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    EXPANDABLE DRONE
    21.
    发明公开
    EXPANDABLE DRONE 审中-公开

    公开(公告)号：US20240076051A1

    公开(公告)日：2024-03-07

    申请号：US18459622

    申请日：2023-09-01

    Applicant: University of South Carolina

    Inventor： Ron Fulbright

    IPC: B64D29/06 ,  B64U20/40 ,  B64U20/87

    CPC classification number: B64D29/06 ,  B64U20/40 ,  B64U20/87

    Abstract: Described herein are drone designs, and methods for making same, wherein drones are constructed from interlocking modular nacelles of different types allowing nacelles to be added as needed to increase payload capacity, flight time, and mission capability as the application requires. The modular nacelles can be attached together into an aggregate structure, called a collective, as desired to yield an infinitely expandable and extensible drone. Since each nacelle includes its own battery, nacelles can be added as needed to achieve whatever capability is needed for the application at hand without having to customize the design of the drone.

    ASSIGNING TRUST RATING TO AI SERVICES USING CAUSAL IMPACT ANALYSIS
    23.
    发明公开
    ASSIGNING TRUST RATING TO AI SERVICES USING CAUSAL IMPACT ANALYSIS 审中-公开

    公开(公告)号：US20240062079A1

    公开(公告)日：2024-02-22

    申请号：US18448369

    申请日：2023-08-11

    Applicant: UNIVERSITY OF SOUTH CAROLINA

    Inventor： BIPLAV SRIVASTAVA ,  KAUSIK LAKKARAJU ,  MARCO VALTORTA

    IPC: G06N5/022

    CPC classification number: G06N5/022

    Abstract: A method and system relates to assigning ratings (i.e., labels) to convey the trustability of AI systems grounded in its cause-and-effect behavior of significant inputs and outputs of the AI. Sentiment Analysis Systems (SASs) are data-driven Artificial Intelligence (AI) systems that, given a piece of text, assign a score conveying the sentiment and emotion intensity. The present disclosure uses the approach that protected attributes like gender and race influence the output (sentiment) given by SASs or if the sentiment is based on other components of the textual input, e.g., chosen emotion words. The presently disclosed rating methodology assigns ratings at fine-grained and overall levels, to rate SASs grounded in a causal setup, and provides an open-source implementation of both SASs—two deep-learning based, one lexicon-based, and two custom-built models—for this rating implementation. This allows users to understand the behavior of SAS in real-world applications.

    METHOD FOR ASSESSING DISPARATE IMPACT IN INTERNET MARKETS
    26.
    发明公开
    METHOD FOR ASSESSING DISPARATE IMPACT IN INTERNET MARKETS 审中-公开

    公开(公告)号：US20230394513A1

    公开(公告)日：2023-12-07

    申请号：US18299929

    申请日：2023-04-13

    Applicant: UNIVERSITY OF SOUTH CAROLINA

    Inventor： RAFAEL BECERRIL ARREOLA

    IPC: G06Q30/0201 ,  G06Q30/0204 ,  G06Q30/0203

    CPC classification number: G06Q30/0206 ,  G06Q30/0205 ,  G06Q30/0203

    Abstract: Disclosed methodology assesses the existence of disparate impact in internet markets that serve geographically dispersed consumers. Implementations of the method can collect unbiased offering (e.g., pricing and/or other fees and costs) data for a large number of products and geographic areas, so that marketing decisions, such as price, recommendations, and delivery fees can be matched to consumer demographic data from established sources such as censuses and large scale surveys. The combined data can then be used to investigate the presence and nature of disparate impact and can be used by internet platforms and retailers to audit their algorithms for disparate impact without collecting or holding the demographic data of their own users. Thus, a methodology is provided for the collection of data required to study the extent to which algorithms in internet markets may induce disparities across demographic consumer groups and whether disparities can be justified by valid interests.

    LEBESGUE SAMPLING-BASED DEEP BELIEF NETWORK FOR LITHIUM-ION BATTERY DIAGNOSIS AND PROGNOSIS
    28.
    发明公开
    LEBESGUE SAMPLING-BASED DEEP BELIEF NETWORK FOR LITHIUM-ION BATTERY DIAGNOSIS AND PROGNOSIS 审中-公开

    公开(公告)号：US20230375636A1

    公开(公告)日：2023-11-23

    申请号：US18317472

    申请日：2023-05-15

    Applicant: UNIVERSITY OF SOUTH CAROLINA

    Inventor： GUANGXING NIU ,  BIN ZHANG

    IPC: G01R31/392 ,  G01R31/367 ,  G01R31/36

    CPC classification number: G01R31/392 ,  G01R31/367 ,  G01R31/3648

    Abstract: Fault diagnosis and prognosis (FDP) is critical for ensuring system reliability and reducing operation and maintenance (O&M) costs. Lebesgue sampling based FDP (LS-FDP) is an event-based approach with the advantages of cost-efficiency, uncertainty management, and less computation. In previous works, LS-FDP approaches are mainly model-based. However, fault dynamic modeling is difficult and time consuming for some complex systems and this severely hinders the applications of LS-FDP. To address this problem, this present disclosure presents a data-driven based LS-FDP framework in which deep belief networks (DBN) and particle filter (PF) are integrated to achieve fault state estimation and remaining useful life (RUL) prediction. In the proposed approach, DBN learns the state evolution model and the Lebesgue time transition model, which are used as diagnostic and prognostic models in PF for FDP. The proposed approach has higher efficiency in terms of computation and better performance in terms of FDP accuracy and precision.

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