Experimental Platform Research Articles

An integrated empirical and computational study to decipher help-seeking behaviors and vocal stigma

BackgroundProfessional voice users often experience stigma associated with voice disorders and are reluctant to seek medical help. This study deployed empirical and computational tools to (1) quantify the experience of vocal stigma and help-seeking behaviors in performers; and (2) predict their modulations with peer influences in social networks.MethodsExperience of vocal stigma and information-motivation-behavioral (IMB) skills were prospectively profiled using online surveys from a total of 403 Canadians (200 singers and actors and 203 controls). Data were used to formulate an agent-based network model of social interactions on vocal stigma (self-stigma and social-stigma) and help-seeking behaviors. Network analysis was performed to evaluate the effect of social network structure on the flow of IMB among virtual agents.ResultsLarger social networks are more likely to contribute to an increase in vocal stigma. For small social networks, total stigma is reduced with higher total IMB but not much so for large networks. For agents with high social-stigma and risk for voice disorder, their vocal stigma is resistant to large changes in IMB ( > 2 standard deviations). Agents with extreme IMB and stigma values are likely to polarize their networks faster in larger social groups.ConclusionsWe integrated empirical surveys and computational techniques to contextualize vocal stigma and IMB beyond theory and to quantify the interaction among stigma, health-seeking behavior and influence of social interactions. This work establishes an effective, predictable experimental platform to provide scientific evidence in developing interventions to reduce health stigma in voice disorders and other medical conditions.

Research on the Sensing Properties and Vibration Reduction Performance of Self-Sensing Self-Tuning Magnetic Fluid Damper

Abstract The semi-active control damping system has gained popularity due to its quick response time and versatility. However, external sensors are susceptible to environmental interference, affecting system reliability and increasing complexity and maintenance costs, restricting their use. To address this, a self-sensing self-tuning magnetic fluid damper is proposed. The vibration-measuring induction coil is wound on the damper to sense the magnetic fluid vibration information in real time, and the vibration signal is communicated to the self-tuning control circuit. The control circuit calculates and determines the dominant frequency of structural vibration, then outputs the relevant current signal to set the damper's natural frequency to track the excitation frequency, resulting in self-tuning vibration reduction. First, the self-sensing unit's output induced electromotive force model is created, followed by an expression of the damper's natural frequency, indicating that the self-sensing unit can achieve self-tuning vibration reduction by tracking the excitation frequency. The multi-field coupling simulation model of the magnetic fluid damper is generated, and the induction coil coupling mode and damper excitation angle are defined to obtain the maximum induced voltage.
Finally, an experimental platform was developed to assess the damper's self-sensing and self-tuning vibration reduction performance. The experimental results demonstrate that the suggested self-tuning magnetic fluid damper exhibits excellent self-sensing capabilities and effectively reduces vibrations. This offers an innovative research concept for using magnetic fluid in self-sensing technology and vibration reduction domains.

Transactive Memory in Caregiver Networks Using Artificial Intelligence

As the population ages and an increasing number of adults want to age in place in their homes, they will rely on a network of family, friends, and other caregivers to provide various forms of assistance. Coordination across this loosely connected network is a common challenge, requiring information sharing, schedule alignment and task coordination. Here, we propose that artificial intelligence (AI) may be used to develop tools to help loosely connected care networks develop better collective cognition. Specifically, we focus on helping members of care networks develop a transactive memory system, or a shared system for storing and retrieving knowledge that expands the capacity of a group to effectively use information. In this paper, we describe the motivation for our study, and our planned research program based on the use of an online experimental platform facilitating human-AI collaboration to develop and test tools to enhance collective cognition in care networks.

Experimental study of dual-source heat pump coupled with water-cooled photovoltaic/thermal system

This paper presents a study on an innovative solar-air dual heat source heat pump coupled with a water-cooled photovoltaic/thermal system. The research investigated the real-time operation of solar electrical and thermal efficiency, as well as the coefficient of performance Field experimental platform construction and experimental testing were conducted. The experimental results show that when running the system in composite mode, the average electrical efficiency in the two phases was 16.79% and 18.33%, while the average collector efficiency reached 42.72% and 98.94%, respectively. Under typical autumn working conditions, the heat pump’s coefficient of performance with the fans off and on was 4.37 and 4.73 respectively on sunny days, and under cloudy day conditions, it reached 3.76 and 4.20 respectively. With the fans on, the average collector efficiency on a cloudy day increased by 3.34% compared with that without the fans, and the total average collector efficiency reached 310.5%. Compared with conventional single water-cooled photovoltaic/thermal systems or solar-assisted heat pump systems, the combination of these two systems, with the addition of both fins and fans which increased the heat transfer performance of the system and improved the stability and economy of the system’s operation, providing data to support the feasibility of a water-cooled photovoltaic/thermal coupled dual-source heat pump system.

