Vibration Patterns Research Articles

Control design of vibrotactile stimulation on weighted vest for deep pressure therapy

Introduction: This study presents a novel weighted vest integrated with vibrotactile stimulation for deep pressure therapy. Following the principles of Morrison's research, we assessed its effectiveness in calming users with four distinct vibration patterns. Method: Ten male participants without ASD history, aged 19-25 and weighing 48-73 kg, provided subjective evaluations using the Comfort Rating Scale (CRS). Result: Results showed that participants reported increased calmness after using the device, as evidenced by the overall ratings of the "calming" term, with the "down" pattern receiving the highest ratings. The study advocates for future work involving physiological sensors to measure the device's effectiveness objectively. While promising, this innovation has limitations, such as fixed vibration frequency and reliance on an adapter for power. Future iterations could address these issues to enhance the device's portability and customizable vibration frequencies. Conclusion: This research contributes to deep pressure therapy and vibration therapy by focusing on responsive patterns and subjective evaluations, opening doors for future developments.

Vocal fold fibroblasts and exposure to vibration in vitro: Does sex matter?

Studies have shown that certain vocal fold pathologies are more common in one sex than the other. This is often explained by differences in the composition of the lamina propria and anatomical differences between female and male vocal folds, resulting in e.g. different fundamental frequencies. Here, we investigated a potential sex-specific voice frequency effect in an in vitro setting using vocal fold fibroblasts from one male and one female donor with and without cigarette smoke extract (CSE) addition. After exposure to either male or female vibration frequency with or without CSE, cells and supernatants were harvested. Gene and protein analysis were performed by means of qPCR, western blot, ELISA and Luminex. We found that exposure of cells to both male and female vibration pattern did not elicit significant changes in the expression of extracellular matrix-, inflammation-, and fibrosis-related genes, compared to control cells. The addition of CSE to vibration downregulated the gene expression of COL1A1 in cells exposed to the female vibration pattern, as well as induced MMP1 and PTGS2 in cells exposed to both female and male vibration pattern. The protein expression of MMP1 and COX2 was found to be significantly upregulated only in cells exposed to CSE and female vibration pattern. To conclude, different vibration patterns alone did not cause different responses of the cells. However, the female vibration pattern in combination with CSE had a tendency to elicit/maintain more pro-inflammatory responses in cells than the male vibration pattern.

Up beating skull vibration induced nystagmus in a case of bilateral sequential superior branch vestibular neuritis.

Vestibular neuritis is one of the most common reasons that someone may experience an episode of acute spontaneous vertigo, with the majority cases impacting only one ear. Cases of bilateral vestibular neuritis are rare and are thought to account for less than 10% of all cases of vestibular neuritis. 18,1,9,15,12 Skull vibration testing is an efficient means to screen for asymmetry in vestibular function but is still in its infancy in clinical use. The ideal assessment methods and the typical patterns of skull vibration induced nystagmus are relatively well understood, however, the presentation of skull vibration induced nystagmus in atypical labyrinthine pathology is less clear. Skull vibration induces a horizontal nystagmus that beats toward the healthy labyrinth in most instances of significant labyrinthine asymmetry. We pose a case report of a patient that's symptomology and clinical test findings are most consistent with bilateral sequential superior branch vestibular neuritis with an up beating skull vibration induced nystagmus.

Investigating the Performance of Gammatone Filters and Their Applicability to Design Cochlear Implant Processing System

Commercially available cochlear implants are designed to aid profoundly deaf people in understanding speech and environmental sounds. A typical cochlear implant uses a bank of bandpass filters to decompose an audio signal into a set of dynamic signals. These filters’ critical center frequencies f0 imitate the human cochlea’s vibration patterns caused by audio signals. Gammatone filters (GTFs), with two unique characteristics: (a) an appropriate “pseudo resonant” frequency transfer function, mimicking the human cochlea, and (b) realizing efficient hardware implementation, could demonstrate them as unique candidates for cochlear implant design. Although GTFs have recently attracted considerable attention from researchers, a comprehensive exposition of GTFs is still absent in the literature. This paper starts by enumerating the impulse response of GTFs. Then, the magnitude spectrum, |H(f)|, and bandwidth, more specifically, the equivalent rectangular bandwidth (ERB) of GTFs, are derived. The simulation results suggested that optimally chosen filter parameters, e.g., critical center frequencies,f0; temporal decay parameter, b; and order of the filter, n, can minimize the interference of the filter bank frequencies and very likely model the filter bandwidth (ERB), independent of f0b. Finally, these optimized filters are applied to delineate a filter bank for a cochlear implant design based on the Clarion processor model.

