Abstract
In aluminum electrolysis, anode current signals can not only provide an insight into the localized anodic dynamic behavior, but also can be used as a new way to study the process in the harsh industrial environment. This kind of the data is stored in the form of time series, which is a sequence of real numeric values. Because the data is huge and growing fast, the number of elements in anode current signals must be reduced to make further analysis much easier and faster, which is a typical time series representation problem. In this paper, an adaptive time series representation method for anode current signals is proposed for this purpose. The essence of this method is that the time series representation problem is transformed into the optimization problem. In addition, a new cognitively inspired optimization method named state transition algorithm (STA) is introduced to solve the optimization problem. The experimental results indicate that the proposed method outperforms common methods used for time series representation in aluminum electrolysis.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call