The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic...Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.展开更多
At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown ...At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.展开更多
In this paper,we propose a three-term conjugate gradient method for solving unconstrained optimization problems based on the Hestenes-Stiefel(HS)conjugate gradient method and Polak-Ribiere-Polyak(PRP)conjugate gradien...In this paper,we propose a three-term conjugate gradient method for solving unconstrained optimization problems based on the Hestenes-Stiefel(HS)conjugate gradient method and Polak-Ribiere-Polyak(PRP)conjugate gradient method.Under the condition of standard Wolfe line search,the proposed search direction is the descent direction.For general nonlinear functions,the method is globally convergent.Finally,numerical results show that the proposed method is efficient.展开更多
In the area of reservoir engineering,the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system's infrastructure.Traditionall...In the area of reservoir engineering,the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system's infrastructure.Traditionally,this optimization process was centered on a single objective,such as net present value,return on investment,cumulative oil production,or cumulative water production.However,the inherent complexity of reservoir exploration necessitates a departure from this single-objective approach.Mul-tiple conflicting production and economic indicators must now be considered to enable more precise and robust decision-making.In response to this challenge,researchers have embarked on a journey to explore field development optimization of multiple conflicting criteria,employing the formidable tools of multi-objective optimization algorithms.These algorithms delve into the intricate terrain of production strategy design,seeking to strike a delicate balance between the often-contrasting objectives.Over the years,a plethora of these algorithms have emerged,ranging from a priori methods to a posteriori approach,each offering unique insights and capabilities.This survey endeavors to encapsulate,catego-rize,and scrutinize these invaluable contributions to field development optimization,which grapple with the complexities of multiple conflicting objective functions.Beyond the overview of existing methodologies,we delve into the persisting challenges faced by researchers and practitioners alike.Notably,the application of multi-objective optimization techniques to production optimization is hin-dered by the resource-intensive nature of reservoir simulation,especially when confronted with inherent uncertainties.As a result of this survey,emerging opportunities have been identified that will serve as catalysts for pivotal research endeavors in the future.As intelligent and more efficient algo-rithms continue to evolve,the potential for addressing hitherto insurmountable field development optimization obstacles becomes increasingly viable.This discussion on future prospects aims to inspire critical research,guiding the way toward innovative solutions in the ever-evolving landscape of oil and gas production optimization.展开更多
Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power o...Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.展开更多
This study investigates the potential of Prosopis cineraria Leaves Powder(PCLP)as a biosorbent for removing lead(Pb)and zinc(Zn)from aqueous solutions,optimizing the process using Response Surface Methodology(RSM).Pro...This study investigates the potential of Prosopis cineraria Leaves Powder(PCLP)as a biosorbent for removing lead(Pb)and zinc(Zn)from aqueous solutions,optimizing the process using Response Surface Methodology(RSM).Prosopis cineraria,commonly known as Khejri,is a drought-resistant tree with significant promise in environmental applications.The research employed a Central Composite Design(CCD)to examine the independent and combined effects of key process variables,including initial metal ion concentration,contact time,pH,and PCLP dosage.RSM was used to develop mathematical models that explain the relationship between these factors and the efficiency of metal removal,allowing the determination of optimal operating conditions.The experimental results indicated that the Langmuir isotherm model was the most appropriate for describing the biosorption of both metals,suggesting favorable adsorption characteristics.Additionally,the D-R isotherm confirmed that chemisorption was the primary mechanism involved in the biosorption process.For lead removal,the optimal conditions were found to be 312.23 K temperature,pH 4.72,58.5 mg L-1 initial concentration,and 0.27 g biosorbent dosage,achieving an 83.77%removal efficiency.For zinc,the optimal conditions were 312.4 K,pH 5.86,53.07 mg L-1 initial concentration,and the same biosorbent dosage,resulting in a 75.86%removal efficiency.These findings highlight PCLP’s potential as an effective,eco-friendly biosorbent for sustainable heavy metal removal in water treatment.展开更多
Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convol...Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.展开更多
