Current methods for predicting missing values in datasets often rely on simplistic approaches such as taking median value of attributes, limiting their applicability. Real-world observations can be diverse, taking sto...Current methods for predicting missing values in datasets often rely on simplistic approaches such as taking median value of attributes, limiting their applicability. Real-world observations can be diverse, taking stock price as example, ranging from prices post-IPO to values before a company’s collapse, or instances where certain data points are missing due to stock suspension. In this paper, we propose a novel approach using Nonlinear Matrix Completion (NIMC) and Deep Matrix Completion (DIMC) to predict associations, and conduct experiment on financial data between dates and stocks. Our method leverages various types of stock observations to capture latent factors explaining the observed date-stock associations. Notably, our approach is nonlinear, making it suitable for datasets with nonlinear structures, such as the Russell 3000. Unlike traditional methods that may suffer from information loss, NIMC and DIMC maintain nearly complete information, especially in high-dimensional parameters. We compared our approach with state-of-the-art linear methods, including Inductive Matrix Completion, Nonlinear Inductive Matrix Completion, and Deep Inductive Matrix Completion. Our findings show that the nonlinear matrix completion method is particularly effective for handling nonlinear structured data, as exemplified by the Russell 3000. Additionally, we validate the information loss of the three methods across different dimensionalities.展开更多
Current research on Digital Twin(DT)is largely focused on the performance of built assets in their operational phases as well as on urban environment.However,Digital Twin has not been given enough attention to constru...Current research on Digital Twin(DT)is largely focused on the performance of built assets in their operational phases as well as on urban environment.However,Digital Twin has not been given enough attention to construction phases,for which this paper proposes a Digital Twin framework for the construction phase,develops a DT prototype and tests it for the use case of measuring the productivity and monitoring of earthwork operation.The DT framework and its prototype are underpinned by the principles of versatility,scalability,usability and automation to enable the DT to fulfil the requirements of large-sized earthwork projects and the dynamic nature of their operation.Cloud computing and dashboard visualisation were deployed to enable automated and repeatable data pipelines and data analytics at scale and to provide insights in near-real time.The testing of the DT prototype in a motorway project in the Northeast of England successfully demonstrated its ability to produce key insights by using the following approaches:(i)To predict equipment utilisation ratios and productivities;(ii)To detect the percentage of time spent on different tasks(i.e.,loading,hauling,dumping,returning or idling),the distance travelled by equipment over time and the speed distribution;and(iii)To visualise certain earthwork operations.展开更多
文摘Current methods for predicting missing values in datasets often rely on simplistic approaches such as taking median value of attributes, limiting their applicability. Real-world observations can be diverse, taking stock price as example, ranging from prices post-IPO to values before a company’s collapse, or instances where certain data points are missing due to stock suspension. In this paper, we propose a novel approach using Nonlinear Matrix Completion (NIMC) and Deep Matrix Completion (DIMC) to predict associations, and conduct experiment on financial data between dates and stocks. Our method leverages various types of stock observations to capture latent factors explaining the observed date-stock associations. Notably, our approach is nonlinear, making it suitable for datasets with nonlinear structures, such as the Russell 3000. Unlike traditional methods that may suffer from information loss, NIMC and DIMC maintain nearly complete information, especially in high-dimensional parameters. We compared our approach with state-of-the-art linear methods, including Inductive Matrix Completion, Nonlinear Inductive Matrix Completion, and Deep Inductive Matrix Completion. Our findings show that the nonlinear matrix completion method is particularly effective for handling nonlinear structured data, as exemplified by the Russell 3000. Additionally, we validate the information loss of the three methods across different dimensionalities.
基金supported by UKRI Innovate UK (Project number:44584,Project Title AI-driven and real-time command and control centre for site equipment in infrastructure projectsProject number,Funding body:UKRI Innovate UK,United Kingdom)under the‘Increase productivity,performance and quality in UK construction’competition.
文摘Current research on Digital Twin(DT)is largely focused on the performance of built assets in their operational phases as well as on urban environment.However,Digital Twin has not been given enough attention to construction phases,for which this paper proposes a Digital Twin framework for the construction phase,develops a DT prototype and tests it for the use case of measuring the productivity and monitoring of earthwork operation.The DT framework and its prototype are underpinned by the principles of versatility,scalability,usability and automation to enable the DT to fulfil the requirements of large-sized earthwork projects and the dynamic nature of their operation.Cloud computing and dashboard visualisation were deployed to enable automated and repeatable data pipelines and data analytics at scale and to provide insights in near-real time.The testing of the DT prototype in a motorway project in the Northeast of England successfully demonstrated its ability to produce key insights by using the following approaches:(i)To predict equipment utilisation ratios and productivities;(ii)To detect the percentage of time spent on different tasks(i.e.,loading,hauling,dumping,returning or idling),the distance travelled by equipment over time and the speed distribution;and(iii)To visualise certain earthwork operations.
