Various types of data from environment are used to provide user-adaptive services. Among them, data of a user's past moving paths are useful to predict a moving user's next location and provide related services. This paper proposes a method to predict a moving user's location through analyzing his/her past moving paths. This method analyzes the user's moving path by using three elements of distance, time and direction of moving paths and Dynamic Time Warping (DTW), and selects the representative path, which is the one most similar to the current moving path in the past paths. The selected path can be used to provide service like space and time estimation.

Clickstreams in users' navigation logs have various data which are related to users' web surfing. Those are visit counts, stay times, product types, etc. When we observe these data, we can divide clickstreams into sub-clickstreams so that the pages in a sub-clickstream share more contexts with each other than with the pages in other sub-clickstreams. In this paper, we propose a method which extracts more informative rules from clickstreams for web page recommendation based on genetic programming and association rules. First, we split clickstreams into sub-clickstreams by contexts for generating more informative rules. In order to split clickstreams in consideration of context, we extract six features from users' navigation logs. A set of split rules is generated by combining those features through genetic programming, and then informative rules for recommendation are extracted with the association rule mining algorithm. Through experiments, we verify that the proposed method is more effective than the other methods in various conditions.

It is shown that bounded impulse action can be combined with usual bang-bang control input to minimize the performance index. Especially for unstable oscillators, the size of controllable region can be increased. We present results on how to minimize the performance index using both ordinary bang-bang control and impulse actions with a recharge constraint on impulse firing. Following the maximum principle and necessary conditions induced from usual perturbation arguments, the mixed control input (bang-bang and impulse actions) is represented in adjoint state and then state variable feedback form. Simulation results show how the switch curves can be used to determine the optimal control value.

Ranking fuzzy numbers is one of very important research topics in fuzzy set theory because it is a base of decision-making in applications. However, fuzzy numbers may not be easily ordered into one sequence according to their magnitudes because they represent uncertain values. When two fuzzy numbers overlap with each other, a fuzzy number may not be considered absolutely larger than the other. That is, even when a fuzzy number may be considered larger than the other, it may also be considered smaller than the other. It means that for a given set of fuzzy numbers, several ranking sequences possibly exist. However, most of the existing ranking methods produce only one ranking sequence. They ignore other possible sequences due to the overlap between fuzzy numbers. We propose a ranking method which generates possible ranking sequences of given fuzzy numbers. Our method takes a viewpoint from users, and uses it for evaluation of fuzzy numbers. Fuzzy numbers will be ranked based on the evaluations and a fuzzy set of sequences of fuzzy numbers will be produced as a ranking results. Numeric examples and comparisons with other methods are also presented.