Temporally adaptive estimation of logistic classifiers on data streams

Modern technology has allowed real-time data collection in a variety of domains, ranging from environmental monitoring to healthcare. Consequently, there is a growing need for algorithms capable of performing inferential tasks in an online manner, continuously revising their estimates to reflect the current status of the underlying process. In particular, we are interested in constructing online and temporally adaptive classifiers capable of handling the possibly drifting decision boundaries arising in streaming environments. We first make a quadratic approximation to the log-likelihood that yields a recursive algorithm for fitting logistic regression online. We then suggest a novel way of equipping this framework with self-tuning forgetting factors. The resulting scheme is capable of tracking changes in the underlying probability distribution, adapting the decision boundary appropriately and hence maintaining high classification accuracy in dynamic or unstable environments. We demonstrate the scheme's effectiveness in both real and simulated streaming environments. © Springer-Verlag 2009.

Advanced DC link capacitor technology application for a stiff voltage-source inverter

Problem of DC link size in a stiff voltage-source inverter for electric drive is described in the paper. Advantages of advanced film capacitor technology over conventional one for DC link application are reviewed. Conventional DC link capacitor selection methods are questioned in view of advanced capacitor technology utilization in stiff voltage-source inverter. For capacitor selection maximum ripple rms current point is shown. DC link ripple current spectrum analysis under modern PWM techniques is presented. Some capacitor selection recommendations are given. The analysis has been aided greatly by computer modeling in PSpice. ©2005 IEEE.