Metal Oxidation Kinetics and the Transition from Thin to Thick Films

We report an investigation of growth kinetics and transition from thin to thick films during metal oxidation. In the thin film limit (< 20 nm), Cabrera and Mott's theory is usually adopted by explicitly considering ionic drift through the oxide in response to electric fields, where the growth kinetics follow an inverse logarithmic law. It is generally accepted that Wagner's theory, involving self-diffusion, is valid only in the limit of thick film regime and leads to parabolic growth kinetics. Theory presented here unifies the two models and provides a complete description of oxidation including the transition from thin to thick film. The range of validity of Cabrera and Mott's theory and Wagner's theory can be well defined in terms of the Debye-Huckel screening length. The transition from drift-dominated ionic transport for thin film to diffusion-dominated transport for thick film is found to strictly follow the direct logarithmic law that is frequently observed in many experiments

A Convex Cycle-based Degradation Model for Battery Energy Storage Planning and Operation

A vital aspect in energy storage planning and operation is to accurately model its operational cost, which mainly comes from the battery cell degradation. Battery degradation can be viewed as a complex material fatigue process that based on stress cycles. Rainflow algorithm is a popular way for cycle identification in material fatigue process, and has been extensively used in battery degradation assessment. However, the rainflow algorithm does not have a closed form, which makes the major difficulty to include it in optimization. In this paper, we prove the rainflow cycle-based cost is convex. Convexity enables the proposed degradation model to be incorporated in different battery optimization problems and guarantees the solution quality. We provide a subgradient algorithm to solve the problem. A case study on PJM regulation market demonstrates the effectiveness of the proposed degradation model in maximizing the battery operating profits as well as extending its lifetime

MDP Optimal Control under Temporal Logic Constraints

In this paper, we develop a method to automatically generate a control policy for a dynamical system modeled as a Markov Decision Process (MDP). The control specification is given as a Linear Temporal Logic (LTL) formula over a set of propositions defined on the states of the MDP. We synthesize a control policy such that the MDP satisfies the given specification almost surely, if such a policy exists. In addition, we designate an "optimizing proposition" to be repeatedly satisfied, and we formulate a novel optimization criterion in terms of minimizing the expected cost in between satisfactions of this proposition. We propose a sufficient condition for a policy to be optimal, and develop a dynamic programming algorithm that synthesizes a policy that is optimal under some conditions, and sub-optimal otherwise. This problem is motivated by robotic applications requiring persistent tasks, such as environmental monitoring or data gathering, to be performed.Comment: Technical report accompanying the CDC2011 submissio

LTL Control in Uncertain Environments with Probabilistic Satisfaction Guarantees

We present a method to generate a robot control strategy that maximizes the probability to accomplish a task. The task is given as a Linear Temporal Logic (LTL) formula over a set of properties that can be satisfied at the regions of a partitioned environment. We assume that the probabilities with which the properties are satisfied at the regions are known, and the robot can determine the truth value of a proposition only at the current region. Motivated by several results on partitioned-based abstractions, we assume that the motion is performed on a graph. To account for noisy sensors and actuators, we assume that a control action enables several transitions with known probabilities. We show that this problem can be reduced to the problem of generating a control policy for a Markov Decision Process (MDP) such that the probability of satisfying an LTL formula over its states is maximized. We provide a complete solution for the latter problem that builds on existing results from probabilistic model checking. We include an illustrative case study.Comment: Technical Report accompanying IFAC 201

Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach

Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and spatial transforms cannot account for common changes due to pathologies. Low-rank/Sparse (LRS) decomposition removes pathologies prior to registration; however, LRS is memory-demanding and slow, which limits its use on larger data sets. Additionally, LRS blurs normal tissue regions, which may degrade registration performance. This paper proposes an efficient alternative to LRS: (1) normal tissue appearance is captured by principal component analysis (PCA) and (2) blurring is avoided by an integrated model for pathology removal and image reconstruction. Results on synthetic and BRATS 2015 data demonstrate its utility.Comment: Accepted as a conference paper for ISBI 201