Recently,Luolai Group released its Q12025 quarterly report.As a leading Chinese home textile enterprise listed on the Shenzhen Stock Exchange in 2009,the company covers the research,design,production,and sales of home...Recently,Luolai Group released its Q12025 quarterly report.As a leading Chinese home textile enterprise listed on the Shenzhen Stock Exchange in 2009,the company covers the research,design,production,and sales of home textile products,and has multiple brands covering different consumer markets.It has expanded its online and offline comprehensive multichannel sales system and is committed to creating a win-win home furnishings and textile industry ecosystem.展开更多
The tighten couplings of game strategies with adjoint methods for multi-criterion aerodynamic design optimization are ad-dressed. Its numerical implementation is also described in details. In cooperative game,adjoint ...The tighten couplings of game strategies with adjoint methods for multi-criterion aerodynamic design optimization are ad-dressed. Its numerical implementation is also described in details. In cooperative game,adjoint methods are coupled in parallel to compute Pareto front collaboratively. Conversely in a Nash game,adjoint methods are coupled in each player s decision making to achieve Nash equilibrium competitively. In Stackelberg game,adjoint methods used by players are nested hierarchically through incomp...展开更多
A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of avail...A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.展开更多
Dynamic spectrum access(DSA) based on cognitive radios(CR) technique is an effective approach to address the "spectrum scarcity" issue. However, traditional CR-enabled DSA system employs only single DSA stra...Dynamic spectrum access(DSA) based on cognitive radios(CR) technique is an effective approach to address the "spectrum scarcity" issue. However, traditional CR-enabled DSA system employs only single DSA strategy, which might not be suited to the dynamic network environment. In this paper, we propose a multi-strategy DSA(MS-DSA) system, where the primary and the secondary system share spectrum resources with multiple DSA strategies simultaneously. To analyze the performance of the proposed MS-DSA system, we model it as a continuous-time Markov chain(CTMC) and derive the expressions to compute the corresponding performance metrics. Based on this, we define a utility function involving the concerns of effective throughput, interference quantity on primary users, and spectrum leasing cost. Two optimization schemes, named as spectrum allocation and false alarm probability selection, are proposed to maximize the utility function. Finally, numerical simulations are provided to validate our analysis and demonstrate that the performance can be significantly improved caused by virtues of the proposed MS-DSA system.展开更多
The multi-roller straightening process of section steel is analyzed by the springback theory of small curva- ture plane bending. The theoretical analysis results prove the curvature unification in straightening proces...The multi-roller straightening process of section steel is analyzed by the springback theory of small curva- ture plane bending. The theoretical analysis results prove the curvature unification in straightening process and clear- ly reveal the principle of the multi-roller straightening process. The principle can be described as~ the initial curva- tures are reduced by several times anti-bendingl meanwhile the initial curvature differences are diminished and the residual curvatures are unified~ finally, the member after curvature unification is straightened by the last anti-ben- ding. With the plastic region ratios becoming larger, the initial curvatures are more easily unified in straightening process. Based on the plastic region ratios and the required number of roller systems for unifying the initial curva- tures, the large deformation straightening strategy and the small deformation straightening strategy are redefined. The new definition provides an important theoretical basis for setting reliable reduction rules. Through the theoretical analysis results, a new straightener design philosophy is proposed to improve the straightening quality and further increase the adjustment precision as well as the flexibility of the last roller system. The adjustable end roller emerges as the times required, achieving a good effect in practical application.展开更多
Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for ...Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for impulse effects. Firstly, to address the inequality constraints,the penalty method is introduced. Then, a novel optimization strategy is developed, which only requires that the team objective function be strongly convex.展开更多
Maintaining stable high temperatures under pressure remains a challenge in high-pressure,high-temperature experiments using multi-anvil presses(MAPs).Temperature fluctuations exceeding 10℃ at high pressures are commo...Maintaining stable high temperatures under pressure remains a challenge in high-pressure,high-temperature experiments using multi-anvil presses(MAPs).Temperature fluctuations exceeding 10℃ at high pressures are common and particularly problematic with LaCrO_(3) heaters,which can experience significant power fluctuations and even failure due to substantial resistance changes—an issue conventional thyristorcontrolled heating systems cannot effectively manage.To address this limitation,we have developed the Multi-Anvil Stable Temperature controller(MASTer),a high-performance heating system optimized for MAP experiments.MASTer enables precise,high-speed measurement of heating parameters and power output control,incorporating a gentle regulation strategy to enhance stability.It ensures consistent heating across various heater types,including LaCrO_(3),with power fluctuations limited to±0.1 W and temperature fluctuations to within±2℃ in most cases.The design,operating principles,user interface,functionality,and performance of the heating system are discussed in detail.展开更多
Optical solitons,as self-sustaining waveforms in a nonlinear medium where dispersion and nonlinear effects are balanced,have key applications in ultrafast laser systems and optical communications.Physics-informed neur...Optical solitons,as self-sustaining waveforms in a nonlinear medium where dispersion and nonlinear effects are balanced,have key applications in ultrafast laser systems and optical communications.Physics-informed neural networks(PINN)provide a new way to solve the nonlinear Schrodinger equation describing the soliton evolution by fusing data-driven and physical constraints.However,the grid point sampling strategy of traditional PINN suffers from high computational complexity and unstable gradient flow,which makes it difficult to capture the physical details efficiently.In this paper,we propose a residual-based adaptive multi-distribution(RAMD)sampling method to optimize the PINN training process by dynamically constructing a multi-modal loss distribution.With a 50%reduction in the number of grid points,RAMD significantly reduces the relative error of PINN and,in particular,optimizes the solution error of the(2+1)Ginzburg–Landau equation from 4.55%to 1.98%.RAMD breaks through the lack of physical constraints in the purely data-driven model by the innovative combination of multi-modal distribution modeling and autonomous sampling control for the design of all-optical communication devices.RAMD provides a high-precision numerical simulation tool for the design of all-optical communication devices,optimization of nonlinear laser devices,and other studies.展开更多
In this paper, a compound Poisson risk model with time-dependent claims is studiedunder a multi-layer dividend strategy. A piecewise integro-differential equation for the Gerber- Shiu function is derived and solved. A...In this paper, a compound Poisson risk model with time-dependent claims is studiedunder a multi-layer dividend strategy. A piecewise integro-differential equation for the Gerber- Shiu function is derived and solved. Asymptotic formulas of the ruin probability are obtained when the claim size distributions are heavy-tailed.展开更多
Aiming at constructing the multi-knapsack model of collaborative portfolio configurations in multi-strategy oriented, the hybrid evolutionary algorithm was designed based on greedy method, combining with the organizat...Aiming at constructing the multi-knapsack model of collaborative portfolio configurations in multi-strategy oriented, the hybrid evolutionary algorithm was designed based on greedy method, combining with the organization of the multiple strategical guidance and multi-knapsack model. Furthermore, the organizing resource utility and risk management of portfolio were considered. The experiments were conducted on three main technological markets which contain communication, transportation and industry. The results demonstrated that the proposed model and algorithm were feasible and reliable.展开更多
