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
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
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
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
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
A curriculum is a complex system that includes a set of core competencies, objectives, contents, methodological and evaluation criteria, regulation among other things. In order to represent a curriculum as a piece of ...A curriculum is a complex system that includes a set of core competencies, objectives, contents, methodological and evaluation criteria, regulation among other things. In order to represent a curriculum as a piece of software the common tools used are databases, trees and lists of courses. However, none of these tools can capture the deep and complex relationships among the elements of a curriculum. To avoid this problem, a more complete representation of an engineering curriculum using ontologies has been developed. This paper presents the construction of an ontology for undergraduate electrical engineering curriculum domain at Universidad Nacional de Colombia, which aims to represent, organize, formalize and standardize the knowledge of this domain, so that it can be shared and reused by different groups of people in the field of education and engineering. The ontology includes four curriculum aspects： knowledge in electrical engineering, skills in engineering, electrical engineering curriculum and regulation. For the ontology development, Methontology was selected as methodology and Protege as implementation tool. In addition, there is a proposal of documentation for this methodology, based on principles of quality management systems. This ontology is designed in order to be used in any field of engineering.展开更多
With the integration of large-scale renewable energy sources(RESs),the line commutated converter(LCC)based high-voltage direct-current(HVDC)inverter side suffers from degraded grid strength and escalating commutation ...With the integration of large-scale renewable energy sources(RESs),the line commutated converter(LCC)based high-voltage direct-current(HVDC)inverter side suffers from degraded grid strength and escalating commutation failure(CF)risks.In comparison with the widely used grid-following(GFL)RESs,grid-forming(GFM)RESs exhibit the favorable characteristics of voltage sources.Therefore,the hybrid operation of GFL-/GFM-RESs holds great potential in voltage support and may help CF suppression.This paper first analyzes the impacts of RESs on the first and subsequent CFs,respectively.It reveals that GFM-RESs alleviate the deterioration of grid voltage induced by GFL-RESs,thereby suppressing the first CF.However,current fault ride-through controls of RESs cannot fulfill the reactive power dynamic demand during the CF recovery process and rarely help mitigate subsequent CFs.Moreover,the limited overcurrent capability of GFM-RES hinders its ability to ride through CF.Based on the mechanism analysis,a CF suppression strategy based on the hybrid GFL/GFM operation is proposed.The proposed strategy optimized the controls of RESs to fulfill reactive power dynamics and offer voltage support during the CF process.Case studies are undertaken on the studied system and the CIGRE benchmark,respectively.The simulation results confirm the effectiveness of the proposed strategy for suppressing CF.展开更多
The rapid advancement of wearable electronic devices has paved the way for a more intelligent and interconnected world.However,ensuring the sustainable energy supply for these devices remains a critical challenge,part...The rapid advancement of wearable electronic devices has paved the way for a more intelligent and interconnected world.However,ensuring the sustainable energy supply for these devices remains a critical challenge,particularly for specialized populations and professionals in demanding environments,where a lack of power can pose life-threatening risks.Herein,we propose a mechanically intelligent biomechanical energy harvesting approach that adapts to complex human motion excitations,thereby improving the energy harvesting performance.Leveraging a mechanical intelligence mechanism,the energy harvester aligns with human physiological habits,selectively activating or deactivating as needed.The system can also adapt to excitations of varying directions,amplitudes,and frequencies.Furthermore,the string tension helps reduce the impact forces on the knee joint during foot strikes.A theoretical model for the biomechanical energy harvesting system is developed to describe its dynamic and electrical characteristics,and a prototype is fabricated and tested under diverse conditions.The experimental results are in good agreement with the simulation trends,validating the effectiveness of the theoretical model.A test subject running at 8 km/h for 90 seconds can successfully power a smartphone for 20 seconds,demonstrating the viability of self-powered applications.This mechanically intelligent biomechanical energy harvesting method holds a promising solution for the sustainable power supply for wearable electronic devices.展开更多
