In this paper,electrically excited synchronous machines(EESMs)using copper(Cu)and aluminum(Al)windings are compared for the feasibility of replacing Cu windings with Al windings in electric vehicle(EV)applications sin...In this paper,electrically excited synchronous machines(EESMs)using copper(Cu)and aluminum(Al)windings are compared for the feasibility of replacing Cu windings with Al windings in electric vehicle(EV)applications since Al windings have lower mass density and cost per weight,but higher resistivity and lower thermal conductivity than Cu windings.The EESMs with four winding configurations are optimized with an electromagnetic-thermal co-optimization method.The optimized EESM with only Cu windings is considered as the baseline in this study.Results show that the EESM with stator-Cu/rotor-Al windings has the least torque reduction(12.1%)compared to the baseline among the three EESMs with Al windings and the highest torque mass density among all EESMs.Meanwhile,although the new European driving cycle efficiency of the stator-Cu/rotor-Al EESM is 1.8%lower than that of the baseline,the torque per cost is 71%higher,and the maximum rotor mechanical stress is 8%lower.Therefore,the EESMs with stator-Cu/rotor-Al windings are prospective substitutions of those with only Cu windings for EV applications considering the trade-off between performance and cost.展开更多
This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational ...This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational efficiencies,and technological advancements.Linear generators,such as Induction,permanent magnet synchronous,and switched reluctance types,are highlighted for their direct conversion capability,eliminating the need for mechanical gearboxes.Rotary Induction generators,permanent magnet synchronous generators,and doubly-fed Induction generators are evaluated for their established engineering principles and integration with existing grid infrastructure.The paper discusses the historical development,environmental benefits,and ongoing advancements in wave energy technologies,emphasizing the increasing feasibility and scalability of wave energy as a renewable source.Through a comprehensive analysis,this review provides insights into the current state and future prospects of electrical generators in wave energy conversion,underscoring their potential to significantly reduce reliance on fossil fuels and mitigate environmental impacts.展开更多
Reconfigurable surface acoustic wave(SAW)phase shifters have garnered significant attention owing to their potential applications in emerging fields such as secure wireless communication,adaptable signal processing,an...Reconfigurable surface acoustic wave(SAW)phase shifters have garnered significant attention owing to their potential applications in emerging fields such as secure wireless communication,adaptable signal processing,and intelligent sensing systems.Among various modulation methods,employing gate voltage-controlled tuning methodologies that leverage acoustoelectric interactions has proven to be an efficient modulation approach that requires a low bias voltage.However,current acoustoelectric devices suffer from limited tunability,intricate heterogeneous structures,and complex manufacturing processes,all of which impede their practical applications.In this study,we present a novel material system for voltage-tunable SAW phase shifters.This system incorporates an atomic layer deposition ZnO thin-film transistors on LiNbO_(3)structure.This structure combines the benefits of LiNbO_(3)'s high electromechanical coupling coefficient(K^(2))and ZnO's superior conductivity adjustability.Besides,the device possesses a simplified structural configuration,which is easy to fabricate.Devices with different mesa lengths were fabricated and measured,and two of the different modes were compared.The results indicate that both the maximum phase shift and attenuation of the Rayleigh mode and longitudinal leaky SAW(LLSAW)increase proportionally with mesa length.Furthermore,LLSAW with larger effective electromechanical coupling coefficients(K_(eff)^(2))values exhibits greater phase velocity shifts and attenuation coefficients,with a maximum phase velocity tuning of 1.22%achieved.It is anticipated that the proposed devices will find utility in a variety of applications necessitating tunable acoustic components.展开更多
Noise is inevitable in electrical capacitance tomography(ECT)measurements.This paper describes the influence of noise on ECT performance for measuring gas-solids fluidized bed characteristics.The noise distribution is...Noise is inevitable in electrical capacitance tomography(ECT)measurements.This paper describes the influence of noise on ECT performance for measuring gas-solids fluidized bed characteristics.The noise distribution is approximated by the Gaussian distribution and added to experimental capacitance data with various intensities.The equivalent signal strength(Ф)that equals the signal-to-noise ratio of packed beds is used to evaluate noise levels.Results show that the Pearson correlation coefficient,which indicates the similarity of solids fraction distributions over pixels,increases with Ф,and reconstructed images are more deteriorated at lower Ф.Nevertheless,relative errors for average solids fraction and bubble size in each frame are less sensitive to noise,attributed to noise compromise caused by the process of pixel values.These findings provide useful guidance for assessing the accuracy of ECT measurements of multiphase flows.展开更多
Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combinin...Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.展开更多
To address the challenge of balancing thermal management and thermal runaway mitigation,it is crucial to explore effective methods for enhancing the safety of lithium-ion battery systems.Herein,an innovative hydrated ...To address the challenge of balancing thermal management and thermal runaway mitigation,it is crucial to explore effective methods for enhancing the safety of lithium-ion battery systems.Herein,an innovative hydrated salt composite phase change material(HSCPCM)with dual phase transition temperature zones has been proposed.This HSCPCM,denoted as SDMA10,combines hydrophilic modified expanded graphite,an acrylic emulsion coating,and eutectic hydrated salts to achieve leakage prevention,enhanced thermal stability,cycling stability,and superior phase change behavior.Battery modules incorporating SDMA10 demonstrate significant thermal control capabilities.Specifically,the cylindrical battery modules with SDMA10 can maintain maximum operating temperatures below 55°C at 4 C discharge rate,while prismatic battery modules can keep maximum operating temperatures below 65°C at 2 C discharge rate.In extreme battery overheating conditions simulated using heating plates,SDMA10 effectively suppresses thermal propagation.Even when the central heating plate reaches 300°C,the maximum temperature at the module edge heating plates remains below 85°C.Further,compared to organic composite phase change materials(CPCMs),the battery module with SDMA10 can further reduce the peak thermal runaway temperature by 93°C and delay the thermal runaway trigger time by 689 s,thereby significantly decreasing heat diffusion.Therefore,the designed HSCPCM integrates excellent latent heat storage and thermochemical storage capabilities,providing high thermal energy storage density within the thermal management and thermal runaway threshold temperature range.This research will offer a promising pathway for improving the thermal safety performance of battery packs in electric vehicles and other energy storage systems.展开更多
