In this paper,a linear optimization method(LOM)for the design of terahertz circuits is presented,aimed at enhancing the simulation efficacy and reducing the time of the circuit design workflow.This method enables the ...In this paper,a linear optimization method(LOM)for the design of terahertz circuits is presented,aimed at enhancing the simulation efficacy and reducing the time of the circuit design workflow.This method enables the rapid determination of optimal embedding impedance for diodes across a specific bandwidth to achieve maximum efficiency through harmonic balance simulations.By optimizing the linear matching circuit with the optimal embedding impedance,the method effectively segregates the simulation of the linear segments from the nonlinear segments in the frequency multiplier circuit,substantially improving the speed of simulations.The design of on-chip linear matching circuits adopts a modular circuit design strategy,incorporating fixed load resistors to simplify the matching challenge.Utilizing this approach,a 340 GHz frequency doubler was developed and measured.The results demonstrate that,across a bandwidth of 330 GHz to 342 GHz,the efficiency of the doubler remains above 10%,with an input power ranging from 98 mW to 141mW and an output power exceeding 13 mW.Notably,at an input power of 141 mW,a peak output power of 21.8 mW was achieved at 334 GHz,corresponding to an efficiency of 15.8%.展开更多
The frequency regulation reserve setting of wind-PV-storage power stations is crucial.However,the existing grid codes set up the station reserve in a static manner,where the synchronous generator characteristics and f...The frequency regulation reserve setting of wind-PV-storage power stations is crucial.However,the existing grid codes set up the station reserve in a static manner,where the synchronous generator characteristics and frequency-step disturbance scenario are considered.Thus,the advantages of flexible regulation of renewable generations are wasted,resulting in excessive curtailment of wind and solar resources.In this study,a method for optimizing the frequency regulation reserve of wind PV storage power stations was developed.Moreover,a station frequency regulation model was constructed,considering the field dynamic response and the coupling between the station and system frequency dynamics.Furthermore,a method for the online evaluation of the station frequency regulation was proposed based on the benchmark governor fitting.This method helps in overcoming the capacity-based reserve static setting.Finally,an optimization model was developed,along with the proposal of the linearized solving algorithm.The field data from the JH4#station in China’s MX power grid was considered for validation.The proposed method achieves a 24.77%increase in the station income while ensuring the system frequency stability when compared with the grid code-based method.展开更多
This paper presents an innovative and effective control strategy tailored for a deregulated,diversified energy system involving multiple interconnected area.Each area integrates a unique mix of power generation techno...This paper presents an innovative and effective control strategy tailored for a deregulated,diversified energy system involving multiple interconnected area.Each area integrates a unique mix of power generation technologies:Area 1 combines thermal,hydro,and distributed generation;Area 2 utilizes a blend of thermal units,distributed solar technologies(DST),and hydro power;andThird control area hosts geothermal power station alongside thermal power generation unit and hydropower units.The suggested control system employs a multi-layered approach,featuring a blended methodology utilizing the Tilted Integral Derivative controller(TID)and the Fractional-Order Integral method to enhance performance and stability.The parameters of this hybrid TID-FOI controller are finely tuned using an advanced optimization method known as the Walrus Optimization Algorithm(WaOA).Performance analysis reveals that the combined TID-FOI controller significantly outperforms the TID and PID controllers when comparing their dynamic response across various system configurations.The study also incorporates investigation of redox flow batteries within the broader scope of energy storage applications to assess their impact on system performance.In addition,the research explores the controller’s effectiveness under different power exchange scenarios in a deregulated market,accounting for restrictions on generation ramp rates and governor hysteresis effects in dynamic control.To ensure the reliability and resilience of the presented methodology,the system transitions and develops across a broad range of varying parameters and stochastic load fluctuation.To wrap up,the study offers a pioneering control approach-a hybrid TID-FOI controller optimized via the Walrus Optimization Algorithm(WaOA)-designed for enhanced stability and performance in a complex,three-region hybrid energy system functioning within a deregulated framework.展开更多
