Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the m...Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the main contributions is proving this property using linear algebra instead of profound knowledge.This makes it easy to read and understand this fundamental fact.The proof of linear independence of a set of Gauss functions relies on the constructing method for one-dimensional space and on the deducing method for higher dimensions.Additionally,under the condition of preserving the same moments between the original function and interpolating function,both the interpolating existence and uniqueness are proven for GRBF in one-dimensional space.The final work demonstrates the application of the GRBF method to locate lunar olivine.By combining preprocessed data using GRBF with the removing envelope curve method,a program is created to find the position of lunar olivine based on spectrum data,and the numerical experiment shows that it is an effective scheme.展开更多
Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the...Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.展开更多
Earth-to-Moon missions with low thrust-to-weight ratios present unique challenges for exoatmospheric guidance,and the existing algorithms are ineffective for the unprecedentedly long burn arcs and high orbital eccentr...Earth-to-Moon missions with low thrust-to-weight ratios present unique challenges for exoatmospheric guidance,and the existing algorithms are ineffective for the unprecedentedly long burn arcs and high orbital eccentricities.To address these challenges,a Long Burn Arc Powered Explicit Guidance(LBA-PEG)algorithm is developed and compared with the existing algorithms.In the proposed LBA-PEG algorithm,a fully numerical thrust prediction method is developed to accurately predict the highly nonlinear thrust effects over long burn arcs.Moreover,a real-time Newton correction method is proposed to correct the orbit injection point,remedying the position-velocity coupling induced by high orbital eccentricities.The comparison between the proposed algorithm and the existing algorithm shows that the proposed algorithm surpasses the existing ones by significantly enhancing fuel efficiency and improving tolerance to thrust decrease.The proposed LBA-PEG algorithm can adapt to a 65%thrust decrease,which is 12%–22%larger than that of the existing algorithms,and it can still reliably converge and complete the guidance mission even when the length of the burn arc exceeds 90°.The proposed LBA-PEG highlights the algorithm's adaptability for long burn arc missions,especially in critical scenarios such as manned Earth-to-Moon missions.展开更多
Aiming to address the Unmanned Aerial Vehicle(UAV) formation collision avoidance problem in Three-Dimensional(3-D) low-altitude environments where dense various obstacles exist, a fluid-based path planning framework n...Aiming to address the Unmanned Aerial Vehicle(UAV) formation collision avoidance problem in Three-Dimensional(3-D) low-altitude environments where dense various obstacles exist, a fluid-based path planning framework named the Formation Interfered Fluid Dynamical System(FIFDS) with Moderate Evasive Maneuver Strategy(MEMS) is proposed in this study.First, the UAV formation collision avoidance problem including quantifiable performance indexes is formulated. Second, inspired by the phenomenon of fluids continuously flowing while bypassing objects, the FIFDS for multiple UAVs is presented, which contains a Parallel Streamline Tracking(PST) method for formation keeping and the traditional IFDS for collision avoidance. Third, to rationally balance flight safety and collision avoidance cost, MEMS is proposed to generate moderate evasive maneuvers that match up with collision risks. Comprehensively containing the time and distance safety information, the 3-D dynamic collision regions are modeled for collision prediction. Then, the moderate evasive maneuver principle is refined, which provides criterions of the maneuver amplitude and direction. On this basis, an analytical parameter mapping mechanism is designed to online optimize IFDS parameters. Finally, the performance of the proposed method is validated by comparative simulation results and real flight experiments using fixed-wing UAVs.展开更多
The increasing use of renewable energy sources,combined with the increase in electricity demand,has highlighted the importance of energy flexibility management in electrical grids.Energy flexibility is the capacity th...The increasing use of renewable energy sources,combined with the increase in electricity demand,has highlighted the importance of energy flexibility management in electrical grids.Energy flexibility is the capacity that generators and consumers have to change production and/or consumption to support grid operation,ensuring the stability and efficiency of the grid.Thus,Local Flexibility Markets(LFMs)are market-oriented mechanisms operated at different time horizons that support flexibility provision and trading at the distribution level,where the Distribution System Operators(DSOs)are the flexibility-demanding actors,and prosumers are the flexibility providers.This paper investigates the requirements and constraints of forecasting algorithms required to participate in LFMs.The paper analyses the adequacy of current load forecasting algorithms to fulfill the requirements of LFMs.The work extracts the forecasting requirements for data granularity,forecasting horizon,participants aggregation,and their relevance for market operation;highlighting the implications of data availability at both training and forecasting stages related to the different localmarket actors(i.e.,DSO,aggregator,prosumer)and market operation timing.The analysis evidences the relevance of load aggregation and forecasting horizon in the performance of forecasting algorithms and their impact on the accuracy,depending on the actors and stages duringmarket operation.It evaluates howdata volume,forecasting horizon,and participant aggregation affect the performance of forecasting models.Key findings show that aggregating participants and reducing the forecasting horizon considerably improve forecasting accuracy.The accuracy of DSO forecasting is usually better due to the availability and completeness of aggregated data at the system level(i.e.,feeder,transformer,substation).Main findings show that increasing training data further than half a year does not keep improving forecasting accuracy,using a next-hour time horizon achieves around 29%better accuracy than a nextday time horizon,aggregating LFM participants can increase forecasting up to 100%depending on the aggregation number.The findings are discussed in the context of LFM operated with current data infrastructures and provide recommendations for improving the integration of forecasting algorithms to enhance flexibility management.展开更多
