Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,co...Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,counter-terrorism,firefighting,surveillance,and cargo transportation.While performing these tasks,quadrotors must operate in noisy environments.Therefore,a robust controller design that can control the altitude and attitude of the quadrotor in noisy environments is of great importance.Many researchers have focused only on white Gaussian noise in their studies,whereas researchers need to consider the effects of all colored noises during the operation of the quadrotor.This study aims to design a robust controller that is resistant to all colored noises.Firstly,a nonlinear quadrotormodel was created with MATLAB.Then,a backstepping controller resistant to colored noises was designed.Thedesigned backstepping controller was tested under Gaussian white,pink,brown,blue,and purple noises.PID and Lyapunov-based controller designswere also carried out,and their time responses(rise time,overshoot,settling time)were compared with those of the backstepping controller.In the simulations,time was in seconds,altitude was in meters,and roll,pitch,and yaw references were in radians.Rise and settling time values were in seconds,and overshoot value was in percent.When the obtained values are examined,simulations prove that the proposed backstepping controller has the least overshoot and the shortest settling time under all noise types.展开更多
Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progr...Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progressive Layered U-Net(PLU-Net),designed to improve brain tumor segmentation accuracy from Magnetic Resonance Imaging(MRI)scans.The PLU-Net extends the standard U-Net architecture by incorporating progressive layering,attention mechanisms,and multi-scale data augmentation.The progressive layering involves a cascaded structure that refines segmentation masks across multiple stages,allowing the model to capture features at different scales and resolutions.Attention gates within the convolutional layers selectively focus on relevant features while suppressing irrelevant ones,enhancing the model's ability to delineate tumor boundaries.Additionally,multi-scale data augmentation techniques increase the diversity of training data and boost the model's generalization capabilities.Evaluated on the BraTS 2021 dataset,the PLU-Net achieved state-of-the-art performance with a dice coefficient of 0.91,specificity of 0.92,sensitivity of 0.89,Hausdorff95 of 2.5,outperforming other modified U-Net architectures in segmentation accuracy.These results underscore the effectiveness of the PLU-Net in improving brain tumor segmentation from MRI scans,supporting clinicians in early diagnosis,treatment planning,and the development of new therapies.展开更多
This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss r...This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss reduction and voltage profile improvement,(2)minimization of voltage and current unbalance indices under various operational cases,and(3)multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index,active power loss,and current unbalance index.Unlike previous research that oftensimplified system components,this work maintains all equipment,including capacitor banks,transformers,and voltage regulators,to ensure realistic results.The study evaluates twelve metaheuristic algorithms to solve the reconfiguration problem(RecPrb)in UPDNs.A comprehensive statistical analysis is conducted to identify the most efficient algorithm for solving the RecPrb in the 123-Bus UPDN,employing multiple performance metrics and comparative techniques.The Artificial Hummingbird Algorithm emerges as the top-performing algorithm and is subsequently applied to address a multi-objective optimization challenge in the 123-Bus UPDN.This research contributes valuable insights for network operators and researchers in selecting suitable algorithms for specific reconfiguration scenarios,advancing the field of UPDN optimization and management.展开更多
This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and i...This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.展开更多
Detecting multiple analytes simultaneously,crucial in disease diagnosis and treatment prognosis,remains challenging.While planar sensing platforms demonstrate this capability,optical fiber sensors still lag behind.An ...Detecting multiple analytes simultaneously,crucial in disease diagnosis and treatment prognosis,remains challenging.While planar sensing platforms demonstrate this capability,optical fiber sensors still lag behind.An operando dual lossy mode resonance(LMR)biosensor fabricated on a D-shaped single-mode fiber(SMF)is proposed for quantification of clinical indicators of inflammatory process,like in COVID-19 infection.Dual LMRs,created via two-step deposition process,yield a nanostructure with distinct SnO_(2) thicknesses on the flat surface of the fiber.Theoretical and experimental analyses confirm its feasibility,showing a sensitivity around 4500 nm/RIU for both LMRs.A novel insight in spatially-separated biofunctionalization of the sensitive fiber regions is validated through fluorescence assays,showcasing selectivity for different immunoglobulins.Real-time and label-free detection of two inflammatory markers,C-reactive protein and Ddimer,empowers the platform capability with a minimum detectable concentration below 1μg/mL for both biomolecules,which is of clinical interest.This proof-of-concept work provides an important leap in fiber-based biosensing for effective and reliable multi-analyte detection,presenting a novel,compact and multi-functional analytical tool.展开更多
Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic(PV)systems integration associated with v...Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic(PV)systems integration associated with varying loading and climate conditions.This paper provides a comprehensive overview on the exit strategies to enhance distribution system operation,with a focus on harmonic mitigation,voltage regulation,power factor correction,and optimization techniques.The impact of passive and active filters,custom power devices such as dynamic voltage restorers(DVRs)and static synchronous compensators(STATCOMs),and grid modernization technologies on power quality is examined.Additionally,this paper specifically explores machine learning and AI-driven solutions for power quality enhancement,discussing their potential to optimize system performance and facilitate renewable energy integration.Modern optimization algorithms are also discussed as effective procedures to find the settings for power system components for optimal operation,including the allocation of distributed energy resources and the tuning of control parameters.Added to that,this paper explores the methods to maximize renewable energy hosting capacity while ensuring reliable and efficient system operation.By synthesizing existing research,this review aims to provide insights into the challenges and opportunities in distribution system operation and optimization,highlighting future research directions that enhance power quality and facilitate renewable energy integration.展开更多
The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the c...The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the current 5G networks have not achieved the promised 5G goals, including the projected 2000 times EE improvement over the legacy 4G Long Term Evolution (LTE) networks. This paper provides a comprehensive survey of Artificial Intelligence (AI)-enabled MA techniques, emphasizing their roles in Spectrum Sensing (SS), Dynamic Resource Allocation (DRA), user scheduling, interference mitigation, and protocol adaptation. In particular, we systematically analyze the progression of traditional and modern MA schemes, from Orthogonal Multiple Access (OMA)-based approaches like Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) to advanced Non-Orthogonal Multiple Access (NOMA) methods, including power domain-NOMA, Sparse Code Multiple Access (SCMA), and Rate Splitting Multiple Access (RSMA). The study further categorizes AI techniques—such as Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Explainable AI (XAI)—and maps them to practical challenges in Dynamic Spectrum Management (DSM), protocol optimization, and real-time distributed decision-making. Optimization strategies, including metaheuristics and multi-agent learning frameworks, are reviewed to illustrate the potential of AI in enhancing energy efficiency, system responsiveness, and cross-layer RA. Additionally, the review addresses security, privacy, and trust concerns, highlighting solutions like privacy-preserving ML, FL, and XAI in 6G and beyond. By identifying research gaps, challenges, and future directions, this work offers a structured resource for researchers and practitioners aiming to integrate AI into 6G MA systems for intelligent, scalable, and secure wireless communications.展开更多
This paper presents a template-based control method for achieving diverse trotting motions in quadrupedal systems,with a focus on smooth transitions between walking trot,regular trot,and flying(running)trot.First,we e...This paper presents a template-based control method for achieving diverse trotting motions in quadrupedal systems,with a focus on smooth transitions between walking trot,regular trot,and flying(running)trot.First,we extend the Clock Torque Actuated Spring-Loaded Inverted Pendulum(CT-SLIP)template to three dimensions,creating a comprehensive control framework.A template-based control strategy is then developed to compute joint torques for stable locomotion,along with a detailed approach for transitioning between gaits.To enable the flight phase in the running trot,a projectile motion model is incorporated into the template.For improved turning,we implement a yaw control method that rotates the swing foot plane to enhance stability,enabling higher turning rates while maintaining steady forward motion and balance.To further enhance locomotion stability and performance,a Whole-Body Controller(WBC)is integrated.The proposed method is implemented and rigorously evaluated in the MuJoCo simulator,with experiments testing gait transitions and disturbance rejection.Additionally,comparative studies assess the impacts of both swing foot plane rotation and the WBC on overall system performance.Furthermore,the approach is validated through real hardware experiments on Unitree GO1 quadrupedal robot,successfully demonstrating smooth gait transitions,stable locomotion,and practical applicability in real-world scenarios.展开更多
The rapid advancement of the Internet ofThings(IoT)has heightened the importance of security,with a notable increase in Distributed Denial-of-Service(DDoS)attacks targeting IoT devices.Network security specialists fac...The rapid advancement of the Internet ofThings(IoT)has heightened the importance of security,with a notable increase in Distributed Denial-of-Service(DDoS)attacks targeting IoT devices.Network security specialists face the challenge of producing systems to identify and offset these attacks.This researchmanages IoT security through the emerging Software-Defined Networking(SDN)standard by developing a unified framework(RNN-RYU).We thoroughly assess multiple deep learning frameworks,including Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),Feed-Forward Convolutional Neural Network(FFCNN),and Recurrent Neural Network(RNN),and present the novel usage of Synthetic Minority Over-Sampling Technique(SMOTE)tailored for IoT-SDN contexts to manage class imbalance during training and enhance performance metrics.Our research has significant practical implications as we authenticate the approache using both the self-generated SD_IoT_Smart_City dataset and the publicly available CICIoT23 dataset.The system utilizes only eleven features to identify DDoS attacks efficiently.Results indicate that the RNN can reliably and precisely differentiate between DDoS traffic and benign traffic by easily identifying temporal relationships and sequences in the data.展开更多