Moles exhibit highly effective capabilities due to their unique body structures and digging techniques,making them ideal models for biomimetic research.However,a major challenge for mole-inspired robots lies in overco...Moles exhibit highly effective capabilities due to their unique body structures and digging techniques,making them ideal models for biomimetic research.However,a major challenge for mole-inspired robots lies in overcoming resistance in granular media when burrowing with forelimbs.In the absence of effective forepaw design strategies,most robotic designs rely on increased power to enhance performance.To address this issue,this paper employs Resistive Force Theory to optimize mole-inspired forepaws,aiming to enhance burrowing efficiency.By analyzing the relationship between geometric parameters and burrowing forces,we propose several forepaw design variations.Through granular resistance assessments,an effective forepaw configuration is identified and further refined using parameters such as longitudinal and transverse curvature.Subsequently,the Particle Swarm Optimization algorithm is applied to determine the optimal forepaw design.In force-loading tests,the optimized forepaw demonstrated a 79.44%reduction in granular lift force and a 22.55%increase in propulsive force compared with the control group.In robotic burrowing experiments,the optimized forepaw achieved the longest burrow displacement(179.528 mm)and the lowest burrowing lift force(0.9355 mm/s),verifying its effectiveness in reducing the lift force and enhancing the propulsive force.展开更多
Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Ext...Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.展开更多
The simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)can independently adjust surface’s reflection and transmission coefficients so as to enhance space coverage.For a multiple-inpu...The simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)can independently adjust surface’s reflection and transmission coefficients so as to enhance space coverage.For a multiple-input multiple-output(MIMO)communication system with a STAR-RIS,a base station(BS),an eavesdropper,and multiple users,the system security rate is studied.A joint design of the power allocation at the transmitter and phase shift matrices for reflection and transmission at the STAR-RIS is conducted,in order to maximize the worst achievable security data rate(ASDR).Since the problem is nonconvex and hence challenging,a particle swarm optimization(PSO)based algorithm is developed to tackle the problem.Both the cases of continuous and discrete phase shift matrices at the STAR-RIS are considered.Simulation results demonstrate the effectiveness of the proposed algorithm and shows the benefits of using STAR-RIS in MIMO mutliuser systems.展开更多
Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,th...Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization algorithm.The proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop control.Then,it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control strategy.Precise pre-synchronization is achieved by regulating the virtual current to zero and aligning the photovoltaic storage hybrid inverter with the grid voltage.Additionally,two novel operators,learning and emotional behaviors are introduced to enhance the optimization precision of the chimpanzee algorithm.These operators ensure high-precision and high-reliability optimization of the droop control parameters for photovoltaic storage hybrid inverters.A Simulink model was constructed for simulation analysis,which validated the optimized control strategy’s ability to evenly distribute power under load transients.This strategy effectively mitigated transient voltage and current surges during mode transitions.Consequently,seamless and efficient switching between gridconnected and island modes was achieved for the photovoltaic storage hybrid inverter.The enhanced energy utilization efficiency,in turn,offers robust technical support for grid stability.展开更多
Dear Editor,This letter proposes a convex optimization-based model predictive control(MPC)autonomous guidance method for the Mars ascent vehicle(MAV).We use the modified chebyshev-picard iteration(MCPI)to solve optimi...Dear Editor,This letter proposes a convex optimization-based model predictive control(MPC)autonomous guidance method for the Mars ascent vehicle(MAV).We use the modified chebyshev-picard iteration(MCPI)to solve optimization sub-problems within the MPC framework,eliminating the dynamic constraints in solving the optimal control problem and enhancing the convergence performance of the algorithm.Moreover,this method can repeatedly perform trajectory optimization calculations at a high frequency,achieving timely correction of the optimal control command.Numerical simulations demonstrate that the method can satisfy the requirements of rapid computation and reliability for the MAV system when considering uncertainties and perturbations.展开更多
