Measured Voltage Signals Research Articles

Solving the MPI reconstruction problem with automatically tuned regularization parameters

In the field of medical imaging, magnetic particle imaging (MPI) poses a promising non-ionizing tomographic technique with high spatial and temporal resolution. In MPI, iterative solvers are used to reconstruct the particle distribution out of the measured voltage signal based on a system matrix. The amount of regularization needed to reconstruct an image of good quality differs from measurement to measurement, depending on the MPI system and the measurement settings. Finding the right choice for the three major parameters controlling the regularization is commonly done by hand and requires time and experience. In this work, we study the reduction to a single regularization parameter and propose a method that enables automatic reconstruction. The method is qualitatively and quantitatively validated on several MPI data sets showing promising results.

Research on Frequency‐Domain Electromagnetic Detection Signal Acquisition Technology Based on Multi‐frequencyLCFiltering

Frequency‐domain electromagnetic (FDEM) instruments are effective tools for detecting underground electrical abnormal targets. However, due to the influence of various noises in the detection process, would lead to l poor signal‐to‐noise ratio of the system and would ultimately affect the detection accuracy. And FDEM detection systems have a defined measuring range. Excessive noise will lead to saturation of the system acquisition and distortion of the measurement. Because of the above problems, an acquisition technique for the FDEM receiving system is proposed to achieve noise suppression in the detection process. The scheme proposes a multi‐frequency LC filtering circuit to optimize the analog signal acquisition module of the receiver system. The circuit converts the measured voltage signal into a current signal, improving the system's immunity to interference. At the same time, the noise of non‐target frequencies in the measured signal is suppressed, avoiding saturation of the receiving system acquisition, and realizing high signal‐to‐noise acquisition of multiple target frequencies. First, the principle of circuit design is analyzed, and the design flow of circuit parameters is proposed. Further, the validity of the design is verified via simulation results. Finally, the optimization scheme is validated with an actual circuit. The results show that for the measurement environment set in this paper, this optimization scheme can suppress the noise of non‐target frequency in the measured signal, so that the noise amplitude can be reduced by more than 90%, and the signal‐to‐noise ratio of the filtered signal can be improved by 23.35 dB. This study helps to facilitate FDEM instruments to achieve high signal‐to‐noise ratio detection. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Simultaneous Measurement of Flow Velocity and Electrical Conductivity of a Liquid Metal Using an Eddy Current Flow Meter in Combination with a Look-Up-Table Method.

The Eddy Current Flow Meter (ECFM) is a commonly employed inductive sensor for assessing the local flow rate or flow velocity of liquid metals with temperatures up to 700 ∘C. One limitation of the ECFM lies in its dependency on the magnetic Reynolds number for measured voltage signals. These signals are influenced not only by the flow velocity but also by the electrical conductivity of the liquid metal. In scenarios where temperature fluctuations are significant, leading to corresponding variations in electrical conductivity, it becomes imperative to calibrate the ECFM while concurrently monitoring temperature to discern the respective impacts of flow velocity and electrical conductivity on the acquired signals. This paper introduces a novel approach that enables the concurrent measurement of electrical conductivity and flow velocity, even in the absence of precise knowledge of the liquid metal's conductivity or temperature. This method employs a Look-Up-Table methodology. The feasibility of this measurement technique is substantiated through numerical simulations and further validated through experiments conducted on the liquid metal alloy GaInSn at room temperature.

Capacity estimation of lithium-ion batteries based on adaptive empirical wavelet transform and long short-term memory neural network

To ensure the durability and safety of electric vehicles (EVs), it is vital to monitor the capacity deterioration of lithium-ion batteries (LIBs). However, due to complex physicochemical interactions and temperature effects, the capacity of LIBs cannot be directly measured. This work presents a novel generalized approach for estimating the capacity of LIBs based on the adaptive empirical wavelet transform (EWT) and long-short-term memory neural network (LSTM). The adaptive EWT is a potent tool for analyzing the charging-discharging terminal voltage signal with non-stationary and transient phenomena of the LIBs in the time-frequency domain. Specifically, the measured voltage signals of the LIBs are decomposed into nine multi-resolution modes to display the high and low-frequency components. Then, 13 statistical features are extracted to investigate the correlation between the capacity degradation and the extracted features. Afterwards, the LSTM model is developed to estimate the capacity of the LIBs. The proposed approach has been validated using two datasets: (1) NASA's randomized dataset with 24 LIBs cycled under generally varying operational conditions and (2) Stanford University's dataset with 10 LIBs cycled with EV discharge current profile. Compared with the state-of-the-art, the proposed method accurately estimates LIB capacity with an average root mean square error of 1.26 % and a maximum error of 2.74 %.

