The continuous top-t most influential place （CTtMIP） query is defined formally and solved efficiently in this paper. A CTtMIP query continuously monitors the t places with the maximum influence from the set of place...The continuous top-t most influential place （CTtMIP） query is defined formally and solved efficiently in this paper. A CTtMIP query continuously monitors the t places with the maximum influence from the set of places, where the influence of a place is defined as the number of its bichromatic reverse k nearest neighbors （BRkNNs）. Two new metrics and their corresponding rules are introduced to shrink the search region and reduce the candidates of BRkNNs checked. Extensive experiments confirm that our proposed approach outperforms the state-of-the-art competitor significantly.展开更多
Vast amounts of valuable historical tunnelling site investigation data remain underutilized due to inefficient content-based archiving and searching tools.This study introduces a novel data-driven framework that integ...Vast amounts of valuable historical tunnelling site investigation data remain underutilized due to inefficient content-based archiving and searching tools.This study introduces a novel data-driven framework that integrates transfer learning with reverse image search to revolutionize the utilization of historical data in tunnelling projects.The method indexes excavated tunnel sections with corresponding tunnel face images and identifies similarities between projects based on geological features.Transfer learning with pre-trained deep learning models is employed to compress tunnel face images into compact,lower-dimensional vectors,enabling efficient similarity searches.This transformation converts geological information into comparable vectors,enhancing the efficiency and speed of data searches.An online cloud service is developed to allow engineers to access similar historical projects in real-time.To enhance the quality of the compressed vectors,this study developed a multi-level feature extraction method.This method markedly improves the deep learning models’ability to accurately identify major features from rock images.When applied to a diverse range of tunnel excavation projects in China,the model exhibited an impressive accuracy of over 90%in retrieving projects with similar geological features.This underscores the model’s potential as a robust tool for enhancing data management and decision-making in tunnelling engineering.展开更多
基金Supported by the National Natural Science Foundation of China (61003049)the Natural Science Foundation of Zhejiang Province (Y110278, 2010QNA5051)Zheda Zijin Plan
文摘The continuous top-t most influential place （CTtMIP） query is defined formally and solved efficiently in this paper. A CTtMIP query continuously monitors the t places with the maximum influence from the set of places, where the influence of a place is defined as the number of its bichromatic reverse k nearest neighbors （BRkNNs）. Two new metrics and their corresponding rules are introduced to shrink the search region and reduce the candidates of BRkNNs checked. Extensive experiments confirm that our proposed approach outperforms the state-of-the-art competitor significantly.
基金funded by the Research and Development Program of the Department of Transportation Zhejiang,China(Grant No.202213)Australian Government through the Australian Research Council’s Discovery Projects funding scheme(Project No.DP220103381)+1 种基金the National Natural Science Foundation of China(Grant Nos.52222905,52179103 and 42272326)Jiangxi Provincial Natural Science Foundation(Grant Nos.20232ACB204031 and 20224ACB204019).
文摘Vast amounts of valuable historical tunnelling site investigation data remain underutilized due to inefficient content-based archiving and searching tools.This study introduces a novel data-driven framework that integrates transfer learning with reverse image search to revolutionize the utilization of historical data in tunnelling projects.The method indexes excavated tunnel sections with corresponding tunnel face images and identifies similarities between projects based on geological features.Transfer learning with pre-trained deep learning models is employed to compress tunnel face images into compact,lower-dimensional vectors,enabling efficient similarity searches.This transformation converts geological information into comparable vectors,enhancing the efficiency and speed of data searches.An online cloud service is developed to allow engineers to access similar historical projects in real-time.To enhance the quality of the compressed vectors,this study developed a multi-level feature extraction method.This method markedly improves the deep learning models’ability to accurately identify major features from rock images.When applied to a diverse range of tunnel excavation projects in China,the model exhibited an impressive accuracy of over 90%in retrieving projects with similar geological features.This underscores the model’s potential as a robust tool for enhancing data management and decision-making in tunnelling engineering.
