In this paper,electrically excited synchronous machines(EESMs)using copper(Cu)and aluminum(Al)windings are compared for the feasibility of replacing Cu windings with Al windings in electric vehicle(EV)applications sin...In this paper,electrically excited synchronous machines(EESMs)using copper(Cu)and aluminum(Al)windings are compared for the feasibility of replacing Cu windings with Al windings in electric vehicle(EV)applications since Al windings have lower mass density and cost per weight,but higher resistivity and lower thermal conductivity than Cu windings.The EESMs with four winding configurations are optimized with an electromagnetic-thermal co-optimization method.The optimized EESM with only Cu windings is considered as the baseline in this study.Results show that the EESM with stator-Cu/rotor-Al windings has the least torque reduction(12.1%)compared to the baseline among the three EESMs with Al windings and the highest torque mass density among all EESMs.Meanwhile,although the new European driving cycle efficiency of the stator-Cu/rotor-Al EESM is 1.8%lower than that of the baseline,the torque per cost is 71%higher,and the maximum rotor mechanical stress is 8%lower.Therefore,the EESMs with stator-Cu/rotor-Al windings are prospective substitutions of those with only Cu windings for EV applications considering the trade-off between performance and cost.展开更多
This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational ...This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational efficiencies,and technological advancements.Linear generators,such as Induction,permanent magnet synchronous,and switched reluctance types,are highlighted for their direct conversion capability,eliminating the need for mechanical gearboxes.Rotary Induction generators,permanent magnet synchronous generators,and doubly-fed Induction generators are evaluated for their established engineering principles and integration with existing grid infrastructure.The paper discusses the historical development,environmental benefits,and ongoing advancements in wave energy technologies,emphasizing the increasing feasibility and scalability of wave energy as a renewable source.Through a comprehensive analysis,this review provides insights into the current state and future prospects of electrical generators in wave energy conversion,underscoring their potential to significantly reduce reliance on fossil fuels and mitigate environmental impacts.展开更多
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
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
A wind-turbine power system is often challenged by voltage instability,reactive power imbalance,and limited fault ride-through capability under grid disturbances.Doubly Fed Induction Generator based wind farms,owing t...A wind-turbine power system is often challenged by voltage instability,reactive power imbalance,and limited fault ride-through capability under grid disturbances.Doubly Fed Induction Generator based wind farms,owing to their partial coupling with the grid,are particularly vulnerable to voltage dips and excessive reactive power absorption during fault events.This study proposes an adaptive control strategy based on Model Reference Adaptive Control integrated with stator flux-oriented vector control to regulate active and reactive power of a DFIG-based wind farm connected to a standard IEEE 9-bus power system under fault conditions.The proposed control scheme is developed and validated using detailed MATLAB/Simulink modeling under normal operation,symmetrical three-phase fault conditions,and post-fault recovery scenarios.A three-phase-to-ground fault is applied at the wind farm interconnection bus for a duration of 150 ms to evaluate transient performance.Simulation results demonstrate that the adaptive controller ensures fast power tracking,effective reactive power support,and enhanced voltage recovery compared to a conventional proportional–integral controller.Quantitatively,the proposed method improves voltage recovery time by approximately 45%,reduces active power overshoot by 38%,and lowers total harmonic distortion by 52% following fault clearance.Furthermore,the adaptive controller maintains stable operation under variations in wind speed and machine parameters without requiring retuning,highlighting its robustness against system uncertainties.The results confirm that the proposed control strategy significantly enhances fault ride-through capability,power quality,and dynamic stability of grid-interfaced wind farms.These findings demonstrate the practical applicability of adaptive control techniques for improving the reliability and resilience of modern power systems with high wind energy penetration.展开更多
Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitiv...Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption.展开更多
