Search Results (3,078)

In the field of multi-robot cooperative localization and task planning, traditional filtering algorithms encounter synchronization and consistency issues during multi-source data fusion. These challenges result in cumulative localization errors and inefficient information sharing, which limits the system’s collaborative capabilities and control accuracy. To overcome these limitations, a distributed cooperative navigation strategy is introduced. Initially, a Distributed Adaptive Extended Kalman Filter (DAEKF) is implemented, which adaptively adjusts the noise covariance matrix to effectively manage nonlinearities and multi-source noise conditions. Subsequently, a Distributed Model Predictive Control (DMPC) framework is introduced. This framework predicts and optimizes each robot’s kinematic model, thereby improving the system’s collaborative operations and dynamic decision-making capabilities. Finally, the efficacy of this strategy is confirmed through detailed simulations and robotic experiments. The simulation results for cooperative localization demonstrate that DAEKF outperforms Kalman Filter (KF) and Extended Kalman Filter (EKF) in terms of localization accuracy. In the straight-line path-tracking experiments, DAEKF effectively reduced both lateral and heading errors for both robots. For Robot 1, DAEKF reduced the lateral error Root Mean Squared Error (RMSE) by 68.87%, 27.80%, and 25.76%, compared to No Filtering, KF, and EKF. In heading error, DAEKF reduced the RMSE by 52.29%, 41.89%, and 36.47%. For Robot 2, DAEKF reduced the lateral error RMSE by 51.30%, 22.88%, and 11.60%, compared to No Filtering, KF, and EKF. In heading error, DAEKF reduced the RMSE by 39.55%, 37.15%, and 26.00%. In the curved path-tracking experiments, both robots demonstrated high trajectory conformity while traveling along a predefined path combining straight-line and circular arc segments, with lateral errors in the straight-line segments all below 0.05 m. The strategy proposed in this study significantly enhanced the precision and stability of multi-robot collaborative navigation, demonstrating strong practicality and scalability. Full article

Fluid dynamic noise produced by eddy disturbances and friction along pipe walls poses a significant challenge in natural gas transmission and distribution stations. (K)TS control valves are widely used in natural gas transmission and distribution stations across Southwest China and are among the primary sources of noise in these facilities. In this study, a 3D geometric model of the (K)TS valve was developed, and the gas flow characteristics were simulated to analyze the gas flow field and sound field within the valve under varying pipeline flow velocities, outlet pressures, and valve openings. The results demonstrate that accurate calculations of the 3D valve model can be achieved with a grid cell size of 3.6 mm and a boundary layer set to 3. The noise-generating regions of the valve are concentrated around the throttle port, valve chamber, and valve inlet. The primary factors contributing to the aerodynamic noise include high gas flow velocity gradients, intense turbulence, rapid turbulent energy dissipation, and vortex formation and shedding within the valve. An increase in inlet flow velocity intensifies turbulence and energy dissipation inside the valve, while valve opening primarily influences the size of vortex rings in the valve chamber and throttle outlet. In contrast, outlet pressure exerts a relatively weak effect on the flow field characteristics within the valve. Under varying operating conditions, the noise directivity distribution remains consistent, exhibiting symmetrical patterns along the central axis of the flow channel and forming six-leaf or four-leaf flower shapes. As the distance from the monitoring point to the valve increases, noise propagation becomes more concentrated in the vertical direction of the valve. These findings provide a theoretical basis for understanding the mechanisms of aerodynamic noise generation within (K)TS control valves during natural gas transmission, and can also offer guidance for designing noise reduction solutions for valves. Full article

The reduction of noise and vibration is possible with passive, semi-active and active control strategies. Especially where self-adaptive control is required, it is necessary to evaluate the noise reduction potential before the control approach is applied to the real-world problem. This evaluation can be based on a virtual model that contains all relevant sub-systems, transfer paths and coupling effects on the one hand. On the other hand, the complexity of such a model has to be limited to focus on principal findings such as convergence speed, power consumption, and noise reduction potential. The present paper proposes a fully coupled electro-vibro-acoustic model for the evaluation of self-adaptive control strategies. This model consists of discrete electrical and mechanical networks that are applied to model the electro-acoustic behavior of noise and anti-noise sources. The acoustic field inside a duct, terminated by these electro-acoustic sources, is described by finite elements. The resulting multi-physical model is capable of describing all relevant coupling effects and enables an efficient evaluation of different control strategies such as the local control of sound pressure or active control of acoustic absorption. It is designed as a benchmark model for the benefit of the scientific community. Full article