Optimizing frequency configurations for qubits and gates in superconducting quantum chips presents a complex NP-complete challenge,critical for mitigating decoherence and crosstalk.This paper introduces a neural netwo...Optimizing frequency configurations for qubits and gates in superconducting quantum chips presents a complex NP-complete challenge,critical for mitigating decoherence and crosstalk.This paper introduces a neural network-based approach,leveraging the network as a surrogate model to predict frequency errors.The method employs a closed-loop Bayesian optimization framework to iteratively refine configurations,guided by the network’s knowledge of nonlinear error mechanisms.By focusing on localized chip windows,the optimization identifies optimal frequency settings that minimize errors.The approach is validated through randomized and cross-entropy benchmarking,showing improved energy calculations when optimizing frequency configurations for a crosstalkaware hardware-efficient ansatz in variational quantum eigensolvers on superconducting quantum chips.展开更多
Vibration detection using sensors with both wide working frequency range,good sensitivity,and other good performances is a topic of great interest in fields such as inertial navigation,deep-sea fishing boat engines co...Vibration detection using sensors with both wide working frequency range,good sensitivity,and other good performances is a topic of great interest in fields such as inertial navigation,deep-sea fishing boat engines condition monitoring,seismic monitoring,attitude,and heading reference system,etc.This paper investigates two 6H-SIC MEMS diaphragms,one triangular and the other square,used in a fiber optic Fabry–Perot(FP)accelerometer in an experimental scenario.The triangular chip shows a wide working frequency range of 630 Hz–5300 Hz,a natural frequency of 44.3 k Hz,and a mechanical sensitivity of 0.154 nm/g.An optimal structure of the square chip used in a probe such as a fiber optic FP accelerometer also shows a wide working frequency range of 120 Hz–2300 Hz;a good sensitivity of 31.5 m V/g,a resonance frequency of7873 Hz,an accuracy of 0.96%F.S.,a frequency measurement error of 1.15%,and an excellent linearity of 0.9995.展开更多
BACKGROUND Current drugs primarily target inflammation control but do not reverse tissue remodeling changes for asthma.Human mesenchymal stem cells are known for their anti-inflammatory and tissue remodeling capabilit...BACKGROUND Current drugs primarily target inflammation control but do not reverse tissue remodeling changes for asthma.Human mesenchymal stem cells are known for their anti-inflammatory and tissue remodeling capabilities.However,limited research has explored the therapeutic impact of varying doses and frequencies of human umbilical cord blood-derived mesenchymal stem cells(HUC-MSCs)on established airway remodeling in experimental asthma.AIM To explore and optimize the dosage and administration frequency of HUC-MSCs in experimental models of ovalbumin(OVA)-induced asthma.METHODS BALB/c mice underwent sensitization and were challenged using OVA.Control animals were administered a saline solution following the same protocol.HUC-MSCs were identified using flow cytometry.HUC-MSCs at incremental dosages(1×105,2×105,4×105)were injected via tail veins on day 30(the second after the final stimulation).After comparing each group and determining the optimal dose,supplement the optimal dose twice on day 30 and day 33(the second and fifth day after the final stimulation).Bronchoalveolar lavage fluid(BALF)and serum were harvested for analysis of concentrations of interleukin-4(IL-4),IL-13,immunoglobulin E and interferon-gamma(IFN-γ)by enzyme-linked immunosorbent assay.Pharmacology of airways and lung functions were also evaluated to identify the optimal group.RESULTS The study shows that HUC-MSC transplantation ameliorates OVA-induced asthma by significantly reducing airway inflammation and obstruction in preclinical models.This effect is associated with decreased Th2 cytokines IL-4 and IL-13,and increased Th1 cytokine IFN-γ.The optimal dose of 2×105 cells/mouse was identified as the most effective in reducing local asthmatic airway inflammation and changing levels of IL-4,IL-13,and IFN-γin serum and BALF compared to other single doses of HUC-MSC.Multiple treatments with the medium dose(2×105 cells)of HUC-MSCs on days 30 and 33 yield the best pathological and lung function outcomes.However,double treatments do not reduce IL-4 and IL-13 expression or enhance IFN-γproduction in serum or BALF more effectively than a single medium dose.CONCLUSION HUC-MSCs effectively regulate pro-inflammatory mediators in serum and BALF,modulating airway remodeling and lung function.In this acute mouse asthma model,a single dosage of 2×105 is optimal,with more significant effects of decreasing airway obstruction requiring repeated administration.展开更多
