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The severity of global magnetic disturbances in Near-Earth space can crucially affect human life. These geomagnetic disturbances are often indicated by a Kp index, which is derived from magnetic field data from ground stations, and is known to be correlated with solar wind observations. Forecasting of Kp index is important for understanding the dynamic relationship between the magnetosphere and solar...
In this paper, a new global time series averaging method is proposed for improving k-means clustering performance for observed signals or time series data. The proposed method is different from most commonly used time series averaging methods, such as pairwise averaging method (PA), nonlinear alignment and averaging filter (NLAAF), prioritized shape averaging (PSA) and dynamic time warping (DTW) barycentre...
As the most important equipment in compressor station, the performance of centrifugal compressor unit is highly concerned with safety production and economic benefits of gas-transmission enterprise. In order to ensure well operating condition of centrifugal compressor unit, we should firstly make clear the design principle of index system of risk assessment, and establish the reliability block diagram...
In order to identify oil pipeline work conditions accurately and quickly, fuzzy C-means algorithm method is applied to this paper. For obtaining clustering standard, sixteen groups of raw data, which include each work condition, are selected from massive pressure data collected in the field. Analyzed data for convenience, each group of raw data is normalized with mean zero and high-frequency noise...
It is well known that the work condition of pipeline, the leak included, can be identified by a pressure signal analysis. Because of the high frequency data collection and always on-line pipeline leak detection, the pressure signal brings up massive data. A methodology for pipeline leak detection using data mining technology and work condition identification is presented here. Sixteen groups of raw...
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