Microelectromechanical systems(MEMS)technology has gained significant attention over the past decade for measuring inertial angular velocity.However,due to inherent complexity,MEMS gyroscopes typically feature up to t...Microelectromechanical systems(MEMS)technology has gained significant attention over the past decade for measuring inertial angular velocity.However,due to inherent complexity,MEMS gyroscopes typically feature up to ten times more parameters than traditional sensors,making selection a challenging task even for experts.This study addresses this challenge,focusing on defensive guidance,navigation,and control(GNC)systems where precise and reliable angular velocity measurement is critical to overall performance.A comprehensive mathematical model is introduced to encapsulate all key MEMS parameters,accompanied by discussions on calibration and Allan variance interpretation.For six leading MEMS gyroscope applications,namely inertial navigation,integrated navigation,autopilot systems,rotating projectiles,homing guidance,and north finding,the most critical parameters are identified,distinguishing suitable and unsuitable sensor choices.Special emphasis is placed on inertial navigation systems,where practical rules of thumb for error evaluation are derived using six degrees of freedom motion equations.Rigorous simulations demonstrate the influence of various sensor parameters through real-world case studies,including static navigation,multi-rotor attitude estimation,gimbal stabilization,and north finding via a turntable.This work aims to be a beacon for practitioners across diverse fields,empowering them to make more informed design decisions.展开更多
Inspections of power transmission lines(PTLs)conducted using unmanned aerial vehicles(UAVs)are complicated by the fine structure of the lines and complex backgrounds,making accurate and efficient segmentation challeng...Inspections of power transmission lines(PTLs)conducted using unmanned aerial vehicles(UAVs)are complicated by the fine structure of the lines and complex backgrounds,making accurate and efficient segmentation challenging.This study presents the Wavelet-Guided Transformer U-Net(WGT-UNet)model,a new hybrid net-work that combines Convolutional Neural Networks(CNNs),Discrete Wavelet Transform(DWT),and Transformer architectures.The model’s primary contribution is based on spatial and channel attention mechanisms derived from wavelet subbands to guide the Transformer’s self-attention structure.Thus,low and high frequency components are separated at each stage using DWT,suppressing structural noise and making linear objects more prominent.The developed design is supported by multi-component hybrid cost functions that simultaneously solve class imbalance,edge sharpness,structural integrity,and spatial regularity issues.Furthermore,high segmentation success has been achieved in producing sharp boundaries and continuous line structures with the DWT-guided attention mechanism.Experiments conducted on the TTPLA dataset reveal that the version using the ConvNeXt backbone outperforms the current state-of-the-art approaches with an F1-Score of 79.33%and an Intersection over Union(IoU)value of 68.38%.The models and visual outputs of the developed method and all compared models can be accessed at https://github.com/burhanbarakli/WGT-UNET.展开更多
Arc faults within the transformers can generate sudden pressure surges,constituting significant hazards that may precipitate oil tank explosions and severely compromise power system stability.Conventional power−freque...Arc faults within the transformers can generate sudden pressure surges,constituting significant hazards that may precipitate oil tank explosions and severely compromise power system stability.Conventional power−frequency arc discharge experiments encounter limitations in isolating pressure wave characteristics due to persistent gas generation and arc reignition.To circumvent these challenges,an oil-immersed impulse voltage discharge platform was conceived and engineered to investigate pressure wave propagation dynamics.A pressure numerical simulation model and theoretical model of oil−solid interface reflection and refraction were subsequently established to elucidate the pressure propagation mechanism.The experimental and simulation results show that the pressure wave generated by pulsed arc discharge in oil propagates radially in the form of spherical waves.Due to the viscous loss and wave front expansion of transformer oil,the peak pressure decays exponentially with distance,with a decay coefficientβ=1.15.When pressure waves encounter metal obstacles inside transformer oil,there are two propagation paths:direct transmission through and multiple reflections through,and a mode transformation of pressure waves occurs at the oil−solid interface,mainly propagating through obstacles in the form of transverse waves.This work quantitatively delineates the energy pressure wave coupling,propagation dynamics,and attenuation mechanisms,providing critical insights for assessing and mitigating arc fault-induced transformer explosion risks.展开更多
We present a computer-modeling framework for photovoltaic(PV)source emulation that preserves the exact single-diode physics while enabling iteration-free,real-time evaluation.We derive two closed-form explicit solvers...We present a computer-modeling framework for photovoltaic(PV)source emulation that preserves the exact single-diode physics while enabling iteration-free,real-time evaluation.We derive two closed-form explicit solvers based on the Lambert W function:a voltage-driven V-Lambert solver for high-fidelity I–V computation and a resistance-driven R-Lambert solver designed for seamless integration in a closed-loop PV emulator.Unlike Taylor-linearized explicit models,our proposed formulation retains the exponential nonlinearity of the PV equations.It employs a numerically stable analytical evaluation that eliminates the need for lookup tables and root-finding,all while maintaining limited computational costs and a small memory footprint.The R-Lambert model is integrated into a buck-converter emulator equipped with a discrete PI regulator,which generates current references directly from sensed operating points,thus supporting hardware-constrained implementation.Comprehensive numerical experiments conducted on six commercial modules from various technologies(mono,poly,and multicrystalline)demonstrate significant accuracy improvements under the IEC EN 50530 near-MPP criterion:the V-Lambert solver reduces the±10%Vmpp band error by up to 61 times compared to an explicit-model baseline.Dynamic simulations under varying irradiance,temperature,and load conditions achieve millisecond-scale settling with accurate trajectory tracking.Additionally,processor-in-the-loop experimental validation on an embedded microcontroller supports the simulation results.By unifying exact analytical modeling with embedded realization,this work advances computer modeling for PV emulation,MPPT benchmarking,and controller verification in integrated renewable energy systems.展开更多
Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods a...Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.展开更多
Location-Based Services(LBS)have greatly improved efficiency and functionality in various domains,but privacy and security concerns remain due to the centralized nature of many existing systems.To address these issues...Location-Based Services(LBS)have greatly improved efficiency and functionality in various domains,but privacy and security concerns remain due to the centralized nature of many existing systems.To address these issues,this paper introduces the V-Track system,a decentralized architecture using blockchain technology for reliable vehicle location verification.By integrating GPS devices(Spark Fun GPS NEO-M9),IoT-enabled sensors,and a Cosmos blockchain-based ledger(network of interconnected blockchains),V-Track aims to solve centralized LBS problems.Through rigorous simulation experiments,this paper evaluates the performance and security of the V-Track system and demonstrates its potential to provide reliable location verification while preserving user privacy.This paper makes significant contributions by presenting V-Track as a decentralized solution to centralized LBS privacy and security problems,enhancing reliability and trustworthiness through blockchain integration,improving tracking mechanisms with GPS devices and IoT sensors for improved accuracy,and providing a privacy-preserving alternative to centralized LBS through its decentralized design and use of blockchain technology.These advancements hold promise for applications across multiple sectors,including logistics,supply chain management,urban planning,and emerging fields such as autonomous vehicles and augmented reality.展开更多
WE observe that the response speed of a linear timeinvariant system to a step reference input depends not only on the system parameters but also on the magnitude of the step input.Based on this observation,we demonstr...WE observe that the response speed of a linear timeinvariant system to a step reference input depends not only on the system parameters but also on the magnitude of the step input.Based on this observation,we demonstrate a method to schedule the magnitude of the reference input to achieve a faster response.展开更多
With the recent increase in data volume and diversity,traditional text representation techniques are struggling to capture context,particularly in environments with sparse data.To address these challenges,this study p...With the recent increase in data volume and diversity,traditional text representation techniques are struggling to capture context,particularly in environments with sparse data.To address these challenges,this study proposes a new model,the Masked Joint Representation Model(MJRM).MJRM approximates the original hypothesis by leveraging multiple elements in a limited context.It dynamically adapts to changes in characteristics based on data distribution through three main components.First,masking-based representation learning,termed selective dynamic masking,integrates topic modeling and sentiment clustering to generate and train multiple instances across different data subsets,whose predictions are then aggregated with optimized weights.This design alleviates sparsity,suppresses noise,and preserves contextual structures.Second,regularization-based improvements are applied.Third,techniques for addressing sparse data are used to perform final inference.As a result,MJRM improves performance by up to 4%compared to existing AI techniques.In our experiments,we analyzed the contribution of each factor,demonstrating that masking,dynamic learning,and aggregating multiple instances complement each other to improve performance.This demonstrates that a masking-based multi-learning strategy is effective for context-aware sparse text classification,and can be useful even in challenging situations such as data shortage or data distribution variations.We expect that the approach can be extended to diverse fields such as sentiment analysis,spam filtering,and domain-specific document classification.展开更多
Grid-Forming(GFM)converters are prone to fault-induced overcurrent and power angle instability during grid fault-induced voltage sags.To address this,this paper develops a multi-loop coordinated fault ridethrough(FRT)...Grid-Forming(GFM)converters are prone to fault-induced overcurrent and power angle instability during grid fault-induced voltage sags.To address this,this paper develops a multi-loop coordinated fault ridethrough(FRT)control strategy based on a power outer loop and voltage-current inner loops,aiming to enhance the stability and current-limiting capability of GFM converters during grid fault conditions.During voltage sags,the GFM converter’s voltage source behavior is maintained by dynamically adjusting the reactive power reference to provide voltage support,thereby effectively suppressing the steady-state component of the fault current.To address the active power imbalance induced by voltage sags,a dynamic active power reference correction method based on apparent power is designed to mitigate power angle oscillations and limit transient current.Moreover,an adaptive virtual impedance loop is implemented to enhance dynamic transient current-limiting performance during the fault initiation phase.This approach improves the responsiveness of the inner loop and ensures safe system operation under various fault severities.Under asymmetric fault conditions,a negative-sequence reactive current compensation strategy is incorporated to further suppress negative-sequence voltage and improve voltage symmetry.The proposed control scheme enables coordinated operation of multiple control objectives,including voltage support,current suppression,and power angle stability,across different fault scenarios.Finally,MATLAB/Simulink simulation results validate the effectiveness of the proposed strategy,showcasing its superior performance in current limiting and power angle stability,thereby significantly enhancing the system’s fault ride-through capability.展开更多
