This article focuses on the optimization of water supply and drainage systems,involving theories such as hydraulic models of pipeline systems and multi-objective collaborative optimization.It introduces the system dyn...This article focuses on the optimization of water supply and drainage systems,involving theories such as hydraulic models of pipeline systems and multi-objective collaborative optimization.It introduces the system dynamics model of sewage treatment facility expansion.Elaborating on detection technology,construction of an intelligent operation and maintenance system,and factors to be considered for sewage plant expansion,it emphasizes the importance of collaborative development and verifies benefits through the PSR model.展开更多
Mobile network operators are facing many challenges to satisfy their subscribers in terms of quality of service and quality of experience provided. To achieve this goal, technological progress and scientific advances ...Mobile network operators are facing many challenges to satisfy their subscribers in terms of quality of service and quality of experience provided. To achieve this goal, technological progress and scientific advances offer good opportunities for efficiency in the management of faults occurring in a mobile network. Machine learning techniques allow systems to learn from past experiences and can predict, solutions to be applied to correct the root cause of a failure. This paper evaluates machine learning techniques and identifies the decision tree as a learning model that provides the most optimal error rate in predicting outages that may occur in a mobile network. Three machine learning techniques are presented in this study and compared with regard to accuracy. This study demonstrates that the appropriate machine learning technique improves the accuracy of the model. By using the decision tree as a machine learning model, it was possible to predict solutions to network failures, with an error rate less than 2%. In addition, the use of Machine Learning makes it possible to eliminate steps in the network failure processing chain;resulting in reduced service disruption time and improved the network availability which is a key network performance index.展开更多
In recent years, with the increase of the speed of social development, the network level of its computer has also been continuously improved, in its computer network application and popularization stage, has achieved ...In recent years, with the increase of the speed of social development, the network level of its computer has also been continuously improved, in its computer network application and popularization stage, has achieved good results. But there are also more and more problems, such as data theft and system damage, which need to be analyzed and applied in security technologies to maintain network security.展开更多
文摘This article focuses on the optimization of water supply and drainage systems,involving theories such as hydraulic models of pipeline systems and multi-objective collaborative optimization.It introduces the system dynamics model of sewage treatment facility expansion.Elaborating on detection technology,construction of an intelligent operation and maintenance system,and factors to be considered for sewage plant expansion,it emphasizes the importance of collaborative development and verifies benefits through the PSR model.
文摘Mobile network operators are facing many challenges to satisfy their subscribers in terms of quality of service and quality of experience provided. To achieve this goal, technological progress and scientific advances offer good opportunities for efficiency in the management of faults occurring in a mobile network. Machine learning techniques allow systems to learn from past experiences and can predict, solutions to be applied to correct the root cause of a failure. This paper evaluates machine learning techniques and identifies the decision tree as a learning model that provides the most optimal error rate in predicting outages that may occur in a mobile network. Three machine learning techniques are presented in this study and compared with regard to accuracy. This study demonstrates that the appropriate machine learning technique improves the accuracy of the model. By using the decision tree as a machine learning model, it was possible to predict solutions to network failures, with an error rate less than 2%. In addition, the use of Machine Learning makes it possible to eliminate steps in the network failure processing chain;resulting in reduced service disruption time and improved the network availability which is a key network performance index.
文摘In recent years, with the increase of the speed of social development, the network level of its computer has also been continuously improved, in its computer network application and popularization stage, has achieved good results. But there are also more and more problems, such as data theft and system damage, which need to be analyzed and applied in security technologies to maintain network security.
