Evaluation of Cognitive Architectures for Cyber-Physical Production Systems

Cyber-physical production systems (CPPS) integrate physical and computational resources due to increasingly available sensors and processing power. This enables the usage of data, to create additional benefit, such as condition monitoring or optimization. These capabilities can lead to cognition, such that the system is able to adapt independently to changing circumstances by learning from additional sensors information. Developing a reference architecture for the design of CPPS and standardization of machines and software interfaces is crucial to enable compatibility of data usage between different machine models and vendors. This paper analysis existing reference architecture regarding their cognitive abilities, based on requirements that are derived from three different use cases. The results from the evaluation of the reference architectures, which include two instances that stem from the field of cognitive science, reveal a gap in the applicability of the architectures regarding the generalizability and the level of abstraction. While reference architectures from the field of automation are suitable to address use case specific requirements, and do not address the general requirements, especially w.r.t. adaptability, the examples from the field of cognitive science are well usable to reach a high level of adaption and cognition. It is desirable to merge advantages of both classes of architectures to address challenges in the field of CPPS in Industrie 4.0

Developing cognitive advisor agents for operators in industry 4.0

Human cyber-physical systems (CPS) are an important component in the development of Industry 4.0. The paradigm shift of doing to thinking has allowed the emergence of cognition as a new perspective for intelligent systems. Currently, different platforms offer several cognitive solutions. Within this space, user assistance systems become increasingly necessary not as a tool but as a function that amplifies the capabilities of the operator in the work environment. There exist different perspectives of cognition. In this study cognition is introduced from the point of view of joint cognitive systems (JCSs); the synergistic combination of different technologies such as artificial intelligence (AI), the Internet of Things (IoT) and multi-agent systems (MAS) allows the operator and the process to provide the necessary conditions to do their work effectively and efficientlyPostprint (published version

Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems

This paper presents the cognitive module of the cognitive architecture for artificial intelligence (CAAI) in cyber-physical production systems (CPPS). The goal of this architecture is to reduce the implementation effort of artificial intelligence (AI) algorithms in CPPS. Declarative user goals and the provided algorithm-knowledge base allow the dynamic pipeline orchestration and configuration. A big data platform (BDP) instantiates the pipelines and monitors the CPPS performance for further evaluation through the cognitive module. Thus, the cognitive module is able to select feasible and robust configurations for process pipelines in varying use cases. Furthermore, it automatically adapts the models and algorithms based on model quality and resource consumption. The cognitive module also instantiates additional pipelines to test algorithms from different classes. CAAI relies on well-defined interfaces to enable the integration of additional modules and reduce implementation effort. Finally, an implementation based on Docker, Kubernetes, and Kafka for the virtualization and orchestration of the individual modules and as messaging-technology for module communication is used to evaluate a real-world use case

Big data reference architecture for industry 4.0: including economic and ethical Implications

El rápido progreso de la Industria 4.0 se consigue gracias a las innovaciones en varios campos, por ejemplo, la fabricación, el big data y la inteligencia artificial. La tesis explica la necesidad de una arquitectura del Big Data para implementar la Inteligencia Artificial en la Industria 4.0 y presenta una arquitectura cognitiva para la inteligencia artificial - CAAI - como posible solución, que se adapta especialmente a los retos de las pequeñas y medianas empresas. La tesis examina las implicaciones económicas y éticas de esas tecnologías y destaca tanto los beneficios como los retos para los países, las empresas y los trabajadores individuales. El "Cuestionario de la Industria 4.0 para las PYME" se realizó para averiguar los requisitos y necesidades de las pequeñas y medianas empresas. Así, la nueva arquitectura de la CAAI presenta un modelo de diseño de software y proporciona un conjunto de bloques de construcción de código abierto para apoyar a las empresas durante la implementación. Diferentes casos de uso demuestran la aplicabilidad de la arquitectura y la siguiente evaluación verifica la funcionalidad de la misma.The rapid progress in Industry 4.0 is achieved through innovations in several fields, e.g., manufacturing, big data, and artificial intelligence. The thesis motivates the need for a Big Data architecture to apply artificial intelligence in Industry 4.0 and presents a cognitive architecture for artificial intelligence – CAAI – as a possible solution, which is especially suited for the challenges of small and medium-sized enterprises. The work examines the economic and ethical implications of those technologies and highlights the benefits but also the challenges for countries, companies and individual workers. The "Industry 4.0 Questionnaire for SMEs" was conducted to gain insights into smaller and medium-sized companies’ requirements and needs. Thus, the new CAAI architecture presents a software design blueprint and provides a set of open-source building blocks to support companies during implementation. Different use cases demonstrate the applicability of the architecture and the following evaluation verifies the functionality of the architecture

Hyperparameter Tuning for Machine and Deep Learning with R

This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here. The case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required. The idea for the book originated in a study conducted by Bartz & Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building on that study, the book is addressed to practitioners in industry as well as researchers, teachers and students in academia. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II). Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms. The book presents analyses of more than 30 hyperparameters from six relevant ML and DL methods, and provides source code so that users can reproduce the results. Accordingly, it serves as a handbook and textbook alike

Hyperparameter Tuning for Machine and Deep Learning with R

This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here. The case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required. The idea for the book originated in a study conducted by Bartz & Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building on that study, the book is addressed to practitioners in industry as well as researchers, teachers and students in academia. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II). Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms. The book presents analyses of more than 30 hyperparameters from six relevant ML and DL methods, and provides source code so that users can reproduce the results. Accordingly, it serves as a handbook and textbook alike

Cyber-physical production system assessment within the manufacturing industries in the Amazon

[EN] Cyber-physical production systems (CPPS) represent a relevant aspect related to industry 4.0 and the advances promoted by the digitization and use of artificial intelligence in the production environment in the search for the development of smart factories. This study aims to assess the maturity level of cyber-physical production system (CPPS) within manufacturing industries in the Amazon. The research uses a quali-quantitative approach to analyze the problem by conducting exploratory case studies (indepth case) and the research framework used aimed to evaluate and measure the CPPS within three manufacturing industries in the Amazon (n = 3) to measure their maturity. Findings reveal a positive relationship between the type of production system adopted by the company, the level of automation, and the maturity of the CPPS. 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