The detection of high-impedance faults(HIFs)in microgrid feeders is a serious issue due to low fault current levels during HIFs.This issue becomes especially problematic when microgrids are operating in an islanded mo...The detection of high-impedance faults(HIFs)in microgrid feeders is a serious issue due to low fault current levels during HIFs.This issue becomes especially problematic when microgrids are operating in an islanded mode integrated with inverter-based distributed generators(IBDGs).This paper proposes an innovative relaying scheme for the protection of microgrid feeders using incremental negative sequence power.After a fault occurs,the negative sequence voltage and currents are collected at the ends of the protected feeder and then its incremental values are measured.After that,incremental negative sequence real power is obtained at the ends of the feeder to obtain the proposed relaying feature,i.e.,a ratio of the sum of incremental negative sequence real power(∆RSNSP).The∆RSNSP is defined as the ratio of the sum of incremental negative sequence real power at the two ends of the feeder to the minimum of the powers among the ends.Simulation studies on a modified IEEE 13-bus system have shown that this scheme can detect HIFs and low-impedance faults(LIFs).It has been rigorously tested under various operating conditions,like variations in fault inception angles,faults during islanding,simultaneous faults,evolving faults,composite faults,capacitors,and load switching.This scheme is not only fast and accurate but also performs well even in noisy conditions,changes in topologies(i.e.,radial or mesh),synchronization errors,and transient faults.A comparative chart comparing its performance with other recent schemes is also included.Finally,the scheme is also validated on a real-time simulator which proves that the proposed scheme can work effectively under various fault conditions.展开更多
Advanced adiabatic compressed air energy storage(AA-CAES),with its dual capability for electricity-heat cogeneration and energy storage,offers significant potential as an energy hub for integrated electricity and heat...Advanced adiabatic compressed air energy storage(AA-CAES),with its dual capability for electricity-heat cogeneration and energy storage,offers significant potential as an energy hub for integrated electricity and heat systems(IEHS).While synergies in the electricity-heat market are known to enhance economic efficiency,it is hard to achieve cooperative operation due to the inherent differences among participants of IEHS and the absence of an incentive-compatible mechanism.To address this challenge,this paper proposes a Nash bargaining-based cooperative operation strategy for IEHS with AA-CAES.First,a cooperative alliance framework based on the Nash bargaining is proposed to optimize energy trading.Second,to overcome computational complexity,the non-convex,nonlinear Nash bargaining problem is decomposed into a two-stage optimization approach.In the first stage,a joint planning model maximizes the total profit of the alliance,determining the optimal energy interaction for each participant.In the second stage,a subsequent model ensures fair profit distribution by optimizing pricing and benefit-sharing mechanisms.Subsequently,a distributed solution strategy based on the self-adaptive alternating direction method of multipliers is utilized to preserve operator privacy and improve computational efficiency.Finally,case studies demonstrate that within the electricity-heat co-supply mode,the daily profit of AA-CAES can improve by approximately 4137.45 CNY.Meanwhile,through the proposed cooperative strategy,participants in the IEHS can obtain greater profits,which validates the effectiveness of this strategy.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
Grid-forming(GFM)control is a key technique for power systems with high penetration of converter-interfaced generation.However,its application to photovoltaic(PV)systems faces challenges related to DC voltage transien...Grid-forming(GFM)control is a key technique for power systems with high penetration of converter-interfaced generation.However,its application to photovoltaic(PV)systems faces challenges related to DC voltage transient stability.This paper investigates a common countermeasure involving a PI-based DC voltage controller for GFM-PV systems,revealing that their small-signal stability is sensitive to parameter tuning.The study develops a generalized DC voltage-dominated 2nd-order GFM model and successfully conducts complex torque analysis,showing that this approach can be effectively extended to other dynamics governed by DC voltage-dominated GFM systems.Subsequently,the paper establishes a stability criterion for GFM-PV systems and proposes a parameter tuning method for DC voltage controllers that incorporates damping margin considerations.The performance of the tuned single-machine-infinite-bus GFM-PV system is validated on the RT-LAB real-time simulation platform under scenarios involving solar irradiance fluctuations and grid frequency disturbances.The proposed method proves effective in ensuring the stability of the GFM-PV system,with robust theoretical support.展开更多
