Search Results (882)

Promoting alternative modes of transportation such as cycling represents a valuable strategy to minimize environmental impacts, as confirmed in the main targets set out by the European Commission. In this regard, in cities throughout the world, there has been a significant increase in the construction of bicycle paths in recent years, requiring effective maintenance strategies to preserve their service levels. The continuous monitoring of road networks is required to ensure the timely scheduling of optimal maintenance activities. This involves regular inspections of the road surface, but there are currently no automated systems for monitoring cycle paths. In this study, an integrated monitoring and assessment system for cycle paths was developed exploiting Raspberry Pi technologies. In more detail, a low-cost Inertial Measurement Unit (IMU), a Global Positioning System (GPS) module, a magnetic Hall Effect sensor, a camera module, and an ultrasonic distance sensor were connected to a Raspberry Pi 4 Model B. The novel system was mounted on a e-bike as a test vehicle to monitor the road conditions of various sections of cycle paths in Rome, characterized by different pavement types and decay levels as detected using the whole-body vibration $a_{wz}$ index (ISO 2631 standard). Repeated testing confirmed the system’s reliability by assigning the same vibration comfort class in 74% of the cases and an adjacent one in 26%, with an average difference of 0.25 m/s², underscoring its stability and reproducibility. Data post-processing was also focused on integrating user comfort perception with image data, and it revealed anomaly detections represented by numerical acceleration spikes. Additionally, data positioning was successfully implemented. Finally, $a_{wz}$ measurements with GPS coordinates and images were incorporated into a Geographic Information System (GIS) to develop a database that supports the efficient and comprehensive management of surface conditions. The proposed system can be considered as a valuable tool to assess the pavement conditions of cycle paths in order to implement preventive maintenance strategies within budget constraints. Full article

The precise real-time detection of polytetrafluoroethylene (PTFE) emulsion rotary vibration sieve levels is critical for improving production efficiency, ensuring product quality, and safeguarding personnel safety. This research presents a deep-learning-oriented video surveillance model for the intelligent level detection of vibrating screens, waste drums, and emulsion outlets, effectively addressing the limitations of traditional methods. With the introduction of TransResNet, which combines Vision Transformer (ViT) with ResNet, we can utilize the advantages of both approaches. ViT has excellent global information capture capability for processing image features, while ResNet excels in local feature extraction. The combined model effectively recognizes level changes in complex backgrounds, enhancing overall detection performance. During model training, synthetic data generation is used to alleviate the marker scarcity problem and generate synthetic images under different liquid level states to further enrich the training dataset, solve the issue of unequal data distribution, and enhance the model’s capacity to generalize. In order to validate the efficacy of our proposed model, we carried out a performance test with real-world data obtained from a material production site. The test results show that the model achieves 96%, 99%, and 99% accuracy at three test points, respectively: the vibrating screen, waste drum, and emulsion. These results not only prove the efficiency of the model but also highlight its significant value in practical applications. Full article

Spaceborne inverse synthetic aperture ladar (ISAL) can achieve high-resolution imaging of satellite targets. However, because the amplitudes of satellite microvibration are comparable to the ladar wavelength, the echoes will contain both space-variant and space-invariant phase errors. These errors will lead to azimuthal image defocus and impede target analysis and identification. In this paper, we establish a phase error estimation model based on satellite vibration characteristics. Based on this model, we propose a vibration phase error compensation algorithm using prior information and adaptive windowing. Compared to conventional algorithms, this algorithm utilizes prior information to improve estimation accuracy while significantly reducing computational complexity. Furthermore, high-accuracy phase function estimation can be achieved through maximum likelihood estimation and adaptive window filtering, thereby enabling the compensation of vibration phase errors. Both simulation and real imaging experiments validate the effectiveness and robustness of the proposed algorithm. Full article

The imaging processes of optoelectronic devices are affected by vibration in the transportation platform, which can cause image shaking and blurring. Nowadays, devices often solve problems of image shaking and blurring using motion rotors. However, there is relatively little research on the influence of optical fixtures themselves under vibration conditions. This article analyzes the influence of sinusoidal vibrations on the MTF of an imaging process, pointing out the randomness of imaging effects under conditions of low-frequency vibration. To address the issue of low-frequency vibration effects, an analysis of the designs, and experimental verification, of a specific optical system mount were conducted to verify the influence of the mount’s own properties on imaging under random vibration conditions, providing a basis for the design of future optical mechanical systems. 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