This paper discusses the problem of direction of departure （DOD） and direction of arrival （DOA） estimation for a bistatic multiple input multiple output （MIMO） radar, and proposes an improved reduced-dimension C...This paper discusses the problem of direction of departure （DOD） and direction of arrival （DOA） estimation for a bistatic multiple input multiple output （MIMO） radar, and proposes an improved reduced-dimension Capon algorithm therein. Compared with the reduced-dimension Capon algorithm which requires pair matching between the two-dimensional angle estimation, the pro- posed algorithm can obtain automatically paired DOD and DOA estimation without debasing the performance of angle estimation in bistatic MIMO radar. Furthermore, the proposed algorithm has a lower complexity than the reduced-dimension Capon algorithm, and it is suitable for non-uniform linear arrays. The complexity of the proposed algorithm is analyzed and the Cramer-Rao bound （CRB） is also derived. Simulation results verify the usefulness of the proposed algorithm.展开更多
Centrifugal pumps always work under steady conditions,and many researches focus on the steady operation.But transient conditions,such as sudden startup and shutdown,are inevitable.The researches on the inner flow of c...Centrifugal pumps always work under steady conditions,and many researches focus on the steady operation.But transient conditions,such as sudden startup and shutdown,are inevitable.The researches on the inner flow of centrifugal pumps under transient conditions have been done,and they show that the transient operation is different from the steady operation.In order to research the evolution of unsteady flow in a centrifugal pump under transient conditions,and to investigate the mechanism of transient effects by analyzing the unsteady flow in a centrifugal pump,the external characteristic experiment and the internal flow numerical calculation of the centrifugal pump with an open impeller during startup is presented.The relationships of the rotation speed,capacity and head between start-time are obtained by the external characteristics experiment.The numerical calculations under startup process are carried out by using the k-e model and N-S equation.The distribution of velocity and pressure in the inner channel of the tested pump was obtained by choosing fourteen start-time points and twelve geometrical points in the impeller channel during startup.The calculation results show that the velocity and the pressure increase linearly with the start-time before rotation＇s speed gets steady,then changes almost horizontally after rotation speed becomes steady,then fluctuates until being steady.The internal flow characteristics are in good agreement with the external characteristic experimental results and numerical calculation.The simulation methods and results make the basis for the diagnosis and optimization of under flow in the centrifugal pump during transient operation.展开更多
With the high penetration of renewable energy,new challenges,such as power fluctuation suppression and inertial support capability,have arisen in the power sector.Battery energy storage systems play an essential role ...With the high penetration of renewable energy,new challenges,such as power fluctuation suppression and inertial support capability,have arisen in the power sector.Battery energy storage systems play an essential role in renewable energy integration.In this paper,a distributed virtual synchronous generator(VSG)control method for a battery energy storage system(BESS)with a cascaded H-bridge converter in a grid-connected mode is proposed.The VSG is developed without communication dependence,and state-of-charge(SOC)balancing control is achieved using the distributed average algorithm.Owing to the low varying speed of SOC,the bandwidth of the distributed communication networks is extremely slow,which decreases the cost.Therefore,the proposed method can simultaneously provide inertial support and accurate SOC balancing.The stability is also proved using root locus analysis.Finally,simulations under different conditions are carried out to verify the effectiveness of the proposed method.展开更多
Sliding mode control(SMC)has been studied since the 1950s and widely used in practical applications due to its insensitivity to matched disturbances.The aim of this paper is to present a review of SMC describing the k...Sliding mode control(SMC)has been studied since the 1950s and widely used in practical applications due to its insensitivity to matched disturbances.The aim of this paper is to present a review of SMC describing the key developments and examining the new trends and challenges for its application to power electronic systems.The fundamental theory of SMC is briefly reviewed and the key technical problems associated with the implementation of SMC to power converters and drives,such chattering phenomenon and variable switching frequency,are discussed and analyzed.The recent developments in SMC systems,future challenges and perspectives of SMC for power converters are discussed.展开更多
A realizable quantum encryption algorithm for qubits is presented by employing bit-wise quantum computation. System extension and bit-swapping are introduced into the encryption process, which makes the ciphertext spa...A realizable quantum encryption algorithm for qubits is presented by employing bit-wise quantum computation. System extension and bit-swapping are introduced into the encryption process, which makes the ciphertext space expanded greatly. The security of the proposed algorithm is analysed in detail and the schematic physical implementation is also provided. It is shown that the algorithm, which can prevent quantum attack strategy as well as classical attack strategy, is effective to protect qubits. Finally, we extend our algorithm to encrypt classical binary bits and quantum entanglements.展开更多
