Storm is the most popular realtime stream processing platform, which can be used to deal with online machine learning. Similar to how Hadoop provides a set of general primitives for doing batch processing, Storm provides a set of general primitives for doing realtime computation. SAMOA includes distributed algorithms for the most common machine learning tasks like Mahout for Hadoop. SAMOA is both a platform and a library. In this paper, Forest cover types, a large benchmaking dataset available at the UCI KDD Archive is used as the data stream source. Vertical Hoeffding Tree, a parallelizing streaming decision tree induction for distributed enviroment, which is incorporated in SAMOA API is applied on Storm platform. This study compared stream prcessing technique for predicting forest cover types from cartographic variables with traditional classic machine learning algorithms applied on this dataset. The test then train method used in this system is totally different from the traditional train then test. The results of the stream processing technique indicated that it’s output is aymptotically nearly identical to that of a conventional learner, but the model derived from this system is totally scalable, real-time, capable of dealing with evolving streams and insensitive to stream ordering.

This paper presents a new topology of main circuit based on soft switching technology of ZVS-PWM. At present, hard switching is applied in most inverters. Because the switching power isnt equal to zero, a great number of losses on the switching are generated when the switching at a high frequency. To solve the fault of hard switching, this paper presents that apply the inverter circuits based on soft switching technology of ZVS-PWM to high frequency APF, which will decrease switching losses and noise greatly. The proposed topology and operation principle of the control method is discussed in detail, finally simulated results verify the effect of research.