Hybrid commutation converters(HCCs)utilizing reverse-blocking integrated gate commutation thyristors(IGCTs)have gained significant attention due to their immunity to commutation failure.Leveraging the recovery enhance...Hybrid commutation converters(HCCs)utilizing reverse-blocking integrated gate commutation thyristors(IGCTs)have gained significant attention due to their immunity to commutation failure.Leveraging the recovery enhancement characteristics of IGCTs,HCCs demonstrate superior performance at reduced extinction angles,thereby minimizing reactive power consumption.This study presents a comprehensive investigation into reactive power control strategies for HCCs operating at small extinction angles.First,the topological configuration and commutation principle of HCC are elucidated.Subsequently,the mechanism of HCC reactive power control is analyzed,and a reactive power control strategy is proposed by combining the converter transformer taps with extinction angles.Moreover,the relationship between transformer taps and reactive power exchange under different rated extinction angles is calculated,and the theoretically rated extinction angle is proposed.Finally,to validate the proposed control strategy,a four-terminal ultra-high voltage direct current power grid incorporating HCC technology is modeled and sim-ulated using PSCAD/EMTDC.The simulation results demonstrate that the proposed strategy effectively supports AC systems by reducing reactive power absorption in HCCs,while simultaneously exhibiting enhanced reli-ability and economic efficiency.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
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
In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In additi...In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In addition,methods for deploying multiple cameras for motion capture of users(e.g.,performers)are widely used in computer graphics.As the need to minimize and optimize the number of cameras grows to reduce costs,various technologies and research approaches focused on Optimal Camera Placement(OCP)are continually being proposed.However,as most existing studies assume homogeneous camera setups,there is a growing demand for studies on heterogeneous camera setups.For instance,technical demands keep emerging in scenarios with minimal camera configurations,especially regarding cost factors,the physical placement of cameras given the spatial structure,and image capture strategies for heterogeneous cameras,such as high-resolution RGB cameras and depth cameras.In this study,we propose a pre-visualization and simulation method for the optimal placement of heterogeneous cameras in XR environments,accounting for both the specifications of heterogeneous cameras(e.g.,field of view)and the physical configuration(e.g.,wall configuration)in real-world spaces.The proposed method performs a visibility analysis of cameras by considering each camera’s field-of-view volume,resolution,and unique characteristics,along with physicalspace constraints.This approach enables the optimal position and rotation of each camera to be recommended,along with the minimum number of cameras required.In the results of our study conducted in heterogeneous camera combinations,the proposed method achieved 81.7%~82.7%coverage of the target visual information using only 2~3 cameras.In contrast,single(or homogeneous)-typed cameras were required to use 11 cameras for 81.6%coverage.Accordingly,we found that camera deployment resources can be reduced with the proposed approaches.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
The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT n...The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.展开更多
Mid-wavelength infrared(MWIR)and long-wavelength infrared(LWIR)detectors,which operate within the 3-14µm wavelength range,have been extensively employed in various fields,including military,space exploration,envi...Mid-wavelength infrared(MWIR)and long-wavelength infrared(LWIR)detectors,which operate within the 3-14µm wavelength range,have been extensively employed in various fields,including military,space exploration,environmental monitoring,biomedicine,and chemical analysis.While thermal detectors are commonly used,their limitations in sensitivity and response time render them less suitable for next-generation MWIR and LWIR applications.These advanced applications necessitate the use of narrow bandgap semiconductor-based photodetectors,which offer tunable optoelectronic properties and higher specific detectivity compared to thermal detectors.In this review,we provide a detailed analysis of the operational principles and manufacturing strategies of infrared photodetectors based on narrow bandgap semiconductors,which enable high-performance detection in the MWIR and LWIR regions.Our focus is specifically on scalable fabrication of MWIR and LWIR photodetectors,emphasizing devices with active areas ranging from millimeters to centimeters.Researches on large-scale fabrication of infrared photodetectors using quantum dots,two-dimensional(2D)van der Waals(vdW)materials,and three-dimensional(3D)bulk semiconductors are investigated.Finally,we summarize the remaining challenges in developing scalable narrow bandgap semiconductor-based MWIR and LWIR photodetectors for commercialization.By addressing the obstacles such as the difficulty in large-scale unform film synthesis,the requirement for cryogenic device operation,and the introduction of high-density of defect states during the hybridization processes,MWIR and LWIR photodetectors based on narrow bandgap semiconductors will pave the way for designing new sensory systems and applications in a wavelength regime that has been less developed compared to the visible and near-infrared(NIR)ranges.展开更多
基金supported in part by China Scholarship Council(CSC)under Grant 202206160023.