To fulfill the training requirements for the daily operations of multirotor unmanned aerial vehicles(UAVs)clusters,a UAV cluster collaborative task integrated simulation platform(UAV-TISP)was developed.The platform in...To fulfill the training requirements for the daily operations of multirotor unmanned aerial vehicles(UAVs)clusters,a UAV cluster collaborative task integrated simulation platform(UAV-TISP)was developed.The platform integrates a suite of hardware and software to simulate a range of collaborative UAV cluster operation scenarios.It features modules for collaborative task planning,UAV cluster simulations,and tactical monitoring.The platform significantly reduces training costs by eliminating physical drone dependencies while offering a flexible environment for testing swarm algorithms.UAV-TISP supports both individual UAV and swarm operations,incorporating high-fidelity flight dynamics,real-time communication via user datagram protocol(UDP),and collision avoidance strategies.Utilizing the OSGEarth engine,it enables dynamic 3D environment visualization and scenario customization.Three key task scenarios-route flight,formation reconstruction,and formation transformation-were tested to validate the platform’s efficacy.Results demonstrated robust formation maintenance,adaptive collision avoidance,and seamless task execution.Comparative analysis with Gazebo Sim revealed lower trajectory deviations in UAV-TISP,highlighting its superior accuracy in simulating real-world flight dynamics.Future work will focus on enhancing scalability for diverse UAV models,optimizing swarm networking under communication constraints,and expanding mission scenarios.UAV-TISP serves as a versatile tool for both operational training and advanced algorithm development in UAV cluster applications.展开更多
Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway s...Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.展开更多
The purpose of this study is to analyze the galloping characteristics of the catenary positive feeder in fluctuating wind areas considering dynamic-wind angle of attack and aerodynamic damping.Firstly,the flow field m...The purpose of this study is to analyze the galloping characteristics of the catenary positive feeder in fluctuating wind areas considering dynamic-wind angle of attack and aerodynamic damping.Firstly,the flow field model of the catenary positive feeder was established,the fluctuating wind field was simulated by Davenport wind power spectrum and linear filtering method,and the wind speed at inlet in calculation domain was controlled by editing the profile file to simulate and calculate the aerodynamic characteristics of the positive feeder in the fluctuating wind area.Then,taking the positive feeder as the research object,the mathematical model of actual structure and the corresponding finite element model were established.By applying the wind load to the finite element model,the influence of aerodynamic damping caused by the self-movement of the positive feeder on the galloping response was analyzed,and the frequency domain characteristics of galloping displacement of the positive feeder considering aerodynamic damping were studied.Finally,the calculation method of aerodynamic damping by the Guidelines for Electrical Transmission Line Structural Loading(ASCE No.74)was used for the galloping response of the positive feeder and compared with the proposed method.The results show that when considering aerodynamic damping,the galloping amplitude of the positive feeder decreases significantly,and the first-order resonance effect on the vertical displacement and horizontal displacement decreases significantly.The galloping trajectories calculated by the two methods are consistent.Therefore,this study is of great significance to further clarify the ice-free galloping mechanism of the catenary positive feeder in violent wind areas.展开更多
To eliminate distortion caused by vertical drift and illusory slopes in atomic force microscopy(AFM)imaging,a lifting-wavelet-based iterative thresholding correction method is proposed in this paper.This method achiev...To eliminate distortion caused by vertical drift and illusory slopes in atomic force microscopy(AFM)imaging,a lifting-wavelet-based iterative thresholding correction method is proposed in this paper.This method achieves high-quality AFM imaging via line-by-line corrections for each distorted profile along the fast axis.The key to this line-by-line correction is to accurately simulate the profile distortion of each scanning row.Therefore,a data preprocessing approach is first developed to roughly filter out most of the height data that impairs the accuracy of distortion modeling.This process is implemented through an internal double-screening mechanism.A line-fitting method is adopted to preliminarily screen out the obvious specimens.Lifting wavelet analysis is then carried out to identify the base parts that are mistakenly filtered out as specimens so as to preserve most of the base profiles and provide a good basis for further distortion modeling.Next,an iterative thresholding algorithm is developed to precisely simulate the profile distortion.By utilizing the roughly screened base profile,the optimal threshold,which is used to screen out the pure bases suitable for distortion modeling,is determined through iteration with a specified error rule.On this basis,the profile distortion is accurately modeled through line fitting on the finely screened base data,and the correction is implemented by subtracting the modeling result from the distorted profile.Finally,the effectiveness of the proposed method is verified through experiments and applications.展开更多
With the development of civil aviation industry,the number of retired aircraft is increasing year by year.How to deal with retired aircraft,build aviation,avoid damage to the ecological environment,and develop their r...With the development of civil aviation industry,the number of retired aircraft is increasing year by year.How to deal with retired aircraft,build aviation,avoid damage to the ecological environment,and develop their residual value has attracted widespread attention internationally,and gradually formed dismantling industry for the commercial and reuse of retired aircraft.From the perspective of the industrial chain,the essence of aircraft dismantling is how to maximize the value of highvalue assets at the of their life cycle,that is,to balance the value of aircraft parts and the value of the whole aircraft,which is the last chain in the complete industrial of civil aircraft from design,manufacturing to usage and retirement.The paper studied the dismantling industrial modes of civil aircraft,analyzed the problems and challenges faced by aircraft dismantling,and put forward relevant measures and suggestions,which point out the direction for the development of domestic civil aircraft dismantling industry.展开更多
Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,...Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.展开更多
Active disturbance rejection control(ADRC)exhibits notable resilience against both internal and external disturbances.Its straightforward implementation further enhances its appeal for controlling a diverse class of s...Active disturbance rejection control(ADRC)exhibits notable resilience against both internal and external disturbances.Its straightforward implementation further enhances its appeal for controlling a diverse class of systems.However,the high-gain nature of the extended state observer,which is the core of ADRC,may degrade performance when faced with high-frequency sensing noise—a common challenge in real-world settings.This article addresses this issue through a specifically placed and particularly designed low-pass filterwhile preserving the ease of implementation characteristic of ADRC.This article proposes a simple tuning method for the filter-controller structure to improve the scheme’s design process.Theoretical results simplify the design process based on the Routh–Hurwitz criterion such that the additional low-pass filter does not affect the closedloop stability.The maximum power point tracking task on a wind turbine—a nonlinear system requiring the measurement of inherently noisy signals,such as electrical currents—is addressed to illustrate the design process of the proposed approach.Real-time experiments on a laboratory platform emulating a Permanent Magnet Synchronous Generator-based wind turbine endorse the enhanced scheme’s effectiveness in mitigating high-frequency sensing noise.展开更多
