The aim of this paper is to investigate a regional constrained optimal control problem for a class of semi[inear distributed systems, which are linear in the control but nonlinear in the state. For a quadratic cost fu...The aim of this paper is to investigate a regional constrained optimal control problem for a class of semi[inear distributed systems, which are linear in the control but nonlinear in the state. For a quadratic cost functional and a closed convex set of admissible controls, the existence of an optimal control is proven, and then this is characterized for three cases of constraints. A useful algorithm is developed, and the approach is illustrated through simulations for a heat equation.展开更多
The optimized strategy made a comprehensive consideration of resources, technology, market orientation, production scale, industry basis and layout based on the principle of crop security and farmers’ income increasi...The optimized strategy made a comprehensive consideration of resources, technology, market orientation, production scale, industry basis and layout based on the principle of crop security and farmers’ income increasing, and determined the general planning on layout and structure optimization of future crop production ar-eas, with present crop production, market outlook, future industry development, con-cluding crop production characteristics of the 4 crop regions, and proposing function orientation and highlights.展开更多
In this paper, several sets of observing system simulation experiments (OSSEs) were designed for three typhoon cases to determine whether or not the additional observation data in the sensitive regions identified by c...In this paper, several sets of observing system simulation experiments (OSSEs) were designed for three typhoon cases to determine whether or not the additional observation data in the sensitive regions identified by conditional nonlinear optimal perturbations (CNOPs) could improve the short-range forecast of typhoons. The results show that the CNOPs capture the sensitive regions for typhoon forecasts, which implies that conducting additional observation in these specific regions and eliminating initial errors could reduce forecast errors. It is inferred from the results that dropping sondes in the CNOP sensitive regions could lead to improvements in typhoon forecasts.展开更多
Reinforcement learning encounters formidable challenges when tasked with intricate decision-making scenarios,primarily due to the expansive parameterized action spaces and the vastness of the corresponding policy land...Reinforcement learning encounters formidable challenges when tasked with intricate decision-making scenarios,primarily due to the expansive parameterized action spaces and the vastness of the corresponding policy landscapes.To surmount these difficulties,we devise a practical structured action graph model augmented by guiding policies that integrate trust region constraints.Based on this,we propose guided proximal policy optimization with structured action graph(GPPO-SAG),which has demonstrated pronounced efficacy in refining policy learning and enhancing performance across sophisticated tasks characterized by parameterized action spaces.Rigorous empirical evaluations of our model have been performed on comprehensive gaming platforms,including the entire suite of StarCraft II and Hearthstone,yielding exceptionally favorable outcomes.Our source code is at https://github.com/sachiel321/GPPO-SAG.展开更多
Metamaterial design,encompassing both microstructure topology selection and geometric parameter optimization,constitutes a high-dimensional optimization problem,with computationally expensive and time-consuming design...Metamaterial design,encompassing both microstructure topology selection and geometric parameter optimization,constitutes a high-dimensional optimization problem,with computationally expensive and time-consuming design evaluations.Bayesian optimization(BO)offers a promising approach for black-box optimization involved in various material designs,and this work presents several advanced techniques to adapt BO to address the challenges associated with metamaterial design.First,variational autoencoders(VAEs)are employed for efficient dimensionality reduction,mapping complex,high-dimensional metamaterial microstructures into a compact latent space.Second,mutual information maximization is incorporated into the VAE to enhance the quality of the learned latent space,ensuring that the most relevant features for optimization are retained.Third,trust region-based Bayesian optimization(TuRBO)dynamically adjusts local search regions,ensuring stability and convergence in high-dimensional spaces.The proposed techniques are well incorporated with conventional Gaussian processes(GP)-based BO framework.We applied the proposed method for the design of electromagnetic metamaterial microstructures.Experimental results show that we achieve a significantly high probability of finding the ground-truth topology types and their geometric parameters,leading to high accuracy in matching the design target.Moreover,our approach demonstrates significant time efficiency compared with traditional design methods.展开更多
