This book provides a comprehensive in-depth look into the practical application of AutomationML Edition 2 from an industrial perspective. It is a cookbook for advanced users and describes re-usable pattern solutions for a variety of industrial applications and how to implement it in software. Just to name some: AutomationML modelling of AAS, MTP, SCD, OPC UA, Automation Components, Automation Projects, drive configurations, requirement models, communication systems, electrical interfaces and cables, or semantic integration aspects as eClass integration or handling of semantic heterogeneity. This book guides through the universe of AutomationML from industrial perspective. It is written by AutomationML experts that have industrially implemented AutomationML in pattern solutions for a large variety of applications. This book is structured into three major parts. • Part I: software implementation for developers • Part II: re-usable industrial pattern solutions and domain models • Part III: outlook into future AutomationML applications Additional material to the book and more information about AutomationML on the website: https://www.automationml.org/about-automationml/publications/amlbook/

This book provides a comprehensive in-depth look into the practical application of AutomationML Edition 2 from an industrial perspective. It is a cookbook for advanced users and describes re-usable pattern solutions for a variety of industrial applications and how to implement it in software. Just to name some: AutomationML modelling of AAS, MTP, SCD, OPC UA, Automation Components, Automation Projects, drive configurations, requirement models, communication systems, electrical interfaces and cables, or semantic integration aspects as eClass integration or handling of semantic heterogeneity. This book guides through the universe of AutomationML from industrial perspective. It is written by AutomationML experts that have industrially implemented AutomationML in pattern solutions for a large variety of applications. This book is structured into three major parts. • Part I: software implementation for developers • Part II: re-usable industrial pattern solutions and domain models • Part III: outlook into future AutomationML applications Additional material to the book and more information about AutomationML on the website: https://www.automationml.org/about-automationml/publications/amlbook/

This book is a beginner's guide to AutomationML Edition 2, written for students, engineers, lecturers, developers and those interested. In guides through the basics of AutomationML Edition 2, CAEX and the AutomationML Editor. AutomationML stands for digitisation of engineering data and engineering workflows. AutomationML achieves both human readability and machine-readability. It is a method for converting data into digital information, and it supports the special needs of iterative engineering data exchange. AutomationML is in the hot spot of the digitisation of automation engineering data. It enables the modelling and transport of engineering data in a vendor neutral and machine-readable models, a valuable source of digital innovation. Machine readable engineering data makes the data accessible and interpretable by software, enabling a plethora of opportunities. This book carefully introduces AutomationML, its goals, values and innovations. It teaches the architecture of AutomationML and explains the language elements with a multitude of examples and step-by-step instructions. Additional material to the book and more information about AutomationML on the website: https://www.automationml.org/about-automationml/publications/amlbook/

The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, September 29th, 2016. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

This two-volume set (LNAI 10448 and LNAI 10449) constitutes the refereed proceedings of the 9th International Conference on Collective Intelligence, ICCCI 2017, held in Nicosia, Cyprus, in September 2017. The 117 full papers presented were carefully reviewed and selected from 248 submissions. The conference focuseson the methodology and applications of computational collective intelligence, included: multi-agent systems, knowledge engineering and semantic web, social networks and recommender systems, text processing and information retrieval, data mining methods and applications, sensor networks and internet of things, decision support & control systems, and computer vision techniques.

This book discusses challenges and solutions for the required information processing and management within the context of multi-disciplinary engineering of production systems. The authors consider methods, architectures, and technologies applicable in use cases according to the viewpoints of product engineering and production system engineering, and regarding the triangle of (1) product to be produced by a (2) production process executed on (3) a production system resource. With this book industrial production systems engineering researchers will get a better understanding of the challenges and requirements of multi-disciplinary engineering that will guide them in future research and development activities. Engineers and managers from engineering domains will be able to get a better understanding of the benefits and limitations of applicable methods, architectures, and technologies for selected use cases. IT researchers will be enabled to identify research issues related to the development of new methods, architectures, and technologies for multi-disciplinary engineering, pushing forward the current state of the art.

This book examines the requirements, risks, and solutions to improve the security and quality of complex cyber-physical systems (C-CPS), such as production systems, power plants, and airplanes, in order to ascertain whether it is possible to protect engineering organizations against cyber threats and to ensure engineering project quality. The book consists of three parts that logically build upon each other. Part I "Product Engineering of Complex Cyber-Physical Systems" discusses the structure and behavior of engineering organizations producing complex cyber-physical systems, providing insights into processes and engineering activities, and highlighting the requirements and border conditions for secure and high-quality engineering. Part II "Engineering Quality Improvement" addresses quality improvements with a focus on engineering data generation, exchange, aggregation, and use within an engineering organization, and the need for proper data modeling and engineering-result validation. Lastly, Part III "Engineering Security Improvement" considers security aspects concerning C-CPS engineering, including engineering organizations’ security assessments and engineering data management, security concepts and technologies that may be leveraged to mitigate the manipulation of engineering data, as well as design and run-time aspects of secure complex cyber-physical systems. The book is intended for several target groups: it enables computer scientists to identify research issues related to the development of new methods, architectures, and technologies for improving quality and security in multi-disciplinary engineering, pushing forward the current state of the art. It also allows researchers involved in the engineering of C-CPS to gain a better understanding of the challenges and requirements of multi-disciplinary engineering that will guide them in their future research and development activities. Lastly, it offers practicing engineers and managers with engineering backgrounds insights into the benefits and limitations of applicable methods, architectures, and technologies for selected use cases.