文摘In this paper,electrically excited synchronous machines(EESMs)using copper(Cu)and aluminum(Al)windings are compared for the feasibility of replacing Cu windings with Al windings in electric vehicle(EV)applications since Al windings have lower mass density and cost per weight,but higher resistivity and lower thermal conductivity than Cu windings.The EESMs with four winding configurations are optimized with an electromagnetic-thermal co-optimization method.The optimized EESM with only Cu windings is considered as the baseline in this study.Results show that the EESM with stator-Cu/rotor-Al windings has the least torque reduction(12.1%)compared to the baseline among the three EESMs with Al windings and the highest torque mass density among all EESMs.Meanwhile,although the new European driving cycle efficiency of the stator-Cu/rotor-Al EESM is 1.8%lower than that of the baseline,the torque per cost is 71%higher,and the maximum rotor mechanical stress is 8%lower.Therefore,the EESMs with stator-Cu/rotor-Al windings are prospective substitutions of those with only Cu windings for EV applications considering the trade-off between performance and cost.
文摘This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational efficiencies,and technological advancements.Linear generators,such as Induction,permanent magnet synchronous,and switched reluctance types,are highlighted for their direct conversion capability,eliminating the need for mechanical gearboxes.Rotary Induction generators,permanent magnet synchronous generators,and doubly-fed Induction generators are evaluated for their established engineering principles and integration with existing grid infrastructure.The paper discusses the historical development,environmental benefits,and ongoing advancements in wave energy technologies,emphasizing the increasing feasibility and scalability of wave energy as a renewable source.Through a comprehensive analysis,this review provides insights into the current state and future prospects of electrical generators in wave energy conversion,underscoring their potential to significantly reduce reliance on fossil fuels and mitigate environmental impacts.
基金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.
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.
文摘A wind-turbine power system is often challenged by voltage instability,reactive power imbalance,and limited fault ride-through capability under grid disturbances.Doubly Fed Induction Generator based wind farms,owing to their partial coupling with the grid,are particularly vulnerable to voltage dips and excessive reactive power absorption during fault events.This study proposes an adaptive control strategy based on Model Reference Adaptive Control integrated with stator flux-oriented vector control to regulate active and reactive power of a DFIG-based wind farm connected to a standard IEEE 9-bus power system under fault conditions.The proposed control scheme is developed and validated using detailed MATLAB/Simulink modeling under normal operation,symmetrical three-phase fault conditions,and post-fault recovery scenarios.A three-phase-to-ground fault is applied at the wind farm interconnection bus for a duration of 150 ms to evaluate transient performance.Simulation results demonstrate that the adaptive controller ensures fast power tracking,effective reactive power support,and enhanced voltage recovery compared to a conventional proportional–integral controller.Quantitatively,the proposed method improves voltage recovery time by approximately 45%,reduces active power overshoot by 38%,and lowers total harmonic distortion by 52% following fault clearance.Furthermore,the adaptive controller maintains stable operation under variations in wind speed and machine parameters without requiring retuning,highlighting its robustness against system uncertainties.The results confirm that the proposed control strategy significantly enhances fault ride-through capability,power quality,and dynamic stability of grid-interfaced wind farms.These findings demonstrate the practical applicability of adaptive control techniques for improving the reliability and resilience of modern power systems with high wind energy penetration.
文摘Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption.