In the 5^(th) generation mobile communication(5G)system,better bitrate,better power consumption,and better Bit Error Rate are needed.To meet all these requirements in 5G,the waveform is important.Although the Orthogon...In the 5^(th) generation mobile communication(5G)system,better bitrate,better power consumption,and better Bit Error Rate are needed.To meet all these requirements in 5G,the waveform is important.Although the Orthogonal Frequency Division Multiplexing(OFDM)modulation scheme has been commonly used in 4G,it is difficult to meet the requirements of 5G.A lot of candidate waveforms are discussed in the 5G system included Filter Bank Multicarrier(FBMC),Universal Filtered Multicarrier(UFMC),Filtered OFDM(F-OFDM),and Generalized Frequency Division Multiplexing(GFDM),etc.However,since GFDM is no longer widely used,in this paper,FBMC,UFMC,and F-OFDM modulation schemes are discussed and compared with OFDM.Power Spectral Density(PSD),Bit Error Rate(BER),Transmitted Signal Power,Peak Average Power Ratio(PAPR)and Bit Rate parameters of the modulation schemes are studied and compared with OFDM.FBMC demonstrates to be the most promising modulation scheme for 5G systems.展开更多
The main contribution of this paper is the design of an event-triggered formation control for leader-following consensus in second-order multi-agent systems(MASs)under communication faults.All the agents must follow t...The main contribution of this paper is the design of an event-triggered formation control for leader-following consensus in second-order multi-agent systems(MASs)under communication faults.All the agents must follow the trajectories of a virtual leader despite communication faults considered as smooth time-varying delays dependent on the distance between the agents.Linear matrix inequalities(LMIs)-based conditions are obtained to synthesize a controller gain that guarantees stability of the synchronization error.Based on the closed-loop system,an event-triggered mechanism is designed to reduce the control law update and information exchange in order to reduce energy consumption.The proposed approach is implemented in a real platform of a fleet of unmanned aerial vehicles(UAVs)under communication faults.A comparison between a state-of-the-art technique and the proposed technique has been provided,demonstrating the performance improvement brought by the proposed approach.展开更多
Ultra-Wide Bandwidth(UWB)localization based on time of arrival(TOA)and angle of arrival(AOA)has attracted increasing interest owing to its high accuracy and low cost.However,existing localization methods often fail to...Ultra-Wide Bandwidth(UWB)localization based on time of arrival(TOA)and angle of arrival(AOA)has attracted increasing interest owing to its high accuracy and low cost.However,existing localization methods often fail to achieve satisfactory accuracy in realistic environments due to multipath effects and non-line-of-sight(NLOS)propagation.In this paper,we propose a passive anchor assisted localization(PAAL)scheme,where the active anchor obtains TOA/AOA measurements to the agent while the passive anchors capture the signals from the active anchor and agent.The proposed method fully exploits the time-difference-of-arrival(TDOA)information from the measurements at the passive anchors to complement single-anchor joint TOA/AOA localization.The performance limits of the PAAL system are derived as a benchmark via the information inequality.Moreover,we implement the PAAL system on a low-cost UWB platform,which can achieve 20 cm localization accuracy in NLOS environments.展开更多
Hierarchical Support Vector Machine （H-SVM） is faster in training and classification than other usual multi-class SVMs such as ＂1-V-R＂and ＂1-V-1＂. In this paper, a new multi-class fault diagnosis algorithm based...Hierarchical Support Vector Machine （H-SVM） is faster in training and classification than other usual multi-class SVMs such as ＂1-V-R＂and ＂1-V-1＂. In this paper, a new multi-class fault diagnosis algorithm based on H-SVM is proposed and applied to aero-engine. Before SVM training, the training data are first clustered according to their class-center Euclid distances in some feature spaces. The samples which have close distances are divided into the same sub-classes for training, and this makes the H-SVM have reasonable hierarchical construction and good generalization performance. Instead of the common C-SVM, the v-SVM is selected as the binary classifier, in which the parameter v varies only from 0 to 1 and can be determined more easily. The simulation results show that the designed H-SVMs can fast diagnose the multi-class single faults and combination faults for the gas path components of an aero-engine. The fault classifiers have good diagnosis accuracy and can keep robust even when the measurement inputs are disturbed by noises.展开更多
文摘Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,counter-terrorism,firefighting,surveillance,and cargo transportation.While performing these tasks,quadrotors must operate in noisy environments.Therefore,a robust controller design that can control the altitude and attitude of the quadrotor in noisy environments is of great importance.Many researchers have focused only on white Gaussian noise in their studies,whereas researchers need to consider the effects of all colored noises during the operation of the quadrotor.This study aims to design a robust controller that is resistant to all colored noises.Firstly,a nonlinear quadrotormodel was created with MATLAB.Then,a backstepping controller resistant to colored noises was designed.Thedesigned backstepping controller was tested under Gaussian white,pink,brown,blue,and purple noises.PID and Lyapunov-based controller designswere also carried out,and their time responses(rise time,overshoot,settling time)were compared with those of the backstepping controller.In the simulations,time was in seconds,altitude was in meters,and roll,pitch,and yaw references were in radians.Rise and settling time values were in seconds,and overshoot value was in percent.When the obtained values are examined,simulations prove that the proposed backstepping controller has the least overshoot and the shortest settling time under all noise types.