This paper proposes a distributed nonsingular cooperative guidance law for multiple flight vehicles with Field-of-View(FOV)constraints.First,a novel time-to-go estimation is developed based on a FOV-constrained Propor...This paper proposes a distributed nonsingular cooperative guidance law for multiple flight vehicles with Field-of-View(FOV)constraints.First,a novel time-to-go estimation is developed based on a FOV-constrained Proportional Navigation Guidance(FPNG)law.Then,the FPNG law is augmented with a cooperative guidance term to achieve consensus of time-to-go with predefined-time convergence prior to the impact time.A continuous auxiliary function is introduced in the bias term to avoid the singularity of guidance command.Moreover,the proposed guidance law is extended to the three-dimensional guidance scenarios and the moving target with the help of a predicted interception point.Finally,several numerical simulations are conducted,and the results verify the effectiveness,robustness,and advantages of the proposed cooperative guidance law.展开更多
Source term estimation(STE)of hazardous gas leakages in chemical industrial parks(CIPs)is important for addressing environmental pollution and improving safety and reliability in engineering practice.To achieve real-t...Source term estimation(STE)of hazardous gas leakages in chemical industrial parks(CIPs)is important for addressing environmental pollution and improving safety and reliability in engineering practice.To achieve real-time STE,least squares-based STE methods have recently been developed.However,these methods require the number and locations of potential hazardous gas leakage sources are known as a priori,which is difficult in many practical scenarios.To address this limitation,we propose a new datadriven STE approach,which enables the STE to be implemented in real time and applicable to complicated turbulent dispersion scenarios.The linear independent analysis in data science is applied to historically collected concentration data of a hazardous gas of concern from a network of sensors to extract the sensor data which represent independent hazardous gas leakage scenarios(IHGLSs).An appropriate Gaussian model approximation to a high-fidelity computational fluid dynamics(CFD)model that must be used to represent the hazardous gas leakage scenarios of concern is built,and the off-line STE of IHGLSs using the approximating Gaussian model is then performed to build the datadriven STE model.The performance of the proposed approach is evaluated by using data that are generated by simulating ethane leakage scenarios in a CIP using a CFD model.Results indicate that the leakage localization accuracy is 100%and the mean relative estimation error for the leakage strength is6.76%.Moreover,the proposed approach is validated with real data in Prairie Grass field dispersion experiments,demonstrating the practical applicability of the proposed approach.展开更多
In this article,a novel model-free coordinated optimal regulation design methodology is proposed for the rigidly connected dual permanent magnet synchronous motor(PMSM)system via adaptive dynamic programming(ADP).Firs...In this article,a novel model-free coordinated optimal regulation design methodology is proposed for the rigidly connected dual permanent magnet synchronous motor(PMSM)system via adaptive dynamic programming(ADP).First,we adopt the classical master-slave structure to maintain torque synchronization by virtue of field-oriented control.Then,a reducedorder model of the dual-PMSM system is established through the application of singular perturbation theory(SPT),which is of significance to decrease the learning time and computational complexity in the outer speed loop design.Afterwards,we design a coordinated adaptive optimal regulator in framework of ADP to drive the speed of girth gear asymptotic tracking the reference signal and accommodate the load torque disturbance,which is independent of the knowledge of model parameters of the system.According to SPT,we analyze the suboptimality,closed-loop stability,and robustness properties of the obtained controller under mild conditions.Finally,comprehensive experimental studies are provided to verify that the proposed control strategy can achieve the speed regulation and the torque synchronization,as well as ameliorate the transient response.展开更多
In this paper,we show the performance benefits of connecting multiple observers within a control system.We focus here on a particular observer-based control approach,namely the active disturbance rejection control(ADR...In this paper,we show the performance benefits of connecting multiple observers within a control system.We focus here on a particular observer-based control approach,namely the active disturbance rejection control(ADRC)with cascade extended state observer(ESO).For this framework,we analyze the control performance in terms of quality of observer estimation,reference tracking,disturbance rejection,sensitivity to measurement noise/unmodeled dynamics,and overall stability.A comprehensive frequency response analysis is performed to study the influence of cascading the observers on the selected quality criteria.To make the inquiry beneficial also to practitioners,FPGA-in-the-loop tests are conducted using a guided missiles gimbaled seeker.They validate the theoretical findings in discretetime settings,where the sampling time and hardware resource requirements become a factor.The results of the investigation are distilled into guidelines for prospective users on when and how a cascade observer structure can be useful for controls.展开更多
Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images...Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images,where targets are often small and similar within categories,detectingthese fine-grained targets is challenging.To address this,we constructed a fine-grained dataset ofremotely sensed airplanes;for the problems of remote sensing fine-grained targets with obvious head-to-tail distributions and large variations in target sizes,we proposed the DWDet fine-grained tar-get detection and recognition algorithm.First,for the problem of unbalanced category distribution,we adopt an adaptive sampling strategy.In addition,we construct a deformable convolutional blockand improve the decoupling head structure to improve the detection effect of the model ondeformed targets.Then,we design a localization loss function,which is used to improve the model’slocalization ability for targets of different scales.The experimental results show that our algorithmimproves the overall accuracy of the model by 4.1%compared to the baseline model,and improvesthe detection accuracy of small targets by 12.2%.The ablation and comparison experiments alsoprove the effectiveness of our algorithm.展开更多
