Developing a chiral material as versatile and universal chiral stationary phase(CSP) for chiral separation in diverse chromatographic techniques simultaneously is of great significance.In this study,we demonstrated fo...Developing a chiral material as versatile and universal chiral stationary phase(CSP) for chiral separation in diverse chromatographic techniques simultaneously is of great significance.In this study,we demonstrated for the first time that a chiral metal-organic cage(MOC),[Zn_(6)M_(4)],as a universal chiral recognition material for both multi-mode high-performance liquid chromatography(HPLC) and capillary gas chromatography(GC) enantioseparation.Two novel HPLC CSPs with different bonding arms(CSP-A with a cationic imidazolium bonding arm and CSP-B with an alkyl chain bonding arm) were prepared by clicking of functionalized chiral MOC [Zn_(6)M_(4)] onto thiolated silica via thiol-ene click chemistry.Meanwhile,a capillary GC column statically coated with the chiral MOC [Zn_(6)M_(4)] was also fabricated.The results showed that the chiral MOC exhibits excellent enantioselectivity not only in normal phase HPLC(NP-HPLC) and reversed phase(RP-HPLC) but also in GC,and various racemates were well separated,including alcohols,diols,esters,ketones,ethers,amines,and epoxides.Importantly,CSP-A and CSP-B are complementary to commercially available Chiralcel OD-H and Chiralpak AD-H columns in enantioseparation,which can separate some racemates that could not be or could not well be separated by the two widely used commercial columns,suggesting the great potential of the two prepared CSPs in enantioseparation.This work reveals that the chiral MOC is potential versatile chiral recognition materials for both HPLC and GC,and also paves the way to expand the potential applications of MOCs.展开更多
The open-source finite element software,OpenSees,is widely used in the earthquake engineering community.However,the shell elements and explicit algorithm in OpenSees still require further improvements.Therefore,in thi...The open-source finite element software,OpenSees,is widely used in the earthquake engineering community.However,the shell elements and explicit algorithm in OpenSees still require further improvements.Therefore,in this work,a triangular shell element,NLDKGT,and an explicit algorithm are proposed and implemented in OpenSees.Specifically,based on the generalized conforming theory and the updated Lagrangian formulation,the proposed NLDKGT element is suitable for problems with complicated boundary conditions and strong nonlinearity.The accuracy and reliability of the NLDKGT element are validated through typical cases.Furthermore,by adopting the leapfrog integration method,an explicit algorithm in OpenSees and a modal damping model are developed.Finally,the stability and efficiency of the proposed shell element and explicit algorithm are validated through the nonlinear time-history analysis of a highrise building.展开更多
Metasurfaces,composed of planar arrays of intricately designed meta-atom structures,possess remarkable capabilities in controlling electromagnetic waves in various ways.A critical aspect of metasurface design involves...Metasurfaces,composed of planar arrays of intricately designed meta-atom structures,possess remarkable capabilities in controlling electromagnetic waves in various ways.A critical aspect of metasurface design involves selecting suitable meta-atoms to achieve target functionalities such as phase retardation,amplitude modulation,and polarization conversion.Conventional design processes often involve extensive parameter sweeping,a laborious and computationally intensive task heavily reliant on designer expertise and judgement.Here,we present an efficient genetic algorithm assisted meta-atom optimization method for high-performance metasurface optics,which is compatible to both single-and multiobjective device design tasks.We first employ the method for a single-objective design task and implement a high-efficiency Pancharatnam-Berry phase based metalens with an average focusing efficiency exceeding 80%in the visible spectrum.We then employ the method for a dual-objective metasurface design task and construct an efficient spin-multiplexed structural beam generator.The device is capable of generating zeroth-order and first-order Bessel beams respectively under right-handed and left-handed circular polarized illumination,with associated generation efficiencies surpassing 88%.Finally,we implement a wavelength and spin co-multiplexed four-channel metahologram capable of projecting two spin-multiplexed holographic images under each operational wavelength,with efficiencies over 50%.Our work offers a streamlined and easy-to-implement approach to meta-atom design and optimization,empowering designers to create diverse high-performance and multifunctional metasurface optics.展开更多
