Power Module Research Articles

Brain wave modulation and EEG power changes during auditory beats stimulation

Auditory beats stimulation (ABS) has received increased attention for its potential to modulate neural oscillations through a phenomenon described as brain entrainment (i.e synchronization of brain’s electrocortical activity to external stimuli at a specific frequency). Recently, a new form of ABS has emerged, inspired by isochronic tones stimulation (ITd).This study investigated neural oscillatory responses induced by ITd in comparison with formerly well-established ABS protocols, such as gamma-binaural beats (BB) and white noise (WN).We recorded the electroencephalographic brain activity in 28 participants during 4 min of BB, ITd, and WN presentation.Data demonstrated that while both BB and WN enhanced oscillatory power on the EEG gamma band, consistently with the expected brain entrainment effect, ITd yielded greater changes in EEG power (p < 0.001). This was confirmed by time-based analysis, which showed a progressive increase in normalized EEG power within the ITd window compared to BB (p < 0.05). Findings also revealed that ITd elicited acute changes in the alpha band of EEG oscillations, through a progressive decrease in power over time, which was distinctly different from the pattern observed while listening BB and WN. Such dual alpha-gamma effects underline the promising and unique potential of ITd to modulate neural oscillations which selectively differ from BB and WN.This study contributes to the evolution of ABS research, highlighting the promise of ITd for cognitive enhancement and clinical applications.

Research on driving technology of radio‐over‐fiber (ROF) compact optical transmitter module

AbstractIn response to the problems of communication capacity and spectrum resource constraints, radio over fiber (ROF) technology has gained widespread adoption, and the quality and performance of the optical transmitter module also directly affect the transmission of the link. This paper presents the design of a direct modulation optical transmitter module in the frequency band ranging from 2.6 kHz to 206.8 MHz using OPA690 broadband voltage feedback op‐amp. The module features a tunable bias voltage range from 0 to 5 V to accommodate different threshold voltages for modulation power supply. It offers a gain of 12 dB and a signal‐to‐noise ratio exceeding 50 dB while demonstrating excellent stability, high signal purity, good spectral response characteristics, and compact size advantages. Based on this design, we further optimized the system circuit by adding a fourth‐order Butterworth filter structure, reducing the lower cut‐off frequency to about 500 Hz, increasing the gain by 6 dB, reducing the return loss to −26.91 dB, and the module's signal‐to‐noise ratio exceeding 90 dB. This optimization scheme improves the stability and signal purity of the optical transmitter module and improves the spectral response characteristics. In this paper, Multisim is used to analyze and verify the proposed optical transmitter circuit. In addition, a complete link experiment was carried out using an optical receiving module designed in the same laboratory to verify the feasibility of our proposed module.

Formation and mechanical properties of AZ31B magnesium alloy welds by power-modulated oscillating laser

an energy-space modulated laser was proposed in welding workpieces with AZ31B magnesium alloy to enhance the efficiency of heat transfer and reduce defects of welds, and the proposed laser was power-modulated oscillating laser (PMOL). To validate the performance of the proposed technique, non-penetration welding and butt welding were conducted on the workpieces with a thickness of 5 mm. The impact of power-modulation parameters was investigated at the aspects of micro-structures, appearance, and mechanical properties of welds. The results showed that in comparison with a conventional laser (CL) or circular oscillation laser (COL), an energy-space modulated laser led to (1) an increased depth of weld penetration and (2) an enhanced coupling efficiency of laser energy. The parameters of power modulation could be optimized to reduce the defects of welds such as spatter, underfill, and thick fish scales significantly reduced; it refined the equiaxed grains in the center area of weld. When the frequency and amplitude of power-modulation were set as 50 Hz and 500 W, respectively, the mechanical properties of the weld were improved with the ultimate tensile strength of 238 MPa and an elongation ratio of 13.9 % which were 96.1 % and 58.7 % of those of the base material (BM), respectively. In comparison with the results welded by CL and COL, the ultimate tensile strength by PMOL was increased by 13.9 % and 4.4 %, and the elongation was increased by 54.4 % and 8.6 %, respectively.

Advances in Energy Harvesting Technologies for Wearable Devices.

