Voltage Signal Research Articles

Neuro-Actuating Photonic Skin Enabled by Ion-Gel Transistor with Thermo-Adaptive Block Copolymer.

Despite significant progress in developing artificial synapses to emulate the human nervous system for bio-signal transmission, synapses with thermo-adaptive coloration and soft actuators driven by temperature change have seldom been reported. Herein, a photonic neuro-actuating synaptic skin is presented enabling thermoresponsive synaptic signal transmission, color variation, and actuation. First, a thermoresponsive display synapse is developed based on a 3-terminal ion-gel transistor with a poly (3,4-ethylene dioxythiophene):poly (styrene sulfonate) (PEDOT:PSS) semiconducting channel mixed with 2D titanium carbide (Ti3C2Tx) MXene and a thermo-adaptive 1D block copolymer (BCP) photonic crystal (PC) gate insulator. Temperature-dependent synaptic behavior is successfully observed in the ion-gel transistor with the corresponding structural colors, leading to a thermo-adaptive display synapse. The 3 × 3 arrays of thermo-adaptive display synapses with Joule heaters show that each pixel is controlled by the thermoresponsive structural color and synaptic output. The synaptic output current from the MXene ion-gel transistor can be converted and amplified to a voltage signal, which powers a soft actuator connected to the ion-gel display synapse and triggers temperature-dependent actuation related to the thermoresponsive synaptic performance. This study showcases a thermo-adaptive photonic neuro-actuating artificial skin that emulates muscle-combined neuronal human skin with visualization capability.

Bioinspired Tilted Magnetized Flakes as a Self-Powered and Antislip Smart Outsole for Healthcare Monitoring and Human-Machine Interaction.

Footwear smart devices capable of reliably capturing body actions and conveniently transmitting human-made information are of great interest to advance healthcare monitoring, human-machine interactions (HMIs), etc. while remaining challenging. Herein, we present a self-powered, antislip, and multifunctional smart outsole based on the gecko toe-inspired tilted magnetized flakes (TMFs) and underlying flexible coils. With the pressure-induced flake deflection and the built-in magnetic moment alignment, the TMF can produce a variable magnetic field to induce the voltage signals in coils for precise pressure perception and linear velocity sensing. The TMF-based smart outsole can thus serve as a real-time footwear recorder to monitor various body actions for exercise analysis and to track the abnormal landing speed for alerting potential injuries. The gecko toe-like flakes also enable the excellent antislip capability of the outsole with a much higher friction coefficient than the standard one of the low slip risk. By programming the magnetic moment alignments of the TMFs, a single-circuit outsole can further output multiple signals as encoded instructions for controlling the racing game. Along with excellent abrasion resistance and environmental immunity, the proposed outsole exhibits great potential as a convenient platform for reliable healthcare monitoring and efficient HMI.

Intelligent fault diagnosis in power distribution networks using LSTM-DenseNet network

This paper introduces a novel fault diagnosis method that combines DenseNet and Long Short-Term Memory (LSTM) networks. The DenseNet utilizes its unique dense block structure to detect subtle variations in three-phase voltage and zero-sequence current signals. In addition, the Squeeze-and-Excitation (SE) module is introduced in DenseNet. The SE module enhances DenseNet's feature representation by adapting the importance of each channel in the feature map. Furthermore, integrating the LSTM model enables capturing time-domain features of fault signals, enhancing the analysis of waveform changes and trends. These extracted features are subsequently fused in a cascaded manner, leveraging the strengths of both approaches to obtain a more comprehensive information representation. To better explain the capability of feature extraction in each part of the model, t-distributed Stochastic Neighbor Embedding (t-SNE) method is used for visual analysis. The proposed method is evaluated using two distribution network models, namely the 10 kV and IEEE34 networks, in simulation. The verification results indicate that the proposed method achieves exceptionally high accuracy in fault identification for both tested distribution network models, with rates of 99.87 % and 99.82 %, respectively, while also demonstrating robust performance in noisy environments. This performance surpasses that of other related methods, underscoring the enhanced effectiveness of our approach.