文摘Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progressive Layered U-Net(PLU-Net),designed to improve brain tumor segmentation accuracy from Magnetic Resonance Imaging(MRI)scans.The PLU-Net extends the standard U-Net architecture by incorporating progressive layering,attention mechanisms,and multi-scale data augmentation.The progressive layering involves a cascaded structure that refines segmentation masks across multiple stages,allowing the model to capture features at different scales and resolutions.Attention gates within the convolutional layers selectively focus on relevant features while suppressing irrelevant ones,enhancing the model's ability to delineate tumor boundaries.Additionally,multi-scale data augmentation techniques increase the diversity of training data and boost the model's generalization capabilities.Evaluated on the BraTS 2021 dataset,the PLU-Net achieved state-of-the-art performance with a dice coefficient of 0.91,specificity of 0.92,sensitivity of 0.89,Hausdorff95 of 2.5,outperforming other modified U-Net architectures in segmentation accuracy.These results underscore the effectiveness of the PLU-Net in improving brain tumor segmentation from MRI scans,supporting clinicians in early diagnosis,treatment planning,and the development of new therapies.
基金supported by the Scientific and Technological Research Council of Turkey(TUBITAK)under Grant No.124E002(1001-Project).
文摘This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss reduction and voltage profile improvement,(2)minimization of voltage and current unbalance indices under various operational cases,and(3)multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index,active power loss,and current unbalance index.Unlike previous research that oftensimplified system components,this work maintains all equipment,including capacitor banks,transformers,and voltage regulators,to ensure realistic results.The study evaluates twelve metaheuristic algorithms to solve the reconfiguration problem(RecPrb)in UPDNs.A comprehensive statistical analysis is conducted to identify the most efficient algorithm for solving the RecPrb in the 123-Bus UPDN,employing multiple performance metrics and comparative techniques.The Artificial Hummingbird Algorithm emerges as the top-performing algorithm and is subsequently applied to address a multi-objective optimization challenge in the 123-Bus UPDN.This research contributes valuable insights for network operators and researchers in selecting suitable algorithms for specific reconfiguration scenarios,advancing the field of UPDN optimization and management.
文摘This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.
基金financial support from the Spanish Agencia Estatal de Investigación (AEI) through project PID2023-149895OB-I00a predoctoral research grant from the Public University of Navarrafinancial support under the National Recovery and Resilience Plan (NRRP),Mission 4,Component 2,Investment 1.1,Call for tender No.1409 published on 14.9.2022 by the Italian Ministry of University and Research (MUR),funded by the European Union–NextGenerationEU–Project Title‘‘Fiber optics sensors as a platform for cancer diagnosis and in vitro model testing”–CUP B53D23024170001-Grant Assignment Decree No.1383 adopted on 01/09/2023 by the Italian MUR.
文摘Detecting multiple analytes simultaneously,crucial in disease diagnosis and treatment prognosis,remains challenging.While planar sensing platforms demonstrate this capability,optical fiber sensors still lag behind.An operando dual lossy mode resonance(LMR)biosensor fabricated on a D-shaped single-mode fiber(SMF)is proposed for quantification of clinical indicators of inflammatory process,like in COVID-19 infection.Dual LMRs,created via two-step deposition process,yield a nanostructure with distinct SnO_(2) thicknesses on the flat surface of the fiber.Theoretical and experimental analyses confirm its feasibility,showing a sensitivity around 4500 nm/RIU for both LMRs.A novel insight in spatially-separated biofunctionalization of the sensitive fiber regions is validated through fluorescence assays,showcasing selectivity for different immunoglobulins.Real-time and label-free detection of two inflammatory markers,C-reactive protein and Ddimer,empowers the platform capability with a minimum detectable concentration below 1μg/mL for both biomolecules,which is of clinical interest.This proof-of-concept work provides an important leap in fiber-based biosensing for effective and reliable multi-analyte detection,presenting a novel,compact and multi-functional analytical tool.
