A Deep Learning Module Design for Workspace Identification in Manufacturing Industry

2021 ◽  
Author(s):  
Jeong-Su Kim ◽  
Dong Myung Lee
Keyword(s):  
Deep Learning ◽  
Manufacturing Industry ◽  
Module Design ◽  
Learning Module

scholarly journals Deep Learning Method for Quantitative Analysis of Covid -19 using CT Chest Images

2021 ◽  
Author(s):  
Indrajeet Kumar ◽  
Jyoti Rawat
Keyword(s):  
Deep Learning ◽  
Diagnostic Tests ◽  
Ct Images ◽  
Rapid Diagnosis ◽  
Chest Ct ◽  
Visual Features ◽  
Learning Module ◽  
Volumetric Ct ◽  
The Cost

Abstract The manual diagnostic tests performed in laboratories for pandemic disease such as COVID19 is time-consuming, requires skills and expertise of the performer to yield accurate results. Moreover, it is very cost ineffective as the cost of test kits is high and also requires well-equipped labs to conduct them. Thus, other means of diagnosing the patients with presence of SARS-COV2 (the virus responsible for COVID19) must be explored. A radiography method like chest CT images is one such means that can be utilized for diagnosis of COVID19. The radio-graphical changes observed in CT images of COVID19 patient helps in developing a deep learning-based method for extraction of graphical features which are then used for automated diagnosis of the disease ahead of laboratory-based testing. The proposed work suggests an Artificial Intelligence (AI) based technique for rapid diagnosis of COVID19 from given volumetric CT images of patient’s chest by extracting its visual features and then using these features in the deep learning module. The proposed convolutional neural network is deployed for classifying the infectious and non-infectious SARS-COV2 subjects. The proposed network utilizes 746 chests scanned CT images of which 349 images belong to COVID19 positive cases while remaining 397 belong negative cases of COVID19. The extensive experiment has been completed with the accuracy of 98.4 %, sensitivity of 98.5 %, the specificity of 98.3 %, the precision of 97.1 %, F1score of 97.8 %. The obtained result shows the outstanding performance for classification of infectious and non-infectious for COVID19 cases.

Modular Neural Network for Learning Visual Features, Routes, and Operation Through Human Driving Data Toward Automatic Driving System

2020 ◽  
Vol 24 (3) ◽  
pp. 368-376
Author(s):  
Shun Otsubo ◽  
Yasutake Takahashi ◽  
Masaki Haruna ◽  
◽  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Visual Information ◽  
Machine Learning Techniques ◽  
Driving System ◽  
Modular Neural Networks ◽  
Automatic Driving ◽  
Learning Module ◽  
Learning Techniques ◽  
Almost All

This paper proposes an automatic driving system based on a combination of modular neural networks processing human driving data. Research on automatic driving vehicles has been actively conducted in recent years. Machine learning techniques are often utilized to realize an automatic driving system capable of imitating human driving operations. Almost all of them adopt a large monolithic learning module, as typified by deep learning. However, it is inefficient to use a monolithic deep learning module to learn human driving operations (accelerating, braking, and steering) using the visual information obtained from a human driving a vehicle. We propose combining a series of modular neural networks that independently learn visual feature quantities, routes, and driving maneuvers from human driving data, thereby imitating human driving operations and efficiently learning a plurality of routes. This paper demonstrates the effectiveness of the proposed method through experiments using a small vehicle.

scholarly journals A Review on Machine and Deep Learning for Semiconductor Defect Classification in Scanning Electron Microscope Images

2021 ◽  
Vol 11 (20) ◽  
pp. 9508
Author(s):  
Francisco López de la Rosa ◽  
Roberto Sánchez-Reolid ◽  
José L. Gómez-Sirvent ◽  
Rafael Morales ◽  
Antonio Fernández-Caballero
Keyword(s):  
Deep Learning ◽  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Defect Detection ◽  
Scoping Review ◽  
Semiconductor Manufacturing ◽  
Manufacturing Industry ◽  
Future Research ◽  
Wide Range ◽  
Scanning Electron

Continued advances in machine learning (ML) and deep learning (DL) present new opportunities for use in a wide range of applications. One prominent application of these technologies is defect detection and classification in the manufacturing industry in order to minimise costs and ensure customer satisfaction. Specifically, this scoping review focuses on inspection operations in the semiconductor manufacturing industry where different ML and DL techniques and configurations have been used for defect detection and classification. Inspection operations have traditionally been carried out by specialised personnel in charge of visually judging the images obtained with a scanning electron microscope (SEM). This scoping review focuses on inspection operations in the semiconductor manufacturing industry where different ML and DL methods have been used to detect and classify defects in SEM images. We also include the performance results of the different techniques and configurations described in the articles found. A thorough comparison of these results will help us to find the best solutions for future research related to the subject.

scholarly journals Performance analysis of pre-trained transfer learning models for the classification of the rolling bearing faults

