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A Model for Generating Workplace Procedures Using a CNN-SVM Architecture

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Institute of Computer Science and Production Management, Faculty of Mechanical Engineering, University of Zielona Góra, ul. Licealna 9, 65-417 Zielona Góra, Poland
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Institute of Technical Science, University of Applied Science in Nysa, ul. Armii Krajowej 7, 48-300 Nysa, Poland
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Author to whom correspondence should be addressed.
Symmetry 2019, 11(9), 1151; https://doi.org/10.3390/sym11091151
Received: 22 August 2019 / Revised: 3 September 2019 / Accepted: 5 September 2019 / Published: 10 September 2019
(1) Background: Improving the management and effectiveness of employees’ learning processes within manufacturing companies has attracted a high level of attention in recent years, especially within the context of Industry 4.0. Convolutional Neural Networks with a Support Vector Machine (CNN-SVM) can be applied in this business field, in order to generate workplace procedures. To overcome the problem of usefully acquiring and sharing specialist knowledge, we use CNN-SVM to examine features from video material concerning each work activity for further comparison with the instruction picture’s features. (2) Methods: This paper uses literature studies and a selected workplace procedure: repairing a solid and using a fuel boiler as the benchmark dataset, which contains 20 s of training and a test video, in order to provide a reference model of features for a workplace procedure. In this model, the method used is also known as Convolutional Neural Networks with Support Vector Machine. This method effectively determines features for the further comparison and detection of objects. (3) Results: The innovative model for generating a workplace procedure, using CNN-SVM architecture, once built, can then be used to provide a learning process to the employees of manufacturing companies. The novelty of the proposed methodology is its architecture, which combines the acquisition of specialist knowledge and formalising and recording it in a useful form for new employees in the company. Moreover, three new algorithms were created: an algorithm to match features, an algorithm to detect each activity in the workplace procedure, and an algorithm to generate an activity scenario. (4) Conclusions: The efficiency of the proposed methodology can be demonstrated on a dataset comprising a collection of workplace procedures, such as the repair of the solid fuel boiler. We also highlighted the impracticality for managers of manufacturing companies to support learning processes in a company, resulting from a lack of resources to teach new employees.
Keywords: generation of a workplace procedure; CNN-SVM architecture; employee learning processes generation of a workplace procedure; CNN-SVM architecture; employee learning processes
MDPI and ACS Style

Patalas-Maliszewska, J.; Halikowski, D. A Model for Generating Workplace Procedures Using a CNN-SVM Architecture. Symmetry 2019, 11, 1151.

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