长文本关系识别在科技情报与数字人文领域中具有重要作用,是实现知识重组向知识发现转变的关键。然而,由于长文本存在上下文跨度大、语义线索分散、实体指代复杂等特征,传统大语言模型(large language model,LLM)在处理该类文本时,易出...长文本关系识别在科技情报与数字人文领域中具有重要作用,是实现知识重组向知识发现转变的关键。然而,由于长文本存在上下文跨度大、语义线索分散、实体指代复杂等特征,传统大语言模型(large language model,LLM)在处理该类文本时,易出现上下文理解不足、语义偏移以及幻觉等问题,使得长文本在科技情报与人文计算等领域的实际应用中尚未更好地实现内容增值。为了解决上述问题,首先,本文依据关系触发词的聚类结果构建实体关系体系;其次,针对长文本特征,设计基于LLM-BERT(large language model-bidirectional encoder representations from transformers)协同框架的长文本关系识别算法,提升语义关联性;再其次,融合预训练模型、深度学习网络、注意力机制处理文本特征的优势,构建BERT-CNN-BiLSTM-MHA(BCBM)模型,深层次挖掘文本语义;最后,结合模型置信度和文本相似度,设计一种摘要质量评估机制,以缓解LLM幻觉。研究结果表明,该算法实测效果优于传统模型,能在一定程度上缓解LLM在处理长文本时易产生的上下文理解不足、语义偏移和幻觉等问题。展开更多
为有效提升配电网韧性,提出了一种基于数据-模型混合驱动的多类型移动应急资源优化调度方法。首先,考虑到交通道路状态动态变化对移动储能车(mobile energy storage system,MESS)和应急抢修队(repair crew,RC)策略的影响,构建了以电力-...为有效提升配电网韧性,提出了一种基于数据-模型混合驱动的多类型移动应急资源优化调度方法。首先,考虑到交通道路状态动态变化对移动储能车(mobile energy storage system,MESS)和应急抢修队(repair crew,RC)策略的影响,构建了以电力-交通耦合网总损失成本最小为目标的多类型移动应急资源随机优化调度模型。然后,为了实时准确地求解MESS和RC最优路由和调度策略,提出了一种数据-模型混合驱动方法对所构建的复杂非线性随机优化模型进行求解。在数据驱动部分提出一种图注意力网络多智能体强化学习算法,以求解考虑交通网道路修复时间和移动应急资源邻接关系动态变化等不确定因素的MESS和RC最优路由策略。所提算法有效结合多种改进策略和优先经验回放策略以提高算法的采样效率和训练效果。在模型驱动部分采用二阶锥松弛和大M法将多类型移动应急资源优化调度问题构建为混合整数二阶锥规划模型以求解可再生能源出力和配电网负荷变化影响下MESS和RC最优调度策略。最后,在2个不同规模的电力-交通耦合网中验证所提方法的有效性、泛化能力和可拓展能力。展开更多
文摘Recently,Luolai Group released its Q12025 quarterly report.As a leading Chinese home textile enterprise listed on the Shenzhen Stock Exchange in 2009,the company covers the research,design,production,and sales of home textile products,and has multiple brands covering different consumer markets.It has expanded its online and offline comprehensive multichannel sales system and is committed to creating a win-win home furnishings and textile industry ecosystem.
基金National Natural Science Foundation of China (10872093)
文摘The tighten couplings of game strategies with adjoint methods for multi-criterion aerodynamic design optimization are ad-dressed. Its numerical implementation is also described in details. In cooperative game,adjoint methods are coupled in parallel to compute Pareto front collaboratively. Conversely in a Nash game,adjoint methods are coupled in each player s decision making to achieve Nash equilibrium competitively. In Stackelberg game,adjoint methods used by players are nested hierarchically through incomp...
基金Project(51561135003)supported by the International Cooperation and Exchange of the National Natural Science Foundation of ChinaProject(51338003)supported by the Key Project of National Natural Science Foundation of China
文摘A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.
基金supported in part by the National Natural Sciences Foundation of China (NSFC) under Grant 61525103the National Natural Sciences Foundation of China under Grant 61501140the Shenzhen Fundamental Research Project under Grant JCYJ20150930150304185
文摘Dynamic spectrum access(DSA) based on cognitive radios(CR) technique is an effective approach to address the "spectrum scarcity" issue. However, traditional CR-enabled DSA system employs only single DSA strategy, which might not be suited to the dynamic network environment. In this paper, we propose a multi-strategy DSA(MS-DSA) system, where the primary and the secondary system share spectrum resources with multiple DSA strategies simultaneously. To analyze the performance of the proposed MS-DSA system, we model it as a continuous-time Markov chain(CTMC) and derive the expressions to compute the corresponding performance metrics. Based on this, we define a utility function involving the concerns of effective throughput, interference quantity on primary users, and spectrum leasing cost. Two optimization schemes, named as spectrum allocation and false alarm probability selection, are proposed to maximize the utility function. Finally, numerical simulations are provided to validate our analysis and demonstrate that the performance can be significantly improved caused by virtues of the proposed MS-DSA system.