This study develops a GWO-optimized cascaded fuzzy-PID controller with triangular membership functions for load frequency control in interconnected power systems.The controller’s effectiveness is demonstrated on ther...This study develops a GWO-optimized cascaded fuzzy-PID controller with triangular membership functions for load frequency control in interconnected power systems.The controller’s effectiveness is demonstrated on thermal–thermal and hybrid thermal–hydro–gas power systems.The controller parameters were tuned using the Integral Time Absolute Error(ITAE)objective function,which was also evaluated alongside other objective functions(IAE,ISE,and ITSE)to ensure high precision in frequency stabilization.To validate the effectiveness of the triangular membership function,comparisons were made with fuzzy-PID controllers employing trapezoidal and Gaussian membership functions.Performance metrics,including ITAE,settling time,overshoot,and undershoot of frequency deviation,as well as tie-line power deviation,were evaluated.Robustness was established through a comprehensive sensitivity analysis with T_(G),T_(T),andT_(R) parameter variations(±50%),a non-linearity analysis incorporating Generation Rate Constraint(GRC)and Governor Deadband(GDB),a random Step Load Perturbation(SLP)over 0–100 s,and also Stability analysis of the proposed scheme is conducted using multiple approaches,including frequency-domain analysis,Lyapunov stability theory,and eigenvalue analysis.Additionally,the system incorporating thermal,hydro,and gas turbines,along with advanced components like CES and HVDC links,was analysed.Comparisons were conducted against controllers optimized using Modified Grasshopper Optimization Algorithm(MGOA),Honey Badger Algorithm(HBA),Particle Swarm Optimization(PSO),Artificial Bee Colony(ABC),and Spider Monkey Optimization(SMO)algorithms.Results demonstrate that the GWO-based fuzzy-PID controller outperforms the alternatives,exhibiting superior performance across all evaluated metrics.This highlights the potential of the proposed approach as a robust solution for load frequency control in complex and dynamic power systems.展开更多
In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the op...In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.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
Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced...Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced by the injector critically influences the performance of the entire accelerator-based scientific research apparatus.The injectors of such facilities usually use photocathode and thermionic-cathode electron guns.Although the photocathode injector can produce electron beams of excellent quality,its associated laser system is massive and intricate.The thermionic-cathode electron gun,especially the gridded electron gun injector,has a simple structure capable of generating numerous electron beams.However,its emittance is typically high.In this study,methods to reduce beam emittance are explored through a comprehensive analysis of various grid structures and preliminary design results,examining the evolution of beam phase space at different grid positions.An optimization method for reducing the emittance of a gridded thermionic-cathode electron gun is proposed through theoretical derivation,electromagnetic-field simulation,and beam-dynamics simulation.A 50%reduction in emittance was achieved for a 50 keV,1.7 A electron gun,laying the foundation for the subsequent design of a high-current,low-emittance injector.展开更多
To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobje...To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobjective optimization.First,a dual-convolution enhanced improved Crossformer prediction model is constructed,which employs parallel 1×1 global and 3×3 local convolutionmodules(Integrated Convolution Block,ICB)formultiscale feature extraction,combinedwith anAdaptive Spectral Block(ASB)to enhance time-series fluctuationmodeling.Based on high-precision predictions,a carbon-electricity cost joint optimization model is further designed to balance economic,environmental,and grid-friendly objectives.The model’s superiority was validated through a case study using real-world data from a renewable-heavy grid.Simulation results show that the proposed multi-objective strategy demonstrated a superior balance compared to baseline and benchmark models,achieving a 15.8%reduction in carbon emissions and a 5.2%reduction in economic costs,while still providing a substantial 22.2%reduction in the peak-valley difference.Its balanced performance significantly outperformed both a single-objective strategy and a state-of-the-art Model Predictive Control(MPC)benchmark,highlighting the advantage of a global optimization approach.This study provides theoretical and technical pathways for dynamic carbon factor-driven EV charging optimization.展开更多
Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame...Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.展开更多
As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays...As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.展开更多
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
Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural ...Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.展开更多
Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when ta...Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems.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
Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method f...Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.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
The 193 nm deep-ultraviolet(DUV)laser plays a critical role in advanced semiconductor chip manufacturing[1,2],micro-nano material characterization[3,4]and biomedical analysis[5,6],due to its high spatial resolution an...The 193 nm deep-ultraviolet(DUV)laser plays a critical role in advanced semiconductor chip manufacturing[1,2],micro-nano material characterization[3,4]and biomedical analysis[5,6],due to its high spatial resolution and short wavelength.Efficient and compact 193 nm DUV laser source thus becomes a hot research area.Currently,193 nm Ar F excimer gas laser is widely employed in DUV lithography systems and serves as the enabling technology for 7 and 5 nm semiconductor fabrication.展开更多
基金Supported by the Beijing Municipal Science&Technology Commission(Z211100004421012),the Key Reaserch and Development Pro⁃gram of China(2022YFF0605902)。
文摘In this paper,a linear optimization method(LOM)for the design of terahertz circuits is presented,aimed at enhancing the simulation efficacy and reducing the time of the circuit design workflow.This method enables the rapid determination of optimal embedding impedance for diodes across a specific bandwidth to achieve maximum efficiency through harmonic balance simulations.By optimizing the linear matching circuit with the optimal embedding impedance,the method effectively segregates the simulation of the linear segments from the nonlinear segments in the frequency multiplier circuit,substantially improving the speed of simulations.The design of on-chip linear matching circuits adopts a modular circuit design strategy,incorporating fixed load resistors to simplify the matching challenge.Utilizing this approach,a 340 GHz frequency doubler was developed and measured.The results demonstrate that,across a bandwidth of 330 GHz to 342 GHz,the efficiency of the doubler remains above 10%,with an input power ranging from 98 mW to 141mW and an output power exceeding 13 mW.Notably,at an input power of 141 mW,a peak output power of 21.8 mW was achieved at 334 GHz,corresponding to an efficiency of 15.8%.
基金supported by the Scientific Research Project of China Three Gorges Group Co.LTD(Contract Number:202103368).
文摘The frequency regulation reserve setting of wind-PV-storage power stations is crucial.However,the existing grid codes set up the station reserve in a static manner,where the synchronous generator characteristics and frequency-step disturbance scenario are considered.Thus,the advantages of flexible regulation of renewable generations are wasted,resulting in excessive curtailment of wind and solar resources.In this study,a method for optimizing the frequency regulation reserve of wind PV storage power stations was developed.Moreover,a station frequency regulation model was constructed,considering the field dynamic response and the coupling between the station and system frequency dynamics.Furthermore,a method for the online evaluation of the station frequency regulation was proposed based on the benchmark governor fitting.This method helps in overcoming the capacity-based reserve static setting.Finally,an optimization model was developed,along with the proposal of the linearized solving algorithm.The field data from the JH4#station in China’s MX power grid was considered for validation.The proposed method achieves a 24.77%increase in the station income while ensuring the system frequency stability when compared with the grid code-based method.
