Adaptive Synchronization Interface for Hardware-Software Co-Simulation based on SystemC and QEMU - Kleinert, Bruno; Weiß, Simone; Schäfer, Franziska; Bakakeu Ngassam, Romuald Jupiter; Fey, Dietmar (Simutools: 9th EAI International Conference on Simulation Tools and Techniques, 2016)

An Artificial Intelligence Approach for Online Energy Optimization of Flexible Manufacturing Systems - Bakakeu Ngassam, Romuald Jupiter; Bauer, Jochen; Tolksdorf, Schirin; Klos, Hans-Henning; Peschke, Jörn; Fehrle, Adrian; Eberlein, Werner; Bürner, Johannes; Brossog, Matthias; Jahn, Lars; Franke, Jörg (Energy Efficiency in Strategy of Sustainable Production IV, 2018)

Automated reasoning and knowledge inference on OPC UA information models - Bakakeu, Jupiter; Brossog, Matthias; Zeitler, Jochen; Franke, Jörg; Tolksdorf, S.; Klos, H.; Peschke, J. (Proceedings - 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019, 2019)
The fourth industrial revolution demands flexibility, adaptability, transparency and semantic interoperability. Within the German Industry 4.0 initiative, the Reference Architecture Model Industrie 4.0 (RAMI4.0) has recently been standardized and OPC Unified Architecture (OPC UA) is listed as the sole recommendation for implementation of a communication layer. Even though OPC UA helps bridge the interoperability gap at the automation level, its semantic has not yet been formally defined and an efficient automated reasoning and knowledge inference on the OPC UA address space is therefore not yet possible. This paper addresses this issue by presenting a solution to infer knowledge from OPC UA information models. By analyzing and comparing the semantic expressiveness of the OPC UA address space with semantic knowledge representation formalisms such as RDF and OWL, we derived and implemented a solution to transform an OPC UA information model into an RDF-Graph expressing an OWL Ontology. Using the generated ontology, we were able to run a true reasoning task directly on the OPC UA address space. In this paper, the developed approach is conveniently validated on various case studies involving online factory reconfiguration, intelligent energy management, and human-machine interfaces.
CRIS-Team Scopus Importer:2019-08-30
Keywords: Industrie 4.0; Knowledge Inference; OPC UA; Sementic Web; SPARQL.

Building Cyber-Physical Systems - A Smart BuildingUse Case - Bakakeu Ngassam, Romuald Jupiter; Schäfer, Franziska; Bauer, Jochen; Michl, Markus; Franke, Jörg (Smart Cities: Foundations, Principles, and Applications, 2017)
This chapter explores the concept of smart home and smart building based on internet-of-things (IoT) and cyber-physical system (CPS) technologies. By reviewing the challenges of the smart home vision, it highlights that CPS-based smart environments are best suited to support the integration of heterogeneous devices with different application domains and architectures. To illustrate the concept of a CPS-based smart building installation with self-configuration and self-organization abilities, the chapter presents implementation of smart home use cases based on the paradigms of "Industry 4.0". In addition, it also analyzes and discusses the performance of the implemented prototype. The concept of "Industry 4.0" provides a vision for the application of CPS in industrial automation. The chapter then proposes an approach based on OPC unified architecture (OPC UA) that supports self-description by using a common ontology and self-organization through the use of multi-agent systems (MAS) theory.
CRIS-Team Scopus Importer:2023-03-07
Keywords: Cyber-physical ecosystem; Industrial automation; Internet-of-things; Multi-agent systems theory; OPC unified architecture; Smart building; Smart home use cases

Designing an OPC UA based Ecosystem for Smarter Homes - Schäfer, Franziska; Bakakeu Ngassam, Romuald Jupiter; Kleinert, Bruno; Michl, Markus; Fey, Dietmar; Franke, Jörg (Advanced Engineering Forum 19, 2016)