Study of High-Frequency Pulsating Flow Control Valves

Pulsating flow signals are widely used in the fields of dynamic calibration of flow meters and reliability testing of fluid transmission pipelines. A high-frequency pulsating flow control valve is proposed, its structure and working principle are introduced, and its mathematical model is also established. Finally, an experimental platform is built to experimentally verify the performance of the high-frequency pulsating flow signal generation of this flow control valve prototype. When applying sinusoidal flow signals of 1–25 Hz, the amplitude of the output flow starts to decay after 2 Hz, and the frequency corresponding to the decay to −3 dB is about 23.5 Hz. The experimental results show that the valve can effectively generate unidirectional sinusoidal flow with a maximum amplitude of 25 L/min and a frequency of 23.5 Hz.

Riemannian Manifold-Aided Data-driven Diagnosis of Photovoltaic Hot Spots

Abstract This paper proposes a novel data-driven approach for hot-spot fault detection in photovoltaic (PV) modules, utilizing a curved Riemannian manifold (RM) to characterize the sample space. The proposed method detects hot spots in PV systems by mining geometric features in high-dimensional data. The RM-based method combines the geometric properties of Riemannian manifolds for fault detection. In addition, it also provides information about the severity of the hot spots. The proposed method has three main advantages:
1) it is capable of actively learning fault characteristics and can be applied to diagnose various degrees of hot spots;
2) unlike other data-driven methods, the proposed method considers non-Euclidean spatial data features, which further improves the accuracy of the method and reduces the false alarm rate (FAR);
3) it does not rely on mathematical models or expert knowledge, making it able to meet the real-time monitoring needs of actual PV systems.
The effectiveness and superiority of the proposed approach are verified through 12 sets of hot spots tests conducted on a PV experimental platform.

Gearbox Fault Diagnosis Based on Adaptive Variational Mode Decomposition–Stationary Wavelet Transform and Ensemble Refined Composite Multiscale Fluctuation Dispersion Entropy

Existing gearbox fault diagnosis methods are prone to noise interference and cannot extract comprehensive fault signals, leading to misdiagnosis or missed diagnosis. This paper proposes a method for gearbox fault diagnosis based on adaptive variational mode decomposition–stationary wavelet transform (AVMD-SWT) and ensemble refined composite multiscale fluctuation dispersion entropy (ERCMFDE). Initially, the kurtosis coefficient and autocorrelation coefficient are presented, and the Intrinsic Mode Functions are denoised through the application of AVMD-SWT. Secondly, the coarse-grained processing method of composite multiscale fluctuation dispersion entropy is extended to encompass three additional approaches: first-order central moment, second-order central moment, and third-order central moment. This enables the comprehensive extraction of feature information from the time series, thereby facilitating the formation of an initial hybrid feature set. Subsequently, recursive feature elimination (RFE) is employed for feature selection. Ultimately, the outcomes of the faults diagnoses are derived through the utilization of a Support Vector Machine with a Sparrow Search Algorithm (SSA-SVM), with the actual faults data collection and analysis conducted on an experimental platform for gearbox fault diagnosis. The experiments demonstrate that the method can accurately identify gearbox faults and achieve a high diagnostic accuracy of 98.78%.

Simulating quantum chaos on a quantum computer

Noisy intermediate-scale quantum (NISQ) computers provide a new experimental platform for investigating the behaviour of complex quantum systems. We show that currently available NISQ devices can be used for versatile quantum simulations of chaotic systems. We introduce a classical-quantum hybrid approach for exploring the dynamics of the chaotic quantum kicked top (QKT) on a quantum computer. The programmability of this approach allows us to experimentally explore a broad range of QKT chaoticity parameter regimes inaccessible to previous studies. Furthermore, the number of gates in our simulation does not increase with the number of kicks, thus making it possible to study the QKT evolution for arbitrary number of kicks without fidelity loss. Using a publicly accessible NISQ computer (IBMQ), we observe periodicities in the evolution of the 2-qubit QKT, as well as signatures of chaos in the time-averaged 2-qubit entanglement. We also demonstrate a connection between entanglement and delocalization in the 2-qubit QKT, confirming theoretical predictions.