Dual-Modal Fusion PRI-SWT Model for Eddy Current Detection of Cracks, Delamination, and Impact Damage in Carbon Fiber-Reinforced Plastic Materials

Carbon fiber-reinforced plastic (CFRP) composites are prone to damage during both manufacturing and operational phases, making the classification and identification of defects critical for maintaining structural integrity. This paper presents a novel dual-modal feature classification approach for the eddy current detection of CFRP defects, utilizing a Parallel Real–Imaginary/Swin Transformer (PRI-SWT) model. Built using the Transformer architecture, the PRI-SWT model effectively integrates the real and imaginary components of sinusoidal voltage signals, demonstrating a significant performance improvement over traditional classification methods such as Support Vector Machine (SVM) and Vision Transformer (ViT). The proposed model achieved a classification accuracy exceeding 95%, highlighting its superior capability in terms of addressing the complexities of defect detection. Furthermore, the influence of key factors—including the real–imaginary fusion layer, the number of layers, the window shift size, and the model’s scale—on the classification performance of the PRI-SWT model was systematically evaluated.

Enhancing power quality in distributed generation systems using three-phase shunt active power filter

The current paper presents a new reference current generation algorithm for shunt active power filter (SAPF) combined with distributed generation systems using the instantaneous power theory (IPT) which is organized in two main blocks. The first is the synchronization technique; we propose a new quasi open loop technique that combines a Decoupled double self-tuning filter (DDSTF) and a single-phase frequency locked loop (FLL). The DDSTF is a new technique that extracts and separates the positive and negative sequence voltage under highly unbalanced and distorted conditions with no need to the symmetrical component and/or signals delay. The FLL achieve the grid frequency adaptation. Unlike the existing frequency estimators, this technique is motivated by its simplicity and the immunity to the voltage signal amplitude and phase variation or disturbance, which is a great advantage. The second is an ADALINE in power space to separate the fundamental harmonic from the distortion harmonics of the measured currents. The effectiveness of the proposed OLS technique in their application to the SAPF is validated by simulation.

Spin conduction and interfacial effects in La2/3Sr1/3MnO3/NiO/Pt heterostructures

In this work we report on the generation and transport of pure spin currents in La2/3Sr1/3MnO3(LSMO)/NiO/Pt heterostructures. Pure spin currents, generated by means of spin pumping in the LSMO layer, are transmitted through the NiO layer and detected by measuring the transverse voltage signal generated by the inverse spin Hall effect (ISHE) in the Pt layer. The spin current transmission is studied as a function of the NiO thickness and temperature. NiO layers grown at room temperature are polycrystalline and their microstructural and magnetic features clearly depend on thickness. Scanning transmission electron microscopy techniques revealed that homogeneous conformal coating of the LSMO layer is only achieved for NiO layer thicknesses above about 2 nm. Below this critical thickness the NiO layer is discontinuous and its main role would be to disturb the LSMO/Pt interface causing it to behave as something similar to an insulating barrier. Magnetic measurements indicate that NiO layers are paramagnetic (PM) well below room temperature. In agreement with these observations, the amplitude of the ISHE voltage signal decreases monotonically on lowering temperature and makes evident that there are two spin conduction regimes as a function of the NiO layer thickness with different spin diffusion lengths. For thin discontinuous NiO layers a spin diffusion length of λSDL≈0.8 nm is found, which is slightly larger than typical values found for insulating barriers, but definitely smaller than λSDL≈3.8 nm found for NiO layer thickness above 2 nm. The latter being similar to λSDL previously reported for epitaxial NiO layers. Our findings indicate that inserting a PM NiO insulating layer of optimal thickness improves the spin transparency of the LSMO/Pt interface. Additionally, the results suggest that spin conduction through the NiO layer is likely driven by magnetic correlations and short-range thermal magnons.

Fluid Classification via the Dual Functionality of Moisture-Enabled Electricity Generation Enhanced by Deep Learning.