Digitalisierung im Engineering – Ein Ansatz für ein Vorgehensmodell zur durchgehenden, arbeitsteiligen Modellierung am Beispiel von AutomationML - Schäffer, Eike; Penczek, Lars; Mayr, Andreas; Bakakeu Ngassam, Romuald Jupiter; Franke, Jörg; Kuhlenkötter, Bernd (Industrie 4.0 Management : Gegenwart und Zukunft industrieller Geschäftsprozesse, 2019)
Die Digitalisierung im Engineering verspricht automatisierte Arbeitsabläufe, höhere Geschwindigkeiten und sinkende Kosten bei der Entwicklung von Automatisierungslösungen. Voraussetzung hierfür ist nicht nur die Modularisierung auf Basis einer strukturierten Beschreibungssprache, sondern auch eine einheitliche, aufeinander aufbauende Modellierung, welche einen automatisierbaren Datenaustausch über die Systemgrenzen hinweg ermöglicht. Um eine breite Anwendung zu erzielen, sollte die zugrundeliegende Ontologie auf bestehenden Normen und Standards aufbauen und in Open-Source-Anwendungen zur Verfügung stehen. Für die kollaborative und konsistente Entwicklung einer solchen Ontologie bedarf es eines strukturierten, methodischen Vorgehens sowie einer damit verbundenen Modellierungslandkarte, welche als Orientierung zur standardisierten, arbeitsteiligen Modellierung dient. Ein möglicher Ansatz für das benötigte Vorgehensmodell sowie der zugehörigen Landkarte wird im Rahmen dieses Beitrags vorgestellt und unter Verwendung von AutomationML validiert. Der vorgestellte Ansatz soll eine mögliche Richtung aufzeigen und weitere prozessgesteuerte Modellierungsbestrebungen von Ontologien anregen. Digitization in engineering promises automated workflows, higher speed and lower costs in the development of automation solutions. The prerequisite for this is not only modularization based on a structured description language, but also uniform, interdependent modeling that ensures automated data exchange across system boundaries. In order to achieve a broad application, the underlying ontology should be based on existing norms and standards and be available in open source applications. However, the collaborative and consistent development of such an ontology requires a structured, methodical procedure and an associated modelling map that serves as an orientation for standardized, work-sharing modelling. A possible approach for the required procedure model and the related map will be presented in this article and validated using AutomationML. The presented approach should point out a possible direction and stimulate further process-controlled modelling efforts of ontologies.<br
Keywords: Durchgängiges Engineering; AutomationML; Modularisierung; Standardisierung; maschineninterpretierbar; Modellierungsmethoden; continuous engineering; modularization; standardization; machine interpretable; modelling methods

Hardware-Software Co-Simulation of Self-Organizing Smart Home Networks: Who am I and where are the others? - Kleinert, Bruno; Schäfer, Franziska; Weiß, Simone; Bakakeu Ngassam, Romuald Jupiter; Fey, Dietmar (2016)

Multi-Agent Reinforcement Learning for the Energy Optimization of Cyber-Physical Production Systems - Bakakeu, Jupiter; Baer, Schirin; Klos, Hans Henning; Peschke, Joern; Brossog, Matthias; Franke, Jörg (Artificial Intelligence in Industry 4.0, 2021)
This chapter proposes an artificial intelligence based solution for the efficient operation of a heterogeneous cluster of flexible manufacturing machines with energy generation and storage capabilities in an electricity micro-grid featuring a high volatility of electricity prices. The problem of finding the optimal control policy is first formulated as a game theoretic sequential decision making problem under uncertainty, where at every time step the uncertainty is characterized by future weather dependent energy prices, high demand fluctuation, as well as random unexpected disturbances on the factory floor. Because of the parallel interaction of the machines with the grid, the local viewpoints of an agent are non-stationary and non-Markovian. Therefore, traditional methods such as standard reinforcement learning approaches that learn a specialized policy for a single machine are not applicable. To address this problem, we propose a multi-agent actor-critic method that takes into account the policies of other participants to achieve explicit coordination between a large numbers of actors. We show the strength of our approach in mixed cooperative and competitive scenarios where different production machines were able to discover different coordination strategies in order to increase the energy efficiency of the whole factory floor.
CRIS-Team Scopus Importer:2021-03-19

