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

Abstract

(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.

Design and Implementation of Intelligent Automated Production-Line Control System

“…With the development of Industry 4.0, production in the factories will be more flexible and must be more intelligent. Related research topics about automated production control systems [18]; automation of product, design, management, and control systems [2]; intelligent manufacturing [20,21]; and CPS [3,6] have been proposed. Whether the research topic is intelligent manufacturing, CPS, or automated production control systems, most people tend to study methods and introductions and do less with the combination and application of the actual production line.…”

“…The automation of a robot control system based on an automatic programming environment minimizes user rework and analysis time when there is a frequent need to redistribute the robot task through robot self-observational learning, independent reprogramming, or-through the integration of the M&S environment-maintain and promote model reuse, and to provide an automatic model-generation function to obtain the best solutions [19,20]. At present, even internal personnel training of enterprises has introduced intelligent learning [21]. CNN neural networks with SVM classifiers can extract and share expert knowledge according to film materials, effectively improve the learning process of employees in manufacturing companies, and shorten the time cost of personnel training.…”

A Deep Learning-Based Model for the Automated Assessment of the Activity of a Single Worker

“…The elements of the model ( Figure 1 ) highlighted in green are discussed in detail in [ 11 ]. In this article, CNN was pledged using ROI and YOLOv3.…”

“…Several research studies have been conducted that implement classifiers such as the support vector machine (SVM), principal component analysis (PCA), linear discriminant analysis (LDA), random forest, K-nearest neighbours, the hidden Markov model [ 1 , 2 ] and deep learning algorithms [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] to develop recognition of human activity (HAR); these research studies have achieved promising results; see [ 11 ] for a more comprehensive overview. Deep learning is also used, successfully, in object and video recognition [ 12 ] and has the ability to automatically learn from unlabelled, raw, sensor data [ 13 ].…”

Hybrid Algorithm Based on Simulated Annealing and Bacterial Foraging Optimization for Mining Imbalanced Data

“…Data mining for imbalanced data can be performed using a decision tree (DT), artificial neural network (ANN), genetic algorithm (GA), and support vector machine (SVM). (3)(4)(5)(6) Some methods such as the cost-sensitive classifier and snowball methods have been proposed to process imbalanced data. (7) The cost-sensitive classifier method focuses on minimizing misclassification costs as well as other types of cost with overspecific rules.…”