This paper delves into the H∞optimal output regulation problem for continuous-time linear systems with an unknown system model.By integrating the internal model principle with optimal control,we derive an optimal con...This paper delves into the H∞optimal output regulation problem for continuous-time linear systems with an unknown system model.By integrating the internal model principle with optimal control,we derive an optimal control policy and a worst-case disturbance policy through the formulation and solution of a zero-sum game problem.Subsequently,leveraging adaptive dynamic programming,we propose a policy iteration learning algorithm capable of learning both the optimal control policy and the worst-case disturbance policy directly from system data.The existing algorithms necessitate an initial stabilizing policy,a full-rank condition,and the storage of historical data to guarantee algorithm convergence.In contrast,we design a dual policy iteration algorithm equipped with an online learning mechanism,thereby eliminating these additional prerequisites.Simulation results with an antonomous ground vehicle underscore the effectiveness of our proposed algorithm,and its superiority is further demonstrated through comparisons with existing methodologies.展开更多
Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to en...Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.展开更多
To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the mod...To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the model predictive static programming method is developed by incorporating neighboring terms and trust region,enabling rapid generation of precise optimal solutions.Next,an adaptive fish swarm optimization technique is employed to identify a sub-optimal solution,while a momentum gradient descent method with learning rate decay ensures the convergence to the global optimal solution.To validate the feasibility and accuracy of the proposed method,a near-space vehicle example is analyzed and simulated during its glide phase.The simulation results demonstrate that the proposed method aligns with theoretical derivations and outperforms existing methods in terms of convergence speed and accuracy.Therefore,the proposed method offers significant practical value for solving the fast trajectory optimization problem in near-space vehicle applications.展开更多
Theoretical frameworks for the strategic placement of Road Side Units(RSUs)along highways are currently insufficient.In the context of emerging Vehicleto-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication set...Theoretical frameworks for the strategic placement of Road Side Units(RSUs)along highways are currently insufficient.In the context of emerging Vehicleto-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication settings,the impact of the growing presence of smart vehicles on the existing deployment strategy has been overlooked.The current paper therefore introduces a framework for optimizing RSU placement that accounts for the influence of V2V and V2I interactions.To optimize the advantages provided by RSU,the enhancement of RSU deployment scope is realized by leveraging the relay and forwarding capabilities inherent in V2V communications,which helps identify the most efficient deployment intervals,thereby reducing costs.After ascertaining how the intelligent vehicle's transmission range affects the time taken for flooding and recognizing the influence of packet length on the sensor network's energy usage,a novel bilevel programming framework has been introduced.The upper layer model minimizes the flooding time by setting the optimal intelligent vehicle transmission radius.In contrast the lower layer model,under the influence of the upper layer model,maximizes the energy efficiency of the sensing network by setting the optimal packet length.In addition,the conventional interlinking of information and traffic flow theories is restructured for RSU placement,innovatively modeling the benefits throughout the information lifecycle.Regarding information transmission loss,a node energy loss model is determined based on the bilevel programming framework.For construction and maintenance costs,a cost model under different cluster lengths is constructed.Employing MATLAB,a study is executed to scrutinize the multifaceted interdependencies among the density of highway traffic,the saturation of intelligent vehicles,and the distribution of roadside RSUs,to establish the most advantageous spacing for RSU installations.This research lays the groundwork for the deployment of sensor networks along highways.In conclusion,the model's accuracy is confirmed by employing the Warshall algorithm and clustering routing methodologies.展开更多
[Objectives]To develop an emergency skills training program for primary care nurses based on the ADDIE(Analysis,Design,Development,Implementation,Evaluation)model and to validate its effectiveness within the context o...[Objectives]To develop an emergency skills training program for primary care nurses based on the ADDIE(Analysis,Design,Development,Implementation,Evaluation)model and to validate its effectiveness within the context of a medical consortium.[Methods]A mixed-methods design was employed to develop the training program,incorporating a cross-sectional survey(n=318),semi-structured interviews(n=15),and a Delphi expert consultation(n=15).Besides,a quasi-experimental study design was adopted.A total of 126 primary care nurses from the medical consortium who met the eligibility criteria were enrolled.The six-month training program was implemented,and changes in emergency competence,self-efficacy,core competence,as well as first-aid knowledge and skills were compared before and after the training.[Results]The developed training program was comprised of five modules encompassing 23 core components.The expert authority coefficient(Cr)was 0.877,and the Kendall's coordination coefficient(W)was 0.362(P<0.001).After the intervention,the nurses'total emergency competence score significantly increased from(90.12±14.85)points to(118.67±11.23)points(t=-18.742,P<0.001).Their self-efficacy score rose from(25.31±5.67)points to(29.84±4.12)points(t=-8.912,P<0.001).The total core competence score improved from(198.45±28.34)points to(235.76±22.15)points(t=-13.565,P<0.001).The pass rate for the first-aid theory assessment increased from 45.24%to 89.68%(χ2=55.632,P<0.001),and the pass rate for the operational skills assessment increased from 38.10%to 85.71%(χ2=61.274,P<0.001).[Conclusions]The training program constructed based on the ADDIE model can systematically improve the emergency competence,self-efficacy,and core competencies of primary care nurses,providing an actionable practical model for implementing standardized emergency training within the medical consortium.展开更多