Research on the High Voltage Near Electric Safety Distance Alarm Device

In the process of construction near overhead transmission lines, the safety distance between the crane, tower crane, and live wire is insufficient, which leads to the tripping of lines. It not only causes huge economic losses, but also endangers human life safety. In order to avoid the car accidentally touching live wire, this paper studies a high voltage near electric safety distance alarm device, and introduces the structure and working principle of the device. The voltage signal is measured by the test to reflect the distance between the vehicle and the live wire. The measured voltage signal data is processed and compared with the alarm voltage signal threshold. According to different voltage levels and distances, the high voltage near electric safety distance alarm device can realize voice alarm and remind operators to pay attention to keep a safe distance.

Development of fiber-based piezoelectric sensors for the load monitoring of dynamically stressed fiber-reinforced composites

Continuous load monitoring of fiber-reinforced composites represents a complex challenge for the composite sector. In this paper, the development and characterization of piezoelectric sensors for structural health monitoring applications based on polyvinylidene fluoride (PVDF) are presented. The basic sensor structures are melt spun bicomponent filaments in a core-sheath configuration. The core is a volumetric mixture of polypropylene (PP) and Pre-Elec PP, a PP-compound modified with carbon black. The sheath is made of PVDF. Three variants with increasing volumetric Pre-Elec ratio in the core (50 vol.%, 60 vol.% and 70 vol.%) are manufactured and analyzed by conducting optical and resistance measurements as well as tensile tests. In the subsequent process step, the bicomponent filaments are braided with copper fine wires. The PP core and copper braiding of the coaxial tricomponent yarn are used as inner and outer electrode, respectively. By generating an electrostatic high voltage field between these electrodes, the PVDF interlayer is subjected to contact polarization to initialize the piezoelectric effect. The piezoelectric characteristics and thereby the sensory potential of the produced sensor yarns are quantified and analyzed on the fiber and composite scale by investigating their piezoelectric behavior during cyclic tensile tests. The results of the different sensor yarn variants are compared with each other by evaluating the measured voltage signal as a function of the induced cyclic stresses.

Cathode Position Detection in a Transferred Arc Plasma Using Artificial Neural Network

In a transferred arc plasma system, the position of the cathode is difficult to detect during the smelting process as it remains inside the cylindrical anode. Real-time and accurate cathode position detection leads to efficient smelting operation with optimal use of electrical energy. In this article, a machine learning technique is proposed to accurately detect the position of the cathode in a direct current (DC) transferred arc plasma system. The measured voltage signal sampled at 20 kHz is processed using a tunable Q-factor wavelet transform (TQWT) followed by statistical features extraction and a machine learning algorithm to provide accurate cathode position information. Two different machine learning algorithms are used in this work, namely, single hidden layer neural network (SHLNN) and single-layer extreme learning machine (SELM). The output of these machine learning algorithms provides accurate position information and is also compared to the traditional voltage-related position information. The experimental signal of a 30-kW DC plasma system and cathode position detection results is shown.

Genetic Algorithm Optimization of Rainfall Impact Force Piezoelectric Sensing Device, Analytical and Finite Element Investigation.

In this paper, rainfall droplet impact force is transformed into a measurable voltage signal output via the piezoelectric material direct effect utilized for sensing purposes. The motivating sensor is utilized to measure the peak impact forces of rainfall droplets for further analysis and processing. Constructing a sense for the impact force of rainfall droplets has great implications in many real-life applications that can provide vital information regarding the amplifications of the impact force of rainfall on soil erosion, and the impact on small creatures and plants, etc. The rainfall droplet is set to collide on a very thin aluminum plate with negligible mass that can be presented geometrically as an extended segment of the proposed sensing device. The proposed sensing device is composed of a bimorph simply supported composite-piezoelectric beam that buckles due to the effect of the rain droplets' vertical impact force. The proposed device is designed for optimal performance in terms of the amount of voltage that can be measured. This is accomplished by having the first critical buckling load of the device as less than the impact force of the rainfall droplet. Accordingly, the well-known genetic algorithm (GA) automated optimization technique is utilized in this paper to enhance the measured voltage signal. A proof mass is added to the middle of the beam to amplify the magnitude of the measured voltage signal. The voltage signal is intended to be transferred to the PC via a data acquisition system. The rainfall droplets' peak impact forces are obtained analytically due to the nonlinear behavior of the beam using the Euler-Bernoulli thin beams assumptions. The FE model using COMSOL 6.0 Multiphysics commercial software is used to verify the analytical results.