The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary gro...The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary growth of mobile communications,the data traffic has dramatically expanded,which has led to massive grid power consumption and incurred high operating expenditure(OPEX).However,the majority of current network designs struggle to efficientlymanage a massive amount of data using little power,which degrades energy efficiency performance.Thereby,it is necessary to have an efficient mechanism to reduce power consumption when processing large amounts of data in network data centers.Utilizing renewable energy sources to power the Cloud Radio Access Network(C-RAN)greatly reduces the need to purchase energy from the utility grid.In this paper,we propose a bandwidth-aware hybrid energypowered C-RAN that focuses on throughput and energy efficiency(EE)by lowering grid usage,aiming to enhance the EE.This paper examines the energy efficiency,spectral efficiency(SE),and average on-grid energy consumption,dealing with the major challenges of the temporal and spatial nature of traffic and renewable energy generation across various network setups.To assess the effectiveness of the suggested network by changing the transmission bandwidth,a comprehensive simulation has been conducted.The numerical findings support the efficacy of the suggested approach.展开更多
Recent studies indicate that millions of individuals suffer from renal diseases,with renal carcinoma,a type of kidney cancer,emerging as both a chronic illness and a significant cause of mortality.Magnetic Resonance I...Recent studies indicate that millions of individuals suffer from renal diseases,with renal carcinoma,a type of kidney cancer,emerging as both a chronic illness and a significant cause of mortality.Magnetic Resonance Imaging(MRI)and Computed Tomography(CT)have become essential tools for diagnosing and assessing kidney disorders.However,accurate analysis of thesemedical images is critical for detecting and evaluating tumor severity.This study introduces an integrated hybrid framework that combines three complementary deep learning models for kidney tumor segmentation from MRI images.The proposed framework fuses a customized U-Net and Mask R-CNN using a weighted scheme to achieve semantic and instance-level segmentation.The fused outputs are further refined through edge detection using Stochastic FeatureMapping Neural Networks(SFMNN),while volumetric consistency is ensured through Improved Mini-Batch K-Means(IMBKM)clustering integrated with an Encoder-Decoder Convolutional Neural Network(EDCNN).The outputs of these three stages are combined through a weighted fusion mechanism,with optimal weights determined empirically.Experiments on MRI scans from the TCGA-KIRC dataset demonstrate that the proposed hybrid framework significantly outperforms standalone models,achieving a Dice Score of 92.5%,an IoU of 87.8%,a Precision of 93.1%,a Recall of 90.8%,and a Hausdorff Distance of 2.8 mm.These findings validate that the weighted integration of complementary architectures effectively overcomes key limitations in kidney tumor segmentation,leading to improved diagnostic accuracy and robustness in medical image analysis.展开更多
The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigm...The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigms have fundamental issues in data privacy,regulatory compliance,and ownership silo alongside the scaled limitations of the real-life application.The concept of Federated Deep Learning(FDL)is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings.It is an overview of the privacy-preserving developments in FDL as of 2018-2025 with a narrow scope on its usage in smart cities(traffic prediction,environmental monitoring,energy grids),smart homes/buildings/IoT(non-intrusive load monitoring,HVAC optimization,anomaly detection)and the healthcare application(medical imaging,Electronic Health Records(EHR)analysis,remote monitoring).It gives coherent taxonomy,domain pipelines,comparative analyses of privacy mechanisms(differential privacy,secure aggregation,Homomorphic Encryption(HE),Trusted Execution Environments(TEEs),blockchain enhanced and hybrids),system structures,security/robustness defense,deployment/Machine Learning Operation(MLOps)issues,and the longstanding challenges(non-IID heterogeneity,communication efficiency,fairness,and sustainability).Some of the contributions made are structured comparisons of privacy threats,practical design advice on urban areas,recognition of open problems,and a research roadmap into the future up to 2035.The paper brings out the transformational worth of FDL in building credible,scalable,and sustainable intelligent urban ecosystems and the need to do further interdisciplinary research in standardization,real-world testbeds,and ethical governance.展开更多
Halide perovskites have emerged as promising materials for X-ray detection with exceptional properties and reasonable costs.Among them,heterostructures between 3D perovskites and low-dimensional perovskites attract in...Halide perovskites have emerged as promising materials for X-ray detection with exceptional properties and reasonable costs.Among them,heterostructures between 3D perovskites and low-dimensional perovskites attract intensive studies of their advantages due to low-level ion migration and decent stability.However,there is still a lack of methods to precisely construct heterostructures and a fundamental understanding of their structure-dependent optoelectronic properties.Herein,a gas-phase method was developed to grow 2D perovskites directly on 3D perovskites with nanoscale accuracy.In addition,the larger steric hindrance of organic layers of 2D perovskites was proved to enable slower ion migration,which resulted in reduced trap states and better stability.Based on MAPbBr_(3)single crystals with the(PA)_(2)PbBr_(4)capping layer,the X-ray detector achieved a sensitivity of 22,245μC Gy_(air)^(−1)cm^(−2),a response speed of 240μs,and a dark current drift of 1.17.10^(–4)nA cm^(−1)s^(−1)V^(−1),which were among the highest reported for state-of-the-art perovskite-based X-ray detectors.This study presents a precise synthesis method to construct perovskite-based heterostructures.It also brings an in-depth understanding of the relationship between lattice structures and properties,which are beneficial for advancing high-performance and cost-effective X-ray detectors.展开更多