In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved D...In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved Dung Beetle Optimizer(IDBO)with VariationalMode Decomposition(VMD).The IDBO-VMD method is designed to enhance the accuracy and efficiency of wind-speed time-series decomposition and to effectively smooth photovoltaic power fluctuations.This study innovatively improves the traditional variational mode decomposition(VMD)algorithm,and significantly improves the accuracy and adaptive ability of signal decomposition by IDBO selfoptimization of key parameters K and a.On this basis,Fourier transform technology is used to define the boundary point between high frequency and low frequency signals,and a targeted energy distribution strategy is proposed:high frequency fluctuations are allocated to supercapacitors to quickly respond to transient power fluctuations;Lowfrequency components are distributed to lead-carbon batteries,optimizing long-term energy storage and scheduling efficiency.This strategy effectively improves the response speed and stability of the energy storage system.The experimental results demonstrate that the IDBO-VMD algorithm markedly outperforms traditional methods in both decomposition accuracy and computational efficiency.Specifically,it effectively reduces the charge–discharge frequency of the battery,prolongs battery life,and optimizes the operating ranges of the state-of-charge(SOC)for both leadcarbon batteries and supercapacitors.In addition,the energy management strategy based on the algorithm not only improves the overall energy utilization efficiency of the system,but also shows excellent performance in the dynamic management and intelligent scheduling of renewable energy generation.展开更多
In contemporary computer vision,convolutional neural networks(CNNs)and vision transformers(ViTs)represent the two primary architectural paradigms for image recognition.While both approaches have been widely adopted in...In contemporary computer vision,convolutional neural networks(CNNs)and vision transformers(ViTs)represent the two primary architectural paradigms for image recognition.While both approaches have been widely adopted in medical imaging applications,they operate based on fundamentally different computational principles.This report attempts to provide brief application notes on ViTs and CNNs,particularly focusing on scenarios that guide the selection of one architecture over the other in practical medical implementations.Generally,CNNs rely on convolutional kernels,localized receptive fields,and weight sharing,enabling efficient hierarchical feature extraction.These properties contribute to strong performance in detecting spatially constrained patterns such as textures,edges,and anatomical boundaries,while maintaining relatively low computational requirements.ViTs,on the other hand,decompose images into smaller segments referred to as tokens and employ self-attention mechanisms to model relationships across the entire image.This global modeling capability allows ViTs to capture long-range dependencies that may be difficult for convolution-based architectures to learn.However,ViTs typically achieve optimal performance when trained on extremely large datasets or when supported by extensive pretraining,as their reduced inductive bias requires greater data exposure to learn robust representations.This report briefly examines the architectural structure,underlying mathematical foundations,and relative performance characteristics of CNNs and ViTs,drawing upon recent findings from contemporary research.Emphasis is placed on understanding how differences in data availability,computational resources,and task requirements influence model effectiveness across medical imaging domains.Most importantly,the report serves as a concise application guide for practitioners seeking informed implementation decisions between these two influential deep learning frameworks.展开更多
Analog reservoir computing(ARC)systems offer an energy-efficient platform for temporal information processing.However,their physical implementation typically requires disparate materials and device architectures for d...Analog reservoir computing(ARC)systems offer an energy-efficient platform for temporal information processing.However,their physical implementation typically requires disparate materials and device architectures for different system components,leading to complicated fabrication processes and increased system complexity.In this work,we present a coplanar floating-gate antiferroelectric field-effect transistor(FG AFeFET)that unifies multiple neural functionalities within a single device,enabling the physical implementation of a complete ARC system.By combining a coplanar layout design with an area ratio engineering strategy,we achieve tunable device behaviors,including volatile responses for artificial neuron emulation,nonvolatile states for synaptic functions,and fading memory dynamics for reservoir operations.The mechanisms underlying these functionalities and their operating mechanism are systematically elucidated using load line analysis and energy band diagrams.Leveraging these insights,we demonstrate an all-in-one ARC system based on the unified coplanar FG AFeFET architecture,which achieves recognition accuracies of 95.6%and 83.4%on the MNIST and Fashion-MNIST datasets,respectively.These findings highlight the potential of coplanar FG AFeFETs to deliver area-efficient,design-flexible neuromorphic hardware for next-generation computing systems.展开更多
The increasing integration of electric vehicle(EV)loads into power systems necessitates understanding their impact on stability.Small-magnitude perturbations,if persistent,can cause low-frequency oscillations,leading ...The increasing integration of electric vehicle(EV)loads into power systems necessitates understanding their impact on stability.Small-magnitude perturbations,if persistent,can cause low-frequency oscillations,leading to synchronism loss and mechanical stress.This work analyzes the effect of voltage-dependent EV loads on this small-signal stability.The study models an EV load within a Single-Machine Infinite Bus(SMIB)system.It specifically evaluates the influence of EV charging through the DC link capacitor of a Unified Power Flow Controller(UPFC),a key device for damping oscillations.The system’s performance is compared to a modified version equipped with both a UPFC and a Linear Quadratic Regulator(LQR)controller.Results confirm the significant influence of EV charging on the power network.The analysis demonstrates that the best performance is achieved with the SMIB system utilizing the combined UPFC and LQR controller.This configuration effectively dampens low-frequency oscillations,yielding superior results by reducing the system’s rise time,settling time,and peak overshoot.展开更多
The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)h...The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)have emerged as a transformative solution for aggregating and controlling heterogeneously distributed energy resources(DERs)flexibly and dynamically.This paper presents a comprehensive review of DVPPs,covering their conceptual evolution—from microgrids to virtual power plants(VPPs)and fast-acting VPPs—culminating in the dynamic DVPP paradigm.This review explores key architectural frameworks,including grid-forming and grid-following roles,as well as AC/DC interfacing strategies.Emphasis is placed on secondary frequency and voltage control mechanisms,dynamic-based and market-based disaggregation,and control methodologies tailored to DERs.展开更多
基金supported in part by China Scholarship Council(CSC)under Grant 202206160023.