Background: Prematurely-born individuals tend to exhibit higher resting oxidative stress, although evidence suggests they may be more resistant to acute hypoxia-induced redox balance alterations. We aimed to investiga...Background: Prematurely-born individuals tend to exhibit higher resting oxidative stress, although evidence suggests they may be more resistant to acute hypoxia-induced redox balance alterations. We aimed to investigate the redox balance changes across a 3-day hypobaric hypoxic exposure at 3375 m in healthy adults born preterm(gestational age ≤ 32 weeks) and their term-born(gestational age ≥ 38 weeks)counterparts.Methods: Resting venous blood was obtained in normoxia(prior to altitude exposure), immediately upon arrival to altitude, and the following 3mornings. Antioxidant(superoxide dismutase(SOD), catalase, glutathione peroxidase(GPx), and ferric reducing antioxidant power(FRAP)),pro-oxidant(xanthine oxidase(XO) and myeloperoxidase(MPO)) enzyme activity, oxidative stress markers(advanced oxidation protein product(AOPP) and malondialdehyde(MDA)), nitric oxide(NO) metabolites(nitrites, nitrates, and total nitrite and nitrate(NOx)), and nitrotyrosine were measured in plasma.Results: SOD increased only in the preterm group(p < 0.05). Catalase increased at arrival in preterm group(p < 0.05). XO activity increased at Day 3 for the preterm group, while it increased acutely(arrival and Day 1) in control group. MPO increased in both groups throughout the3 days(p < 0.05). AOPP only increased at arrival in the preterm(p < 0.05) whereas it decreased at arrival up to Day 3(p < 0.05) for control.MDA decreased in control group from arrival onward. Nitrotyrosine decreased in both groups(p < 0.05). Nitrites increased on Day 3(p < 0.05)in control group and decreased on Day 1(p < 0.05) in preterm group.Conclusion: These data indicate that antioxidant enzymes seem to increase immediately upon hypoxic exposure in preterm adults. Conversely, the blunted pro-oxidant enzyme response to prolonged hypoxia exposure suggests that these enzymes may be less sensitive in preterm individuals.These findings lend further support to the potential hypoxic preconditioning effect of preterm birth.展开更多
Fiber-reinforced polymer(FRP)composites are renowned for their high mechanical strength,durability,and lightweight properties,making them integral to civil engineering,aerospace,and automotive manufacturing.Traditiona...Fiber-reinforced polymer(FRP)composites are renowned for their high mechanical strength,durability,and lightweight properties,making them integral to civil engineering,aerospace,and automotive manufacturing.Traditionally,the simulation and optimization of FRP materials have relied on finite element(FE)methods,which,while effective,often fall short in capturing the intricate behaviors of these composites under diverse conditions.Concrete examples in this regard involve modeling interfacial cracks,delaminations,or environmental effects that involve nonlinear phenomena.These degradation mechanisms exceed the capacity of classical FE models,as they are not detailed to the required level of detail.This aspect increases the time and computational resources required,leading to a need for optimization regarding fiber reinforcement configurations or multiple scenario load analysis.Thus,FE methods are inefficient compared to AI-based approaches that generalize material behavior based on extensive datasets.The advent of artificial intelligence(AI)has introduced advanced tools capable of enhancing the analysis and design of FRP materials.This review examines the current landscape of AI applications in FRP composite simulations,highlighting existing research gaps.Through a comprehensive bibliometric analysis,the study underscores the limited number of investigations focused on leveraging AI for FRP optimization.Furthermore,it synthesizes findings related to AI-driven simulation techniques,the mechanical properties of FRP composites,and strategies for predicting and improving their durability.This review comprehensively explores the potential of AI to overcome these limitations by synthesizing over 170 scientific works published between 2015 and 2025.Key findings highlight that supervised learning methods—especially neural networks,support vector machines,and gradient boosting models—achieve prediction accuracies above 90%for mechanical properties and defect classification.However,bibliometric analysis reveals that there are limited studies that address AI-driven optimization or standardized datasets for FRP applications.This review identifies eight core classification domains and eight regression domains where AI excels,including defect detection,bond strength prediction,and fiber orientation optimization.展开更多
High-performance terahertz(THz)logic gate devices are crucial components for signal processing and modulation,playing a significant role in the application of THz communication and imaging.Here,we propose a THz broadb...High-performance terahertz(THz)logic gate devices are crucial components for signal processing and modulation,playing a significant role in the application of THz communication and imaging.Here,we propose a THz broadband NOR logic encoder based on a graphene-metal hybrid metasurface.The unit structure consists of two symmetrical dual-gap metal split-ring resonators(DSRRs)arranged in a staggered configuration,with graphene strips embedded in their gaps.The NOR logic gate metadevice is controlled by the bias voltages independently applied to the two electrodes.Experiments show that when the bias voltages are applied to both electrodes,the metadevice achieves the NOR logic gate within a 0.52 THz bandwidth,with an average modulation depth above 80%.The experimental results match well with theoretical simulations.Additionally,the strong near-field coupling induced by the staggered DSRRs causes redshift at both LC resonance and dipole resonance.This phenomenon was demonstrated by coupled mode theory.Besides,we analyze the surface current distribution at resonances and propose four equivalent circuit models to elucidate the physical mechanisms of modulation under distinct loaded voltage conditions.The results not only advance modulation and logic gate designs for THz communication but also demonstrate significant potential applications in 6G networks,THz imaging,and radar systems.展开更多
基金Supported by the National Basic Research Program of China(2012CB025904)Zhengzhou Shengda University of Economics,Business and Management(SD-YB2025085)。
文摘Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the main contributions is proving this property using linear algebra instead of profound knowledge.This makes it easy to read and understand this fundamental fact.The proof of linear independence of a set of Gauss functions relies on the constructing method for one-dimensional space and on the deducing method for higher dimensions.Additionally,under the condition of preserving the same moments between the original function and interpolating function,both the interpolating existence and uniqueness are proven for GRBF in one-dimensional space.The final work demonstrates the application of the GRBF method to locate lunar olivine.By combining preprocessed data using GRBF with the removing envelope curve method,a program is created to find the position of lunar olivine based on spectrum data,and the numerical experiment shows that it is an effective scheme.