With the widespread application of large language models(LLMs)in natural language processing and code generation,traditional High-Level Language Programming courses are facing unprecedented challenges and opportunitie...With the widespread application of large language models(LLMs)in natural language processing and code generation,traditional High-Level Language Programming courses are facing unprecedented challenges and opportunities.As a core programming language for computer science majors,C language remains irreplaceable due to its foundational nature and engineering adaptability.This paper,based on the rapid development of large model technologies,proposes a systematic reform design for C language teaching,focusing on teaching objectives,content structure,teaching methods,and evaluation systems.The article suggests a teaching framework centered on“human-computer collaborative programming,”integrating prompt training,AI-assisted debugging,and code generation analysis,aiming to enhance students’problem modeling ability,programming expression skills,and AI collaboration literacy.展开更多
This study presented a simulation-based two-stage interval-stochastic programming (STIP) model to support water resources management in the Kaidu-Konqi watershed in Northwest China. The modeling system coupled a dis...This study presented a simulation-based two-stage interval-stochastic programming (STIP) model to support water resources management in the Kaidu-Konqi watershed in Northwest China. The modeling system coupled a distributed hydrological model with an interval two-stage stochastic programing (ITSP). The distributed hydrological model was used for establishing a rainfall-runoff forecast system, while random parameters were pro- vided by the statistical analysis of simulation outcomes water resources management planning in Kaidu-Konqi The developed STIP model was applied to a real case of watershed, where three scenarios with different water re- sources management policies were analyzed. The results indicated that water shortage mainly occurred in agri- culture, ecology and forestry sectors. In comparison, the water demand from municipality, industry and stock- breeding sectors can be satisfied due to their lower consumptions and higher economic values. Different policies for ecological water allocation can result in varied system benefits, and can help to identify desired water allocation plans with a maximum economic benefit and a minimum risk of system disruption under uncertainty.展开更多
The automatic algorithm programming model can increase the dependability and efficiency of algorithm program development,including specification generation,program refinement,and formal verification.However,the existi...The automatic algorithm programming model can increase the dependability and efficiency of algorithm program development,including specification generation,program refinement,and formal verification.However,the existing model has two flaws:incompleteness of program refinement and inadequate automation of formal verification.This paper proposes an automatic algorithm programming model based on the improved Morgan’s refinement calculus.It extends the Morgan’s refinement calculus rules and designs the C++generation system for realizing the complete process of refinement.Meanwhile,the automation tools VCG(Verification Condition Generator)and Isabelle are used to improve the automation of formal verification.An example of a stock’s maximum income demonstrates the effectiveness of the proposed model.Furthermore,the proposed model has some relevance for automatic software generation.展开更多
A weak nonlinear model of a two-layer barotropic ocean with Rayleigh dissipation is built.The analytic asymptotic solution is derived in the mid-latitude stationary wind field,and the physical meaning of the correspon...A weak nonlinear model of a two-layer barotropic ocean with Rayleigh dissipation is built.The analytic asymptotic solution is derived in the mid-latitude stationary wind field,and the physical meaning of the corresponding problem is discussed.展开更多
Considering the decision-making variables of the capacities of branch roads and the optimization targets of lowering the saturation of arterial roads and the reconstruction expense of branch roads, the bi-level progra...Considering the decision-making variables of the capacities of branch roads and the optimization targets of lowering the saturation of arterial roads and the reconstruction expense of branch roads, the bi-level programming model for reconstructing the branch roads was set up. The upper level model was for determining the enlarged capacities of the branch roads, and the lower level model was for calculating the flows of road sections via the user equilibrium traffic assignment method. The genetic algorithm for solving the bi-level model was designed to obtain the reconstruction capacities of the branch roads. The results show that by the bi-level model and its algorithm, the optimum scheme of urban branch roads reconstruction can be gained, which reduces the saturation of arterial roads apparently, and alleviates traffic congestion. In the data analysis the arterial saturation decreases from 1.100 to 0.996, which verifies the micro-circulation transportation's function of urban branch road network.展开更多
