Voltage Signal Research Articles

Degradation prediction of proton exchange membrane fuel cell using a novel neuron-fuzzy model based on light spectrum optimizer

Proton exchange membrane fuel cell (PEMFC) is regarded as the most promising clean energy to address the fossil energy crisis and environmental pollution. However, it is susceptible to frequently variable load and the impurities of hydrogen, which can directly cause the degradation of performance over time during operations. Degradation prediction has received much attention in recent years, as it can improve the durability and reliability of the PEMFC system. This paper proposes an effective multi-step-ahead prediction for PEMFC degradation under various operational conditions by using variational mode decomposition (VMD), a double recurrent fuzzy neural network (DRFNN), and a light spectrum optimizer (LSO). The integrated method enables precise prediction of degradation trends of PEMFC using historical testing data, which brings together their advantages. To better learn degradation trends, VMD is applied to decompose the input voltage signal into a series of sub-signals with a simpler structure. Then, DRFNN with a feedback loop is developed to train each sub-signal model, which can learn and memorize past information. To further enhance the prediction precision of degradation model, LSO is adopted to automatically update the network’s weights. Finally, the prediction performance of the proposed method is experimentally verified under different load conditions. Compared with other degradation methods, the test results reveal that the proposed method can achieve significant improvements in terms of multi-step-ahead prediction accuracy and robustness.

Phase-stabilised self-injection-locked microcomb

Microresonator frequency combs (microcombs) hold great potential for precision metrology within a compact form factor, impacting a wide range of applications such as point-of-care diagnostics, environmental monitoring, time-keeping, navigation and astronomy. Through the principle of self-injection locking, electrically-driven chip-based microcombs with minimal complexity are now feasible. However, phase-stabilisation of such self-injection-locked microcombs—a prerequisite for metrological frequency combs—has not yet been attained. Here, we address this critical need by demonstrating full phase-stabilisation of a self-injection-locked microcomb. The microresonator is implemented in a silicon nitride photonic chip, and by controlling a pump laser diode and a microheater with low voltage signals (less than 1.57 V), we achieve independent control of the comb’s offset and repetition rate frequencies. Both actuators reach a bandwidth of over 100 kHz, enabling phase-locking of the microcomb to external frequency references. These results establish photonic chip-based, self-injection-locked microcombs as low-complexity yet versatile sources for coherent precision metrology in emerging applications.

Using Flexible-Printed Piezoelectric Sensor Arrays to Measure Plantar Pressure during Walking for Sarcopenia Screening.

Sarcopenia is an age-related syndrome characterized by the loss of skeletal muscle mass and function. Community screening, commonly used in early diagnosis, usually lacks features such as real-time monitoring, low cost, and convenience. This study introduces a promising approach to sarcopenia screening by dynamic plantar pressure monitoring. We propose a wearable flexible-printed piezoelectric sensing array incorporating barium titanate thin films. Utilizing a flexible printer, we fabricate the array with enhanced compressive strength and measurement range. Signal conversion circuits convert charge signals of the sensors into voltage signals, which are transmitted to a mobile phone via Bluetooth after processing. Through cyclic loading, we obtain the average voltage sensitivity (4.844 mV/kPa) of the sensing array. During a 6 m walk, the dynamic plantar pressure features of 51 recruited participants are extracted, including peak pressures for both sarcopenic and control participants before and after weight calibration. Statistical analysis discerns feature significance between groups, and five machine learning models are employed to screen for sarcopenia with the collected features. The results show that the features of dynamic plantar pressure have great potential in early screening of sarcopenia, and the Support Vector Machine model after feature selection achieves a high accuracy of 93.65%. By combining wearable sensors with machine learning techniques, this study aims to provide more convenient and effective sarcopenia screening methods for the elderly.

Open-source synthetic photoplethysmographic signal generator with analog output.

