A Novel Short-Memory Sequence-Based Model for Variable-Length Reading Recognition of Multi-Type Digital Instruments in Industrial Scenarios

As a practical application of Optical Character Recognition (OCR) for the digital situation, the digital instrument recognition is significant to achieve automatic information management in real-industrial scenarios. However, different from the normal digital recognition task such as license plate recognition, CAPTCHA recognition and handwritten digit recognition, the recognition task of multi-type digital instruments faces greater challenges due to the reading strings are variable-length with different fonts, different spacing and aspect ratios. In order to overcome this shortcoming, we propose a novel short-memory sequence-based model for variable-length reading recognition. First, we involve shortcut connection strategy into traditional convolutional structure to form a feature extractor for capturing effective features from characters with different fonts of multi-type digital instruments images. Then, we apply an RNN-based sequence module, which strengthens short-distance dependencies while reducing the long-distance trending memory of the reading string, to greatly improve the robustness and generalization of the model for invisible data. Finally, a novel short-memory sequence-based model consisting of a feature extractor, an RNN-based sequence module and the CTC, is proposed for variable-length reading recognition of multi-type digital instruments. Experimental results show that this method is effective on variable-length instrument reading recognition task, especially for invisible data, which proves that our method has outstanding generalization and robustness in real-industrial applications

Impact of Driver Age and Experience in Software Usage on Driving Safety and Usability of Car-Sharing Software

Car-sharing economy has caused new driving safety and usability problems, which have not been well studied. This study aims at analyzing the effects of users age and the user experience (UX) of the car-sharing software (e.g., DiDi travel app) on overall usability and the level of distraction for drivers. To this end, 48 experienced Chinese drivers were recruited to perform various tasks with the car-sharing software using a driving simulator. The variables of driving safety and usability were analyzed by two-way analysis of variance (ANOVA) and independent sample Kruskal–Wallis nonparametric test. As expected, it was found that car-sharing software has a significant negative impact on driving distraction and usability. The overall performance of young drivers is better than that of the elderly, but it seems that young drivers are more likely to be led to errors by car-sharing software. In most aspects, experienced drivers perform better than inexperienced drivers and have a better in-depth understanding of car-sharing software weaknesses. However, inexperienced drivers performed better regarding braking time and interaction time. Although young inexperienced drivers performed worst in driving safety, they exhibited the lowest cognitive load and the highest interaction efficiency. The experience of using car-sharing software may improve driver’s ability to deal with driving distractions. The above conclusions provide theoretical support for optimizing the UX of car-sharing software and some references for driver’s screening and training

Simulation of edge cracks using pulsed eddy current stimulated thermography

Thermography has proven to be one of the most effective approaches to detect cracks in conductive specimens over a relatively large area. Pulsed eddy current stimulated thermography is an emerging integrative nondestructive approach for the detection and characterization of surface and subsurface cracks. In this paper, heating behaviors of edge cracks, excited by pulsed eddy currents, are examined using numerical simulations. The simulations are performed using COMSOL multiphysics finite element method simulation software using the AC/DC module. The simulation results show that in the early heating stage, the temperature increases more quickly at the crack tip compared with other points on the sample. The results indicate that to maximize sensitivity, the response should be analyzed in the early stages of the heating period, no more than 100 ms for samples in which we are interested. The eddy current density distribution is changed with a variation in inductor orientation, but the crack tips remain the “hottest” points during the excitation period, which can be used for robust quantitative defect evaluation. Signal feature selection, transient temperature profile of the sample, and influence of the inductor orientation on the detection sensitivity for edge cracks are investigated. The work shows that positioning of the inductor, perpendicular to the crack line, results in the highest sensitivity for defect detection and characterization. The crack orientation can be estimated through the rotation of the linear inductor near the sample edge and the crack tips.</jats:p