This paper presents the design of an integrated micro-vibration isolation and pointing platform with a four-leg structure, incorporating pitch and yaw adjustment functions using voice coil motors. The primary objective is to mitigate the impact of spacecraft-generated micro-vibrations on the pointing accuracy and imaging clarity of large space optical payloads while adhering to lightweight requirements. The research methodology encompasses three main phases. Initially, a simplified dynamic model of the integrated platform is established, and dynamic control equations are derived based on the proportional–integral–derivative (PID) control strategy. The effects of centroid deviation and control parameters on the control efficacy are analyzed. Subsequently, a principle prototype of the two-dimensional micro-vibration isolation and pointing platform is designed, detailing the development of the membrane, actuator, legs, and integrated system. Finally, a ground test verification system is implemented under gravity unloading conditions using elastic strings. The experimental results demonstrate the platform’s effective vibration isolation and pointing capabilities, achieving a 23 dB attenuation effect at the fundamental frequency. Furthermore, the PID control algorithm exhibits enhanced isolation performance at low frequencies and facilitates directional tracking of target signals. Full article

Various metal-based nanomaterials have been the focus of research regarding their use in controlling pests and diseases and in improving crop yield and quality. In this study, we synthesized via a solvothermal procedure pegylated zinc-doped ferrite (ZnFer) NPs and characterized their physicochemical properties by X-ray diffraction (XRD), vibrating sample magnetometry (VSM), thermogravimetric analysis (TGA), FT-IR and UV–Vis spectroscopies, as well as transmission electron microscopy (TEM). Subsequently, their impact on tomato photosynthetic efficiency was evaluated by using chlorophyll a fluorescence imaging analysis to estimate the light energy use efficiency of photosystem II (PSII), 30, 60, and 180 min after foliar spray of tomato plants with distilled water (control plants) or 15 mg L⁻¹ and 30 mg L⁻¹ ZnFer NPs. The PSII responses of tomato leaves to foliar spray with ZnFer NPs showed time- and dose-dependent biphasic hormetic responses, characterized by a short-time inhibitory effect by the low dose and stimulatory effect by the high dose, while at a longer exposure period, the reverse phenomenon was recorded by the low and high doses. An inhibitory effect on PSII function was observed after more than ~120 min exposure to both ZnFer NPs concentrations, implying a negative effect on PSII photochemistry. We may conclude that the synthesized ZnFer NPs, despite their ability to induce hormesis of PSII photochemistry, have a negative impact on photosynthetic function. Full article

In industrial settings, machinery components inevitably wear and degrade due to friction between moving parts. To address this, various maintenance strategies, including corrective, preventive, and predictive maintenance, are commonly employed. This paper focuses on predictive maintenance through vibration analysis, utilizing data-driven models. This study explores the application of unsupervised learning methods, particularly Convolutional Autoencoders (CAEs) and variational Autoencoders (VAEs), for anomaly detection (AD) in vibration signals. By transforming vibration signals into images using the Synchrosqueezing Transform (SST), this research leverages the strengths of convolutional neural networks (CNNs) in image processing, which have proven effective in AD, especially at the pixel level. The methodology involves training CAEs and VAEs on data from machinery in healthy condition and testing them on new data samples representing different levels of system degradation. The results indicate that models with spatial latent spaces outperform those with dense latent spaces in terms of reconstruction accuracy and AD capabilities. However, VAEs did not yield satisfactory results, likely because reconstruction-based metrics are not entirely useful for AD purposes in such models. This study also highlights the potential of ReLU residuals in enhancing the visibility of anomalies. The data used in this study are openly available. Full article

This paper presents a distributed vibration control method using attachable absorbers for micro-vibration control of large space payload structures. The distributed vibration control system is modeled at three levels. The simplification of the attachable absorber model is discussed, and the single-channel ANC controller is extended to a multi-channel configuration. Based on the models, simulation analysis is conducted, revealing that the voltage–force output of the absorber in the low-frequency range can be simplified to a second-order system. During the distributed vibration control system simulation, a Simulink–GA hybrid optimization is applied to address the large number of converging parameters. The optimized parameters successfully control the vibration of all channels. Further analysis indicates that the coupling between control channels slightly reduces convergence speed but has no impact on the final control effect. Additionally, the control system can achieve the same results by independently tuning parameters for each channel. The experimental results, using absorber prototypes and a model with 12 sub-mirror structures, demonstrate that the method can control sub-mirror vibrations simultaneously, maintaining the flatness of the main mirror under disturbance, with a 90% reduction in vibration within 4 s. The coupling effect reduces the final convergence speed by approximately 10%, with a time difference of around 1 s. Full article