This paper considers the swarm vigilance problem for multi-agent systems(MAS),where multiple agents are deployed within a rectangular region for perception-based vigilance.There are two main challenges,namely the task...This paper considers the swarm vigilance problem for multi-agent systems(MAS),where multiple agents are deployed within a rectangular region for perception-based vigilance.There are two main challenges,namely the task allocation for vigilance roles and the coverage planning of the perception ranges.Firstly,vigilance behavioral patterns and processes in animal populations within natural habitats are investigated.Inspired by these biological vigilance behaviors,an efficient vigilance task allocation model for MAS is proposed.Secondly,the subsequent optimization of task layouts can achieve efficient surveillance coverage with fewer agents,minimizing resource consumption.Thirdly,an improved particle swarm optimization(IPSO)algorithm is proposed,which incorporates fitness-driven adaptive inertia weight dynamics.According to simulation analysis and comparative studies,optimal parameter configurations for genetic algorithm(GA)and IPSO are determined.Finally,the results indicate the proposed IPSO's superior performance to both GA and standard particle swarm optimization(PSO)in vigilance task allocation optimization,with satisfying advantages in computational efficiency and solution quality.展开更多
This study introduces a novel voice cloning framework driven by Mordukhovich Subdifferential Optimization(MSO)to address the complex multi-objective challenges of pathological speech synthesis in underresourced Lithua...This study introduces a novel voice cloning framework driven by Mordukhovich Subdifferential Optimization(MSO)to address the complex multi-objective challenges of pathological speech synthesis in underresourced Lithuanian language with unique phonemes not present in most pre-trained models.Unlike existing voice synthesis models that often optimize for a single objective or are restricted to major languages,our approach explicitly balances four competing criteria:speech naturalness,speaker similarity,computational efficiency,and adaptability to pathological voice patterns.We evaluate four model configurations combining Lithuanian and English encoders,synthesizers,and vocoders.The hybrid model(English encoder,Lithuanian synthesizer,English vocoder),optimized via MSO,achieved the highest Mean Opinion Score(MOS)of 4.3 and demonstrated superior intelligibility and speaker fidelity.The results confirm that MSO enables effective navigation of trade-offs in multilingual pathological voice cloning,offering a scalable path toward high-quality voice restoration in clinical speech applications.This work represents the first integration of Mordukhovich optimization into pathological TTS,setting a new benchmark for speech synthesis under clinical and linguistic constraints.展开更多
This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational mach...This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational machine learning models are applied in reservoir development optimization.Traditional regression-based models often struggle in complex scenarios,but the proposed relational and regression-based composite differential evolution(RRCODE)method combines a Gaussian naive Bayes relational model with a radial basis function network regression model.This integration effectively captures complex relationships in the optimization process,improving both accuracy and convergence speed.Experimental tests on a multi-layer multi-channel reservoir model,the Egg reservoir model,and a real-field reservoir model(the S reservoir)demonstrate that RRCODE significantly reduces water injection and production volumes while increasing economic returns and cumulative oil recovery.Moreover,the surrogate models employed in RRCODE exhibit lightweight characteristics with low computational overhead.These results highlight RRCODE's superior performance in the integrated optimization of reservoir production and layer configurations,offering more efficient and economically viable solutions for oilfield development.展开更多
Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device ...Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy utilization.To tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network edge.This architecture effectively reduces transmission costs by minimizing data travel time.In addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation cost.The IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal solutions.Experimental results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and discharging.It successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy sources.The edge-computing framework significantly reduces transmission delays between energy devices.Furthermore,IWOA outperforms traditional algorithms in optimizing the objective function.展开更多
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01264).
文摘Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.
文摘At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.
基金Supported by the Science and Technology Project of Guangxi(Guike AD23023002)。
文摘In this paper,we propose a three-term conjugate gradient method for solving unconstrained optimization problems based on the Hestenes-Stiefel(HS)conjugate gradient method and Polak-Ribiere-Polyak(PRP)conjugate gradient method.Under the condition of standard Wolfe line search,the proposed search direction is the descent direction.For general nonlinear functions,the method is globally convergent.Finally,numerical results show that the proposed method is efficient.
