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
In recent years, functional data has been widely used in finance, medicine, biology and other fields. The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be direc...In recent years, functional data has been widely used in finance, medicine, biology and other fields. The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be directly used for the clustering of functional data. In this paper, we propose a new unsupervised clustering algorithm based on adaptive weights. In the absence of initialization parameter, we use entropy-type penalty terms and fuzzy partition matrix to find the optimal number of clusters. At the same time, we introduce a measure based on adaptive weights to reflect the difference in information content between different clustering metrics. Simulation experiments show that the proposed algorithm has higher purity than some algorithms.展开更多
Information about the relative importance of each criterion or theweights of criteria can have a significant influence on the ultimate rank of alternatives.Accordingly,assessing the weights of criteria is a very impor...Information about the relative importance of each criterion or theweights of criteria can have a significant influence on the ultimate rank of alternatives.Accordingly,assessing the weights of criteria is a very important task in solving multi-criteria decision-making problems.Three methods are commonly used for assessing the weights of criteria:objective,subjective,and integrated methods.In this study,an objective approach is proposed to assess the weights of criteria,called SPCmethod(Symmetry Point of Criterion).This point enriches the criterion so that it is balanced and easy to implement in the process of the evaluation of its influence on decision-making.The SPC methodology is systematically presented and supported by detailed calculations related to an artificial example.To validate the developed method,we used our numerical example and calculated the weights of criteria by CRITIC,Entropy,Standard Deviation and MEREC methods.Comparative analysis between these methods and the SPC method reveals that the developedmethod is a very reliable objective way to determine the weights of criteria.Additionally,in this study,we proposed the application of SPCmethod to evaluate the efficiency of themulti-criteria partitioning algorithm.The main idea of the evaluation is based on the following fact:the greater the uniformity of the weights of criteria,the higher the efficiency of the partitioning algorithm.The research demonstrates that the SPC method can be applied to solving different multi-criteria problems.展开更多
In the context of the joint operation of China’s intra-provincial markets and inter-provincial trading,how to meet the load demand and energy consumption using inter-provincial renewable energy trading is a key probl...In the context of the joint operation of China’s intra-provincial markets and inter-provincial trading,how to meet the load demand and energy consumption using inter-provincial renewable energy trading is a key problem.The combined operation of intra-provincial and inter-provincial markets provides a new way for provincial power companies to optimize and clear the intra-provincial power market,complete the intra-provincial consumption responsibility weight index,and consume renewable energy across provinces and regions.This paper combines power generation and consumption within the province,uses inter-provincial renewable energy trading tomeet the load demand within the province and completes the index of intra-provincial consumption responsibility weights.The intra-provincial market trading and inter-provincial market clearing are respectively taken as the upper and lower levels of the model.Under the two-level electricity market operation framework,the upper-level model aims to minimize the expected total operating cost within the province considering the carbon emission cost and the weight of the consumption responsibility,while the lower-level model aims to minimize the inter-provincial renewable energy purchasing cost.Finally,the influence of inter-provincial transaction mechanism,risk aversion coefficient,voucher price,and responsibility weight on operating cost is analyzed.Simulation is used to verify that the proposed model can meet the requirements of the provincial load power consumption and the consumption responsibility weight index,and promote the consumption of renewable energy.展开更多