The development of wearable electronics is revolutionizing human health monitoring, intelligent robotics, and informatics. Yet the reliance on traditional batteries limits their wearability, user comfort, and continuous use. Energy harvesting technologies offer a promising power solution by converting ambient energy from the human body or surrounding environment into electrical power. Despite their potential, current studies often focus on individual modules under specific conditions, which limits practical applicability in diverse real-world environments. Here, this review highlights the recent progress, potential, and technological challenges in energy harvesting technology and accompanying technologies to construct a practical powering module, including power management and energy storage devices for wearable device developments. Also, this paper offers perspectives on designing next-generation wearable soft electronics that enhance quality of life and foster broader adoption in various aspects of daily life.

Multi-objective topology optimization design of silicon carbide metal oxide semiconductor field effect transistors power module liquid-cooled heatsink for electric vehicles

Silicon carbide (SiC) metal oxide semiconductor field effect transistors (MOSFETs) is gradually replacing silicon-based insulated gate bipolar transistor (IGBT) as the switching devices in electric vehicle power inverters due to its higher operating temperatures, switching speeds, and frequencyies. However, the high switching frequency of SiC MOSFETs can increase the losses of the power inverter, resulting in an increase in the temperature, which will limit the performance of power inverters. The improvement of power module performance is mainly controlled by optimizing its control methods during operation and improving its cooling system. Currently, the liquid-cooled heatsink used in power modules have disadvantages such as large flow resistance and poor heat dissipation performance. In order to enhance the performance of the heatsink, this study first established a three-dimensional finite element model of SiC MOSFETs power module based on a pin fin heatsink. The accuracy of the model was verified by numerical and experimental methods. Secondly, based on numerical analysis, a multi-objective topology optimization method for the power module liquid-cooled heatsink is proposed. This method takes heat exchange and fluid dissipation energy as the objective function and uses weight factors to adjust the influence of each on the objective functions. Additionally, the temperature distribution of the heatsink is considered to optimize its performance. Finally, the numerical results show that the topology-optimized heatsink exhibits better performance at high coolant volumetric flow rates. When the coolant volumetric flow rate is 14 L/min, the pressure drops of topology-optimized heatsink is reduced by 63.94 % and the junction temperature of the power module is reduced by 0.24 % compared with the pin fin heatsink. This topologically optimized heatsink could be helpful to improve the performance of existing power module heatsinks.

A Simplified Model Predictive Current Control Strategy for Six-phase H-bridge Inverters

Background: The double-end H-bridge inverters can realize independent control of the stator voltage in each phase and have flexible advantages of modulation and fault tolerance; thus, they are more suitable for multi-phase fault-tolerant motor drives. However, due to the increase of voltage vectors and the coupling between the electromagnetic and non-electromagnetic quantities, the normal control strategies for traditional three-phase motor drives do not work anymore. Objective: This paper aimed to propose a simplified model predictive current control strategy based on the vector space decomposition (VSD) and the vectors’ gradual simplification for the three-level six-phase H-bridge inverters. Methods: Firstly, the 729 physical variables were decomposed and mapped onto the fundamental αβ subspace, harmonic xy subspace, and the zero-sequence o1o2 subspace based on the VSD. Then, to eliminate the influence of the harmonic and zero-sequence components on the control performance and make an easy digital implementation, vectors’ simplification has been proposed based on the in-depth analysis of the relationship between the voltage vectors mapped onto different subspaces and vectors’ stratification. With the simplification method, the number of voltage vectors was simplified from 729 to 12, and then the selected voltage vectors were used in the rolling optimization of the model predictive current control (MPCC) to choose the optimal one. Finally, sufficient experiments were carried out including static and dynamic conditions, different modulation index and power factor, etc., to verify the feasibility of the proposed strategy. Results: The simulation and experimental results show that with the simplified MPCC strategy, both the static and dynamic performances are relatively good, and the THDs of the phase current under different modulations and power factors are relatively low. Conclusion: The proposed MPCC algorithm for the three-level six-phase H-bridge inverters has shown obvious improvement in solving the control problems of multi-vectors and complex redundancy issues.