文摘In this paper,electrically excited synchronous machines(EESMs)using copper(Cu)and aluminum(Al)windings are compared for the feasibility of replacing Cu windings with Al windings in electric vehicle(EV)applications since Al windings have lower mass density and cost per weight,but higher resistivity and lower thermal conductivity than Cu windings.The EESMs with four winding configurations are optimized with an electromagnetic-thermal co-optimization method.The optimized EESM with only Cu windings is considered as the baseline in this study.Results show that the EESM with stator-Cu/rotor-Al windings has the least torque reduction(12.1%)compared to the baseline among the three EESMs with Al windings and the highest torque mass density among all EESMs.Meanwhile,although the new European driving cycle efficiency of the stator-Cu/rotor-Al EESM is 1.8%lower than that of the baseline,the torque per cost is 71%higher,and the maximum rotor mechanical stress is 8%lower.Therefore,the EESMs with stator-Cu/rotor-Al windings are prospective substitutions of those with only Cu windings for EV applications considering the trade-off between performance and cost.
文摘This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational efficiencies,and technological advancements.Linear generators,such as Induction,permanent magnet synchronous,and switched reluctance types,are highlighted for their direct conversion capability,eliminating the need for mechanical gearboxes.Rotary Induction generators,permanent magnet synchronous generators,and doubly-fed Induction generators are evaluated for their established engineering principles and integration with existing grid infrastructure.The paper discusses the historical development,environmental benefits,and ongoing advancements in wave energy technologies,emphasizing the increasing feasibility and scalability of wave energy as a renewable source.Through a comprehensive analysis,this review provides insights into the current state and future prospects of electrical generators in wave energy conversion,underscoring their potential to significantly reduce reliance on fossil fuels and mitigate environmental impacts.
基金supported by National Natural Science Foundation of China(Grant Nos:62122004 and 62274082)Beijing Natural Science Foundation(Grant No.Z210006)+5 种基金Hong Kong Research Grant Council(Grant Nos.27206321,17205922,17212923,C1009-22G and T45-701/22-R)Shenzhen Science and Technology Innovation Commission(SGDX20220530111405040,JCYJ20220530115411025 and JCYJ20210324120409025)Research on mechanism of source/drain ohmic contact and the related Ga N p-FET(Grant No:2023A1515030034)Research on high-reliable Ga N power device and the related industrial power system(Grant No:HZQB-KCZYZ-2021052)supported by ACCESS-AI Chip Center for Emerging Smart Systems,sponsored by Innovation and Technology Fund(ITF),Hong Kong SARthe assistance of SUSTech Core Research Facilities。
文摘Reconfigurable surface acoustic wave(SAW)phase shifters have garnered significant attention owing to their potential applications in emerging fields such as secure wireless communication,adaptable signal processing,and intelligent sensing systems.Among various modulation methods,employing gate voltage-controlled tuning methodologies that leverage acoustoelectric interactions has proven to be an efficient modulation approach that requires a low bias voltage.However,current acoustoelectric devices suffer from limited tunability,intricate heterogeneous structures,and complex manufacturing processes,all of which impede their practical applications.In this study,we present a novel material system for voltage-tunable SAW phase shifters.This system incorporates an atomic layer deposition ZnO thin-film transistors on LiNbO_(3)structure.This structure combines the benefits of LiNbO_(3)'s high electromechanical coupling coefficient(K^(2))and ZnO's superior conductivity adjustability.Besides,the device possesses a simplified structural configuration,which is easy to fabricate.Devices with different mesa lengths were fabricated and measured,and two of the different modes were compared.The results indicate that both the maximum phase shift and attenuation of the Rayleigh mode and longitudinal leaky SAW(LLSAW)increase proportionally with mesa length.Furthermore,LLSAW with larger effective electromechanical coupling coefficients(K_(eff)^(2))values exhibits greater phase velocity shifts and attenuation coefficients,with a maximum phase velocity tuning of 1.22%achieved.It is anticipated that the proposed devices will find utility in a variety of applications necessitating tunable acoustic components.
基金National Key Research and Development Program of China(2021YFA1501302)the National Natural Science Foundation of China(22121004,22122808)+1 种基金the Haihe Laboratory of Sustainable Chemical Transformations and the Program of Introducing Talents of Discipline to Universities(BP0618007)for financial supportsupported by the XPLORER PRIZE.
文摘Noise is inevitable in electrical capacitance tomography(ECT)measurements.This paper describes the influence of noise on ECT performance for measuring gas-solids fluidized bed characteristics.The noise distribution is approximated by the Gaussian distribution and added to experimental capacitance data with various intensities.The equivalent signal strength(Ф)that equals the signal-to-noise ratio of packed beds is used to evaluate noise levels.Results show that the Pearson correlation coefficient,which indicates the similarity of solids fraction distributions over pixels,increases with Ф,and reconstructed images are more deteriorated at lower Ф.Nevertheless,relative errors for average solids fraction and bubble size in each frame are less sensitive to noise,attributed to noise compromise caused by the process of pixel values.These findings provide useful guidance for assessing the accuracy of ECT measurements of multiphase flows.
文摘Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.