In electrical circuit analysis, it is often necessary to find the set of all direct current （d.c.） operating points （either voltages or currents） of nonlinear circuits. In general, these nonlinear equations are of...In electrical circuit analysis, it is often necessary to find the set of all direct current （d.c.） operating points （either voltages or currents） of nonlinear circuits. In general, these nonlinear equations are often represented as polynomial systems. In this paper, we address the problem of finding the solutions of nonlinear electrical circuits, which are modeled as systems of n polynomial equations contained in an n-dimensional box. Branch and Bound algorithms based on interval methods can give guaranteed enclosures for the solution. However, because of repeated evaluations of the function values, these methods tend to become slower. Branch and Bound algorithm based on Bernstein coefficients can be used to solve the systems of polynomial equations. This avoids the repeated evaluation of function values, but maintains more or less the same number of iterations as that of interval branch and bound methods. We propose an algorithm for obtaining the solution of polynomial systems, which includes a pruning step using Bernstein Krawczyk operator and a Bernstein Coefficient Contraction algorithm to obtain Bernstein coefficients of the new domain. We solved three circuit analysis problems using our proposed algorithm. We compared the performance of our proposed algorithm with INTLAB based solver and found that our proposed algorithm is more efficient and fast.展开更多
Trichloroethylene （TCE） pretreatment of Si surface prior to HfO2 deposition is employed to fabricate HfO2 gatedielectric MOS capacitors. Influence of this processing procedure on interlayer growth, HfO2/Si interface...Trichloroethylene （TCE） pretreatment of Si surface prior to HfO2 deposition is employed to fabricate HfO2 gatedielectric MOS capacitors. Influence of this processing procedure on interlayer growth, HfO2/Si interface properties, gate-oxide leakage and device reliability is investigated. Among the surface pretreatments in NH3, NO, N2O and TCE ambients, the TCE pretreatment gives the least interlayer growths the lowest interface-state density, the smallest gate leakage and the highest reliability. All these improvements should be ascribed to the passivation effects of Cl2 and HC1 on the structural defects in the interlayer and at the interface, and also their gettering effects on the ion contamination in the gate dielectric.展开更多
A smart grid will require, to greater or lesser degrees, advanced tools for planning and operation, broadly accepted communications platforms, smart sensors and controls, and real-time pricing. The smart grid has been...A smart grid will require, to greater or lesser degrees, advanced tools for planning and operation, broadly accepted communications platforms, smart sensors and controls, and real-time pricing. The smart grid has been described as something of an ecosystem with constantly communication, proactive, and virtually self-aware. The use of smart grid has a lot of economical and environmental advantages;however it has a downside of instability and unpredictability introduced by distributed generation (DG) from renewable energy into the public electric systems. Variable energies such as solar and wind power have a lack of stability and to avoid short-term fluctuations in power supplied to the grid, a local storage subsystem could be used to provide higher quality and stability in the fed energy. Energy storage systems (ESSs) would be a facilitator of smart grid deployment and a “small amount” of storage would have a “great impact” on the future power grid. The smart grid, with its various superior communications and control features, would make it possible to integrate the potential application of widely dispersed battery storage systems as well other ESSs. This work deals with a detailed updated review on available ESSs applications in future smart power grids. It also highlights latest projects carried out on different ESSs throughout all around the world.展开更多
The expansion planning and operation of all three sectors, generation, transmission and distribution, of power system essentially require load forecasting. Weather conditions have significant impacts on forecasted loa...The expansion planning and operation of all three sectors, generation, transmission and distribution, of power system essentially require load forecasting. Weather conditions have significant impacts on forecasted load, especially short-term and mid-term. A momentous portion of the electrical energy is consumed, especially in cold or hot countries, to mitigate the impact of weather on the daily life of human society. Usually, weather dependent component of load is identified by fitting appropriate non-linear curve to the scatter plot of weather-load model. This technique some times shows lower correlation with weather variables. This paper proposes a new methodology to identify the weather sensitive component of electrical load using empirical mode decomposition (EMD) technique. The proposed methodology is applied to the daily peak load of Dhaka zone of Bangladesh Power System (BPS) of the year 2012. A detailed numerical process to evaluate the weather sensitive portion of the load is also presented. The proposed methodology is validated through statistical error evaluation process. Finally the salient features of the results are discussed.展开更多
