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Noises exist in many measured systems inevitably. Therefore, noise resistance ability analysis is of great importance to accurately evaluate the performance of information analysis methods. In this paper, we systematically test the noise resistance ability of visibility graph (VG) and its generalization, i.e., limited penetrable visibility graph (LPVG). Taking the Lorenz system as the example, we...
Characterizing the time series measured from different flow environments is of great importance in diverse research fields. In this paper, we first systematically record three groups of 3D wind speed data from indoor and outdoor environments separately. Then, we employ a modality transition-based approach for mapping the experimental multivariate data into a directed weighted complex network. For...
Uncovering the correlations of different flow fields, in terms of time series network analysis, is important and relevant to both engineers and businesses. We first systematically carry out experiments and record different groups of time series signals from the wind field and the gas concentration field simultaneously. Then, we employ the visibility graph method for mapping the experimental measurements...
Better understanding of boundary-layer wind field is fundamental for modeling of various wind-related phenomena, e.g., gas dispersal and energy generation. In this paper, we first record three groups of the near surface wind speed time series in different locations using a high-resolution 3D ultrasonic anemometer. Then, we map these experimental time series into complex networks and detect the corresponding...
Uncovering the cross-correlation between wind signals and gas concentration signals is a challenging problem of significant importance. We first measure six groups of multivariate signals which contain wind speed time series, wind direction time series and gas concentration time series. Then, the order patterns analysis is employed to construct recurrence matrices and we can infer multivariate recurrence...
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