The photoplethysmographic (PPG) signal of the finger is being used to create embedded devices that estimate physiological variables. This project outlines an innovative method for developing a synthetic PPG generator that produces both actual reference digital signals and their equivalent analog signals using open-source technology. A series of PPG profiles is synthesized using three variant Gaussian functions. A low-frequency trend induced by respiratory frequency and background noise are then added. To generate a diverse range of continuously variable PPG profiles within specified boundaries and customizable levels of interference, all parameters undergo random fluctuations on a cycle-by-cycle basis, as per user-defined constraints. The generated signal is then converted into its equivalent analog form through the use of an RC filter that low-frequency filters a Pulse-Width Modulation square wave that is modulated directly by the generated signal. The software returns different PPG profiles and allows the signal comparison before vs after the addition of different-intensity modulated respiratory trends and background noise. The digital signal is faithfully converted into an equivalent analog voltage signal capable of reproducing not only the waveform profile but also the respiratory trend and various levels of noise.

(Invited) Cardiac Engineering: Optical Mapping in Cellular Communication

Microfluidic platforms have emerged as valuable tools for replicating the adverse effects of chemotherapy drugs on cardiac and non-cardiac cells in vitro. However, these systems currently fall short of comprehensively capturing the intricate nature of human responses to chemotherapy, necessitating further validation to enhance model accuracy. While innovative chip systems provide insightful means for screening cardiac toxicity, they encounter challenges in addressing individual variations in drug responses and the influence of patient-specific factors.A novel cardiac chip incorporating intracellular electrophysiological techniques was developed to monitor acute hypoxia-induced responses in cardiac cells. Despite its promising potential, the model exhibits limitations inherent to its simplified two-dimensional microfluidic system, warranting additional validation and optimization efforts to enhance physiological relevance and facilitate applications in clinical translation.Moreover, previous research has proposed the formation of functional connections between human neurons and myocardial cells in a microfluidic chip. However, this study primarily focused on one-way connections between neurons and myocardial cells, overlooking the complexities of bidirectional neuro-muscular connections. Consequently, further research is imperative to deepen our understanding of the intricate interactions between neurons and myocardial cells.Optical mapping techniques assessed transmembrane voltage, mitochondrial activity, and calcium signals during electric pacing. These comprehensive assessments aim to advance our understanding of cardiac electrophysiology under drug exposure, providing crucial insights into the intricate responses of cardiac cells to chemotherapy. This research has significant implications for enhancing our understanding of cardiac safety in the context of chemotherapy.

Object slip detection sensor based on triboelectric nanogenerator

Existing gripping devices limit the way of gripping the object, and the object may slide due to insufficient friction, when the manipulator grips the object, the object may slip phenomenon, which leads to the manipulator can not complete the gripping work normally. In order to solve this problem, this paper proposes a robotic slipping sensor to detect the slipping state of the object and its slipping distance, the sensor through the friction of two different materials and electrostatic induction phenomenon of triboelectricity and the peak voltage signal to determine whether the contact object produces the phenomenon of slipping and its slipping distance. This design integrates two rectangular copper foils and two polytetrafluoroethylene (PTFE) films together to form a triboelectricity nanogenerator in independent layer mode, which judges the slip distance of an object by the peak voltage signal generated by the object’s slip, which is flexible and can be combined with a robot to make the robot more flexible and convenient in its work. In order to verify the performance of this sensor, horizontal slip test and vertical slip test were conducted. In the horizontal slip test and vertical slip test, the peak voltage signal output from the TENG sensor has a linear relationship with the slip distance of the object. The sensor and the object contact slip process ends after 100 ms, the oscilloscope will output the peak voltage signal, so that according to the size of the peak voltage signal to determine the object in the range of 0–10 cm slip distance, for judging whether the object appears to slip phenomenon and the occurrence of the phenomenon of the slip distance it produces provides a flexible program.