Harmonic Signature-Based One-Class Classifier for Islanding Detection in Microgrids

This article presents a new passive islanding detection technique in MGs that uses locally measured voltage signals at the PoC of DERs. The proposed method distinguishes islanding events from normal/non-islanding conditions by utilizing superimposed harmonic spectra extracted through a full-cycle discrete Fourier transform. Our solution utilizes a machine-learning-based one-class classifier to define and adjust thresholds for full harmonic spectra. Unlike other methods, our approach does not require data synchronization or communication infrastructure, nor does it suffer from common errors that often arise in current transformers. Moreover, our design is compatible with distributed and decentralized control strategies, as it relies solely on local voltage measurements at the PoC. Another advantage of this method is its low sampling frequency requirement, in the range of 1 kHz, making it cost-effective and implementable in most existing systems. In a comprehensive evaluation of a typical MG test system that included synchronous and inverter-based DERs, the proposed scheme demonstrated exceptional performance. Specifically, the scheme was able to detect 99.06% of different islanding events within the training range, with a detection time of just 10 to 21 ms. Additionally, the scheme remained 100% stable during various normal conditions, short-circuit faults, load changes, voltage changes, capacitor switching, and frequency changes.

Improvement of longitudinal beam tracking simulation considering the frequency response of the cavity gap voltage monitor

Wideband RF cavities are employed in the Rapid Cycling Synchrotron of the Japan Proton Accelerator Research Complex. Not only the driving harmonics, but also the higher harmonics due to the wake voltage, are excited in the wideband RF cavities during a high power beam acceleration. A longitudinal beam tracking simulation including the contribution of the higher harmonics is required to evaluate the longitudinal beam behavior during the high power beam acceleration. The longitudinal beam tracking simulation using the measured voltage is performed. The frequency response of the cavity gap voltage monitor must be taken into account to use the measured voltage signal in the tracking simulation. The theoretical frequency response models of the voltage divider and the coaxial cable, which are the primary components of the cavity gap voltage monitor, are developed. By taking the frequency response into account, the tracking simulation well reproduces the measured bunch shape at 1 MW. The tracking simulation shows that the higher harmonics are large enough to deform the bunch shape. The longitudinal beam behavior can be evaluated by using the higher harmonics properly.

Modeling the magnetization dynamics for large ensembles of immobilized magnetic nanoparticles in multi-dimensional magnetic particle imaging

Magnetic nanoparticles (MNPs) play an important role in biomedical applications including imaging modalities such as magnetic resonance imaging (MRI) and magnetic particle imaging (MPI). The latter one exploits the non-linear magnetization response of a large ensemble of magnetic nanoparticles to magnetic fields which allows determining the spatial distribution of the MNP concentration from measured voltage signals. The image-to-voltage mapping is linear and described by a system matrix. Currently, modeling the voltage signals of large ensembles of MNPs in an MPI environment is not yet accurately possible, especially for liquid tracers in multi-dimensional magnetic excitation fields. As an immediate consequence, the system matrix is still obtained in a time consuming calibration procedure. While the ferrofluidic case can be seen as the typical setting, more recently immobilized and potentially oriented MNPs have received considerable attention. By aligning the particles magnetic easy axis during immobilization one can encode the angle of the particle’s magnetic easy axis into the magnetization response providing a relevant benchmark system for model-based approaches. In this work we address the modeling problem for immobilized and oriented MNPs in the context of MPI. We investigate a model-based approach where the magnetization response is simulated by a Néel rotation model for the particle’s magnetic moments and the ensemble magnetization is obtained by solving a Fokker–Planck equation approach. Since the parameters of the model are a-priori unknown, we investigate different methods for performing a parameter identification and discuss two different models: One where a single function vector is used from the space spanned by the model parameters and another where a superposition of function vectors is considered. We show that our model can much more accurately reproduce the orientation dependent signal response when compared to the equilibrium model, which marks the current state-of-the-art for model-based system matrix simulations in MPI.

Observed Oscillating Anodic Plasma Plume Phenomena in High Current Vacuum Arcs

The impact of current density on the anode attachment characteristics of high current vacuum arcs was investigated using high-speed imaging techniques. The current density determines the occurrence of attachment transition into an anode spot type 2. Electrodes were made of Cu/Cr 50%/50%. In combination with the adequate amplitude of current, the anode spot type 2 was accompanied by first observed oscillating anodic plasma plumes by the higher current densities. Resulting electrode overheating processes, which depend on the instantaneous power delivered, determine the formation of a plasma region in front of the anode. Image processing indicates that this region interacts actively with the cathode, displacing the radiation intensity region, and influencing the behavior of its arc spots. In addition, increased high-frequency contents on the measured voltage signals accompanied the observed oscillations of the anodic plume. An explanation through a double-layer effect for the anode spot type 2 is proposed.

