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Marine electromagnetic transmitter transmits electromagnetic waves with large power frequency conversion to the seabed to obtain the submarine structure and mineral resources. However, the current transmitter presents several problems, such as low efficiency, serious heat, and poor adaptability to the load. Soft-switching controlled-source circuit is used to reduce circuit losses. The mathematical model of controlled-source circuit should be established to realize a closed-loop control for increasing the output transient performance of electromagnetic waves. Given that the soft-switching controlled-source circuit has more status and that direct modeling is difficult, small-signal model of soft-switching controlled-source circuit is established based on that of hard-switching controlled-source circuit by analyzing the effect of output filter inductor current transformer leakage inductance and input voltage soft-switching controlled circuit on change in the duty cycle. Finally, experiments verify the accuracy and validity of the model.

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User related data indicate user interests in a certain environment. In the context of massive data from the Web, if an application wants to provide more personalized service (e.g. search) for users, an investigation on user interests is needed. User interests are usually distributed in different sources. In order to provide a more comprehensive understanding, user related data from multiple sources need to be integrated together for deeper analysis. Web based social networks have become typical platforms for extracting user interests. In addition, there are various types of interests from these social networks. In this paper, we provide an algorithmic framework for retrieving semantic data based on user interests from multiple sources (such as multiple social networking sites). We design several algorithms to deal with interests based retrieval based on single and multiple types of interests. We utilize publication data from Semantic Web Dog Food (which can be considered as an academic collaboration based social network), and microblogging data from Twitter to validate our framework. The Active Academic Visit Recommendation Application (AAVRA) is developed as a concrete usecase to show the potential effectiveness of the proposed framework for user interests driven Web personalization based on multiple social networks.