Indium tin oxide(ITO) thin film was deposited on glass substrate by means of vacuum evaporation technique and annealed at 200 ℃, 300℃ and 400 ℃ in air for 1 h. The characterization and properties of the deposited f...Indium tin oxide(ITO) thin film was deposited on glass substrate by means of vacuum evaporation technique and annealed at 200 ℃, 300℃ and 400 ℃ in air for 1 h. The characterization and properties of the deposited film samples were analyzed by X-ray diffraction(XRD), scanning electron microscopy(SEM), and UV-VIS-NIR spectroscopy techniques. From the XRD patterns, it was found that the deposited thin film was of crystalline at an annealing temperature of 400℃. The crystalline phase was indexed as cubic structure with lattice constant and crystallite size of 0.511 nm and 40 nm, respectively. The SEM images showed that the films exhibited uniform surface morphology with well-defined spherical grains. The optical transmittance of ITO thin film annealed at 400 ℃ was improved from 44% to 84% in the wavelength range from 250 nm to 2 100 nm and an optical band gap was measured as 3.86 e V. Hall effect measurement was used to measure the resistivity and conductivity of the prepared film.展开更多
Supercapacitors are gaining popularity due to their high cycling stability,power density,and fast charge and discharge rates.Researchers are ex-ploring electrode materials,electrolytes,and separat-ors for cost-effecti...Supercapacitors are gaining popularity due to their high cycling stability,power density,and fast charge and discharge rates.Researchers are ex-ploring electrode materials,electrolytes,and separat-ors for cost-effective energy storage systems.Ad-vances in materials science have led to the develop-ment of hybrid nanomaterials,such as combining fil-amentous carbon forms with inorganic nanoparticles,to create new charge and energy transfer processes.Notable materials for electrochemical energy-stor-age applications include MXenes,2D transition met-al carbides,and nitrides,carbon black,carbon aerogels,activated carbon,carbon nanotubes,conducting polymers,carbon fibers,and nanofibers,and graphene,because of their thermal,electrical,and mechanical properties.Carbon materials mixed with conducting polymers,ceramics,metal oxides,transition metal oxides,metal hydroxides,transition metal sulfides,trans-ition metal dichalcogenide,metal sulfides,carbides,nitrides,and biomass materials have received widespread attention due to their remarkable performance,eco-friendliness,cost-effectiveness,and renewability.This article explores the development of carbon-based hybrid materials for future supercapacitors,including electric double-layer capacitors,pseudocapacitors,and hy-brid supercapacitors.It investigates the difficulties that influence structural design,manufacturing(electrospinning,hydro-thermal/solvothermal,template-assisted synthesis,electrodeposition,electrospray,3D printing)techniques and the latest car-bon-based hybrid materials research offer practical solutions for producing high-performance,next-generation supercapacitors.展开更多
The paper reports that Hfrio dielectric is deposited by reactive co-sputtering of Hf and Ti targets in an Ar/O2 ambience, followed by an annealing in different gas ambiences of N2, NO and NH3 at 600℃ for 2 min. Capac...The paper reports that Hfrio dielectric is deposited by reactive co-sputtering of Hf and Ti targets in an Ar/O2 ambience, followed by an annealing in different gas ambiences of N2, NO and NH3 at 600℃ for 2 min. Capacitancevoltage and gate-leakage properties are characterized and compared. The results indicate that the NO-annealed sample exhibits the lowest interface-state and dielectric-charge densities and best device reliability. This is attributed to the fact that nitridation can create strong Si≡N bonds to passivate dangling Si bonds and replace strained Si-O bonds, thus the sample forms a hardened dielectric/Si interface with high reliability.展开更多
Si-doped zinc oxide（SZO） thin films are deposited by using a co-sputtering method,and used as the channel active layers of ZnO-based TFTs with single and dual active layer structures.The effects of silicon content o...Si-doped zinc oxide（SZO） thin films are deposited by using a co-sputtering method,and used as the channel active layers of ZnO-based TFTs with single and dual active layer structures.The effects of silicon content on the optical transmittance of the SZO thin film and electrical properties of the SZO TFT are investigated.Moreover,the electrical performances and bias-stress stabilities of the single- and dual-active-layer TFTs are investigated and compared to reveal the effects of the Si doping and dual-active-layer structure.The average transmittances of all the SZO films are about 90% in the visible light region of 400 nm-800 nm,and the optical band gap of the SZO film gradually increases with increasing Si content.The Si-doping can effectively suppress the grain growth of ZnO,revealed by atomic force microscope analysis.Compared with that of the undoped ZnO TFT,the off-state current of the SZO TFT is reduced by more than two orders of magnitude and it is 1.5 × 10^-12 A,and thus the on/off current ratio is increased by more than two orders of magnitude.In summary,the SZO/ZnO TFT with dual-active-layer structure exhibits a high on/off current ratio of 4.0 × 10^6 and superior stability under gate-bias and drain-bias stress.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.
