Search Results (3,331)

Operational modal analysis (OMA) is essential for long-term health monitoring of offshore wind turbines (OWTs), helping identifying changes in structural dynamic characteristics. OMA has been applied under parked or idle states for OWTs, assuming a linear and time-invariant dynamic system subjected to white noise excitations. The impact of complex operating environmental conditions on structural modal identification therefore requires systematic investigation. This paper studies the applicability of OMA based on covariance-driven stochastic subspace identification (SSI-COV) under various non-white noise excitations, using a DTU 10 MW jacket OWT model as a basis for a case study. Then, a scaled (1:75) 10 MW jacket OWT model test is used for the verification. For pure wave conditions, it is found that accurate identification for the first and second FA/SS modes can be achieved with significant wave energy. Under pure wind excitations, the unsteady servo control behavior leads to significant identification errors. The combined wind and wave actions further complicate the picture, leading to more scattered identification errors. The SSI-COV based modal identification method is suggested to be reliably applied for wind speeds larger than the rated speed and with sufficient wave energy. In addition, this method is found to perform better with larger misalignment of wind and wave directions. This study provides valuable insights in relation to the engineering applications of in situ modal identification techniques under operating conditions in real OWT projects. Full article

Water level forecasting has significant impacts on transportation, agriculture, and flood control measures. Accurate water level values can enhance the safety and efficiency of water conservancy hub operation scheduling, reduce flood risks, and are essential for ensuring sustainable regional development. Addressing the nonlinearity and non-stationarity characteristics of gate-front water level sequences, this paper introduces a gate-front water level forecasting method based on a GRU–TCN–Transformer coupled model and permutation entropy (PE) algorithm. Firstly, an analysis method combining Singular Spectrum Analysis (SSA) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is used to separate the original water level data into different frequency modal components. The PE algorithm subsequently divides each modal component into sequences of high and low frequencies. The GRU model is applied to predict the high-frequency sequence part, while the TCN–Transformer combination model is used for the low-frequency sequence part. The forecasting from both models are combined to obtain the final water level forecasting value. Multiple evaluation metrics are used to assess the forecasting performance. The findings indicate that the combined GRU–TCN–Transformer model achieves a Mean Absolute Error (MAE) of 0.0154, a Root Mean Square Error (RMSE) of 0.0205, and a Coefficient of Determination (R²) of 0.8076. These metrics indicate that the model outperforms machine learning Support Vector Machine (SVM) models, GRU models, Transformer models, and TCN–Transformer combination models in forecasting performance. The forecasting results have high credibility. This model provides a new reference for improving the accuracy of gate-front water level forecasting and offers significant insights for water resource management and flood prevention, demonstrating promising application prospects. Full article

The proliferation of low-cost, small radar cross-section UAVs (unmanned aerial vehicles) necessitates innovative solutions for countering them. Since these UAVs typically operate with a radio control link, a promising defense technique involves passive scanning of the radio frequency (RF) spectrum to detect UAV control signals. This approach is enhanced when integrated with machine-learning (ML) and deep-learning (DL) methods. Currently, this field is actively researched, with various studies proposing different ML/DL architectures competing for optimal accuracy. However, there is a notable gap regarding robustness, which refers to a UAV detector’s ability to maintain high accuracy across diverse scenarios, rather than excelling in just one specific test scenario and failing in others. This aspect is critical, as inaccuracies in UAV detection could lead to severe consequences. In this work, we introduce a new dataset specifically designed to test for robustness. Instead of the existing approach of extracting the test data from the same pool as the training data, we allowed for multiple categories of test data based on channel conditions. Utilizing existing UAV detectors, we found that although coefficient classifiers have outperformed CNNs in previous works, our findings indicate that image classifiers exhibit approximately 40% greater robustness than coefficient classifiers under low signal-to-noise ratio (SNR) conditions. Specifically, the CNN classifier demonstrated sustained accuracy in various RF channel conditions not included in the training set, whereas the coefficient classifier exhibited partial or complete failure depending on channel characteristics. Full article