A High Voltage Signal Conditioner for the Quench Detection System of the ITER Superconducting Magnet

A High Voltage Signal Conditioner for the Quench Detection System of the ITER Superconducting Magnet

Energy efficiency and electrochemical characteristic assessment of PEMFC-supercapacitor hybridization via railway profile

Electric trains equipped with efficient electric motors and powered by high-capacity hydrogen fuel cells are a choice of the global transportation industry to accelerate the global transition to a low-carbon transportation system. The operation of distinct vehicle types in varying geographical regions is characterized by unique features. Concurrently, the continually fluctuating demand for electricity necessitates the utilization of fuel cells in conjunction with energy storage devices. Thus, this study centers on examining the role of the PEMFC-supercapacitor direct hybridization system within the authentic driving profile of the subway in the Bangkok metropolitan region. The supercapacitor within the hybridization system serves as an energy management device, enhancing the durability of fuel cells when subjected to diverse load demands associated with train propulsion. In order to validate this hypothesis, an MRT (Mass Rapid Transit) protocol was formulated to emulate and serve as the load demand for the investigation, and then the PEMFC-supercapacitor direct hybridization system was operated using the created profile. Subsequently, the hybridization system undergoes electrochemical characterizations and fuel cell performance tests for diagnostic purposes. Empirical findings validated that the supercapacitor operates as a filter, generating a stable voltage response to varying load requirements, and possesses the capability to assimilate energy originating from regenerative braking. The integration of supercapacitors in stabilizing the operational parameters of proton exchange membrane fuel cells (PEMFCs) has markedly prolonged their operational lifespan. This assertion is substantiated by voltage signal analysis, which demonstrated a negligible increase in surface resistance of merely 0.23%, as depicted by Nyquist plots. Furthermore, cyclic voltammetry revealed that the system attained a peak electrochemical surface area (ECSA) of 0.0089 (106 cm2/mg of Pt). The information presented in this article constitutes data that has been minimally explored in existing research. Specifically, the study concentrates on the practical driving pattern of electric trains. Consequently, experimental results were deemed advantageous for the advancement of heavy-duty electric vehicles.

Research on degradation analysis and health condition assessment method of phase shifter

Abstract In high-power, high-reliability power supply systems, the switching operation of power devices is driven by phase shifters. Degradation or failure of the phase shifters leads to power device shoot-through and other failures, and reduces the power devices’ service lifetime. Aiming at the problem of phase shifter condition monitoring, a degradation model is proposed by analysing the degradation mechanism and aging process of the sensitive key components of the phase shifter, and then a health condition monitoring method with multi-dimensional features is presented in this paper. The multidimensional feature vectors in the time domain are extracted from the output voltage signals of different aging stages, and the time-domain feature separability of different health conditions is verified. Then the deep neural networks identification model based on deep learning technology is proposed to recognize the health condition of phase shifter. At last, the performance degradation simulation and experimental evaluation show that the proposed method can identify the phase shifter health conditions more accurately, and the accuracy rate can reach 95.892%.

AC metrology applications of the Josephson effect

The performance of programmable voltage signals that exploit the quantum behavior of superconducting Josephson junctions continues to improve and enhance measurements in metrology, communications, and quantum control. We review advances in pulse-driven digital synthesis techniques with Josephson-junction-based devices. Quantum-based synthesis of voltage waveforms has been demonstrated at frequencies up to 3 GHz and rms amplitudes up to 4 V. Josephson pulse generators have also been used to control and characterize superconducting qubits.

Voltage inspector: Sender identification for in-vehicle CAN bus using voltage slice

Controller Area Network (CAN) serves as the neural system of modern cars, connecting and coordinating various electronic control units (ECUs) responsible for vehicle operation. However, the inherent features of CAN, such as broadcast communication and lack of authentication, make it increasingly vulnerable to cyberattacks. Although existing intrusion detection systems (IDSs) perform well in detecting malicious attacks, they often lack the ability to accurately locate the senders of these malicious messages. In this paper, we propose an efficient sender identification method called Voltage Inspector, which leverages physical voltage signal slice to accurately identify the source of messages for CAN bus. We start by extracting voltage slices from the raw physical signals of the CAN bus. Next, we leverage clustering technology to infer the ECU mapping information, which is typically considered confidential. This mapping information, combined with a machine learning classifier, is then utilized to construct an identification model capable of accurately identifying the sender of each message. To validate the effectiveness of our proposed method, we conducted extensive experiments using a publicly available voltage dataset collected from ten real vehicles. The experimental results demonstrate the remarkable accuracy of our approach, achieving a minimum identification accuracy of 99%. Furthermore, our method significantly reduces the data volume by half and reduces the identification time by a quarter when compared to state-of-the-art methods. Our research reveals that even a small portion of the voltage signal can be used to uniquely fingerprint an ECU. We emphasize that our method serves as an alternative identification approach and can complement existing works in the field.