文摘The detection of high-impedance faults(HIFs)in microgrid feeders is a serious issue due to low fault current levels during HIFs.This issue becomes especially problematic when microgrids are operating in an islanded mode integrated with inverter-based distributed generators(IBDGs).This paper proposes an innovative relaying scheme for the protection of microgrid feeders using incremental negative sequence power.After a fault occurs,the negative sequence voltage and currents are collected at the ends of the protected feeder and then its incremental values are measured.After that,incremental negative sequence real power is obtained at the ends of the feeder to obtain the proposed relaying feature,i.e.,a ratio of the sum of incremental negative sequence real power(∆RSNSP).The∆RSNSP is defined as the ratio of the sum of incremental negative sequence real power at the two ends of the feeder to the minimum of the powers among the ends.Simulation studies on a modified IEEE 13-bus system have shown that this scheme can detect HIFs and low-impedance faults(LIFs).It has been rigorously tested under various operating conditions,like variations in fault inception angles,faults during islanding,simultaneous faults,evolving faults,composite faults,capacitors,and load switching.This scheme is not only fast and accurate but also performs well even in noisy conditions,changes in topologies(i.e.,radial or mesh),synchronization errors,and transient faults.A comparative chart comparing its performance with other recent schemes is also included.Finally,the scheme is also validated on a real-time simulator which proves that the proposed scheme can work effectively under various fault conditions.
基金supported in part by the National Natural Science Foundation of China(No.52407115)State Key Laboratory of Power System Operation and Control(61011000223).
文摘Advanced adiabatic compressed air energy storage(AA-CAES),with its dual capability for electricity-heat cogeneration and energy storage,offers significant potential as an energy hub for integrated electricity and heat systems(IEHS).While synergies in the electricity-heat market are known to enhance economic efficiency,it is hard to achieve cooperative operation due to the inherent differences among participants of IEHS and the absence of an incentive-compatible mechanism.To address this challenge,this paper proposes a Nash bargaining-based cooperative operation strategy for IEHS with AA-CAES.First,a cooperative alliance framework based on the Nash bargaining is proposed to optimize energy trading.Second,to overcome computational complexity,the non-convex,nonlinear Nash bargaining problem is decomposed into a two-stage optimization approach.In the first stage,a joint planning model maximizes the total profit of the alliance,determining the optimal energy interaction for each participant.In the second stage,a subsequent model ensures fair profit distribution by optimizing pricing and benefit-sharing mechanisms.Subsequently,a distributed solution strategy based on the self-adaptive alternating direction method of multipliers is utilized to preserve operator privacy and improve computational efficiency.Finally,case studies demonstrate that within the electricity-heat co-supply mode,the daily profit of AA-CAES can improve by approximately 4137.45 CNY.Meanwhile,through the proposed cooperative strategy,participants in the IEHS can obtain greater profits,which validates the effectiveness of this strategy.
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 in part by Northeast Branch of State Grid Corporation of China(52992624000W).
文摘Grid-forming(GFM)control is a key technique for power systems with high penetration of converter-interfaced generation.However,its application to photovoltaic(PV)systems faces challenges related to DC voltage transient stability.This paper investigates a common countermeasure involving a PI-based DC voltage controller for GFM-PV systems,revealing that their small-signal stability is sensitive to parameter tuning.The study develops a generalized DC voltage-dominated 2nd-order GFM model and successfully conducts complex torque analysis,showing that this approach can be effectively extended to other dynamics governed by DC voltage-dominated GFM systems.Subsequently,the paper establishes a stability criterion for GFM-PV systems and proposes a parameter tuning method for DC voltage controllers that incorporates damping margin considerations.The performance of the tuned single-machine-infinite-bus GFM-PV system is validated on the RT-LAB real-time simulation platform under scenarios involving solar irradiance fluctuations and grid frequency disturbances.The proposed method proves effective in ensuring the stability of the GFM-PV system,with robust theoretical support.
