As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and s...As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and smart maintenance.While promising,both methods have issues that need to be addressed.For example,model-based methods are limited by low computational accuracy and a high computational burden,and data-driven methods always suffer from poor interpretability and redundant features.To address these issues,the concept of data-model fusion(DMF)emerges as a promising solution.DMF involves integrating model-based methods with data-driven methods by incorporating big data into model-based methods or embedding relevant domain knowledge into data-driven methods.Despite growing efforts in the field of DMF,a unanimous definition of DMF remains elusive,and a general framework of DMF has been rarely discussed.This paper aims to address this gap by providing a thorough overview and categorization of both data-driven methods and model-based methods.Subsequently,this paper also presents the definition and categorization of DMF and discusses the general framework of DMF.Moreover,the primary seven applications of DMF are reviewed within the context of smart manufacturing and digital engineering.Finally,this paper directs the future directions of DMF.展开更多
This paper presents a method for measuring stress fields within the framework of coupled data models,aimed at determining stress fields in isotropic material structures exhibiting localized deterioration behavior with...This paper presents a method for measuring stress fields within the framework of coupled data models,aimed at determining stress fields in isotropic material structures exhibiting localized deterioration behavior without relying on constitutive equations in the deteriorated region.This approach contributes to advancing the field of intrinsic equation-free mechanics.The methodology combines measured strain fields with data-model coupling driven algorithms.The gradient and Canny operators are utilized to process the strain field data,enabling the determination of the deterioration region's location.Meanwhile,an adaptive model building method is proposed for constructing coupling driven models.To address the issue of unknown datasets during computation,a dataset updating strategy based on a differential evolutionary algorithm is introduced.The resulting optimal dataset is then used to generate stress field results.Validation against finite element method calculations demonstrates the accuracy of the proposed method in obtaining full-field stresses in specimens with local degradation behavior.展开更多
现有GNSS水汽层析研究主要聚焦于如何提升卫星观测数据利用率,但在卫星信号数据优选方面研究较少,导致穿过同一组网格集的层析观测方程线性近似且方程系数矩阵列向量元素多数为零,水汽层析模型病态严重。针对该现状,本文提出一种GNSS卫...现有GNSS水汽层析研究主要聚焦于如何提升卫星观测数据利用率,但在卫星信号数据优选方面研究较少,导致穿过同一组网格集的层析观测方程线性近似且方程系数矩阵列向量元素多数为零,水汽层析模型病态严重。针对该现状,本文提出一种GNSS卫星信号自适应优选的水汽层析方法,解决层析模型设计矩阵零元素较多和层析模型病态的难题。该方法基于网格覆盖率最大原则确定层析区域水平网格划分,并发展联合卫星高度角与方位角阈值的卫星信号自适应优选方法,克服水汽层析模型观测方程线性近似的难题。本文选取香港地区2013年5月2日—2013年5月7日共6 d 12个GNSS测站及1个无线电探空站数据为例进行试验。与现有方法相比,本文方法能在降低卫星信号利用率的同时保证网格覆盖率,克服相似卫星信号造成层析模型设计矩阵病态的现状。以无线电探空数据为真值,发现本文方法反演水汽密度廓线的平均RMS、MAE和Bias分别为1.03、0.80和0.13 g/m^(3),优于传统方法的1.25、0.97和0.10 g/m^(3),其RMS改善率为20.78%;此外,本文方法在模型解算效率方面也优于传统方法,其模型计算效率平均提升9.51%。展开更多
