An ontological characterization of time-series and state-sequences for data mining

Time-series and sequences are important patterns in data mining. Based on an ontology of time-elements, this paper presents a formal characterization of time-series and state-sequences, where a state denotes a collection of data whose validation is dependent on time. While a time-series is formalized as a vector of time-elements temporally ordered one after another, a state-sequence is denoted as a list of states correspondingly ordered by a time-series. In general, a time-series and a state-sequence can be incomplete in various ways. This leads to the distinction between complete and incomplete time-series, and between complete and incomplete state-sequences, which allows the expression of both absolute and relative temporal knowledge in data mining.

Design, modeling and characterization of a microinductor for future DC-DC power converters

Experimental, analytical and simulated data are presented in this article to assess the performance of electrodeposited nickel-iron within a novel solenoid microinductor. A design flowchart highlights the primary design principles when developing a microscale magnetic component for DC-DC power converters. Thermal modeling is used to predict the operational conditions that generate undesirable thermal generation within the component. Operating at 0.5MHz, the microinductor achieves an efficiency and power density of 78% and 7.8 W/cm3, respectively.