For the Battery Management System (BMS) to manage and control the battery, State of Charge (SOC) is an important battery performance indicator. In order to identify the parameters of the LiFePO₄ battery, this paper employs the forgetting factor recursive least squares (FFRLS) method, which considers the computational volume and model correctness, to determine the parameters of the LiFePO₄ battery. On this basis, the two resistor-capacitor equivalent circuit model is selected for estimating the SOC of the Li-ion battery by combining the extended Kalman filter (EKF) with the Sage–Husa adaptive algorithm. The positivity is improved by modifying the system noise estimation matrix. The paper concludes with a MATLAB 2016B simulation, which serves to validate the SOC estimation algorithm. The results demonstrate that, in comparison to the conventional EKF, the enhanced EKF exhibits superior estimation precision and resilience to interference, along with enhanced convergence during the estimation process. Full article

Space-based gravitational wave (GW) detection employs the Michelson interferometry principle to construct ultra-long baseline laser interferometers in space for detecting GW signals with a frequency band of 10⁻⁴–1 Hz. The spaceborne telescope, as a core component directly integrated into the laser link, comes in various configurations, with the off-axis four-mirror design being the most prevalent. In this paper, we present a high-performance design based on this configuration, which exhibits a stable structure, ultra-low wavefront aberration, and high-level stray light suppression capabilities, effectively eliminating background noise. Also, a scientifically justified positioning of the entrance and exit pupils has been implemented, thereby paving adequate spatial provision for the integration of subsequent optical systems. The final design realizes a wavefront error of less than λ/500 in the science field of view, and after tolerance allocation and Monte Carlo analysis, a wavefront error of less than λ/30 can be achieved with a probability of 92%. The chief ray spot diagram dimensions are significantly small, indicating excellent control of pupil aberrations. Additionally, the tilt-to-length (TTL) noise and stray light meet the stringent requirements for space-based gravitational wave detection. The refined design presented in this paper proves to be a more fitting candidate for GW detection projects, offering more accurate and rational guidance. Full article

Knock detection is critical for maintaining engine performance and preventing damage in spark-ignition engines. This study explores the use of ion current and knock indicators derived from a vibration sensor ($K{I}_v$) and ion current ($K{I}_i$) to improve knock detection accuracy. Traditional threshold-based methods rely on $K{I}_v$, but they are susceptible to mechanical noise and cylinder variations. In this work, we applied both logistic regression and neural networks, including fully connected (FCNN) and convolutional neural networks (CNN), to classify knock events based on these indicators. The CNN models used ion current as the primary input, with an extended version incorporating both $K{I}_v$ and $K{I}_i$ into the fully connected layers. The models were evaluated using area under the curve (AUC) as the primary performance metric. The results show that the CNN model with additional inputs outperformed the other models, achieving a better and more consistent performance across cylinders. The dual-input logistic regression and CNN models demonstrated reduced cylinder-to-cylinder variation in classification performance, providing a more consistent knock detection accuracy across all cylinders. These findings suggest that combining ion current and knock indicators enhances knock detection reliability, offering a robust solution for real-time applications in engine control systems. Full article

As a key pressure control component of a hydraulic system, the noise of the direct-acting pressure reducing valve affects the working state of the system directly. However, the existing pressure reducing valves generally have the problem of excessive pure noise. In order to solve this problem, this study explored various structural combinations with the aim of improving the noise level of a direct-acting pressure reducing valve. Firstly, the flow field model of the direct-acting pressure reducing valve was established by using FEA (Finite Element Analysis), and the relationship between the flow field state and noise state was demonstrated through CFD (Computational Fluid Dynamics) simulation. Secondly, the position, number, and diameter of the oil holes on the valve spool were comprehensively analyzed, and the sound field analysis using LMS Virtual Lab was carried out. Finally, a prototype of the pressure reducing valve was manufactured, and the noise level before and after improvement was compared. The results showed that the effective sound pressure after improvement was reduced by 6.1% compared with that before at 50% of the opening, which verified the precision of the simulation model. The research results could provide a guideline for the design and improvement of direct-acting pressure reducing valves. Full article