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 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A curriculum is a complex system that includes a set of core competencies, objectives, contents, methodological and evaluation criteria, regulation among other things. In order to represent a curriculum as a piece of software the common tools used are databases, trees and lists of courses. However, none of these tools can capture the deep and complex relationships among the elements of a curriculum. To avoid this problem, a more complete representation of an engineering curriculum using ontologies has been developed. This paper presents the construction of an ontology for undergraduate electrical engineering curriculum domain at Universidad Nacional de Colombia, which aims to represent, organize, formalize and standardize the knowledge of this domain, so that it can be shared and reused by different groups of people in the field of education and engineering. The ontology includes four curriculum aspects： knowledge in electrical engineering, skills in engineering, electrical engineering curriculum and regulation. For the ontology development, Methontology was selected as methodology and Protege as implementation tool. In addition, there is a proposal of documentation for this methodology, based on principles of quality management systems. This ontology is designed in order to be used in any field of engineering.
基金supported by the National Natural Science Foundation of China under Grant 52225704 and Grant U23B20120.
文摘With the integration of large-scale renewable energy sources(RESs),the line commutated converter(LCC)based high-voltage direct-current(HVDC)inverter side suffers from degraded grid strength and escalating commutation failure(CF)risks.In comparison with the widely used grid-following(GFL)RESs,grid-forming(GFM)RESs exhibit the favorable characteristics of voltage sources.Therefore,the hybrid operation of GFL-/GFM-RESs holds great potential in voltage support and may help CF suppression.This paper first analyzes the impacts of RESs on the first and subsequent CFs,respectively.It reveals that GFM-RESs alleviate the deterioration of grid voltage induced by GFL-RESs,thereby suppressing the first CF.However,current fault ride-through controls of RESs cannot fulfill the reactive power dynamic demand during the CF recovery process and rarely help mitigate subsequent CFs.Moreover,the limited overcurrent capability of GFM-RES hinders its ability to ride through CF.Based on the mechanism analysis,a CF suppression strategy based on the hybrid GFL/GFM operation is proposed.The proposed strategy optimized the controls of RESs to fulfill reactive power dynamics and offer voltage support during the CF process.Case studies are undertaken on the studied system and the CIGRE benchmark,respectively.The simulation results confirm the effectiveness of the proposed strategy for suppressing CF.
基金Project supported by the National Natural Science Foundation of China(Nos.12202262,12172127,12032015,and 12121002)the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology(No.2023QNRC001)the Hunan Province Science and Technology Innovation Program of China(Nos.2025JJ20012 and 2025RC4022)。
文摘The rapid advancement of wearable electronic devices has paved the way for a more intelligent and interconnected world.However,ensuring the sustainable energy supply for these devices remains a critical challenge,particularly for specialized populations and professionals in demanding environments,where a lack of power can pose life-threatening risks.Herein,we propose a mechanically intelligent biomechanical energy harvesting approach that adapts to complex human motion excitations,thereby improving the energy harvesting performance.Leveraging a mechanical intelligence mechanism,the energy harvester aligns with human physiological habits,selectively activating or deactivating as needed.The system can also adapt to excitations of varying directions,amplitudes,and frequencies.Furthermore,the string tension helps reduce the impact forces on the knee joint during foot strikes.A theoretical model for the biomechanical energy harvesting system is developed to describe its dynamic and electrical characteristics,and a prototype is fabricated and tested under diverse conditions.The experimental results are in good agreement with the simulation trends,validating the effectiveness of the theoretical model.A test subject running at 8 km/h for 90 seconds can successfully power a smartphone for 20 seconds,demonstrating the viability of self-powered applications.This mechanically intelligent biomechanical energy harvesting method holds a promising solution for the sustainable power supply for wearable electronic devices.
