This paper develops an advanced framework for the operational optimization of integrated multi-energy systems that encompass electricity,gas,and heating networks.Introducing a cutting-edge stochastic gradient-enhanced...This paper develops an advanced framework for the operational optimization of integrated multi-energy systems that encompass electricity,gas,and heating networks.Introducing a cutting-edge stochastic gradient-enhanced distributionally robust optimization approach,this study integrates deep learning models,especially generative adversarial networks,to adeptly handle the inherent variability and uncertainties of renewable energy and fluctuating consumer demands.The effectiveness of this framework is rigorously tested through detailed simulations mirroring real-world urban energy consumption,renewable energy production,and market price fluctuations over an annual period.The results reveal substantial improvements in the resilience and efficiency of the grid,achieving a reduction in power distribution losses by 15%and enhancing voltage stability by 20%,markedly outperforming conventional systems.Additionally,the framework facilitates up to 25%in cost reductions during peak demand periods,significantly lowering operational costs.The adoption of stochastic gradients further refines the framework’s ability to continually adjust to real-time changes in environmental and market conditions,ensuring stable grid operations and fostering active consumer engagement in demand-side management.This strategy not only aligns with contem-porary sustainable energy practices but also provides scalable and robust solutions to pressing challenges in modern power network management.展开更多
Inverse design of advanced materials represents a pivotal challenge in materials science.Leveraging the latent space of Variational Autoencoders(VAEs)for material optimization has emerged as a significant advancement ...Inverse design of advanced materials represents a pivotal challenge in materials science.Leveraging the latent space of Variational Autoencoders(VAEs)for material optimization has emerged as a significant advancement in the field of material inverse design.However,VAEs are inherently prone to generating blurred images,posing challenges for precise inverse design and microstructure manufacturing.While increasing the dimensionality of the VAE latent space can mitigate reconstruction blurriness to some extent,it simultaneously imposes a substantial burden on target optimization due to an excessively high search space.To address these limitations,this study adopts a Variational Autoencoder guided Conditional Diffusion Generative Model(VAE-CDGM)framework integrated with Bayesian optimization to achieve the inverse design of composite materials with targeted mechanical properties.The VAE-CDGM model synergizes the strengths of VAEs and Denoising Diffusion Probabilistic Models(DDPM),enabling the generation of high-quality,sharp images while preserving a manipulable latent space.To accommodate varying dimensional requirements of the latent space,two optimization strategies are proposed.When the latent space dimensionality is excessively high,SHapley Additive exPlanations(SHAP)sensitivity analysis is employed to identify critical latent features for optimization within a reduced subspace.Conversely,direct optimization is performed in the low-dimensional latent space of VAE-CDGM when dimensionality is modest.The results demonstrate that both strategies accurately achieve the targeted design of composite materials while circumventing the blurred reconstruction flaws of VAEs,which offers a novel pathway for the precise design of advanced materials.展开更多
Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises stru...Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.展开更多