目的采用数据挖掘方法总结肺纤维化动物模型的特点及建立药效指标评价体系。方法通过中国知网(CNKI)、万方数据库(Wangfang)、维普中文科技期刊全文数据库(VIP)、PubMed、Web of Science、Embase数据库检索与肺纤维化动物药效研究相关...目的采用数据挖掘方法总结肺纤维化动物模型的特点及建立药效指标评价体系。方法通过中国知网(CNKI)、万方数据库(Wangfang)、维普中文科技期刊全文数据库(VIP)、PubMed、Web of Science、Embase数据库检索与肺纤维化动物药效研究相关的文献,归纳整理、分析肺纤维化动物模型的造模方法、干预药物等,并统计检测指标类型、方法等,构建肺纤维化动物药效指标体系。结果共纳入1174篇文献,动物造模常见的诱导因素有肿瘤药物、环境/职业暴露颗粒、物理因素等,以C57 BL/6小鼠、SD大鼠为主要研究对象,其中博来霉素以无创性气管滴注诱导肺纤维化动物模型最常见。肺纤维化动物药效研究中常见的干预药物有化学药、抑制剂/激动剂、天然药物、中药复方等。肺纤维化动物药效研究中检测指标包括一般情况、肺功能、肺组织病理、细胞外基质、上皮间质转化、细胞因子、氧化应激等七类,其中一般情况以体质量、肺系数、生存分析检测为主;肺功能指标主要包括用力肺活量、动态肺顺应性、潮气量等;常见的肺组织病理染色方法有HE染色、Masson染色及天狼猩红染色等;细胞外基质检测指标以Ⅰ型胶原蛋白、羟脯氨酸、纤维连接蛋白等为主;上皮间质转化指标有α-平滑肌肌动蛋白、E-钙黏蛋白、波形蛋白等;细胞因子检测指标主要有转化生长因子β1、肿瘤坏死因子α、白细胞介素6等;氧化应激检测指标主要包括丙二醛、超氧化物歧化酶、谷胱甘肽等。根据检测指标频次≥200次作为肺纤维化动物药效研究检测的强推荐指标,将一般情况(体质量、肺系数)、肺病理(HE染色、Masson染色等)、细胞外基质(羟脯氨酸、Ⅰ型胶原蛋白、Ⅲ型胶原蛋白、纤维连接蛋白)、上皮间质化(α-平滑肌肌动蛋白)、细胞因子(肿瘤坏死因子α、白细胞介素6/1β、转化生长因子β1)作为肺纤维化动物药效研究检测的强推荐指标。结论本研究为肺纤维化动物模型构建及药效指标评价体系的建立提供了更多参考。展开更多
随着大型语言模型(LLMs)在超大规模语料库上开展预训练,数据污染问题逐渐凸显,这对模型性能评估的准确性构成了直接威胁。提出了一种创新的动态数据评估方法EdEval(equal distribution dynamic evaluation),旨在降低数据污染对评估准确...随着大型语言模型(LLMs)在超大规模语料库上开展预训练,数据污染问题逐渐凸显,这对模型性能评估的准确性构成了直接威胁。提出了一种创新的动态数据评估方法EdEval(equal distribution dynamic evaluation),旨在降低数据污染对评估准确性的影响。EdEval通过提取核心知识点与主旨,确保生成的评估问题在本质上与静态数据一致,并结合联网检索对知识点进行深入阐述,生成具有高质量知识支撑的评估样本。此外,EdEval通过控制问题数量和复杂度,实现动态对齐与灵活调节,以匹配静态数据的难度水平并满足不同复杂度的需求。采用布鲁姆分类法,EdEval从记忆、理解、应用、分析、评价和创造六个维度对LLMs进行综合评估。实验结果表明,EdEval在多个数据集上有效减轻了数据污染的影响,显著提高了评估的公正性和准确性。展开更多
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grants(52275471 and 52120105008)the Beijing Outstanding Young Scientist Program,and the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and smart maintenance.While promising,both methods have issues that need to be addressed.For example,model-based methods are limited by low computational accuracy and a high computational burden,and data-driven methods always suffer from poor interpretability and redundant features.To address these issues,the concept of data-model fusion(DMF)emerges as a promising solution.DMF involves integrating model-based methods with data-driven methods by incorporating big data into model-based methods or embedding relevant domain knowledge into data-driven methods.Despite growing efforts in the field of DMF,a unanimous definition of DMF remains elusive,and a general framework of DMF has been rarely discussed.This paper aims to address this gap by providing a thorough overview and categorization of both data-driven methods and model-based methods.Subsequently,this paper also presents the definition and categorization of DMF and discusses the general framework of DMF.Moreover,the primary seven applications of DMF are reviewed within the context of smart manufacturing and digital engineering.Finally,this paper directs the future directions of DMF.
基金supported by the Fundamental Research Fund for the Central Universities(Grant No.BLX202226)。
文摘This paper presents a method for measuring stress fields within the framework of coupled data models,aimed at determining stress fields in isotropic material structures exhibiting localized deterioration behavior without relying on constitutive equations in the deteriorated region.This approach contributes to advancing the field of intrinsic equation-free mechanics.The methodology combines measured strain fields with data-model coupling driven algorithms.The gradient and Canny operators are utilized to process the strain field data,enabling the determination of the deterioration region's location.Meanwhile,an adaptive model building method is proposed for constructing coupling driven models.To address the issue of unknown datasets during computation,a dataset updating strategy based on a differential evolutionary algorithm is introduced.The resulting optimal dataset is then used to generate stress field results.Validation against finite element method calculations demonstrates the accuracy of the proposed method in obtaining full-field stresses in specimens with local degradation behavior.
