ALBERT

Description: People leave traces of movements that might affect the behavior of others both online in cyberspace and offline in real space. Previous studies, however, have used only questionnaires, network data, or GPS data to study spatio-temporal behaviors, ignoring the relationship between online and offline activities, and overlooking the influence of previous activities on future behaviors. We propose a Pheromone-based Fusion Model, viewing human behaviors as similar to insect foraging behaviors to model spatio-temporal recreational activity patterns, on and offline. In our model, website data were combined with GPS data to evaluate the attractiveness of destinations over time using twenty-nine landscapes in Beijing, China; big website data and GPS trajectories were gathered from 181 users for 57 months. The datasets were portioned into two periods. Online and offline recreational pheromones were calculated from the first period, and the visitation rates were extracted from the second period. These data were subsequently applied in a regression analysis to determine unknown parameters and estimate the attractiveness of destinations. The proposed method was compared with two other approaches that use either GPS data or online data alone, in order to verify effectiveness. The results show that the proposed method can estimate future behaviors, based on real world and online past actions.

Description: Equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) measurements are a basic requirement of power systems. In order to predict the site pollution severity (SPS) of insulators, a new method based on random forests (RFs) is proposed. Using mutual information (MI) theory and RFs, the weights of factors related to the SPS of insulators are analyzed. The samples of contaminated insulators are extracted from the transmission lines of high voltage alternating current (HVAC) and high voltage direct current transmission (HVDC). The regression models of RFs and support vector machines (SVM) are constructed and compared, which helps to support the lack of information in predicting NSDD in previous works. The results are as follows: according to the mean decrease accuracy (MDA), mean decrease Gini, (MDG), and MI, the types of the insulators (including surface area, surface orientation, and total length) as well as the hydrophobicity are the main factors affecting both ESDD and NSDD. Compared with NSDD, the electrical parameters have a significant effect on ESDD. For the influence factors of ESDD, the weights of the insulator type, hydrophobicity, and meteorological factors are 52.94%, 6.35%, and 21.88%, respectively. For the influence factors of NSDD, the weights of the insulator type, hydrophobicity, and meteorological factors are 55.37%, 11.04%, and 14.26%, respectively. The influence voltage level (vl), voltage type (vt), polarity/phases (pp) exerted on ESDD are 1.5 times, 3 times, and 4.5 times of NSDD, respectively. The influence that distance from the coastline (d), wind velocity (wv), and rainfall (rf) exert on NSDD are 1.5 times, 2 times, and 2.5 times that of ESDD, respectively. Compared with the natural contamination test and the SVM regression model, the RFs regression model can effectively predict the contamination degree of insulators, and the relative error of the predicted ESDD and NSDD is 8.31% and 9.62%, respectively.