate modeling runs were exclusively tested externally. An ANN model was created based on the Adam optimizer, where the loss was taken to be Mean Squared Error (MSE). The level of model robustness was assessed employing 5-fold cross-validation and grouped validation (LOPCO, LOFLO-RPM, and LOFLO-TA). Under 5-fold cross-validation, the ANN had mean R² values equal to 0.940 (VHN), 0.920 (TS). In normalized training, the model achieves MAE = 0.26 and R² = 0.97, whereas testing in physical units has developed MAE values of 1.0 and 2.0, respectively (VHN and TS). These results correspond with the high predictive ability and generalization of the ANN model, as indicated by the uniform performance of the ANN model on training, cross-validation, internal testing, and independent validation. The importance analysis of features revealed that rotational speed was the most significant factor that influenced the tensile strength and microhardness. The constructed ANN model is a credible and sound system for optimizing and replicating processes from other friction-stir processing methods on AA8090 alloy. Full article

This study addresses the problem of improving the efficiency of fine grinding of bulk materials in a spring-rotor mill. The objective is to determine technologically sound operating parameters based on mathematical modeling, design of experiments, and multi-objective optimization. The methodology relies on a full-factorial experimental design according to the Hartley plan, with five control factors: rotor rotational speed, material loading ratio, overlap of the working zones, grinding chamber clearance, and grinding duration. The analyzed responses include grinding fineness, throughput, power consumption, specific energy consumption, and specific metal intensity. Based on experimental data, adequate second-order polynomial regression models were obtained for all response variables using the least-squares method. Statistical analysis showed that grinding time and rotational speed had the most significant influence on the process. Multi-objective optimization using the weighted-sum method enabled the identification of optimal operating conditions that balance product quality, throughput, and energy consumption. Verification experiments confirmed the adequacy of the developed models. Practical implementation of the optimized regimes increases throughput by 15–20% while simultaneously reducing energy consumption by 8–12% compared with empirically selected operating conditions. The proposed models and recommendations provide a quantitative basis for tuning and controlling grinding equipment in processing industries. Full article

Ultrasonic testing is a prevalent method for non-destructive evaluation of railway rails; however, conventional Time-of-Flight (ToF) approaches applied in practical dry-coupled inspections often rely on simplified assumptions regarding wave propagation velocity and neglect complex waveform characteristics. This paper presents a robust depth estimation framework for surface-breaking cracks that enhances sizing accuracy through effective velocity calibration and Hilbert envelope extraction. Unlike standard methods that assume the free-space speed of sound in air (343 m/s) for wave propagation within the air-filled gap of a surface-breaking crack, we propose an effective velocity model derived from in situ calibration to account for the boundary layer viscosity and thermal conduction effects within narrow crack geometries. The signal processing chain incorporates spectral analysis, band-pass filtering, and Hilbert Transform-based envelope detection to mitigate noise and resolve phase ambiguities. Experimental validation on steel specimens with controlled defects (0.2–10.0 mm) demonstrates that the proposed method achieves an exceptional linear correlation (R² ≈ 0.9976). The calibrated effective velocity was determined to be 289.3 m/s, approximately 15.6% lower than the speed of sound in air, confirming the significant influence of confinement effects. Furthermore, excitation parameters were optimized, identifying that high-voltage excitation (≥110 V) and a tuned pulse width (≈150 ns) are critical for maximizing the signal-to-noise ratio. The results confirm that combining physical model calibration with advanced signal analysis significantly reduces systematic errors, paving the way for portable, high-precision rail inspection systems. Full article