Classifications of fluids using miniaturized sensors are of substantial importance for various fields of application. Modified with functional nanomaterials, a moisture-enabled electricity generation (MEG) device can execute a dual-purpose operation as both a self-powered framework and a fluid detection platform. In this study, a novel intelligent self-sustained sensing approach was implemented by integrating MEG with deep learning in microfluidics. Following a multilayer design, the MEG device including three individual units for power generation/fluid classification was fabricated in this study by using nonwoven fabrics, hydroxylated carbon nanotubes, poly(vinyl alcohol)-mixed gels, and indium tin bismuth liquid alloy. A composite configuration utilizing hydrophobic microfluidic channels and hydrophilic porous substrates was conducive to self-regulation of the on-chip flow. As a generator, the MEG device was capable of maintaining a continuous and stable power output for at least 6 h. As a sensor, the on-chip units synchronously measured the voltage (V), current (C), and resistance (R) signals as functions of time, whose transitions were completed using relays. These signals can serve as straightforward indicators of a fluid presence, such as the distinctive "fingerprint". After normalization and Fourier transform of raw V/C/R signals, a lightweight deep learning model (wide-kernel deep convolutional neural network, WDCNN) was employed for classifying pure water, kiwifruit, clementine, and lemon juices. In particular, the accuracy of the sample distinction using the WDCNN model was 100% within 15 s. The proposed integration of MEG, microfluidics, and deep learning provides a novel paradigm for the development of sustainable intelligent environmental perception, as well as new prospects for innovations in analytical science and smart instruments.

Extraction of two-magnon scattering and detection of inverse spin Hall effect up to 40 GHz in low-damping Fe65Co35/Pt bilayer thin film

Abstract We report an investigation of the inverse spin Hall effect (ISHE), as a measure of the pure spin current (J S ), induced by spin pumping in a Fe65Co35/Pt bilayer using coplanar waveguide ferromagnetic resonance (CPW-FMR) in the in-plane (IP) geometry over the broadband microwave frequency range of 8–40 GHz. From the IP-FMR measurements, the defect-induced two-magnon scattering (TMS) contribution is evaluated by analyzing the frequency dependence of FMR linewidth using Arias and Mills approach [Arias R and Mills D L (1999 Phys. Rev. B 60, 7395), Mills D L and Arias R (2006 Physica B 384 147–151)]. Here, we have determined a very low Gilbert damping constant (α G ) for Fe65Co35 bare film around 1.3 × 10−3, which is essential for the high spin current (>107 A/m2) generation. The enhanced value of α G ≈3.2 × 10−3 obtained for Fe65Co35/Pt bilayer film is due to spin pumping damping, α SP = 1.9 × 10−3, confirmed by the ISHE induced DC voltage (V DC ) signal measured across the edges of Pt layer. Out-of-plane (OP) angular ISHE measurements have been performed to disentangle spin pumping from spin rectification effects in the V DC signal. Further, we evaluated the effective spin-mixing conductance g eff ↑ ↓ = 3.33 × 1019 m−2, which is higher than the values reported in FeCo-alloy-based bilayer systems. The present study aims to detect the J S injected by low-damping Fe65Co35 thin films, leading to the realization of energy-efficient spintronic devices.

A Hybrid Data-Driven Method for Main Circuit Gound Faults Diagnosis in Electrical Traction Drive Systems

A main circuit ground fault (MCGF) is a typical system fault in an electrical traction drive system (ETDS). When two or more MCGFs occur, it will cause serious accidents. Therefore, it is particularly important to detect and handle MCGFs in a timely manner. To improve the efficiency of train operation and ensure driving safety, this paper proposes a hybrid data-driven MCGF diagnosis method. First, the voltage signals related to the fault are selected according to the mechanism analysis of the MCGF, and then the initial feature variables are constructed according to these voltage signals. Secondly, the initial feature variables of different types of MCGF are analyzed in the time and frequency domains by wavelet transform, and four feature indicators are calculated. Finally, the fault feature indicators are trained by random forest to obtain a model for subsequent fault diagnosis. After comparative experiments using various machine learning methods, it was found that the RF used in the proposed method has a better diagnostic effect, and the correct isolation rate exceeds 99%.