Noise mechanism clarification in external-modulation Brillouin optical correlation-domain reflectometry with double-sideband modulator

Brillouin optical correlation-domain reflectometry (BOCDR) allows for relatively high spatial resolution and random accessibility with single-end light injection into the sensing fiber. Typically, BOCDR relies on directly modulating the laser diode’s driving current, which facilitates sinusoidal frequency modulation for distributed sensing but also introduces unintended power modulation that can degrade performance. To address these power variations, external-modulation BOCDR using a double-sideband modulator has been developed. However, this method generates substantial noise, overpowering the Brillouin signal and impeding accurate strain and temperature measurements. This study clarifies the previously unexplained noise mechanisms and suggests system design optimizations to mitigate their impact.

Coordinated Frequency Modulation Control Strategy of Wind Power and Energy Storage Considering Mechanical Load Optimization

When a doubly fed induction generator (DFIG) participates in primary frequency modulation by rotor kinetic energy control, the torque of the generator is changed sharply and the mechanical load pressure of the shaft increases rapidly, which aggravates the fatigue damage of shafting. In order to alleviate the fatigue load of shafting, energy storage was added in the primary frequency modulation of a wind turbine, and a coordinated frequency modulation control strategy of wind power and energy storage based on fuzzy control was proposed. The wind-storage frequency modulation power command was allocated to reduce the response speed of the wind turbine to alleviate the load pressure on the shafting by the fuzzy controller considering the rotor speed range and the state of energy storage charge, and the remaining demand power was supplemented by energy storage. Finally, the joint simulation model based on GH Bladed–Matlab was used to verify the effectiveness of the proposed control strategy. Compared with the traditional integrated control of virtual inertia, the proposed method can reduce the load pressure and fatigue damage of the shafting while satisfying the requirement of frequency modulation.

Design of a high-efficient SiC-based interleaved voltage source converter

The paper deals with some peculiar aspects of the design of interleaved voltage source converter (IVSC) topology in high-power applications such as renewable energy systems and electric vehicles. The IVSC offers advantages like power sharing among multiple modules, improved power quality, redundancy, and fault tolerance. The design of an IVSC mainly involves the selection of the number of deployed converters and the choice of suitable semiconductor devices with the aim to reach a desired overall efficiency for an assigned operating condition. The paper develops an approach to characterize through a straightforward procedure the dependence of the IVSC' power losses on the number of the interlaced converters, on the employed semiconductor devices used and on a properly conceived set of key parameters which define the system operating conditions. The proposed methodology allows to highlight the system performance' dependence on the chosen power devices and it is showcased in this work by considering silicon carbide (SIC) devices with different current ratings. The main goal of the paper is to formulate a smart design procedure able to guide the configuration of an IVSC towards an optimal choice with respect to the power module rating, the number of interleaved levels, the system efficiency, and the output current waveforms. The proposed procedure has been experimentally validated.

Application of artificial neuronal networks for the prediction of monthly photovoltaic power using tilt angle as input in Rabat city, Morocco

The use of artificial neural networks (ANNs) is attracting interest in the field of renewable energies, particularly for predicting operational parameters of photovoltaic (PV) systems. Thus, the present study aims to develop an ANN model for predicting the output power of a photovoltaic module. In this context, two ANN models with two and three hidden layers, respectively, were trained and validated to predict the average monthly photovoltaic power Pp produced by a 0. 325 kW photovoltaic module by varying the tilt angle from 0° to 90° of Rabat city, Morocco. The training data used in this study include longitude, latitude, calculated photovoltaic power, tilt angle, and months as inputs, while the predicted Pp is the target. These two proposed models are trained using the Levenberg-Marquardt (LM) optimiser. Furthermore, to confirm the reliability of each of these two proposed ANN models, existing photovoltaic power data were used to validate the two proposed models by predicting the average monthly power of the photovoltaic module. For the tilt angle of 34°, which corresponds to the latitude of the city of Rabat, the ANN model with three hidden layers performed better with an R2 value of 0.9997.

Segmenting and Predicting Musical Phrase Structure Exploits Neural Gain Modulation and Phase Precession.

