In this paper, we prove an extrapolation theorem of operators in martingale spaces with Ap weights, which shows that for operators T defined on martingale space Lu^I, if T is bounded on martingale space L^P0 （w） for...In this paper, we prove an extrapolation theorem of operators in martingale spaces with Ap weights, which shows that for operators T defined on martingale space Lu^I, if T is bounded on martingale space L^P0 （w） for some 1 〈 P0 〈 ∞ and every w ∈ A∞, so it is on L^P,S（w） for every 1〈p, s〈∞ , and w∈ A∞. We also get some properties of Ap weights and prove that if w ∈ AI, then the maximal operator M is bounded on L^p,q（w） with 1〈p〈∞, 1〈q≤∞.展开更多
This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,i...This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,in which only one parameter needs to be adjusted in the power-law terms;this greatly decreases the inconvenience of parameter adjustment.Second,several fixed-time passivity criteria with LMI forms are derived by using a Gauss divergence theorem to deal with the spatial diffusion of nodes and by applying the Hölder’s inequality to dispose rigorously the power-law term greater than one in the designed control scheme;this improves the previous theoretical analysis.Additionally,the fixed-time synchronization of spatiotemporal directed networks with multi-weights is addressed as a direct result of fixed-time strict passivity.Finally,a numerical example is presented in order to show the validity of the theoretical analysis.展开更多
We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training ph...We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.展开更多
In this paper,it is shown that the harmonic Bergman projection P_(ω)^(h),induced by a radial,induced by a radial weightω,is bounded and onto from L^(∞)(D)to the harmonic Bloch space B_(h)if and only ifω∈D,,which ...In this paper,it is shown that the harmonic Bergman projection P_(ω)^(h),induced by a radial,induced by a radial weightω,is bounded and onto from L^(∞)(D)to the harmonic Bloch space B_(h)if and only ifω∈D,,which is a class of radial weights satisfying the two-sided doubling conditions.As an application,the bounded and compact positive Toeplitz operators T_(μ,ω)on the endpoint case weighted harmonic Bergman space L_(h,ω)^(1)(D)are characterized.展开更多
Grain filling is a critical determinant of yield and quality in rice.This study aims to clarify the association between grain photosynthesis and the filling rate of rice varieties with different grain weights,providin...Grain filling is a critical determinant of yield and quality in rice.This study aims to clarify the association between grain photosynthesis and the filling rate of rice varieties with different grain weights,providing a theoretical foundation for optimizing grain-filling processes.Two rice varieties with similar growth duration but different grain weights were selected:a large-grain variety,Lingliangyou 268(L268),and a small-grain variety,Ruiliangyou 1053(R1053).Differences in grain filling,grain photosynthetic rate,and grain chlorophyll content were systematically examined during the filling stage.Results showed significant differences in grain-filling,grain photosynthetic rate,and grain chlorophyll content between large-grain and small-grain rice varieties.The grain photosynthetic rate of L268 was a significantly higher than R1053.L268 also exhibited significantly higher initial grain filling rate,maximum grainfilling rate,and mean grain filling rate compared to R1053.Throughout the grain filling period,L268 showed higher grain chlorophyll content(including chlorophyll a,chlorophyll b,and total chlorophyll)than R1053.The increase in chlorophyll content,particularly total chlorophyll,enhanced the grain photosynthetic rate during the early and middle stages of grain filling significantly.These findings suggested that rice varieties with higher grain weights exhibited stronger panicle photosynthetic capacity due to their higher chlorophyll content.The enhanced grain photosynthetic rate contributed to improved grain filling and increased grain weight.展开更多
Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery conditi...Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery condition monitoring because that can fully use available data and computational power.Since significant accidents might be caused if wrong fault alarms are given for machine condition monitoring,interpretable machine learning models,integrate signal processing knowledge to enhance trustworthiness of models,are gradually becoming a research hotspot.A previous spectrum-based and interpretable optimized weights method has been proposed to indicate faulty and fundamental frequencies when the analyzed data only contains a healthy type and a fault type.Considering that multiclass fault types are naturally met in practice,this work aims to explore the interpretable optimized weights method for multiclass fault type scenarios.Therefore,a new multiclass optimized weights spectrum(OWS)is proposed and further studied theoretically and numerically.It is found that the multiclass OWS is capable of capturing the characteristic components associated with different conditions and clearly indicating specific fault characteristic frequencies(FCFs)corresponding to each fault condition.This work can provide new insights into spectrum-based fault classification models,and the new multiclass OWS also shows great potential for practical applications.展开更多
目的针对飞机货舱配载方案评估中多属性决策的复杂性,以及现有评估方法在权重确定上过度依赖专家经验导致的主观偏差,或单纯依赖客观数据忽略决策者偏好的局限性,提出一种融合主观先验与客观数据驱动的混合赋权评估模型,以提供更为合理...目的针对飞机货舱配载方案评估中多属性决策的复杂性,以及现有评估方法在权重确定上过度依赖专家经验导致的主观偏差,或单纯依赖客观数据忽略决策者偏好的局限性,提出一种融合主观先验与客观数据驱动的混合赋权评估模型,以提供更为合理、可靠的配载方案择优决策支持。方法首先,引入大型语言模型(Large language model,LLM),构建“虚拟专家委员会”,通过精心设计的提示词工程,获取多维度、多情境下的主观权重。其次,针对传统熵权法对数据分布敏感、难以有效区分指标优劣等问题,提出一种改进的数据预处理熵权法(Improved data preprocessing entropy weighting method,IDPEW),该方法结合指标值的辨识度和信息熵的均衡性来确定客观权重。最后,将LLM生成的主观权重与IDPEW计算的客观权重进行加权组合,构建综合评价函数,对飞机货舱配载方案进行全面评估和排序。结果实验结果表明,LLM模拟专家意见时最关注“装载率”(主观权重0.2250),而IDPEW方法从数据中识别出“横向不平衡度”最具区分力(客观权重0.2481)。混合赋权模型(α=0.5)有效平衡了主客观偏好,在24个方案中精准识别出综合性能最优的方案,验证了模型在复杂情境下的稳定性。结论创新性地利用LLM低成本构建“虚拟专家”获取先验知识,并通过耦合指标辨识度与均衡性的IDPEW方法,提升了客观赋权精度。该模型克服了单一赋权的局限,为飞机货舱配载方案的科学评估提供了一种兼具可解释性和实用性的新范式。展开更多
基金Supported by the National Natural Science Foundation of China(10671147)
文摘In this paper, we prove an extrapolation theorem of operators in martingale spaces with Ap weights, which shows that for operators T defined on martingale space Lu^I, if T is bounded on martingale space L^P0 （w） for some 1 〈 P0 〈 ∞ and every w ∈ A∞, so it is on L^P,S（w） for every 1〈p, s〈∞ , and w∈ A∞. We also get some properties of Ap weights and prove that if w ∈ AI, then the maximal operator M is bounded on L^p,q（w） with 1〈p〈∞, 1〈q≤∞.
基金supported by the National Natural Science Foundation of China(62373317)the Tianshan Talent Training Program(2022TSYCCX0013)+3 种基金the Key Project of Natural Science Foundation of Xinjiang(2021D01D10)the Basic Research Foundation for Universities of Xinjiang(XJEDU2023P023)the Xinjiang Key Laboratory of Applied Mathematics(XJDX1401)the Intelligent Control and Optimization Research Platform in Xinjiang University.
文摘This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,in which only one parameter needs to be adjusted in the power-law terms;this greatly decreases the inconvenience of parameter adjustment.Second,several fixed-time passivity criteria with LMI forms are derived by using a Gauss divergence theorem to deal with the spatial diffusion of nodes and by applying the Hölder’s inequality to dispose rigorously the power-law term greater than one in the designed control scheme;this improves the previous theoretical analysis.Additionally,the fixed-time synchronization of spatiotemporal directed networks with multi-weights is addressed as a direct result of fixed-time strict passivity.Finally,a numerical example is presented in order to show the validity of the theoretical analysis.
基金supported by the Natural Science Research Project of Colleges and Universities in Anhui Province (No.KJ2021A0479)the Science Research Program of Anhui University of Finance and Economics (No.ACKYC22082)。
文摘We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.
基金supported by the National Natural Science Foundation of China(12171075)the Science and Technology Research Project of Education Department of Jilin Province(JJKH20241406KJ)Zhan’s research was supported by the Doctoral Startup Fund of Liaoning University of Technology(XB2024029).
