Abstract: An air-cooled condenser system for steam condensing applications in a power plant Rankine cycle includes an air cooled condenser having a plurality of interconnected modular cooling cells. A method for forming an axial flow baffle for a shell and tube heat exchanger includes providing a baffle workpiece, locating a centerpoint of a first axial flow tube aperture, drilling flow holes around the centerpoint of the flow aperture, and drilling a central tube hole at the centerpoint. A method of cooling high level waste includes surrounding a cask comprising an external surface and an internal storage cavity containing the high level radioactive waste which emits heat with a cooling water header; and discharging cooling water radially inwards from the cooling water header onto the external surface of the cask from the plurality of water dispensing outlets arranged on the cooling water header.

Abstract: Disclosed herein are system, method, and computer program product embodiments for surface automation in black box environments. An embodiment operates by determining scenarios of an application for automation; detecting the scenario during an execution of an application; capturing and storing one or more user interface screenshots of the scenario; identifying and storing user interface information from the user interface screenshot; implementing a sequential set of instructions comprising at least one textual element detection technique and at least one non-textual element detection technique; and executing the sequential set of instructions.

Abstract: In an example, a computerized neural fabric is created by representing each pattern of learned weights of one or more machine learning algorithm-trained models specifying a specific set of products as a column in the computerized neural fabric, each pattern comprising one or more clusters representing combinations of convolutional filters, each cluster learning low level features and sending output via a vertical flow up the corresponding column to a final classification within the corresponding pattern. One or more potential lateral flows between patterns in the computerized neural fabrics is dynamically determined based on resemblance of a new product in a candidate image to the specific sets of products in each of the patterns and identifying possible mutations of the patterns based on the resemblance. Then, one of the one or more potential lateral flows is selected as a new model.

Abstract: In an example, a first machine learning algorithm is used to train a smart contour model to identify contours of product shapes in input images and to identify backgrounds in the input images. A second machine learning algorithm is used to train a plurality of shape-specific classification models to output identifications of products in input images. A candidate image of one or more products is obtained. The candidate image is passed to the smart contour model, obtaining output of one or more tags identifying product contours in the candidate image. The candidate image and the one or more tags are passed to an ultra-large scale multi-hierarchy classification system to identify one or more classification models for one or more individual product shapes in the candidate image. The one or more classification models are used to distinguish between one or more products and one or more unknown products in the image.

Abstract: In an example, a computerized neural fabric is created by representing each pattern of learned weights of one or more machine learning algorithm-trained models specifying a specific set of products as a column in the computerized neural fabric, each pattern comprising one or more clusters representing combinations of convolutional filters, each cluster learning low level features and sending output via a vertical flow up the corresponding column to a final classification within the corresponding pattern. One or more potential lateral flows between patterns in the computerized neural fabrics is dynamically determined based on resemblance of a new product in a candidate image to the specific sets of products in each of the patterns and identifying possible mutations of the patterns based on the resemblance. Then, one of the one or more potential lateral flows is selected as a new model.

Abstract: In an example, a first machine learning algorithm is used to train a smart contour model to identify contours of product shapes in input images and to identify backgrounds in the input images. A second machine learning algorithm is used to train a plurality of shape-specific classification models to output identifications of products in input images. A candidate image of one or more products is obtained. The candidate image is passed to the smart contour model, obtaining output of one or more tags identifying product contours in the candidate image. The candidate image and the one or more tags are passed to an ultra-large scale multi-hierarchy classification system to identify one or more classification models for one or more individual product shapes in the candidate image. The one or more classification models are used to distinguish between one or more products and one or more unknown products in the image.

Abstract: Various embodiments of systems and methods for rendering an optimized metrics topology on a monitoring tool are described herein. A monitoring tool, installed on a computer, displays a list of monitorable systems and a plurality of components of a system selected from the list. Each component is analyzed under a selected category. Each component includes a set of metrics associated with the selected category. Each metric from the set of metrics for a component is ranked. A rank for each metric is determined based upon at least a navigation behavior of a user of the monitoring tool and a metric characteristic. Based upon their ranks, the metrics are arranged in an optimized metrics topology. Higher ranked metrics are arranged in relatively higher topology level thereby delivering critical or key metrics, up front, in which the user is interested in.