During the steel pipeline installation, special attention is paid to the butt weld control performed by fusion welding. The operation of the currently popular automated X-ray and ultrasonic testing complexes is associated with high resource and monetary costs. In this regard, this work is devoted to the development of alternative and cost-effective means of preliminary quality control of the work performed based on the visual testing method. To achieve this goal, a hardware platform based on a single board Raspberry Pi4 minicomputer and a set of available modules and expansion cards is proposed, and software whose main functionality is implemented based on the systemic application of computer vision algorithms and machine learning methods. The YOLOv5 object detection algorithm and the random forest machine learning model were used as a defect detection and classification system. The mean average precision (mAP) of the trained YOLOv5 algorithm based on extracted weld contours is 86.9%. A copy of YOLOv5 trained on the images of control objects showed a mAP result of 96.8%. Random forest identifying of the defect precursor based on the point clouds of the weld surface achieved a mAP of 87.5%.
The pattern suggested for the structure-function superfamily of cytochromes P450 is composed by combining the conserved amino acid motifs. The sizes of P450 cytochromes were estimated according to their length. The empirical coefficients reflecting the peculiarities of the primary structure of these enzymes are calculated. We propose an approach for determining novel proteins sequences to the mentioned superfamily on the ground of the complex of these parameters. A number of the hypothetical proteins from the international databases is related to the cytochromes P450 by means of our pattern.
In the field of intelligent surface inspection systems, particular attention is paid to decision making problems, based on data from different sensors. The combination of such data helps to make an intelligent decision. In this research, an approach to intelligent decision making based on a data integration strategy to raise awareness of a controlled object is used. In the following article, this approach is considered in the context of reasonable decisions when detecting defects on the surface of welds that arise after the metal pipe welding processes. The main data types were RGB, RGB-D images, and acoustic emission signals. The fusion of such multimodality data, which mimics the eyes and ears of an experienced person through computer vision and digital signal processing, provides more concrete and meaningful information for intelligent decision making. The main results of this study include an overview of the architecture of the system with a detailed description of its parts, methods for acquiring data from various sensors, pseudocodes for data processing algorithms, and an approach to data fusion meant to improve the efficiency of decision making in detecting defects on the surface of various materials.
Microfluidic devices have opened new opportunities for functional material chemical synthesis in a few applications. The screening of microfluidic synthesis processes is an urgent task of the experimental process in terms of automation and intellectualization. This study proposes a methodology and software for extracting the morphological and dynamic characteristics of generated monodisperse droplets from video data streams obtained from a digital microscope. For this purpose, the paper considers an approach to generating an extended feature space characterizing the process of droplet generation using a microfluidic device based on the creation of synthetic image datasets. YOLOv7 was used as an algorithm for detecting objects in the images. When training this algorithm, the values in the test dataset mAP@0.5 0.996 were obtained. The algorithms proposed for image processing and analysis implement the basic functionality to extract the morphological and dynamic characteristics of monodisperse droplets in the synthesis process. Laboratory validation and verification of the software demonstrated high results of the identification of key characteristics of the monodisperse droplets generated by the microfluidic device with the average deviation from the real values not exceeding 8%.
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