The human brain is asymmetrical in function, with each of its two hemispheres being somewhat responsible for distinct cognitive and motor tasks, to include writing. It stands to reason that engineering students who ha...The human brain is asymmetrical in function, with each of its two hemispheres being somewhat responsible for distinct cognitive and motor tasks, to include writing. It stands to reason that engineering students who have established entrance into their upper-division programs will have demonstrated cognitive proficiency in math and logical operations, abstract and analytical reasoning and language usage, to include writing. In this study the question was asked: is there a correlation between an upper-division electrical engineering students’ analytical reasoning ability and their descriptive writing ability? Descriptive writing is taken here to mean a students’ ability to identify key physical aspects of a mathematical model and to express—in words—a concise and well-balanced description that demonstrates a deep conceptual understanding of the model. This includes more than a description of the variables or the particular application to an engineering problem;it includes a demonstrated recognition of the basic physics that govern the model, certain limitations (idealizations) inherent in the model, and an understanding of how to make practical experimental measurements to verify the governing physics in the model. A student at this level may demonstrate proficiency in their analytical reasoning skills and hence be capable of correctly solving a given problem. However, this does not guarantee that the same student is skilled in associating equations with their physical meaning on a deep conceptual level or in understanding physical limitations of the equation. Consequently, such a student may demonstrate difficulty in mapping their comprehension of the model into written language that demonstrates a sound conceptual understanding of the governing physics. The findings represent a sample of two independent class sections of Electrical and Computer Engineering junior’s first course in Microe-lectronic Devices and Circuits during fall semesters 2012 and 2013 at a private mid-size university in NW Oregon. A total of three exams were administered to each of the 2012/2013 groups. Correlations between exam scores that students achieved on their descriptive writing of microelectronics phenomena and their analytical problem-solving abilities were examined and found to be quite significant.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
In the quest to enhance energy efficiency and reduce environmental impact in the transportation sector,the recovery of waste heat from diesel engines has become a critical area of focus.This study provided an exhausti...In the quest to enhance energy efficiency and reduce environmental impact in the transportation sector,the recovery of waste heat from diesel engines has become a critical area of focus.This study provided an exhaustive thermodynamic analysis optimizing Organic Rankine Cycle(ORC)systems forwaste heat recovery fromdiesel engines.Thestudy assessed the performance of five candidateworking fluids—R11,R123,R113,R245fa,and R141b—under a range of operating conditions,specifically varying overheat temperatures and evaporation pressures.The results indicated that the choice of working fluid substantially influences the system’s exergetic efficiency,net output power,and thermal efficiency.R245fa showed an outstanding net output power of 30.39 kW at high overheat conditions,outperforming R11,which is significant for high-temperature waste heat recovery.At lower temperatures,R11 and R113 demonstrated higher exergetic efficiencies,with R11 reaching a peak exergetic efficiency of 7.4%at an evaporation pressure of 10 bar and an overheat of 10℃.The study also revealed that controlling the overheat and optimizing the evaporation pressure are crucial for enhancing the net output power of the ORC system.Specifically,at an evaporation pressure of 30 bar and an overheat of 0℃,R113 exhibited the lowest exergetic destruction of 544.5 kJ/kg,making it a suitable choice for minimizing irreversible losses.These findings are instrumental for understanding the performance of ORC systems in waste heat recovery applications and offer valuable insights for the design and operation of more efficient and environmentally friendly diesel engine systems.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
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
This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obsta...This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.展开更多
Synchronous reluctance motors(SynRM)are widely employed in industrial applications due to their high robustness,low cost,and absence of permanent magnets.In recent years,significant research efforts have focused on im...Synchronous reluctance motors(SynRM)are widely employed in industrial applications due to their high robustness,low cost,and absence of permanent magnets.In recent years,significant research efforts have focused on improving the controllability and efficiency of SynRM.Accurate rotor position information is essential for the controller to generate appropriate current and voltage references corresponding to the desired speed and load torque.Shaft-mounted position sensors are generally undesirable because of their high cost,sensitivity to harsh operating conditions,maintenance requirements,and reduced reliability in environments characterized by high vibration.Consequently,sensorless control techniques that estimate rotor position using measured stator currents and voltages have attracted increasing attention.However,magnetic saturation,parameter nonlinearities,and cross-coupling effects significantly degrade position estimation accuracy and may compromise the stability of sensorless SynRM drives.In this paper,a nonlinear SynRM model is developed using finite element analysis(FEA)to accurately capture