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    Systems and methods for detecting a fault or a model mismatch
    56.
    发明授权
    Systems and methods for detecting a fault or a model mismatch 有权

    公开(公告)号：US11093315B2

    公开(公告)日：2021-08-17

    申请号：US16361780

    申请日：2019-03-22

    Applicant: TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC. ,  UNIVERSITY OF CONNECTICUT

    Inventor： Donald McMenemy ,  Weiqiang Chen ,  Ali M. Bazzi ,  Krishna R. Pattipati ,  Shailesh N. Joshi

    IPC: G06F11/07

    Abstract: Systems and methods for detecting a fault or model mismatch are disclosed. A system includes a processor, a memory, and one or more sensors. The sensors may detect data associated with an electronic device. The memory may store processor executable instructions to: compute T2 and Q statistics, over a time period, and apply a model mismatch and fault detection logic based on the T2 and Q statistics. The model mismatch and fault detection logic may: count consecutive instances where a T2 statistic exceeds a T2 threshold via a T2 counter, update a probability of fault based on the T2 counter, count consecutive instances where a Q statistic exceeds a Q threshold via a Q counter, update a probability of model mismatch based on the Q counter, and detect one of a fault or a model mismatch based on a probability of fault threshold and a probability of model mismatch threshold.

    STRETCHABLE THERMAL RADIATION MODULATION SYSTEM VIA MECHANICALLY TUNABLE SURFACE EMISSIVITY
    58.
    发明申请
    STRETCHABLE THERMAL RADIATION MODULATION SYSTEM VIA MECHANICALLY TUNABLE SURFACE EMISSIVITY 有权

    公开(公告)号：US20210213718A1

    公开(公告)日：2021-07-15

    申请号：US17149333

    申请日：2021-01-14

    Applicant: University of Connecticut

    Inventor： Luyi Sun ,  Songshan Zeng ,  Kuangyu Shen

    IPC: B32B27/30 ,  B32B7/12 ,  B32B27/28 ,  B32B27/18 ,  B32B15/082 ,  B32B7/023 ,  B32B7/06

    Abstract: Disclosed herein is a thermal radiation modulation system comprising a first low emissivity layer comprising a plurality of distributed, strain-dependent cracks, the first low emissivity layer comprising a first polymer composite layer and a first mirror-like metal layer with low emissivity covering a surface of the first polymer composite layer; a first elastomer layer bonded to the first low emissivity layer opposite to the mirror-like metal layer; and optionally a first stretchable heater, the first stretchable heater is attached to the first elastomer layer opposite to the first low emissivity layer, wherein a top surface of the first low emissivity layer comprising the mirror-like metal layer has a lower emissivity relative to the first elastomer layer. Methods of making and use of the system are further described.

    Artificial Intelligence (AI) based Decision-Making Model for Orthodontic Diagnosis and Treatment Planning
    59.
    发明申请
    Artificial Intelligence (AI) based Decision-Making Model for Orthodontic Diagnosis and Treatment Planning 有权

    公开(公告)号：US20210196428A1

    公开(公告)日：2021-07-01

    申请号：US17107193

    申请日：2020-11-30

    Applicant: University of Connecticut

    Inventor： Madhur Upadhyay ,  Yasir Suhail ,  Kshitz Gupta ,  Aditya Chhibber

    IPC: A61C7/00 ,  G16H50/20 ,  G06N5/00

    Abstract: A computer-implemented method and corresponding system provide orthodontic treatment options for use in orthodontic diagnosis or treatment planning. The method performs a rules-based expert system analysis on a given feature variable to produce expert system treatment options. The given feature variable represents an orthodontic feature of a patient. The method applies a computer-implemented multi-component model to a given set of feature variables to produce multi-component model-based treatment options that include primary and secondary model-based treatment options. The method compares the expert system treatment options to the multi-component model-based treatment options to determine disagreement or agreement between each other, enabling a suitable treatment decision to be arrived at which can be valuable to clinicians for verifying treatment plans, minimizing human error, training orthodontists, and improving reliability.

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