基金the support of EPIC - Energy Production Innovation Center, hosted by the University of Campinas (UNICAMP) and sponsored by Equinor Brazil and FAPESP - Sao Paulo Research Foundation (2021/04878- 7 and 2017/15736-3)financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nível Superior Brasil (CAPES) - Financing Code 001
文摘In the area of reservoir engineering,the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system's infrastructure.Traditionally,this optimization process was centered on a single objective,such as net present value,return on investment,cumulative oil production,or cumulative water production.However,the inherent complexity of reservoir exploration necessitates a departure from this single-objective approach.Mul-tiple conflicting production and economic indicators must now be considered to enable more precise and robust decision-making.In response to this challenge,researchers have embarked on a journey to explore field development optimization of multiple conflicting criteria,employing the formidable tools of multi-objective optimization algorithms.These algorithms delve into the intricate terrain of production strategy design,seeking to strike a delicate balance between the often-contrasting objectives.Over the years,a plethora of these algorithms have emerged,ranging from a priori methods to a posteriori approach,each offering unique insights and capabilities.This survey endeavors to encapsulate,catego-rize,and scrutinize these invaluable contributions to field development optimization,which grapple with the complexities of multiple conflicting objective functions.Beyond the overview of existing methodologies,we delve into the persisting challenges faced by researchers and practitioners alike.Notably,the application of multi-objective optimization techniques to production optimization is hin-dered by the resource-intensive nature of reservoir simulation,especially when confronted with inherent uncertainties.As a result of this survey,emerging opportunities have been identified that will serve as catalysts for pivotal research endeavors in the future.As intelligent and more efficient algo-rithms continue to evolve,the potential for addressing hitherto insurmountable field development optimization obstacles becomes increasingly viable.This discussion on future prospects aims to inspire critical research,guiding the way toward innovative solutions in the ever-evolving landscape of oil and gas production optimization.
基金funded by the“Research and Application Project of Collaborative Optimization Control Technology for Distribution Station Area for High Proportion Distributed PV Consumption(4000-202318079A-1-1-ZN)”of the Headquarters of the State Grid Corporation.
文摘Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.
文摘This study investigates the potential of Prosopis cineraria Leaves Powder(PCLP)as a biosorbent for removing lead(Pb)and zinc(Zn)from aqueous solutions,optimizing the process using Response Surface Methodology(RSM).Prosopis cineraria,commonly known as Khejri,is a drought-resistant tree with significant promise in environmental applications.The research employed a Central Composite Design(CCD)to examine the independent and combined effects of key process variables,including initial metal ion concentration,contact time,pH,and PCLP dosage.RSM was used to develop mathematical models that explain the relationship between these factors and the efficiency of metal removal,allowing the determination of optimal operating conditions.The experimental results indicated that the Langmuir isotherm model was the most appropriate for describing the biosorption of both metals,suggesting favorable adsorption characteristics.Additionally,the D-R isotherm confirmed that chemisorption was the primary mechanism involved in the biosorption process.For lead removal,the optimal conditions were found to be 312.23 K temperature,pH 4.72,58.5 mg L-1 initial concentration,and 0.27 g biosorbent dosage,achieving an 83.77%removal efficiency.For zinc,the optimal conditions were 312.4 K,pH 5.86,53.07 mg L-1 initial concentration,and the same biosorbent dosage,resulting in a 75.86%removal efficiency.These findings highlight PCLP’s potential as an effective,eco-friendly biosorbent for sustainable heavy metal removal in water treatment.
基金supported by Science and Technology Innovation Programfor Postgraduate Students in IDP Subsidized by Fundamental Research Funds for the Central Universities(Project No.ZY20240335)support of the Research Project of the Key Technology of Malicious Code Detection Based on Data Mining in APT Attack(Project No.2022IT173)the Research Project of the Big Data Sensitive Information Supervision Technology Based on Convolutional Neural Network(Project No.2022011033).
文摘Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.
基金financially supported in-part by the National Natural Science Foundation of China(52275011)the Natural Science Foundation of Guangdong Province(2023B1515020080)+3 种基金the Natural Science Foundation of Guangzhou(2024A04J2552)the Fundamental Research Funds for the Central Universities,the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology(CAST)(2021QNRC001)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515011253)the Higher Education Institution Featured Innovation Project of Department of Education of Guangdong Province(GrantNo.2023KTSCX138).