Purpose–The purpose of this paper is to employ stochastic techniques to increase efficiency of the classical algorithms for solving nonlinear optimization problems.Design/methodology/approach–The well-known simulate...Purpose–The purpose of this paper is to employ stochastic techniques to increase efficiency of the classical algorithms for solving nonlinear optimization problems.Design/methodology/approach–The well-known simulated annealing strategy is employed to search successive neighborhoods of the classical trust region(TR)algorithm.Findings–An adaptive formula for computing the TR radius is suggested based on an eigenvalue analysis conducted on the memoryless Broyden-Fletcher-Goldfarb-Shanno updating formula.Also,a(heuristic)randomized adaptive TR algorithm is developed for solving unconstrained optimization problems.Results of computational experiments on a set of CUTEr test problems show that the proposed randomization scheme can enhance efficiency of the TR methods.Practical implications–The algorithm can be effectively used for solving the optimization problems which appear in engineering,economics,management,industry and other areas.Originality/value–The proposed randomization scheme improves computational costs of the classical TR algorithm.Especially,the suggested algorithm avoids resolving the TR subproblems for many times.展开更多
In this paper, a nonmonotonic trust region method for optimization problems with equality constraints is proposed by introducing a nonsmooth merit function and adopting a correction step. It is proved that all accumul...In this paper, a nonmonotonic trust region method for optimization problems with equality constraints is proposed by introducing a nonsmooth merit function and adopting a correction step. It is proved that all accumulation points of the iterates generated by the proposed algorithm are Kuhn-Tucker points and that the algorithm is q-superlinearly convergent.展开更多
To improve the driving efficiency of unmanned vehicles in a complex urban traffic flow environment and the safety and passenger comfort of vehicles when changing lanes,we propose a hierarchical reinforcement learning(...To improve the driving efficiency of unmanned vehicles in a complex urban traffic flow environment and the safety and passenger comfort of vehicles when changing lanes,we propose a hierarchical reinforcement learning(HRL)-based vehicle trajectory planning and tracking method.First,we present a hierarchical control framework for vehicle trajectory tracking that is based on deep reinforcement learning(DRL)and model predictive control(MPC).We design an upper-level decision model based on the trust region policy optimization algorithm integrated with long short-term memory to obtain more accurate strategies.Second,to improve stability and passenger comfort,we constructed a lower controller that combines the Bezier curve fitting method and an MPC controller.Finally,the proposed method was simulated via the car learning to act(CARLA)simulator,which is based on an unreal engine.Random urban traffic-flow test scenarios were used to simulate a real urban road-traffic environment.The simulation results illustrate that the proposed method can complete the vehicle trajectory planning and tracking task well.Compared with the existing RL methods,our proposed method has the lowest collision rate of 1.5%and achieves an average speed improvement of 7.04%.Moreover,our proposed method has better comfort performance and lower fuel consumption during the driving process.展开更多
INTRODUCTION An effective coordination of immune and non-immune cells is essential for generating optimal regional immunity to combat tumorigenesis and infection at barrier tissues such as lung.Regional immune structu...INTRODUCTION An effective coordination of immune and non-immune cells is essential for generating optimal regional immunity to combat tumorigenesis and infection at barrier tissues such as lung.Regional immune structures such as inducible bronchus-associated lymphoid tissue(iBALT)and tertiary lymphoid structure(TLS)play essential roles in modulating lung local immune responses.While the identification of iBALTs or TLS is generally dependent on conventional histology,it remains poorly understood how immune cells are spatiotemporally coordinated in the lung at single-cell resolution to effectively eliminate malignant cells and invading pathogens.Recently studies have revealed the presence of dendritic cell(DC)-T immunity hubs in human lung with close association with tumor immunotherapy response[1],antiviral immunity[2],and inflammation resolution[3].展开更多