文摘The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary growth of mobile communications,the data traffic has dramatically expanded,which has led to massive grid power consumption and incurred high operating expenditure(OPEX).However,the majority of current network designs struggle to efficientlymanage a massive amount of data using little power,which degrades energy efficiency performance.Thereby,it is necessary to have an efficient mechanism to reduce power consumption when processing large amounts of data in network data centers.Utilizing renewable energy sources to power the Cloud Radio Access Network(C-RAN)greatly reduces the need to purchase energy from the utility grid.In this paper,we propose a bandwidth-aware hybrid energypowered C-RAN that focuses on throughput and energy efficiency(EE)by lowering grid usage,aiming to enhance the EE.This paper examines the energy efficiency,spectral efficiency(SE),and average on-grid energy consumption,dealing with the major challenges of the temporal and spatial nature of traffic and renewable energy generation across various network setups.To assess the effectiveness of the suggested network by changing the transmission bandwidth,a comprehensive simulation has been conducted.The numerical findings support the efficacy of the suggested approach.
基金funded by the Ongoing Research Funding Program-Research Chairs(ORF-RC-2025-2400),King Saud University,Riyadh,Saudi Arabia。
文摘Recent studies indicate that millions of individuals suffer from renal diseases,with renal carcinoma,a type of kidney cancer,emerging as both a chronic illness and a significant cause of mortality.Magnetic Resonance Imaging(MRI)and Computed Tomography(CT)have become essential tools for diagnosing and assessing kidney disorders.However,accurate analysis of thesemedical images is critical for detecting and evaluating tumor severity.This study introduces an integrated hybrid framework that combines three complementary deep learning models for kidney tumor segmentation from MRI images.The proposed framework fuses a customized U-Net and Mask R-CNN using a weighted scheme to achieve semantic and instance-level segmentation.The fused outputs are further refined through edge detection using Stochastic FeatureMapping Neural Networks(SFMNN),while volumetric consistency is ensured through Improved Mini-Batch K-Means(IMBKM)clustering integrated with an Encoder-Decoder Convolutional Neural Network(EDCNN).The outputs of these three stages are combined through a weighted fusion mechanism,with optimal weights determined empirically.Experiments on MRI scans from the TCGA-KIRC dataset demonstrate that the proposed hybrid framework significantly outperforms standalone models,achieving a Dice Score of 92.5%,an IoU of 87.8%,a Precision of 93.1%,a Recall of 90.8%,and a Hausdorff Distance of 2.8 mm.These findings validate that the weighted integration of complementary architectures effectively overcomes key limitations in kidney tumor segmentation,leading to improved diagnostic accuracy and robustness in medical image analysis.
文摘The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigms have fundamental issues in data privacy,regulatory compliance,and ownership silo alongside the scaled limitations of the real-life application.The concept of Federated Deep Learning(FDL)is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings.It is an overview of the privacy-preserving developments in FDL as of 2018-2025 with a narrow scope on its usage in smart cities(traffic prediction,environmental monitoring,energy grids),smart homes/buildings/IoT(non-intrusive load monitoring,HVAC optimization,anomaly detection)and the healthcare application(medical imaging,Electronic Health Records(EHR)analysis,remote monitoring).It gives coherent taxonomy,domain pipelines,comparative analyses of privacy mechanisms(differential privacy,secure aggregation,Homomorphic Encryption(HE),Trusted Execution Environments(TEEs),blockchain enhanced and hybrids),system structures,security/robustness defense,deployment/Machine Learning Operation(MLOps)issues,and the longstanding challenges(non-IID heterogeneity,communication efficiency,fairness,and sustainability).Some of the contributions made are structured comparisons of privacy threats,practical design advice on urban areas,recognition of open problems,and a research roadmap into the future up to 2035.The paper brings out the transformational worth of FDL in building credible,scalable,and sustainable intelligent urban ecosystems and the need to do further interdisciplinary research in standardization,real-world testbeds,and ethical governance.
基金support from National Key Research and Development Program of China(2024YFE0217100)the National Natural Science Foundation of China(21905006,22261160370,and 62105075)+7 种基金the Guangdong Provincial Science and Technology Plan(2021A0505110003)the Natural Science Foundation of Hunan Province,China(2023JJ50132)Guangxi Department of Science and Technology(2020GXNSFBA159049 and AD19110030)the Shenzhen Science and Technology Program(SGDX20230116093205009,JCYJ20220818100211025 and 2022378670)the Natural Science Foundation of Top Talent of SZTU(GDRC202343)financial support of Innovation and Technology Fund(#GHP/245/22SZ)The University Grant Council of the University of Hong Kong(grant No.2302101786)General Research Fund(grant Nos.17200823 and 17310624)from the Research Grants Council.