Scalable Multispecies Ion Transport in a Grid-Based Surface-Electrode Trap

Quantum processors based on linear arrays of trapped ions have achieved exceptional performance, but scaling to large qubit numbers requires realizing two-dimensional ion arrays as envisioned in the quantum charge-coupled device (QCCD) architecture. Here, we present a scalable method for the control of ion crystals in a grid-based surface-electrode Paul trap and characterize it in the context of transport operations that sort and reorder multispecies crystals. By combining cowiring of control electrodes at translationally symmetric locations in each grid site with the sitewise ability to exchange the voltages applied to two special electrodes gated by a binary input, site-dependent operations can be achieved using only a fixed number of analog voltage signals and a single digital input per site. In two separate experimental systems containing nominally identical grid traps, one using Yb+171−Ba+138 crystals and the other Ba+137−Sr+88, we demonstrate this method by characterizing the conditional intrasite crystal reorder and the conditional exchange of ions between adjacent sites on the grid. Averaged across a multisite region of interest, we measure subquanta motional excitation in the axial in-phase and out-of-phase modes of the crystals following these operations at exchange rates of 2.5 kHz. In this initial demonstration, the logic controlling the voltage exchange occurs in software, but the applied signals mimic a proposed hardware implementation using crossover switches. These techniques can be further extended to implement other conditional operations in the QCCD architecture such as gates, initialization, and measurement. Published by the American Physical Society 2024

Tin can telephone-inspired self-powered mechanical wave communication integrated with self-charge excitation triboelectric nanogenerator

As an alternative to electromagnetic wave communication, mechanical waves (MW) retain their advantage in several aspects, including cost-effectiveness, simplicity, interference resistance, and short-distance transmission. Here, a string-vibrated self-powered mechanical wave communication system (SSMWC) is proposed. Inspired by tin can telephone, modulated voltage signals are converted into MW by a vibrator and then transmitted along a string to the receiver. The receiver integrates a string-vibrated triboelectric nanogenerator (SV-TENG) and a self-charge excitation triboelectric nanogenerator (SCE-TENG). The SV-TENG can detect MW and convert them into electrical signals without external power sources, while the SCE-TENG is integrated to improve the sensitivity of the receiver. After charges pumping of SCE-TENG, the average output of SV-TENG can increase by 88 times within the frequency range of 0-1000Hz, indicating the successful application of self-charge excitation in triboelectric sensing for the first time. Furthermore, a demonstration of information coding and real-time decoding proves the potential application of SSMWC as an alternative in specific environments. This work shows the breakthrough of the bottlenecks faced by triboelectric sensors that the essential preparatory contact electrification before packaging, which indeed affect sensitivity and durability. Therefore, this scheme can be widely applied for output enhancement, especially for triboelectric sensors with lower output.

Design and real-time implementation of a sliding mode observer utilizing voltage signal injection and PLL for sensorless control of IPMSMs

In this study, a sliding mode observer (SMO) based on high-frequency (HF) voltage signal injection and a phase-locked loop (PLL) is proposed for estimating the extended electromotive force (EEMF), rotor position, and rotor velocity of an interior permanent magnet synchronous machine (IPMSM).This approach addresses real-time estimation challenges associated with standard SMO and PLL at very low speeds and standstill. A reliable and accurate sensorless speed control system for IPMSM is then developed and implemented in real time using the proposed SMO and PLL, covering a wide range of speeds, including low-speed and standstill conditions. The SMO effectively estimates the EEMF, while the PLL extracts the rotor velocity and position based on these estimates. Compared to conventional SMO and PLL methods, real-time results from an 8-pole, 0.4 kW IPMSM demonstrate the superior efficiency of the proposed system.

Chaotic dynamics in a class of generalized memristive maps.

The memory effects of the memristors in nonlinear systems make the systems generate complicated dynamics, which inspires the development of the applications of memristors. In this article, the model of the discrete memristive systems with the generalized Ohm's law is introduced, where the classical Ohm's law is a linear relationship between voltage and current, and a generalized Ohm's law is a nonlinear relationship. To illustrate the rich dynamics of this model, the complicated dynamical behavior of three types of maps with three types of discrete memristances is investigated, where a cubic function representing a kind of generalized Ohm's law is used, and this cubic function is a simplified characteristic of the famous tunnel diode. The existence of attractors with one or two positive Lyapunov exponents (corresponding to chaotic or hyperchaotic dynamics) is obtained, and the coexistence of (infinitely) many attractors is observable. A hardware device is constructed to implement these maps and the analog voltage signals are experimentally acquired.