基金financially supported by Natural Science Foundation of Guangdong province(2024A1515010228)CATARC Automotive Inspection Center Excellent Engineer Program(2023B0909050007).
文摘To address the challenge of balancing thermal management and thermal runaway mitigation,it is crucial to explore effective methods for enhancing the safety of lithium-ion battery systems.Herein,an innovative hydrated salt composite phase change material(HSCPCM)with dual phase transition temperature zones has been proposed.This HSCPCM,denoted as SDMA10,combines hydrophilic modified expanded graphite,an acrylic emulsion coating,and eutectic hydrated salts to achieve leakage prevention,enhanced thermal stability,cycling stability,and superior phase change behavior.Battery modules incorporating SDMA10 demonstrate significant thermal control capabilities.Specifically,the cylindrical battery modules with SDMA10 can maintain maximum operating temperatures below 55°C at 4 C discharge rate,while prismatic battery modules can keep maximum operating temperatures below 65°C at 2 C discharge rate.In extreme battery overheating conditions simulated using heating plates,SDMA10 effectively suppresses thermal propagation.Even when the central heating plate reaches 300°C,the maximum temperature at the module edge heating plates remains below 85°C.Further,compared to organic composite phase change materials(CPCMs),the battery module with SDMA10 can further reduce the peak thermal runaway temperature by 93°C and delay the thermal runaway trigger time by 689 s,thereby significantly decreasing heat diffusion.Therefore,the designed HSCPCM integrates excellent latent heat storage and thermochemical storage capabilities,providing high thermal energy storage density within the thermal management and thermal runaway threshold temperature range.This research will offer a promising pathway for improving the thermal safety performance of battery packs in electric vehicles and other energy storage systems.
文摘Microelectromechanical systems(MEMS)technology has gained significant attention over the past decade for measuring inertial angular velocity.However,due to inherent complexity,MEMS gyroscopes typically feature up to ten times more parameters than traditional sensors,making selection a challenging task even for experts.This study addresses this challenge,focusing on defensive guidance,navigation,and control(GNC)systems where precise and reliable angular velocity measurement is critical to overall performance.A comprehensive mathematical model is introduced to encapsulate all key MEMS parameters,accompanied by discussions on calibration and Allan variance interpretation.For six leading MEMS gyroscope applications,namely inertial navigation,integrated navigation,autopilot systems,rotating projectiles,homing guidance,and north finding,the most critical parameters are identified,distinguishing suitable and unsuitable sensor choices.Special emphasis is placed on inertial navigation systems,where practical rules of thumb for error evaluation are derived using six degrees of freedom motion equations.Rigorous simulations demonstrate the influence of various sensor parameters through real-world case studies,including static navigation,multi-rotor attitude estimation,gimbal stabilization,and north finding via a turntable.This work aims to be a beacon for practitioners across diverse fields,empowering them to make more informed design decisions.
文摘Inspections of power transmission lines(PTLs)conducted using unmanned aerial vehicles(UAVs)are complicated by the fine structure of the lines and complex backgrounds,making accurate and efficient segmentation challenging.This study presents the Wavelet-Guided Transformer U-Net(WGT-UNet)model,a new hybrid net-work that combines Convolutional Neural Networks(CNNs),Discrete Wavelet Transform(DWT),and Transformer architectures.The model’s primary contribution is based on spatial and channel attention mechanisms derived from wavelet subbands to guide the Transformer’s self-attention structure.Thus,low and high frequency components are separated at each stage using DWT,suppressing structural noise and making linear objects more prominent.The developed design is supported by multi-component hybrid cost functions that simultaneously solve class imbalance,edge sharpness,structural integrity,and spatial regularity issues.Furthermore,high segmentation success has been achieved in producing sharp boundaries and continuous line structures with the DWT-guided attention mechanism.Experiments conducted on the TTPLA dataset reveal that the version using the ConvNeXt backbone outperforms the current state-of-the-art approaches with an F1-Score of 79.33%and an Intersection over Union(IoU)value of 68.38%.The models and visual outputs of the developed method and all compared models can be accessed at https://github.com/burhanbarakli/WGT-UNET.
基金funded by the Science and Technology Program of State Grid Corporation of China(5500-202356358A-2-1-ZX).
文摘Arc faults within the transformers can generate sudden pressure surges,constituting significant hazards that may precipitate oil tank explosions and severely compromise power system stability.Conventional power−frequency arc discharge experiments encounter limitations in isolating pressure wave characteristics due to persistent gas generation and arc reignition.To circumvent these challenges,an oil-immersed impulse voltage discharge platform was conceived and engineered to investigate pressure wave propagation dynamics.A pressure numerical simulation model and theoretical model of oil−solid interface reflection and refraction were subsequently established to elucidate the pressure propagation mechanism.The experimental and simulation results show that the pressure wave generated by pulsed arc discharge in oil propagates radially in the form of spherical waves.Due to the viscous loss and wave front expansion of transformer oil,the peak pressure decays exponentially with distance,with a decay coefficientβ=1.15.When pressure waves encounter metal obstacles inside transformer oil,there are two propagation paths:direct transmission through and multiple reflections through,and a mode transformation of pressure waves occurs at the oil−solid interface,mainly propagating through obstacles in the form of transverse waves.This work quantitatively delineates the energy pressure wave coupling,propagation dynamics,and attenuation mechanisms,providing critical insights for assessing and mitigating arc fault-induced transformer explosion risks.
基金funded by Scientific Research Deanship at University of Ha’il-Saudi Arabia through project number(RG-24014).