magnetic saturation and cross-coupling effects,thereby providing a precise representation of the machine’s electromagnetic behavior under varying load and flux conditions.A series of magnetostatic FEA simulations is performed.To reduce computational complexity,only one motor pole is analyzed by applying anti-periodic boundary conditions along the domain sides and enforcing a zero magnetic vector potential on the external stator boundary.Nonlinear iron material properties are modeled using the appropriate B-H curve.The simulations are carried out by imposing d-and q-axis current components and computing the corresponding flux linkages and electromagnetic torque.Based on these results,both apparent and incremental inductances are extracted and incorporated into the control algorithm.An advanced fictitious flux linkage method combined with a phase-locked loop(PLL)is employed for accurate rotor position estimation.Simulation results confirm that the proposed sensorless control strategy ensures stable operation and high position estimation accuracy over the entire speed range.展开更多
Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows rais...Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.展开更多
A novel T-type specimen enabled the separation of the thermal and athermal contributions of electropulsing treatment(EPT)in pretwinned AZ31 Mg alloy.The combination of T-type geometry and carefully selected EPT condit...A novel T-type specimen enabled the separation of the thermal and athermal contributions of electropulsing treatment(EPT)in pretwinned AZ31 Mg alloy.The combination of T-type geometry and carefully selected EPT conditions equalized the thermal contributions at two distinct locations(J70-C and J77-D)in a thermally stable regime,which isolated the athermal effect in J70-C.The results were verified using finite element analysis.Although the athermal EPT contribution did not alter the fundamental microstructural evolution mechanisms,the evolution rate increased significantly.As a result,the J70-C region exhibited faster grain growth,annihilation of twin boundaries,reduction of low-angle grain boundaries,dislocation annihilation,and static recrystallization compared to its counterpart,as further supported by the microhardness trends.These results were interpreted in light of the additional driving force by the athermal EPT contribution,which accelerated the strain-induced boundary migrations beyond Joule heating alone.The proposed T-type specimen methodology offers a robust framework for decoupling the thermal and athermal effects in macroscale EPT processes.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
Automated detection of Motor Imagery(MI)tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation.Prediction of MI tasks can be performed with the help of Electroencephalogram(...Automated detection of Motor Imagery(MI)tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation.Prediction of MI tasks can be performed with the help of Electroencephalogram(EEG)signals recorded by placing electrodes on the scalp of subjects;however,accurate prediction of MI tasks remains a challenge due to noise that is incurred during the EEG signal recording process,the extraction of a feature vector with high interclass variance,and accurate classification.The proposed method consists of preprocessing,feature extraction,and classification.First,EEG signals are denoised using a bandpass filter followed by Independent Component Analysis(ICA).Multiple channels are combined to form a single surrogate channel.Short Time Fourier Transform(STFT)is then applied to convert time domain EEG signals into the frequency domain.Handcrafted and automated features are extracted from EEG signals and then concatenated to form a single feature vector.We propose a customized two-dimensional Convolutional Neural Network(CNN)for automated feature extraction with high interclass variance.Feature selection is performed using Particle Swarm Optimization(PSO)to obtain optimal features.The final feature vector is passed to three different classifiers:Support Vector Machine(SVM),Random Forest(RF),and Long Short-Term Memory(LSTM).The final decision is made using the Model-Agnostic Meta Learning(MAML).The Proposed method has been tested on two datasets,including PhysioNet and BCI Competition IV-2a,and it achieved better results in terms of accuracy and F1 score than existing state-of-the-art methods.The proposed framework achieved an accuracy and F1 score of 96%on the PhysioNet dataset and 95.5%on the BCI Competition IV,dataset 2a.We also present SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM)explainable techniques to enhance model interpretability in a clinical setting.展开更多
As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation c...As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation capability and promoting renewable integration.However,evaluating the adjustable capability of such hybrid stations while considering security constraints remains a major challenge.This paper first analyzes the adjustable capabilities of all the resources within such a station based on the power-energy boundary(PEB)model.Then,an optimal formulation is proposed to obtain the adjusted parameters of the aggregate feasible region(AFR)model,which embeds low-dimensional linear models within high-dimensional linear models to improve the accuracy.To solve this formulation,it is transformed using duality theory and an alternating optimization algorithm is designed to obtain the solution.Finally,a multi-station adjustable capability aggregation method considering security constraints is introduced.Simulation results verify that the proposed method effectively reduces infeasible regions and improves smoothness of aggregated boundaries,providing an accurate and practical tool for flexibility evaluation in PSCSs and offering guidance for aggregators and system planners.展开更多