The coefficients embodied in a Boussinesq-type model are very important since they are determined to optimize the linear and nonlinear properties.In most conventional Boussinesq-type models,these coefficients are assi...The coefficients embodied in a Boussinesq-type model are very important since they are determined to optimize the linear and nonlinear properties.In most conventional Boussinesq-type models,these coefficients are assigned the specific values.As for the multi-layer Boussinesq-type models with the inclusion of the vertical velocity,however,the effect of the different values of these coefficients on linear and nonlinear performances has never been investigated yet.The present study focuses on a two-layer Boussinesq-type model with the highest spatial derivatives being 2 and theoretically and numerically examines the effect of the coefficient on model performance.Theoretical analysis show that different values for(0.13≤α≤0.25)do not have great effects on the high accuracy of the linear shoaling,linear phase celerity and even third-order nonlinearity for water depth range of 0<kh≤10(k is wave number and h is water depth).The corresponding errors using different values are restricted within 0.1%,0.1%and 1%for the linear shoaling amplitude,dispersion and nonlinear harmonics,respectively.Numerical tests including regular wave shoaling over mildly varying slope from deep to shallow water,regular wave propagation over submerged bar,bichromatic wave group and focusing wave propagation over deep water are conducted.The comparison between numerical results using different values of,experimental data and analytical solutions confirm the theoretical analysis.The flexibility and consistency of the two-layer Boussinesq-type model is therefore demonstrated theoretically and numerically.展开更多
Estimation of the rock mass modulus of deformation(Em)is one of the most important design parameters in designing many structures in and on rock.This parameter can be obtained by in situ tests,empirical relations betw...Estimation of the rock mass modulus of deformation(Em)is one of the most important design parameters in designing many structures in and on rock.This parameter can be obtained by in situ tests,empirical relations between deformation modulus and rock mass classifcation,and estimating from laboratory tests results.In this paper,a back analysis calculation is performed to present an equation for estimation of the rock mass modulus of deformation using genetic programming(GP)and numerical modeling.A database of 40,960 datasets,including vertical stress(rz),horizontal to vertical stresses ratio(k),Poisson’s ratio(m),radius of circular tunnel(r)and wall displacement of circular tunnel on the horizontal diameter(d)for input parameters and modulus of deformation for output,was established.The selected parameters are easy to determine and rock mass modulus of deformation can be obtained from instrumentation data of any size circular galleries.The resulting RMSE of 0.86 and correlation coeffcient of97%of the proposed equation demonstrated the capability of the computer program(CP)generated by GP.展开更多
A novel Parsimonious Genetic Programming (PGP) algorithm together with a novel aero-engine optimum data-driven dynamic start process model based on PGP is proposed. In application of this method, first, the traditio...A novel Parsimonious Genetic Programming (PGP) algorithm together with a novel aero-engine optimum data-driven dynamic start process model based on PGP is proposed. In application of this method, first, the traditional Genetic Programming(GP) is used to generate the nonlinear input-output models that are represented in a binary tree structure; then, the Orthogonal Least Squares algorithm (OLS) is used to estimate the contribution of the branches of the tree (refer to basic function term that cannot be decomposed anymore according to special rule) to the accuracy of the model, which contributes to eliminate complex redundant subtrees and enhance GP's convergence speed; and finally, a simple, reliable and exact linear-in-parameter nonlinear model via GP evolution is obtained. The real aero-engine start process test data simulation and the comparisons with Support Vector Machines (SVM) validate that the proposed method can generate more applicable, interpretable models and achieve comparable, even superior results to SVM.展开更多
In order to balance the temporal-spatial distribution of urban traffic flow, a model is established for combined urban traffic signal control and traffic flow guidance. With consideration of the wide use of fixed sign...In order to balance the temporal-spatial distribution of urban traffic flow, a model is established for combined urban traffic signal control and traffic flow guidance. With consideration of the wide use of fixed signal control at intersections, traffic assignment under traffic flow guidance, and dynamic characteristics of urban traffic management, a tri-level programming model is presented. To reflect the impact of intersection delay on traffic assignment, the lower level model is set as a modified user equilibrium model. The middle level model, which contains several definitional constraints for different phase modes, is built for the traffic signal control optimization. To solve the problem of tide lane management, the upper level model is built up based on nonlinear 0-1 integer programming. A heuristic iterative optimization algorithm(HIOA) is set up to solve the tri-level programming model. The lower level model is solved by method of successive averages(MSA), the middle level model is solved by non-dominated sorting genetic algorithm II(NSGA II), and the upper level model is solved by genetic algorithm(GA). A case study is raised to show the efficiency and applicability of the proposed modelling and computing method.展开更多
The current portfolio model for property-liability insurance company is only single period that can not meet the practical demands of portfolio management, and the purpose of this paper is to develop a multiperiod mod...The current portfolio model for property-liability insurance company is only single period that can not meet the practical demands of portfolio management, and the purpose of this paper is to develop a multiperiod model for its portfolio problem. The model is a multistage stochastic programming which considers transaction costs, cash flow between time periods, and the matching of asset and liability; it does not depend on the assumption for normality of return distribution. Additionally, an investment constraint is added. The numerical example manifests that the multiperiod model can more effectively assist the property-liability insurer to determine the optimal composition of insurance and investment portfolio and outperforms the single period one.展开更多