文摘We present a computer-modeling framework for photovoltaic(PV)source emulation that preserves the exact single-diode physics while enabling iteration-free,real-time evaluation.We derive two closed-form explicit solvers based on the Lambert W function:a voltage-driven V-Lambert solver for high-fidelity I–V computation and a resistance-driven R-Lambert solver designed for seamless integration in a closed-loop PV emulator.Unlike Taylor-linearized explicit models,our proposed formulation retains the exponential nonlinearity of the PV equations.It employs a numerically stable analytical evaluation that eliminates the need for lookup tables and root-finding,all while maintaining limited computational costs and a small memory footprint.The R-Lambert model is integrated into a buck-converter emulator equipped with a discrete PI regulator,which generates current references directly from sensed operating points,thus supporting hardware-constrained implementation.Comprehensive numerical experiments conducted on six commercial modules from various technologies(mono,poly,and multicrystalline)demonstrate significant accuracy improvements under the IEC EN 50530 near-MPP criterion:the V-Lambert solver reduces the±10%Vmpp band error by up to 61 times compared to an explicit-model baseline.Dynamic simulations under varying irradiance,temperature,and load conditions achieve millisecond-scale settling with accurate trajectory tracking.Additionally,processor-in-the-loop experimental validation on an embedded microcontroller supports the simulation results.By unifying exact analytical modeling with embedded realization,this work advances computer modeling for PV emulation,MPPT benchmarking,and controller verification in integrated renewable energy systems.
文摘Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.
文摘Location-Based Services(LBS)have greatly improved efficiency and functionality in various domains,but privacy and security concerns remain due to the centralized nature of many existing systems.To address these issues,this paper introduces the V-Track system,a decentralized architecture using blockchain technology for reliable vehicle location verification.By integrating GPS devices(Spark Fun GPS NEO-M9),IoT-enabled sensors,and a Cosmos blockchain-based ledger(network of interconnected blockchains),V-Track aims to solve centralized LBS problems.Through rigorous simulation experiments,this paper evaluates the performance and security of the V-Track system and demonstrates its potential to provide reliable location verification while preserving user privacy.This paper makes significant contributions by presenting V-Track as a decentralized solution to centralized LBS privacy and security problems,enhancing reliability and trustworthiness through blockchain integration,improving tracking mechanisms with GPS devices and IoT sensors for improved accuracy,and providing a privacy-preserving alternative to centralized LBS through its decentralized design and use of blockchain technology.These advancements hold promise for applications across multiple sectors,including logistics,supply chain management,urban planning,and emerging fields such as autonomous vehicles and augmented reality.
文摘WE observe that the response speed of a linear timeinvariant system to a step reference input depends not only on the system parameters but also on the magnitude of the step input.Based on this observation,we demonstrate a method to schedule the magnitude of the reference input to achieve a faster response.
基金supported by the SungKyunKwan University and the BK21 FOUR(Graduate School Innovation)funded by the Ministry of Education(MOE,Korea)and National Research Foundation of Korea(NRF).
文摘With the recent increase in data volume and diversity,traditional text representation techniques are struggling to capture context,particularly in environments with sparse data.To address these challenges,this study proposes a new model,the Masked Joint Representation Model(MJRM).MJRM approximates the original hypothesis by leveraging multiple elements in a limited context.It dynamically adapts to changes in characteristics based on data distribution through three main components.First,masking-based representation learning,termed selective dynamic masking,integrates topic modeling and sentiment clustering to generate and train multiple instances across different data subsets,whose predictions are then aggregated with optimized weights.This design alleviates sparsity,suppresses noise,and preserves contextual structures.Second,regularization-based improvements are applied.Third,techniques for addressing sparse data are used to perform final inference.As a result,MJRM improves performance by up to 4%compared to existing AI techniques.In our experiments,we analyzed the contribution of each factor,demonstrating that masking,dynamic learning,and aggregating multiple instances complement each other to improve performance.This demonstrates that a masking-based multi-learning strategy is effective for context-aware sparse text classification,and can be useful even in challenging situations such as data shortage or data distribution variations.We expect that the approach can be extended to diverse fields such as sentiment analysis,spam filtering,and domain-specific document classification.
文摘Grid-Forming(GFM)converters are prone to fault-induced overcurrent and power angle instability during grid fault-induced voltage sags.To address this,this paper develops a multi-loop coordinated fault ridethrough(FRT)control strategy based on a power outer loop and voltage-current inner loops,aiming to enhance the stability and current-limiting capability of GFM converters during grid fault conditions.During voltage sags,the GFM converter’s voltage source behavior is maintained by dynamically adjusting the reactive power reference to provide voltage support,thereby effectively suppressing the steady-state component of the fault current.To address the active power imbalance induced by voltage sags,a dynamic active power reference correction method based on apparent power is designed to mitigate power angle oscillations and limit transient current.Moreover,an adaptive virtual impedance loop is implemented to enhance dynamic transient current-limiting performance during the fault initiation phase.This approach improves the responsiveness of the inner loop and ensures safe system operation under various fault severities.Under asymmetric fault conditions,a negative-sequence reactive current compensation strategy is incorporated to further suppress negative-sequence voltage and improve voltage symmetry.The proposed control scheme enables coordinated operation of multiple control objectives,including voltage support,current suppression,and power angle stability,across different fault scenarios.Finally,MATLAB/Simulink simulation results validate the effectiveness of the proposed strategy,showcasing its superior performance in current limiting and power angle stability,thereby significantly enhancing the system’s fault ride-through capability.
基金funded by the Institute of Smart Energy,Huaiyin Institute of Technology,under Grant No.HIT-ISE-2024-07.