An Early Warning Method for Volleyball Players with Human Bioelectric Energy

Bioelectric energy is a weak electric current generated by the human body itself, and the change in its value can indicate human injury, which can be used as a competitive physical injury judgment information. However, in the past, bioelectric energy measurement was mainly based on the standard voltage of 220 V, and it was not possible to conduct follow-up tests, so the scope of application was limited. Photovoltaic equipment in new energy has the characteristics of being easy to carry and can meet the needs of bioelectric energy testing at the theoretical level. In order to improve the application depth of new energy equipment and improve the safety of volleyball competition. Therefore, from the perspective of new energy, this paper tests the human bioelectric energy, collects current, voltage and power flow signals, combines the different functions to reduce the data dimensionality, calculates the abnormal changes of bioelectricity, and forms an abnormal data set. Finally, according to the injury characteristics of the human body, the volleyball economy is given an early warning. The results show that photovoltaic cells can meet the measurement requirements of human bioenergy, and the detection accuracy rate of joint injury, negative emotion, muscle strain and respiratory system injury reaches 80~83%, the abnormal electrical energy recognition rate reaches 90~95%, and the athlete satisfaction rate reaches 98%, and the wearing time is 1~3 months. Therefore, photovoltaic equipment can meet the identification requirements of human bioelectricity and has practical value in assisting competitive training and injury early warning.

Diagnostics of unmanned aerial vehicle with recurrence based approach of piezo-element voltage signals

This work experimentally addresses damage calibration of an unmanned aerial vehicle in operational condition. A wide range of damage level and types are simulated and controlled by an electric motor via pulse width modulation in this regard. The measurement is carried out via established protocols of using a piezo-patch on one of the 8 arms, utilising the vibration sensitivity and flexibility of the arms, demonstrating repeatability of such protocol. Subsequently, recurrence analysis on the voltage time series data is performed for detection of damage. Quantifiers of damage extent are then created for the full range of damage conditions, including the extreme case of complete loss of power. Experimental baseline condition for no damage condition is also established in this regard. Both diagonal-line and vertical-line based indicators from recurrence analysis are sensitive to the quantitative estimates of damage levels and a statistical test of significance analysis confirms that it is possible to automate distinguishing the levels of damage. The damage quantifiers proposed in this paper are useful for rapid monitoring of unmanned aerial vehicle operations of connection.

Epitaxial growth of c-axis inclined Sr3YCo4O10.5+ δ thin film: Insights into laser-induced voltage signals arising from magnetic anisotropy

We report the laser-induced voltage (LIV) signals in cubic phase Sr3YCo4O10.5+δ (CP-SYCO) thin films with a c-axis tilted and discuss the relationship between the LIV signals and magnetic anisotropy. CP-SYCO thin films were epitaxially deposited on 0°, 5°, 10°, 15°, and 20° miscut LaAlO3 (001) substrates using pulsed laser deposition (laser energy of 200 mJ, post-annealed at 760 °C). The peak voltage of the LIV for the 15° tilted CP-SYCO film is 310 mV (under laser energy of 400 mJ/pulse), with a rising edge of 26 ns. Compared with G-type antiferromagnetic ordered tetragonal phase Sr3YCo4O10.5+δ(OT-SYCO, deposited at a laser energy of 300 mJ, post-annealed at 790 °C) film, the CP-SYCO exhibits much stronger magnetization anisotropy along in-plane and out-plane due to the possible A-type antiferromagnetic moment arrangement. CP-SYCO films also demonstrate significant thermoelectric anisotropy, supporting the transverse thermoelectric effect, which is partially attributed to the contribution of spin entropy. This study provides an understanding of the anisotropic behavior in atomic layer thermoelectric stack materials and explores magnetic anisotropic material systems.

