Abstract: A system for conducting a subterranean operation can include a retention feature in a drill floor with the retention feature configured to suspend a tubular string from the drill floor via engagement of the retention feature with the tubular string, an imaging system configured to capture images of a visible feature on a portion of the tubular string that extends above the drill floor; and a processor configured to detect a change in a position of the visible feature while the tubular string is engaged with the retention feature, and the processor being configured to calculate a wear status of the retention feature based upon the change in the position of the tubular string.

Abstract: Document portion identification in a recorded video is disclosed, including: obtaining a recorded video; identifying a document portion that appears during the recorded video, wherein the document portion belongs to a document; and determining a video segment during which the document portion appears in the recorded video.

Abstract: Document portion identification in a recorded video is disclosed, including: obtaining a recorded video; identifying a document portion that appears during the recorded video, wherein the document portion belongs to a document; and determining a video segment during which the document portion appears in the recorded video.

Abstract: A system may include a processor, a night vision sensor, and an objective lens assembly comprising four lenses. Each of a first lens, a second lens, a third lens, and a fourth lens may be configured to project a real world scene onto one of four portions of the night vision sensor. The night vision sensor may be configured to provide four images of the real world scene from the objective lens assembly to the processor. The processor may be configured to: receive a first image, a second image, a third image, and a fourth image from the night vision sensor; generate color night vision image data based at least on the first image, the second image, the third image, and the fourth image; and output the color night vision image data.

Abstract: Data includes data with labels and data without labels. For data without labels a fuzzy rules system assigns pseudo labels. A computer processes the data with labels using a first cognitive neural network; processes the data with pseudo labels using a second cognitive neural network; and produces system outcomes by combining the results of the first and second cognitive neural networks. The computer obtains feedback on the system outcomes, and modifies parameters of the fuzzy rule system in response to the feedback.

Abstract: Data includes data with labels and data without labels. For data without labels a fuzzy rules system assigns pseudo labels. A computer processes the data with labels using a first cognitive neural network; processes the data with pseudo labels using a second cognitive neural network; and produces system outcomes by combining the results of the first and second cognitive neural networks. The computer obtains feedback on the system outcomes, and modifies parameters of the fuzzy rule system in response to the feedback.