Takahiro Kudoh

Travel destination prediction using frequent crossing pattern from driving history

The modeling of user behavior patterns for personalized information services in mobile environments has recently become a popular research theme. Most of the research aims at predicting the users future behavior (and/or location) by extracting frequent patterns from the history of location data sequences. However, sometimes user behavior changes according to the external information such as date, time, weather, etc., and we cannot accurately predict it based on the location data sequences alone. In this paper, we propose a new travel destination prediction method including day and time as external information. First, the users travel history information including the location, date and time is stored. Then, from the external information, time/day categories that have correlation to the users destination based on entropy are determined. Finally, using the categories, a destination that depends on the external information can be successfully predicted. An application of the method to data collected from a car navigation system showed possibility for an improved performance comparing to the conventional methods. Higher destination prediction accuracy during the first several minutes after users departure was reported.

H ∞ control of microgrids involving gas turbine engines and batteries

This paper proposes a new power management control method for microgrids based on H∞ control theory. Microgrid systems consist of distributed power sources such as cogeneration systems and photovoltaic systems with batteries. In general, power generation by photovoltaic systems and power consumption of various loads cause large fluctuations depending on weather conditions and varied lifestyles. Moreover, efficient management of battery capacity is a critical issue to prolong the battery life. Therefore, in power management control of microgrids, it is necessary to take account of a number of aspects such as power balancing performance, maintaining battery capacity, and robustness against power fluctuations. For such multiobjective control problems, we apply H∞ control theory which offers a unified robust control method with desirable power balancing performance and efficient battery management. The experimental results illustrate the effectiveness of the proposed control method.