Road noise significantly impacts how customers perceive vehicle noise, especially in electric vehicles, where it becomes more noticeable with the lack of the masking effect of the internal combustion engine. In this study, a novel sound quality (SQ) metric to capture the perception of road noise was established with the help of both objective measurements and subjective evaluations on six different vehicles under smooth and rough road conditions. A jury of 50 individuals participated in subjective evaluations in controlled settings, experiencing road noise on six vehicles under both smooth and rough conditions. The same vehicles were also objectively measured in these conditions. Using subjective responses and objective measurements, this study identified key sound quality parameters influencing perception. These parameters were used to develop a new regression model predicting customer perception of road noise, considering both aspects of comfort and satisfaction to follow as a key indicator for road noise, particularly in electric vehicles. While an R² of 0.312 was obtained with SPL, R² of 0.972 and 0.999 were obtained with the new comfort and satisfaction metrics, respectively. The effectiveness of the newly created SQ metrics was further validated across various vehicles. Full article

This paper addresses the challenge of docking an Autonomous Underwater Vehicle (AUV) under realistic conditions. Traditional model-based controllers are often constrained by the complexity and variability of the ocean environment. To overcome these limitations, we propose a Deep Reinforcement Learning (DRL) approach to manage the homing and docking maneuver. First, we define the proposed docking task in terms of its observations, actions, and reward function, aiming to bridge the gap between theoretical DRL research and docking algorithms tested on real vehicles. Additionally, we introduce a novel observation space that combines raw noisy observations with filtered data obtained using an Extended Kalman Filter (EKF). We demonstrate the effectiveness of this approach through simulations with various DRL algorithms, showing that the proposed observations can produce stable policies in fewer learning steps, outperforming not only traditional control methods but also policies obtained by the same DRL algorithms in noise-free environments. Full article

Noise reduction methods have been proposed for various loud noises. However, in a quiet indoor environment, even small noises often cause discomfort. One of the small noises that causes discomfort is noise with resonant frequencies. Since resonant frequencies are often high frequencies, it is difficult to apply conventional active noise control methods to them. To solve this problem, we focused on auditory masking, a phenomenon in which synthesized sounds increase the audible threshold. We have performed several studies on reducing discomfort based on auditory masking. However, it was difficult for comfortable sound design to be achieved using the previously proposed methods, even though they were able to reduce feelings of discomfort. Here, we focus on a pleasant sound: music. Comfortable sound design is made possible by introducing music theory into the design of masker signals. In this paper, we therefore propose comfortable sound design based on auditory masking with chord progression and melody generation to match the peak frequencies of dental treatment noises. Full article

As machine learning continues to evolve, traditional image steganography techniques find themselves increasingly unable to meet the dual challenge of deceiving both the human visual system and machine learning models. In response, this paper introduces the Visually Robust Image Steganography (VRIS) model, specifically tailored for this dual deception task. The VRIS model elevates visual security through the use of specialized feature extraction and processing methodologies. It meticulously conducts feature-level fusion between secret images and cover images, ensuring a high level of visual similarity between them. To effectively mislead machine learning models, the VRIS model incorporates a sophisticated strategy utilizing random noise factors and discriminators. This involves adding controlled amounts of random Gaussian noise to the encrypted image, thereby enhancing the difficulty for machine learning frameworks to recognize it. Furthermore, the discriminator is trained to discern between the noise-infused encrypted image and the original cover image. Through adversarial training, the discriminator and VRIS model refine each other, successfully deceiving the machine learning systems. Additionally, the VRIS model presents an innovative method for extracting and reconstructing secret images. This approach safeguards secret information from unauthorized access while enabling legitimate users to non-destructively extract and reconstruct images by leveraging multi-scale features from the encrypted image, combined with advanced feature fusion and reconstruction techniques. Experimental results validate the effectiveness of the VRIS model, achieving high PSNR and SSIM scores on the LFW dataset and demonstrating significant deception capabilities against the ResNet50 model on the Mini-ImageNet dataset, with an impressive misclassification rate of 99.24%. Full article