文摘Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic(PV)systems integration associated with varying loading and climate conditions.This paper provides a comprehensive overview on the exit strategies to enhance distribution system operation,with a focus on harmonic mitigation,voltage regulation,power factor correction,and optimization techniques.The impact of passive and active filters,custom power devices such as dynamic voltage restorers(DVRs)and static synchronous compensators(STATCOMs),and grid modernization technologies on power quality is examined.Additionally,this paper specifically explores machine learning and AI-driven solutions for power quality enhancement,discussing their potential to optimize system performance and facilitate renewable energy integration.Modern optimization algorithms are also discussed as effective procedures to find the settings for power system components for optimal operation,including the allocation of distributed energy resources and the tuning of control parameters.Added to that,this paper explores the methods to maximize renewable energy hosting capacity while ensuring reliable and efficient system operation.By synthesizing existing research,this review aims to provide insights into the challenges and opportunities in distribution system operation and optimization,highlighting future research directions that enhance power quality and facilitate renewable energy integration.
文摘The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the current 5G networks have not achieved the promised 5G goals, including the projected 2000 times EE improvement over the legacy 4G Long Term Evolution (LTE) networks. This paper provides a comprehensive survey of Artificial Intelligence (AI)-enabled MA techniques, emphasizing their roles in Spectrum Sensing (SS), Dynamic Resource Allocation (DRA), user scheduling, interference mitigation, and protocol adaptation. In particular, we systematically analyze the progression of traditional and modern MA schemes, from Orthogonal Multiple Access (OMA)-based approaches like Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) to advanced Non-Orthogonal Multiple Access (NOMA) methods, including power domain-NOMA, Sparse Code Multiple Access (SCMA), and Rate Splitting Multiple Access (RSMA). The study further categorizes AI techniques—such as Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Explainable AI (XAI)—and maps them to practical challenges in Dynamic Spectrum Management (DSM), protocol optimization, and real-time distributed decision-making. Optimization strategies, including metaheuristics and multi-agent learning frameworks, are reviewed to illustrate the potential of AI in enhancing energy efficiency, system responsiveness, and cross-layer RA. Additionally, the review addresses security, privacy, and trust concerns, highlighting solutions like privacy-preserving ML, FL, and XAI in 6G and beyond. By identifying research gaps, challenges, and future directions, this work offers a structured resource for researchers and practitioners aiming to integrate AI into 6G MA systems for intelligent, scalable, and secure wireless communications.
基金supported by The Scientific and Technological Research Council of Türkiye(TUBITAK)1515 Frontier R&D Laboratories Support Program for Turk Telekom neXt Generation Technologies Lab(XGeNTT)under Project No.5249902supported by the Scientific Research Projects Coordination Unit of Middle East Technical University(METU)under Project No.ADEP-301-2025-11613.
文摘This paper presents a template-based control method for achieving diverse trotting motions in quadrupedal systems,with a focus on smooth transitions between walking trot,regular trot,and flying(running)trot.First,we extend the Clock Torque Actuated Spring-Loaded Inverted Pendulum(CT-SLIP)template to three dimensions,creating a comprehensive control framework.A template-based control strategy is then developed to compute joint torques for stable locomotion,along with a detailed approach for transitioning between gaits.To enable the flight phase in the running trot,a projectile motion model is incorporated into the template.For improved turning,we implement a yaw control method that rotates the swing foot plane to enhance stability,enabling higher turning rates while maintaining steady forward motion and balance.To further enhance locomotion stability and performance,a Whole-Body Controller(WBC)is integrated.The proposed method is implemented and rigorously evaluated in the MuJoCo simulator,with experiments testing gait transitions and disturbance rejection.Additionally,comparative studies assess the impacts of both swing foot plane rotation and the WBC on overall system performance.Furthermore,the approach is validated through real hardware experiments on Unitree GO1 quadrupedal robot,successfully demonstrating smooth gait transitions,stable locomotion,and practical applicability in real-world scenarios.
基金supported by NSTC 113-2221-E-155-055NSTC 113-2222-E-155-007,Taiwan.
