The influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling engineering.Whereas,there lacks robust methods to predict excavation-induced tunnel displacements.In this study,an au...The influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling engineering.Whereas,there lacks robust methods to predict excavation-induced tunnel displacements.In this study,an auto machine learning(AutoML)-based approach is proposed to precisely solve the issue.Seven input parameters are considered in the database covering two physical aspects,namely soil property,and spatial characteristics of the deep excavation.The 10-fold cross-validation method is employed to overcome the scarcity of data,and promote model’s robustness.Six genetic algorithm(GA)-ML models are established as well for comparison.The results indicated that the proposed AutoML model is a comprehensive model that integrates efficiency and robustness.Importance analysis reveals that the ratio of the average shear strength to the vertical effective stress E_(ur)/σ′_(v),the excavation depth H,and the excavation width B are the most influential variables for the displacements.Finally,the AutoML model is further validated by practical engineering.The prediction results are in a good agreement with monitoring data,signifying that our model can be applied in real projects.展开更多
CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferrin...CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.展开更多
为了理解甲醇/柴油双燃料机的自燃特性并为燃烧计算所需骨架机理提供理论依据,以正庚烷作为柴油替代物,应用快速压缩机对宽广实验条件下甲醇/正庚烷混合燃料的自燃特性进行了研究。实验条件覆盖了甲醇/柴油双燃料机的典型工况。实验研...为了理解甲醇/柴油双燃料机的自燃特性并为燃烧计算所需骨架机理提供理论依据,以正庚烷作为柴油替代物,应用快速压缩机对宽广实验条件下甲醇/正庚烷混合燃料的自燃特性进行了研究。实验条件覆盖了甲醇/柴油双燃料机的典型工况。实验研究结果显示,随着压力升高、甲醇比例减少或当量比增大,混合燃料滞燃期变短。根据实验数据验证了爱尔兰国立大学(National University of Ireland,NUI)的正庚烷详细机理对甲醇/正庚烷的适用性,并利用该机理在CHEMKIN PRO软件中进行了化学动力学分析。结果表明,甲醇与正庚烷竞争羟基(hydroxyl,OH)从而抑制系统氧化过程。敏感性分析结果显示,超氧化氢(HO_(2))反应生成过氧化氢(H_(2)O_(2))是燃烧过程中最敏感的反应,抑制系统氧化过程的进行。本研究可为获得适用于甲醇/柴油双燃料机燃烧计算的骨架机理提供理论依据。展开更多
由于文档纸张的几何形变、拍摄场景的干扰及拍摄角度不理想导致的透视失真,移动设备获取的文档图像的光学字符识别(Optical character recognition,OCR)性能受到很大挑战。针对折叠和扭曲的畸变文档图像预处理问题,设计了两种基于自编...由于文档纸张的几何形变、拍摄场景的干扰及拍摄角度不理想导致的透视失真,移动设备获取的文档图像的光学字符识别(Optical character recognition,OCR)性能受到很大挑战。针对折叠和扭曲的畸变文档图像预处理问题,设计了两种基于自编码器的网络结构,以实现自适应性图像矫正并提高文字识别正确率。首先提出空洞残差块和非对称卷积残差块两种残差块,然后将残差块与自编码器相结合,设计了一种非对称空洞自编码器网络;同时利用空间金字塔池化代替全连接层,并用非对称卷积残差块实现特征提取,设计了另一种空间金字塔自编码器网络。实验结果表明,与畸变图像相比,经非对称空洞自编码器网络矫正后的图像在OCR正确率、OCR召回率和文本相似度上分别提高了26.3%、20.4%和12.3%,而经空间金字塔自编码器网络矫正后的图像在正确率、召回率和文本相似度上分别提高了27.7%、22.0%和15.5%。与RectiNet等其他图像矫正网络相比,这两种网络可以自适应矫正多种类型的畸变文档图像,且矫正后的图像在文字识别上表现更为优异。本文提出的两种矫正网络能有效提高图像文字识别正确率、召回率和文本相似度,同时在鲁棒性、泛化性等方面与现有矫正网络相比具有明显的优势。展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.51978517,52090082,and 52108381)Innovation Program of Shanghai Municipal Education Commission(Grant No.2019-01-07-00-07-456 E00051)Shanghai Science and Technology Committee Program(Grant Nos.21DZ1200601 and 20DZ1201404).
文摘The influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling engineering.Whereas,there lacks robust methods to predict excavation-induced tunnel displacements.In this study,an auto machine learning(AutoML)-based approach is proposed to precisely solve the issue.Seven input parameters are considered in the database covering two physical aspects,namely soil property,and spatial characteristics of the deep excavation.The 10-fold cross-validation method is employed to overcome the scarcity of data,and promote model’s robustness.Six genetic algorithm(GA)-ML models are established as well for comparison.The results indicated that the proposed AutoML model is a comprehensive model that integrates efficiency and robustness.Importance analysis reveals that the ratio of the average shear strength to the vertical effective stress E_(ur)/σ′_(v),the excavation depth H,and the excavation width B are the most influential variables for the displacements.Finally,the AutoML model is further validated by practical engineering.The prediction results are in a good agreement with monitoring data,signifying that our model can be applied in real projects.
文摘CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.
文摘为了理解甲醇/柴油双燃料机的自燃特性并为燃烧计算所需骨架机理提供理论依据,以正庚烷作为柴油替代物,应用快速压缩机对宽广实验条件下甲醇/正庚烷混合燃料的自燃特性进行了研究。实验条件覆盖了甲醇/柴油双燃料机的典型工况。实验研究结果显示,随着压力升高、甲醇比例减少或当量比增大,混合燃料滞燃期变短。根据实验数据验证了爱尔兰国立大学(National University of Ireland,NUI)的正庚烷详细机理对甲醇/正庚烷的适用性,并利用该机理在CHEMKIN PRO软件中进行了化学动力学分析。结果表明,甲醇与正庚烷竞争羟基(hydroxyl,OH)从而抑制系统氧化过程。敏感性分析结果显示,超氧化氢(HO_(2))反应生成过氧化氢(H_(2)O_(2))是燃烧过程中最敏感的反应,抑制系统氧化过程的进行。本研究可为获得适用于甲醇/柴油双燃料机燃烧计算的骨架机理提供理论依据。
文摘由于文档纸张的几何形变、拍摄场景的干扰及拍摄角度不理想导致的透视失真,移动设备获取的文档图像的光学字符识别(Optical character recognition,OCR)性能受到很大挑战。针对折叠和扭曲的畸变文档图像预处理问题,设计了两种基于自编码器的网络结构,以实现自适应性图像矫正并提高文字识别正确率。首先提出空洞残差块和非对称卷积残差块两种残差块,然后将残差块与自编码器相结合,设计了一种非对称空洞自编码器网络;同时利用空间金字塔池化代替全连接层,并用非对称卷积残差块实现特征提取,设计了另一种空间金字塔自编码器网络。实验结果表明,与畸变图像相比,经非对称空洞自编码器网络矫正后的图像在OCR正确率、OCR召回率和文本相似度上分别提高了26.3%、20.4%和12.3%,而经空间金字塔自编码器网络矫正后的图像在正确率、召回率和文本相似度上分别提高了27.7%、22.0%和15.5%。与RectiNet等其他图像矫正网络相比,这两种网络可以自适应矫正多种类型的畸变文档图像,且矫正后的图像在文字识别上表现更为优异。本文提出的两种矫正网络能有效提高图像文字识别正确率、召回率和文本相似度,同时在鲁棒性、泛化性等方面与现有矫正网络相比具有明显的优势。