文摘In this paper,it is shown that the harmonic Bergman projection P_(ω)^(h),induced by a radial,induced by a radial weightω,is bounded and onto from L^(∞)(D)to the harmonic Bloch space B_(h)if and only ifω∈D,,which is a class of radial weights satisfying the two-sided doubling conditions.As an application,the bounded and compact positive Toeplitz operators T_(μ,ω)on the endpoint case weighted harmonic Bergman space L_(h,ω)^(1)(D)are characterized.
基金supported by the Hunan Provincial Natural Science Foundation of China(Grant No.2023JJ40309)the Changsha Outstanding Innovative Youth Training Program(kq2306015).
文摘Grain filling is a critical determinant of yield and quality in rice.This study aims to clarify the association between grain photosynthesis and the filling rate of rice varieties with different grain weights,providing a theoretical foundation for optimizing grain-filling processes.Two rice varieties with similar growth duration but different grain weights were selected:a large-grain variety,Lingliangyou 268(L268),and a small-grain variety,Ruiliangyou 1053(R1053).Differences in grain filling,grain photosynthetic rate,and grain chlorophyll content were systematically examined during the filling stage.Results showed significant differences in grain-filling,grain photosynthetic rate,and grain chlorophyll content between large-grain and small-grain rice varieties.The grain photosynthetic rate of L268 was a significantly higher than R1053.L268 also exhibited significantly higher initial grain filling rate,maximum grainfilling rate,and mean grain filling rate compared to R1053.Throughout the grain filling period,L268 showed higher grain chlorophyll content(including chlorophyll a,chlorophyll b,and total chlorophyll)than R1053.The increase in chlorophyll content,particularly total chlorophyll,enhanced the grain photosynthetic rate during the early and middle stages of grain filling significantly.These findings suggested that rice varieties with higher grain weights exhibited stronger panicle photosynthetic capacity due to their higher chlorophyll content.The enhanced grain photosynthetic rate contributed to improved grain filling and increased grain weight.
基金supported by the National Natural Science Foundation of China under Grant Nos.523B2043 and 52475112.
文摘Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery condition monitoring because that can fully use available data and computational power.Since significant accidents might be caused if wrong fault alarms are given for machine condition monitoring,interpretable machine learning models,integrate signal processing knowledge to enhance trustworthiness of models,are gradually becoming a research hotspot.A previous spectrum-based and interpretable optimized weights method has been proposed to indicate faulty and fundamental frequencies when the analyzed data only contains a healthy type and a fault type.Considering that multiclass fault types are naturally met in practice,this work aims to explore the interpretable optimized weights method for multiclass fault type scenarios.Therefore,a new multiclass optimized weights spectrum(OWS)is proposed and further studied theoretically and numerically.It is found that the multiclass OWS is capable of capturing the characteristic components associated with different conditions and clearly indicating specific fault characteristic frequencies(FCFs)corresponding to each fault condition.This work can provide new insights into spectrum-based fault classification models,and the new multiclass OWS also shows great potential for practical applications.
文摘目的针对飞机货舱配载方案评估中多属性决策的复杂性,以及现有评估方法在权重确定上过度依赖专家经验导致的主观偏差,或单纯依赖客观数据忽略决策者偏好的局限性,提出一种融合主观先验与客观数据驱动的混合赋权评估模型,以提供更为合理、可靠的配载方案择优决策支持。方法首先,引入大型语言模型(Large language model,LLM),构建“虚拟专家委员会”,通过精心设计的提示词工程,获取多维度、多情境下的主观权重。其次,针对传统熵权法对数据分布敏感、难以有效区分指标优劣等问题,提出一种改进的数据预处理熵权法(Improved data preprocessing entropy weighting method,IDPEW),该方法结合指标值的辨识度和信息熵的均衡性来确定客观权重。最后,将LLM生成的主观权重与IDPEW计算的客观权重进行加权组合,构建综合评价函数,对飞机货舱配载方案进行全面评估和排序。结果实验结果表明,LLM模拟专家意见时最关注“装载率”(主观权重0.2250),而IDPEW方法从数据中识别出“横向不平衡度”最具区分力(客观权重0.2481)。混合赋权模型(α=0.5)有效平衡了主客观偏好,在24个方案中精准识别出综合性能最优的方案,验证了模型在复杂情境下的稳定性。结论创新性地利用LLM低成本构建“虚拟专家”获取先验知识,并通过耦合指标辨识度与均衡性的IDPEW方法,提升了客观赋权精度。该模型克服了单一赋权的局限,为飞机货舱配载方案的科学评估提供了一种兼具可解释性和实用性的新范式。