Voice onset is the sequence of events between the first detectable movement of the vocal folds (VFs) and the stable vibration of the vocal folds. It is considered a critical phase of phonation, and the different modalities of voice onset and their distinctive characteristics are analysed. Oscillation of the VFs can start from either a closed glottis with no airflow or an open glottis with airflow. The objective of this article is to provide a comprehensive survey of this transient phenomenon, from a biomechanical point of view, in normal modal (i.e., nonpathological) conditions of vocal emission. This synthetic overview mainly relies upon a number of recent experimental studies, all based on in vivo physiological measurements, and using a common, original and consistent methodology which combines high-speed imaging, sound analysis, electro-, photo-, flow- and ultrasound glottography. In this way, the two basic parameters—the instantaneous glottal area and the airflow—can be measured, and the instantaneous intraglottal pressure can be automatically calculated from the combined records, which gives a detailed insight, both qualitative and quantitative, into the onset phenomenon. The similarity of the methodology enables a link to be made with the biomechanics of sustained phonation. Essential is the temporal relationship between the glottal area and intraglottal pressure. The three key findings are (1) From the initial onset cycles onwards, the intraglottal pressure signal leads that of the opening signal, as in sustained voicing, which is the basic condition for an energy transfer from the lung pressure to the VF tissue. (2) This phase lead is primarily due to the skewing of the airflow curve to the right with respect to the glottal area curve, a consequence of the compressibility of air and the inertance of the vocal tract. (3) In case of a soft, physiological onset, the glottis shows a spindle-shaped configuration just before the oscillation begins. Using the same parameters (airflow, glottal area, intraglottal pressure), the mechanism of triggering the oscillation can be explained by the intraglottal aerodynamic condition. From the first cycles on, the VFs oscillate on either side of a paramedian axis. The amplitude of these free oscillations increases progressively before the first contact on the midline. Whether the first movement is lateral or medial cannot be defined. Moreover, this comprehensive synthesis of onset biomechanics and the links it creates sheds new light on comparable phenomena at the level of sound attack in wind instruments, as well as phenomena such as the production of intervals in the sung voice. Full article

To tackle the challenges associated with conventional methods of diagnosing hydraulic turbine faults, which depend heavily on expert knowledge and exhibit low efficiency and precision, a model for detecting hydraulic turbine faults has been developed that integrates the synchrosqueezed wavelet transform (SWT) with SE-ResNet. Initially, a 1D non-stationary vibration signal is converted into a high-frequency time–frequency representation in two dimensions using SWT, which then acts as the input for the convolutional neural network. Secondly, a model based on SE-ResNet is designed, incorporating an attention mechanism that enhances the extraction of features from two-dimensional images, thereby increasing the emphasis on crucial features and bolstering the model’s representation capabilities. Finally, results related to fault detection are produced via the softmax layer. To evaluate the proposed model’s efficiency, two datasets were utilized for the experiments conducted, one sourced from Case Western Reserve University and the other from hydraulic turbine vibration signals. Compared to conventional approaches, this technique demonstrates significant practicality and effective convergence characteristics, offering considerable value in real-world applications. Full article

Repeatable and reliable assessment of corneal biomechanics with spatial resolution remains a challenge. Vibrational Optical Computerized Tomography (V-OCT), based on sound-wave elastography, has made it possible to investigate the natural resonant modes of the cornea and obtain the elastic moduli non-invasively. This pilot study presents a characterization of four corneal vibrational modes from aberrometric, geometrical, and biomechanical approaches in the living human cornea of five healthy volunteers by combining a corneal sound-wave generator, dual Placido–Scheimpflug corneal imaging, and the Ocular Response Analyzer (ORA) devices. Sound-induced corneal wavefront aberration maps were reconstructed as a function of sound frequency and isolated from the natural state. While maps of low-order aberrations (LOA) revealed symmetric geometrical patterns, those corresponding to high-order aberrations (HOA) showed complex non-symmetric patterns. Corneal geometry was evaluated by reconstructing corneal elevation maps through biconical fitting, and the elastic and viscous components were calculated by applying the standard linear solid model to the ORA measurements. The results showed that sound-wave modulation can increase high-order corneal aberrations significantly. Two frequencies rendered the corneal shape more prolate (50 Hz) and oblate (150 Hz) with respect to the baseline, respectively. Finally, both the elastic and viscous properties are sensitive to sound-induced vibrational modes, which can also modulate the corneal stress-strain response. The cornea exhibits natural resonant modes influenced by its optical, structural, and biomechanical properties. Full article