In real industrial microgrids(MGs),the length of the primary delivery feeder to the connection point of the main substation is sometimes long.This reduces the power factor and increases reactive power absorption along...In real industrial microgrids(MGs),the length of the primary delivery feeder to the connection point of the main substation is sometimes long.This reduces the power factor and increases reactive power absorption along the primary delivery feeder from the external network.Besides,the giant induction electro-motors as the working horse of industries requires remarkable amounts of reactive power for electro-mechanical energy conversions.To reduce power losses and operating costs of the MG as well as to improve the voltage quality,this study aims at providing an insightful model for optimal placement and sizing of reactive power compensation capacitors in an industrial MG.In the presented model,the objective function considers voltage profile and network power factor improvement at the MG connection point.Also,it realizes power flow equations within which all operational security constraints are considered.Various reactive power compensation strategies including distributed group compensation,centralized compensation at the main substation,and distributed compensation along the primary delivery feeder are scrutinized.A real industrial MG,say as Urmia Petrochemical plant,is considered in numerical validations.The obtained results in each scenario are discussed in depth.As seen,the best performance is obtained when the optimal location and sizing of capacitors are simultaneously determined at the main buses of the industrial plants,at the main substation of the MG,and alongside the primary delivery feeder.In this way,74.81%improvement in power losses reduction,1.3%lower active power import from the main grid,23.5%improvement in power factor,and 37.5%improvement in network voltage deviation summation are seen in this case compared to the base case.展开更多
Although wind energy is volatile,the output of a wind-storage plant is partially dispatchable,making it a promising paradigm on the generation side.A grid-friendly wind-storage plant ought to be able to continuously o...Although wind energy is volatile,the output of a wind-storage plant is partially dispatchable,making it a promising paradigm on the generation side.A grid-friendly wind-storage plant ought to be able to continuously output the desired power over a certain period of time.This paper proposes a dependable dynamic capacity provision scheme of a wind-storage plant over a daily horizon.It stipulates a minimum number of periods during which the committed capacity must be fulfilled and a maximum mismatch during the remaining periods when the desired power output is not achievable.In the general case,the day-ahead piecewise constant capacity provision results in a two-stage stochastic program formulated as a mixed-integer linear program.Specifically,for constant capacity provision,a decomposition algorithm is developed to determine the global optimal solution,and the complexity grows linearly with the number of scenarios.Given the committed capacity trajectory,the real-time operation problem is modeled as a four-state stochastic dynamic program.The discrete state-action values are derived recursively via the principle of optimality.Real-time dispatch actions are generated by using the action-value tabular leveraging inexact ultra-short-term forecasts.Numerical tests over one year demonstrate that the proposed method successfully fulfills reliable operation on 355 days and achieve an optimality gap of 9.47%compared with the ex-post optimum,which is comparable to model predictive control using exact 2–3-hour-ahead wind power forecasts.展开更多
The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods.To this end,the pa...The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods.To this end,the paper proposes a novel high-dimensional memristor synapse-coupled hyperchaotic neural network by using the designed memristor as the synapse to connect an inertial neuron(IN)and a Hopfield neural network(HNN).By using numerical tools including bifurcation plots,phase plots,and basins of attraction,it is found that the dynamics of this system are closely related to the memristor coupling strength,self-connection synaptic weights,and inter-connection synaptic weights,and it can exhibit excellent hyperchaotic behaviors and coexisting multi-stable patterns.Through PSIM circuit simulations,the complex dynamics of the coupled IN-HNN system are verified.Furthermore,a DNA-encoded encryption algorithm is given,which utilizes generated hyperchaotic sequences to achieve encoding,operation,and decoding of DNA.The results show that this algorithm possesses strong robustness against statistical attacks,differential attacks,and noise interference,and can effectively resist known/selected plaintext attacks.This work will provide new ideas for the modeling of large-scale brainlike neural networks and high-security image encryption.展开更多
This study explores the thin-layer convective solar drying of Marrubium vulgare L.leaves under conditions typical of sun-rich semi-arid climates.Drying experiments were conducted at three inlet-air temperatures(40℃,5...This study explores the thin-layer convective solar drying of Marrubium vulgare L.leaves under conditions typical of sun-rich semi-arid climates.Drying experiments were conducted at three inlet-air temperatures(40℃,50℃,60℃)and two air velocities(1.5 and 2.5 m·s^(-1))using an indirect solar dryer with auxiliary temperature control.Moisture-ratio data were fitted with eight widely used thin-layer models and evaluated using correlation coefficient(r),root-mean-square error(RMSE),and Akaike information criterion(AIC).A complementary heattransfer analysis based on Reynolds and Prandtl numbers with appropriate Nusselt correlations was used to relate flow regime to drying performance,and an energy balance quantified the relative contributions of solar and auxiliary heat.The logarithmic model consistently achieved the lowest RMSE/AIC with r>0.99 across all conditions.Higher temperature and air velocity significantly reduced drying time during the decreasing-rate period,with no constantrate stage observed.On average,solar input supplied the large majority of the thermal demand,while the auxiliary heater compensated short irradiance drops to maintain setpoints.These findings provide a reproducible dataset and a modelling benchmark for M.vulgare leaves,and they support energy-aware design of hybrid solar dryers formedicinal plants in sun-rich regions.展开更多