Music, like spoken language, is often characterized by hierarchically organized structure. Previous experiments have shown neural tracking of notes and beats, but little work touches on the more abstract question: how does the brain establish high-level musical structures in real time? We presented Bach chorales to participants (20 females and 9 males) undergoing electroencephalogram (EEG) recording to investigate how the brain tracks musical phrases. We removed the main temporal cues to phrasal structures, so that listeners could only rely on harmonic information to parse a continuous musical stream. Phrasal structures were disrupted by locally or globally reversing the harmonic progression, so that our observations on the original music could be controlled and compared. We first replicated the findings on neural tracking of musical notes and beats, substantiating the positive correlation between musical training and neural tracking. Critically, we discovered a neural signature in the frequency range ∼0.1 Hz (modulations of EEG power) that reliably tracks musical phrasal structure. Next, we developed an approach to quantify the phrasal phase precession of the EEG power, revealing that phrase tracking is indeed an operation of active segmentation involving predictive processes. We demonstrate that the brain establishes complex musical structures online over long timescales (>5 s) and actively segments continuous music streams in a manner comparable to language processing. These two neural signatures, phrase tracking and phrasal phase precession, provide new conceptual and technical tools to study the processes underpinning high-level structure building using noninvasive recording techniques.

Production of High-Power Nitrogen Sputtering Plasma for TiN Film Preparation

High-density nitrogen plasma was produced using a high-power pulsed power modulator to sputter titanium targets for the preparation of titanium nitride film. The high-power pulsed sputtering discharge unit consisted of two targets facing each other with the same electrical potential. The titanium target plates were used as target materials with dimensions of 60 mm length, 20 mm height, and 5 mm thickness. The gap length was set to be 10 mm. The magnetic field was created with a permanent magnet array behind the targets. The magnetic field strength at the gap between the target plates was 70 mT. The electrons were trapped by the magnetic and electric fields to enhance the ionization in the gap. The nitrogen and argon gases were injected into the chamber with 4 Pa gas pressure. The applied voltage to the target plates had an amplitude from −600 V to −1000 V with 600 μs in pulse width. The target current was approximately 10 A with the consumed power of 13 kW. The discharge sustaining voltage was almost constant and independent of the applied voltage, in the same manner as the conventional normal glow discharge. The ion density and electron temperature at the surface of the ionization region were obtained as 1.7 × 1019 m−3 and 3.4 eV, respectively, by the double probe measurements. The vertical distribution of ion density and electron temperature ranged from 1.1 × 1017 m−3 (at 6 cm from the target edge) to 1.7 × 1019 m−3 and from 2.4 eV (at 6 cm from the target edge) to 3.4 eV, respectively. From the emission spectra, the intensities of titanium atoms (Ti I), titanium ions (Ti II), and nitrogen ions (N2+) increased with increasing input power. However, the intensities ratio of Ti II to Ti I was not affected by the intensities from N2+.

Flexible, Wearable Wireless-Charging Power System Incorporating Piezo-Ultrasonic Arrays and MXene-Based Solid-State Supercapacitors.

With the continuous development of wearable electronics, higher requirements are put forward for flexible, detachable, stable output, and long service life power modules. Given the limited capacity of energy storage devices, the integration of energy capture and storage is a viable approach. Here, we present a flexible, wearable, wireless-charging power system that integrates a piezoelectric ultrasonic array harvester (PUAH) with MXene-based solid-state supercapacitors (MSSSs) in a soft wristband format for sustainable applications. The MSSS as the energy storage module is developed by using Ti3C2Tx nanosheet-loaded inserted finger-like carbon cloth skeletons as electrodes and poly(vinyl alcohol)/H3PO4 gel as electrolytes, with high energy density (58.74 Wh kg-1) and long cycle life (99.37%, 10,000 cycles). A two-dimensional stretchable piezoelectric array as a wireless-charging module hybridizes high-performance 1-3 composite units with serpentine electrodes, which allows wireless power via ultrasonic waves, with a maximum power density of 1.56 W cm-2 and an output voltage of 20.75 V. The overall PUAH-MSSS wireless energy supply system is 2 mm thick and offers excellent energy conversion/storage performance, cyclic stability, and mechanical flexibility. The results of this project will lay the foundation for the development of next-generation wearable electronics.

