Abstract

The difficulty in teaching control theory is that the lecturer must not only provide all the theoretical concepts but also visualize the control system in time and frequency domains. In control system courses, the visualizations are usually provided with roughly sketches on whiteboards and thus might be difficult to understand. In this paper, a Deep Learning (DL) based pipeline is proposed that is capable to recognize Handwritten Feedback Control Architectures (HFCAs) on the whiteboard and to transform them into Matlab ® for visualization and analysis of control systems interactively. The proposed DL pipeline consists of 5 main steps that take up the frameworks of deep learning, pattern recognition and image processing. The main challenges of constructing such a pipeline are the uncertainties resulting from the lecturer's handwriting quality and lighting conditions in the classroom, which can be seen as inter- and intra-quality uncertainties. Therefore, we employed and trained deep Convolutional Neural Networks (CNNs) to recognize the HFCAs with a high performance. In the training of deep CNNs, we integrated the transfer learning approach with the deep CNN ResNet-50. To capture the inter- and intra-quality uncertainties, we constructed an image dataset of HFCAs collected from control system lecturers, who have different levels of experience, in a small-sized classroom under different lighting conditions. We provide all the details on the design of the DL based pipeline and present experimental results to show that the pipeline is a powerful tool to visualize HFCAs in real-time by using the advantages of Matlab ® .

Highlights

  • Teaching control theory is difficult as there are many theoretical concepts to be addressed

  • We proposed a Deep Learning (DL) based the pipeline that is capable to recognize Handwritten Feedback Control Architectures (FCAs) (HFCAs) on the whiteboard and to transform them into Matlab R for visualization and analysis of FCAs

  • WORK In this paper, we provided a DL based pipeline that provides the opportunity to the lecturers/ researchers to visualize and analyze Handwritten Feedback Control Architectures (HFCAs) during a lecture by transforming the HFCA into Matlab R in real-time

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Summary

INTRODUCTION

Teaching control theory is difficult as there are many theoretical concepts to be addressed. The lighting conditions in the classroom (as it can be seen from Fig. 2) and more importantly the lecturer’s handwriting quality result with difficulties in grasping the concepts of control theory since the students have to put an extra effort to understand/ recognize the handwritings on the whiteboard These issues can be seen as uncertainties that can be categorized in following forms: FIGURE 1. We proposed a DL based the pipeline that is capable to recognize Handwritten FCAs (HFCAs) on the whiteboard and to transform them into Matlab R for visualization and analysis of FCAs. The main design challenge of the proposed DL based pipeline are the aforementioned uncertainties, namely Inter- and Intra- quality uncertainties.

BRIEF OVERVIEW ON CNNs
RESNET-50 AND TRANSFER LEARNING
HANDWRITTEN CHARACTER RECOGNITION WITH DEEP LEARNING
Findings
CONCLUSIONS AND FUTURE WORK
Full Text
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