2021 ◽  
Vol 2070 (1) ◽  
pp. 012141
Author(s):  
Pavan Sharma ◽  
Hemant Amhia ◽  
Sunil Datt Sharma
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Manufacturing Industry ◽  
Rolling Bearing ◽  
Performance Comparison ◽  
Learning Methods ◽  
Training Time ◽  
Bearing Faults ◽  
Artificial Intelligence Techniques

Abstract Nowadays, artificial intelligence techniques are getting popular in modern industry to diagnose the rolling bearing faults (RBFs). The RBFs occur in rotating machinery and these are common in every manufacturing industry. The diagnosis of the RBFs is highly needed to reduce the financial and production losses. Therefore, various artificial intelligence techniques such as machine and deep learning have been developed to diagnose the RBFs in the rotating machines. But, the performance of these techniques has suffered due the size of the dataset. Because, Machine learning and deep learning methods based methods are suitable for the small and large datasets respectively. Deep learning methods have also been limited to large training time. In this paper, performance of the different pre-trained models for the RBFs classification has been analysed. CWRU Dataset has been used for the performance comparison.

scholarly journals Building Compassion Capacity: Chester Retold and Storyhouse, a Case Study

2018 ◽  
Vol 6 (3) ◽  
pp. 75-82
Author(s):  
Eileen Pollard
Keyword(s):  
Higher Education ◽  
Experiential Learning ◽  
Module Design ◽  
Learning Module ◽  
Arts And Humanities ◽  
The University

This article is a case study of a level five experiential learning module that I designed and taught at the University of Chester in the summer term of 2018 in collaboration with the city’s innovative new arts hub, Storyhouse. As a case study, it will demonstrate how ‘compassion’ can be placed at the heart of module design within Higher Education Arts and Humanities teaching, as well as how compassionate practice can emerge organically from innovation.

scholarly journals Pengembangan Modul Pendidikan Kewarganegaraan Berbasis Kearifan Lokal di Sekolah Menengah Atas Negeri 1 Kunto Darussalam

2020 ◽  
Vol 12 (2) ◽  
pp. 362
Author(s):  
Nurjanah Nurjanah ◽  
Isnarmi Isnarmi
Keyword(s):  
Learning Outcomes ◽  
Citizenship Education ◽  
Real Life ◽  
Student Learning Outcomes ◽  
Material System ◽  
Research Model ◽  
Average Value ◽  
Module Design ◽  
Learning Module ◽  
Improving Student Learning

This article aims to develop the learning of Citizenship Education based on local wisdom in improving student learning outcomes on material systems and the dynamics of Pancasila democracy in State Senior High School 1 Kunto Darussalam. The research model used is Research and Development. The focus of the study is in the field of Citizenship Education learning module design which collaborates on the material system and the dynamics of Pancasila democracy with local wisdom of Malay Malay culture. In accordance with Dewey's opinion; learning must be contextualized and adjusted for real life situations (Rusmiati et al, 2013). The data of this study were collected using tests, questionnaires, observations and interviews analyzed qualitatively. From the results of the study showed an increase in the average value of students from 2.1 to 3.1 with a total completeness level of 63.3% to 86.7%. 94.5% of students gave positive responses to learning. From the results of the study concluded that the learning module based on local wisdom can increase students' interest and learning outcomes in the material system and the dynamics of Pancasila democracy.

scholarly journals Automatic Reclaimed Wafer Classification Using Deep Learning Neural Networks

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 705
Author(s):  
Po-Chou Shih ◽  
Chun-Chin Hsu ◽  
Fang-Chih Tien
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Superior Performance ◽  
Asymmetric Distribution ◽  
Learning Capability ◽  
Competitive Edge ◽  
Proper Design ◽  
The Cost

Silicon wafer is the most crucial material in the semiconductor manufacturing industry. Owing to limited resources, the reclamation of monitor and dummy wafers for reuse can dramatically lower the cost, and become a competitive edge in this industry. However, defects such as void, scratches, particles, and contamination are found on the surfaces of the reclaimed wafers. Most of the reclaimed wafers with the asymmetric distribution of the defects, known as the “good (G)” reclaimed wafers, can be re-polished if their defects are not irreversible and if their thicknesses are sufficient for re-polishing. Currently, the “no good (NG)” reclaimed wafers must be first screened by experienced human inspectors to determine their re-usability through defect mapping. This screening task is tedious, time-consuming, and unreliable. This study presents a deep-learning-based reclaimed wafers defect classification approach. Three neural networks, multilayer perceptron (MLP), convolutional neural network (CNN) and Residual Network (ResNet), are adopted and compared for classification. These networks analyze the pattern of defect mapping and determine not only the reclaimed wafers are suitable for re-polishing but also where the defect categories belong. The open source TensorFlow library was used to train the MLP, CNN, and ResNet networks using collected wafer images as input data. Based on the experimental results, we found that the system applying CNN networks with a proper design of kernels and structures gave fast and superior performance in identifying defective wafers owing to its deep learning capability, and the ResNet averagely exhibited excellent accuracy, while the large-scale MLP networks also acquired good results with proper network structures.

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