文摘The rapid advancement of the Internet ofThings(IoT)has heightened the importance of security,with a notable increase in Distributed Denial-of-Service(DDoS)attacks targeting IoT devices.Network security specialists face the challenge of producing systems to identify and offset these attacks.This researchmanages IoT security through the emerging Software-Defined Networking(SDN)standard by developing a unified framework(RNN-RYU).We thoroughly assess multiple deep learning frameworks,including Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),Feed-Forward Convolutional Neural Network(FFCNN),and Recurrent Neural Network(RNN),and present the novel usage of Synthetic Minority Over-Sampling Technique(SMOTE)tailored for IoT-SDN contexts to manage class imbalance during training and enhance performance metrics.Our research has significant practical implications as we authenticate the approache using both the self-generated SD_IoT_Smart_City dataset and the publicly available CICIoT23 dataset.The system utilizes only eleven features to identify DDoS attacks efficiently.Results indicate that the RNN can reliably and precisely differentiate between DDoS traffic and benign traffic by easily identifying temporal relationships and sequences in the data.
基金supported by the National Natural Science Foundation of China(6080105261271327)+2 种基金Jiangsu Planned Projects for Postdoctoral Research Funds(1201039C)the China Postdoctoral Science Foundation (2012M521099)Hubei Key Laboratory of Intelligent Wireless Communications(IWC2012002)
文摘This paper discusses the problem of direction of departure （DOD） and direction of arrival （DOA） estimation for a bistatic multiple input multiple output （MIMO） radar, and proposes an improved reduced-dimension Capon algorithm therein. Compared with the reduced-dimension Capon algorithm which requires pair matching between the two-dimensional angle estimation, the pro- posed algorithm can obtain automatically paired DOD and DOA estimation without debasing the performance of angle estimation in bistatic MIMO radar. Furthermore, the proposed algorithm has a lower complexity than the reduced-dimension Capon algorithm, and it is suitable for non-uniform linear arrays. The complexity of the proposed algorithm is analyzed and the Cramer-Rao bound （CRB） is also derived. Simulation results verify the usefulness of the proposed algorithm.
基金supported by National Natural Science Foundation of China (Grant No. 50879080, Grant No. 50609025)Zhejiang Provincial Natural Science Foundation of China (Grant No. Y1100013,Grant No. R1100530)
文摘Centrifugal pumps always work under steady conditions,and many researches focus on the steady operation.But transient conditions,such as sudden startup and shutdown,are inevitable.The researches on the inner flow of centrifugal pumps under transient conditions have been done,and they show that the transient operation is different from the steady operation.In order to research the evolution of unsteady flow in a centrifugal pump under transient conditions,and to investigate the mechanism of transient effects by analyzing the unsteady flow in a centrifugal pump,the external characteristic experiment and the internal flow numerical calculation of the centrifugal pump with an open impeller during startup is presented.The relationships of the rotation speed,capacity and head between start-time are obtained by the external characteristics experiment.The numerical calculations under startup process are carried out by using the k-e model and N-S equation.The distribution of velocity and pressure in the inner channel of the tested pump was obtained by choosing fourteen start-time points and twelve geometrical points in the impeller channel during startup.The calculation results show that the velocity and the pressure increase linearly with the start-time before rotation＇s speed gets steady,then changes almost horizontally after rotation speed becomes steady,then fluctuates until being steady.The internal flow characteristics are in good agreement with the external characteristic experimental results and numerical calculation.The simulation methods and results make the basis for the diagnosis and optimization of under flow in the centrifugal pump during transient operation.
基金This work was supported by National Natural Science Foundation of China under Grant U1909201,Distributed active learning theory and method for operational situation awareness of active distribution network.
文摘With the high penetration of renewable energy,new challenges,such as power fluctuation suppression and inertial support capability,have arisen in the power sector.Battery energy storage systems play an essential role in renewable energy integration.In this paper,a distributed virtual synchronous generator(VSG)control method for a battery energy storage system(BESS)with a cascaded H-bridge converter in a grid-connected mode is proposed.The VSG is developed without communication dependence,and state-of-charge(SOC)balancing control is achieved using the distributed average algorithm.Owing to the low varying speed of SOC,the bandwidth of the distributed communication networks is extremely slow,which decreases the cost.Therefore,the proposed method can simultaneously provide inertial support and accurate SOC balancing.The stability is also proved using root locus analysis.Finally,simulations under different conditions are carried out to verify the effectiveness of the proposed method.
基金supported in part by the National Key R&D Program of China(2019YFB1312000)the National Natural Science Foundation of China(62022030 and 62033005)+2 种基金the Fundamental Research Funds for the Central Universities(HIT.OCEF.2021005)the Heilongjiang Provincial Natural Science Foundation of China(62033005)the SelfPlanned Task of State Key Laboratory of Advanced Welding and Joining(HIT)。
文摘Sliding mode control(SMC)has been studied since the 1950s and widely used in practical applications due to its insensitivity to matched disturbances.The aim of this paper is to present a review of SMC describing the key developments and examining the new trends and challenges for its application to power electronic systems.The fundamental theory of SMC is briefly reviewed and the key technical problems associated with the implementation of SMC to power converters and drives,such chattering phenomenon and variable switching frequency,are discussed and analyzed.The recent developments in SMC systems,future challenges and perspectives of SMC for power converters are discussed.