[Objectives]To establish an HPLC method for the quantitative determination of multiple phenolic acid components in Tetracera asiatica medicinal material,providing a basis for establishing its quality standards.[Method...[Objectives]To establish an HPLC method for the quantitative determination of multiple phenolic acid components in Tetracera asiatica medicinal material,providing a basis for establishing its quality standards.[Methods]An Inertsil ODS-C 18 column(250 mm×4.6 mm,5μm)was used.The mobile phase consisted of acetonitrile-0.2% phosphoric acid solution(10:90).The flow rate was 1.0 mL/min.The detection wavelength was 274 nm.The column temperature was 25℃.The injection volume was 10μL.The content of three components,gallic acid,protocatechuic acid,and protocatechualdehyde,was determined in 13 batches of T.asiatica.[Results]Gallic acid showed good linearity within the range of 0.020-6.400μg/mL,protocatechuic acid within 0.201-6.432μg/mL,and protocatechualdehyde within 0.202-6.464μg/mL(r>0.9990).The average recovery rates ranged from 98.61%to 101.17%,with RSD s between 1.21%and 2.69%.[Conclusions]The quantitative determination method established in this study is simple and feasible,and can provide a basis for the quality evaluation of T.asiatica.展开更多
Covalent organic frameworks(COFs)have demonstrated great potential in chromatographic separation because of unique structure and superior performance.Herein,single-crystal three-dimensional(3D)COFs with regular morpho...Covalent organic frameworks(COFs)have demonstrated great potential in chromatographic separation because of unique structure and superior performance.Herein,single-crystal three-dimensional(3D)COFs with regular morphology,good monodispersity and high specific surface area,were used as a stationary phase for high-performance liquid chromatography(HPLC).The single-crystal 3D COFs packed column not only exhibits high efficiency in separating hydrophobic molecules involving substituted benzenes,halogenated benzenes,halogenated nitrobenzenes,aromatic amines,aromatic hydrocarbons(PAHs)and phthalate esters(PAEs),but also achieves baseline separation of acenaphthene and acenaphthylene with similar physical and chemical properties as well as environmental pollutants,which cannot be quickly separated on commercial C18 column and a polycrystalline 3D COFs packed column.Especially,the column efficiency of 17303-24255 plates/m was obtained for PAEs,and the resolution values for acenaphthene and acenaphthylene,and carbamazepine(CBZ)and carbamazepine-10,11-epoxide(CBZEP)were 1.7and 2.2,respectively.This successful application not only confirmed the great potential of the singlecrystal 3D COFs in HPLC separation of the organic molecules,but also facilitates the application of COFs in separation science.展开更多
Fiber reinforcement significantly enhances the strength,toughness,and durability of concrete by reducing the propagation of microcracks in the concrete matrix.With the rising demand for high-performance concrete(HPC),...Fiber reinforcement significantly enhances the strength,toughness,and durability of concrete by reducing the propagation of microcracks in the concrete matrix.With the rising demand for high-performance concrete(HPC),this study investigates the mechanical properties of HPC with varying proportions of polypropylene(PP)and steel(ST)fibers.Supplementary cementitious materials(SCMs)toward partial replacement of ordinary Portland cement(OPC)were incorporated to prepare HPC mixes as a ternary composite system using Fly Ash(FA),Silica Fume(SF),and Ground Granulated Blast Furnace Slag(GGBS).Each HPC mix comprised two SCMs,accounting for 20%of the mass fraction of the OPC binder.The study encompassed fiber percentages ranging from 0 to 0.075%PP and 0 to 2%ST,incorporating them into the HPC mixes with gradual increases of 0.025%for PP and 0.5%for ST fiber by mass fraction.All HPC mixes were tested for mechanical properties using compressive and split tensile strength tests.The influence of SCMs on HPC was studied using X-ray diffraction(XRD)for microstructural analyses.It was found that the compressive and split tensile strengths of HPC increased up to an optimal fiber percentage and then decreased.A comparison of the test results of high-performance fiber-reinforced concrete with those of plain HPC revealed significant improvements in compressive and splitting tensile strengths by 26.59%and 57.74%,respectively.Also,the XRD analysis revealed that the composition of the SCMs in HPC was a significant and effective solution for the mechanical properties of the concrete.展开更多
Compared to subtractive manufacturing and casting,3D printing(additive manufacturing)offers advantages,such as the rapid production of complex structures,reduced material waste,and environmental friendliness.Direct in...Compared to subtractive manufacturing and casting,3D printing(additive manufacturing)offers advantages,such as the rapid production of complex structures,reduced material waste,and environmental friendliness.Direct ink writing(DIW)is one of the most popular 3D printing techniques owing to its ability to print multiple materials simultaneously and its high compatibility with printing inks.However,DIW presents significant challenges,particularly in the printing of high-performance polymers.The main challenges are as follows:1.The rigid structures and reaction kinetics of high-performance polymers make developing new inks difficult.2.The limited types of available high-performance polymers underscore the need for new DIW-suitable materials.3.Layer-by-layer stacking weakens interlayer bonding,affecting the mechanical properties of the printed product.4.The accuracy and speed of DIW printing are insufficient for large-scale manufacturing.After introducing the topic,the requirements for DIW printing inks are first reviewed,emphasizing the importance of thixotropic agents.Then,research progress regarding DIW printing of high-performance polymers is comprehensively reviewed according to the requirements of different polymer inks.Additionally,the applications of these materials across various fields are summarized.Finally,the challenges in DIW printing of high-performance polymers,along with corresponding solutions and future development prospects,are discussed in detail.展开更多
