4results about How to "Facilitates automated analysis" patented technology

The invention relates to the technical field of non-destructive testing, and discloses a non-destructive testing method for enhancing defect detection efficiency through deep learning, which comprises the following steps: step 1, acquiring multi-modal data of a target material, including data acquired through X-ray imaging, ultrasonic detection and infrared thermal imaging; 2, signal denoising processing is carried out on the obtained multi-modal data, signal denoising is carried out through wavelet transformation, and the wavelet transformation comprises the steps that multi-scale decomposition is carried out on signals, and high-frequency noise is removed through a soft threshold method; and step 3, carrying out image enhancement processing on the de-noised multi-modal data. The internal structural features of the material are captured from multiple physical dimensions by fusing multi-modal data of X-ray imaging, ultrasonic detection and infrared thermal imaging. The multi-modal fusion can make up for the deficiency of a single detection technology, so that the defect identification in complex structures and materials is more comprehensive and accurate, thereby significantly improving the detection precision.

The invention discloses a power and environment centralized monitoring method, system and device, a medium and a product, and relates to the field of abnormity monitoring, and the method comprises the steps: obtaining a video image of an inspection scene; preprocessing the video image to obtain a preprocessed image; constructing a machine vision inspection scene AI model; inputting the preprocessed image into a machine vision inspection scene AI model to obtain a machine vision feature detection result; determining whether the inspection result is abnormal or not based on the machine visual feature detection result, a preset threshold value, the permission information and the inspection task; when the inspection result is abnormal, determining an abnormal type and an abnormal result, generating alarm notification information and an intelligent regulation and control instruction, and storing the abnormal information; and when the inspection result is not abnormal, storing the machine visual feature detection result. According to the invention, the problems of low efficiency, high error rate and the like of the existing power and environment centralized monitoring mode can be solved, and the requirements of modern informatization management are met.

Methods and systems for tracking elongated objects in image data are disclosed. Methods and systems are operable, for example, for neurite tracking. A turn prediction is inferred using an artificial intelligence model. The turn prediction is processed to determine a next point of the series of points (60) on the centerline (50) of the elongate object.

The application provides a molecular marker and identification method for identifying genuine producing areas and non-genuine producing areas of Astragalus membranaceus. The molecular marker contains a nucleotide sequence with a polymorphism of A / T at the 131st base of the sequence shown in SEQ ID NO: 1 of the 5th exon of Am02G035490 of the Astragalus membranaceus genome. The application also provides a method for identifying the genuine producing areas of Astragalus membranaceus by using the SNP molecular marker, which provides technical support for the precise traceability, stable quality and healthy and orderly development of the Astragalus membranaceus industry of the genuine producing areas of Astragalus membranaceus.