This work presents a systematic analysis of proton-induced total ionizing dose(TID)effects in 1.2 k V silicon carbide(SiC)power devices with various edge termination structures.Three edge terminations including ring-a...This work presents a systematic analysis of proton-induced total ionizing dose(TID)effects in 1.2 k V silicon carbide(SiC)power devices with various edge termination structures.Three edge terminations including ring-assisted junction termination extension(RA-JTE),multiple floating zone JTE(MFZ-JTE),and field limiting rings(FLR)were fabricated and irradiated with45 Me V protons at fluences ranging from 1×10^(12) to 1×10^(14) cm^(-2).Experimental results,supported by TCAD simulations,show that the RA-JTE structure maintained stable breakdown performance with less than 1%variation due to its effective electric field redistribution by multiple P+rings.In contrast,MFZ-JTE and FLR exhibit breakdown voltage shifts of 6.1%and 15.2%,respectively,under the highest fluence.These results demonstrate the superior radiation tolerance of the RA-JTE structure under TID conditions and provide practical design guidance for radiation-hardened Si C power devices in space and other highradiation environments.展开更多
The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials off...The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials offer unique advantages in photovoltaics due to their tunable optoelectronic properties,high surface area and efficient charge transport capabilities.This review explores recent progress in photovoltaics incorporating 2D materials,focusing on their application as hole and electron transport layers to optimize bandgap alignment,enhance carrier mobility and improve chemical stability.A comprehensive analysis is presented on perovskite solar cells utilizing 2D materials,with a particular focus on strategies to enhance crystallization,passivate defects and improve overall cell efficiency.Additionally,the application of 2D materials in organic solar cells is examined,particularly for reducing recombination losses and enhancing charge extraction through work function modification.Their impact on dye-sensitized solar cells,including catalytic activity and counter electrode performance,is also explored.Finally,the review outlines key challenges,material limitations and performance metrics,offering insight into the future development of nextgeneration photovoltaic devices encouraged by 2D materials.展开更多
The effect of sintering temperature on microstructure, electrical properties, and pulse aging behavior of （V2O5-Mn3O4-Er2O3）-doped zinc oxide varistor ceramics was systematically studied. When the sintering temperat...The effect of sintering temperature on microstructure, electrical properties, and pulse aging behavior of （V2O5-Mn3O4-Er2O3）-doped zinc oxide varistor ceramics was systematically studied. When the sintering temperature increased, the average grain size increased from 6.1 to 8.7μm and the sintered density decreased from 5.52 to 5.43 g/cm3. The breakdown field decreased from 3856 to 922 V/cm with an increase in the sintering temperature up to 900 °C, whereas a further increase to 2352 V/cm at 925 °C. The nonlinear coefficient increased pronouncedly from 4.6 to 30.0 with an increase in the sintering temperature. The varistor ceramics sintered at 850 °C exhibited the best clamping characteristics, with the clamp voltage ratio of the range of 2.22-2.88 for pulse current of 1-25 A. The varistor ceramics sintered at 925 °C exhibited the strongest stability, with %ΔE1 mA/cm2=-8.8% after applying the multi-pulse current of 25 A.展开更多
The characteristic evaluation of aluminum oxide （A1203）/carbon nanotubes （CNTs） hybrid composites for micro-electrical discharge machining （EDM） was described. Alumina matrix composites reinforced with CNTs were...The characteristic evaluation of aluminum oxide （A1203）/carbon nanotubes （CNTs） hybrid composites for micro-electrical discharge machining （EDM） was described. Alumina matrix composites reinforced with CNTs were fabricated by a catalytic chemical vapor deposition method. A1203 composites with different CNT concentrations were synthesized. The electrical characteristic of A1203/CNTs composites was examined. These composites were machined by the EDM process according to the various EDM parameters, and the characteristics of machining were analyzed using field emission scanning electron microscope （FESEM）. The electrical conductivity has a increasing tendency as the CNTs content is increased and has a critical point at 5% A1203 （volume fraction）. In the machining accuracy, many tangles of CNT in A1203/CNTs composites cause violent spark. Thus, it causes the poor dimensional accuracy and circularity. The results show that conductivity of the materials and homogeneous distribution of CNTs in the matrix are important factors for micro-EDM of A1203/CNTs hybrid composites.展开更多