A method for data conversion of exposure level testers using sinusoidal alternating magnetic fields

Flux density measurements provide valuable information on the state of electrical appliances, which can be utilised for improving the safety and the performance of the associated system. Exposure level testers with a B-field probe are widely used for flux density measurements but they can only perform a time-domain measurement and display it as output voltage related to flux density. In other words, they cannot output flux density directly, which brings many inconveniences. These drawbacks can be improved using data conversion coefficients. However, the test frequencies of most exposure level testers are greater than 1 Hz. They are unable to calculate the data conversion coefficients using a constant magnetic field. This paper proposes a method for data conversion of exposure level testers using alternating magnetic fields to measure flux density directly: the flux density conversion model was established; the mathematical relationship between the mean value of the voltage signal output by the exposure level testers and its stable display value was deduced; and an experimental test of the conversion coefficients was carried out. The results show that the error between the value measured using the new technique and the display value of the exposure level tester at different frequencies is small. This method can trace the flux density in both the time and frequency domains through software processing algorithms without changing the exposure level tester itself. By applying sinusoidal magnetic fields and using the conversion factor, then existing testers can be used to measure flux density directly.

Self-Powered Machine-Learning-Assisted Material Identification Enabled by a Thermogalvanic Dual-Network Hydrogel with a High Thermopower.

Wearable devices equipped with high-performance flexible sensors that can identify diverse physical information free from batteries are playing an indispensable role in various fields. However, previous studies on flexible sensors have primarily focused on their elasticity and temperature-sensing capability, with few reports on material identification. In this paper, a thermogalvanic dual-network hydrogel is fabricated with [Fe(CN)6]3-/4- as a redox couple and lithium magnesium silicate, Gdm+ and lithium bromideas key electrolytes to optimize the interconnected porous structure of the gel, which shows excellent mechanical and thermoelectric properties with a thermopower as high as 4.01mV K-1. A self-powered material identification ring is developed based on the temperature-triggered thermoelectric response of the gel in conjunction with machine learning, which can actively infer materials without an external power connection by analyzing the voltage signals correlated with interfacial heat transfer produced upon contact with different materials. The proposed gel ring has important applications for future areas such as human-computer interaction and haptic-associated artificial intelligence.

Dynamic Monitoring the Friction Transmission of Mine Hoist Using Triboelectric Nanogenerators Self‐Powered Sensor Based on Different Surface Structures Embedded in the Friction Lining

The friction lining is a critical component of the friction hoist, serving as the driving force for lifting through its interaction with the wire rope. Monitoring the friction state between the wire rope and the friction lining is crucial as it directly impacts lifting capacity, work efficiency, and overall safety. By employing finite element simulation and creating surface microstructures on triboelectric nanogenerators (TENG) using ultraviolet laser, this study demonstrats that microstructure can improve voltage output. Compared with TENG without morphology, the voltage is increased by nearly seven times, reaching 2.28 V. Moreover, experiments revealed that embedding TENG at the optimal standard point of the friction lining enables effective monitoring of friction transmission under varied conditions, with the voltage signal showing synchronization with friction force. Notably, the voltage reached 520 mV under increasing specific pressures and stabilized around 670 mV with rising sliding speeds. This research represents a significant step toward real‐time monitoring of intelligent mining systems by dynamically observing friction transmission in mine hoists.