工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小...工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小迭代修复和改进WGAN混合模型的时序数据修复方法.首先,在预处理阶段,保留异常数据,进行信息标注等处理,从而充分挖掘异常值与真实值之间的特征约束.其次,在噪声模块提出了近邻参数裁剪规则,用于修正最小迭代修复公式生成的噪声向量.将其传递至模拟分布模块的生成器中,同时设计了一个动态时间注意力网络层,用于提取时序特征权重并与门控循环单元串联组合捕捉不同步长的特征依赖,并引入递归多步预测原理共同提升模型的表达能力;在判别器中设计了Abnormal and Truth奖励机制和Weighted Mean Square Error损失函数共同反向优化生成器修复数据的细节和质量.最后,在公开数据集和真实数据集上的实验结果表明,该方法的修复准确度与模型稳定性显著优于现有方法.展开更多
下丘脑是参与进食调控的重要脑区,具有复杂的环路调控机制。然而,是否存在下丘脑以外同样发挥体重调节功能的神经核团尚不清楚。本试验利用神经环路示踪技术,鉴定向背肩胛棕色脂肪组织(interscapular brown adipose tissue, IBAT)发送...下丘脑是参与进食调控的重要脑区,具有复杂的环路调控机制。然而,是否存在下丘脑以外同样发挥体重调节功能的神经核团尚不清楚。本试验利用神经环路示踪技术,鉴定向背肩胛棕色脂肪组织(interscapular brown adipose tissue, IBAT)发送神经元投射的神经元类型和分布,探究中枢神经元及神经环路调节能量稳态的形态学基础。首先通过报告基因和逆行示踪识别囊泡型谷氨酸转运体2(vesicular glutamate transporter 2, VGlut2)和细胞外调节蛋白激酶(extracellular regulated protein kinase, ERK)神经元,顺行示踪试验拓展VGlut2神经元发送神经支配的大脑区域。化学遗传技术验证VGlut2免疫阳性神经元的体重和采食调控作用。结果显示:LC^(VGlut2::ERK)神经元向IBAT发送密集的神经支配信号。ERK免疫阳性神经元富集表达在LC,可作为LC特异性标记物。LC^(VGlut2)神经元向纹状体(neurons project to the striatum, CPu)、第二运动皮层(secondary motor cortex, M2)、下丘脑腹内侧核(ventromedial hypothalamic nucleus, VMH)和迷走神经背核(dorsal motor nucleus of the vagus, DMV)发送神经元投射。化学遗传激活LC^(VGlut2)神经元显著降低小鼠体重(P=0.016 5)和采食(P=0.029 0)。综上,LC^(VGlut2)神经元参与小鼠的体重调节过程。鉴定除下丘脑室旁核(paraventricular nucleus of hypothalamus, PVN)以外的谷氨酸能神经元及调节体重的下游神经环路,将进一步加深对于摄食行为和摄食过程中神经调控机制的了解,为研究和干预肥胖相关的疾病提供新思路。展开更多
基于GIS的多元信息成矿预测方法可以利用数据信息,圈定不同级别的成矿远景区,已经在航空物探铀矿勘查工作中得到了广泛应用。铀成矿有利信息权重赋值主要采用经验赋值法,存在主观性强、缺乏分析利用数据特征的问题,文章通过引入CRITIC(C...基于GIS的多元信息成矿预测方法可以利用数据信息,圈定不同级别的成矿远景区,已经在航空物探铀矿勘查工作中得到了广泛应用。铀成矿有利信息权重赋值主要采用经验赋值法,存在主观性强、缺乏分析利用数据特征的问题,文章通过引入CRITIC(Criteria Importance Though Intercrieria Correlation)权重法对传统的经验赋值法进行约束,建立了改进经验赋值法,并以二连盆地赛汉高毕地区的铀成矿预测为例,通过预测结果与已知钻孔、矿体等铀矿信息的比较,确定了改进经验赋值法最终圈定的成矿有利区带与客观事实吻合。结果表明,通过CRITIC权重法约束的改进经验赋值法可以同时利用数据本身信息和预测专家的经验,在铀成矿有利信息的权重赋值方面有着更好的效果,圈定的铀成矿有利区带更加准确,值得在其他区域开展进一步应用。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≤∞.
文摘In recent years, functional data has been widely used in finance, medicine, biology and other fields. The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be directly used for the clustering of functional data. In this paper, we propose a new unsupervised clustering algorithm based on adaptive weights. In the absence of initialization parameter, we use entropy-type penalty terms and fuzzy partition matrix to find the optimal number of clusters. At the same time, we introduce a measure based on adaptive weights to reflect the difference in information content between different clustering metrics. Simulation experiments show that the proposed algorithm has higher purity than some algorithms.
文摘Information about the relative importance of each criterion or theweights of criteria can have a significant influence on the ultimate rank of alternatives.Accordingly,assessing the weights of criteria is a very important task in solving multi-criteria decision-making problems.Three methods are commonly used for assessing the weights of criteria:objective,subjective,and integrated methods.In this study,an objective approach is proposed to assess the weights of criteria,called SPCmethod(Symmetry Point of Criterion).This point enriches the criterion so that it is balanced and easy to implement in the process of the evaluation of its influence on decision-making.The SPC methodology is systematically presented and supported by detailed calculations related to an artificial example.To validate the developed method,we used our numerical example and calculated the weights of criteria by CRITIC,Entropy,Standard Deviation and MEREC methods.Comparative analysis between these methods and the SPC method reveals that the developedmethod is a very reliable objective way to determine the weights of criteria.Additionally,in this study,we proposed the application of SPCmethod to evaluate the efficiency of themulti-criteria partitioning algorithm.The main idea of the evaluation is based on the following fact:the greater the uniformity of the weights of criteria,the higher the efficiency of the partitioning algorithm.The research demonstrates that the SPC method can be applied to solving different multi-criteria problems.
基金supported by National Natural Science Foundation of China (51977127)Shanghai Municipal Science and Technology Commission (19020500800)“Shuguang Program” (20SG52)Shanghai Education Development Foundation and Shanghai Municipal Education Commission.