Distributed Acoustic Sensing (DAS) offers numerous advantages, including resistance to electromagnetic interference, long-range dynamic monitoring, dense spatial sensing, and low deployment costs. We initially deployed a water–land DAS system at the Xinfengjiang (XFJ) Reservoir in Guangdong Province, China, to monitor earthquake events. Environmental noise analysis identified three distinct noise zones based on deployment conditions: periodic 18 Hz signals near surface-laid segments, attenuated low-frequency signals (<10 Hz) in the buried terrestrial sections, and elevated noise at transition zones due to water–cable interactions. The system successfully detected hundreds of teleseismic and regional earthquakes, including a Mw7.3 earthquake in Hualien and a local ML0.5 microseismic event. One year later, the DAS system was upgraded with two types of spiral sensor cables at the end of the submarine cable, extending the total length to 5.51 km. The results of detecting both active (transducer) and passive sources (cooperative vessels) highlight the potential of integrating DAS interrogators with spiral sensor cables for the accurate tracking of underwater moving targets. This field trial demonstrates that DAS technology holds promise for the integrated joint monitoring of underwater acoustics and seismic signals beneath lake or ocean bottoms. Full article

Hydrodynamic noise is induced by hydrodynamic phenomena, such as pressure fluctuations, shear layers, and eddy currents, which have a significant impact on ship performance, pumping equipment efficiency, detection accuracy, and the living environment of marine organisms. Specifically, hydrodynamic noise increases fluid resistance around the hull, reduces speed and fuel efficiency, and affects the stealthiness of military vessels; whereas, in pumping equipment, noise generation is usually accompanied by energy loss and mechanical vibration, resulting in reduced efficiency and accelerated wear and tear of the equipment. Traditional physical experiments, theoretical modeling, and numerical simulation methods occupy a key position in hydrodynamic noise research, but each have their own limitations: physical experiments are limited by experimental conditions, which make it difficult to comprehensively reproduce the characteristics of the complex flow field; theoretical modeling appears to be simplified and idealized to cope with the multiscale noise mechanism; and numerical simulation methods, although accurate, are deficient in the sense that they are computationally expensive and difficult to adapt to complex boundary conditions. In recent years, intelligent algorithms represented by data-driven algorithms and heuristic algorithms have gradually emerged, showing great potential for development in hydrodynamic noise optimization applications. To this end, this paper systematically reviews progress in the application of intelligent algorithms in hydrodynamic noise research, focusing on their advantages in the optimal design of noise sources, noise prediction, and control strategy optimization. Meanwhile, this paper analyzes the problems of data scarcity, computational efficiency, and model interpretability faced in the current research, and looks forward to the possible improvements brought by hybrid methods, including physical information neural networks, in future research directions. It is hoped that this review can provide useful references for theoretical research and practical engineering applications involving hydrodynamic noise, and point the way toward further exploration in related fields. Full article

With the ever-increasing volume of maritime traffic, the risks of ship navigation are becoming more significant, making the use of advanced multi-source perception strategies and AI technologies indispensable for obtaining information about ship navigation status. In this paper, first, the ship tracking system was optimized using the Bi-YOLO network based on the C2f_BiFormer module and the OC-SORT algorithms. Second, to extract the visual trajectory of the target ship without a reference object, an absolute position estimation method based on binocular stereo vision attitude information was proposed. Then, a perception data fusion framework based on ship spatio-temporal trajectory features (ST-TF) was proposed to match GPS-based ship information with corresponding visual target information. Finally, AR technology was integrated to fuse multi-source perceptual information into the real-world navigation view. Experimental results demonstrate that the proposed method achieves a mAP0.5:0.95 of 79.6% under challenging scenarios such as low resolution, noise interference, and low-light conditions. Moreover, in the presence of the nonlinear motion of the own ship, the average relative position error of target ship visual measurements is maintained below 8%, achieving accurate absolute position estimation without reference objects. Compared to existing navigation assistance, the AR-based navigation assistance system, which utilizes ship ST-TF-based perception data fusion mechanism, enhances ship traffic situational awareness and provides reliable decision-making support to further ensure the safety of ship navigation. Full article

When detecting respiratory rate and heart rate in an FMCW radar room, there is a lot of static clutter and white Gaussian noise generated by hardware heat loss in the environment, which makes the separation of respiratory and heartbeat signals poor. At the same time, the harmonic component of the respiratory signal in the frequency domain will affect the estimation of heart rate. To solve the above problems, a spatial blind source estimation method was proposed to accurately estimate respiratory heart rate. Firstly, the weighted principal component analysis (WPCA) algorithm was used to extract the features of the target signal from the IF signal, and then the respiratory heart rate signal was reconstructed according to the different features. Then, the multi-signal classification (MUSIC) algorithm is used to convert the respiration and heartbeat signals into the zero domain to avoid the influence of the respective harmonic components on the detection results. The experimental results showed that the accuracy of respiratory rate detection and heart rate detection was 94.51% and 97.79%, respectively. Compared with the traditional algorithm, the proposed method is stable and has higher detection accuracy. Full article