A theoretical model for irreversible double resonance ESE(energy selective electron)device with phonon induced bypass heat leakage which is operating as heat engine system is proposed.The thermodynamic performance is ...A theoretical model for irreversible double resonance ESE(energy selective electron)device with phonon induced bypass heat leakage which is operating as heat engine system is proposed.The thermodynamic performance is optimized and the impacts of heat leakage and structure parameters of the electron system on its performance are discussed in detail by using FTT(finite time thermodynamics).Moreover,performances of the ESE system with multiple optimization objective functions,including power output,thermal efficiency,ecological function and efficient power,are explored by numerical examples.New optimal performance regions and the selection plans of optimization objective functions of the ESE system are obtained.It reveals that the characteristic of power versus efficiency behave as loop-shaped curves in spite of the heat leakage which will always decrease the efficiency of the electron engine.By properly choosing the design parameters,the ESE engine can be designed to operate at optimal conditions according to different design purpose.The preferred design area should be located between the optimal effective power condition and the optimal ecological function condition.展开更多
Purpose-Breast cancer is one of the most common malignant tumors in women,which badly have an effect on women’s physical and psychological health and even danger to life.Nowadays,mammography is considered as a fundam...Purpose-Breast cancer is one of the most common malignant tumors in women,which badly have an effect on women’s physical and psychological health and even danger to life.Nowadays,mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer.Though,due to the intricate formation of mammogram images,it is reasonably hard for practitioners to spot breast cancer features.Design/methodology/approach-Breast cancer is one of the most common malignant tumors in women,which badly have an effect on women’s physical and psychological health and even danger to life.Nowadays,mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer.Though,due to the intricate formation of mammogram images,it is reasonably hard for practitioners to spot breast cancer features.Findings-The performance analysis was done for both segmentation and classification.From the analysis,the accuracy of the proposed IAP-CSA-based fuzzy was 41.9%improved than the fuzzy classifier,2.80%improved than PSO,WOA,and CSA,and 2.32%improved than GWO-based fuzzy classifiers.Additionally,the accuracy of the developed IAP-CSA-fuzzy was 9.54%better than NN,35.8%better than SVM,and 41.9%better than the existing fuzzy classifier.Hence,it is concluded that the implemented breast cancer detection model was efficient in determining the normal,benign and malignant images.Originality/value-This paper adopts the latest Improved Awareness Probability-based Crow Search Algorithm(IAP-CSA)-based Region growing and fuzzy classifier for enhancing the breast cancer detection of mammogram images,and this is the first work that utilizes this method.展开更多
文摘The aim of this paper is to investigate a regional constrained optimal control problem for a class of semi[inear distributed systems, which are linear in the control but nonlinear in the state. For a quadratic cost functional and a closed convex set of admissible controls, the existence of an optimal control is proven, and then this is characterized for three cases of constraints. A useful algorithm is developed, and the approach is illustrated through simulations for a heat equation.
基金Supported by S&T Innovation Foundation of Hunan Academy of Agricultural Sciences~~
文摘The optimized strategy made a comprehensive consideration of resources, technology, market orientation, production scale, industry basis and layout based on the principle of crop security and farmers’ income increasing, and determined the general planning on layout and structure optimization of future crop production ar-eas, with present crop production, market outlook, future industry development, con-cluding crop production characteristics of the 4 crop regions, and proposing function orientation and highlights.
基金sponsored by the National Natural Science Foundation of China (Grant Nos. 40830955 and 40821092)the Project of China Meteorological Administration (Grant No. GYHY200906009)
文摘In this paper, several sets of observing system simulation experiments (OSSEs) were designed for three typhoon cases to determine whether or not the additional observation data in the sensitive regions identified by conditional nonlinear optimal perturbations (CNOPs) could improve the short-range forecast of typhoons. The results show that the CNOPs capture the sensitive regions for typhoon forecasts, which implies that conducting additional observation in these specific regions and eliminating initial errors could reduce forecast errors. It is inferred from the results that dropping sondes in the CNOP sensitive regions could lead to improvements in typhoon forecasts.
基金supported by National Nature Science Foundation of China(Nos.62073324,6200629,61771471 and 91748131)in part by the InnoHK Project,China.