文摘The human brain is asymmetrical in function, with each of its two hemispheres being somewhat responsible for distinct cognitive and motor tasks, to include writing. It stands to reason that engineering students who have established entrance into their upper-division programs will have demonstrated cognitive proficiency in math and logical operations, abstract and analytical reasoning and language usage, to include writing. In this study the question was asked: is there a correlation between an upper-division electrical engineering students’ analytical reasoning ability and their descriptive writing ability? Descriptive writing is taken here to mean a students’ ability to identify key physical aspects of a mathematical model and to express—in words—a concise and well-balanced description that demonstrates a deep conceptual understanding of the model. This includes more than a description of the variables or the particular application to an engineering problem;it includes a demonstrated recognition of the basic physics that govern the model, certain limitations (idealizations) inherent in the model, and an understanding of how to make practical experimental measurements to verify the governing physics in the model. A student at this level may demonstrate proficiency in their analytical reasoning skills and hence be capable of correctly solving a given problem. However, this does not guarantee that the same student is skilled in associating equations with their physical meaning on a deep conceptual level or in understanding physical limitations of the equation. Consequently, such a student may demonstrate difficulty in mapping their comprehension of the model into written language that demonstrates a sound conceptual understanding of the governing physics. The findings represent a sample of two independent class sections of Electrical and Computer Engineering junior’s first course in Microe-lectronic Devices and Circuits during fall semesters 2012 and 2013 at a private mid-size university in NW Oregon. A total of three exams were administered to each of the 2012/2013 groups. Correlations between exam scores that students achieved on their descriptive writing of microelectronics phenomena and their analytical problem-solving abilities were examined and found to be quite significant.
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 the Huaiyin Institute of Technology—Institute of Smart Energy.
文摘In the quest to enhance energy efficiency and reduce environmental impact in the transportation sector,the recovery of waste heat from diesel engines has become a critical area of focus.This study provided an exhaustive thermodynamic analysis optimizing Organic Rankine Cycle(ORC)systems forwaste heat recovery fromdiesel engines.Thestudy assessed the performance of five candidateworking fluids—R11,R123,R113,R245fa,and R141b—under a range of operating conditions,specifically varying overheat temperatures and evaporation pressures.The results indicated that the choice of working fluid substantially influences the system’s exergetic efficiency,net output power,and thermal efficiency.R245fa showed an outstanding net output power of 30.39 kW at high overheat conditions,outperforming R11,which is significant for high-temperature waste heat recovery.At lower temperatures,R11 and R113 demonstrated higher exergetic efficiencies,with R11 reaching a peak exergetic efficiency of 7.4%at an evaporation pressure of 10 bar and an overheat of 10℃.The study also revealed that controlling the overheat and optimizing the evaporation pressure are crucial for enhancing the net output power of the ORC system.Specifically,at an evaporation pressure of 30 bar and an overheat of 0℃,R113 exhibited the lowest exergetic destruction of 544.5 kJ/kg,making it a suitable choice for minimizing irreversible losses.These findings are instrumental for understanding the performance of ORC systems in waste heat recovery applications and offer valuable insights for the design and operation of more efficient and environmentally friendly diesel engine systems.
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.
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.
基金supported by the National Science and Technology Council of under Grant NSTC 114-2221-E-130-007.
文摘This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.
文摘Synchronous reluctance motors(SynRM)are widely employed in industrial applications due to their high robustness,low cost,and absence of permanent magnets.In recent years,significant research efforts have focused on improving the controllability and efficiency of SynRM.Accurate rotor position information is essential for the controller to generate appropriate current and voltage references corresponding to the desired speed and load torque.Shaft-mounted position sensors are generally undesirable because of their high cost,sensitivity to harsh operating conditions,maintenance requirements,and reduced reliability in environments characterized by high vibration.Consequently,sensorless control techniques that estimate rotor position using measured stator currents and voltages have attracted increasing attention.However,magnetic saturation,parameter nonlinearities,and cross-coupling effects significantly degrade position estimation accuracy and may compromise the stability of sensorless SynRM drives.In this paper,a nonlinear SynRM model is developed using finite element analysis(FEA)to accurately capture magnetic saturation and cross-coupling effects,thereby providing a precise representation of the machine’s electromagnetic behavior under varying load and flux conditions.A series of magnetostatic FEA simulations is performed.To reduce computational complexity,only one motor pole is analyzed by applying anti-periodic boundary conditions along the domain sides and enforcing a zero magnetic vector potential on the external stator boundary.Nonlinear iron material properties are modeled using the appropriate B-H curve.The simulations are carried out by imposing d-and q-axis current components and computing the corresponding flux linkages and electromagnetic torque.Based on these results,both apparent and incremental inductances are extracted and incorporated into the control algorithm.An advanced fictitious flux linkage method combined with a phase-locked loop(PLL)is employed for accurate rotor position estimation.Simulation results confirm that the proposed sensorless control strategy ensures stable operation and high position estimation accuracy over the entire speed range.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.