THD improvement of piezoelectric MEMS speakers by dual cantilever units with well-designed resonant frequencies

In this study, a piezoelectric MEMS (Micro-Electro-Mechanical-System) speaker with thoughtfully selected resonant frequencies of dual cantilever units is designed and implemented. Based on the lead zirconium titanate (PZT) thin film with superior piezoelectric coefficient, the cantilever diaphragm is designed through the modal analysis. In the 1.5 mm by 1.5 mm cantilever array, a high-frequency actuation unit with resonance of 13.1 kHz and a low-frequency actuation unit with resonance of 6.35 kHz constitute the proposed piezoelectric MEMS speaker. The boost of sound pressure level (SPL) due to the resonant mode of low-frequency actuation unit reduces the total harmonic distortion (THD) around the subharmonic frequency of high-frequency actuation unit. Meanwhile, the asymmetric triangle diaphragm reduces the maximum stress in the structure when operating at the resonant frequency, thereby mitigating the THD spikes resulting from the nonlinear structural behavior. In pressure-field measurements, the proposed design can reach SPL ≥ 70.0 dB from 2.7 kHz to 15.0 kHz with only 0.179 Vrms input. At the same time, THD remains below 3.0 % from 3.2 kHz to 20 kHz. The outstanding performance under larger input signal and bias voltage is also verified. Actuated by 0.354 Vrms and 2 VDC bias, the maximum SPL achieves 111.9 dB and the SPL is higher than 80.0 dB from 3.3 kHz to 14.1 kHz. Furthermore, THD is lower than 3.0 % from 1.0 kHz to 8.9 kHz. A promising solution of piezoelectric MEMS speaker for in-ear applications is demonstrated in this study.

Superimposed Positive Sequence Impedance for Detecting Unintentional Islanding in Microgrid

Incorporation of environmentally friendly energy sources (RESs) into the electricity grid has many benefits, including economic, technological, and environmental. However, excessive renewable energy sources (RES) in the power grid provide technical problems, including equipment protection, DG operation, and islanding detection. One of the most serious challenges is the islanding phenomenon. Islanding can cause several problems, such as frequency instability and voltage fluctuations resulting in damage to electrical equipment or threatening utility workers who may be working/accessing the equipment. This research proposes an efficient islanding detection algorithm to lessen the impact of such threats. This novel passive islanding detection scheme is based on superimposed positive sequence impedance (SPSI). For calculating the superimposed positive sequence impedance (SPSI), the voltage and current signals are obtained from targeted DG points. The scheme’s performance is tested on multiple bus systems across islanding and non-islanding conditions using a MATLAB/Simulink environment. It is shown that even in the presence of noise, the algorithm can determine an islanding decision with high accuracy and a short detection time of 84 ms. In comparison to other algorithms, it operates at zero power mismatch (ZPM) and does not affect power quality.

Emergent Inhomogeneity and Nonlocality in a Graphene Field-Effect Transistor on a Near-Parallel Moiré Superlattice of Transition Metal Dichalcogenides.

At near-parallel orientation, twisted bilayers of transition metal dichalcogenides exhibit interlayer charge transfer-driven out-of-plane ferroelectricity. Here, we report detailed electrical transport in a dual-gated graphene field-effect transistor placed on a 2.1° twisted bilayer WSe2. We observe hysteretic transfer characteristics and an emergent charge inhomogeneity with multiple local Dirac points evolving with an increasing electric displacement field (D). Concomitantly, we also observe a strong nonlocal voltage signal at D ∼ 0 V/nm that decreases rapidly with increasing D. A linear scaling of the nonlocal signal with longitudinal resistance suggests edge mode transport, which we attribute to the breaking of valley symmetry of graphene due to the spatially fluctuating electric field from the underlying polarized moiré domains. A quantitative analysis suggests the emergence of finite-size domains in graphene that modulate the charge and the valley currents simultaneously. This work underlines the impact of interfacial ferroelectricity that can trigger a new generation of devices.