Automatic detection of water pipeline leakage via unsupervised learning applied on ground penetrating radar data

ABSTRACT Aging urban water supply pipelines are facing significant leakage issues. Ground penetrating radar (GPR) is an effective non-destructive method for leakage localization, but its image analysis mainly relies on interpreter experience, leading to uncertainty and inefficiency. To address this issue, an unsupervised learning method based on GPR data attributes is proposed for automatic leakage detection. First, velocity and energy attributes are extracted from B-Scan profiles to differentiate wet areas caused by leakage from normal areas. Then, the density-based spatial clustering of applications with noise (DBSCAN) clustering method is applied to automatically classify these two types of areas. The proposed method was evaluated on an experimental platform with constant water pressure and pre-drilled leakage points. Experiments show that the velocity and energy attributes decrease by 25.2 and 26.6% after leakage. Using velocity attributes yields better results, and suggestions for DBSCAN hyperparameters are provided. Feature importance analysis indicates that two-way-travel-time with a score of 46.1 and energy attribution index with a score of 32.7 significantly influence leakage identification. This method can also estimate affected leakage areas with an error not exceeding the spacing of three measurement lines, and is available across various underground conditions without needing well-labeled training data.

Attack Reconstruction and Attack-Resilient Consensus Control for Fuzzy Markovian Jump Multi-Agent Systems

Driven by the rapid development of modern industrial applications, multi-agent systems (MASs), integrating computational and physical resources, have become increasingly important in recent years. However, the performance of MASs can be easily compromised by malicious false data injection attacks (FDIAs) due to the inherent vulnerability of the cyber layer. This work focuses on an event-triggered framework for secure reconstruction and consensus control in MASs subject to both sensor and actuator attacks. First, we introduce a class of Takagi–Sugeno fuzzy multi-agent systems that relax the traditional Lipschitz condition and incorporate realistic system dynamics by considering parameter variations governed by Markovian jump principles. Second, an adaptive fuzzy estimator is developed for the simultaneous reconstruction of states and attacks in MASs. The derived estimates are utilized to design an attack-resilient consensus control strategy that compensates for the effects of FDIAs on the closed-loop consensus error dynamics. Meanwhile, the sufficient conditions for the convergence of both estimation and consensus errors are presented and rigorously proved. Finally, evaluation results on an experimental platform through multiple truck-trailer systems are provided to demonstrate the effectiveness and performance of the proposed approach.

Active noise control of refrigerator based on cascaded notch feedback algorithm

Refrigerators bring convenience to people, but the noise they produce can also affect people’s lives. Refrigerator noise is dominated by low-frequency noise, and the active noise control method is more effective for this kind of noise. However, the existing active noise control methods for refrigerators do not take into account factors such as the limited space and complex structure of the refrigerator compressor room, and external interference signals will be introduced to affect the noise reduction performance during the error signal collection. Given that, this paper proposes the cascade notch feedback algorithm, which can well reduce the influence of external interference signals to better achieve the refrigerator noise reduction. The algorithm consists of the cascaded notch filtering algorithm and the feedback algorithm. The cascade notch filtering algorithm is formed by cascading the adaptive notch filtering algorithm, which is used to deal with the main frequency and harmonic noise generated by the refrigerator. The feedback algorithm consists of a robust algorithm for dealing with external interference signals introduced during error signal collection. The simulation experiment proves that the algorithm has advantages in terms of noise reduction and computational cost compared with the adaptive notch filtering algorithm and the improved algorithm. The experimental test platform is set up to carry out the actual refrigerator noise reduction experiments under different conditions. The algorithm has the effect of noise reduction under different robust algorithms.

Study on the effect pattern of moisture on the oxidized combustion of ventilation air methane

AbstractVentilation air methane (VAM) is one of the main greenhouse gas sources. Owing to the characteristics of low concentration of ventilation air methane and high moisture content, we build an experimental platform and take the oxidative combustion temperature and methane conversion rate as the research indexes, and the systematic research finds that the inhibitory effect of moisture on the oxidative combustion of ultra‐low concentration of methane (<1%) is a nonlinear polynomial law. In the meantime, we constructed OH(H2O)n+CH4 and studied its reaction potential energy surface using quantum chemical calculations, which used the most significant primitive reaction of methane combustion, OH+CH4→H2O+CH3, as the theoretical basis. We found that as moisture content increased, so did its reaction energy barrier, making the reactants more stable, strengthening the three‐body collision effect, and reducing the number of free radicals, all of which hindered the methane chain reaction. The study aimed to validate the experimental finding that moisture inhibits the oxidative combustion of ventilation air methane by examining the internal mechanism of methane oxidation. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