基金Item Sponsored by National Natural Science Foundation of China(51178452)
文摘The multi-roller straightening process of section steel is analyzed by the springback theory of small curva- ture plane bending. The theoretical analysis results prove the curvature unification in straightening process and clear- ly reveal the principle of the multi-roller straightening process. The principle can be described as~ the initial curva- tures are reduced by several times anti-bendingl meanwhile the initial curvature differences are diminished and the residual curvatures are unified~ finally, the member after curvature unification is straightened by the last anti-ben- ding. With the plastic region ratios becoming larger, the initial curvatures are more easily unified in straightening process. Based on the plastic region ratios and the required number of roller systems for unifying the initial curva- tures, the large deformation straightening strategy and the small deformation straightening strategy are redefined. The new definition provides an important theoretical basis for setting reliable reduction rules. Through the theoretical analysis results, a new straightener design philosophy is proposed to improve the straightening quality and further increase the adjustment precision as well as the flexibility of the last roller system. The adjustable end roller emerges as the times required, achieving a good effect in practical application.
基金supported in part by the National Natural Science Foundation of China(62276119)the Natural Science Foundation of Jiangsu Province(BK20241764)the Postgraduate Research & Practice Innovation Program of Jiangsu Province(KYCX22_2860)
文摘Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for impulse effects. Firstly, to address the inequality constraints,the penalty method is introduced. Then, a novel optimization strategy is developed, which only requires that the team objective function be strongly convex.
基金supported by the National Science Fund for Distinguished Young Scholars(Grant No.T2225027)the National Key R&D Program of China(Grant No.2023YFA1608902).
文摘Maintaining stable high temperatures under pressure remains a challenge in high-pressure,high-temperature experiments using multi-anvil presses(MAPs).Temperature fluctuations exceeding 10℃ at high pressures are common and particularly problematic with LaCrO_(3) heaters,which can experience significant power fluctuations and even failure due to substantial resistance changes—an issue conventional thyristorcontrolled heating systems cannot effectively manage.To address this limitation,we have developed the Multi-Anvil Stable Temperature controller(MASTer),a high-performance heating system optimized for MAP experiments.MASTer enables precise,high-speed measurement of heating parameters and power output control,incorporating a gentle regulation strategy to enhance stability.It ensures consistent heating across various heater types,including LaCrO_(3),with power fluctuations limited to±0.1 W and temperature fluctuations to within±2℃ in most cases.The design,operating principles,user interface,functionality,and performance of the heating system are discussed in detail.
基金supported by the National Key R&D Program of China(Grant No.2022YFA1604200)National Natural Science Foundation of China(Grant No.12261131495)+1 种基金Beijing Municipal Science and Technology Commission,Adminitrative Commission of Zhongguancun Science Park(Grant No.Z231100006623006)Institute of Systems Science,Beijing Wuzi University(Grant No.BWUISS21)。
文摘Optical solitons,as self-sustaining waveforms in a nonlinear medium where dispersion and nonlinear effects are balanced,have key applications in ultrafast laser systems and optical communications.Physics-informed neural networks(PINN)provide a new way to solve the nonlinear Schrodinger equation describing the soliton evolution by fusing data-driven and physical constraints.However,the grid point sampling strategy of traditional PINN suffers from high computational complexity and unstable gradient flow,which makes it difficult to capture the physical details efficiently.In this paper,we propose a residual-based adaptive multi-distribution(RAMD)sampling method to optimize the PINN training process by dynamically constructing a multi-modal loss distribution.With a 50%reduction in the number of grid points,RAMD significantly reduces the relative error of PINN and,in particular,optimizes the solution error of the(2+1)Ginzburg–Landau equation from 4.55%to 1.98%.RAMD breaks through the lack of physical constraints in the purely data-driven model by the innovative combination of multi-modal distribution modeling and autonomous sampling control for the design of all-optical communication devices.RAMD provides a high-precision numerical simulation tool for the design of all-optical communication devices,optimization of nonlinear laser devices,and other studies.
