EDUCATIONAL TOPICS

• Excursions to the companyBosch Powertrain s.r.o. Jihlava (8 hours)

The aim of the excursion to Bosch is to introduce Industry 4.0 in practice. In the first part of the excursion, the Bosch company will be introduced as well as the history of putting Industry 4.0 into practice. In the next part, the participants will learn in detail about the individual applications of Industry 4.0: autonomous transport systems, vibration diagnostics, MES systems and the field of robotics (anomaly detection, object detection). Activities.

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• Industry 4.0 Communication Protocols (2 hours of exercises, 4 hours of self-study, 3 hours to complete 2 assignments)

An introduction to current communication protocols applicable to communication in distributed control systems and IoT area. Content: OPC UA, MQTT and their comparison with proprietary protocols, using tools for managing communication protocols, basic implementation in C language, study of the use of the mentioned communication standards in practice. Activities.

• Virtualization and Simulation of Industrial Systems (2 hours of tutorial, 6 hours of self-study, 3 hours for completing 2 assignments)

Creation of digital shadows of industrial systems using OPC UA communication. Content. Demonstration of the principle of creating a digital model, its animation and connection to a real system based on Simatic PLC. Activities.

• Programming in Matlab and its applicability in Industry 4.0 applications (1 hour tutorial, 2 hours self-study, 2 hours to complete 1 assignment)

Introduce participants to programming in Matlab. Content. Basic principles of programming in Matlab. Solving basic programming problems. Activities. Online Matlab Onramp courses.

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• Introduction to image processing, use of image processing in Industry 4.0, digital representation of image data, basics of image preprocessing (1 hour tutorial, 2 hours self-study)

Introduction to image processing issues and their use in Industry 4.0. Content. Introduction to image processing in Industry 4.0. Digital representation of image data. Basic methods of image data preprocessing. Activities. Online course Matlab Image Processing Onramp.

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• Image filtering, image sharpening, image attribute representation, texture analysis, morphological operations, image segmentation, basic principles of image recognition and classification (2 hours of tutorial, 3 hours of self-study, 2 hours to complete 2 assignments)

Learn how to implement the individual steps of image processing leading to its detailed analysis and subsequent use in Industry 4.0 tasks. Content. Image sharpening and edge detection. Texture analysis. Morphological operations with image. Image data segmentation. Basic principles of image recognition and classification. Activities. Online course in Matlab Image Processing.

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• Practical implementation of Industry 4.0 tasks - object detection in image, object classification and more (2 hours of tutorial, 1 hour of self-study, 2 hours to complete 2 tasks)

Learn how to solve specific problems dealing with image processing and use the knowledge acquired in previous topics. Content : Detection and classification of different types of objects for the purpose of robotic manipulation of them. Solution of problems designed by students. Activities. Elaboration of image processing tasks.

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• History and definition of AI (1 hour tutorial, 1 hour self-study, 0.5 hours to complete 1 assignment)

An introduction to the origin and development of the field of Artificial Intelligence (AI), placing the field in the context of Industry 4.0. Content. The concept of intelligent agent. AI and Industry 4.0. Activities.

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• Theoretical Foundations of AI(1 hour tutorial, 3 hours self-study, 0.5 hours to complete 1 assignment)

An introduction to basic AI methods and algorithms with regard to their use in industrial applications. Content. Automated scheduling. Machine learning. Deep learning, neural networks. Reinforcement learning. Activities: online tests, offline chat to discuss the material.  

• String Learning and Neural Networks in Matlab (1 hour self-study, 4 hours to complete 4 assignments)

An introduction to the capabilities of Matlab in machine learning tasks and neural networks. Content. Neural networks in Matlab. Reinforcement learning in Matlab. Activities.

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• UI in industry (1 hour tutorial, 2 hours self-study, 0.5 hours to complete 1 task)

Presentation of the current state of the art in industrial AI applications. Content. Quality Control. Production planning, process optimization. Collaborative robotics. Production simulation. Digital twin. Activities.

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• Ethics and Safety of AI (1 hour tutorial, 3 hours self-study, 0.5 hours to complete 1 assignment)

Introduce participants to the ethical and safety challenges associated with the use of AI in various domains. Content: Recognition of ethical challenges and safety risks associated with AI and risk minimization. Ethical issues associated with the use of AI. Security risks associated with AI. Regulation of artificial intelligence. Activities.

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• Consultation on solved tasks (1 hour online consultation)
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Consultation of the participants with the teacher on solved tasks.

• Excursion to Škoda Auto a.s. Mladá Boleslav (8 hours)

Introduction to Industry 4.0 in practice - Škoda Auto a.s. Content: introduction about the company, introduction of Industry 4.0 into the company, application of Industry 4.0, visit of selected plants with interpretation. Activities: visit to the museum, depository, selected plant, parts dispatch centre, battery manufacturing plant, discussion with experts from practice.

• Using Image Data for Robot Control (2 hours of hands-on, 3 hours of self-study, 2.5 hours to complete 1 assignment)

Learn about the control of Mitsubishi industrial robots using image processing technologies and camera-based object detection and identification. Content: Updating robot data using TCP/IP or OPC UA communication protocols. Programming robots. Activities: programming robots in the classroom. Solving tasks on robot programming.

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• Measuring real data and using it to optimize robot motion (2 hours practice, 3 hours self-study)

Learn how to read and use real data (voltages, currents, torques) from Mitsubishi industrial robots. Content. Data processing using Matlab. Comparison of robot trajectory and motion options in terms of efficiency and energy consumption and selection of the optimal option. Activities.

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• Programming Robotino robot (2 hours of practice, 3 hours of self-study, 2.5 hours to complete 1 task)

Learn how to program Robotino autonomous mobile robots. Content. Tracking a given trajectory. Solving tasks in the virtual simulation environment Robotino SIM. Solving tasks on real robots. Activities.