文摘Reinforcement learning encounters formidable challenges when tasked with intricate decision-making scenarios,primarily due to the expansive parameterized action spaces and the vastness of the corresponding policy landscapes.To surmount these difficulties,we devise a practical structured action graph model augmented by guiding policies that integrate trust region constraints.Based on this,we propose guided proximal policy optimization with structured action graph(GPPO-SAG),which has demonstrated pronounced efficacy in refining policy learning and enhancing performance across sophisticated tasks characterized by parameterized action spaces.Rigorous empirical evaluations of our model have been performed on comprehensive gaming platforms,including the entire suite of StarCraft II and Hearthstone,yielding exceptionally favorable outcomes.Our source code is at https://github.com/sachiel321/GPPO-SAG.
基金supported by the National Key R&D Program of China No.2021YFB3802103Open Project of the State Key Laboratory of Metamaterial Electromagnetic Modulation Technology:“Research on Optimization Algorithms for Antenna Wideband Matching Network Parameters”.
文摘Metamaterial design,encompassing both microstructure topology selection and geometric parameter optimization,constitutes a high-dimensional optimization problem,with computationally expensive and time-consuming design evaluations.Bayesian optimization(BO)offers a promising approach for black-box optimization involved in various material designs,and this work presents several advanced techniques to adapt BO to address the challenges associated with metamaterial design.First,variational autoencoders(VAEs)are employed for efficient dimensionality reduction,mapping complex,high-dimensional metamaterial microstructures into a compact latent space.Second,mutual information maximization is incorporated into the VAE to enhance the quality of the learned latent space,ensuring that the most relevant features for optimization are retained.Third,trust region-based Bayesian optimization(TuRBO)dynamically adjusts local search regions,ensuring stability and convergence in high-dimensional spaces.The proposed techniques are well incorporated with conventional Gaussian processes(GP)-based BO framework.We applied the proposed method for the design of electromagnetic metamaterial microstructures.Experimental results show that we achieve a significantly high probability of finding the ground-truth topology types and their geometric parameters,leading to high accuracy in matching the design target.Moreover,our approach demonstrates significant time efficiency compared with traditional design methods.
基金the anonymous reviewers for their valuable comments and suggestions helped to improve the quality of this work.
文摘Purpose–The purpose of this paper is to employ stochastic techniques to increase efficiency of the classical algorithms for solving nonlinear optimization problems.Design/methodology/approach–The well-known simulated annealing strategy is employed to search successive neighborhoods of the classical trust region(TR)algorithm.Findings–An adaptive formula for computing the TR radius is suggested based on an eigenvalue analysis conducted on the memoryless Broyden-Fletcher-Goldfarb-Shanno updating formula.Also,a(heuristic)randomized adaptive TR algorithm is developed for solving unconstrained optimization problems.Results of computational experiments on a set of CUTEr test problems show that the proposed randomization scheme can enhance efficiency of the TR methods.Practical implications–The algorithm can be effectively used for solving the optimization problems which appear in engineering,economics,management,industry and other areas.Originality/value–The proposed randomization scheme improves computational costs of the classical TR algorithm.Especially,the suggested algorithm avoids resolving the TR subproblems for many times.
文摘In this paper, a nonmonotonic trust region method for optimization problems with equality constraints is proposed by introducing a nonsmooth merit function and adopting a correction step. It is proved that all accumulation points of the iterates generated by the proposed algorithm are Kuhn-Tucker points and that the algorithm is q-superlinearly convergent.
基金supported in part by the Jiaxing Public Welfare Research Program(Grant No.2023AY11034)the Zhejiang Provincial Natural Science Foundation of China under(Grant No.LTGS23F030002)+1 种基金the National Natural Science Foundation of China(Grant No.61603154)the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(Grant No.ICT2022B52).
