Equifax Inc.

Soneter, Inc.

Georgia Tech Research Corporation

Reliance Worldwide Corporation

Jeffery Dugger

Jeffrey L Leaders

Matthew Turner

Paul Edward Hasler

Michael McBurnett

David Verl Anderson

Paul Smith

Xiao Huang

Jonathan Boardman

Matthew Raymond Kucic

Abhishek Bandyopadhyay

Md Shafiul Alam

Tyson S Hall

Systems and methods for automated historical risk assessment for risk mitigation in online access control are provided. An entity assessment server can receive a request to assess a risk indicator change from a first risk indicator to a second risk indicator. For each attribute used to generate the first risk indicator and second risk indicator, a first impact can be determined for changing from the first risk indicator to a third risk indicator between the first risk indicator and the second risk indicator. A second impact similarly can be determined for changing from the third risk indicator to the second risk indicator. Aggregating the first impact and the second impact can determine a total impact of each attribute. Assessment results can be generated to include a list of attributes ordered according to the respective total impact and transmitted to a remote computing device for use in improving the risk indicator.

Historical risk assessment for risk mitigation in online access control

Certain aspects involve building timing-prediction models for predicting timing of events that can impact one or more operations of machine-implemented environments. For instance, a computing system can generate program code executable by a host system for modifying host system operations based on the timing of a target event. The program code, when executed, can cause processing hardware to a compute set of probabilities for the target event by applying a set of trained timing-prediction models to predictor variable data. A time of the target event can be computed from the set of probabilities. To generate the program code, the computing system can build the set of timing-prediction models from training data. Building each timing-prediction model can include training the timing-prediction model to predict one or more target events for a different time bin within the training window. The computing system can generate and output program code implementing the models' functionality.

Training or using sets of explainable machine-learning modeling algorithms for predicting timing of events

A fluid flow meter estimates the velocity of water or another fluid flowing through pipe by comparing measurements of the water velocity to one or more pre-determined templates. The fluid flow meter may collect measurement signals from one or more flow sensors, estimate the fluid velocity or flow rate by comparing the measurement signals to the template, and either store the comparison results in local memory, transmit the results to a remote memory or server, or both. In some embodiments, the fluid flow meter transmits the results to a server via a wireless interface. The transducers and processing system can be powered by a battery, a power line, or, for manifolds installed outdoors, a solar cell. Example transducers and processing systems may also have a passive wake-up feature for power reduction; that is, they may only draw power when water or another fluid flows through the pipe.

Methods and apparatus for ultrasonic fluid flow measurement and fluid flow data analysis

Methods and apparatus for fluid flow measurement

A fluid flow meter estimates the velocity of water or another fluid flowing through pipe by comparing measurements of the water velocity to one or more pre-determined templates. The fluid flow meter may collect measurement signals from one or more flow sensors (e.g., ultrasonic transducers), estimate the fluid velocity or flow rate by comparing the measurement signals to the template(s), and either store the comparison results in local memory, transmit the results to a remote memory or server, or both. In some embodiments, the fluid flow meter transmits the results to a server via a wireless interface (e.g., a Zigbee, Bluetooth, or Wi-fi interface). The transducers and processing system can be powered by a battery, a power line, or, for manifolds installed outdoors, a solar cell. Example transducers and processing systems may also have a passive wake-up feature for power reduction; that is, they may only draw power when water or another fluid flows through the pipe.

In one exemplary embodiment, a programmable analog array (PAA) contains a configurable analog matrix having two floating-gate field effect transistors (FETs). Also contained in the PAA is an interconnect circuit that is programmable to configure the configurable analog matrix to operate in one or more of several matrix modes. A few examples of such matrix modes include a switching matrix mode, a memory matrix mode, and a computing matrix mode. In an exemplary method of configuring the PAA. PAA, the the method includes programming an interconnection, for example, between a first terminal of the first floating-gate FET and a first terminal of the second floating-gate FET. The method further includes programming an interconnection, for example, between a gate terminal of the first floating-gate FET and a fixed voltage source, for setting a floating gate charge on the first floating-gate FET.

Floating-gate analog circuit

Growing community of inventors

Atlanta, GA, United States of America

Jeffery Dugger