Aiming at the fundamental role of railway wagon number recognition in railway freight management and railway wagon inspection, a railway wagon number recognition method based on Combined Grey Wolf Optimizer Support Vector Machine (C-GWO-SVM) is proposed. Aiming at the confusing numbers and letters in the railway wagon number dataset, the algorithm in this article first uses a set of GWO-SVM to perform multi-classification processing on the railway wagon number characters, and divides the numbers and letters in the dataset into easily distinguishable number and letter characters, and easily confusing number and letter characters, with a classification accuracy of 98.875%; Then two sets of GWO-SVM are used to classify and recognize the numbers and letters in the railway wagon number characters, with classification accuracy of 99.70% and 99.99%, respectively. The experimental results show that compared with Sparrow Search Algorithm (SSA) to optimize SVM multi-classification algorithm, the GWO-SVM algorithm has shorter parameter optimization time, higher recognition accuracy and faster recognition speed in the application of railway wagon number and letter characters recognition.
An embedded micro-deep hole drilling force measurement system is proposed and verified experimentally, it is based on resistance strain gauge, weak signal detection method and embedded technology. The system can measure the thrust force, tangential force and two orthogonal components of radial force precisely, continuously and stably in real time, while other similar systems can measure only thrust force usually. The system can replace the complex modelling of drilling process to obtain random changing details of drilling forces, and can also be used effectively in intelligent drilling control research.
The traditional dimensional measurement of gate tile steel back is still under the status quo of relying on manual work, which is not conducive to industrialization and automated production. In this paper, we rely on machine vision and image feature extraction to achieve contactless measurement of gate tile backs, preprocess the acquired images, binarization operation, edge detection using Canny operator, and finally obtain the product length and width parameters, square hole length and width parameters, central round hole diameter parameters using Hough transform and least squares method. The experimental results show that the method can accurately measure the index parameters of the gate tile steel backing products, and the detection error is within ±2mm. The method reduces the influence of human factors and environmental factors on the inspection of the gate tile steel backing, improves the measurement accuracy and speed of the quality inspection of the gate tile steel backing, and promotes the automation of the production of the gate tile steel backing to a certain extent.
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