The underappreciated role of vocal fold medial surface thickness in vocal control

Clinicians and singers are used to viewing vocal fold vibration from a superior endoscopic view. However, both vocal fold vibration and vocal control are three-dimensional. Although it is hidden from the superior view, the vocal fold medial surface shape in the vertical dimension plays an important part in determining both the vibratory pattern as viewed from above and the produced voice.

Adaptive rearrangement of actuator layout for the cable-driven vibration control of a membrane antenna

Although various studies on vibration control schemes exist, most of them focus on a one-time optimization of actuator layout. However, numerical results indicate significant drawbacks of fixed layouts on control performance, a factor rarely mentioned in the field of vibration control. This study aims to address this gap by proposing a novel rearrangement strategy that adaptively varies the actuator layout based on the vibration intensity pattern. The inspiration for this approach comes from the reorientation of cortical microtubules in plant cells, which adapt to stress patterns. The study first derives the definitions of vibration patterns and the index of intensity based on energy transfer. These definitions then guide the optimization of actuator layout throughout the control process. The study compares the fixed layout with the proposed adaptive varying layout, considering both full-state feedback and recognition feedback. The numerical results demonstrate that the proposed adaptive rearrangement of actuator layout significantly improves suppression performance, leading to higher utilization efficiency of control forces. Additionally, the presented strategy is tolerant to computation errors from recognition feedback, resulting in robust enhancements in vibration attenuation. Our study has introduced new possibilities for improving existing control schemes.

Enhanced Bearing Fault Analysis under Inconstant Loads Conditions by Cosine Weighted K-Nearest Neighbours Model

Bearing faults often lead to machinery failures, underscoring the importance of analyzing bearing vibrations to avert undesirable consequences. Leveraging Artificial Intelligence (AI) in this context benefits from the strides in intelligent data processing and computing capabilities. Traditionally, signal processing and feature engineering play pivotal roles in achieving accurate classifications. However, classification accuracy can decline notably during variable loading scenarios due to the diverse vibration patterns exhibited under different loads. This study assesses an AI model's performance under variable loading conditions using raw vibration signals, without recourse to signal processing or feature engineering. Introducing an enhanced AI model, known as Cosine Weighted K-Nearest Neighbours (CWKNN), resulted in a slightly improved 85.2–88.7% under stable loading conditions and 64.3–72.6% under variable loading conditions.

Structural damage recognition based on wavelet transform and improved most valuable player algorithm

The probability of damage to infrastructure increases with age. Timely health monitoring of infrastructure is essential since it is linked to the safety of people's lives and property. The study employed the wavelet transform approach to discretize the wavelet transform of the observed vibration patterns of the damaged structure in order to identify and localize the structural damage in infrastructure. The study also employed natural excitation techniques to obtain the structural multi-order modal parameters. To quantify the degree of structural damage, the study designed an objective function for damage quantification and improved the most valuable player algorithm. The study avoided the most valuable player algorithm from falling into local optimality by introducing the elite inverse strategy and simplex strategy. The wavelet transform and the improved most valuable player algorithm were able to successfully identify and localize the structural damages and quantify the degree of the damages, thereby providing technical support for the daily monitoring of the infrastructure.

Класифікація низькочастотних сигналів за допомогою методів кластеризаці

The article considers the problem of low frequency signal classification, as sound or vibration pattern footprints may describe types of objects very well. In cases of a priori absence of object signal pattern information, the unsupervised learning methods based on clustering looks good enough for classification, and outperform neural net-based methods in case of limited power envelope. We have used big real-world sound and vibration data set to check several clustering methods (K-Means, OPTICS) for classification without any a priori data and have got good enough results. The article considers data set preparations including primary signal processing and the parameters to select appropriate clustering algorithms, which depends on input data shape. There are several examples of data classification, also cascaded methods for data set improvement are considered. Finally, we provide a good and practical guide for exploring low frequency signals using clustering methods, which can be used for real world observations and analysis for open space and inside buildings.