文摘In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved Dung Beetle Optimizer(IDBO)with VariationalMode Decomposition(VMD).The IDBO-VMD method is designed to enhance the accuracy and efficiency of wind-speed time-series decomposition and to effectively smooth photovoltaic power fluctuations.This study innovatively improves the traditional variational mode decomposition(VMD)algorithm,and significantly improves the accuracy and adaptive ability of signal decomposition by IDBO selfoptimization of key parameters K and a.On this basis,Fourier transform technology is used to define the boundary point between high frequency and low frequency signals,and a targeted energy distribution strategy is proposed:high frequency fluctuations are allocated to supercapacitors to quickly respond to transient power fluctuations;Lowfrequency components are distributed to lead-carbon batteries,optimizing long-term energy storage and scheduling efficiency.This strategy effectively improves the response speed and stability of the energy storage system.The experimental results demonstrate that the IDBO-VMD algorithm markedly outperforms traditional methods in both decomposition accuracy and computational efficiency.Specifically,it effectively reduces the charge–discharge frequency of the battery,prolongs battery life,and optimizes the operating ranges of the state-of-charge(SOC)for both leadcarbon batteries and supercapacitors.In addition,the energy management strategy based on the algorithm not only improves the overall energy utilization efficiency of the system,but also shows excellent performance in the dynamic management and intelligent scheduling of renewable energy generation.
文摘In contemporary computer vision,convolutional neural networks(CNNs)and vision transformers(ViTs)represent the two primary architectural paradigms for image recognition.While both approaches have been widely adopted in medical imaging applications,they operate based on fundamentally different computational principles.This report attempts to provide brief application notes on ViTs and CNNs,particularly focusing on scenarios that guide the selection of one architecture over the other in practical medical implementations.Generally,CNNs rely on convolutional kernels,localized receptive fields,and weight sharing,enabling efficient hierarchical feature extraction.These properties contribute to strong performance in detecting spatially constrained patterns such as textures,edges,and anatomical boundaries,while maintaining relatively low computational requirements.ViTs,on the other hand,decompose images into smaller segments referred to as tokens and employ self-attention mechanisms to model relationships across the entire image.This global modeling capability allows ViTs to capture long-range dependencies that may be difficult for convolution-based architectures to learn.However,ViTs typically achieve optimal performance when trained on extremely large datasets or when supported by extensive pretraining,as their reduced inductive bias requires greater data exposure to learn robust representations.This report briefly examines the architectural structure,underlying mathematical foundations,and relative performance characteristics of CNNs and ViTs,drawing upon recent findings from contemporary research.Emphasis is placed on understanding how differences in data availability,computational resources,and task requirements influence model effectiveness across medical imaging domains.Most importantly,the report serves as a concise application guide for practitioners seeking informed implementation decisions between these two influential deep learning frameworks.
基金supported by the National Research Foundation,Prime Minister's Office,Singapore,under its Competitive Research Program(NRF-CRP24-2020-0002)。
文摘Analog reservoir computing(ARC)systems offer an energy-efficient platform for temporal information processing.However,their physical implementation typically requires disparate materials and device architectures for different system components,leading to complicated fabrication processes and increased system complexity.In this work,we present a coplanar floating-gate antiferroelectric field-effect transistor(FG AFeFET)that unifies multiple neural functionalities within a single device,enabling the physical implementation of a complete ARC system.By combining a coplanar layout design with an area ratio engineering strategy,we achieve tunable device behaviors,including volatile responses for artificial neuron emulation,nonvolatile states for synaptic functions,and fading memory dynamics for reservoir operations.The mechanisms underlying these functionalities and their operating mechanism are systematically elucidated using load line analysis and energy band diagrams.Leveraging these insights,we demonstrate an all-in-one ARC system based on the unified coplanar FG AFeFET architecture,which achieves recognition accuracies of 95.6%and 83.4%on the MNIST and Fashion-MNIST datasets,respectively.These findings highlight the potential of coplanar FG AFeFETs to deliver area-efficient,design-flexible neuromorphic hardware for next-generation computing systems.
文摘The increasing integration of electric vehicle(EV)loads into power systems necessitates understanding their impact on stability.Small-magnitude perturbations,if persistent,can cause low-frequency oscillations,leading to synchronism loss and mechanical stress.This work analyzes the effect of voltage-dependent EV loads on this small-signal stability.The study models an EV load within a Single-Machine Infinite Bus(SMIB)system.It specifically evaluates the influence of EV charging through the DC link capacitor of a Unified Power Flow Controller(UPFC),a key device for damping oscillations.The system’s performance is compared to a modified version equipped with both a UPFC and a Linear Quadratic Regulator(LQR)controller.Results confirm the significant influence of EV charging on the power network.The analysis demonstrates that the best performance is achieved with the SMIB system utilizing the combined UPFC and LQR controller.This configuration effectively dampens low-frequency oscillations,yielding superior results by reducing the system’s rise time,settling time,and peak overshoot.
文摘The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)have emerged as a transformative solution for aggregating and controlling heterogeneously distributed energy resources(DERs)flexibly and dynamically.This paper presents a comprehensive review of DVPPs,covering their conceptual evolution—from microgrids to virtual power plants(VPPs)and fast-acting VPPs—culminating in the dynamic DVPP paradigm.This review explores key architectural frameworks,including grid-forming and grid-following roles,as well as AC/DC interfacing strategies.Emphasis is placed on secondary frequency and voltage control mechanisms,dynamic-based and market-based disaggregation,and control methodologies tailored to DERs.