Real-time lithology identification while drilling is widely applied in oil and gas exploration, development drilling, geo-steering, unconventional resource extraction, well logging, and environmental monitoring, enhancing efficiency and accuracy in subsurface operations. This study investigates the frequency characteristics of rock-drilling sounds generated during drilling operations and explores their potential for real-time lithology identification. Experiments were conducted using 8 mm and 14 mm drill bits at both high and low rotational speeds on four types of rock samples: sandstone, limestone, granite, and shaly sandstone. Sound signals were recorded both within the rock and in air using high-fidelity sensors. The results reveal distinct frequency patterns for each rock type, with sandstone exhibiting dominant low-frequency energy, limestone and granite showing broader frequency bands with strong high-frequency components, and shaly sandstone displaying a mix of low- and high-frequency energy. Quadratic polynomial regression models between the Vp or Vs and the peak frequencies of the four distinct rock samples are built, and the corresponding coefficients of determination are 0.9878 and 0.9799. The study also demonstrates that drilling parameters, such as drill bit diameter and revolutions per minute (RPM), significantly influence the frequency distribution of rock-drilling sounds, with larger drill bits and higher RPMs producing broader frequency bands and stronger high-frequency energy. Comparisons between in-rock and in-air recordings show that the latter captures richer high-frequency information, though the overall trends remain consistent. These findings provide an experimental foundation for using rock-breaking sounds as a potential tool for lithology identification during drilling operations. The study highlights the importance of considering rock heterogeneity and drilling conditions when interpreting acoustic data and suggests future work to validate the method in field conditions and integrate advanced data processing techniques. Full article

Background/Objectives: Whispering has frequently been recommended to patients in order to avoid vocal overuse, particularly in those with organic dysphonia. However, there is controversy as to whether whispering may negatively affect vocal function by promoting malregulatory phonatory patterns. The aim of this study was to investigate whether a standardized short-term whisper loading task induces measurable changes in vocal function and vocal fold vibration characteristics in patients with organic dysphonia. Methods: Eight patients with clinically diagnosed vocal fold mass lesions scheduled for phonosurgery were examined before and immediately after a 10 min forced whisper loading task. Vocal function was assessed using the Dysphonia Severity Index. Vocal fold vibration and phonatory characteristics were analyzed using transnasal high-speed videolaryngoscopy, electroglottography, and acoustic measures. Pre–post differences were evaluated using non-parametric statistical testing with Bonferroni correction. Results: All participants completed the whisper loading task, although most were unable to consistently reach the target sound pressure level. No statistically significant pre–post differences were found in DSI, acoustic measures, perturbation parameters, or glottal vibration indices derived from high-speed recordings and electroglottography. Pathological vibration patterns related to the underlying mass lesions were present but did not show systematic changes following whisper loading. Conclusions: In patients with organic dysphonia, the short-term forced whisper loading task did not result in statistically significant alterations of vocal function or vocal fold vibration patterns. These findings suggest that short-term whispering does not acutely exacerbate biomechanical or vibratory impairments in organic vocal fold pathology. Full article

Subtle vibrations induced in everyday objects by ambient sound, especially speech, carry rich acoustic cues that can potentially be transformed into meaningful text, with potential implications for monitoring and security-related scenarios. This paper presents a Vision-based Subtitle Generator (VSG). This is the first attempt to recover text directly from high-speed videos of sound-induced object vibrations using a generative approach. To this end, VSG introduces a phase-based motion estimation (PME) technique that treats each pixel as an “independent microphone”, and extracts thousands of pseudo-acoustic signals from a single video. Meanwhile, the pretrained Hidden-unit Bidirectional Encoder Representations from Transformers (HuBERT) serves as the encoder of the proposed VSG-Transformer architecture, effectively transferring large-scale acoustic representation knowledge to the vibration-to-text task. These strategies significantly reduce reliance on large volumes of video data. Experimentally, text was generated from vibrations induced in a bag of chips by AISHELL-1 audio samples. Two VSG-Transformer variants with different parameter scales (Base and Large) achieved character error rates of 13.7 and 12.5%, respectively, demonstrating the remarkable effectiveness of the proposed generative approach. Furthermore, experiments using signal upsampling techniques show that the VSG-Transformer maintains effective performance when operating on videos with limited temporal sampling, indicating robustness to lower sampling rates. Full article

Laser metal deposition operates on the principle of layer-by-layer material addition, wherein each layer is formed by overlapping individual single tracks. Consequently, clads formed serve as the fundamental building blocks for this technology. Their quality directly affects the overall build quality, particularly the geometric characteristics, which are also critical to process productivity. In the present work, geometric characteristics of TiC/Ti6Al4V single tracks fabricated via laser metal deposition are optimised. An artificial neural network model was developed to predict the clad width, height, and dilution using processing parameters, laser power, scan speed, and powder feed rate, as model inputs. The Particle Swarm Optimisation algorithm was employed for hyperparameter selection. The hyperparameter-optimised model achieved a mean squared error of 0.00183 and an R² score of 0.979 during training, and a mean squared error of 0.00709 and an R² score of 0.887 during testing. Although the small discrepancy between training and testing metrics suggests slight overfitting, likely due to the size of the dataset, the model achieved a mean absolute percentage error of less than 10% during testing. Subsequently, process plots generated by the model predictions were used to identify suitable parameters, and a processing map was developed to highlight the window that achieves suitable dilution (14–24%), defect-free sound bonding, and thick and dense clads. Full article