文摘Hybrid commutation converters(HCCs)utilizing reverse-blocking integrated gate commutation thyristors(IGCTs)have gained significant attention due to their immunity to commutation failure.Leveraging the recovery enhancement characteristics of IGCTs,HCCs demonstrate superior performance at reduced extinction angles,thereby minimizing reactive power consumption.This study presents a comprehensive investigation into reactive power control strategies for HCCs operating at small extinction angles.First,the topological configuration and commutation principle of HCC are elucidated.Subsequently,the mechanism of HCC reactive power control is analyzed,and a reactive power control strategy is proposed by combining the converter transformer taps with extinction angles.Moreover,the relationship between transformer taps and reactive power exchange under different rated extinction angles is calculated,and the theoretically rated extinction angle is proposed.Finally,to validate the proposed control strategy,a four-terminal ultra-high voltage direct current power grid incorporating HCC technology is modeled and sim-ulated using PSCAD/EMTDC.The simulation results demonstrate that the proposed strategy effectively supports AC systems by reducing reactive power absorption in HCCs,while simultaneously exhibiting enhanced reli-ability and economic efficiency.
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.
基金supported by the 2024 Research Fund of University of Ulsan.
文摘In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In addition,methods for deploying multiple cameras for motion capture of users(e.g.,performers)are widely used in computer graphics.As the need to minimize and optimize the number of cameras grows to reduce costs,various technologies and research approaches focused on Optimal Camera Placement(OCP)are continually being proposed.However,as most existing studies assume homogeneous camera setups,there is a growing demand for studies on heterogeneous camera setups.For instance,technical demands keep emerging in scenarios with minimal camera configurations,especially regarding cost factors,the physical placement of cameras given the spatial structure,and image capture strategies for heterogeneous cameras,such as high-resolution RGB cameras and depth cameras.In this study,we propose a pre-visualization and simulation method for the optimal placement of heterogeneous cameras in XR environments,accounting for both the specifications of heterogeneous cameras(e.g.,field of view)and the physical configuration(e.g.,wall configuration)in real-world spaces.The proposed method performs a visibility analysis of cameras by considering each camera’s field-of-view volume,resolution,and unique characteristics,along with physicalspace constraints.This approach enables the optimal position and rotation of each camera to be recommended,along with the minimum number of cameras required.In the results of our study conducted in heterogeneous camera combinations,the proposed method achieved 81.7%~82.7%coverage of the target visual information using only 2~3 cameras.In contrast,single(or homogeneous)-typed cameras were required to use 11 cameras for 81.6%coverage.Accordingly,we found that camera deployment resources can be reduced with the proposed approaches.
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.
文摘The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2022R1C1C100923512)by the Yonsei University Research Fund of 2023-22-0076supported by the POSCO Science Fellowship from POSCO TJ Park Foundation。
文摘Mid-wavelength infrared(MWIR)and long-wavelength infrared(LWIR)detectors,which operate within the 3-14µm wavelength range,have been extensively employed in various fields,including military,space exploration,environmental monitoring,biomedicine,and chemical analysis.While thermal detectors are commonly used,their limitations in sensitivity and response time render them less suitable for next-generation MWIR and LWIR applications.These advanced applications necessitate the use of narrow bandgap semiconductor-based photodetectors,which offer tunable optoelectronic properties and higher specific detectivity compared to thermal detectors.In this review,we provide a detailed analysis of the operational principles and manufacturing strategies of infrared photodetectors based on narrow bandgap semiconductors,which enable high-performance detection in the MWIR and LWIR regions.Our focus is specifically on scalable fabrication of MWIR and LWIR photodetectors,emphasizing devices with active areas ranging from millimeters to centimeters.Researches on large-scale fabrication of infrared photodetectors using quantum dots,two-dimensional(2D)van der Waals(vdW)materials,and three-dimensional(3D)bulk semiconductors are investigated.Finally,we summarize the remaining challenges in developing scalable narrow bandgap semiconductor-based MWIR and LWIR photodetectors for commercialization.By addressing the obstacles such as the difficulty in large-scale unform film synthesis,the requirement for cryogenic device operation,and the introduction of high-density of defect states during the hybridization processes,MWIR and LWIR photodetectors based on narrow bandgap semiconductors will pave the way for designing new sensory systems and applications in a wavelength regime that has been less developed compared to the visible and near-infrared(NIR)ranges.
