The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In the GIS applications, massive mark point display has become a very important problem. Especially for the application in vehicle networking, high-load vehicle position moving increases the difficulty of the display problem. Many existing efforts have been taken on proposing aggregation algorithms for map mark points. However, all these works suffer from efficiency and scalability problems when we...
This paper describe a feasible scheme of local visual navigation, local visual navigation application scenarios often some with complex background, target species more real scenario that the obstacle avoidance is particularly important. In particular the visual navigation target segmentation in the background and the foreground objects more complex scenarios important for predicting pre-step. This...
Many-objective optimization problems involving a large number (more than four) of objectives have aroused extensive attention. It is known that problems with a high number of objectives cause additional difficulties in visualization of the objective space, stagnation in search process and high computational cost. In this paper, a special class of many objective problems, which can be degenerated to...
In this paper, an electricity consumption prediction model is proposed and built up by the following steps: 1) characterize historical data via fuzzy clustering method; 2) reduce the characterized data based on rough set; 3) extract the correlation between the attribute equivalence and the predicted variable; 4) set up electricity consumption prediction model finally.
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.