This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in ...This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in turbulent carrier flow.The Eulerian framework numerically resolves turbulent carrier flow using a parallelized,finite-volume DNS solver on a staggered Cartesian grid.Particles are tracked using a point-particle method utilizing a Lagrangian particle tracking(LPT)algorithm.The proposed Eulerian-Lagrangian algorithm is validated using an inertial particle-laden turbulent channel flow for different Stokes number cases.The particle concentration profiles and higher-order statistics of the carrier and dispersed phases agree well with the benchmark results.We investigated the effect of fluid velocity interpolation and numerical integration schemes of particle tracking algorithms on particle dispersion statistics.The suitability of fluid velocity interpolation schemes for predicting the particle dispersion statistics is discussed in the framework of the particle tracking algorithm coupled to the finite-volume solver.In addition,we present parallelization strategies implemented in the algorithm and evaluate their parallel performance.展开更多
As electronic devices continue to evolve toward higher power densities,faster speeds,and smaller form factors,the demand for high-performance electronic packaging materials has become increasingly critical.These mater...As electronic devices continue to evolve toward higher power densities,faster speeds,and smaller form factors,the demand for high-performance electronic packaging materials has become increasingly critical.These materials serve as the physical and functional interface between semiconductor components and their operating environment,impacting the overall reliability,thermal management,mechanical protection,and electrical performance of modern electronic systems.This study investigates the development,formulation,and performance evaluation of advanced packaging materials,focusing on polymer-based composites,metal and ceramic matrix systems,and nanomaterial-enhanced formulations.A comprehensive analysis of key performance metrics-including thermal conductivity,electrical insulation,mechanical robustness,and environmental resistance-is presented,alongside strategies for material optimization through interface engineering and processing innovations.Furthermore,the study explores cutting-edge integration technologies such as 3D packaging compatibility,low-temperature co-firing,and high-density interconnects.The findings provide critical insights into the structure-property-processing relationships that define the effectiveness of next-generation packaging materials and offer a roadmap for material selection and system integration in high-reliability electronic applications.展开更多
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises stru...Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.展开更多
This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japo...This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japonicus.The math-ematical model is developed by simulating various biological behaviors of the Gekko japonicus,such as hybrid loco-motion patterns,directional olfactory guidance,implicit group advantage tendencies,and the tail autotomy mechanism.By integrating multi-stage mutual constraints and dynamically adjusting parameters,GJA maintains an optimal balance between global exploration and local exploitation,thereby effectively solving complex optimization problems.To assess the performance of GJA,comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithms using the CEC2017 and CEC2022 benchmark test sets.Additionally,a Friedman test was performed on the experimen-tal results to assess the statistical significance of differences between various algorithms.And GJA was evaluated using multiple qualitative indicators,further confirming its superiority in exploration and exploitation.Finally,GJA was utilized to solve four engineering optimization problems and further implemented in robotic path planning to verify its practical applicability.Experimental results indicate that,compared to other high-performance algorithms,GJA demonstrates excep-tional performance as a powerful optimization algorithm in complex optimization problems.We make the code publicly available at:https://github.com/zhy1109/Gekko-japonicusalgorithm.展开更多
The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,convention...The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications.展开更多
In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h...In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.展开更多
ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the ...ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants.展开更多
With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-...With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.展开更多
In reliability analyses,the absence of a priori information on the most probable point of failure(MPP)may result in overlooking critical points,thereby leading to biased assessment outcomes.Moreover,second-order relia...In reliability analyses,the absence of a priori information on the most probable point of failure(MPP)may result in overlooking critical points,thereby leading to biased assessment outcomes.Moreover,second-order reliability methods exhibit limited accuracy in highly nonlinear scenarios.To overcome these challenges,a novel reliability analysis strategy based on a multimodal differential evolution algorithm and a hypersphere integration method is proposed.Initially,the penalty function method is employed to reformulate the MPP search problem as a conditionally constrained optimization task.Subsequently,a differential evolution algorithm incorporating a population delineation strategy is utilized to identify all MPPs.Finally,a paraboloid equation is constructed based on the curvature of the limit-state function at the MPPs,and the failure probability of the structure is calculated by using the hypersphere integration method.The localization effectiveness of the MPPs is compared through multiple numerical cases and two engineering examples,with accuracy comparisons of failure probabilities against the first-order reliability method(FORM)and the secondorder reliability method(SORM).The results indicate that the method effectively identifies existing MPPs and achieves higher solution precision.展开更多
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting...Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.展开更多
This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the compl...This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.展开更多
Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting incr...Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting increased interest in swarm intelligence algorithms.Among these,the Cuckoo Search(CS)algorithm stands out for its promising global search capabilities.However,it often suffers from premature convergence when tackling complex problems.To address this limitation,this paper proposes a Grouped Dynamic Adaptive CS(GDACS)algorithm.Theenhancements incorporated intoGDACS can be summarized into two key aspects.Firstly,a chaotic map is employed to generate initial solutions,leveraging the inherent randomness of chaotic sequences to ensure a more uniform distribution across the search space and enhance population diversity from the outset.Secondly,Cauchy and Levy strategies replace the standard CS population update.This strategy involves evaluating the fitness of candidate solutions to dynamically group the population based on performance.Different step-size adaptation strategies are then applied to distinct groups,enabling an adaptive search mechanism that balances exploration and exploitation.Experiments were conducted on six benchmark functions and four constrained engineering design problems,and the results indicate that the proposed GDACS achieves good search efficiency and produces more accurate optimization results compared with other state-of-the-art algorithms.展开更多
基金supported by the National Natural Science Foundation of China (Nos.22064020,22364022,and 22174125)the Applied Basic Research Foundation of Yunnan Province (Nos.202101AT070101 and 202201AT070029)。
文摘Developing a chiral material as versatile and universal chiral stationary phase(CSP) for chiral separation in diverse chromatographic techniques simultaneously is of great significance.In this study,we demonstrated for the first time that a chiral metal-organic cage(MOC),[Zn_(6)M_(4)],as a universal chiral recognition material for both multi-mode high-performance liquid chromatography(HPLC) and capillary gas chromatography(GC) enantioseparation.Two novel HPLC CSPs with different bonding arms(CSP-A with a cationic imidazolium bonding arm and CSP-B with an alkyl chain bonding arm) were prepared by clicking of functionalized chiral MOC [Zn_(6)M_(4)] onto thiolated silica via thiol-ene click chemistry.Meanwhile,a capillary GC column statically coated with the chiral MOC [Zn_(6)M_(4)] was also fabricated.The results showed that the chiral MOC exhibits excellent enantioselectivity not only in normal phase HPLC(NP-HPLC) and reversed phase(RP-HPLC) but also in GC,and various racemates were well separated,including alcohols,diols,esters,ketones,ethers,amines,and epoxides.Importantly,CSP-A and CSP-B are complementary to commercially available Chiralcel OD-H and Chiralpak AD-H columns in enantioseparation,which can separate some racemates that could not be or could not well be separated by the two widely used commercial columns,suggesting the great potential of the two prepared CSPs in enantioseparation.This work reveals that the chiral MOC is potential versatile chiral recognition materials for both HPLC and GC,and also paves the way to expand the potential applications of MOCs.
