scholarly journals Low-Cost Education Kit for Teaching Basic Skills for Industry 4.0 Using Deep-Learning in Quality Control Tasks

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 230
Author(s):  
Martin Pajpach ◽  
Oto Haffner ◽  
Erik Kučera ◽  
Peter Drahoš
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
Engineering Education ◽  
Industry 4.0 ◽  
Low Cost ◽  
Basic Skills ◽  
Free Software ◽  
Educational Institutions ◽  
Next Generation ◽  
Suitable Environment

The main purposes of this paper are to offer a low-cost solution that can be used in engineering education and to address the challenges that Industry 4.0 brings with it. In recent years, there has been a great shortage of engineering experts, and therefore it is necessary to educate the next generation of experts, but the hardware and software tools needed for education are often expensive and access to them is sometimes difficult, but most importantly, they change and evolve rapidly. Therefore, the use of cheaper hardware and free software helps to create a reliable and suitable environment for the education of engineering experts. Based on the overview of related works dealing with low-cost teaching solutions, we present in this paper our own low-cost Education Kit, for which the price can be as low as approximately EUR 108 per kit, for teaching the basic skills of deep learning in quality-control tasks in inspection lines. The solution is based on Arduino, TensorFlow and Keras, a smartphone camera, and is assembled using LEGO kit. The results of the work serve as inspiration for educators and educational institutions.

DESIGN OF SMARTSCHOOL WEBCAM SECURITY SYSTEM USING RASPBERRY PI WITH MOTION DETECTION IN SMK PGRI SUKODADI

JOURNAL ASRO ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 202
Author(s):  
Firman Yudianto ◽  
Fajar Annas Susanto
Keyword(s):  
Motion Detection ◽  
Industry 4.0 ◽  
Low Cost ◽  
Security System ◽  
Educational Institutions ◽  
Raspberry Pi ◽  
File Size ◽  
Moving Image ◽  
Moving Images ◽  
Web Camera

Currently a low cost security system is needed and easy to apply especially at educational institutions that willimplement smart school and industry 4.0. Needed devices are raspberry pi and web camera. Raspberry pi willonly save moving images taken from the web camera. Because by storing an image whose file size is not toolarge will ease the performance of the server. In this study, a design for the raspberry pi based motion detectionsystem will be applied at SMK PGRI Sukodadi Lamongan Regency which has not have security system. Thissystem will save the file in the form of an image that will be put together into a moving image that looks like avideo that will displayed in a LED monitor.Keywords: smart school, motion detection, moving image.

scholarly journals S3E2: a web-based GIS for the visualization and analysis of socioeconomic segregation in Chile's elementary education system

10.29007/jrhj ◽  
2019 ◽  
Author(s):  
Julio Rojas-Mora ◽  
Ignacio Lincolao-Venegas ◽  
Felipe Schneeberger-León
Keyword(s):  
Information System ◽  
Elementary Education ◽  
Education System ◽  
Low Cost ◽  
Free Software ◽  
Educational Institutions ◽  
Basic Information ◽  
Ministry Of Education ◽  
Web Based ◽  
Socioeconomic Segregation

S3E2 is a web-based geographic information system (GIS) designed for the visualization and analysis of the socioeconomic segregation of Chile’s elementary education system of Chile. It consists of a frontend developed in JavaScript using ReactJS, React-Redux, Leaflet and D3.js, an API developed in Go and ECHO, and a documentary database man- aged with MongoDB. Data comes from Chile’s Ministry of Education, while the provisions of Law 20.248 serve as indicators of vulnerability. S3E2 graphically shows different segregation indices found in the literature, calculated at the commune level. It also allows visualizing, at this same level, the educational institutions that compose it, their basic information, and time series associated with them. S3E2 is a flexible and fast web-based GIS, with a low cost of implementation, due to the usage of free software -or at least free licensing- tools, thus serving as a template for new web-based GIS in different contexts.

scholarly journals Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3987 ◽  
Author(s):  
Javier Villalba-Diez ◽  
Daniel Schmidt ◽  
Roman Gevers ◽  
Joaquín Ordieres-Meré ◽  
Martin Buchwitz ◽  
...  
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Control Process ◽  
Optical Quality ◽  
Soft Sensor ◽  
Training Data ◽  
Printing Industry ◽  
Quality Control Process