基金supported by the Spanish Ministry of Science and Innovation under Projects PID2022-137680OB-C32 and PID2022-139187OB-I00.
文摘Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.
文摘Earth-to-Moon missions with low thrust-to-weight ratios present unique challenges for exoatmospheric guidance,and the existing algorithms are ineffective for the unprecedentedly long burn arcs and high orbital eccentricities.To address these challenges,a Long Burn Arc Powered Explicit Guidance(LBA-PEG)algorithm is developed and compared with the existing algorithms.In the proposed LBA-PEG algorithm,a fully numerical thrust prediction method is developed to accurately predict the highly nonlinear thrust effects over long burn arcs.Moreover,a real-time Newton correction method is proposed to correct the orbit injection point,remedying the position-velocity coupling induced by high orbital eccentricities.The comparison between the proposed algorithm and the existing algorithm shows that the proposed algorithm surpasses the existing ones by significantly enhancing fuel efficiency and improving tolerance to thrust decrease.The proposed LBA-PEG algorithm can adapt to a 65%thrust decrease,which is 12%–22%larger than that of the existing algorithms,and it can still reliably converge and complete the guidance mission even when the length of the burn arc exceeds 90°.The proposed LBA-PEG highlights the algorithm's adaptability for long burn arc missions,especially in critical scenarios such as manned Earth-to-Moon missions.
基金supported in part by the National Natural Science Foundations of China(Nos.61175084,61673042 and 62203046)the China Postdoctoral Science Foundation(No.2022M713006).
文摘Aiming to address the Unmanned Aerial Vehicle(UAV) formation collision avoidance problem in Three-Dimensional(3-D) low-altitude environments where dense various obstacles exist, a fluid-based path planning framework named the Formation Interfered Fluid Dynamical System(FIFDS) with Moderate Evasive Maneuver Strategy(MEMS) is proposed in this study.First, the UAV formation collision avoidance problem including quantifiable performance indexes is formulated. Second, inspired by the phenomenon of fluids continuously flowing while bypassing objects, the FIFDS for multiple UAVs is presented, which contains a Parallel Streamline Tracking(PST) method for formation keeping and the traditional IFDS for collision avoidance. Third, to rationally balance flight safety and collision avoidance cost, MEMS is proposed to generate moderate evasive maneuvers that match up with collision risks. Comprehensively containing the time and distance safety information, the 3-D dynamic collision regions are modeled for collision prediction. Then, the moderate evasive maneuver principle is refined, which provides criterions of the maneuver amplitude and direction. On this basis, an analytical parameter mapping mechanism is designed to online optimize IFDS parameters. Finally, the performance of the proposed method is validated by comparative simulation results and real flight experiments using fixed-wing UAVs.
基金funded by RESCHOOL,grant agreement No.101096490.
文摘The increasing use of renewable energy sources,combined with the increase in electricity demand,has highlighted the importance of energy flexibility management in electrical grids.Energy flexibility is the capacity that generators and consumers have to change production and/or consumption to support grid operation,ensuring the stability and efficiency of the grid.Thus,Local Flexibility Markets(LFMs)are market-oriented mechanisms operated at different time horizons that support flexibility provision and trading at the distribution level,where the Distribution System Operators(DSOs)are the flexibility-demanding actors,and prosumers are the flexibility providers.This paper investigates the requirements and constraints of forecasting algorithms required to participate in LFMs.The paper analyses the adequacy of current load forecasting algorithms to fulfill the requirements of LFMs.The work extracts the forecasting requirements for data granularity,forecasting horizon,participants aggregation,and their relevance for market operation;highlighting the implications of data availability at both training and forecasting stages related to the different localmarket actors(i.e.,DSO,aggregator,prosumer)and market operation timing.The analysis evidences the relevance of load aggregation and forecasting horizon in the performance of forecasting algorithms and their impact on the accuracy,depending on the actors and stages duringmarket operation.It evaluates howdata volume,forecasting horizon,and participant aggregation affect the performance of forecasting models.Key findings show that aggregating participants and reducing the forecasting horizon considerably improve forecasting accuracy.The accuracy of DSO forecasting is usually better due to the availability and completeness of aggregated data at the system level(i.e.,feeder,transformer,substation).Main findings show that increasing training data further than half a year does not keep improving forecasting accuracy,using a next-hour time horizon achieves around 29%better accuracy than a nextday time horizon,aggregating LFM participants can increase forecasting up to 100%depending on the aggregation number.The findings are discussed in the context of LFM operated with current data infrastructures and provide recommendations for improving the integration of forecasting algorithms to enhance flexibility management.