基金funded by the Defense Acquisition Program Administration(DAPA)(Grant No.KRIT-CT-23-059)supported by the‘Human Resources Program in Energy Technology’of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),which was funded by the Ministry of Trade,Industry,and Energy(MOTIE)(Grant No.RS-2024-00398425).
文摘A novel T-type specimen enabled the separation of the thermal and athermal contributions of electropulsing treatment(EPT)in pretwinned AZ31 Mg alloy.The combination of T-type geometry and carefully selected EPT conditions equalized the thermal contributions at two distinct locations(J70-C and J77-D)in a thermally stable regime,which isolated the athermal effect in J70-C.The results were verified using finite element analysis.Although the athermal EPT contribution did not alter the fundamental microstructural evolution mechanisms,the evolution rate increased significantly.As a result,the J70-C region exhibited faster grain growth,annihilation of twin boundaries,reduction of low-angle grain boundaries,dislocation annihilation,and static recrystallization compared to its counterpart,as further supported by the microhardness trends.These results were interpreted in light of the additional driving force by the athermal EPT contribution,which accelerated the strain-induced boundary migrations beyond Joule heating alone.The proposed T-type specimen methodology offers a robust framework for decoupling the thermal and athermal effects in macroscale EPT processes.
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.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘Automated detection of Motor Imagery(MI)tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation.Prediction of MI tasks can be performed with the help of Electroencephalogram(EEG)signals recorded by placing electrodes on the scalp of subjects;however,accurate prediction of MI tasks remains a challenge due to noise that is incurred during the EEG signal recording process,the extraction of a feature vector with high interclass variance,and accurate classification.The proposed method consists of preprocessing,feature extraction,and classification.First,EEG signals are denoised using a bandpass filter followed by Independent Component Analysis(ICA).Multiple channels are combined to form a single surrogate channel.Short Time Fourier Transform(STFT)is then applied to convert time domain EEG signals into the frequency domain.Handcrafted and automated features are extracted from EEG signals and then concatenated to form a single feature vector.We propose a customized two-dimensional Convolutional Neural Network(CNN)for automated feature extraction with high interclass variance.Feature selection is performed using Particle Swarm Optimization(PSO)to obtain optimal features.The final feature vector is passed to three different classifiers:Support Vector Machine(SVM),Random Forest(RF),and Long Short-Term Memory(LSTM).The final decision is made using the Model-Agnostic Meta Learning(MAML).The Proposed method has been tested on two datasets,including PhysioNet and BCI Competition IV-2a,and it achieved better results in terms of accuracy and F1 score than existing state-of-the-art methods.The proposed framework achieved an accuracy and F1 score of 96%on the PhysioNet dataset and 95.5%on the BCI Competition IV,dataset 2a.We also present SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM)explainable techniques to enhance model interpretability in a clinical setting.
基金supported by Science and Technology Project of China Southern Power Grid Company(036000KK52222007(GDKJXM20222121)).
文摘As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation capability and promoting renewable integration.However,evaluating the adjustable capability of such hybrid stations while considering security constraints remains a major challenge.This paper first analyzes the adjustable capabilities of all the resources within such a station based on the power-energy boundary(PEB)model.Then,an optimal formulation is proposed to obtain the adjusted parameters of the aggregate feasible region(AFR)model,which embeds low-dimensional linear models within high-dimensional linear models to improve the accuracy.To solve this formulation,it is transformed using duality theory and an alternating optimization algorithm is designed to obtain the solution.Finally,a multi-station adjustable capability aggregation method considering security constraints is introduced.Simulation results verify that the proposed method effectively reduces infeasible regions and improves smoothness of aggregated boundaries,providing an accurate and practical tool for flexibility evaluation in PSCSs and offering guidance for aggregators and system planners.
文摘In real industrial microgrids(MGs),the length of the primary delivery feeder to the connection point of the main substation is sometimes long.This reduces the power factor and increases reactive power absorption along the primary delivery feeder from the external network.Besides,the giant induction electro-motors as the working horse of industries requires remarkable amounts of reactive power for electro-mechanical energy conversions.To reduce power losses and operating costs of the MG as well as to improve the voltage quality,this study aims at providing an insightful model for optimal placement and sizing of reactive power compensation capacitors in an industrial MG.In the presented model,the objective function considers voltage profile and network power factor improvement at the MG connection point.Also,it realizes power flow equations within which all operational security constraints are considered.Various reactive power compensation strategies including distributed group compensation,centralized compensation at the main substation,and distributed compensation along the primary delivery feeder are scrutinized.A real industrial MG,say as Urmia Petrochemical plant,is considered in numerical validations.The obtained results in each scenario are discussed in depth.As seen,the best performance is obtained when the optimal location and sizing of capacitors are simultaneously determined at the main buses of the industrial plants,at the main substation of the MG,and alongside the primary delivery feeder.In this way,74.81%improvement in power losses reduction,1.3%lower active power import from the main grid,23.5%improvement in power factor,and 37.5%improvement in network voltage deviation summation are seen in this case compared to the base case.