Rapid and accurate industrial inspection to ensure the highest quality standards at a competitive price is one of the biggest challenges in the manufacturing industry. This paper shows an application of how a Deep Learning soft sensor application can be combined with a high-resolution optical quality control camera to increase the accuracy and reduce the cost of an industrial visual inspection process in the Printing Industry 4.0. During the process of producing gravure cylinders, mistakes like holes in the printing cylinder are inevitable. In order to improve the defect detection performance and reduce quality inspection costs by process automation, this paper proposes a deep neural network (DNN) soft sensor that compares the scanned surface to the used engraving file and performs an automatic quality control process by learning features through exposure to training data. The DNN sensor developed achieved a fully automated classification accuracy rate of 98.4%. Further research aims to use these results to three ends. Firstly, to predict the amount of errors a cylinder has, to further support the human operation by showing the error probability to the operator, and finally to decide autonomously about product quality without human involvement.

A low-cost, free-software platform with hard real-time performance for control engineering education

2018 ◽  
Vol 27 (2) ◽  
pp. 406-418 ◽  
Author(s):  
Andrés M. González-Vargas ◽  
Juan M. Serna-Ramirez ◽  
Carlos Fory-Aguirre ◽  
Alejandro Ojeda-Misses ◽  
John M. Cardona-Ordoñez ◽  
...  
Keyword(s):  
Engineering Education ◽  
Real Time ◽  
Low Cost ◽  
Free Software ◽  
Software Platform ◽  
Control Engineering ◽  
Time Performance ◽  
Hard Real Time

scholarly journals Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yong He ◽  
Hong Zeng ◽  
Yangyang Fan ◽  
Shuaisheng Ji ◽  
Jianjian Wu
Keyword(s):  
Deep Learning ◽  
Integrated Pest Management ◽  
Pest Management ◽  
Real Time ◽  
Oilseed Rape ◽  
Data Augmentation ◽  
Low Cost ◽  
Response Speed ◽  
Original Model ◽  
Real Time System

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).

scholarly journals New Omnidirectional Sensor Based on Open-Source Software and Hardware for Tracking and Backtracking of Dual-Axis Solar Trackers in Photovoltaic Plants

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 726
Author(s):  
Francisco J. Gómez-Uceda ◽  
José Ramirez-Faz ◽  
Marta Varo-Martinez ◽  
Luis Manuel Fernández-Ahumada
Keyword(s):  
Open Source Software ◽  
Energy Production ◽  
Scientific Community ◽  
Radiative Capture ◽  
Low Cost ◽  
Free Software ◽  
Solar Tracking ◽  
Software And Hardware ◽  
Celestial Sphere ◽  
Photovoltaic Plants

In this work, an omnidirectional sensor that enables identification of the direction of the celestial sphere with maximum solar irradiance is presented. The sensor, based on instantaneous measurements, functions as a position server for dual-axis solar trackers in photovoltaic plants. The proposed device has been developed with free software and hardware, which makes it a pioneering solution because it is open and accessible as well as capable of being improved by the scientific community, thereby contributing to the rapid advancement of technology. In addition, the device includes an algorithm developed ex professo that makes it possible to predetermine the regions of the celestial sphere for which, according to the geometric characteristics of the PV plant, there would be shading between the panels. In this way, solar trackers do not have to locate the Sun’s position at all times according to astronomical models, while taking into account factors such as shadows or cloudiness that also affect levels of incident irradiance on solar collectors. Therefore, with this device, it is possible to provide photovoltaic plants with dual-axis solar tracking with a low-cost device that helps to optimise the trajectory of the trackers and, consequently, their radiative capture and energy production.

scholarly journals Using Digital Twin Technology in Engineering Education – Course Concept to Explore Benefits and Barriers

2020 ◽  
Vol 10 (1) ◽  
pp. 377-385 ◽  
Author(s):  
Antti Liljaniemi ◽  
Heikki Paavilainen
Keyword(s):  
Engineering Education ◽  
Industry 4.0 ◽  
Digital Technologies ◽  
Digital Twin ◽  
New Knowledge

AbstractDigital Twin (DT) technology is an essential technology related to the Industry 4.0. In engineering education, it is important that the curricula are kept up-to-date. By adopting new digital technologies, such as DT, we can provide new knowledge for students, teachers, and companies. The main aim of this research was to create a course concept to research benefits and barriers of DT technology in engineering education. The research confirmed earlier findings concerning digitalization in engineering education. DT technology can increase motivation for studying and improve learning when applied correctly.

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