An Energy Management System for Distributed Energy Storage System Considering Time-Varying Linear Resistance

As the proportion of renewable energy in energy use continues to increase, to solve the problem of line impedance mismatch leading to the difference in the state of charge (SOC) of each distributed energy storage unit (DESU) and the DC bus voltage drop, a distributed energy storage system control strategy considering the time-varying line impedance is proposed in this paper. By analyzing the fundamental frequency harmonic components of the pulse width modulation (PWM) signal carrier of the converter output voltage and output current, we can obtain the impedance information and, thus, compensate for the bus voltage drop. Then, a novel, droop-free cooperative controller is constructed to achieve SOC equalization, current sharing, and voltage regulation. Finally, the validity of the system is verified by a hardware-in-the-loop experimental platform.

Control of transient power compensation for virtual impedance VSG in Quasi-Z-source photovoltaic grid integration system

The virtual impedance solution effectively addresses the power coupling issues and improves small-signal stability in grid-connected Virtual Synchronous Generator (VSG) systems but can deteriorate the system's transient synchronous stability, particularly in high-voltage lines with significant line inductance exacerbating this effect. To address these contradictions, the paper first analyzes the power coupling mechanisms of the system, highlighting how the line reactance ratio (R/X) and power angle (δ) affect system stability, and describes the transient stability of VSGs using the equal area criterion. Leveraging the advantages of quasi-Z-source inverters, they are applied to VSG grid-connected systems, proposing an improved VSG control strategy tailored for quasi-Z-source photovoltaic grid-connected systems. This strategy introduces virtual active power at the point of common coupling (PCC) and uses it as power feedback in the control loop, instead of using real active power. The control model is validated on an experimental platform, demonstrating that the VSG control strategy with transient power compensation significantly enhances system transient stability while preserving small-signal stability. This comprehensive approach bridges the gap between maintaining essential stability functions and enhancing dynamic performance under various operational conditions.

Adaptive Virtual Synchronous Generator Control Strategy Based on Frequency Integral Compensation

With the increasing proportion of power electronic equipment in the power system, improving the inertia and damping characteristics of the system through virtual synchronous generator (VSG) control technology has become a hot research topic. In terms of adjusting the output frequency, traditional primary frequency modulation control cannot ensure that the output frequency is maintained within the safe operating range when the load disturbance is large, so secondary frequency modulation with the integral link is usually adopted. However, the fixed integral coefficient cannot solve the contradiction between system regulation time and frequency oscillation. In this paper, a strategy of coordinated adaptive control based on the integral coefficient, moment of inertia, and damping coefficient is proposed. According to the offset of frequency and the change rate of the frequency offset, the value of parameters is determined, and the specific parameter setting method is determined. Finally, the correctness of the proposed control strategy is verified on a simulation platform and a semi-physical experimental platform.

Modeling and precise tracking control of spatial bending pneumatic soft actuators

In recent years, a variety of pneumatic soft actuators (PSAs) have been proposed due to the development of soft robots in biomimetic robots, medical devices, etc. At the same time, the modeling and control of PSAs remains an open question. In this paper, a spatial bending pneumatic soft actuator (SBPSA) modeling method based on the Prandtl–Ishlinskii (PI) model is proposed, and the inverse model is designed to compensate for hysteresis nonlinearity. Furthermore, an adaptive feedback controller combined with a hysteresis compensator is proposed for the precise control and tracking of SBPSAs. Finally, an experimental platform is built, and experimental results demonstrate the effectiveness of the proposed method for precise tracking.