A knowledge representation has been proposed using the state space theory of Artificial Intelligence for Dynamic Programming Model, in which a model can be defined as a six tuple M=(I,G,O,T,D,S). A building block mode...A knowledge representation has been proposed using the state space theory of Artificial Intelligence for Dynamic Programming Model, in which a model can be defined as a six tuple M=(I,G,O,T,D,S). A building block modeling method uses the modules of a six tuple to form a rule based solution model. Moreover, a rule based system has been designed and set up to solve the Dynamic Programming Model. This knowledge based representation can be easily used to express symbolical knowledge and dynamic characteristics for Dynamic Programming Model, and the inference based on the knowledge in the process of solving Dynamic Programming Model can also be conveniently realized in computer.展开更多
This study introduces FTCSEM,a FORTRAN-based,parallelized one-dimensional controlledsource electromagnetic(CSEM)forward modeling and inversion software capable of accommodating arbitrary source-receiver confi guration...This study introduces FTCSEM,a FORTRAN-based,parallelized one-dimensional controlledsource electromagnetic(CSEM)forward modeling and inversion software capable of accommodating arbitrary source-receiver confi gurations.In comparison to existing one-dimensional CSEM tools,FTCSEM incorporates several signifi cant enhancements:it supports transmitters of diverse shapes,quantities,and spatial locations;permits receivers to be positioned flexibly on the surface,subsurface,or in the atmosphere;facilitates simulations and inversions in both frequency and time domains;integrates an adaptive regularized inversion algorithm with multiple model constraints;and leverages GPU-accelerated parallel computing to attain high computational efficiency.Validation through numerical experiments and field data inversion confirms the program’s accuracy and practical applicability.The findings indicate that FTCSEM performs robustly in complex geoelectric environments,multi-source and multi-receiver arrangements,as well as multi-component joint inversion scenarios,thereby offering a versatile and powerful tool for advancing CSEM research and applications.展开更多
The hybrid neural differentiable models mark a significant advancement in the field of scientific machine learning.These models,integrating numerical representations of known physics into deep neural networks,offer en...The hybrid neural differentiable models mark a significant advancement in the field of scientific machine learning.These models,integrating numerical representations of known physics into deep neural networks,offer enhanced predictive capabilities and show great potential for data-driven modeling of complex physical systems.However,a critical and yet unaddressed challenge lies in the quantification of inherent uncertainties stemming from multiple sources.Addressing this gap,we introduce a novel method,uncertainty quantification for hybrid neural differentiable modeling,for effective and efficient uncertainty propagation and estimation in hybrid neural differentiable models,leveraging the strengths of deep ensemble Bayesian learning and nonlinear transformations.Specifically,our approach effectively discerns and quantifies both aleatoric uncertainties,arising from data noise,and epistemic uncertainties,resulting from model-form discrepancies and data sparsity.This is achieved within a Bayesian model averaging framework,where aleatoric uncertainties are modeled through hybrid neural models.The unscented transformation plays a pivotal role in enabling the flow of these uncertainties through the nonlinear functions within the hybrid model.In contrast,epistemic uncertainties are estimated using an ensemble of stochastic gradient descent trajectories.This approach offers a practical approximation to the posterior distribution of both the network parameters and the physical parameters.Notably,our framework is designed for simplicity in implementation and high scalability,making it suitable for parallel computing environments.The merits of the proposed method have been demonstrated through problems governed by both ordinary and partial differentiable equations.展开更多
This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) ...This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.展开更多
In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite netw...In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite networks.Setting up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the servers.However,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache contents.To address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is proposed.The two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground station.Probabilistic caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of content.The cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached content.In the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing data.Simulation results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.展开更多
基金supported by the National Natural Science Foundation of China(62322305,62495090,62495095)。
摘要This paper delves into the H∞optimal output regulation problem for continuous-time linear systems with an unknown system model.By integrating the internal model principle with optimal control,we derive an optimal control policy and a worst-case disturbance policy through the formulation and solution of a zero-sum game problem.Subsequently,leveraging adaptive dynamic programming,we propose a policy iteration learning algorithm capable of learning both the optimal control policy and the worst-case disturbance policy directly from system data.The existing algorithms necessitate an initial stabilizing policy,a full-rank condition,and the storage of historical data to guarantee algorithm convergence.In contrast,we design a dual policy iteration algorithm equipped with an online learning mechanism,thereby eliminating these additional prerequisites.Simulation results with an antonomous ground vehicle underscore the effectiveness of our proposed algorithm,and its superiority is further demonstrated through comparisons with existing methodologies.