Optimal PMU placement in the Southern Cameroon interconnected grid considering the effect of a group of ZIBs for complete observability and dynamic stability

In this paper, a solution for optimal PMU placement in the southern Cameroon interconnected grid (SIG) is proposed to the issue of dynamic stability within the network. The power system under consideration relies on SCADA parameter monitoring, which offers unsynchronized measurements and inconsistent state estimation of the grid. Moreover, SCADA system's data scan rate hinders its efficiency in capturing the dynamic and transient behaviours in the power grid. The Approach in this research is based on the integer linear programming (ILP) considering the effect of a group of zero-injection buses (ZIBs). Subsequently, a comparative analysis is conducted to evaluate the performance of the ILP method in terms of the number of PMUs, the number of buses observable from one or more nodes, and the sum of redundancy index (SORI) in comparison to other optimization algorithms. The results obtained using MATLAB 2020a demonstrate that the ILP method, considering the effect of a group of ZIBs and the SORI, provides the most optimal solution. Furthermore, a PMU-based dynamic stability assessment is performed, validating the improvement of the visibility of voltage signals.

Identification of transmission line voltage sag sources based on multi‐location information convolutional transformer

AbstractConventional methods for identifying voltage sag sources are difficult to categorize accurately due to the complexity of transmission lines and the influence of noise. In order to solve the problem of difficulty in recognizing voltage sag sources of transmission lines under different locations, this paper proposes a new method for transmission line fault diagnosis based on modified wavelet denoising and multi‐location information convolution transformer. The improved wavelet denoising method proposed in this paper solves the problems of discontinuity and bias of the traditional wavelet denoising method, and is able to better reconstruct the original voltage signal in a strong noise environment. In addition, multi‐location information convolution transformer adopts a new model combining multi‐location information convolution and multi‐scale convolution transformer, which realizes the combination of global information and local context information capturing ability under multi‐location faults. This paper validates the method through simulation experiments and practical situations, and the results show that the method can well classify and identify the type and location of voltage sag sources in transmission lines.

Monitoring performance of a new smart geosynthetic under drawing test

Geosynthetics are widely used in civil engineering reinforcements owing to their high strength, acceptable toughness, and ease of transportation. However, traditional geosynthetics do not have the capability to monitor damage inside the soil. Therefore, in this paper, a new sensor-enabled piezoelectric geobelt (SPGB) is developed to measure the deformation of reinforced-soil structures. In-soil drawing tests are conducted to investigate the sensing performance of the SPGB. Variations in the voltage and impedance signals of the SPGB with the drawing displacement under different damage conditions are investigated. The results show that with the increase of drawing displacement, SPGB undergoes tensile deformation followed by pullout damage. In tensile deformation, the signal response of SPGB is related to strain. As the strain increases, the output voltage first increases and then decreases, and the impedance gradually decreases. In the pullout damage phase, the signal response of SPGB is related to the contact area between SPGB and soil. As the drawing displacement increases, the contact area between SPGB and soil gradually decreases, the output voltage gradually decreases, and the impedance gradually increases. Therefore, the SPGB, as a sensor-enabled geosynthetic, provides a reinforcing function to the soil body and simultaneously performs in-soil catastrophe identification.

Deep predictive data representation model control for photovoltaic maximum power point tracking under partial shading conditions

As deep learning progresses in recent years, deep neural networks (DNNs) have penetrated applications in various industries. The global maximum power point (GMPP) tracking issue exists in photovoltaic (PV) systems. This study proposes a deep predictive data representation model control (PDRC)-based PV GMPP tracking method. The PDRC approach establishes a data representation control model centered on deep prediction by extracting the data features such as the current, voltage, and duty cycle of PV systems controllers on a time series basis. The proposed PDRC method can receive signals of voltage and current, then output duty cycle control signals. Thereby PDRC method can control PV output power to track GMPP efficiently. Finally, the proposed PDRC approach is evaluated with the perturbation observation method (P&O), the improved particle swarm algorithm (PSO-P&O), and the modified cuckoo algorithm (CS-P&O) to compare the PV GMPP tracking performance. In the simulation experiments, the PDRC approach increases the average tracking efficiency over the comparison algorithms by at least 7.729 % and decreases the average tracking time by at least 0.0155 s under partial shading conditions (PSCs). In the physical simulation validation, the proposed PDRC approach possesses minimal power perturbation and increases the average tracking efficiency over the comparison algorithms by at least 0.407 %.