Controllable Double‐Emulsion Droplet Manipulation Actuated by a Triboelectric Nanogenerator

AbstractElectrically controlled droplet manipulation is attractive for lots of applications ranging from material synthesis and drug delivery. However, the present techniques using bulky and expensive power supplies to generate high‐voltage electrical signals are easy to cause electrode electrolysis, bubble formation in buffer solution, and are incapable for point‐of‐care testing. Herein, a novel and portable electrical method for self‐powered emulsion droplet core coalescence and release based on triboelectric nanogenerator (TENG) is presented. The electrical signal with a 2.25 kV open‐voltage and a 35 µA short‐circuit current can be conveniently generated via frictional electrification effect, and the harmful electrochemical reaction in solutions can be avoided. The high transient pulse voltage and stable sinusoidal signal between two electrodes in microfluidic device can generate a strong electric field, which causes apparent Maxwell electrical stresses at droplet interfaces and subsequently promotes the core coalescence and release of double‐emulsions. By adjusting the voltage magnitude, the core coalescence and release behaviors of double‐emulsion droplets can be controllably achieved, followed by the synthesis copper‐based metal–organic framework (Cu‐MOF) nanoparticles. Thus, this portable, effective and self‐powered droplet manipulation technique can be attractive for those droplet‐based applications.

Building an Educational Automated Mechatronics-Based Sorting System

This paper discusses the development of an automated sorting machine designed as a comprehensive mechatronics educational project. The project integrates mechanical and electrical design, incorporating a robot arm, a microcontroller, sensors, and actuators. The sorting machine uses color identification to sort wooden blocks of three different colors. The blocks are stacked and dropped onto a conveyor belt by a hopper system that employs a solenoid actuator and a servo to release one block at a time at specific intervals. As the belt runs continuously, each block passes under a color sensor, which monitors the color and signals one of three servo-powered mechanical arms to guide the block into the appropriate chute. Each chute is equipped with a capacitive proximity sensor that sends a voltage signal to the robot controller, queuing commands for the robot to pick up the blocks from the bottom of each chute and return them to the hopper to form a continuously running sorting system. This paper details the design and integration of the system’s various elements and the development of the control software. The designed system can drop blocks every 8.05 s, sort each block within 5 s of being sensed, and return them to the sorting system every 12 s. It has a color-sensing accuracy of 97%, with a failure rate of around 7%. The system achieved quick and reliable sorting using various low-cost, accessible, and open-source parts. The project exemplifies a cost-effective solution suitable for mechatronics education, demonstrating the numerous challenges involved in developing automated sorting systems.

Flexible self-powered triboelectric nanogenerator sensor for wind speed measurement driven by moving trains

Flexible self-powered sensors have been extensively applied to the Internet of Things, structural health monitoring (SHM), and intelligent transportation. It would be more demanding for the power supply to these sensors during the long-term maintenance of the rail transit system. The wind pressure/velocity generated by high-speed trains poses a substantial threat to safety of human, and new sensors without an external power supply should be developed to monitor wind pressure/velocity in the trackside. Flexible self-powered wind triboelectric nanogenerator (W-TENG) sensor with a single-electrode mode based on conductive hydrogel is designed to wind pressure/velocity monitoring without power supply by harvesting wind energy. It is devoted the relationship between the output voltage of the sensors and the wind pressure/velocity driven by high-speed trains. Material selection and structural design methods are adopted to enhance the energy harvesting efficiency and sensing accuracy of self-powered W-TENG sensors. Open-circuit current of 2.8 μA and open-circuit voltage of 12 V are achieved, and the output voltage signal has the linear relationship with trackside wind pressure/velocity. Field tests are implemented to evaluate the performance of self-powered W-TENG sensors in wind pressure/velocity measurement caused by moving trains, providing an idea to SHM application in intelligent transmit systems.