文摘Halide perovskites have emerged as promising materials for X-ray detection with exceptional properties and reasonable costs.Among them,heterostructures between 3D perovskites and low-dimensional perovskites attract intensive studies of their advantages due to low-level ion migration and decent stability.However,there is still a lack of methods to precisely construct heterostructures and a fundamental understanding of their structure-dependent optoelectronic properties.Herein,a gas-phase method was developed to grow 2D perovskites directly on 3D perovskites with nanoscale accuracy.In addition,the larger steric hindrance of organic layers of 2D perovskites was proved to enable slower ion migration,which resulted in reduced trap states and better stability.Based on MAPbBr_(3)single crystals with the(PA)_(2)PbBr_(4)capping layer,the X-ray detector achieved a sensitivity of 22,245μC Gy_(air)^(−1)cm^(−2),a response speed of 240μs,and a dark current drift of 1.17.10^(–4)nA cm^(−1)s^(−1)V^(−1),which were among the highest reported for state-of-the-art perovskite-based X-ray detectors.This study presents a precise synthesis method to construct perovskite-based heterostructures.It also brings an in-depth understanding of the relationship between lattice structures and properties,which are beneficial for advancing high-performance and cost-effective X-ray detectors.
文摘In electrical circuit analysis, it is often necessary to find the set of all direct current （d.c.） operating points （either voltages or currents） of nonlinear circuits. In general, these nonlinear equations are often represented as polynomial systems. In this paper, we address the problem of finding the solutions of nonlinear electrical circuits, which are modeled as systems of n polynomial equations contained in an n-dimensional box. Branch and Bound algorithms based on interval methods can give guaranteed enclosures for the solution. However, because of repeated evaluations of the function values, these methods tend to become slower. Branch and Bound algorithm based on Bernstein coefficients can be used to solve the systems of polynomial equations. This avoids the repeated evaluation of function values, but maintains more or less the same number of iterations as that of interval branch and bound methods. We propose an algorithm for obtaining the solution of polynomial systems, which includes a pruning step using Bernstein Krawczyk operator and a Bernstein Coefficient Contraction algorithm to obtain Bernstein coefficients of the new domain. We solved three circuit analysis problems using our proposed algorithm. We compared the performance of our proposed algorithm with INTLAB based solver and found that our proposed algorithm is more efficient and fast.
基金Project supported by the National Natural Science Foundation of China （Grant No 60376019）.
文摘Trichloroethylene （TCE） pretreatment of Si surface prior to HfO2 deposition is employed to fabricate HfO2 gatedielectric MOS capacitors. Influence of this processing procedure on interlayer growth, HfO2/Si interface properties, gate-oxide leakage and device reliability is investigated. Among the surface pretreatments in NH3, NO, N2O and TCE ambients, the TCE pretreatment gives the least interlayer growths the lowest interface-state density, the smallest gate leakage and the highest reliability. All these improvements should be ascribed to the passivation effects of Cl2 and HC1 on the structural defects in the interlayer and at the interface, and also their gettering effects on the ion contamination in the gate dielectric.
文摘A smart grid will require, to greater or lesser degrees, advanced tools for planning and operation, broadly accepted communications platforms, smart sensors and controls, and real-time pricing. The smart grid has been described as something of an ecosystem with constantly communication, proactive, and virtually self-aware. The use of smart grid has a lot of economical and environmental advantages;however it has a downside of instability and unpredictability introduced by distributed generation (DG) from renewable energy into the public electric systems. Variable energies such as solar and wind power have a lack of stability and to avoid short-term fluctuations in power supplied to the grid, a local storage subsystem could be used to provide higher quality and stability in the fed energy. Energy storage systems (ESSs) would be a facilitator of smart grid deployment and a “small amount” of storage would have a “great impact” on the future power grid. The smart grid, with its various superior communications and control features, would make it possible to integrate the potential application of widely dispersed battery storage systems as well other ESSs. This work deals with a detailed updated review on available ESSs applications in future smart power grids. It also highlights latest projects carried out on different ESSs throughout all around the world.
文摘The expansion planning and operation of all three sectors, generation, transmission and distribution, of power system essentially require load forecasting. Weather conditions have significant impacts on forecasted load, especially short-term and mid-term. A momentous portion of the electrical energy is consumed, especially in cold or hot countries, to mitigate the impact of weather on the daily life of human society. Usually, weather dependent component of load is identified by fitting appropriate non-linear curve to the scatter plot of weather-load model. This technique some times shows lower correlation with weather variables. This paper proposes a new methodology to identify the weather sensitive component of electrical load using empirical mode decomposition (EMD) technique. The proposed methodology is applied to the daily peak load of Dhaka zone of Bangladesh Power System (BPS) of the year 2012. A detailed numerical process to evaluate the weather sensitive portion of the load is also presented. The proposed methodology is validated through statistical error evaluation process. Finally the salient features of the results are discussed.