文摘This paper presents an innovative and effective control strategy tailored for a deregulated,diversified energy system involving multiple interconnected area.Each area integrates a unique mix of power generation technologies:Area 1 combines thermal,hydro,and distributed generation;Area 2 utilizes a blend of thermal units,distributed solar technologies(DST),and hydro power;andThird control area hosts geothermal power station alongside thermal power generation unit and hydropower units.The suggested control system employs a multi-layered approach,featuring a blended methodology utilizing the Tilted Integral Derivative controller(TID)and the Fractional-Order Integral method to enhance performance and stability.The parameters of this hybrid TID-FOI controller are finely tuned using an advanced optimization method known as the Walrus Optimization Algorithm(WaOA).Performance analysis reveals that the combined TID-FOI controller significantly outperforms the TID and PID controllers when comparing their dynamic response across various system configurations.The study also incorporates investigation of redox flow batteries within the broader scope of energy storage applications to assess their impact on system performance.In addition,the research explores the controller’s effectiveness under different power exchange scenarios in a deregulated market,accounting for restrictions on generation ramp rates and governor hysteresis effects in dynamic control.To ensure the reliability and resilience of the presented methodology,the system transitions and develops across a broad range of varying parameters and stochastic load fluctuation.To wrap up,the study offers a pioneering control approach-a hybrid TID-FOI controller optimized via the Walrus Optimization Algorithm(WaOA)-designed for enhanced stability and performance in a complex,three-region hybrid energy system functioning within a deregulated framework.
基金supported by the National Key Research and Development Program of China (Grant No. 2023YFB4502500)。
文摘Optimizing frequency configurations for qubits and gates in superconducting quantum chips presents a complex NP-complete challenge,critical for mitigating decoherence and crosstalk.This paper introduces a neural network-based approach,leveraging the network as a surrogate model to predict frequency errors.The method employs a closed-loop Bayesian optimization framework to iteratively refine configurations,guided by the network’s knowledge of nonlinear error mechanisms.By focusing on localized chip windows,the optimization identifies optimal frequency settings that minimize errors.The approach is validated through randomized and cross-entropy benchmarking,showing improved energy calculations when optimizing frequency configurations for a crosstalkaware hardware-efficient ansatz in variational quantum eigensolvers on superconducting quantum chips.
基金Project supported by the National Natural Science Foundation of China(Grant No.32473216)Ningbo Youth Science and Technology Innovation Leading Talent Project(Grant No.2023QL004)。
文摘Vibration detection using sensors with both wide working frequency range,good sensitivity,and other good performances is a topic of great interest in fields such as inertial navigation,deep-sea fishing boat engines condition monitoring,seismic monitoring,attitude,and heading reference system,etc.This paper investigates two 6H-SIC MEMS diaphragms,one triangular and the other square,used in a fiber optic Fabry–Perot(FP)accelerometer in an experimental scenario.The triangular chip shows a wide working frequency range of 630 Hz–5300 Hz,a natural frequency of 44.3 k Hz,and a mechanical sensitivity of 0.154 nm/g.An optimal structure of the square chip used in a probe such as a fiber optic FP accelerometer also shows a wide working frequency range of 120 Hz–2300 Hz;a good sensitivity of 31.5 m V/g,a resonance frequency of7873 Hz,an accuracy of 0.96%F.S.,a frequency measurement error of 1.15%,and an excellent linearity of 0.9995.