Automatic Zebra Crossing Using Raspberry PI

The Automatic Zebra Crossing project enhances pedestrian safety and convenience at zebra crossings using an Atmega8 microcontroller board, a time display, and Progisp software. The project features a pair of automatic gates positioned at both ends of the zebra crossing, closing whenever the traffic signal turns red to stop vehicles. This closure effectively restricts pedestrian movement beyond the zebra crossing area, ensuring enhanced safety. The system works like an automatic stairway, with the gates being opened or closed depending on the condition of the traffic signal. As the central control device, the Atmega8 microcontroller board processes inputs from the traffic signal and generates control signals to open and close the gates. The time display tells pedestrians how much time is left until they can cross in real time. The Atmega8 microcontroller is programmed using the Progisp software, allowing the construction of the control logic and synchronization with the traffic signal. The software enables the microcontroller to work seamlessly with the gate motors and the time display. The Automatic Zebra Crossing system dramatically improves pedestrian safety by keeping people inside the crossing area when the light is red. This system uses gates for straying, lowering accident rates, and optimizing traffic flow. The project documentation outlines the software implementation with Progisp and explains the hardware setup. This setup involves an Atmega8 microcontroller board, gate motors, and a time display. The code integrates a control logic for synchronization.Hardware Requirement:  Raspberry Pi- Pico  Mobile Wi-fi Module  Power Supply Unit  Motor Unit  Relay Unit  Software Requirement:  Python  PC  Web page

Characterization of double-stranded RNA and its silencing efficiency for insects using hybrid deep-learning framework.

RNA interference (RNAi) technology is widely used in the biological prevention and control of terrestrial insects. One of the main factors with the application of RNAi in insects is the difference in RNAi efficiency, which may vary not only in different insects, but also in different genes of the same insect, and even in different double-stranded RNAs (dsRNAs) of the same gene. This work focuses on the last question and establishes a bioinformatics software that can help researchers screen for the most efficient dsRNA targeting target genes. Among insects, the red flour beetle (Tribolium castaneum) is known to be one of the most sensitive to RNAi. From iBeetle-Base, we extracted 12027 efficient dsRNA sequences with a lethality rate of ≥20% or with experimentation-induced phenotypic changes and processed these data to correspond to specific silence efficiency. Based on the first complied novel benchmark dataset, we specifically designed a deep neural network to identify and characterize efficient dsRNA for RNAi in insects. The dna2vec word embedding model was trained to extract distributed feature representations, and three powerful modules, namely convolutional neural network, bidirectional long short-term memory network, and self-attention mechanism, were integrated to form our predictor model to characterize the extracted dsRNAs and their silencing efficiencies for T. castaneum. Our model dsRNAPredictor showed reliable performance in multiple independent tests based on different species, including both T. castaneum and Aedes aegypti. This indicates that dsRNAPredictor can facilitate prescreening for designing high-efficiency dsRNA targeting target genes of insects in advance.

Dual grid energy management strategy for electric vehicles in hybrid microgrid utilizing matrix pencil method

Abstract This study introduces a cutting-edge Matrix Pencil-based Dual Grid Energy Management System (MP-DEMS) aimed at seamlessly integrating electric vehicles (EVs) into power grids while enhancing power quality (PQ) and reliability (PR). Operating within an AC-DC hybrid microgrid (HMG) framework, the MP-DEMS reduces the necessity for additional conversion devices, simplifying the integration of EVs and batteries. The core of the proposed DEMS comprises three key modules: the Wind Energy Management Module (WEMS), Smart Storage and EV Power Coordination (SS-EVPC), and Coordinated Power Conversion Control (CPCC). These modules work together to optimize energy usage, leverage renewable sources effectively, and manage EV charging/discharging schedules to minimize grid impact. An innovative aspect of the MP-DEMS is its use of Matrix Pencil-based techniques, offering several advantages over traditional Discrete Fourier Transform (DFT) methods. These advantages include swift dynamic response, resilience to voltage variations, and precise voltage phase estimation. Software simulations and Hardware-in-the-Loop (HIL-402) testing validate the efficacy of the MP-DEMS approach, showcasing enhanced power transfer capabilities, improved harmonic performance, and superior voltage/frequency regulation within EV-HMS applications. Overall, the MP-DEMS presents a promising solution for advancing the integration of EVs into microgrid systems, contributing to a more sustainable and reliable energy future.