文摘Moles exhibit highly effective capabilities due to their unique body structures and digging techniques,making them ideal models for biomimetic research.However,a major challenge for mole-inspired robots lies in overcoming resistance in granular media when burrowing with forelimbs.In the absence of effective forepaw design strategies,most robotic designs rely on increased power to enhance performance.To address this issue,this paper employs Resistive Force Theory to optimize mole-inspired forepaws,aiming to enhance burrowing efficiency.By analyzing the relationship between geometric parameters and burrowing forces,we propose several forepaw design variations.Through granular resistance assessments,an effective forepaw configuration is identified and further refined using parameters such as longitudinal and transverse curvature.Subsequently,the Particle Swarm Optimization algorithm is applied to determine the optimal forepaw design.In force-loading tests,the optimized forepaw demonstrated a 79.44%reduction in granular lift force and a 22.55%increase in propulsive force compared with the control group.In robotic burrowing experiments,the optimized forepaw achieved the longest burrow displacement(179.528 mm)and the lowest burrowing lift force(0.9355 mm/s),verifying its effectiveness in reducing the lift force and enhancing the propulsive force.
基金the University of Transport Technology under grant number DTTD2022-12.
文摘Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.
文摘The simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)can independently adjust surface’s reflection and transmission coefficients so as to enhance space coverage.For a multiple-input multiple-output(MIMO)communication system with a STAR-RIS,a base station(BS),an eavesdropper,and multiple users,the system security rate is studied.A joint design of the power allocation at the transmitter and phase shift matrices for reflection and transmission at the STAR-RIS is conducted,in order to maximize the worst achievable security data rate(ASDR).Since the problem is nonconvex and hence challenging,a particle swarm optimization(PSO)based algorithm is developed to tackle the problem.Both the cases of continuous and discrete phase shift matrices at the STAR-RIS are considered.Simulation results demonstrate the effectiveness of the proposed algorithm and shows the benefits of using STAR-RIS in MIMO mutliuser systems.
基金received funding from the Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1633)2023 University Student Innovation and Entrepreneurship Training Program(202311463009Z)+1 种基金Changzhou Science and Technology Support Project(CE20235045)Open Project of Jiangsu Key Laboratory of Power Transmission&Distribution Equipment Technology(2021JSSPD12).
文摘Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization algorithm.The proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop control.Then,it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control strategy.Precise pre-synchronization is achieved by regulating the virtual current to zero and aligning the photovoltaic storage hybrid inverter with the grid voltage.Additionally,two novel operators,learning and emotional behaviors are introduced to enhance the optimization precision of the chimpanzee algorithm.These operators ensure high-precision and high-reliability optimization of the droop control parameters for photovoltaic storage hybrid inverters.A Simulink model was constructed for simulation analysis,which validated the optimized control strategy’s ability to evenly distribute power under load transients.This strategy effectively mitigated transient voltage and current surges during mode transitions.Consequently,seamless and efficient switching between gridconnected and island modes was achieved for the photovoltaic storage hybrid inverter.The enhanced energy utilization efficiency,in turn,offers robust technical support for grid stability.
基金supported by the National Defense Basic Scientific Research Program(JCKY2021603B030)the National Natural Science Foundation of China(62273118,12150008)the Natural Science Foundation of Heilongjiang Province(LH2022F023).
文摘Dear Editor,This letter proposes a convex optimization-based model predictive control(MPC)autonomous guidance method for the Mars ascent vehicle(MAV).We use the modified chebyshev-picard iteration(MCPI)to solve optimization sub-problems within the MPC framework,eliminating the dynamic constraints in solving the optimal control problem and enhancing the convergence performance of the algorithm.Moreover,this method can repeatedly perform trajectory optimization calculations at a high frequency,achieving timely correction of the optimal control command.Numerical simulations demonstrate that the method can satisfy the requirements of rapid computation and reliability for the MAV system when considering uncertainties and perturbations.