文摘In the context of the joint operation of China’s intra-provincial markets and inter-provincial trading,how to meet the load demand and energy consumption using inter-provincial renewable energy trading is a key problem.The combined operation of intra-provincial and inter-provincial markets provides a new way for provincial power companies to optimize and clear the intra-provincial power market,complete the intra-provincial consumption responsibility weight index,and consume renewable energy across provinces and regions.This paper combines power generation and consumption within the province,uses inter-provincial renewable energy trading tomeet the load demand within the province and completes the index of intra-provincial consumption responsibility weights.The intra-provincial market trading and inter-provincial market clearing are respectively taken as the upper and lower levels of the model.Under the two-level electricity market operation framework,the upper-level model aims to minimize the expected total operating cost within the province considering the carbon emission cost and the weight of the consumption responsibility,while the lower-level model aims to minimize the inter-provincial renewable energy purchasing cost.Finally,the influence of inter-provincial transaction mechanism,risk aversion coefficient,voucher price,and responsibility weight on operating cost is analyzed.Simulation is used to verify that the proposed model can meet the requirements of the provincial load power consumption and the consumption responsibility weight index,and promote the consumption of renewable energy.
文摘工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小迭代修复和改进WGAN混合模型的时序数据修复方法.首先,在预处理阶段,保留异常数据,进行信息标注等处理,从而充分挖掘异常值与真实值之间的特征约束.其次,在噪声模块提出了近邻参数裁剪规则,用于修正最小迭代修复公式生成的噪声向量.将其传递至模拟分布模块的生成器中,同时设计了一个动态时间注意力网络层,用于提取时序特征权重并与门控循环单元串联组合捕捉不同步长的特征依赖,并引入递归多步预测原理共同提升模型的表达能力;在判别器中设计了Abnormal and Truth奖励机制和Weighted Mean Square Error损失函数共同反向优化生成器修复数据的细节和质量.最后,在公开数据集和真实数据集上的实验结果表明,该方法的修复准确度与模型稳定性显著优于现有方法.
文摘下丘脑是参与进食调控的重要脑区,具有复杂的环路调控机制。然而,是否存在下丘脑以外同样发挥体重调节功能的神经核团尚不清楚。本试验利用神经环路示踪技术,鉴定向背肩胛棕色脂肪组织(interscapular brown adipose tissue, IBAT)发送神经元投射的神经元类型和分布,探究中枢神经元及神经环路调节能量稳态的形态学基础。首先通过报告基因和逆行示踪识别囊泡型谷氨酸转运体2(vesicular glutamate transporter 2, VGlut2)和细胞外调节蛋白激酶(extracellular regulated protein kinase, ERK)神经元,顺行示踪试验拓展VGlut2神经元发送神经支配的大脑区域。化学遗传技术验证VGlut2免疫阳性神经元的体重和采食调控作用。结果显示:LC^(VGlut2::ERK)神经元向IBAT发送密集的神经支配信号。ERK免疫阳性神经元富集表达在LC,可作为LC特异性标记物。LC^(VGlut2)神经元向纹状体(neurons project to the striatum, CPu)、第二运动皮层(secondary motor cortex, M2)、下丘脑腹内侧核(ventromedial hypothalamic nucleus, VMH)和迷走神经背核(dorsal motor nucleus of the vagus, DMV)发送神经元投射。化学遗传激活LC^(VGlut2)神经元显著降低小鼠体重(P=0.016 5)和采食(P=0.029 0)。综上,LC^(VGlut2)神经元参与小鼠的体重调节过程。鉴定除下丘脑室旁核(paraventricular nucleus of hypothalamus, PVN)以外的谷氨酸能神经元及调节体重的下游神经环路,将进一步加深对于摄食行为和摄食过程中神经调控机制的了解,为研究和干预肥胖相关的疾病提供新思路。
文摘基于GIS的多元信息成矿预测方法可以利用数据信息,圈定不同级别的成矿远景区,已经在航空物探铀矿勘查工作中得到了广泛应用。铀成矿有利信息权重赋值主要采用经验赋值法,存在主观性强、缺乏分析利用数据特征的问题,文章通过引入CRITIC(Criteria Importance Though Intercrieria Correlation)权重法对传统的经验赋值法进行约束,建立了改进经验赋值法,并以二连盆地赛汉高毕地区的铀成矿预测为例,通过预测结果与已知钻孔、矿体等铀矿信息的比较,确定了改进经验赋值法最终圈定的成矿有利区带与客观事实吻合。结果表明,通过CRITIC权重法约束的改进经验赋值法可以同时利用数据本身信息和预测专家的经验,在铀成矿有利信息的权重赋值方面有着更好的效果,圈定的铀成矿有利区带更加准确,值得在其他区域开展进一步应用。