Theoretical design of a single-mode fiber-based bi-order Bessel beam for a STED system

Stimulated emission depletion (STED) microscopy has attracted great research attention due to its applications to breaking diffraction limits for imaging and lithography. However, its implementation based on single-mode fibers often encounters challenges such as complex structural integration, costly fabrication processes, and the need for specific fiber designs. Herein, a low-cost bi-order Bessel beam based on one single-mode fiber integrated with a structurally simple wavelength-scale microstructure (WSM) on fiber end was proposed for STED system. Through simulation study for full-scale WSM optimization, we have successfully developed a bicolor laser beam (BLB) consisting of a zero-order Bessel beam at a wavelength of 405 nm and a donuts high-order Bessel beam at a wavelength of 532 nm. This fiber-based configuration allows us to achieve a diffraction-limited spot size with a working distance of 0.67λpump and a minimum FWHM of 0.395λpump. By combining wavelength division multiplexing technology with power modulation of the donuts beam, this work provides a promising way for achieving super-resolution imaging or lithography with only one single-mode fiber.

Lateralized modulation of cortical beta power during human gait is related to arm swing

Human gait is a complex behavior requiring dynamic control of upper and lower extremities that is accompanied by cortical activity in multiple brain areas. We investigated the contribution of beta (15-30Hz) and gamma (30-50Hz) band electroencephalography (EEG) activity during specific phases of the gait cycle, comparing treadmill walking with and without arm swing. Modulations of spectral power in the beta band during early double support and swing phases source-localized to the sensorimotor cortex ipsilateral, but not contralateral, to the leading leg. The lateralization disappeared in the condition with constrained arms, together with an increase of activity in bilateral supplementary motor areas. By contrast, gamma band modulations that localized to the presumed leg area of sensorimotor cortex around the heel-strike events were unaffected by arm movement. Our findings demonstrate that arm swing is accompanied by considerable cortical activation that should not be neglected in gait-related neuroimaging studies.

Power modulation and its effect on microstructural evolution during laser oscillating welding of steel to aluminum alloy

The control of intermetallic compounds (IMCs) poses a challenge in welding Al–Fe joints. To tackle this issue, power-modulated laser welding was utilized to join 6061 aluminum (6061Al) and AISI-304 stainless steel (304SS) in an overlap configuration. This research compares the impacts of two power modes: constant power (CP) and gradient power (GP) on weld formation and interface IMCs to demonstrate the superior performance of the GP mode. The GP mode alters the energy distribution pattern, resulting in distinct weld formation and interface microstructures compared to the CP mode. Notably, the research confirms that the GP mode enhances the tensile shear strength of the joints, highlighting the effectiveness of the GP mode.

A Study on the Mechanism and Properties of a Self-Powered H2O2 Electrochemical Sensor Based on a Fuel Cell Configuration with FePc and Graphene Cathode Catalyst Materials.

Conventional electrochemical sensors use voltammetric and amperometric methods with external power supply and modulation systems, which hinder the flexibility and application of the sensors. To avoid the use of an external power system and to minimize the number of electrochemical cell components, a self-powered electrochemical sensor (SPES) for hydrogen peroxide was investigated here. Iron phthalocyanine, an enzyme mimetic material, and Ni were used as a cathode catalyst and an anode material, respectively. The properties of the iron phthalocyanine catalyst modified by graphene nanoplatelets (GNPs) were investigated. Open circuit potential tests demonstrated the feasibility of this system. The GNP-modulated interface helped to solve the problems of aggregation and poor conductivity of iron phthalocyanine and allowed for the achievement of the best analytical characteristics of the self-powered H2O2 sensor with a low detection limit of 0.6 µM and significantly higher sensitivity of 0.198 A/(M·cm2) due to the enhanced electrochemical properties. The SPES demonstrated the best performance at pH 3.0 compared to pH 7.4 and 12.0. The sensor characteristics under the control of external variable load resistances are discussed and the cell showed the highest power density of 65.9 μW/cm2 with a 20 kOhm resistor. The practical applicability of this method was verified by the determination of H2O2 in blood serum.