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River Publishers Series in Communications Series

Artificial Intelligence for Digitising Industry

Applications

Hardback
November 2021
9788770226646
More details
  • Publisher
    River Publishers
  • Published
    18th November
  • ISBN 9788770226646
  • Language English
  • Pages 430 pp.
  • Size 6" x 9"
$130.00
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November 2021
9788770226639
More details
  • Publisher
    River Publishers
  • Published
    9th November
  • ISBN 9788770226639
  • Language English
  • Pages 430 pp.
  • Size 6" x 9"
$130.00

This book provides in-depth insights into use cases implementing artificial intelligence (AI) applications at the edge. It covers new ideas, concepts, research, and innovation to enable the development and deployment of AI, the industrial internet of things (IIoT), edge computing, and digital twin technologies in industrial environments. The work is based on the research results and activities of the AI4DI (ECSEL JU) project, including an overview of industrial use cases, research, technological innovation, validation, and deployment.

This book's sections build on the research, development, and innovative ideas elaborated for applications in five industries: automotive, semiconductor, industrial machinery, food and beverage, and transportation.

The articles included under each of these five industrial sectors discuss AI-based methods, techniques, models, algorithms, and supporting technologies, such as IIoT, edge computing, digital twins, collaborative robots, silicon-born AI circuit concepts, neuromorphic architectures, and augmented intelligence, that are anticipating the development of Industry 5.0.

Automotive applications cover use cases addressing AI-based solutions for inbound logistics and assembly process optimization, autonomous reconfigurable battery systems, virtual AI training platforms for robot learning, autonomous mobile robotic agents, and predictive maintenance for machines on the level of a digital twin.

AI-based technologies and applications in the semiconductor manufacturing industry address use cases related to AI-based failure modes and effects analysis assistants, neural networks for predicting critical 3D dimensions in MEMS inertial sensors, machine vision systems developed in the wafer inspection production line, semiconductor wafer fault classifications, automatic inspection of scanning electron microscope cross-section images for technology verification, anomaly detection on wire bond process trace data, and optical inspection.

The use cases presented for machinery and industrial equipment industry applications cover topics related to wood machinery, with the perception of the surrounding environment and intelligent robot applications.

AI, IIoT, and robotics solutions are highlighted for the food and beverage industry, presenting use cases addressing novel AI-based environmental monitoring; autonomous environment-aware, quality control systems for Champagne production; and production process optimization and predictive maintenance for soybeans manufacturing.

For the transportation sector, the use cases presented cover the mobility-as-a-service development of AI-based fleet management for supporting multimodal transport.

This book highlights the significant technological challenges that AI application developments in industrial sectors are facing, presenting several research challenges and open issues that should guide future development for evolution towards an environment-friendly Industry 5.0.

The challenges presented for AI-based applications in industrial environments include issues related to complexity, multidisciplinary and heterogeneity, convergence of AI with other technologies, energy consumption and efficiency, knowledge acquisition, reasoning with limited data, fusion of heterogeneous data, availability of reliable data sets, verification, validation, and testing for decision-making processes.