With the advancement of space exploration and optical communication toward deep space, the high-precision evaluation and image stabilization of space optical payloads under micro-vibration have become increasingly critical. To address these challenges and ensure sub-micro-radian pointing accuracy for high-precision space optical payloads (HPSOPs), this paper proposes a high-precision micro-vibration testing scheme and a two-stage image stabilization system. The micro-vibration testing scheme is based on an automated quasi-zero stiffness suspension device (AQZSSD), which enhances testing sensitivity and environmental disturbance resistance, ensuring the accuracy of the results. The two-stage image stabilization system integrates three bipod vibration isolation legs (BVILs) and a decoupled fast steering mirror (FSM), extending control bandwidth and achieving comprehensive vibration suppression. Micro-vibration testing and image stabilization experiments were conducted under disturbances from multiple vibration sources. Experimental results demonstrate that the AQZSSD introduces disturbances below 0.4 Hz, confirming its quasi-zero stiffness characteristics in alignment with theoretical predictions. Furthermore, the line-of-sight (LOS) jitter root mean square (RMS) value is reduced from 1.253 μrad to 0.276 μrad, achieving sub-micro-radian stability. Additionally, due to the coupling effect of the micro-vibration response, the collaborative testing results were found to be lower than the linear superposition of individual sources. This work offers critical theoretical and technical support for the development of HPSOPs, with potential applications in future space missions and advanced optical technologies. Full article

This paper proposes a novel method called Fusion Attention Network for Bearing Diagnosis (FAN-BD) to address the challenges in effectively extracting and fusing key information from current and vibration signals in traditional methods. The research is validated using the public dataset Vibration, Acoustic, Temperature, and Motor Current Dataset of Rotating Machines under Varying Operating Conditions for Fault Diagnosis. The method first converts current and vibration signals into two-dimensional grayscale images, extracts local features through multi-layer convolutional neural networks, and captures global information using the self-attention mechanism in the Vision Transformer (ViT). Furthermore, it innovatively introduces the Channel-Based Multi-Head Attention (CBMA) mechanism for the efficient fusion of features from different modalities, maximizing the complementarity between signals. The experimental results show that compared to mainstream algorithms such as Vision Transformer, Swin Transformer, and ConvNeXt, the Fusion Attention Network for Bearing Diagnosis (FAN-BD) achieves higher accuracy and robustness in fault diagnosis tasks, providing an efficient and reliable solution for bearing fault diagnosis.The proposed model outperforms ViT, Swin Transformer, ConvNeXt, and CBMA-ViT in terms of classification accuracy, achieving an accuracy of 97.5%. The comparative results clearly demonstrate that the proposed Fusion Attention Network for Bearing Diagnosis yields significant improvements in classification outcomes. Full article

The concept of synthetic aperture radar (SAR) has the advantage of being able to obtain high-quality images even when the target area is at night or covered with obstacles such as clouds or fog. These imaging capabilities have led to a rapid increase in demand for space SAR imagery across a variety of sectors, including government, military, and commercial sectors. The SAR-based deployable reflector antenna was developed in this series of paper. The satellite performance is influenced by the aperture size of an antenna. To improve the image acquisition performance, the SAR antenna has the configuration of several foldable CFRP reflectors. In this paper, the experimental investigation of the Structural-thermal model deployable reflector antenna is performed. During the launch condition, the satellite and payload are subjected to the dynamic load. In the STM phase, the acoustic test was conducted to evaluate the structural stability of the deployable reflector antenna within the acoustic environment. The sinusoidal vibration test was implemented to investigate the fundamental frequency for inplane/normal directions and evaluate the structural stability of reflector antenna. By using experimental data obtained from the thermal-balance test, the well-correlated thermal analysis model was established to execute the orbital thermal analysis. The experimental results of the environmental test in STM phase show that the deployable reflector antenna has structural stability for the structural/thermal environments. The configuration of the deployable reflector antenna determined in STM phase can be applied to the qualification model. Full article