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

被引:14
|
作者
Patalas-Maliszewska, Justyna [1 ]
Halikowski, Daniel [2 ]
机构
[1] Univ Zielona Gora, Inst Comp Sci & Prod Management, Fac Mech Engn, Ul Licealna 9, PL-65417 Zielona Gora, Poland
[2] Univ Appl Sci Nysa, Inst Tech Sci, Ul Armii Krajowej 7, PL-48300 Nysa, Poland
来源
SYMMETRY-BASEL | 2019年 / 11卷 / 09期
关键词
generation of a workplace procedure; CNN-SVM architecture; employee learning processes; VIRTUAL-REALITY; LOCALIZATION; RECOGNITION; FEATURES;
D O I
10.3390/sym11091151
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
(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.
引用
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页数:15
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