基金supported by the National Natural Science Foundation of China(No.92371206)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.CX2023063).
摘要Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(No.52425212)National Key Research and Development Program of China(No.2021YFA0717100)National Natural Science Foundation of China(Nos.12072270,U2013206,and 52442214).
摘要To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the model predictive static programming method is developed by incorporating neighboring terms and trust region,enabling rapid generation of precise optimal solutions.Next,an adaptive fish swarm optimization technique is employed to identify a sub-optimal solution,while a momentum gradient descent method with learning rate decay ensures the convergence to the global optimal solution.To validate the feasibility and accuracy of the proposed method,a near-space vehicle example is analyzed and simulated during its glide phase.The simulation results demonstrate that the proposed method aligns with theoretical derivations and outperforms existing methods in terms of convergence speed and accuracy.Therefore,the proposed method offers significant practical value for solving the fast trajectory optimization problem in near-space vehicle applications.
基金supported by the Key Science and Technology Projects in Transportation Industry of the Ministry of Transportation(Grant No.2021-ZD2-047)the Shandong Transportation Science and Technology Planning Project(Grant No.2021B49).
摘要Theoretical frameworks for the strategic placement of Road Side Units(RSUs)along highways are currently insufficient.In the context of emerging Vehicleto-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication settings,the impact of the growing presence of smart vehicles on the existing deployment strategy has been overlooked.The current paper therefore introduces a framework for optimizing RSU placement that accounts for the influence of V2V and V2I interactions.To optimize the advantages provided by RSU,the enhancement of RSU deployment scope is realized by leveraging the relay and forwarding capabilities inherent in V2V communications,which helps identify the most efficient deployment intervals,thereby reducing costs.After ascertaining how the intelligent vehicle's transmission range affects the time taken for flooding and recognizing the influence of packet length on the sensor network's energy usage,a novel bilevel programming framework has been introduced.The upper layer model minimizes the flooding time by setting the optimal intelligent vehicle transmission radius.In contrast the lower layer model,under the influence of the upper layer model,maximizes the energy efficiency of the sensing network by setting the optimal packet length.In addition,the conventional interlinking of information and traffic flow theories is restructured for RSU placement,innovatively modeling the benefits throughout the information lifecycle.Regarding information transmission loss,a node energy loss model is determined based on the bilevel programming framework.For construction and maintenance costs,a cost model under different cluster lengths is constructed.Employing MATLAB,a study is executed to scrutinize the multifaceted interdependencies among the density of highway traffic,the saturation of intelligent vehicles,and the distribution of roadside RSUs,to establish the most advantageous spacing for RSU installations.This research lays the groundwork for the deployment of sensor networks along highways.In conclusion,the model's accuracy is confirmed by employing the Warshall algorithm and clustering routing methodologies.
基金Supported by Research Project of the Tiancheng Science-Education-Industry Collaborative Service Platform of Neijiang Health Vocational College in 2024(TCKY-202402).
