Generating a representation of high-frequency electric power delivery system data using deviations from a trend

    公开(公告)号:US11152916B2

    公开(公告)日:2021-10-19

    申请号:US16871220

    申请日:2020-05-11

    Abstract: A system, method, and computer program product are provided for representation of high-frequency signal data. In use, input data is received including high-frequency signals, wherein the input data is of a first width. Next, the input data is processed to manage display of the input data, where specifically the input data is divided into one or more segments based on first criteria including the first width, and from each segment of the one or more segments, a maximum value is identified and a minimum value is identified. The maximum and minimum may be trend maximum and minimum values. The input data is transformed to a visualizable representation of the high-frequency signals, the visualizable representation of the high-frequency signals including a plot of the maximum value and the minimum value for each segment of the one or more segments. Additionally, the plot is displayed.

    Power distribution bus bar for distributing power to surface mount connectors

    公开(公告)号:US11096298B2

    公开(公告)日:2021-08-17

    申请号:US16785223

    申请日:2020-02-07

    Applicant: Krambu Inc.

    Inventor: Travis Jank

    Abstract: A power distribution bus bar is provided for distributing power to surface mount connectors. In use, the power distribution bus bar includes a circuit board and at least two add-in card connectors each mounted to a first surface of the circuit board. Additionally, at least one power supply connector distributing a power supply to the add-in card connectors is provided. The at least one power supply connector may be mounted to a second surface of the circuit board and connected to the at least two add-in card connectors.

    Photo analytics calibration
    3.
    发明授权

    公开(公告)号:US11030475B2

    公开(公告)日:2021-06-08

    申请号:US15205296

    申请日:2016-07-08

    Abstract: In one general embodiment, an article of manufacture includes an objective reference having at least two optical references. The optical references are selected from a group consisting of: a non-human-visible mark, a ruler, a spaced grid, a color calibration area, an area of reflectivity, a texture, and a pattern. In at other general embodiment, a method includes receiving an image of a product and an objective reference having at least two optical references. The optical references are selected from a group consisting of: a non-human-visible mark, a ruler, a spaced grid, a color calibration area, an area of reflectivity, a texture, and a pattern. The product is evaluated by comparing the product in the image to the optical references in the image.

    System, method, and computer program for automatic root cause analysis

    公开(公告)号:US10061637B1

    公开(公告)日:2018-08-28

    申请号:US15410623

    申请日:2017-01-19

    CPC classification number: G06F11/079

    Abstract: A system, method, and computer program product are provided for automatic root cause analysis. In operation, a root cause analysis system identifies at least one event associated with one or more records for which to perform a root cause analysis. The root cause analysis system performs a root cause analysis of the event by automatically generating a decision tree based on all records in the current time-window such that each leaf in the decision tree represents the probabilities for class labels of a target variable and each branch in the decision tree represents a feature that leads to a corresponding class label probability. The root cause analysis system automatically generates the decision tree by automatically selecting at each step the feature that maximizes information gain based on a current subset of data. The root cause analysis system then classifies which conditioned feature is a causal factor and which is a root cause of the event by using a conditional entropy equation on each branch leading to the tree leaf. The root cause analysis is repeatedly performed on sequential time-window sets of records gathered, per a sufficiently small time window for near-real-time root cause detection, yet sufficiently large records set for statistical significance.

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