The Stackelberg prediction game(SPG)is a bilevel optimization frame-work for modeling strategic interactions between a learner and a follower.Existing meth-ods for solving this problem with general loss functions are ...The Stackelberg prediction game(SPG)is a bilevel optimization frame-work for modeling strategic interactions between a learner and a follower.Existing meth-ods for solving this problem with general loss functions are computationally expensive and scarce.We propose a novel hyper-gradient type method with a warm-start strategy to address this challenge.Particularly,we first use a Taylor expansion-based approach to obtain a good initial point.Then we apply a hyper-gradient descent method with an ex-plicit approximate hyper-gradient.We establish the convergence results of our algorithm theoretically.Furthermore,when the follower employs the least squares loss function,our method is shown to reach an e-stationary point by solving quadratic subproblems.Numerical experiments show our algorithms are empirically orders of magnitude faster than the state-of-the-art.展开更多
Background:Schistosomiasis is a parasitic disease.It is caused by a prevalent infection in tropical areas and is transmitted through contaminated water with larvae parasites.Schistosomiasis is the second most parasiti...Background:Schistosomiasis is a parasitic disease.It is caused by a prevalent infection in tropical areas and is transmitted through contaminated water with larvae parasites.Schistosomiasis is the second most parasitic disease globally,so investigating its prevention and treatment is crucial.Methods:This paper aims to suggest a time-fractional model of schistosomiasis disease(T-FMSD)in the sense of the Caputo operator.The T-FMSD considers the dynamics involving susceptible ones not infected with schistosomiasis(S_(h)(t)),those infected with the infection(Ih(t)),those recovering from the disease(R(t)),susceptible snails with and without schistosomiasis infection,respectively shown by I_(v)(t)and S_(v)(t).We use a new basis function,generalized Bernoulli polynomials,for the approximate solution of T-FMSD.The operational matrices are incorporated into the method of Lagrange multipliers so that the fractional problem can be transformed into an algebraic system of equations.Results:The existence and uniqueness of the solution,and the convergence analysis of the model are established.The numerical computations are graphically presented to depict the variations of the compartments with time for varied fractional order derivatives.Conclusions:The proposed method not only provides an accurate solution but also can accurately predict schistosomiasis transmission.The results of this study will assist medical scientists in taking necessary measures during screening and treatment processes.展开更多
近几年循环流化床锅炉水冷壁磨损较为严重,虽然采用防磨凸台可以有效减缓壁面磨损,然而基于凸台优化设计的文献还鲜见报道,因此文中使用声发射设备研究凸台绕流过程中稠密气固两相流颗粒与壁面接触程度问题,在一冷态实验台密相区,采用...近几年循环流化床锅炉水冷壁磨损较为严重,虽然采用防磨凸台可以有效减缓壁面磨损,然而基于凸台优化设计的文献还鲜见报道,因此文中使用声发射设备研究凸台绕流过程中稠密气固两相流颗粒与壁面接触程度问题,在一冷态实验台密相区,采用声发射对相同尺寸不同截面形状凸台下方颗粒与壁面接触程度进行测量,并用小波包分解处理信号。实验结果表明,0~10.5 k Hz低频段信号可以反应颗粒与壁面接触程度,重点分析该特征频段能量百分比,提出方形凸台为理论优化凸台,对进一步改进循环流化床防磨技术提供指导意见。展开更多
基金supported by the National Key R&D Program of China(No.2021ZD0112700).
文摘This paper develops an advanced framework for the operational optimization of integrated multi-energy systems that encompass electricity,gas,and heating networks.Introducing a cutting-edge stochastic gradient-enhanced distributionally robust optimization approach,this study integrates deep learning models,especially generative adversarial networks,to adeptly handle the inherent variability and uncertainties of renewable energy and fluctuating consumer demands.The effectiveness of this framework is rigorously tested through detailed simulations mirroring real-world urban energy consumption,renewable energy production,and market price fluctuations over an annual period.The results reveal substantial improvements in the resilience and efficiency of the grid,achieving a reduction in power distribution losses by 15%and enhancing voltage stability by 20%,markedly outperforming conventional systems.Additionally,the framework facilitates up to 25%in cost reductions during peak demand periods,significantly lowering operational costs.The adoption of stochastic gradients further refines the framework’s ability to continually adjust to real-time changes in environmental and market conditions,ensuring stable grid operations and fostering active consumer engagement in demand-side management.This strategy not only aligns with contem-porary sustainable energy practices but also provides scalable and robust solutions to pressing challenges in modern power network management.