文摘To improve the driving efficiency of unmanned vehicles in a complex urban traffic flow environment and the safety and passenger comfort of vehicles when changing lanes,we propose a hierarchical reinforcement learning(HRL)-based vehicle trajectory planning and tracking method.First,we present a hierarchical control framework for vehicle trajectory tracking that is based on deep reinforcement learning(DRL)and model predictive control(MPC).We design an upper-level decision model based on the trust region policy optimization algorithm integrated with long short-term memory to obtain more accurate strategies.Second,to improve stability and passenger comfort,we constructed a lower controller that combines the Bezier curve fitting method and an MPC controller.Finally,the proposed method was simulated via the car learning to act(CARLA)simulator,which is based on an unreal engine.Random urban traffic-flow test scenarios were used to simulate a real urban road-traffic environment.The simulation results illustrate that the proposed method can complete the vehicle trajectory planning and tracking task well.Compared with the existing RL methods,our proposed method has the lowest collision rate of 1.5%and achieves an average speed improvement of 7.04%.Moreover,our proposed method has better comfort performance and lower fuel consumption during the driving process.
基金supported by Grants from the National Key R&D Program of China(2023YFA1801400)National Natural Science Foundation of China(92374115 and 82388201).
文摘INTRODUCTION An effective coordination of immune and non-immune cells is essential for generating optimal regional immunity to combat tumorigenesis and infection at barrier tissues such as lung.Regional immune structures such as inducible bronchus-associated lymphoid tissue(iBALT)and tertiary lymphoid structure(TLS)play essential roles in modulating lung local immune responses.While the identification of iBALTs or TLS is generally dependent on conventional histology,it remains poorly understood how immune cells are spatiotemporally coordinated in the lung at single-cell resolution to effectively eliminate malignant cells and invading pathogens.Recently studies have revealed the presence of dendritic cell(DC)-T immunity hubs in human lung with close association with tumor immunotherapy response[1],antiviral immunity[2],and inflammation resolution[3].
基金supported by the National Natural Science Foundation of China(Grant Nos.51576207,51306206)the Hubei Provincial Natural Science Foundation of China(Grant No.2017CFB498)
文摘A theoretical model for irreversible double resonance ESE(energy selective electron)device with phonon induced bypass heat leakage which is operating as heat engine system is proposed.The thermodynamic performance is optimized and the impacts of heat leakage and structure parameters of the electron system on its performance are discussed in detail by using FTT(finite time thermodynamics).Moreover,performances of the ESE system with multiple optimization objective functions,including power output,thermal efficiency,ecological function and efficient power,are explored by numerical examples.New optimal performance regions and the selection plans of optimization objective functions of the ESE system are obtained.It reveals that the characteristic of power versus efficiency behave as loop-shaped curves in spite of the heat leakage which will always decrease the efficiency of the electron engine.By properly choosing the design parameters,the ESE engine can be designed to operate at optimal conditions according to different design purpose.The preferred design area should be located between the optimal effective power condition and the optimal ecological function condition.
文摘Purpose-Breast cancer is one of the most common malignant tumors in women,which badly have an effect on women’s physical and psychological health and even danger to life.Nowadays,mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer.Though,due to the intricate formation of mammogram images,it is reasonably hard for practitioners to spot breast cancer features.Design/methodology/approach-Breast cancer is one of the most common malignant tumors in women,which badly have an effect on women’s physical and psychological health and even danger to life.Nowadays,mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer.Though,due to the intricate formation of mammogram images,it is reasonably hard for practitioners to spot breast cancer features.Findings-The performance analysis was done for both segmentation and classification.From the analysis,the accuracy of the proposed IAP-CSA-based fuzzy was 41.9%improved than the fuzzy classifier,2.80%improved than PSO,WOA,and CSA,and 2.32%improved than GWO-based fuzzy classifiers.Additionally,the accuracy of the developed IAP-CSA-fuzzy was 9.54%better than NN,35.8%better than SVM,and 41.9%better than the existing fuzzy classifier.Hence,it is concluded that the implemented breast cancer detection model was efficient in determining the normal,benign and malignant images.Originality/value-This paper adopts the latest Improved Awareness Probability-based Crow Search Algorithm(IAP-CSA)-based Region growing and fuzzy classifier for enhancing the breast cancer detection of mammogram images,and this is the first work that utilizes this method.
