Deformation monitoring is a critical measure for intuitively reflecting the operational behavior of a dam.However,the deformation monitoring data are often incomplete due to environmental changes,monitoring instrument...Deformation monitoring is a critical measure for intuitively reflecting the operational behavior of a dam.However,the deformation monitoring data are often incomplete due to environmental changes,monitoring instrument faults,and human operational errors,thereby often hindering the accurate assessment of actual deformation patterns.This study proposed a method for quantifying deformation similarity between measurement points by recognizing the spatiotemporal characteristics of concrete dam deformation monitoring data.It introduces a spatiotemporal clustering analysis of the concrete dam deformation behavior and employs the support vector machine model to address the missing data in concrete dam deformation monitoring.The proposed method was validated in a concrete dam project,with the model error maintaining within 5%,demonstrating its effectiveness in processing missing deformation data.This approach enhances the capability of early-warning systems and contributes to enhanced dam safety management.展开更多
Heterogeneous graphs organize data with nodes and edges,and have been widely used in various graph-centric applications.Often,some data are omitted during manual construction,leading to data reduction and performance ...Heterogeneous graphs organize data with nodes and edges,and have been widely used in various graph-centric applications.Often,some data are omitted during manual construction,leading to data reduction and performance degeneration on downstream tasks.Existing methods recover the missing data based on the data already within a single graph,neglecting the fact that graphs from different sources share some common nodes due to scope overlap.In this paper,we concentrate on the missing data recovery task on multi-source heterogeneous graphs under the incremental scenario and design a novel framework to recover the missing data by fusing multi-source complementary data from previously appeared graphs.Our model,namely SIKE,is present with a pre-trained language model and graph-specific adapters.To take advantage of the complementary data of multi-source graphs,we propose an embedding-based data fusion method to gather data among graphs.To evaluate the proposed model,we build two new datasets consisting of multi-source heterogeneous graphs.The experimental results show that our model SIKE achieves significant improvements compared with competitive baseline models,demonstrating the effectiveness of our model and shedding light on multi-source data fusion for data governance.展开更多
基金supported by the National Key R&D Program of China(Grant No.2022YFC3005401)the Fundamental Research Funds for the Central Universities(Grant No.B230201013)+2 种基金the National Natural Science Foundation of China(Grants No.52309152,U2243223,and U23B20150)the Natural Science Foundation of Jiangsu Province(Grant No.BK20220978)the Open Fund of National Dam Safety Research Center(Grant No.CX2023B03).
文摘Deformation monitoring is a critical measure for intuitively reflecting the operational behavior of a dam.However,the deformation monitoring data are often incomplete due to environmental changes,monitoring instrument faults,and human operational errors,thereby often hindering the accurate assessment of actual deformation patterns.This study proposed a method for quantifying deformation similarity between measurement points by recognizing the spatiotemporal characteristics of concrete dam deformation monitoring data.It introduces a spatiotemporal clustering analysis of the concrete dam deformation behavior and employs the support vector machine model to address the missing data in concrete dam deformation monitoring.The proposed method was validated in a concrete dam project,with the model error maintaining within 5%,demonstrating its effectiveness in processing missing deformation data.This approach enhances the capability of early-warning systems and contributes to enhanced dam safety management.
基金supported by the National Natural Science Foundation of China(Grant No.62272219).
文摘Heterogeneous graphs organize data with nodes and edges,and have been widely used in various graph-centric applications.Often,some data are omitted during manual construction,leading to data reduction and performance degeneration on downstream tasks.Existing methods recover the missing data based on the data already within a single graph,neglecting the fact that graphs from different sources share some common nodes due to scope overlap.In this paper,we concentrate on the missing data recovery task on multi-source heterogeneous graphs under the incremental scenario and design a novel framework to recover the missing data by fusing multi-source complementary data from previously appeared graphs.Our model,namely SIKE,is present with a pre-trained language model and graph-specific adapters.To take advantage of the complementary data of multi-source graphs,we propose an embedding-based data fusion method to gather data among graphs.To evaluate the proposed model,we build two new datasets consisting of multi-source heterogeneous graphs.The experimental results show that our model SIKE achieves significant improvements compared with competitive baseline models,demonstrating the effectiveness of our model and shedding light on multi-source data fusion for data governance.