文摘To fulfill the training requirements for the daily operations of multirotor unmanned aerial vehicles(UAVs)clusters,a UAV cluster collaborative task integrated simulation platform(UAV-TISP)was developed.The platform integrates a suite of hardware and software to simulate a range of collaborative UAV cluster operation scenarios.It features modules for collaborative task planning,UAV cluster simulations,and tactical monitoring.The platform significantly reduces training costs by eliminating physical drone dependencies while offering a flexible environment for testing swarm algorithms.UAV-TISP supports both individual UAV and swarm operations,incorporating high-fidelity flight dynamics,real-time communication via user datagram protocol(UDP),and collision avoidance strategies.Utilizing the OSGEarth engine,it enables dynamic 3D environment visualization and scenario customization.Three key task scenarios-route flight,formation reconstruction,and formation transformation-were tested to validate the platform’s efficacy.Results demonstrated robust formation maintenance,adaptive collision avoidance,and seamless task execution.Comparative analysis with Gazebo Sim revealed lower trajectory deviations in UAV-TISP,highlighting its superior accuracy in simulating real-world flight dynamics.Future work will focus on enhancing scalability for diverse UAV models,optimizing swarm networking under communication constraints,and expanding mission scenarios.UAV-TISP serves as a versatile tool for both operational training and advanced algorithm development in UAV cluster applications.
文摘Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.
基金supported by National Natural Science Foundation of China (No.51867013)Natural Science Foundation of Gansu Province (No.20JR5RA414)。
文摘The purpose of this study is to analyze the galloping characteristics of the catenary positive feeder in fluctuating wind areas considering dynamic-wind angle of attack and aerodynamic damping.Firstly,the flow field model of the catenary positive feeder was established,the fluctuating wind field was simulated by Davenport wind power spectrum and linear filtering method,and the wind speed at inlet in calculation domain was controlled by editing the profile file to simulate and calculate the aerodynamic characteristics of the positive feeder in the fluctuating wind area.Then,taking the positive feeder as the research object,the mathematical model of actual structure and the corresponding finite element model were established.By applying the wind load to the finite element model,the influence of aerodynamic damping caused by the self-movement of the positive feeder on the galloping response was analyzed,and the frequency domain characteristics of galloping displacement of the positive feeder considering aerodynamic damping were studied.Finally,the calculation method of aerodynamic damping by the Guidelines for Electrical Transmission Line Structural Loading(ASCE No.74)was used for the galloping response of the positive feeder and compared with the proposed method.The results show that when considering aerodynamic damping,the galloping amplitude of the positive feeder decreases significantly,and the first-order resonance effect on the vertical displacement and horizontal displacement decreases significantly.The galloping trajectories calculated by the two methods are consistent.Therefore,this study is of great significance to further clarify the ice-free galloping mechanism of the catenary positive feeder in violent wind areas.
基金supported by the National Natural Science Foundation of China under Grant No.21933006.
文摘To eliminate distortion caused by vertical drift and illusory slopes in atomic force microscopy(AFM)imaging,a lifting-wavelet-based iterative thresholding correction method is proposed in this paper.This method achieves high-quality AFM imaging via line-by-line corrections for each distorted profile along the fast axis.The key to this line-by-line correction is to accurately simulate the profile distortion of each scanning row.Therefore,a data preprocessing approach is first developed to roughly filter out most of the height data that impairs the accuracy of distortion modeling.This process is implemented through an internal double-screening mechanism.A line-fitting method is adopted to preliminarily screen out the obvious specimens.Lifting wavelet analysis is then carried out to identify the base parts that are mistakenly filtered out as specimens so as to preserve most of the base profiles and provide a good basis for further distortion modeling.Next,an iterative thresholding algorithm is developed to precisely simulate the profile distortion.By utilizing the roughly screened base profile,the optimal threshold,which is used to screen out the pure bases suitable for distortion modeling,is determined through iteration with a specified error rule.On this basis,the profile distortion is accurately modeled through line fitting on the finely screened base data,and the correction is implemented by subtracting the modeling result from the distorted profile.Finally,the effectiveness of the proposed method is verified through experiments and applications.
文摘With the development of civil aviation industry,the number of retired aircraft is increasing year by year.How to deal with retired aircraft,build aviation,avoid damage to the ecological environment,and develop their residual value has attracted widespread attention internationally,and gradually formed dismantling industry for the commercial and reuse of retired aircraft.From the perspective of the industrial chain,the essence of aircraft dismantling is how to maximize the value of highvalue assets at the of their life cycle,that is,to balance the value of aircraft parts and the value of the whole aircraft,which is the last chain in the complete industrial of civil aircraft from design,manufacturing to usage and retirement.The paper studied the dismantling industrial modes of civil aircraft,analyzed the problems and challenges faced by aircraft dismantling,and put forward relevant measures and suggestions,which point out the direction for the development of domestic civil aircraft dismantling industry.
基金FEDER/Ministry of Science and Innovation-State Research Agency/Project PID2020-112667RB-I00 funded by MCIN/AEI/10.13039/501100011033the Basque Government,IT1726-22+2 种基金by the predoctoral contracts PRE_2022_2_0022 and EP_2023_1_0015 of the Basque Governmentpartially supported by the Italian MIUR,PRIN 2020 Project“COMMON-WEARS”,N.2020HCWWLP,CUP:H23C22000230005co-funding from Next Generation EU,in the context of the National Recovery and Resilience Plan,through the Italian MUR,PRIN 2022 Project”COCOWEARS”(A framework for COntinuum COmputing WEARable Systems),N.2022T2XNJE,CUP:H53D23003640006.
文摘Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.
