Active wavelength control of tunable lasers for use in Un cooled WDM systems

It is shown for the first time that uncooled tunable DBR-laser diodes can be used as athermal WDM sources. Using novel bias current control, absolute wavelength control to within 6Å has been achieved for temperatures up to 70°C. © 2000 Optical Society of America.

Model Refinement for the Active Control of Thermoacoustic Instability

Active control has been shown as a feasible technology for suppressing thermoacoustic instability in continuous combustion systems, and the control strategy design is substantially dependent on the reliability of the flame model. In this paper, refinement of G-equation flame model for the dynamics of lean premixed combustion is investigated. Precisely, the dynamics between the flame speed S_u and equivalence ratio phi are proposed based on numerical calculations and physical explanations. Finally, the developed model is tested on one set of experimental data.

Bayesian Active Learning for Classification and Preference Learning

Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classification with nonparametric models, the optimal solution is harder to compute. Current approaches make approximations to achieve tractability. We propose an approach that expresses information gain in terms of predictive entropies, and apply this method to the Gaussian Process Classifier (GPC). Our approach makes minimal approximations to the full information theoretic objective. Our experimental performance compares favourably to many popular active learning algorithms, and has equal or lower computational complexity. We compare well to decision theoretic approaches also, which are privy to more information and require much more computational time. Secondly, by developing further a reformulation of binary preference learning to a classification problem, we extend our algorithm to Gaussian Process preference learning.

IGBT series connection under active voltage control

In this paper an Active Voltage Control (AVC) technique is presented, for series connection of insulated-gate-bipolar-transistors (IGBT) and control of diode recovery. The AVC technique can control the switching trajectory of an IGBT according to a pre-set reference signal. In series connections, every series connected IGBT follows the reference and so that the dynamic voltage sharing is achieved. For the static voltage balancing, the AVC technique can clamp the highest collector-to-emitter voltage to a pre-set clamping voltage level. By selecting the value of the clamping voltage, the difference among series connected IGBTs can be controlled in an accepted range. Another key advantage for AVC is that by changing the reference signal at turn-on, the diode recovery can be optimized. © 2011 EPE Association - European Power Electr.