AbTech (United States)

Demand for high-performance rechargeable batteries has driven the development of novel electrodes with high reversibility and energy density. Tracking the change in electronic and local structure is crucial to investigate and improve the stability and performance of these batteries. Advanced characterization techniques allow the user to monitor electronic and local atomic structure changes to better understand the role and behavior of different elements during cycling of electrode material for rechargeable batteries.X-ray Absorption and Emission Spectroscopy allow us to explore such changes in 3d transition metal elements through the process of generation and quenching of core holes in the sample. Operando measurements enable real-time monitoring of electronic structure changes in electrode materials which enable us to capture phenomena which are only kinetically accessible.In this study, the change in electronic structure of transition metals present in electrode materials is explored as a function of lithium removal through XES Kβ1,3feature along with K-edge XAFS using a lab-scale instrument. The choice of electrodes explored in this study considers the degree of oxidation, structure, and mechanism for ion storage. Change in peak and edge positions of Kβ1,3and K-edge feature is utilized to monitor oxidation/spin changes in electrode materials during charge/discharge. Further the magnetic behavior for ex-situ samples prepared by electrochemical delithiation of LCO in the range of 2-10% delithiation is investigated using Kα and Kβ fluorescence (spin sensitive) followed by K-edge XAFS for Cobalt and Oxygen. Figure 1

With the growing size of target databases and the large number of example images required for target recognition system development, a key requirement in managing ATR system development is the automatic and accurate assessment of target imagery. We define assessment in terms of image similarity of the target subimage to a truth target image set. The goal in this work is to create a system that automates the assessment of images and improves the accuracy of the image database assessment process. Our approach to the database assessment problem combines an image feature based approach with a statistical data modeling approach. The process being two-fold, provides a generic framework for approaching the problem regardless of imaging modalities. The image assessment process must handle a range of both high-level tasks as well as low level tasks, e.g., identifying Regions of Interest, segmenting the target, and computing feature based image metrics and statistical distances between images. This work describes the design and work in progress on the implementation of such a system.

Is the process of inferring facts using neural networks a unique form of reasoning? Is there really a different type of reasoning separate and distinct from deduction and induction? Does there exist a single fundamental form of inference for reasoning symbolically, qualitatively, quantitatively, possibilistically (about "fuzzy" concepts), and probabilistically? YES, it is called abduction. This paper presents abduction and abductory induction. Abduction not only classifies the distinct type of reasoning performed when neural networks are applied, but gives a logical framework for expanding current neural network research to include network concepts not constrained by neuron analogies. These networks are called abductive networks. In describing abductive networks, this paper unveils the true source of the "power" of networks of functional elements. A practical machine learning tool for synthesizing abductive networks from databases of examples, called the Abductory Induction Mechanism (AIMTM), is also presented.