1 AI Automotive
1.0 AI Reshaping the Automotive Industry—Daniel Plorin
1.1 AI for Inbound Logistics Optimisation in An Automotive Industry—Nikolaos Evangeliou, George Stamatis, George Bravos, Daniel Plorin and Dominik Stark
1.2 State of Health Estimation using a Temporal Convolutional Network for an Efficient Use of Retired Electric Vehicle
Batteries within Second-Life Applications—Steffen Bockrath, Stefan Waldhör, Harald Ludwig, Vincent Lorentz
1.3 Optimizing Trajectories in Simulations with Deep Reinforcement Learning for Industrial Robots in Automotive Manufacturing—Noah Klarmann, Mohammadhossein Malmir, Josip Josifovski, Daniel Plorin, Matthias Wagner and Alois C. Knoll
1.4 Foundations of Real Time Predictive Maintenance with Root Cause Analysis—Franz Wotawa, David Kaufmann, Adil Amukhtar, Iulia Nica, Florian Klück, Hermann Felbinger, Petr Blaha, Matus Kozovsky, Zdenek Havranek and Martin Dosedel
1.5 Real-Time Predictive Maintenance – Model-Based, Simulation-Based and Machine Learning Based Diagnosis—Franz Wotawa, David Kaufmann, Adil Amukhtar, Iulia Nica, Florian Klück, Hermann Felbinger, Petr Blaha, Matus Kozovsky,
Zdenek Havranek and Martin Dosedel
1.6 Real-Time Predictive Maintenance – Artificial Neural Network Based Diagnosis—Petr Blaha, Matus Kozovsky, Zdenek Havranek, Martin Dosedel, Franz Wotawa, David Kaufmann, Adil Amukhtar, Iulia Nica, Florian Klück and Hermann Felbinger
2 AI Semiconductor
2.0 AI in Semiconductor Industry—Cristina De Luca, Bernhard Lippmann, Wolfgang Schober, Saad Al-Baddai, Georg Pelz, Andreja Rojko, Frédéric Pétrot, Marcello Coppola and Reiner John
2.1 AI Based Knowledge Management System for Risk Assessment and Root Cause Analysis in Semiconductor Industry—Houssam Razouk, Roman Kern, Martin Mischitz, Josef Moser, Mirhad Memic, Lan Liu, Christian Burmer and Anna Safont
2.2 Efficient Deep Learning Approach for Fault Detection in the Semiconductor Industry—Liliana Andrade, Thomas Baumela, Frédéric Pétrot, David Briand, Olivier Bichler and Marcello Coppola
2.3 Towards Fully Automated Verification of Semiconductor Technologies—Matthias Ludwig, Dinu Purice, Bernhard Lippmann, Ann-Christin Bette and Claus Lenz
2.4 Automated Anomaly Detection Through Assembly and Packaging Process—Saad Al-Baddai, Martin Juhrisch, Jan Papadoudis, Anna Renner, Lippmann Bernhard, Cristina De Luca, Fabian Haas and Wolfgang Schober
3 AI Industrial Machinery
3.0 AI in Industrial Machinery—Giulio Urlini, Janis Arents and Antonio Latella
3.1 AI-Powered Collision Avoidance Safety System for Industrial Woodworking Machinery—Francesco Conti, Fabrizio Indirli, Antonio Latella, Francesco Papariello, Giacomo Michele Puglia, Felice Tecce, Giulio Urlini and Marcello Zanghieri
3.2 Construction of a Smart Vision-Guided Robot System for Manipulation in a Dynamic Environment—Janis Arents, Modris Greitans and Bernd Lesser
3.3 Radar-Based Human-Robot Interfaces—Hans Cappelle, Ali Gorji Daronkolaei, Inton Tsang, Lars Keuninckx, Björn Debaillie and Ilja Ocket
3.4 Touch Identification on Sensitive Robot Skin Using Time Domain Reflectometry and Machine Learning Methods—Pawel Kostka, Anja Winkler, Adnan Haidar, Muhammad Ghufran Khan, Rene Jäkel, Peter Winkler and Ralph Müller-Pfefferkorn
4 AI Food and Beverage
4.0 AI in Food and Beverage Industry—Rachel Ouvinha de Oliveira, Marcello Coppola and Ovidiu Vermesan
4.1 Innovative Vineyards Environmental Monitoring System Using Deep Edge AI—Marcello Coppola, Louis Noaille, Rachel Ouvinha de Oliveira, Nathalie Gaveau, Marine Rondeau, Lucas Mohimont and Luiz Angelo Steffenel
4.2 AI-Driven Yield Estimation Using an Autonomous Robot for Data Acquisition—Lucas Mohimont, Luiz Angelo Steffenel, Mathias Roesler, Nathalie Gaveau, Marine Rondeau, François Alin, Clément Pierlot, Rachel Ouvinha de Oliveira and Marcello Coppola
4.3 AI-Based Quality Control System at the Pressing Stages of the Champagne Production—Lucas Mohimont, Mathias Roesler, Angelo Steffenel, Nathalie Gaveau, Marine Rondeau, François Alin, Clément Pierlot, Rachel
Ouvinha de Oliveira, Marcello Coppola and Philipe Doré
4.4 Optimisation of Soybean Manufacturing Process Using Realtime Artificial Intelligence of Things Technology—Ovidiu Vermesan, Jøran Edell Martinsen, Anders Kristoffersen, Roy Bahr, Ronnie Otto Bellmann, Torgeir Hjertaker, John Breiland, Karl Andersen, Hans Erik Sand, Parsa Rahmanpour and David Lindberg
4.5 AI and IIoT-based Predictive Maintenance System for Soybean Processing—Ovidiu Vermesan, Roy Bahr, Ronnie Otto Bellmann, Jøran Edell Martinsen, Anders Kristoffersen, Torgeir Hjertaker, John Breiland, Karl Andersen, Hans Erik Sand, Parsa Rahmanpour and David Lindberg
5 AI Transportation
5.0 Applications of AI in Transportation Industry—Mathias Schneider, Matti Kutila and Alfred Höß
5.1 AI-Based Vehicle Systems for Mobility-as-a-Service Application—Mikko Tarkiainen, Matti Kutila, Topi Miekkala, Sami Koskinen, Jokke Ruokolainen, Sami Dahlman and Jani Toiminen
5.2 Open Traffic Data for Mobility-as-a-Service Applications – Architecture and Challenges—Mathias Schneider, Mina Marmpena, Haris Zafeiris, Ruben Prokscha, Seifeddine Saadani, Nikolaos Evangeliou, George Bravos and Alfred Höß

