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

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1151 ◽  
Author(s):  
Patalas-Maliszewska ◽  
Halikowski
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Convolutional Neural Networks ◽  
Reference Model ◽  
Learning Processes ◽  
Support Vector ◽  
Manufacturing Companies ◽  
Work Activity ◽  
New Employees ◽  
Specialist Knowledge

(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.

scholarly journals An Automated Recognition of Work Activity in Industrial Manufacturing Using Convolutional Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2946
Author(s):  
Justyna Patalas-Maliszewska ◽  
Daniel Halikowski ◽  
Robertas Damaševičius
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Industry 4.0 ◽  
Reference Frames ◽  
Absolute Error ◽  
Automatic Generation ◽  
Support Vector ◽  
Work Activity ◽  
Work Related ◽  
Industrial Solutions

The automated assessment and analysis of employee activity in a manufacturing enterprise, operating in accordance with the concept of Industry 4.0, is essential for a quick and precise diagnosis of work quality, especially in the process of training a new employee. In the case of industrial solutions, many approaches involving the recognition and detection of work activity are based on Convolutional Neural Networks (CNNs). Despite the wide use of CNNs, it is difficult to find solutions supporting the automated checking of work activities performed by trained employees. We propose a novel framework for the automatic generation of workplace instructions and real-time recognition of worker activities. The proposed method integrates CNN, CNN Support Vector Machine (SVM), CNN Region-Based CNN (Yolov3 Tiny) for recognizing and checking the completed work tasks. First, video recordings of the work process are analyzed and reference video frames corresponding to work activity stages are determined. Next, work-related features and objects are determined using CNN with SVM (achieving 94% accuracy) and Yolov3 Tiny network based on the characteristics of the reference frames. Additionally, matching matrix between the reference frames and the test frames using mean absolute error (MAE) as a measure of errors between paired observations was built. Finally, the practical usefulness of the proposed approach by applying the method for supporting the automatic training of new employees and checking the correctness of their work done on solid fuel boiler equipment in a manufacturing company was demonstrated. The developed information system can be integrated with other Industry 4.0 technologies introduced within an enterprise.

scholarly journals A Hybrid EEG-Based Emotion Recognition Approach Using Wavelet Convolutional Neural Networks (WCNN) and Support Vector Machine

2021 ◽  
pp. 1-29
Author(s):  
Sara Bagherzadeh ◽  
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Convolutional Neural Networks ◽  
Hybrid Approach ◽  
Support Vector ◽  
Emotional States ◽  
Eeg Signals ◽  
Color Representation ◽  
Time Frequency ◽  
Average Accuracy

Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool which makes processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate. In this paper, a hybrid approach based on deep features extracted from Wavelet CNNs (WCNNs) weighted layers and multiclass support vector machine (MSVM) is proposed to improve recognition of emotional states from electroencephalogram (EEG) signals. First, EEG signals were preprocessed and converted to time-frequency (T-F) color representation or scalogram using the continuous wavelet transform (CWT) method. Then, scalograms were fed into four popular pre-trained CNNs, AlexNet, ResNet-18, VGG-19 and Inception-v3 to fine-tune them. Then, the best feature layer from each one was used as input to the MSVM method to classify four quarters of the valence-arousal model. Finally, subject-independent Leave-One-Subject-Out criterion was used to evaluate the proposed method on DEAP and MAHNOB-HCI databases. Results show that extracting deep features from the earlier convolutional layer of ResNet-18 (Res2a) and classifying using the MSVM increases the average accuracy, precision and recall about 20% and 12% for MAHNOB-HCI and DEAP databases, respectively. Also, combining scalograms from four regions of pre-frontal, frontal, parietal and parietal-occipital and two regions of frontal and parietal achieved the higher average accuracy of 77.47% and 87.45% for MAHNOB-HCI and DEAP databases, respectively. Combining CNN and MSVM increased recognition of emotion from EEG signal and results were comparable to state-of-the-art studies.

scholarly journals Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhe Xu ◽  
Xi Guo ◽  
Anfan Zhu ◽  
Xiaolin He ◽  
Xiaomin Zhao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Fine Tuning ◽  
Support Vector ◽  
Test Accuracy ◽  
Nutrient Deficiencies ◽  
Deep Convolutional Neural Networks ◽  
Histogram Of Oriented Gradient

Symptoms of nutrient deficiencies in rice plants often appear on the leaves. The leaf color and shape, therefore, can be used to diagnose nutrient deficiencies in rice. Image classification is an efficient and fast approach for this diagnosis task. Deep convolutional neural networks (DCNNs) have been proven to be effective in image classification, but their use to identify nutrient deficiencies in rice has received little attention. In the present study, we explore the accuracy of different DCNNs for diagnosis of nutrient deficiencies in rice. A total of 1818 photographs of plant leaves were obtained via hydroponic experiments to cover full nutrition and 10 classes of nutrient deficiencies. The photographs were divided into training, validation, and test sets in a 3 : 1 : 1 ratio. Fine-tuning was performed to evaluate four state-of-the-art DCNNs: Inception-v3, ResNet with 50 layers, NasNet-Large, and DenseNet with 121 layers. All the DCNNs obtained validation and test accuracies of over 90%, with DenseNet121 performing best (validation accuracy = 98.62 ± 0.57%; test accuracy = 97.44 ± 0.57%). The performance of the DCNNs was validated by comparison to color feature with support vector machine and histogram of oriented gradient with support vector machine. This study demonstrates that DCNNs provide an effective approach to diagnose nutrient deficiencies in rice.

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