文摘Active disturbance rejection control(ADRC)exhibits notable resilience against both internal and external disturbances.Its straightforward implementation further enhances its appeal for controlling a diverse class of systems.However,the high-gain nature of the extended state observer,which is the core of ADRC,may degrade performance when faced with high-frequency sensing noise—a common challenge in real-world settings.This article addresses this issue through a specifically placed and particularly designed low-pass filterwhile preserving the ease of implementation characteristic of ADRC.This article proposes a simple tuning method for the filter-controller structure to improve the scheme’s design process.Theoretical results simplify the design process based on the Routh–Hurwitz criterion such that the additional low-pass filter does not affect the closedloop stability.The maximum power point tracking task on a wind turbine—a nonlinear system requiring the measurement of inherently noisy signals,such as electrical currents—is addressed to illustrate the design process of the proposed approach.Real-time experiments on a laboratory platform emulating a Permanent Magnet Synchronous Generator-based wind turbine endorse the enhanced scheme’s effectiveness in mitigating high-frequency sensing noise.
基金supported by the National Natural Science Foundation of China(No.91216304)。
文摘This paper proposes a distributed nonsingular cooperative guidance law for multiple flight vehicles with Field-of-View(FOV)constraints.First,a novel time-to-go estimation is developed based on a FOV-constrained Proportional Navigation Guidance(FPNG)law.Then,the FPNG law is augmented with a cooperative guidance term to achieve consensus of time-to-go with predefined-time convergence prior to the impact time.A continuous auxiliary function is introduced in the bias term to avoid the singularity of guidance command.Moreover,the proposed guidance law is extended to the three-dimensional guidance scenarios and the moving target with the help of a predicted interception point.Finally,several numerical simulations are conducted,and the results verify the effectiveness,robustness,and advantages of the proposed cooperative guidance law.
基金supported by the National Natural Science Foundation of China(Basic Science Center Program 61988101,62303186,62203173)。
文摘Source term estimation(STE)of hazardous gas leakages in chemical industrial parks(CIPs)is important for addressing environmental pollution and improving safety and reliability in engineering practice.To achieve real-time STE,least squares-based STE methods have recently been developed.However,these methods require the number and locations of potential hazardous gas leakage sources are known as a priori,which is difficult in many practical scenarios.To address this limitation,we propose a new datadriven STE approach,which enables the STE to be implemented in real time and applicable to complicated turbulent dispersion scenarios.The linear independent analysis in data science is applied to historically collected concentration data of a hazardous gas of concern from a network of sensors to extract the sensor data which represent independent hazardous gas leakage scenarios(IHGLSs).An appropriate Gaussian model approximation to a high-fidelity computational fluid dynamics(CFD)model that must be used to represent the hazardous gas leakage scenarios of concern is built,and the off-line STE of IHGLSs using the approximating Gaussian model is then performed to build the datadriven STE model.The performance of the proposed approach is evaluated by using data that are generated by simulating ethane leakage scenarios in a CIP using a CFD model.Results indicate that the leakage localization accuracy is 100%and the mean relative estimation error for the leakage strength is6.76%.Moreover,the proposed approach is validated with real data in Prairie Grass field dispersion experiments,demonstrating the practical applicability of the proposed approach.
基金supported by the National Natural Science Foundation of China(62073327,62403467,62373090,62273350,62521001)the Natural Science Foundation of Jiangsu Province(BK20241635)+2 种基金the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(CPSF)(GZB20240827)Jiangsu Funding Program for Excellent Postdoctoral Talent(2024ZB604)the China Postdoctoral Science Foundation(2024M763545,2025T054ZGMK).
文摘In this article,a novel model-free coordinated optimal regulation design methodology is proposed for the rigidly connected dual permanent magnet synchronous motor(PMSM)system via adaptive dynamic programming(ADP).First,we adopt the classical master-slave structure to maintain torque synchronization by virtue of field-oriented control.Then,a reducedorder model of the dual-PMSM system is established through the application of singular perturbation theory(SPT),which is of significance to decrease the learning time and computational complexity in the outer speed loop design.Afterwards,we design a coordinated adaptive optimal regulator in framework of ADP to drive the speed of girth gear asymptotic tracking the reference signal and accommodate the load torque disturbance,which is independent of the knowledge of model parameters of the system.According to SPT,we analyze the suboptimality,closed-loop stability,and robustness properties of the obtained controller under mild conditions.Finally,comprehensive experimental studies are provided to verify that the proposed control strategy can achieve the speed regulation and the torque synchronization,as well as ameliorate the transient response.
基金supported by the project of the University of Defence in Belgrade,Serbia(VA/TT/1/25-27)。
文摘In this paper,we show the performance benefits of connecting multiple observers within a control system.We focus here on a particular observer-based control approach,namely the active disturbance rejection control(ADRC)with cascade extended state observer(ESO).For this framework,we analyze the control performance in terms of quality of observer estimation,reference tracking,disturbance rejection,sensitivity to measurement noise/unmodeled dynamics,and overall stability.A comprehensive frequency response analysis is performed to study the influence of cascading the observers on the selected quality criteria.To make the inquiry beneficial also to practitioners,FPGA-in-the-loop tests are conducted using a guided missiles gimbaled seeker.They validate the theoretical findings in discretetime settings,where the sampling time and hardware resource requirements become a factor.The results of the investigation are distilled into guidelines for prospective users on when and how a cascade observer structure can be useful for controls.
基金supported by National Natural Science Foundation of China(No.62471034)Hebei Natural Science Foundation(No.F2023105001).
文摘Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images,where targets are often small and similar within categories,detectingthese fine-grained targets is challenging.To address this,we constructed a fine-grained dataset ofremotely sensed airplanes;for the problems of remote sensing fine-grained targets with obvious head-to-tail distributions and large variations in target sizes,we proposed the DWDet fine-grained tar-get detection and recognition algorithm.First,for the problem of unbalanced category distribution,we adopt an adaptive sampling strategy.In addition,we construct a deformable convolutional blockand improve the decoupling head structure to improve the detection effect of the model ondeformed targets.Then,we design a localization loss function,which is used to improve the model’slocalization ability for targets of different scales.The experimental results show that our algorithmimproves the overall accuracy of the model by 4.1%compared to the baseline model,and improvesthe detection accuracy of small targets by 12.2%.The ablation and comparison experiments alsoprove the effectiveness of our algorithm.