基金Project supported by the National Natural Science Foundation of China （Grant Nos 60472018 and 90104005） and by the Doctoral Programs Foundation of the Ministry of Education of China （Grant No 20020247063）.
文摘A realizable quantum encryption algorithm for qubits is presented by employing bit-wise quantum computation. System extension and bit-swapping are introduced into the encryption process, which makes the ciphertext space expanded greatly. The security of the proposed algorithm is analysed in detail and the schematic physical implementation is also provided. It is shown that the algorithm, which can prevent quantum attack strategy as well as classical attack strategy, is effective to protect qubits. Finally, we extend our algorithm to encrypt classical binary bits and quantum entanglements.
文摘In the 5^(th) generation mobile communication(5G)system,better bitrate,better power consumption,and better Bit Error Rate are needed.To meet all these requirements in 5G,the waveform is important.Although the Orthogonal Frequency Division Multiplexing(OFDM)modulation scheme has been commonly used in 4G,it is difficult to meet the requirements of 5G.A lot of candidate waveforms are discussed in the 5G system included Filter Bank Multicarrier(FBMC),Universal Filtered Multicarrier(UFMC),Filtered OFDM(F-OFDM),and Generalized Frequency Division Multiplexing(GFDM),etc.However,since GFDM is no longer widely used,in this paper,FBMC,UFMC,and F-OFDM modulation schemes are discussed and compared with OFDM.Power Spectral Density(PSD),Bit Error Rate(BER),Transmitted Signal Power,Peak Average Power Ratio(PAPR)and Bit Rate parameters of the modulation schemes are studied and compared with OFDM.FBMC demonstrates to be the most promising modulation scheme for 5G systems.
文摘The main contribution of this paper is the design of an event-triggered formation control for leader-following consensus in second-order multi-agent systems(MASs)under communication faults.All the agents must follow the trajectories of a virtual leader despite communication faults considered as smooth time-varying delays dependent on the distance between the agents.Linear matrix inequalities(LMIs)-based conditions are obtained to synthesize a controller gain that guarantees stability of the synchronization error.Based on the closed-loop system,an event-triggered mechanism is designed to reduce the control law update and information exchange in order to reduce energy consumption.The proposed approach is implemented in a real platform of a fleet of unmanned aerial vehicles(UAVs)under communication faults.A comparison between a state-of-the-art technique and the proposed technique has been provided,demonstrating the performance improvement brought by the proposed approach.
文摘Ultra-Wide Bandwidth(UWB)localization based on time of arrival(TOA)and angle of arrival(AOA)has attracted increasing interest owing to its high accuracy and low cost.However,existing localization methods often fail to achieve satisfactory accuracy in realistic environments due to multipath effects and non-line-of-sight(NLOS)propagation.In this paper,we propose a passive anchor assisted localization(PAAL)scheme,where the active anchor obtains TOA/AOA measurements to the agent while the passive anchors capture the signals from the active anchor and agent.The proposed method fully exploits the time-difference-of-arrival(TDOA)information from the measurements at the passive anchors to complement single-anchor joint TOA/AOA localization.The performance limits of the PAAL system are derived as a benchmark via the information inequality.Moreover,we implement the PAAL system on a low-cost UWB platform,which can achieve 20 cm localization accuracy in NLOS environments.
基金University Science Foundation of Jiangsu Province (04KJD510018)
文摘Hierarchical Support Vector Machine （H-SVM） is faster in training and classification than other usual multi-class SVMs such as ＂1-V-R＂and ＂1-V-1＂. In this paper, a new multi-class fault diagnosis algorithm based on H-SVM is proposed and applied to aero-engine. Before SVM training, the training data are first clustered according to their class-center Euclid distances in some feature spaces. The samples which have close distances are divided into the same sub-classes for training, and this makes the H-SVM have reasonable hierarchical construction and good generalization performance. Instead of the common C-SVM, the v-SVM is selected as the binary classifier, in which the parameter v varies only from 0 to 1 and can be determined more easily. The simulation results show that the designed H-SVMs can fast diagnose the multi-class single faults and combination faults for the gas path components of an aero-engine. The fault classifiers have good diagnosis accuracy and can keep robust even when the measurement inputs are disturbed by noises.