Structural and Modal Analysis of the Unloading Robot on the Broaching Machine

In order to meet the actual production of a machining company in the low production efficiency, labor intensity, process flow complexity and other real problems, the broaching machine processing upgraded and designed a broaching machine for the production of automatic loading and unloading device. This paper analyzes the layout scheme and workflow of automatic loading and unloading, designs the overall structure of the loading and unloading device and key components, and establishes a three-dimensional model through Solidworks software, and carries out a static analysis of the limit position in the operation process by using the finite element analysis software ANSYS Workbench, and obtains the maximum deformation under the limiting conditions of 0.1129mm, and the maximum stress value of 24.236MPa. The maximum deformation is 0.1129mm and the maximum stress is 24.236MPa. Subsequently, modal analysis is carried out to extract the first 6 orders of intrinsic frequency and corresponding vibration pattern, and harmonic response analysis is carried out on the weak components on the basis of modal analysis, which verifies that the structure complies with the design requirements in terms of strength, stiffness and resonance of the whole machine, and it provides the theoretical basis for the subsequent structural improvement and optimization design.

Characterization of the Oxazolone and Macrocyclic Motifs in the bn (n = 2-5) Product Ions from Collision-Induced Dissociation of Protonated Oligoglycine Peptides with Isomer-Selective, Cryogenic Vibrational Spectroscopy.

Collision-induced dissociation (CID) of small, protonated peptides leads to the formation of b-type fragment ions that can occur with several structural motifs driven by different covalent intramolecular bonding arrangements. Here, we characterize the so-called "oxazolone" and "macrocycle" bn ion structures that occur upon CID of oligoglycine peptides (Gn) ions (n = 2-6). This is determined by acquiring the vibrational band patterns of the cryogenically cooled, D2-tagged bn ions obtained using isomer-selective, two-color IR-IR photobleaching and analyzing them with predicted (DFT) harmonic spectra for the candidate structures. Both oxazolone and macrocyclic isomers are formed by b4, whereas only oxazolone species are created for b2 and b3 and the macrocycle is created for b5. As such, n = 4 corresponds to the minimum size where both Oxa and MC forms are present.

A novel hybrid model integrating MFCC and acoustic parameters for voice disorder detection

Voice is an essential component of human communication, serving as a fundamental medium for expressing thoughts, emotions, and ideas. Disruptions in vocal fold vibratory patterns can lead to voice disorders, which can have a profound impact on interpersonal interactions. Early detection of voice disorders is crucial for improving voice health and quality of life. This research proposes a novel methodology called VDDMFS [voice disorder detection using MFCC (Mel-frequency cepstral coefficients), fundamental frequency and spectral centroid] which combines an artificial neural network (ANN) trained on acoustic attributes and a long short-term memory (LSTM) model trained on MFCC attributes. Subsequently, the probabilities generated by both the ANN and LSTM models are stacked and used as input for XGBoost, which detects whether a voice is disordered or not, resulting in more accurate voice disorder detection. This approach achieved promising results, with an accuracy of 95.67%, sensitivity of 95.36%, specificity of 96.49% and f1 score of 96.9%, outperforming existing techniques.

Research on the Multi-Angle Seismic Spectrum Response Law of Auxiliary Feedwater Electric Pumps

The auxiliary feedwater electric pump, which is vital in nuclear power units, demands exceptional seismic reliability. The impact of the seismic excitation direction on the seismic response of the complex auxiliary feedwater electric pump structure remains inadequately understood, thereby introducing uncertainties into seismic analysis and equipment installation procedures. To address this challenge, this study centers on a representative horizontal multi-stage centrifugal pump based on the finite element model and modal analysis. Utilizing the response spectrum method and the square root of the sum of the squares (SRSS) vibration pattern superposition principle, the research comprehensively explores the seismic response characteristics of individual pressure-bearing components and key rotor positions under seismic excitation from various angles. Findings reveal intricate variations in maximum stress and displacement responses for each pressure-bearing component under safe shutdown earthquake (SSE) level seismic excitation, corresponding to different input angles of seismic spectra. In assessing the seismic operability of the clearance between the impeller and the stator, the direction near 45° exhibits the maximum displacement response, highlighting the need for focused attention in testing and verification checks. The methodologies and conclusions presented in this paper offer valuable insights for designing, optimizing, and installing horizontal multi-stage centrifugal pumps, including auxiliary feedwater electric pumps, providing valuable guidance for future applications in the field.