基金The authors would like to acknowledge the financial supports of Beijing Natural Science Foundation(No.8182025).
文摘The open-source finite element software,OpenSees,is widely used in the earthquake engineering community.However,the shell elements and explicit algorithm in OpenSees still require further improvements.Therefore,in this work,a triangular shell element,NLDKGT,and an explicit algorithm are proposed and implemented in OpenSees.Specifically,based on the generalized conforming theory and the updated Lagrangian formulation,the proposed NLDKGT element is suitable for problems with complicated boundary conditions and strong nonlinearity.The accuracy and reliability of the NLDKGT element are validated through typical cases.Furthermore,by adopting the leapfrog integration method,an explicit algorithm in OpenSees and a modal damping model are developed.Finally,the stability and efficiency of the proposed shell element and explicit algorithm are validated through the nonlinear time-history analysis of a highrise building.
基金support from the National Science Foundation of China(Grant Nos.62075078 and 62135004)the Knowledge Innovation Program of Wuhan-Shuguang Project(Grant No.2022010801020095).
文摘Metasurfaces,composed of planar arrays of intricately designed meta-atom structures,possess remarkable capabilities in controlling electromagnetic waves in various ways.A critical aspect of metasurface design involves selecting suitable meta-atoms to achieve target functionalities such as phase retardation,amplitude modulation,and polarization conversion.Conventional design processes often involve extensive parameter sweeping,a laborious and computationally intensive task heavily reliant on designer expertise and judgement.Here,we present an efficient genetic algorithm assisted meta-atom optimization method for high-performance metasurface optics,which is compatible to both single-and multiobjective device design tasks.We first employ the method for a single-objective design task and implement a high-efficiency Pancharatnam-Berry phase based metalens with an average focusing efficiency exceeding 80%in the visible spectrum.We then employ the method for a dual-objective metasurface design task and construct an efficient spin-multiplexed structural beam generator.The device is capable of generating zeroth-order and first-order Bessel beams respectively under right-handed and left-handed circular polarized illumination,with associated generation efficiencies surpassing 88%.Finally,we implement a wavelength and spin co-multiplexed four-channel metahologram capable of projecting two spin-multiplexed holographic images under each operational wavelength,with efficiencies over 50%.Our work offers a streamlined and easy-to-implement approach to meta-atom design and optimization,empowering designers to create diverse high-performance and multifunctional metasurface optics.
基金Supported by Regional Science Foundation of China,National Natural Science Foundation(No.82160820)General Program of Guizhou Provincial Natural Science Foundation[QianKeHe Foundation-ZK(2023)General153].
文摘[Objectives]To establish an HPLC method for the quantitative determination of multiple phenolic acid components in Tetracera asiatica medicinal material,providing a basis for establishing its quality standards.[Methods]An Inertsil ODS-C 18 column(250 mm×4.6 mm,5μm)was used.The mobile phase consisted of acetonitrile-0.2% phosphoric acid solution(10:90).The flow rate was 1.0 mL/min.The detection wavelength was 274 nm.The column temperature was 25℃.The injection volume was 10μL.The content of three components,gallic acid,protocatechuic acid,and protocatechualdehyde,was determined in 13 batches of T.asiatica.[Results]Gallic acid showed good linearity within the range of 0.020-6.400μg/mL,protocatechuic acid within 0.201-6.432μg/mL,and protocatechualdehyde within 0.202-6.464μg/mL(r>0.9990).The average recovery rates ranged from 98.61%to 101.17%,with RSD s between 1.21%and 2.69%.[Conclusions]The quantitative determination method established in this study is simple and feasible,and can provide a basis for the quality evaluation of T.asiatica.
