Most applied time series are non-stationary,or exhibit some kind of non-stationarity for at least parts of the time series.For time series analyses or mathematical modeling purposes,the non-stationarities can be diffi...Most applied time series are non-stationary,or exhibit some kind of non-stationarity for at least parts of the time series.For time series analyses or mathematical modeling purposes,the non-stationarities can be difficult to handle.Therefore,identification of stationary and non-stationary behavior is of great practical interest in time series analysis.In this study a robust and computationally efficient method to identify steady state parts of time series data is presented.The method is based on the class of deterministic trend models using a sliding window,and is focused towards being easy to implement,efficient and practical in use and to preserve data completeness.To demonstrate the performance of the steady state identifier,the method is applied on different sets of time series data from two ships equipped with systems for in-service monitoring.The method is shown to be reliable and practical for identifying steady state parts of time series data,and can serve as a practical preprocessing tool for time series data analysis.展开更多
Voluntary observing ship （VOS） observations are international obligations that must be fulfilled by China. Currently, the number of Chinese VOSs is showing a decreasing trend, which has decreased from more than one ...Voluntary observing ship （VOS） observations are international obligations that must be fulfilled by China. Currently, the number of Chinese VOSs is showing a decreasing trend, which has decreased from more than one hundred ships in the past to the current number of thirty something ships. Moreover, the observation capabilities have many existing problems, such as relatively outdated observation measures, simple observation parameters, and lack of observation data. Fundamentally speaking, the operation mechanism of VOSs lacks effective systematic assurance and protection. Consequently, these VOSs are unable to have sufficient operational capabilities and cannot effectively fulfill their international obligations.展开更多
文摘Most applied time series are non-stationary,or exhibit some kind of non-stationarity for at least parts of the time series.For time series analyses or mathematical modeling purposes,the non-stationarities can be difficult to handle.Therefore,identification of stationary and non-stationary behavior is of great practical interest in time series analysis.In this study a robust and computationally efficient method to identify steady state parts of time series data is presented.The method is based on the class of deterministic trend models using a sliding window,and is focused towards being easy to implement,efficient and practical in use and to preserve data completeness.To demonstrate the performance of the steady state identifier,the method is applied on different sets of time series data from two ships equipped with systems for in-service monitoring.The method is shown to be reliable and practical for identifying steady state parts of time series data,and can serve as a practical preprocessing tool for time series data analysis.
文摘Voluntary observing ship （VOS） observations are international obligations that must be fulfilled by China. Currently, the number of Chinese VOSs is showing a decreasing trend, which has decreased from more than one hundred ships in the past to the current number of thirty something ships. Moreover, the observation capabilities have many existing problems, such as relatively outdated observation measures, simple observation parameters, and lack of observation data. Fundamentally speaking, the operation mechanism of VOSs lacks effective systematic assurance and protection. Consequently, these VOSs are unable to have sufficient operational capabilities and cannot effectively fulfill their international obligations.