Flow-induced oscillations of vocal-fold replicas with tuned extensibility and material properties

Human vocal folds are highly deformable non-linear oscillators. During phonation, they stretch up to 50% under the complex action of laryngeal muscles. Exploring the fluid/structure/acoustic interactions on a human-scale replica to study the role of the laryngeal muscles remains a challenge. For that purpose, we designed a novel in vitro testbed to control vocal-folds pre-phonatory deformation. The testbed was used to study the vibration and the sound production of vocal-fold replicas made of (i) silicone elastomers commonly used in voice research and (ii) a gelatin-based hydrogel we recently optimized to approximate the mechanics of vocal folds during finite strains under tension, compression and shear loadings. The geometrical and mechanical parameters measured during the experiments emphasized the effect of the vocal-fold material and pre-stretch on the vibration patterns and sounds. In particular, increasing the material stiffness increases glottal flow resistance, subglottal pressure required to sustain oscillations and vibratory fundamental frequency. In addition, although the hydrogel vocal folds only oscillate at low frequencies (close to 60 Hz), the subglottal pressure they require for that purpose is realistic (within the range 0.5–2 kPa), as well as their glottal opening and contact during a vibration cycle. The results also evidence the effect of adhesion forces on vibration and sound production.

Laser-Powered Vibrotactile Rendering

We investigate the feasibility of a vibrotactile device that is both battery-free and electronic-free. Our approach leverages lasers as a wireless power transfer and haptic control mechanism, which can drive small actuators commonly used in AR/VR and mobile applications with DC or AC signals. To validate the feasibility of our method, we developed a proof-of-concept prototype that includes low-cost eccentric rotating mass (ERM) motors and linear resonant actuators (LRAs) connected to photovoltaic (PV) cells. This prototype enabled us to capture laser energy from any distance across a room and analyze the impact of critical parameters on the effectiveness of our approach. Through a user study, testing 16 different vibration patterns rendered using either a single motor or two motors, we demonstrate the effectiveness of our approach in generating vibration patterns of comparable quality to a baseline, which rendered the patterns using a signal generator.

Enhancing Chatter Stability for Milling Thin-Walled Blades by Designing Non-Uniform Allowance

During the milling of thin-walled blades, the removal of material exhibits strong time-varying dynamics, leading to chatter and a decrease in surface quality. To address the issue of milling vibrations in the machining of complex thin-walled blades used in aerospace applications, this work proposes a process optimization approach involving non-uniform allowances. The objective is to enhance of he stiffness of the thin-walled parts during the milling process by establishing a non-uniform allowance distribution for the finishing process of thin-walled blades. By applying the theory of sensitive process stiffness and conducting finite element simulations, two processing strategies, namely uniform allowances and non-uniform allowances, are evaluated through cutting experiments. The experimental results demonstrate that the non-uniform allowance processing strategy leads to a more evenly distributed acceleration spectrum and a 50% reduction in amplitude. Moreover, the surface exhibits no discernible vibration pattern, resulting in a 35% decrease in roughness. The non-uniform allowance-processing strategy proves to be effective in significantly improving the rigidity of the thin-walled blade processing system, thereby enhancing the stability of the cutting process. These findings hold significant relevance in guiding the machining of typical complex thin-walled aerospace components.

Utilizing a knowledge-based training algorithm and time-domain extraction for pattern recognition in cylindrical features through vibration and sound signals

This study presents a new solution to address challenges encountered in additive manufacturing, specifically in the context of 3D printing, where failures can occur due to complications associated with the nozzle or filament. The proposed solution in this research involves using a time-domain feature extraction method that leverages sound and vibration patterns. By implementing sensors to capture these signals in a controlled and noise-free environment, and then utilizing a Multi-Layer Perceptron (MLP) model trained accurately to predict upcoming signals and vibrations, proactive anticipation of printing outcomes is facilitated, including potential failures. Simulation results obtained using MATLAB for the MLP showcase the effectiveness of this approach, demonstrating remarkably low error rates. Furthermore, through rigorous data validation, the proposed method's ability to accurately identify sound and vibration signals is confirmed. As a result, the likelihood of failures is significantly reduced, thereby preventing defects in the filament. The implications of this solution hold great promise in substantially enhancing the reliability and efficiency of additive manufacturing processes.