摘要[Objectives]To develop an emergency skills training program for primary care nurses based on the ADDIE(Analysis,Design,Development,Implementation,Evaluation)model and to validate its effectiveness within the context of a medical consortium.[Methods]A mixed-methods design was employed to develop the training program,incorporating a cross-sectional survey(n=318),semi-structured interviews(n=15),and a Delphi expert consultation(n=15).Besides,a quasi-experimental study design was adopted.A total of 126 primary care nurses from the medical consortium who met the eligibility criteria were enrolled.The six-month training program was implemented,and changes in emergency competence,self-efficacy,core competence,as well as first-aid knowledge and skills were compared before and after the training.[Results]The developed training program was comprised of five modules encompassing 23 core components.The expert authority coefficient(Cr)was 0.877,and the Kendall's coordination coefficient(W)was 0.362(P<0.001).After the intervention,the nurses'total emergency competence score significantly increased from(90.12±14.85)points to(118.67±11.23)points(t=-18.742,P<0.001).Their self-efficacy score rose from(25.31±5.67)points to(29.84±4.12)points(t=-8.912,P<0.001).The total core competence score improved from(198.45±28.34)points to(235.76±22.15)points(t=-13.565,P<0.001).The pass rate for the first-aid theory assessment increased from 45.24%to 89.68%(χ2=55.632,P<0.001),and the pass rate for the operational skills assessment increased from 38.10%to 85.71%(χ2=61.274,P<0.001).[Conclusions]The training program constructed based on the ADDIE model can systematically improve the emergency competence,self-efficacy,and core competencies of primary care nurses,providing an actionable practical model for implementing standardized emergency training within the medical consortium.
基金Education and Teaching Research Project of Beijing University of Technology(ER2024KCB08)。
摘要With the widespread application of large language models(LLMs)in natural language processing and code generation,traditional High-Level Language Programming courses are facing unprecedented challenges and opportunities.As a core programming language for computer science majors,C language remains irreplaceable due to its foundational nature and engineering adaptability.This paper,based on the rapid development of large model technologies,proposes a systematic reform design for C language teaching,focusing on teaching objectives,content structure,teaching methods,and evaluation systems.The article suggests a teaching framework centered on“human-computer collaborative programming,”integrating prompt training,AI-assisted debugging,and code generation analysis,aiming to enhance students’problem modeling ability,programming expression skills,and AI collaboration literacy.
基金supported by the National Basic Research Program of China(2010CB951002)the Dr.Western-funded Project of Chinese Academy of Science(XBBS201010 and XBBS201005)+1 种基金the National Natural Sciences Foundation of China (51190095)the Open Research Fund Program of State Key Laboratory of Hydro-science and Engineering(sklhse-2012-A03)
摘要This study presented a simulation-based two-stage interval-stochastic programming (STIP) model to support water resources management in the Kaidu-Konqi watershed in Northwest China. The modeling system coupled a distributed hydrological model with an interval two-stage stochastic programing (ITSP). The distributed hydrological model was used for establishing a rainfall-runoff forecast system, while random parameters were pro- vided by the statistical analysis of simulation outcomes water resources management planning in Kaidu-Konqi The developed STIP model was applied to a real case of watershed, where three scenarios with different water re- sources management policies were analyzed. The results indicated that water shortage mainly occurred in agri- culture, ecology and forestry sectors. In comparison, the water demand from municipality, industry and stock- breeding sectors can be satisfied due to their lower consumptions and higher economic values. Different policies for ecological water allocation can result in varied system benefits, and can help to identify desired water allocation plans with a maximum economic benefit and a minimum risk of system disruption under uncertainty.
基金Supported by the National Natural Science Foundation of China(61862033,61902162)Key Project of Science and Technology Research of Department of Education of Jiangxi Province(GJJ210307)Postgraduate Innovation Fund Project of Education Department of Jiangxi Province(YC2021-S306)。
摘要The automatic algorithm programming model can increase the dependability and efficiency of algorithm program development,including specification generation,program refinement,and formal verification.However,the existing model has two flaws:incompleteness of program refinement and inadequate automation of formal verification.This paper proposes an automatic algorithm programming model based on the improved Morgan’s refinement calculus.It extends the Morgan’s refinement calculus rules and designs the C++generation system for realizing the complete process of refinement.Meanwhile,the automation tools VCG(Verification Condition Generator)and Isabelle are used to improve the automation of formal verification.An example of a stock’s maximum income demonstrates the effectiveness of the proposed model.Furthermore,the proposed model has some relevance for automatic software generation.
基金Project supported by the National Basic Research Program of China (Grant No. 2011CB403501)the National Natural Science Foundation of China (GrantNos. 41175058,41275062,and 11202106)
摘要A weak nonlinear model of a two-layer barotropic ocean with Rayleigh dissipation is built.The analytic asymptotic solution is derived in the mid-latitude stationary wind field,and the physical meaning of the corresponding problem is discussed.
基金Project(2006CB705507) supported by the National Basic Research and Development Program of ChinaProject(20060533036) supported by the Specialized Research Foundation for the Doctoral Program of Higher Education of China
摘要Considering the decision-making variables of the capacities of branch roads and the optimization targets of lowering the saturation of arterial roads and the reconstruction expense of branch roads, the bi-level programming model for reconstructing the branch roads was set up. The upper level model was for determining the enlarged capacities of the branch roads, and the lower level model was for calculating the flows of road sections via the user equilibrium traffic assignment method. The genetic algorithm for solving the bi-level model was designed to obtain the reconstruction capacities of the branch roads. The results show that by the bi-level model and its algorithm, the optimum scheme of urban branch roads reconstruction can be gained, which reduces the saturation of arterial roads apparently, and alleviates traffic congestion. In the data analysis the arterial saturation decreases from 1.100 to 0.996, which verifies the micro-circulation transportation's function of urban branch road network.