基金supported by the Smart Grid-National Science and Technology Major Project of China(2024ZD0802000).
文摘Although wind energy is volatile,the output of a wind-storage plant is partially dispatchable,making it a promising paradigm on the generation side.A grid-friendly wind-storage plant ought to be able to continuously output the desired power over a certain period of time.This paper proposes a dependable dynamic capacity provision scheme of a wind-storage plant over a daily horizon.It stipulates a minimum number of periods during which the committed capacity must be fulfilled and a maximum mismatch during the remaining periods when the desired power output is not achievable.In the general case,the day-ahead piecewise constant capacity provision results in a two-stage stochastic program formulated as a mixed-integer linear program.Specifically,for constant capacity provision,a decomposition algorithm is developed to determine the global optimal solution,and the complexity grows linearly with the number of scenarios.Given the committed capacity trajectory,the real-time operation problem is modeled as a four-state stochastic dynamic program.The discrete state-action values are derived recursively via the principle of optimality.Real-time dispatch actions are generated by using the action-value tabular leveraging inexact ultra-short-term forecasts.Numerical tests over one year demonstrate that the proposed method successfully fulfills reliable operation on 355 days and achieve an optimality gap of 9.47%compared with the ex-post optimum,which is comparable to model predictive control using exact 2–3-hour-ahead wind power forecasts.
基金Project supported by the Training Plan of Young Backbone Teachers in Universities of Henan Province(Grant No.2023GGJS142)the Key Scientific Research of Colleges and Universities in Henan Province,China(Grant No.25A120009)+1 种基金Changzhou Leading Innovative Talent Introduction and Cultivation Project(Grant No.CQ20240102)Changzhou Applied Basic Research Program(Grant No.CJ20253065)。
文摘The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods.To this end,the paper proposes a novel high-dimensional memristor synapse-coupled hyperchaotic neural network by using the designed memristor as the synapse to connect an inertial neuron(IN)and a Hopfield neural network(HNN).By using numerical tools including bifurcation plots,phase plots,and basins of attraction,it is found that the dynamics of this system are closely related to the memristor coupling strength,self-connection synaptic weights,and inter-connection synaptic weights,and it can exhibit excellent hyperchaotic behaviors and coexisting multi-stable patterns.Through PSIM circuit simulations,the complex dynamics of the coupled IN-HNN system are verified.Furthermore,a DNA-encoded encryption algorithm is given,which utilizes generated hyperchaotic sequences to achieve encoding,operation,and decoding of DNA.The results show that this algorithm possesses strong robustness against statistical attacks,differential attacks,and noise interference,and can effectively resist known/selected plaintext attacks.This work will provide new ideas for the modeling of large-scale brainlike neural networks and high-security image encryption.
文摘This study explores the thin-layer convective solar drying of Marrubium vulgare L.leaves under conditions typical of sun-rich semi-arid climates.Drying experiments were conducted at three inlet-air temperatures(40℃,50℃,60℃)and two air velocities(1.5 and 2.5 m·s^(-1))using an indirect solar dryer with auxiliary temperature control.Moisture-ratio data were fitted with eight widely used thin-layer models and evaluated using correlation coefficient(r),root-mean-square error(RMSE),and Akaike information criterion(AIC).A complementary heattransfer analysis based on Reynolds and Prandtl numbers with appropriate Nusselt correlations was used to relate flow regime to drying performance,and an energy balance quantified the relative contributions of solar and auxiliary heat.The logarithmic model consistently achieved the lowest RMSE/AIC with r>0.99 across all conditions.Higher temperature and air velocity significantly reduced drying time during the decreasing-rate period,with no constantrate stage observed.On average,solar input supplied the large majority of the thermal demand,while the auxiliary heater compensated short irradiance drops to maintain setpoints.These findings provide a reproducible dataset and a modelling benchmark for M.vulgare leaves,and they support energy-aware design of hybrid solar dryers formedicinal plants in sun-rich regions.