文摘现有GNSS水汽层析研究主要聚焦于如何提升卫星观测数据利用率,但在卫星信号数据优选方面研究较少,导致穿过同一组网格集的层析观测方程线性近似且方程系数矩阵列向量元素多数为零,水汽层析模型病态严重。针对该现状,本文提出一种GNSS卫星信号自适应优选的水汽层析方法,解决层析模型设计矩阵零元素较多和层析模型病态的难题。该方法基于网格覆盖率最大原则确定层析区域水平网格划分,并发展联合卫星高度角与方位角阈值的卫星信号自适应优选方法,克服水汽层析模型观测方程线性近似的难题。本文选取香港地区2013年5月2日—2013年5月7日共6 d 12个GNSS测站及1个无线电探空站数据为例进行试验。与现有方法相比,本文方法能在降低卫星信号利用率的同时保证网格覆盖率,克服相似卫星信号造成层析模型设计矩阵病态的现状。以无线电探空数据为真值,发现本文方法反演水汽密度廓线的平均RMS、MAE和Bias分别为1.03、0.80和0.13 g/m^(3),优于传统方法的1.25、0.97和0.10 g/m^(3),其RMS改善率为20.78%;此外,本文方法在模型解算效率方面也优于传统方法,其模型计算效率平均提升9.51%。
文摘目的采用数据挖掘方法总结肺纤维化动物模型的特点及建立药效指标评价体系。方法通过中国知网(CNKI)、万方数据库(Wangfang)、维普中文科技期刊全文数据库(VIP)、PubMed、Web of Science、Embase数据库检索与肺纤维化动物药效研究相关的文献,归纳整理、分析肺纤维化动物模型的造模方法、干预药物等,并统计检测指标类型、方法等,构建肺纤维化动物药效指标体系。结果共纳入1174篇文献,动物造模常见的诱导因素有肿瘤药物、环境/职业暴露颗粒、物理因素等,以C57 BL/6小鼠、SD大鼠为主要研究对象,其中博来霉素以无创性气管滴注诱导肺纤维化动物模型最常见。肺纤维化动物药效研究中常见的干预药物有化学药、抑制剂/激动剂、天然药物、中药复方等。肺纤维化动物药效研究中检测指标包括一般情况、肺功能、肺组织病理、细胞外基质、上皮间质转化、细胞因子、氧化应激等七类,其中一般情况以体质量、肺系数、生存分析检测为主;肺功能指标主要包括用力肺活量、动态肺顺应性、潮气量等;常见的肺组织病理染色方法有HE染色、Masson染色及天狼猩红染色等;细胞外基质检测指标以Ⅰ型胶原蛋白、羟脯氨酸、纤维连接蛋白等为主;上皮间质转化指标有α-平滑肌肌动蛋白、E-钙黏蛋白、波形蛋白等;细胞因子检测指标主要有转化生长因子β1、肿瘤坏死因子α、白细胞介素6等;氧化应激检测指标主要包括丙二醛、超氧化物歧化酶、谷胱甘肽等。根据检测指标频次≥200次作为肺纤维化动物药效研究检测的强推荐指标,将一般情况(体质量、肺系数)、肺病理(HE染色、Masson染色等)、细胞外基质(羟脯氨酸、Ⅰ型胶原蛋白、Ⅲ型胶原蛋白、纤维连接蛋白)、上皮间质化(α-平滑肌肌动蛋白)、细胞因子(肿瘤坏死因子α、白细胞介素6/1β、转化生长因子β1)作为肺纤维化动物药效研究检测的强推荐指标。结论本研究为肺纤维化动物模型构建及药效指标评价体系的建立提供了更多参考。
文摘随着大型语言模型(LLMs)在超大规模语料库上开展预训练,数据污染问题逐渐凸显,这对模型性能评估的准确性构成了直接威胁。提出了一种创新的动态数据评估方法EdEval(equal distribution dynamic evaluation),旨在降低数据污染对评估准确性的影响。EdEval通过提取核心知识点与主旨,确保生成的评估问题在本质上与静态数据一致,并结合联网检索对知识点进行深入阐述,生成具有高质量知识支撑的评估样本。此外,EdEval通过控制问题数量和复杂度,实现动态对齐与灵活调节,以匹配静态数据的难度水平并满足不同复杂度的需求。采用布鲁姆分类法,EdEval从记忆、理解、应用、分析、评价和创造六个维度对LLMs进行综合评估。实验结果表明,EdEval在多个数据集上有效减轻了数据污染的影响,显著提高了评估的公正性和准确性。
