Flight delay prediction remains an important research topic due to dynamic nature in flight operation and numerous delay factors.Dynamic data-driven application system in the control area can provide a solution to thi...Flight delay prediction remains an important research topic due to dynamic nature in flight operation and numerous delay factors.Dynamic data-driven application system in the control area can provide a solution to this problem.However,in order to apply the approach,a state-space flight delay model needs to be established to represent the relationship among system states,as well as the relationship between system states and input/output variables.Based on the analysis of delay event sequence in a single flight,a state-space mixture model is established and input variables in the model are studied.Case study is also carried out on historical flight delay data.In addition,the genetic expectation-maximization（EM）algorithm is used to obtain the global optimal estimates of parameters in the mixture model,and results fit the historical data.At last,the model is validated in Kolmogorov-Smirnov tests.Results show that the model has reasonable goodness of fitting the data,and the search performance of traditional EM algorithm can be improved by using the genetic algorithm.展开更多
With rising capacity demand in mobile networks, the infrastructure is also becoming increasingly denser and complex. This results in collection of larger amount of raw data(big data) that is generated at different lev...With rising capacity demand in mobile networks, the infrastructure is also becoming increasingly denser and complex. This results in collection of larger amount of raw data(big data) that is generated at different levels of network architecture and is typically underutilized. To unleash its full value, innovative machine learning algorithms need to be utilized in order to extract valuable insights which can be used for improving the overall network's performance. Additionally, a major challenge for network operators is to cope up with increasing number of complete(or partial) cell outages and to simultaneously reduce operational expenditure. This paper contributes towards the aforementioned problems by exploiting big data generated from the core network of 4 G LTE-A to detect network's anomalous behavior. We present a semi-supervised statistical-based anomaly detection technique to identify in time: first, unusually low user activity region depicting sleeping cell, which is a special case of cell outage; and second, unusually high user traffic area corresponding to a situation where special action such as additional resource allocation, fault avoidance solution etc. may be needed. Achieved results demonstrate that the proposed method can be used for timely and reliable anomaly detection in current and future cellular networks.展开更多
基金Supported by the High Technology Research and Development Programme of China(2006AA12A106)~~
文摘Flight delay prediction remains an important research topic due to dynamic nature in flight operation and numerous delay factors.Dynamic data-driven application system in the control area can provide a solution to this problem.However,in order to apply the approach,a state-space flight delay model needs to be established to represent the relationship among system states,as well as the relationship between system states and input/output variables.Based on the analysis of delay event sequence in a single flight,a state-space mixture model is established and input variables in the model are studied.Case study is also carried out on historical flight delay data.In addition,the genetic expectation-maximization（EM）algorithm is used to obtain the global optimal estimates of parameters in the mixture model,and results fit the historical data.At last,the model is validated in Kolmogorov-Smirnov tests.Results show that the model has reasonable goodness of fitting the data,and the search performance of traditional EM algorithm can be improved by using the genetic algorithm.
基金supported in part by the National Natural Science Foundation of China under the Grants No.61431011 and 61671371the National Science and Technology Major Project under Grant no.2016ZX03001016-005+1 种基金the Key Research and Development Program of Shaanxi Province under Grant No.2017ZDXM-G-Y-012the Fundamental Research Funds for the Central Universities
文摘With rising capacity demand in mobile networks, the infrastructure is also becoming increasingly denser and complex. This results in collection of larger amount of raw data(big data) that is generated at different levels of network architecture and is typically underutilized. To unleash its full value, innovative machine learning algorithms need to be utilized in order to extract valuable insights which can be used for improving the overall network's performance. Additionally, a major challenge for network operators is to cope up with increasing number of complete(or partial) cell outages and to simultaneously reduce operational expenditure. This paper contributes towards the aforementioned problems by exploiting big data generated from the core network of 4 G LTE-A to detect network's anomalous behavior. We present a semi-supervised statistical-based anomaly detection technique to identify in time: first, unusually low user activity region depicting sleeping cell, which is a special case of cell outage; and second, unusually high user traffic area corresponding to a situation where special action such as additional resource allocation, fault avoidance solution etc. may be needed. Achieved results demonstrate that the proposed method can be used for timely and reliable anomaly detection in current and future cellular networks.