基金the National Natural Science Foundation of China(No.22274021)Natural Science Foundation of Fujian Province(No.2022J01535)for financial support。
文摘Covalent organic frameworks(COFs)have demonstrated great potential in chromatographic separation because of unique structure and superior performance.Herein,single-crystal three-dimensional(3D)COFs with regular morphology,good monodispersity and high specific surface area,were used as a stationary phase for high-performance liquid chromatography(HPLC).The single-crystal 3D COFs packed column not only exhibits high efficiency in separating hydrophobic molecules involving substituted benzenes,halogenated benzenes,halogenated nitrobenzenes,aromatic amines,aromatic hydrocarbons(PAHs)and phthalate esters(PAEs),but also achieves baseline separation of acenaphthene and acenaphthylene with similar physical and chemical properties as well as environmental pollutants,which cannot be quickly separated on commercial C18 column and a polycrystalline 3D COFs packed column.Especially,the column efficiency of 17303-24255 plates/m was obtained for PAEs,and the resolution values for acenaphthene and acenaphthylene,and carbamazepine(CBZ)and carbamazepine-10,11-epoxide(CBZEP)were 1.7and 2.2,respectively.This successful application not only confirmed the great potential of the singlecrystal 3D COFs in HPLC separation of the organic molecules,but also facilitates the application of COFs in separation science.
基金financed by the Science and Engineering Research Board,India,in Project No.EEQ/2023/000130CSIR-India in Project No.MLP072002.
文摘Fiber reinforcement significantly enhances the strength,toughness,and durability of concrete by reducing the propagation of microcracks in the concrete matrix.With the rising demand for high-performance concrete(HPC),this study investigates the mechanical properties of HPC with varying proportions of polypropylene(PP)and steel(ST)fibers.Supplementary cementitious materials(SCMs)toward partial replacement of ordinary Portland cement(OPC)were incorporated to prepare HPC mixes as a ternary composite system using Fly Ash(FA),Silica Fume(SF),and Ground Granulated Blast Furnace Slag(GGBS).Each HPC mix comprised two SCMs,accounting for 20%of the mass fraction of the OPC binder.The study encompassed fiber percentages ranging from 0 to 0.075%PP and 0 to 2%ST,incorporating them into the HPC mixes with gradual increases of 0.025%for PP and 0.5%for ST fiber by mass fraction.All HPC mixes were tested for mechanical properties using compressive and split tensile strength tests.The influence of SCMs on HPC was studied using X-ray diffraction(XRD)for microstructural analyses.It was found that the compressive and split tensile strengths of HPC increased up to an optimal fiber percentage and then decreased.A comparison of the test results of high-performance fiber-reinforced concrete with those of plain HPC revealed significant improvements in compressive and splitting tensile strengths by 26.59%and 57.74%,respectively.Also,the XRD analysis revealed that the composition of the SCMs in HPC was a significant and effective solution for the mechanical properties of the concrete.
基金supported by National Key Research and Development Program of China(Grant No.2022YFB3809000)Major Science and Technology Project of Gansu Province(Grant No.23ZDGA011)+1 种基金National Natural Science Foundation of China(Grant No.22275199,52105224)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB04701022021).
文摘Compared to subtractive manufacturing and casting,3D printing(additive manufacturing)offers advantages,such as the rapid production of complex structures,reduced material waste,and environmental friendliness.Direct ink writing(DIW)is one of the most popular 3D printing techniques owing to its ability to print multiple materials simultaneously and its high compatibility with printing inks.However,DIW presents significant challenges,particularly in the printing of high-performance polymers.The main challenges are as follows:1.The rigid structures and reaction kinetics of high-performance polymers make developing new inks difficult.2.The limited types of available high-performance polymers underscore the need for new DIW-suitable materials.3.Layer-by-layer stacking weakens interlayer bonding,affecting the mechanical properties of the printed product.4.The accuracy and speed of DIW printing are insufficient for large-scale manufacturing.After introducing the topic,the requirements for DIW printing inks are first reviewed,emphasizing the importance of thixotropic agents.Then,research progress regarding DIW printing of high-performance polymers is comprehensively reviewed according to the requirements of different polymer inks.Additionally,the applications of these materials across various fields are summarized.Finally,the challenges in DIW printing of high-performance polymers,along with corresponding solutions and future development prospects,are discussed in detail.
