Accurate estimation of the state of health (SOH) is one of the most dominant contents for prognostics and health management of a lithium battery system. However, it has not been well solved owing to its internal electrochemical reactions and complex nonlinear system. Here, an SOH estimation framework for lithium-ion batteries depending on health features (HF) extraction and the construction of the mixed model is proposed. The overall trend and local dynamic changes of capacity degradation was simultaneously considered in the mixed model. First, the double exponential empirical model describing the overall degradation trend of capacity was established from historical data and the error between its output and the SOH reference value was calculated. Then, with the constructed HF as the input and error as the output, the improved gaussian process regression (IGPR) model describing the local dynamic changes of capacity degradation was established. Finally, the output of the mixed model was obtained by feeding back the output of the IGPR model to the result of the empirical model. The proposed framework has been verified on two different data sets. With the result of 5% relative percentage error, the proposed framework shows high accuracy and robustness in a lithium battery system.

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One of the major challenges in seismic imaging is accurately delineating subsurface salt. Since a salt boundary has strong impedance compared with other sediments, we build a saliency map with intensity and orientation to create a pixel-level model for salt interpretation. In this abstract, we train a saliency-map as an additional attribute to combine with the original seismic to predict salt bodies. We also train a saliency-map to classify multiple geological facies in a multi-channel convolutional neural network with residual net architecture to help build subsurface velocity models. Two examples are shown which demonstrate that a saliency-map-plus-seismic model successfully improves the accuracy of salt prediction and reduces artifacts.

Most of the depth from image flow algorithms has to rely on either good initial guesses, or some assumptions about the object surfaces to achieve solutions that agree with the physical world. Waxman and Sinha point out that those restrictions can be relaxed if depth is computed from a relative image flow field. Since image flow determination is relatively much more difficult than normal flow determination, it is of interest to develop an algorithm to recover depth from normal flows. In this paper, we have shown that similar results can be obtained from relative normal flow fields as from relative image flow fields. We have implemented a normal flow estimation algorithm, and applied our algorithm to recover depth from intensity images.