基金funded by the Swiss National Science Foundation(SNSF Grant No.320030L_192073 to GM)the Slovenian Research Agency(ARRS Grant No.N5-0152 to TD).
文摘Background: Prematurely-born individuals tend to exhibit higher resting oxidative stress, although evidence suggests they may be more resistant to acute hypoxia-induced redox balance alterations. We aimed to investigate the redox balance changes across a 3-day hypobaric hypoxic exposure at 3375 m in healthy adults born preterm(gestational age ≤ 32 weeks) and their term-born(gestational age ≥ 38 weeks)counterparts.Methods: Resting venous blood was obtained in normoxia(prior to altitude exposure), immediately upon arrival to altitude, and the following 3mornings. Antioxidant(superoxide dismutase(SOD), catalase, glutathione peroxidase(GPx), and ferric reducing antioxidant power(FRAP)),pro-oxidant(xanthine oxidase(XO) and myeloperoxidase(MPO)) enzyme activity, oxidative stress markers(advanced oxidation protein product(AOPP) and malondialdehyde(MDA)), nitric oxide(NO) metabolites(nitrites, nitrates, and total nitrite and nitrate(NOx)), and nitrotyrosine were measured in plasma.Results: SOD increased only in the preterm group(p < 0.05). Catalase increased at arrival in preterm group(p < 0.05). XO activity increased at Day 3 for the preterm group, while it increased acutely(arrival and Day 1) in control group. MPO increased in both groups throughout the3 days(p < 0.05). AOPP only increased at arrival in the preterm(p < 0.05) whereas it decreased at arrival up to Day 3(p < 0.05) for control.MDA decreased in control group from arrival onward. Nitrotyrosine decreased in both groups(p < 0.05). Nitrites increased on Day 3(p < 0.05)in control group and decreased on Day 1(p < 0.05) in preterm group.Conclusion: These data indicate that antioxidant enzymes seem to increase immediately upon hypoxic exposure in preterm adults. Conversely, the blunted pro-oxidant enzyme response to prolonged hypoxia exposure suggests that these enzymes may be less sensitive in preterm individuals.These findings lend further support to the potential hypoxic preconditioning effect of preterm birth.
文摘Fiber-reinforced polymer(FRP)composites are renowned for their high mechanical strength,durability,and lightweight properties,making them integral to civil engineering,aerospace,and automotive manufacturing.Traditionally,the simulation and optimization of FRP materials have relied on finite element(FE)methods,which,while effective,often fall short in capturing the intricate behaviors of these composites under diverse conditions.Concrete examples in this regard involve modeling interfacial cracks,delaminations,or environmental effects that involve nonlinear phenomena.These degradation mechanisms exceed the capacity of classical FE models,as they are not detailed to the required level of detail.This aspect increases the time and computational resources required,leading to a need for optimization regarding fiber reinforcement configurations or multiple scenario load analysis.Thus,FE methods are inefficient compared to AI-based approaches that generalize material behavior based on extensive datasets.The advent of artificial intelligence(AI)has introduced advanced tools capable of enhancing the analysis and design of FRP materials.This review examines the current landscape of AI applications in FRP composite simulations,highlighting existing research gaps.Through a comprehensive bibliometric analysis,the study underscores the limited number of investigations focused on leveraging AI for FRP optimization.Furthermore,it synthesizes findings related to AI-driven simulation techniques,the mechanical properties of FRP composites,and strategies for predicting and improving their durability.This review comprehensively explores the potential of AI to overcome these limitations by synthesizing over 170 scientific works published between 2015 and 2025.Key findings highlight that supervised learning methods—especially neural networks,support vector machines,and gradient boosting models—achieve prediction accuracies above 90%for mechanical properties and defect classification.However,bibliometric analysis reveals that there are limited studies that address AI-driven optimization or standardized datasets for FRP applications.This review identifies eight core classification domains and eight regression domains where AI excels,including defect detection,bond strength prediction,and fiber orientation optimization.
基金supported by the National Natural Science Foundation of China(Grant Nos.62005058 and 62365006)the Natural Science Foundation of Guangxi,China(Grant No.2020GXNSFBA238012)+2 种基金the China Postdoctoral Science Foundation(Grant No.2020M683726)the Innovation Project of Guangxi Graduate Education(Grant Nos.YCSW2024345 and YCBZ2025157)the Guangxi Key Laboratory of Automatic Detecting Technology and Instruments(Grant No.YQ24101).
文摘High-performance terahertz(THz)logic gate devices are crucial components for signal processing and modulation,playing a significant role in the application of THz communication and imaging.Here,we propose a THz broadband NOR logic encoder based on a graphene-metal hybrid metasurface.The unit structure consists of two symmetrical dual-gap metal split-ring resonators(DSRRs)arranged in a staggered configuration,with graphene strips embedded in their gaps.The NOR logic gate metadevice is controlled by the bias voltages independently applied to the two electrodes.Experiments show that when the bias voltages are applied to both electrodes,the metadevice achieves the NOR logic gate within a 0.52 THz bandwidth,with an average modulation depth above 80%.The experimental results match well with theoretical simulations.Additionally,the strong near-field coupling induced by the staggered DSRRs causes redshift at both LC resonance and dipole resonance.This phenomenon was demonstrated by coupled mode theory.Besides,we analyze the surface current distribution at resonances and propose four equivalent circuit models to elucidate the physical mechanisms of modulation under distinct loaded voltage conditions.The results not only advance modulation and logic gate designs for THz communication but also demonstrate significant potential applications in 6G networks,THz imaging,and radar systems.