文摘Indium tin oxide(ITO) thin film was deposited on glass substrate by means of vacuum evaporation technique and annealed at 200 ℃, 300℃ and 400 ℃ in air for 1 h. The characterization and properties of the deposited film samples were analyzed by X-ray diffraction(XRD), scanning electron microscopy(SEM), and UV-VIS-NIR spectroscopy techniques. From the XRD patterns, it was found that the deposited thin film was of crystalline at an annealing temperature of 400℃. The crystalline phase was indexed as cubic structure with lattice constant and crystallite size of 0.511 nm and 40 nm, respectively. The SEM images showed that the films exhibited uniform surface morphology with well-defined spherical grains. The optical transmittance of ITO thin film annealed at 400 ℃ was improved from 44% to 84% in the wavelength range from 250 nm to 2 100 nm and an optical band gap was measured as 3.86 e V. Hall effect measurement was used to measure the resistivity and conductivity of the prepared film.
文摘Supercapacitors are gaining popularity due to their high cycling stability,power density,and fast charge and discharge rates.Researchers are ex-ploring electrode materials,electrolytes,and separat-ors for cost-effective energy storage systems.Ad-vances in materials science have led to the develop-ment of hybrid nanomaterials,such as combining fil-amentous carbon forms with inorganic nanoparticles,to create new charge and energy transfer processes.Notable materials for electrochemical energy-stor-age applications include MXenes,2D transition met-al carbides,and nitrides,carbon black,carbon aerogels,activated carbon,carbon nanotubes,conducting polymers,carbon fibers,and nanofibers,and graphene,because of their thermal,electrical,and mechanical properties.Carbon materials mixed with conducting polymers,ceramics,metal oxides,transition metal oxides,metal hydroxides,transition metal sulfides,trans-ition metal dichalcogenide,metal sulfides,carbides,nitrides,and biomass materials have received widespread attention due to their remarkable performance,eco-friendliness,cost-effectiveness,and renewability.This article explores the development of carbon-based hybrid materials for future supercapacitors,including electric double-layer capacitors,pseudocapacitors,and hy-brid supercapacitors.It investigates the difficulties that influence structural design,manufacturing(electrospinning,hydro-thermal/solvothermal,template-assisted synthesis,electrodeposition,electrospray,3D printing)techniques and the latest car-bon-based hybrid materials research offer practical solutions for producing high-performance,next-generation supercapacitors.
基金Project supported by the National Natural Science Foundation of China （Grant No 60376019）.
文摘The paper reports that Hfrio dielectric is deposited by reactive co-sputtering of Hf and Ti targets in an Ar/O2 ambience, followed by an annealing in different gas ambiences of N2, NO and NH3 at 600℃ for 2 min. Capacitancevoltage and gate-leakage properties are characterized and compared. The results indicate that the NO-annealed sample exhibits the lowest interface-state and dielectric-charge densities and best device reliability. This is attributed to the fact that nitridation can create strong Si≡N bonds to passivate dangling Si bonds and replace strained Si-O bonds, thus the sample forms a hardened dielectric/Si interface with high reliability.
基金supported by the National Natural Science Foundation of China(Grant Nos.61076113 and 61274085)the Natural Science Foundation of Guangdong Province(Grant No.2016A030313474)the University Development Fund(Nanotechnology Research Institute,Grant No.00600009)of the University of Hong Kong,China
文摘Si-doped zinc oxide（SZO） thin films are deposited by using a co-sputtering method,and used as the channel active layers of ZnO-based TFTs with single and dual active layer structures.The effects of silicon content on the optical transmittance of the SZO thin film and electrical properties of the SZO TFT are investigated.Moreover,the electrical performances and bias-stress stabilities of the single- and dual-active-layer TFTs are investigated and compared to reveal the effects of the Si doping and dual-active-layer structure.The average transmittances of all the SZO films are about 90% in the visible light region of 400 nm-800 nm,and the optical band gap of the SZO film gradually increases with increasing Si content.The Si-doping can effectively suppress the grain growth of ZnO,revealed by atomic force microscope analysis.Compared with that of the undoped ZnO TFT,the off-state current of the SZO TFT is reduced by more than two orders of magnitude and it is 1.5 × 10^-12 A,and thus the on/off current ratio is increased by more than two orders of magnitude.In summary,the SZO/ZnO TFT with dual-active-layer structure exhibits a high on/off current ratio of 4.0 × 10^6 and superior stability under gate-bias and drain-bias stress.