Noise and vibration signal classification can be applied to fault diagnosis in mechanical and electronic systems such as electric vehicles. Traditional signal classification technology uses signal time and frequency domain characteristics as the identification basis. This study proposes a technique for visualizing sound signals using the Wigner–Ville distribution (WVD) method to extract vibration signal characteristics and artificial neural networks as the signal classification basis. A brushless motor is used as the machinery power source to verify the feasibility of this method to classify different signal vibration characteristics. In this experimental work, six states in various brushless motor revolutions were deliberately designed for measuring vibration signals. The brushless motor vibration signal is imaged using the WVD analysis method to extract the vibration signal characteristics. Through the WVD method, the brushless motor data is converted, and the YOLO (you only look once) deep coiling machine neural method is used to identify and classify the brushless motor WVD images. The Wagener analysis method parameters and recognition rates are discussed, thereby improving accurate motor fault diagnostic capabilities. This research provides a method for fault diagnosis that can be accurately performed without dismantling the brushless motor. The proposed approach can improve the reliability and stability of brushless motor applications. Full article

The evaluation of air pollution is a critical concern due to its potential severe impacts on human health. Currently, vast quantities of data are collected at high frequencies, and researchers must navigate multiannual, multisite datasets trying to identify possible pollutant sources while addressing the presence of noise and sparse missing data. To address this challenge, multivariate data analysis is widely used with an increasing interest in neural networks and deep learning networks along with well-established chemometrics methods and receptor models. Here, we report a combined approach involving the Self-Organizing Map (SOM) algorithm, Hierarchical Clustering Analysis (HCA), and Positive Matrix Factorization (PMF) to disentangle multiannual, multisite data in a single elaboration without previously separating the sites and years. The approach proved to be valid, allowing us to detect the site peculiarities in terms of pollutant sources, the variation in pollutant profiles during years and the outliers, affording a reliable interpretation. Full article

This study presents a numerical methodology for analyzing hydroacoustic noise generation and its propagation in a homogeneous domain using Lighthill’s analogy, the finite volume method, and hybrid-Higdon boundary conditions. The approach consists of three key steps: performing an eddy-resolving Large Eddy Simulation to capture the unsteady fluid dynamics, extracting the turbulent field to compute the acoustic source term via Lighthill’s analogy, and solving a homogeneous wave equation to propagate the noise in an open domain. The methodology is applied to a turbulent plane channel flow, simulating the acoustic field for a fluid with water-like density at a Mach number of 0.1. The results reveal the spatial distribution of the acoustic pressure, highlighting the dominant noise sources and their spectral characteristics. The acoustic domain extends beyond the turbulent region, enabling the study of pressure propagation outside the flow. The findings demonstrate that noise generation is strongly linked to turbulent structures near the walls, with significant acoustic radiation occurring in the low-wavenumber range. This framework provides a powerful tool for modeling noise propagation in marine and industrial applications, offering insights into turbulence-induced sound in underwater environments. Future work could extend the approach to more complex geometries, higher Reynolds numbers, and heterogeneous domains, further advancing its applicability to real-world acoustic challenges. Full article

Diffusion models, as representative models in the field of artificial intelligence, have made significant progress in text-to-image synthesis. However, studies of style transfer using diffusion models typically require a large amount of text to describe semantic content or specific painting attributes, and the style and layout of semantic content in synthesized images are frequently uncertain. To accomplish high-quality fixed content style transfer, this paper adopts text-free guidance and proposes a multi-source, training-free and controllable style transfer method by using single image or video as content input and single or multiple style images as style guidance. To be specific, the proposed method firstly fuses the inversion noise of a content image with that of a single or multiple style images as the initial noise of stylized image sampling process. Then, the proposed method extracts the self-attention mechanism’s query, key, and value vectors from the DDIM inversion process of content and style images and injects them into the stylized image sampling process to improve the color, texture and semantics of stylized images. By setting the hyperparameters involved in the proposed method, the style transfer effect of symmetric style proportion and asymmetric style distribution can be achieved. By comparing with state-of-the-art baselines, the proposed method demonstrates high fidelity and excellent stylized performance, and can be applied to numerous image or video style transfer tasks. Full article