基金supported by the P.G.Senapathy Center for Computing Resources at IIT Madrasfunding provided by the Ministry of Education,Government of Indiasupported by the National Natural Science Foundation of China(Grant Nos.12388101,12472224 and 92252104).
文摘This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in turbulent carrier flow.The Eulerian framework numerically resolves turbulent carrier flow using a parallelized,finite-volume DNS solver on a staggered Cartesian grid.Particles are tracked using a point-particle method utilizing a Lagrangian particle tracking(LPT)algorithm.The proposed Eulerian-Lagrangian algorithm is validated using an inertial particle-laden turbulent channel flow for different Stokes number cases.The particle concentration profiles and higher-order statistics of the carrier and dispersed phases agree well with the benchmark results.We investigated the effect of fluid velocity interpolation and numerical integration schemes of particle tracking algorithms on particle dispersion statistics.The suitability of fluid velocity interpolation schemes for predicting the particle dispersion statistics is discussed in the framework of the particle tracking algorithm coupled to the finite-volume solver.In addition,we present parallelization strategies implemented in the algorithm and evaluate their parallel performance.
文摘As electronic devices continue to evolve toward higher power densities,faster speeds,and smaller form factors,the demand for high-performance electronic packaging materials has become increasingly critical.These materials serve as the physical and functional interface between semiconductor components and their operating environment,impacting the overall reliability,thermal management,mechanical protection,and electrical performance of modern electronic systems.This study investigates the development,formulation,and performance evaluation of advanced packaging materials,focusing on polymer-based composites,metal and ceramic matrix systems,and nanomaterial-enhanced formulations.A comprehensive analysis of key performance metrics-including thermal conductivity,electrical insulation,mechanical robustness,and environmental resistance-is presented,alongside strategies for material optimization through interface engineering and processing innovations.Furthermore,the study explores cutting-edge integration technologies such as 3D packaging compatibility,low-temperature co-firing,and high-density interconnects.The findings provide critical insights into the structure-property-processing relationships that define the effectiveness of next-generation packaging materials and offer a roadmap for material selection and system integration in high-reliability electronic applications.
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
文摘Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.
基金CHINA POSTDOCTORAL SCIENCE FOUNDATION(Grant No.2025M771925)Young Scientists Fund(C Class)(Grant No.32501636)Special Fund of Fundamental Scientific Research Business Expense for Higher School of Central Government(Grant No.2572025JT04).
文摘This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japonicus.The math-ematical model is developed by simulating various biological behaviors of the Gekko japonicus,such as hybrid loco-motion patterns,directional olfactory guidance,implicit group advantage tendencies,and the tail autotomy mechanism.By integrating multi-stage mutual constraints and dynamically adjusting parameters,GJA maintains an optimal balance between global exploration and local exploitation,thereby effectively solving complex optimization problems.To assess the performance of GJA,comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithms using the CEC2017 and CEC2022 benchmark test sets.Additionally,a Friedman test was performed on the experimen-tal results to assess the statistical significance of differences between various algorithms.And GJA was evaluated using multiple qualitative indicators,further confirming its superiority in exploration and exploitation.Finally,GJA was utilized to solve four engineering optimization problems and further implemented in robotic path planning to verify its practical applicability.Experimental results indicate that,compared to other high-performance algorithms,GJA demonstrates excep-tional performance as a powerful optimization algorithm in complex optimization problems.We make the code publicly available at:https://github.com/zhy1109/Gekko-japonicusalgorithm.