Investigation to the vibration characteristics of ultrasonic transducer based on the number of piezoelectric ceramics

As the vibration actuator source, PZT applied on the ultrasonic transducers (UTs) plays a crucial role in the vibration characteristics of UTs. Most conventional research focus on the overall size and positional relationship of PZT stack, while the influence of PZT numbers on vibration characteristics of UTs is seldom reported. In this article, we present a comprehensive investigation between the PZT numbers and vibration characteristics of UTs with identical geometric configurations, specifically UTs with 2 (UT2), 4 (UT4) and 6 (UT6) PZT. The electromechanical equivalent circuit and finite element analysis (FEM) based on PZT numbers are established to investigate the impedance and resonant frequency. Furthermore, the dynamic displacement model of the UTs is proposed to study the influence of PZT numbers on amplitude, and the calculation results are consistent with harmonic response analysis. Finally, the experimental platform is established to test the vibration characteristics of the three types UTs. The results show that the resonant frequency of the transducer is not affected by the numbers of PZT, while the impedance and impedance stability can be improved by the increased PZT numbers. Moreover, the amplitude of UTs is negatively correlated with the numbers of PZT. Through experiments, it is verified that UT2 is suitable for the conditions as the load less than 1000g and amplitude less than 2.2µm, and UT4 is applicable to the other conditions. Although the UT6 exhibits excellent impedance stability, its output power is relatively high and is not suitable for the structure used in this article. The findings suggest that the number of PZTs should be designed based on the operational conditions to improve amplitude output and minimize the loss of power. The presented methods can effectively improve energy consumption and working life, making the UTs greener and more efficient.

Simulation and experimental study of aerodynamic characteristics of a rotor adsorption wall-climbing robot

Abstract High-rise buildings are prone to gusts of wind, especially crosswinds. In order to investigate the effect of crosswinds on the aerodynamic characteristics of rotor blades at different near-wall distances, a CFD simulation model is designed for four near-wall distances and five wind speeds, and the change curves of rotor pulling force, pressure cloud diagrams and velocity cloud diagrams are obtained by using Fluent for the various conditions. The mechanism and pattern of its influence on the aerodynamic characteristics of rotor blades are studied by observing the pulling force change curve and cloud diagram. Finally, an experimental platform is built to verify the correctness of the simulation results. The simulation and experimental results have a similar law, i.e., crosswinds do not only have a negative effect on reducing the pulling force generated by the rotor blades, but can also have a gain effect on the pulling force in some cases. This law has some application value, which can be utilized to compensate the rotor speed accordingly. When the pulling force is reduced, the speed is increased appropriately to improve the reliability of the adsorption, and when the pull force is increased, the speed is reduced appropriately, which can reduce the power consumption and increase the range.

Development of inorganic phase change material and cold charging performance analysis based on cold storage refrigerator

Phase change cold storage refrigerators are a core of low-carbon development in cold chain logistics. This study is dedicated to optimizing the performance of phase-change cold storage refrigerators for the refrigerated transport of fruits and vegetables. Firstly, a high-performance inorganic phase change material was developed. By selecting a ratio of 2 wt% Carboxymethyl cellulose and 0.25 wt% Fumed Silica to solve the defects of supercooling and phase separation of this material, Subsequently, an experimental platform was built based on this material to test the charging and cooling characteristics of the phase change module. It was found that the opening of the expansion valve, the compressor’s speed, and the condenser fan’s speed were positively correlated to the charging and cooling efficiencies. Still, the effects of the first two on the system performance coefficient had optimal value points. However, there is an optimum point for the impact of the first two on the system performance coefficient. The optimized system coefficient of performance is increased to 1.62, and the charging time is reduced by 47.6 %, which is a good balance between energy efficiency and transport efficiency. In addition, it was found that the radial thermal resistance of the evaporator tubes in the charging module was more significant than the axial direction, and a new type of finned tube was designed to enhance the heat transfer, which resulted in a further reduction of 29 percent in the charge time.

Experimental study on acoustic and flame responses in an annular combustor under azimuthal force

This study investigates the flame dynamics of an annular combustor under various azimuthal acoustic forces. The experimental platform was applied to external azimuthal acoustic force through four loudspeakers installed on the wall outside the plenum. Experiments were conducted with an equivalence ratio of Ф = 0.71 and a combustion power of P = 9.5 kW. Therein, four loudspeakers were used to precisely control the acoustic pressure amplitude, spinning ratio, and spinning direction, which stimulated the first-order standing mode, spinning modes, and second-order standing mode acoustic mode of the plenum, respectively. Our analysis of flame dynamics and acoustic response characteristics was based on different azimuthal modes, combined with dynamic mode decomposition (DMD) of the flame image sequence and acoustic pressure signal. The results corroborate the consistency between the acoustic field quaternion analysis and the flame image sequence DMD results despite varied external forcing conditions. Specifically, the characteristics of the flame vary by the applied forces. Moreover, DMD results indicate that the flame structure of the eigenfrequency mode is asymmetrical in spinning modes and tilts in the direction of the pressure gradient.