基金supported by the National Natural Science Foundation of China(Grant Nos.51779022,51809053,and 51579034)the Innovation Team Project of Estuary and Coast Protection and Management(Grant No.Y220013)the Open Project Fund of State Key Laboratory of Coastal and Offshore Engineering,Dalian University of Technology(Grant No.LP19015).
摘要The coefficients embodied in a Boussinesq-type model are very important since they are determined to optimize the linear and nonlinear properties.In most conventional Boussinesq-type models,these coefficients are assigned the specific values.As for the multi-layer Boussinesq-type models with the inclusion of the vertical velocity,however,the effect of the different values of these coefficients on linear and nonlinear performances has never been investigated yet.The present study focuses on a two-layer Boussinesq-type model with the highest spatial derivatives being 2 and theoretically and numerically examines the effect of the coefficient on model performance.Theoretical analysis show that different values for(0.13≤α≤0.25)do not have great effects on the high accuracy of the linear shoaling,linear phase celerity and even third-order nonlinearity for water depth range of 0<kh≤10(k is wave number and h is water depth).The corresponding errors using different values are restricted within 0.1%,0.1%and 1%for the linear shoaling amplitude,dispersion and nonlinear harmonics,respectively.Numerical tests including regular wave shoaling over mildly varying slope from deep to shallow water,regular wave propagation over submerged bar,bichromatic wave group and focusing wave propagation over deep water are conducted.The comparison between numerical results using different values of,experimental data and analytical solutions confirm the theoretical analysis.The flexibility and consistency of the two-layer Boussinesq-type model is therefore demonstrated theoretically and numerically.
摘要Estimation of the rock mass modulus of deformation(Em)is one of the most important design parameters in designing many structures in and on rock.This parameter can be obtained by in situ tests,empirical relations between deformation modulus and rock mass classifcation,and estimating from laboratory tests results.In this paper,a back analysis calculation is performed to present an equation for estimation of the rock mass modulus of deformation using genetic programming(GP)and numerical modeling.A database of 40,960 datasets,including vertical stress(rz),horizontal to vertical stresses ratio(k),Poisson’s ratio(m),radius of circular tunnel(r)and wall displacement of circular tunnel on the horizontal diameter(d)for input parameters and modulus of deformation for output,was established.The selected parameters are easy to determine and rock mass modulus of deformation can be obtained from instrumentation data of any size circular galleries.The resulting RMSE of 0.86 and correlation coeffcient of97%of the proposed equation demonstrated the capability of the computer program(CP)generated by GP.
基金National Defense Advanced Research Foundation of China
摘要A novel Parsimonious Genetic Programming (PGP) algorithm together with a novel aero-engine optimum data-driven dynamic start process model based on PGP is proposed. In application of this method, first, the traditional Genetic Programming(GP) is used to generate the nonlinear input-output models that are represented in a binary tree structure; then, the Orthogonal Least Squares algorithm (OLS) is used to estimate the contribution of the branches of the tree (refer to basic function term that cannot be decomposed anymore according to special rule) to the accuracy of the model, which contributes to eliminate complex redundant subtrees and enhance GP's convergence speed; and finally, a simple, reliable and exact linear-in-parameter nonlinear model via GP evolution is obtained. The real aero-engine start process test data simulation and the comparisons with Support Vector Machines (SVM) validate that the proposed method can generate more applicable, interpretable models and achieve comparable, even superior results to SVM.
基金Project(2014BAG01B0403)supported by the High-Tech Research and Development Program of China
摘要In order to balance the temporal-spatial distribution of urban traffic flow, a model is established for combined urban traffic signal control and traffic flow guidance. With consideration of the wide use of fixed signal control at intersections, traffic assignment under traffic flow guidance, and dynamic characteristics of urban traffic management, a tri-level programming model is presented. To reflect the impact of intersection delay on traffic assignment, the lower level model is set as a modified user equilibrium model. The middle level model, which contains several definitional constraints for different phase modes, is built for the traffic signal control optimization. To solve the problem of tide lane management, the upper level model is built up based on nonlinear 0-1 integer programming. A heuristic iterative optimization algorithm(HIOA) is set up to solve the tri-level programming model. The lower level model is solved by method of successive averages(MSA), the middle level model is solved by non-dominated sorting genetic algorithm II(NSGA II), and the upper level model is solved by genetic algorithm(GA). A case study is raised to show the efficiency and applicability of the proposed modelling and computing method.