Urban traffic noise poses escalating environmental challenges in rapidly urbanizing regions with high-density buildings, yet systematic investigations into its spatiotemporal characteristics remain relatively scarce. This study addresses this research gap via the synchronized on-site monitoring of traffic noise and traffic flow on a representative arterial road in Guangzhou, China. The analysis reveals that nighttime equivalent continuous A-weighted sound levels (L_Aeq) are 3.0–4.0 dB(A) higher than those during the congested daytime peak, a phenomenon primarily driven by higher vehicle speeds under nighttime free-flow traffic conditions. The spatial analysis uncovers complex three-dimensional noise propagation dynamics specific to urban street canyons. Vertical profiling demonstrates a counterintuitive pattern where noise levels do not attenuate with building height, and upper floors experience marginally higher noise exposure than the ground floor, which is attributed to the canyon effect, where multiple sound wave reflections offset the natural distance attenuation. A validated three-dimensional computational model was further employed to evaluate the efficacy of noise mitigation strategies, showing that an integrated intervention combining porous asphalt pavement and acoustic barriers achieves a maximum noise attenuation of 19.9 dB(A) at ground-level receptors. This significant reduction stems from a synergistic effect: porous asphalt reduces noise at the source on a global scale, while acoustic barriers provide localized shielding for the lower floors of adjacent buildings. This research concludes that effective traffic noise control in high-density urban areas requires three-dimensional, multi-faceted strategies addressing noise source characteristics, transmission pathways, and receptor vulnerabilities. Full article

Detecting persistent items that recur across multiple time windows is essential for identifying anomalies in high-speed data streams. However, performing such detection under tight memory constraints and high-speed data streams remains a challenge. Existing approaches often suffer from severe hash collisions because they store much redundant information in sketches, which increases hash collisions of persistent items and degrades both accuracy and processing speed. In this paper, we propose TP-Sketch, a novel approximate data structure that efficiently addresses these issues. Instead of recording additional item statistics, TP-Sketch classifies items as promising or unpromising based on a dynamic global threshold; it then protects promising persistent items from eviction while probabilistically replacing unpromising ones. This strategy improves both accuracy and speed. We provide a formal error-bound analysis to establish the theoretical soundness of TP-Sketch. Extensive trace-driven experiments show that TP-Sketch consistently outperforms state-of-the-art methods in both accuracy and throughput across a variety of tests. For example, compared with P-Sketch, TP-Sketch improves the average F1-score by $16.27\%$ and the average throughput by $113.21\%$ on the MAWI 1 dataset. Overall, TP-Sketch achieves the best accuracy and throughput among state-of-the-art algorithms. Full article

The work presents an algorithm for early detection of breast microlesions using a high resolution three-dimensional ultrasound imaging system. The system employs a 1 × 512 ring transducer array and a triaxial displacement platform with an accuracy of 0.1 mm, achieving high-density acquisition of three-dimensional volumetric data through fixed-step scanning. To improve imaging quality, an adaptive beamforming algorithm incorporating optimal sound speed estimation is proposed, effectively compensating for phase distortion caused by sound speed heterogeneity within tissues and improving spatial coherence and imaging resolution. The three-dimensional volumetric data is visualized using volume rendering to achieve high-fidelity three-dimensional ultrasound image reconstruction. The in vitro experimental results demonstrate that the proposed algorithm improves the system’s spatial resolution to 0.5 mm, with a linear measurement accuracy of 2.1%. A preliminary clinical feasibility case study comparing breast image reconstruction with MRI imaging results shows a Dice similarity coefficient of 0.87 for the lesion region, high anatomical structure reconstruction accuracy, and good spatial consistency. These results demonstrate preliminary clinical feasibility for early detection of breast microlesions. Full article