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A configuration of a single-phase full-bridge AC-DC converter and dual active bridge (DAB) converter used in V2G (vehicle to grid) and G2V (grid to vehicle) technology is proposed in this paper. It can well realize the bi-directional energy fluidity. The numerous controllers can be designed by MATLAB SISOTOOL easily and quickly. And PLECS software is used to validate the function of the proposed system. In addition, a power decoupling method for this power interface is presented and verified. The simulation results show this configuration works well in V2G application and the DC-link capacitance could be reduced remarkably using proposed method.

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Purpose Frequent itemset mining (FIM) is a basic topic in data mining. Most FIM methods build itemset database containing all possible itemsets, and use predefined thresholds to determine whether an itemset is frequent. However, the algorithm has some deficiencies. It is more fit for discrete data rather than ordinal/continuous data, which may result in computational redundancy, and some of the results are difficult to be interpreted. The purpose of this paper is to shed light on this gap by proposing a new data mining method. Design/methodology/approach Regression pattern (RP) model will be introduced, in which the regression model and FIM method will be combined to solve the existing problems. Using a survey data of computer technology and software professional qualification examination, the multiple linear regression model is selected to mine associations between items. Findings Some interesting associations mined by the proposed algorithm and the results show that the proposed method can be applied in ordinal/continuous data mining area. The experiment of RP model shows that, compared to FIM, the computational redundancy decreased and the results contain more information. Research limitations/implications The proposed algorithm is designed for ordinal/continuous data and is expected to provide inspiration for data stream mining and unstructured data mining. Practical implications Compared to FIM, which mines associations between discrete items, RP model could mine associations between ordinal/continuous data sets. Importantly, RP model performs well in saving computational resource and mining meaningful associations. Originality/value The proposed algorithms provide a novelty view to define and mine association.