基金Supported by the Joint Innovation Project Funds of Huaqiao University,No.2022YX001。
文摘BACKGROUND Current drugs primarily target inflammation control but do not reverse tissue remodeling changes for asthma.Human mesenchymal stem cells are known for their anti-inflammatory and tissue remodeling capabilities.However,limited research has explored the therapeutic impact of varying doses and frequencies of human umbilical cord blood-derived mesenchymal stem cells(HUC-MSCs)on established airway remodeling in experimental asthma.AIM To explore and optimize the dosage and administration frequency of HUC-MSCs in experimental models of ovalbumin(OVA)-induced asthma.METHODS BALB/c mice underwent sensitization and were challenged using OVA.Control animals were administered a saline solution following the same protocol.HUC-MSCs were identified using flow cytometry.HUC-MSCs at incremental dosages(1×105,2×105,4×105)were injected via tail veins on day 30(the second after the final stimulation).After comparing each group and determining the optimal dose,supplement the optimal dose twice on day 30 and day 33(the second and fifth day after the final stimulation).Bronchoalveolar lavage fluid(BALF)and serum were harvested for analysis of concentrations of interleukin-4(IL-4),IL-13,immunoglobulin E and interferon-gamma(IFN-γ)by enzyme-linked immunosorbent assay.Pharmacology of airways and lung functions were also evaluated to identify the optimal group.RESULTS The study shows that HUC-MSC transplantation ameliorates OVA-induced asthma by significantly reducing airway inflammation and obstruction in preclinical models.This effect is associated with decreased Th2 cytokines IL-4 and IL-13,and increased Th1 cytokine IFN-γ.The optimal dose of 2×105 cells/mouse was identified as the most effective in reducing local asthmatic airway inflammation and changing levels of IL-4,IL-13,and IFN-γin serum and BALF compared to other single doses of HUC-MSC.Multiple treatments with the medium dose(2×105 cells)of HUC-MSCs on days 30 and 33 yield the best pathological and lung function outcomes.However,double treatments do not reduce IL-4 and IL-13 expression or enhance IFN-γproduction in serum or BALF more effectively than a single medium dose.CONCLUSION HUC-MSCs effectively regulate pro-inflammatory mediators in serum and BALF,modulating airway remodeling and lung function.In this acute mouse asthma model,a single dosage of 2×105 is optimal,with more significant effects of decreasing airway obstruction requiring repeated administration.
文摘This study develops a GWO-optimized cascaded fuzzy-PID controller with triangular membership functions for load frequency control in interconnected power systems.The controller’s effectiveness is demonstrated on thermal–thermal and hybrid thermal–hydro–gas power systems.The controller parameters were tuned using the Integral Time Absolute Error(ITAE)objective function,which was also evaluated alongside other objective functions(IAE,ISE,and ITSE)to ensure high precision in frequency stabilization.To validate the effectiveness of the triangular membership function,comparisons were made with fuzzy-PID controllers employing trapezoidal and Gaussian membership functions.Performance metrics,including ITAE,settling time,overshoot,and undershoot of frequency deviation,as well as tie-line power deviation,were evaluated.Robustness was established through a comprehensive sensitivity analysis with T_(G),T_(T),andT_(R) parameter variations(±50%),a non-linearity analysis incorporating Generation Rate Constraint(GRC)and Governor Deadband(GDB),a random Step Load Perturbation(SLP)over 0–100 s,and also Stability analysis of the proposed scheme is conducted using multiple approaches,including frequency-domain analysis,Lyapunov stability theory,and eigenvalue analysis.Additionally,the system incorporating thermal,hydro,and gas turbines,along with advanced components like CES and HVDC links,was analysed.Comparisons were conducted against controllers optimized using Modified Grasshopper Optimization Algorithm(MGOA),Honey Badger Algorithm(HBA),Particle Swarm Optimization(PSO),Artificial Bee Colony(ABC),and Spider Monkey Optimization(SMO)algorithms.Results demonstrate that the GWO-based fuzzy-PID controller outperforms the alternatives,exhibiting superior performance across all evaluated metrics.This highlights the potential of the proposed approach as a robust solution for load frequency control in complex and dynamic power systems.
基金Supported by the National Natural Science Foundation of China(12071133)Natural Science Foundation of Henan Province(252300421993)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110005)。
文摘In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.
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.
基金supported by the Hundred-person Program of Chinese Academy of Sciences and the National Natural Science Foundation of China(No.11905074).