基金The National Natural Science Foundation of China(62203015,62233001,62273351)The Beijing Natural Science Foundation(4242038)。
文摘This paper considers the swarm vigilance problem for multi-agent systems(MAS),where multiple agents are deployed within a rectangular region for perception-based vigilance.There are two main challenges,namely the task allocation for vigilance roles and the coverage planning of the perception ranges.Firstly,vigilance behavioral patterns and processes in animal populations within natural habitats are investigated.Inspired by these biological vigilance behaviors,an efficient vigilance task allocation model for MAS is proposed.Secondly,the subsequent optimization of task layouts can achieve efficient surveillance coverage with fewer agents,minimizing resource consumption.Thirdly,an improved particle swarm optimization(IPSO)algorithm is proposed,which incorporates fitness-driven adaptive inertia weight dynamics.According to simulation analysis and comparative studies,optimal parameter configurations for genetic algorithm(GA)and IPSO are determined.Finally,the results indicate the proposed IPSO's superior performance to both GA and standard particle swarm optimization(PSO)in vigilance task allocation optimization,with satisfying advantages in computational efficiency and solution quality.
基金funding from the Research Council of Lithuania(LMTLT),agreement No.S-MIP-23-46.
文摘This study introduces a novel voice cloning framework driven by Mordukhovich Subdifferential Optimization(MSO)to address the complex multi-objective challenges of pathological speech synthesis in underresourced Lithuanian language with unique phonemes not present in most pre-trained models.Unlike existing voice synthesis models that often optimize for a single objective or are restricted to major languages,our approach explicitly balances four competing criteria:speech naturalness,speaker similarity,computational efficiency,and adaptability to pathological voice patterns.We evaluate four model configurations combining Lithuanian and English encoders,synthesizers,and vocoders.The hybrid model(English encoder,Lithuanian synthesizer,English vocoder),optimized via MSO,achieved the highest Mean Opinion Score(MOS)of 4.3 and demonstrated superior intelligibility and speaker fidelity.The results confirm that MSO enables effective navigation of trade-offs in multilingual pathological voice cloning,offering a scalable path toward high-quality voice restoration in clinical speech applications.This work represents the first integration of Mordukhovich optimization into pathological TTS,setting a new benchmark for speech synthesis under clinical and linguistic constraints.
基金supported by the National Natural Science Foundation of China under Grant 52325402,52274057,and 52074340the National Key R&D Program of China under Grant 2023YFB4104200+2 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSN111 Project under Grant B08028China Scholarship Council under Grant 202306450108.
文摘This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational machine learning models are applied in reservoir development optimization.Traditional regression-based models often struggle in complex scenarios,but the proposed relational and regression-based composite differential evolution(RRCODE)method combines a Gaussian naive Bayes relational model with a radial basis function network regression model.This integration effectively captures complex relationships in the optimization process,improving both accuracy and convergence speed.Experimental tests on a multi-layer multi-channel reservoir model,the Egg reservoir model,and a real-field reservoir model(the S reservoir)demonstrate that RRCODE significantly reduces water injection and production volumes while increasing economic returns and cumulative oil recovery.Moreover,the surrogate models employed in RRCODE exhibit lightweight characteristics with low computational overhead.These results highlight RRCODE's superior performance in the integrated optimization of reservoir production and layer configurations,offering more efficient and economically viable solutions for oilfield development.
基金supported by the Changzhou Science and Technology Support Project(CE20235045)Open Subject of Jiangsu Province Key Laboratory of Power Transmission and Distribution(2021JSSPD12)+1 种基金Talent Projects of Jiangsu University of Technology(KYY20018)Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1633).
文摘Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy utilization.To tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network edge.This architecture effectively reduces transmission costs by minimizing data travel time.In addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation cost.The IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal solutions.Experimental results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and discharging.It successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy sources.The edge-computing framework significantly reduces transmission delays between energy devices.Furthermore,IWOA outperforms traditional algorithms in optimizing the objective function.