基金supported by the NSFC(Grant Nos.62176273,62271070,62441212)The Open Foundation of State Key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications)under Grant SKLNST-2024-1-062025Major Project of the Natural Science Foundation of Inner Mongolia(2025ZD008).
文摘The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications.
基金funding from the European Commission by the Ruralities project(grant agreement no.101060876).
文摘In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.
基金supported by the National Natural Science Foundation of China under grant number 62066016the Natural Science Foundation of Hunan Province of China under grant number 2024JJ7395+2 种基金International and Regional Science and Technology Cooperation and Exchange Program of the Hunan Association for Science and Technology under grant number 025SKX-KJ-04Hunan Provincial Postgraduate Research Innovation Project under grant numberCX20251611Liye Qin Bamboo Slips Research Special Project of JishouUniversity 25LYY03.
文摘ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants.
基金supported in part by the Technical Service for the Development and Application of an Intelligent Visual Management Platformfor Expressway Construction Progress Based on BIM Technology(grant NO.JKYZLX-2023-09)in partby the Technical Service for the Development of an Early Warning Model in the Research and Application of Key Technologies for Tunnel Operation Safety Monitoring and Early Warning Based on Digital Twin(grant NO.JK-S02-ZNGS-202412-JISHU-FA-0035)sponsored by Yunnan Transportation Science Research Institute Co.,Ltd.
文摘With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.
基金National Natural Science Foundation of China(No.52375236)Fundamental Research Funds for the Central Universities of China(No.23D110316)。
文摘In reliability analyses,the absence of a priori information on the most probable point of failure(MPP)may result in overlooking critical points,thereby leading to biased assessment outcomes.Moreover,second-order reliability methods exhibit limited accuracy in highly nonlinear scenarios.To overcome these challenges,a novel reliability analysis strategy based on a multimodal differential evolution algorithm and a hypersphere integration method is proposed.Initially,the penalty function method is employed to reformulate the MPP search problem as a conditionally constrained optimization task.Subsequently,a differential evolution algorithm incorporating a population delineation strategy is utilized to identify all MPPs.Finally,a paraboloid equation is constructed based on the curvature of the limit-state function at the MPPs,and the failure probability of the structure is calculated by using the hypersphere integration method.The localization effectiveness of the MPPs is compared through multiple numerical cases and two engineering examples,with accuracy comparisons of failure probabilities against the first-order reliability method(FORM)and the secondorder reliability method(SORM).The results indicate that the method effectively identifies existing MPPs and achieves higher solution precision.
基金National Key Research and Development Program of China,No.2023YFC3006704National Natural Science Foundation of China,No.42171047CAS-CSIRO Partnership Joint Project of 2024,No.177GJHZ2023097MI。
文摘Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.
基金supported by the Research Project of China Southern Power Grid(No.056200KK52222031).
文摘This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.
基金supported in part by the Ministry of Higher Education Malaysia(MOHE)through Fundamental Research Grant Scheme(FRGS)Ref:FRGS/1/2024/ICT02/UTM/02/10,Vot.No:R.J130000.7828.5F748the Scientific Research Project of Education Department of Hunan Province(Nos.22B1046 and 24A0771).
文摘Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting increased interest in swarm intelligence algorithms.Among these,the Cuckoo Search(CS)algorithm stands out for its promising global search capabilities.However,it often suffers from premature convergence when tackling complex problems.To address this limitation,this paper proposes a Grouped Dynamic Adaptive CS(GDACS)algorithm.Theenhancements incorporated intoGDACS can be summarized into two key aspects.Firstly,a chaotic map is employed to generate initial solutions,leveraging the inherent randomness of chaotic sequences to ensure a more uniform distribution across the search space and enhance population diversity from the outset.Secondly,Cauchy and Levy strategies replace the standard CS population update.This strategy involves evaluating the fitness of candidate solutions to dynamically group the population based on performance.Different step-size adaptation strategies are then applied to distinct groups,enabling an adaptive search mechanism that balances exploration and exploitation.Experiments were conducted on six benchmark functions and four constrained engineering design problems,and the results indicate that the proposed GDACS achieves good search efficiency and produces more accurate optimization results compared with other state-of-the-art algorithms.