Proton exchange membrane fuel cells (PEMFCs) are currently a relatively mature type of hydrogen energy device due to their high efficiency and low noise compared to traditional power devices. However, there are still challenges that hinder the large-scale application of PEMFCs. One key challenge lies in the gas supply system, which is a complex, coupled nonlinear system. Therefore, an effective control strategy is essential for the efficient and stable operation of the gas control system. This paper aims to provide a comprehensive and systematic overview of the control strategies for PEMFC anode and cathode supply systems based on an analysis of 182 papers. The review covers modern control theories and optimization algorithms, including their design, objectives, performance, applications, and so on. Additionally, the advantages and disadvantages of these control methods are thoroughly evaluated and summarized. Full article

This paper presents a programmable digital filtering unit dedicated to operating with signals from infrared (IR) detection modules. The designed device is quite useful for increasing the signal-to-noise ratio due to the reduction in noise and interference from detector–amplifier circuits or external radiation sources. Moreover, the developed device is flexible due to the possibility of programming the desired filter types and their responses. In the circuit, an advanced field-programmable gate array FPGA chip was used to ensure an adequate number of resources that are necessary to implement an effective filtration process. The proposed circuity was assisted by a 32-bit microcontroller to perform controlling functions and could operate at frequency sampling of up to 40 MSa/s with 16-bit resolution. In addition, in our application, the sampling frequency decimation enabled obtaining relatively narrow passband characteristics also in the low frequency range. The filtered signal was available in real time at the digital-to-analog converter output. In the paper, we showed results of simulations and real measurements of filters implementation in the FPGA device. Moreover, we also presented a practical application of the proposed circuit in cooperation with an InAsSb mid-IR detector module, where its self-noise was effectively reduced. The presented device can be regarded as an attractive alternative to the lock-in technique, artificial intelligence algorithms, or wavelet transform in applications where their use is impossible or problematic. Comparing the presented device with the previous proposal, a higher signal-to-noise ratio improvement and wider bandwidth of operation were obtained. Full article

To meet the demands of laser communication, quantum precision measurement, cold atom technology, and other fields for narrow linewidth and low-noise light sources, an external cavity diode laser (ECDL) operating in the wavelength range around 780 nm was set up with a Fabry–Pérot etalon (F–P) and an interference filter (IF) in the experiment. The interference filter type ECDL (IF–ECDL) with butterfly-style packaging configuration has continuous wavelength tuning within a specified range through precise temperature and current control and has excellent single-mode characteristics. Experimental results indicate that the output power of the IF–ECDL is 14 mW, with a side-mode suppression ratio (SMSR) of 54 dB, a temperature-controlled mode-hop-free tuning range of 527 GHz (1.068 nm), and an output linewidth of 570 Hz. Compared to traditional lasers operating at 780 nm, the IF–ECDL exhibits narrower linewidth, lower noise, and higher spectral purity, and its dimensions are merely 25 × 15 × 8.5 mm³ weighing only 19.8 g, showcasing remarkable miniaturization and lightweight advantages over similar products in current research fields. Full article

This study proposes a qualitative analysis for identifying the best predictors for ensuring passive noise control, aiming to achieve superior acoustic comfort in transportation systems. The study is based on real experimental data, collected through acoustic measurements performed by the authors on materials from six different classes and employs a multidisciplinary approach, including Mann–Whitney U tests, Kruskal–Wallis analysis with Dunn’s post hoc multiple comparisons and multilinear regression. This research presents an analysis and evaluation of how the physical properties of various materials influence acoustic comfort, acoustic absorption class and absorption class performance and proposes quantitative models for material selection to address sustainable strategies in the automotive industry. The results highlight significant differences between material categories in terms of acoustic absorption properties and demonstrate the importance of rigorous material selection in vehicle design to enhance acoustic comfort. Additionally, the research contributes to the development of predictive models that estimate acoustic performance based on the physical properties of materials, providing a basis for optimizing material selection in the design phase. Full article