文摘Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced by the injector critically influences the performance of the entire accelerator-based scientific research apparatus.The injectors of such facilities usually use photocathode and thermionic-cathode electron guns.Although the photocathode injector can produce electron beams of excellent quality,its associated laser system is massive and intricate.The thermionic-cathode electron gun,especially the gridded electron gun injector,has a simple structure capable of generating numerous electron beams.However,its emittance is typically high.In this study,methods to reduce beam emittance are explored through a comprehensive analysis of various grid structures and preliminary design results,examining the evolution of beam phase space at different grid positions.An optimization method for reducing the emittance of a gridded thermionic-cathode electron gun is proposed through theoretical derivation,electromagnetic-field simulation,and beam-dynamics simulation.A 50%reduction in emittance was achieved for a 50 keV,1.7 A electron gun,laying the foundation for the subsequent design of a high-current,low-emittance injector.
基金Supported by State Grid Corporation of China Science and Technology Project:Research on Key Technologies for Intelligent Carbon Metrology in Vehicle-to-Grid Interaction(Project Number:B3018524000Q).
文摘To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobjective optimization.First,a dual-convolution enhanced improved Crossformer prediction model is constructed,which employs parallel 1×1 global and 3×3 local convolutionmodules(Integrated Convolution Block,ICB)formultiscale feature extraction,combinedwith anAdaptive Spectral Block(ASB)to enhance time-series fluctuationmodeling.Based on high-precision predictions,a carbon-electricity cost joint optimization model is further designed to balance economic,environmental,and grid-friendly objectives.The model’s superiority was validated through a case study using real-world data from a renewable-heavy grid.Simulation results show that the proposed multi-objective strategy demonstrated a superior balance compared to baseline and benchmark models,achieving a 15.8%reduction in carbon emissions and a 5.2%reduction in economic costs,while still providing a substantial 22.2%reduction in the peak-valley difference.Its balanced performance significantly outperformed both a single-objective strategy and a state-of-the-art Model Predictive Control(MPC)benchmark,highlighting the advantage of a global optimization approach.This study provides theoretical and technical pathways for dynamic carbon factor-driven EV charging optimization.
基金supported by the National Natural Science Foundation of China(Grant No.72161034).
文摘Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.
基金supported by Youth Talent Project of Scientific Research Program of Hubei Provincial Department of Education under Grant Q20241809Doctoral Scientific Research Foundation of Hubei University of Automotive Technology under Grant 202404.
文摘As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.
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supported by the National Key Research and Development Program of China(2023YFF0906502)the Postgraduate Research and Innovation Project of Hunan Province under Grant(CX20240473).
文摘Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.
基金funded by National Natural Science Foundation of China(Nos.12402142,11832013 and 11572134)Natural Science Foundation of Hubei Province(No.2024AFB235)+1 种基金Hubei Provincial Department of Education Science and Technology Research Project(No.Q20221714)the Opening Foundation of Hubei Key Laboratory of Digital Textile Equipment(Nos.DTL2023019 and DTL2022012).
文摘Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems.
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.
基金National Natural Science Foundation of China,No.42301470,No.52270185,No.42171389Capacity Building Program of Local Colleges and Universities in Shanghai,No.21010503300。
文摘Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.
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 National Natural Science Foundation of China(Grant Nos.62450006,62304217,62274157,62127807,62234011,62034008,62074142,62074140)Tianshan Innovation Team Program(Grant No.2022TSYCTD0005)+1 种基金Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0880000)Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant Nos.2023124,Y2023032)。
文摘The 193 nm deep-ultraviolet(DUV)laser plays a critical role in advanced semiconductor chip manufacturing[1,2],micro-nano material characterization[3,4]and biomedical analysis[5,6],due to its high spatial resolution and short wavelength.Efficient and compact 193 nm DUV laser source thus becomes a hot research area.Currently,193 nm Ar F excimer gas laser is widely employed in DUV lithography systems and serves as the enabling technology for 7 and 5 nm semiconductor fabrication.