Energy storage density of tailored relaxor-antiferroelectric state in Gd-doped Ag(Nb0.8Ta0.2)O3 ceramics

Antiferroelectric state AgNb0.8Ta0.2O3 ceramics can be adjusted by doping with Gd to form the relaxor-antiferroelectric state (Ag1-3xGdx)(Nb0.8Ta0.2)O3 ceramics (x = 0, 0.01, 0.02, 0.03, 0.04) for energy storage applications. The superb recoverable energy density (Wrec) of ∼9.47 J cm−3 at x = 0.01 and energy storage efficiency (η) of ∼68 % at x = 0.02 can be achieved at 375 kV cm−1. The superb Wrec can be attributed to phase evolution modulated with optimal fractions of ferroelectric Pmc21, antiferroelectric Pnma, and nonpolar cubic Pm-3m symmetries. Polarization-electric field (P-E) hysteresis loop, dielectric permittivity, atomic vibration, and electron diffraction pattern were used to probe the mechanism of superb recoverable energy density.

Micro-vibration monitoring of pipelines using millimetre-wave MIMO radar

ABSTRACTTo ensure pipelines’ safety it is essential to have a regular and accurate monitoring system to inspect the behaviour of pipelines over time to prevent potential damage. This paper explores the capability of a millimetre-wave multiple-input multiple-output (MIMO) radar system for micro-vibration monitoring of pipelines. Compared to the conventional radars, MIMO radar provides cross-range resolution, while still being capable of monitoring a scene with sub-second data acquisition intervals. This makes it a non-contact system suitable for fast displacement and vibration monitoring. However, the application of MIMO radars has not been fully investigated and analysed for structural monitoring, especially for pipeline vibrations. In this study, a dense time-series of MIMO radar data was collected and processed by proposing a framework based on persistent scatterer interferometry to obtain a map of pipeline vibrations. Experiments were conducted in a controlled environment consisting of both functional and inactive water pipelines. The results showed that the system could detect displacements from micrometre up to sub-millimetre levels, with a displacement error of less than 3 μm. Additionally, the radar could identify the dominant frequencies of 24.375 and 24.500 Hz even in pipelines with very small vibration patterns. These results validate the high potential of millimetre-wave MIMO radar systems for non-contact monitoring of micro-vibrations in pipelines.

Ultrasensitive vibrational resonance induced by small disturbances.

We have found two kinds of ultrasensitive vibrational resonance in coupled nonlinear systems. It is particularly worth pointing out that this ultrasensitive vibrational resonance is transient behavior caused by transient chaos. Considering a long-term response, the system will transform from transient chaos to a periodic response. The pattern of vibrational resonance will also transform from ultrasensitive vibrational resonance to conventional vibrational resonance. This article focuses on the transient ultrasensitive vibrational resonance phenomenon. It is induced by a small disturbance of the high-frequency excitation and the initial simulation conditions, respectively. The damping coefficient and the coupling strength are the key factors to induce the ultrasensitive vibrational resonance. By increasing these two parameters, the vibrational resonance pattern can be transformed from ultrasensitive vibrational resonance to conventional vibrational resonance. The reason for different vibrational resonance patterns to occur lies in the state of the system response. The response usually presents transient chaotic behavior when the ultrasensitive vibrational resonance appears and the plot of the response amplitude vs the controlled parameters shows a highly fractalized pattern. When the response is periodic or doubly periodic, it usually corresponds to the conventional vibrational resonance. The ultrasensitive vibrational resonance not only occurs at the excitation frequency, but it also occurs at some more nonlinear frequency components. The ultrasensitive vibrational resonance as transient behavior and the transformation of vibrational resonance patterns are new phenomena in coupled nonlinear systems.