基金supported by the IITP(Institute for Information&Communications Technology Planning&Evaluation)under the ITRC(Information Technology Research Center)support program(IITP-2025-RS-2024-00438288)grant funded by the Korea government(MSIT)+1 种基金National Research Council of Science&Technology(NST)grant by the MSIT(Aerospace Semiconductor Strategy Research Project No.GTL25051-000)supported by the IC Design Education Center(IDEC),Korea。
文摘This work presents a systematic analysis of proton-induced total ionizing dose(TID)effects in 1.2 k V silicon carbide(SiC)power devices with various edge termination structures.Three edge terminations including ring-assisted junction termination extension(RA-JTE),multiple floating zone JTE(MFZ-JTE),and field limiting rings(FLR)were fabricated and irradiated with45 Me V protons at fluences ranging from 1×10^(12) to 1×10^(14) cm^(-2).Experimental results,supported by TCAD simulations,show that the RA-JTE structure maintained stable breakdown performance with less than 1%variation due to its effective electric field redistribution by multiple P+rings.In contrast,MFZ-JTE and FLR exhibit breakdown voltage shifts of 6.1%and 15.2%,respectively,under the highest fluence.These results demonstrate the superior radiation tolerance of the RA-JTE structure under TID conditions and provide practical design guidance for radiation-hardened Si C power devices in space and other highradiation environments.
基金supported by the IITP(Institute of Information & Communications Technology Planning & Evaluation)-ITRC(Information Technology Research Center) grant funded by the Korea government(Ministry of Science and ICT) (IITP-2025-RS-2024-00437191, and RS-2025-02303505)partly supported by the Korea Basic Science Institute (National Research Facilities and Equipment Center) grant funded by the Ministry of Education. (No. 2022R1A6C101A774)the Deanship of Research and Graduate Studies at King Khalid University, Saudi Arabia, through Large Research Project under grant number RGP-2/527/46
文摘The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials offer unique advantages in photovoltaics due to their tunable optoelectronic properties,high surface area and efficient charge transport capabilities.This review explores recent progress in photovoltaics incorporating 2D materials,focusing on their application as hole and electron transport layers to optimize bandgap alignment,enhance carrier mobility and improve chemical stability.A comprehensive analysis is presented on perovskite solar cells utilizing 2D materials,with a particular focus on strategies to enhance crystallization,passivate defects and improve overall cell efficiency.Additionally,the application of 2D materials in organic solar cells is examined,particularly for reducing recombination losses and enhancing charge extraction through work function modification.Their impact on dye-sensitized solar cells,including catalytic activity and counter electrode performance,is also explored.Finally,the review outlines key challenges,material limitations and performance metrics,offering insight into the future development of nextgeneration photovoltaic devices encouraged by 2D materials.
文摘The effect of sintering temperature on microstructure, electrical properties, and pulse aging behavior of （V2O5-Mn3O4-Er2O3）-doped zinc oxide varistor ceramics was systematically studied. When the sintering temperature increased, the average grain size increased from 6.1 to 8.7μm and the sintered density decreased from 5.52 to 5.43 g/cm3. The breakdown field decreased from 3856 to 922 V/cm with an increase in the sintering temperature up to 900 °C, whereas a further increase to 2352 V/cm at 925 °C. The nonlinear coefficient increased pronouncedly from 4.6 to 30.0 with an increase in the sintering temperature. The varistor ceramics sintered at 850 °C exhibited the best clamping characteristics, with the clamp voltage ratio of the range of 2.22-2.88 for pulse current of 1-25 A. The varistor ceramics sintered at 925 °C exhibited the strongest stability, with %ΔE1 mA/cm2=-8.8% after applying the multi-pulse current of 25 A.
基金Project(2010-0008-277) supported by Program of Establishment of an Infrastructure for Public Usepartly by NCRC (National Core Research Center) through the National Research Foundation of Korea funded by the Ministry of Education
文摘The characteristic evaluation of aluminum oxide （A1203）/carbon nanotubes （CNTs） hybrid composites for micro-electrical discharge machining （EDM） was described. Alumina matrix composites reinforced with CNTs were fabricated by a catalytic chemical vapor deposition method. A1203 composites with different CNT concentrations were synthesized. The electrical characteristic of A1203/CNTs composites was examined. These composites were machined by the EDM process according to the various EDM parameters, and the characteristics of machining were analyzed using field emission scanning electron microscope （FESEM）. The electrical conductivity has a increasing tendency as the CNTs content is increased and has a critical point at 5% A1203 （volume fraction）. In the machining accuracy, many tangles of CNT in A1203/CNTs composites cause violent spark. Thus, it causes the poor dimensional accuracy and circularity. The results show that conductivity of the materials and homogeneous distribution of CNTs in the matrix are important factors for micro-EDM of A1203/CNTs hybrid composites.