摘要The current portfolio model for property-liability insurance company is only single period that can not meet the practical demands of portfolio management, and the purpose of this paper is to develop a multiperiod model for its portfolio problem. The model is a multistage stochastic programming which considers transaction costs, cash flow between time periods, and the matching of asset and liability; it does not depend on the assumption for normality of return distribution. Additionally, an investment constraint is added. The numerical example manifests that the multiperiod model can more effectively assist the property-liability insurer to determine the optimal composition of insurance and investment portfolio and outperforms the single period one.
摘要A knowledge representation has been proposed using the state space theory of Artificial Intelligence for Dynamic Programming Model, in which a model can be defined as a six tuple M=(I,G,O,T,D,S). A building block modeling method uses the modules of a six tuple to form a rule based solution model. Moreover, a rule based system has been designed and set up to solve the Dynamic Programming Model. This knowledge based representation can be easily used to express symbolical knowledge and dynamic characteristics for Dynamic Programming Model, and the inference based on the knowledge in the process of solving Dynamic Programming Model can also be conveniently realized in computer.
基金funded by the National Natural Science Foundation of China(42274192 and 42030106)Youth Innovation Promotion Association CAS(2023070).
摘要This study introduces FTCSEM,a FORTRAN-based,parallelized one-dimensional controlledsource electromagnetic(CSEM)forward modeling and inversion software capable of accommodating arbitrary source-receiver confi gurations.In comparison to existing one-dimensional CSEM tools,FTCSEM incorporates several signifi cant enhancements:it supports transmitters of diverse shapes,quantities,and spatial locations;permits receivers to be positioned flexibly on the surface,subsurface,or in the atmosphere;facilitates simulations and inversions in both frequency and time domains;integrates an adaptive regularized inversion algorithm with multiple model constraints;and leverages GPU-accelerated parallel computing to attain high computational efficiency.Validation through numerical experiments and field data inversion confirms the program’s accuracy and practical applicability.The findings indicate that FTCSEM performs robustly in complex geoelectric environments,multi-source and multi-receiver arrangements,as well as multi-component joint inversion scenarios,thereby offering a versatile and powerful tool for advancing CSEM research and applications.
基金supported by the Air Force Office of Scientific Research(AFOSR),United States of America(Grant No.FA9550-22-10065)the funding support from the Office of Naval Research(Grant No.N00014-23-1-2071)the National Science Foundation(Grant No.OAC-2047127)。
摘要The hybrid neural differentiable models mark a significant advancement in the field of scientific machine learning.These models,integrating numerical representations of known physics into deep neural networks,offer enhanced predictive capabilities and show great potential for data-driven modeling of complex physical systems.However,a critical and yet unaddressed challenge lies in the quantification of inherent uncertainties stemming from multiple sources.Addressing this gap,we introduce a novel method,uncertainty quantification for hybrid neural differentiable modeling,for effective and efficient uncertainty propagation and estimation in hybrid neural differentiable models,leveraging the strengths of deep ensemble Bayesian learning and nonlinear transformations.Specifically,our approach effectively discerns and quantifies both aleatoric uncertainties,arising from data noise,and epistemic uncertainties,resulting from model-form discrepancies and data sparsity.This is achieved within a Bayesian model averaging framework,where aleatoric uncertainties are modeled through hybrid neural models.The unscented transformation plays a pivotal role in enabling the flow of these uncertainties through the nonlinear functions within the hybrid model.In contrast,epistemic uncertainties are estimated using an ensemble of stochastic gradient descent trajectories.This approach offers a practical approximation to the posterior distribution of both the network parameters and the physical parameters.Notably,our framework is designed for simplicity in implementation and high scalability,making it suitable for parallel computing environments.The merits of the proposed method have been demonstrated through problems governed by both ordinary and partial differentiable equations.
基金Project (Nos. 60174009 and 70071017) supported by the National
Natural Science Foundation of China
摘要This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.
基金This research was funded by the National Natural Science Foundation of China(No.U21A20451)the Science and Technology Planning Project of Jilin Province(No.20200401105GX)the China University Industry University Research Innovation Fund(No.2021FNA01003).
摘要In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite networks.Setting up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the servers.However,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache contents.To address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is proposed.The two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground station.Probabilistic caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of content.The cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached content.In the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing data.Simulation results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.