Portable Chemiluminescence-Based Lateral Flow Assay Platform for the Detection of Cortisol in Human Serum.

In this study, we developed the portable chemiluminescence (CL)-based lateral flow assay (LFA) platform for the detection of cortisol in human serum. Cortisol is well-known as a stress hormone due to its high relevancy for human mental and physical health, such as hypertension or depression. To date, a number of optical devices have provided the sensitive determination of levels of analytes. However, this modality type still requires costly optical modules. The developed CL platform is simply composed of two detection modules along with a loading part for the LFA strip. The LFA membrane contains gold nanoparticle probes conjugated with antibodies against cortisol and horseradish peroxidase (HRP), which can also efficiently increase the luminescent signal by providing many areas for anti-cortisol antibody and HRP. The measured voltage signals coming from the photodiode in a CL reader were compared with a standard microplate reader for the evaluation of accuracy. The linear range observed for cortisol was measured to be 0.78–12.5 μg/dL (R2 = 0.99) with a limit of detection (LOD) of 0.342 μg/dL. In addition, the CL-LFA reader showed a high correlation (R2 = 0.96) with the standard cortisol console (COBAS 8000, Roche), suggesting that our developed CL-based LFA platform can be usable in situ.

DEVELOPMENT OF PADDY-FIELD WATER LEVEL GAGE CORRESPONDING TO A SENSOR-NETWORK

This study proposes a measurement system that comprises an e-Tape water level sensor, Arduino and XBee. The system was considered a success because of the linear relation between measured voltage signals and water depths obtained by it. This linearity was essential because Arduino does not have non-linear calculation ability. As a result, the numerical order of RMSE in measuring water depth using this system was obtained as 3.52 mm. For measuring water consumption for 1 day at the standard scale of paddy fields in Japan, water consumption can be estimated using the system below non-flowing water surfaces. However, when there is water flow, it will be difficult to estimate water consumption because discharge errors may be cumulative.

Modernization of laboratory rolling mill 150 for the formation of students 'studies of automation object research

Studying the operation of automated control systems using computers significantly re-duces the time, but does not give a complete picture of the system on a real object. Therefore, the use of real objects for the study of control systems in the educational process is appropriate and useful. The purpose of the study is to modernize the laboratory rolling mill 150, designed for rolling lead, tin and plasticine, and equip it with sensors and actuators. The manual pressure device of the rolling stand was replaced by an automated one, for which an worm gearbox was additionally installed, which allowed to increase the total gear ratio to 94.5. The thrust screws are moved from the AC motor, which is controlled by the DOP-103BQ operator panel and the MS-300 frequency converter with built-in PLC. As a displacement sensor used photopulse sensor PDF-3. The installed equipment and the developed software for the operator panel and the PLC provided high accuracy of in-stallation of pressure screws in the set position. Additionally, software was developed to measure the power parameters of the rolling process: the rolling force and the electrical parameters of the DC motor of the drive of the rolling stand. The software allows you to configure the board, ie select the type of board, the channels used to measure voltage signals, select measurement ranges, signal color on the graph, signal polling frequency, number of points to display on the graph, parameters of graph coordinate axes. In the process of measurement, the output of signals to the monitor screen is performed simultaneously. Before rolling, the measurement process is started using the keyboard or mouse and the change of parameters is displayed on the screen in real time. At the end of the rolling process, the measurement stops, and the graphs of parameter changes over time remain on the monitor screen, which allows you to quickly analyze the process. The measurement results can be saved in an Excel file and then the file can be viewed. The file stores: the time of measurement of parameters and the values of parameters those were measured. Measurement of power parameters and sizes of rolled products before and after rolling allowed to determine the stiffness of the stand and rolled metal, which is necessary to calculate the transmission coefficients of the automated tuning system of the rolling stand.