基金Surported by the Third Stage of 211 ProjectInnovative Talent Training Project of S-09110the Chongqing University Postgraduates’ Science and Innovation Fund (200911B1B0110327)
文摘In this paper, a compound Poisson risk model with time-dependent claims is studiedunder a multi-layer dividend strategy. A piecewise integro-differential equation for the Gerber- Shiu function is derived and solved. Asymptotic formulas of the ruin probability are obtained when the claim size distributions are heavy-tailed.
文摘Aiming at constructing the multi-knapsack model of collaborative portfolio configurations in multi-strategy oriented, the hybrid evolutionary algorithm was designed based on greedy method, combining with the organization of the multiple strategical guidance and multi-knapsack model. Furthermore, the organizing resource utility and risk management of portfolio were considered. The experiments were conducted on three main technological markets which contain communication, transportation and industry. The results demonstrated that the proposed model and algorithm were feasible and reliable.
基金supported by Major International(Regional)Joint Research Project of the National Natural Science Foundation of China(61320106011)National High Technology Research and Development Program of China(863 Program)(2014AA052802)National Natural Science Foundation of China(61573224)
文摘长文本关系识别在科技情报与数字人文领域中具有重要作用,是实现知识重组向知识发现转变的关键。然而,由于长文本存在上下文跨度大、语义线索分散、实体指代复杂等特征,传统大语言模型(large language model,LLM)在处理该类文本时,易出现上下文理解不足、语义偏移以及幻觉等问题,使得长文本在科技情报与人文计算等领域的实际应用中尚未更好地实现内容增值。为了解决上述问题,首先,本文依据关系触发词的聚类结果构建实体关系体系;其次,针对长文本特征,设计基于LLM-BERT(large language model-bidirectional encoder representations from transformers)协同框架的长文本关系识别算法,提升语义关联性;再其次,融合预训练模型、深度学习网络、注意力机制处理文本特征的优势,构建BERT-CNN-BiLSTM-MHA(BCBM)模型,深层次挖掘文本语义;最后,结合模型置信度和文本相似度,设计一种摘要质量评估机制,以缓解LLM幻觉。研究结果表明,该算法实测效果优于传统模型,能在一定程度上缓解LLM在处理长文本时易产生的上下文理解不足、语义偏移和幻觉等问题。
文摘为有效提升配电网韧性,提出了一种基于数据-模型混合驱动的多类型移动应急资源优化调度方法。首先,考虑到交通道路状态动态变化对移动储能车(mobile energy storage system,MESS)和应急抢修队(repair crew,RC)策略的影响,构建了以电力-交通耦合网总损失成本最小为目标的多类型移动应急资源随机优化调度模型。然后,为了实时准确地求解MESS和RC最优路由和调度策略,提出了一种数据-模型混合驱动方法对所构建的复杂非线性随机优化模型进行求解。在数据驱动部分提出一种图注意力网络多智能体强化学习算法,以求解考虑交通网道路修复时间和移动应急资源邻接关系动态变化等不确定因素的MESS和RC最优路由策略。所提算法有效结合多种改进策略和优先经验回放策略以提高算法的采样效率和训练效果。在模型驱动部分采用二阶锥松弛和大M法将多类型移动应急资源优化调度问题构建为混合整数二阶锥规划模型以求解可再生能源出力和配电网负荷变化影响下MESS和RC最优调度策略。最后,在2个不同规模的电力-交通耦合网中验证所提方法的有效性、泛化能力和可拓展能力。