Microwave sources based on ultrastable lasers and optical frequency combs (OFCs) exhibit ultralow phase noise and ultrahigh-frequency stability, which are important for many applications. Herein, we present a microwave source that is phase-locked to an ultrastable continuous-wave laser, with a relative frequency instability of 7 × ${10}^{-16}$ at 1 s. An Er:fiber-based OFC and an optic-to-electronic converter with low residual noise are employed to confer optical frequency stability on the 9.6 GHz microwave signal. Instead of using the normal cascaded Mach–Zehnder interferometer method, we developed a microwave regeneration method for converting optical pulses into microwave signals to further suppress the additional noise in the optic-to-electronic conversion process. The microwave regeneration method employs an optical-to-microwave phase detector based on a fiber-based Sagnac loop to produce the error signal between a 9.6 GHz dielectric resonator oscillator (DRO) and the OFC. The 9.6 GHz microwave (48th harmonic of the comb’s repetition rate) signal with the frequency stability of the ultrastable laser was achieved using a DRO that was phase-locked to the optical comb. Preliminary evaluations showed that the frequency instability of the frequency synthesizer from the optical to the 9.6 GHz microwave signal was approximately 2 × ${10}^{-15}$ at 1 s, the phase noise was $-106$ dBc Hz⁻¹ at 1 Hz, and the timing noise was approximately 9 as Hz^−1/2 (phase noise approx. $-125$ dBc Hz⁻¹). The 9.6 GHz signal from the photonic microwave source exhibited a short-term relative frequency instability of 2.1 × ${10}^{-15}$ at 1 s, which is 1.5 times better than the previous results. Full article

Rapid and accurate leaf area index (LAI) determination is important for monitoring daylily growth, yield estimation, and field management. Because of low estimation accuracy of empirical models based on single-source data, we proposed a machine-learning algorithm combining optical and microwave remote-sensing data as well as the random forest regression (RFR) importance score to select features. A high-precision LAI estimation model for daylilies was constructed by optimizing feature combinations. The RFR importance score screened the top five important features, including vegetation indices land surface water index (LSWI), generalized difference vegetation index (GDVI), normalized difference yellowness index (NDYI), and backscatter coefficients VV and VH. Vegetation index features characterized canopy moisture and the color of daylilies, and the backscatter coefficient reflected dielectric properties and geometric structure. The selected features were sensitive to daylily LAI. The RFR algorithm had good anti-noise performance and strong fitting ability; thus, its accuracy was better than the partial least squares regression and artificial neural network models. Synergistic optical and microwave data more comprehensively reflected the physical and chemical properties of daylilies, making the RFR-VI-BC05 model after feature selection better than the others ( r = 0.711, RMSE = 0.498, and NRMSE = 9.10%). This study expanded methods for estimating daylily LAI by combining optical and radar data, providing technical support for daylily management. Full article

Vibration signals serve as the primary data source for bearing fault diagnosis. However, when collected in complex industrial environments, these signals are often contaminated by noise interference, posing significant challenges to fault feature extraction and diagnostic accuracy. To address these issues, this paper proposes a novel bearing fault diagnosis network architecture: the Multi-Resolution Fusion Selection Network (MR-FuSN). The MR-FuSN effectively extracts domain-invariant features from input data through multi-resolution feature extraction and incorporates an adaptive kernel convolution strategy, thereby enhancing its robustness against environmental noise. Experimental results demonstrate that the MR-FuSN achieves outstanding performance in noisy environments with signal-to-noise ratios (SNRs) ranging from −5 dB to 10 dB, particularly attaining a diagnostic accuracy of 99.97% under 0 dB conditions. This study provides technical support for practical fault diagnosis applications. Full article

The effective monitoring of methane (CH₄) point sources is important for climate change research. Satellite-based observations have demonstrated significant potential for emission estimation. In this study, the methane plumes with different emission rates are modelled and pseudo-observations with diverse spatial resolution, spectral resolution, and signal-to-noise ratios (SNR) are simulated by the radiative transfer model. The iterative maximum a posteriori–differential optical absorption spectroscopy (IMAP-DOAS) algorithm is applied to retrieve the column-averaged methane dry air mole fraction (XCH₄), a three-dimensional matrix of estimated plume emission rates is then constructed. The results indicate that an optimal plume estimation requires high spatial and spectral resolution alongside an adequate SNR. While a spatial resolution degradation within 120 m has little impact on quantification, a high spatial resolution is important for detecting low-emission plumes. Additionally, a fine spectral resolution (<5 nm) is more beneficial than a higher SNR for precise plume retrieval. Scientific SNR settings can also help to accurately quantify methane plumes, but there is no need to pursue an overly extreme SNR. Finally, miniaturized spectroscopic systems, such as dispersive spectrometers or Fabry–Pérot interferometers, meet current detection needs, offering a faster and resource-efficient deployment pathway. The results can provide a reference for the development of current detection instruments for methane plumes. Full article