Zi Yang

Battery evaluation and early prediction software package (BEEP) provides an open-source Python-based framework for the management and processing of high-throughput battery cycling data-streams. BEEPs features include file-system based organization of raw cycling data and metadata received from cell testing equipment, validation protocols that ensure the integrity of such data, parsing and structuring of data into Python-objects ready for analytics, featurization of structured cycling data to serve as input for machine-learning, and end-to-end examples that use processed data for anomaly detection and featurized data to train early-prediction models for cycle life. BEEP is developed in response to the software and expertise gap between cell-level battery testing and data-driven battery development.

We propose a generalization of the best arm identification problem in stochastic multi-armed bandits (MAB) to the setting where every pull of an arm is associated with delayed feedback. The delay in feedback increases the effective sample complexity of standard algorithms, but can be offset if we have access to partial feedback received before a pull is completed. We propose a general framework to model the relationship between partial and delayed feedback, and as a special case we introduce efficient algorithms for settings where the partial feedback are biased or unbiased estimators of the delayed feedback. Additionally, we propose a novel extension of the algorithms to the parallel MAB setting where an agent can control a batch of arms. Our experiments in real-world settings, involving policy search and hyperparameter optimization in computational sustainability domains for fast charging of batteries and wildlife corridor construction, demonstrate that exploiting the structure of partial feedback can lead to significant improvements over baselines in both sequential and parallel MAB.

Abstract Recursive identification algorithms for continuous systems from sampled input and output data are discussed. The continuous system is identified through an approximated discrete-time estimation model with continuous system parameters. An approximated discrete-time model of the continuous system under study is first obtained by bilinear transformation. Then using the estimated denominator of the transfer function of the discrete-time model to construct adaptive IIR filters introduced to avoid direct approximations of differentiations from sampled data, an approximated discrete-time estimation model with continuous system parameters is derived. The discrete-time estimation model is composed of filtered sampled system input and output signals. With filtered inputs and delayed filtered outputs as instrumental variables, some kinds of recursive instrumental variable identification algorithms are proposed to obtain consistent estimates in the presence of noise. The proposed identification algorithms have close relations to standard recursive identification algorithms for common discrete-time systems. Numerical examples are included to illustrate the effectiveness of the recursive identification algorithms.