Numerical analysis and experimental verification of the induced waveform characteristics for aeroengine gas path debris electrostatic sensor

This article introduces the principle of aeroengine gas path electrostatic monitoring and establishes a mathematical model of aeroengine gas path debris electrostatic sensor. In this study, we simulate particle’s movement based on the established model and perform numerical analysis of the induced charge pulse waveform. The simulation results show the quantitative relationship among particle’s charge amount, velocity, and pulse waveform’s features, and obtain the qualitative relationship between particle’s spatial position and pulse waveform’s features. A test rig is designed to verify the correctness of the mathematical model. A measurement mode based on dual-channel sensors has been proposed, and corresponding signal processing methods are used to calculate the velocity of the particle and reconstruct the charge pulse waveform from the measured voltage signal. The conclusions of this study not only avoid the shortcomings of traditional signal processing methods that directly use the measured voltage signal but also have important significance for improving the electrostatic monitoring capability.

Rogowski Coil Transducer-Based Condition Monitoring of High Voltage Insulators

In this paper, a proposed condition monitoring technique of high voltage insulators using Rogowski coil is presented. The proposed technique depends on the voltage signal that generated in Rogowski coil winding due to capacitive coupling between the insulator and the coil. A superimposed voltage signal is generated in the coil winding due to electromagnetic induction that occurs due to leakage current over the insulator surface during fog conditions. The output voltage signal from coil winding (i.e. due to capacitive coupling and electromagnetic induction) is directly analyzed using Fast Fourier Transform (FFT). The direct analysis of the output voltage signal from Rogowski coil without the need for current reconstruction reduces the complexity of signal processing. According to the obtained frequency spectrum of the output voltage signal, pollution level on insulator surface can be determined. In order to validate the proposed technique, an experimental setup is used. In experiments, high voltage pin type insulator is used and connected to a high voltage AC source. The insulator surface is sprayed with saline solution according to IEC 60815 standard to simulate different pollution levels. Also, the insulator surface is polluted with charcoal powder to simulate the non-soluble pollutants. For more validation, the experiments are extended to a cap and pin – type high voltage insulator. The obtained results show that the percent of 3rd/5th harmonics of the measured voltage signal increases with the increase in pollution level. Finally, the efficacy of the proposed technique is validated through the obtained results.

Simultaneous piezoelectric noninvasive detection of multiple vital signs

The monitoring of vital signs plays a key role in the diagnosis of several diseases. Piezoelectric sensors have been utilized to collect a corresponding representative signal from the chest surface. The subject typically needs to hold his or her breath to eliminate the respiration effect. This work further contributes to the extraction of the corresponding representative vital signs directly from the measured respiration signal. The contraction and expansion of the heart muscles, as well as the respiration activities, will induce a mechanical vibration across the chest wall. The induced vibration is then captured by the piezoelectric sensor placed at the chest surface, which produces an electrical output voltage signal conformally mapped with the respiration-cardiac activities. During breathing, the measured voltage signal is composed of the cardiac cycle activities modulated along with the respiratory cycle activity. A representative model that incorporates the cardiac and respiratory activities is developed and adopted. The piezoelectric and the convolution theories along with Fourier transformation are applied to extract the corresponding cardiac activity signal from the respiration signal. All the results were validated step by step by a conventional apparatus, with good agreement observed.

Intelligent Islanding Detection of Multi-distributed Generation Using Artificial Neural Network Based on Intrinsic Mode Function Feature

The integration of distributed energy resources (DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method (IIDM) using an intrinsic mode function (IMF) feature-based grey wolf optimized artificial neural network (GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.

A Method Combining Measurement Tool and Numerical Simulation for Calculating Acoustic Signals of Ultrasonic Flowmeter

Ultrasonic flowmeters (UFMs) based on the principle of transit time difference are widely used in process industries. The transit time difference is obtained through analyzing received acoustic signals generated by a couple of transducers located upstream and downstream respectively. The quality of received acoustic signal will directly affect the measurement accuracy of UFM. For this reason, a measurement-calculation coupled approach combining measurement tools and numerical simulation for calculating the acoustic signal of UFM is proposed in this paper. The method can predict the acoustic signals of UFM with different flowrates. First, important boundary condition (vibration velocity on transmitter transducer surface) is measured by measurement tool, which can reduce computational complexity and also avoid modeling transmitter transducers. Second, simulating the wave propagation (difficult to access experimentally) within moving medium by using the measured vibration velocity. Third, obtaining the transfer function of receiver by comparing estimated force acting on the receiver transducer surface with measured voltage signal from receiver, which can avoid modeling receiver transducer. Finally, predicting the received acoustic signal at different flowrates by using the transfer function. The rationality of predicted data was validated by comparing experimental and predicted acoustic signal on receiver transducer at different flowrates. A good agreement between simulated and experimental data was obtained, which means that the working processes of the acoustic signal generated by actual transducers inside UFMs can be accurately predicted based on this method. Analysis of the predicted results will provide good guides and advices to the design of UFMs involving actual application.