文摘Inverse design of advanced materials represents a pivotal challenge in materials science.Leveraging the latent space of Variational Autoencoders(VAEs)for material optimization has emerged as a significant advancement in the field of material inverse design.However,VAEs are inherently prone to generating blurred images,posing challenges for precise inverse design and microstructure manufacturing.While increasing the dimensionality of the VAE latent space can mitigate reconstruction blurriness to some extent,it simultaneously imposes a substantial burden on target optimization due to an excessively high search space.To address these limitations,this study adopts a Variational Autoencoder guided Conditional Diffusion Generative Model(VAE-CDGM)framework integrated with Bayesian optimization to achieve the inverse design of composite materials with targeted mechanical properties.The VAE-CDGM model synergizes the strengths of VAEs and Denoising Diffusion Probabilistic Models(DDPM),enabling the generation of high-quality,sharp images while preserving a manipulable latent space.To accommodate varying dimensional requirements of the latent space,two optimization strategies are proposed.When the latent space dimensionality is excessively high,SHapley Additive exPlanations(SHAP)sensitivity analysis is employed to identify critical latent features for optimization within a reduced subspace.Conversely,direct optimization is performed in the low-dimensional latent space of VAE-CDGM when dimensionality is modest.The results demonstrate that both strategies accurately achieve the targeted design of composite materials while circumventing the blurred reconstruction flaws of VAEs,which offers a novel pathway for the precise design of advanced materials.
文摘Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.
文摘The Stackelberg prediction game(SPG)is a bilevel optimization frame-work for modeling strategic interactions between a learner and a follower.Existing meth-ods for solving this problem with general loss functions are computationally expensive and scarce.We propose a novel hyper-gradient type method with a warm-start strategy to address this challenge.Particularly,we first use a Taylor expansion-based approach to obtain a good initial point.Then we apply a hyper-gradient descent method with an ex-plicit approximate hyper-gradient.We establish the convergence results of our algorithm theoretically.Furthermore,when the follower employs the least squares loss function,our method is shown to reach an e-stationary point by solving quadratic subproblems.Numerical experiments show our algorithms are empirically orders of magnitude faster than the state-of-the-art.
文摘Background:Schistosomiasis is a parasitic disease.It is caused by a prevalent infection in tropical areas and is transmitted through contaminated water with larvae parasites.Schistosomiasis is the second most parasitic disease globally,so investigating its prevention and treatment is crucial.Methods:This paper aims to suggest a time-fractional model of schistosomiasis disease(T-FMSD)in the sense of the Caputo operator.The T-FMSD considers the dynamics involving susceptible ones not infected with schistosomiasis(S_(h)(t)),those infected with the infection(Ih(t)),those recovering from the disease(R(t)),susceptible snails with and without schistosomiasis infection,respectively shown by I_(v)(t)and S_(v)(t).We use a new basis function,generalized Bernoulli polynomials,for the approximate solution of T-FMSD.The operational matrices are incorporated into the method of Lagrange multipliers so that the fractional problem can be transformed into an algebraic system of equations.Results:The existence and uniqueness of the solution,and the convergence analysis of the model are established.The numerical computations are graphically presented to depict the variations of the compartments with time for varied fractional order derivatives.Conclusions:The proposed method not only provides an accurate solution but also can accurately predict schistosomiasis transmission.The results of this study will assist medical scientists in taking necessary measures during screening and treatment processes.
文摘为了筛选嗜酸乳杆菌固体发酵棉粕的最佳产酸发酵条件,试验采用单因素试验,根据响应面的基本原理,以发酵饲料中乳酸含量为优化指标,利用Design Expert软件对发酵条件(接种量、发酵、温度和时间)进行优化。结果表明:理论最佳发酵条件是接种量为5.96 m L,发酵温度为36.99℃,发酵时间为48.26 h;此条件下发酵产物中乳酸含量预测可达7.57%,实际含量为7.50%。
文摘近几年循环流化床锅炉水冷壁磨损较为严重,虽然采用防磨凸台可以有效减缓壁面磨损,然而基于凸台优化设计的文献还鲜见报道,因此文中使用声发射设备研究凸台绕流过程中稠密气固两相流颗粒与壁面接触程度问题,在一冷态实验台密相区,采用声发射对相同尺寸不同截面形状凸台下方颗粒与壁面接触程度进行测量,并用小波包分解处理信号。实验结果表明,0~10.5 k Hz低频段信号可以反应颗粒与壁面接触程度,重点分析该特征频段能量百分比,提出方形凸台为理论优化凸台,对进一步改进循环流化床防磨技术提供指导意见。