Ovidiu Vermesan, PhD

Dr. Ovidiu Vermesan holds a PhD in Microelectronics and a Master of International Business (MIB) degree. He is Chief Scientist at SINTEF Digital, Oslo, Norway. His research interests are in the area of smart systems integration, mixed-signal embedded electronics, analogue neural networks, artificial intelligence, and cognitive communication systems. Dr. Vermesan received SINTEF’s 2003 award for research excellence for his work on the implementation of a biometric sensor system. He is currently working on projects addressing nanoelectronics, integrated sensor/actuator systems, communication, cyber–physical systems (CPSs) and Industrial Internet of Things (IIoT), with applications in green mobility, energy, autonomous systems, and smart cities. He has authored or co-authored over 85 technical articles and conference papers. He is actively involved in the activities of the Electronic Components and Systems for European Leadership Joint Undertaking (ECSEL JU) and involved in technical activities to define the priorities for the new European partnership for Key Digital Technologies (KDT). He has coordinated and managed various national, EU and other international projects related to smart sensor systems, integrated electronics, electromobility and intelligent autonomous systems such as E3Car, POLLUX, CASTOR, IoE, MIRANDELA, IoF2020, AUTOPILOT, AutoDrive, ArchitectECA2030, AI4DI, AI4CSM. Dr. Vermesan actively participates in national, H2020 EU and other international initiatives by coordinating and managing various projects. He is the coordinator of the IoT European Research Cluster (IERC) and a member of the board of the Alliance for Internet of Things Innovation (AIOTI). He is currently the technical co-coordinator of the Artificial Intelligence for Digitising Industry (AI4DI) project.

Reiner John

Reiner John received his degree in Electrical Engineering from the Fachhochschule des Saarlandes (Germany) in collaboration with the University of Metz / Perpignan (France). In 1984 he started his career with the Siemens Semiconductor Group in Munich, where he worked in automatic test systemdevelopment. In 1989 he was responsible for the consultation and application of embedded control development tools in the Siemens Automation Group. After joining Siemens Corporate Research and Development in 1991, Reiner John researched knowledge-based embedded systems within the Fuzzy group. Moving to Regensburg to work for the Siemens Automotive Division three years later, he developed concepts and implementations for a real-time operating system to manage and control the engine and transmission system. In 1996 he joined Siemens Semiconductors, the later IPO of Infineon Technologies, where he served in several management positions in the Quality and Production Department of the company. In 2000, he further pursued his career in Taiwan, where he set up and managed the Infineon Silicon Foundry Taiwan Office as the Head of Department for seven years. At present, Reiner John is working in AVL List GmbH, Austria, where he oversees the coordination of public-funded R&D projects in the area of trustable AI for industrial and electromobility applications.

Cristina De Luca, PhD

Dr. Cristina De Luca received the Laurea degree in statistical and economic science from, University of Padova (Italy) and the PhD degree in mathematics from, University of Klagenfurt (Austria), 2003. She joined Infineon Technologies Austria AG in 2002. She has worked on a wide range of R2R control applications for lithography, CMP and CVD semiconductor production processes and rollout in Regensburg (Germany), Kulim (Malaysia) and Villach (Austria) and contributed to research on R2R for the epitaxy process. Her research interests included advanced process control, automation and statistical data analysis, production automation, predictive maintenance, virtual metrology and industry 4.0 automation, model predictive control for semiconductor manufacturing. She was an external professor for statistical quality control at the "Fachhochschule Kärnten" for 2004-2008 in cooperation with Infineon Technologies Austria AG. She is certified in Project Management since 2008. In 2009, she became project manager for European projects, first ENIAC and then ECSEL JU. She followed projects at different levels and contributed to their preparation, implementation, and coordination. To cite some of the projects: IMPROVE, EPPL, EPT300, SemI40, PRODUCTIVE4.0, Arrowhead Tools, AI4CSM. She is currently the coordinator of the ArchitectECA2030 project (Automotive) and AI4DI project (artificial intelligence). In 2019 she joined Infineon Technologies AG, Munich (Germany), where she is Senior Manager Funding Projects and Coordination.

Marcello Coppola

Marcello Coppola is technical Director at STMicroelectronics. He has more than 25 years of industry experience with an extended network within the research community and major funding agencies with the primary focus on the development of break-through technologies. He is a technology innovator, with the ability to accurately predict technology trends. He is involved in many European research projects targeting Industrial IoT and IoT, cyber physical systems, Smart Agriculture, AI, Low power, Security, 5G, and design technologies for Multicore and Many-core System-on-Chip, with particular emphasis to architecture and network-on-chip. He has published more than 50 scientific publications, holds over 26 issued patents. He authored chapters in 12 edited print books, and he is one of the main authors of Design of Cost-Efficient Interconnect Processing Units: Spidergon STNoC (CRC Press). Until 2018, he was part of IEEE Computing Now Journal Technical editorial board. He contributed to the security chapter of the Strategic Research Agenda (SRA) to set the scene on R&I on Embedded Intelligent Systems in Europe. He is serving under different roles numerous top international conferences and workshops. Graduated in Computer Science from the University of Pisa, Italy in 1992.

Artificial intelligence (AI); Industrial internet of things (IIoT); Machine learning; Deep learning; Neural Networks; Machine vision; Smart robots; AI at the edge; Silicon-born AI Industrial sectors: automotive, semiconductor, industrial machinery, food and beverage, transportation