Multi-Agent Reinforcement Learning for the Energy Optimization of Cyber-Physical Production Systems - Bakakeu, Jupiter; Kißkalt, Dominik; Franke, Jörg; Baer, Shirin; Klos, Hans Henning; Peschke, Joern (Canadian Conference on Electrical and Computer Engineering, 2020)
The paper proposes an artificial intelligence-based solution for the efficient operation of a heterogeneous cluster of flexible manufacturing machines with energy generation and storage capabilities in an electricity micro-grid featuring high volatility of electricity prices. The problem of finding the optimal control policy is first formulated as a game-theoretic sequential decision-making problem under uncertainty, where at every time step the uncertainty is characterized by future weather-dependent energy prices, high demand fluctuation, as well as random unexpected disturbances on the factory floor. Because of the parallel interaction of the machines with the grid, the local viewpoints of an agent are non-stationary and non-Markovian. Therefore, traditional methods such as standard reinforcement learning approaches that learn a specialized policy for a single machine are not applicable. To address this problem, we propose a multi-agent actor-critic method that takes into account the policies of other participants to achieve explicit coordination between a large numbers of actors. We show the strength of our approach in mixed cooperative and competitive scenarios where different production machines were able to discover different coordination strategies in order to increase the energy efficiency of the whole factory floor.
CRIS-Team Scopus Importer:2020-12-25
Keywords: Actor-Critic; Autocurricula; Flexible Manufacturing System; Industrie 4.0; Load Management; Multi-Agent System; Proximal Policy Optimization; Reinforcement Learning

Multi-Objective Design Space Exploration for the Integration of Advanced Analytics in Cyber-Physical Production Systems - Bakakeu, Jupiter; Fuchs, Jonathan; Javied, Tallal; Brossog, Matthias; Franke, Jörg; Klos, H.; Eberlein, W.; Tolksdorf, S.; Peschke, J.; Jahn, L. (2018 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEE IEEM), 2018)
CRIS-Team WoS Importer:2019-02-27

Reasoning over OPC UA Information Models using Graph Embedding and Reinforcement Learning - Bakakeu, Jupiter; Schäfer, Franziska; Franke, Jörg; Baer, Schirin; Klos, Hans Henning; Peschke, Joern (Proceedings - 2020 3rd International Conference on Artificial Intelligence for Industries, AI4I 2020, 2020)
With the fourth industrial revolution, the OPC Unified Architecture (OPC UA) [1] has emerged as the standard communication framework for the implementation of cyber-physical production systems since it can be used for both, communication and information modeling. Even though OPC UA helps bridge the interoperability gap at the automation level, its semantic has not yet been formally defined and generated or manually created OPC UA information models are often incomplete and inconsistent making an efficient automated reasoning and knowledge inference on the OPC UA address space particularly challenging.In this paper, we show that it is possible to train a machine learning system on OPC UA information models, such that it performs automated reasoning over OPC UA knowledge graphs with high precision and recall. More specifically, we present a reinforcement learning based solution that learns to reason on semantically incomplete OPC UA information models by constructing multi-hop relational paths along an embedded vector space of the knowledge graph representing the information model. The construction of such relational paths allows the discovery of missing relations between the entities and at the same time the evaluation of the truth of the encoded triples, thus enabling consistency checks and questions answering.
CRIS-Team Scopus Importer:2021-04-19
Keywords: entity relation extraction; graph embedding; knowledge inference; link prediction; machine learning; multi-hop reasoning; OPC UA; reinforcement learning

SMARTLOCATE – INDOOR LOCALIZATION WITH BLUETOOTH BEACONS AND SMARTPHONE SENSORS - Bauer, Jochen; Bakakeu Ngassam, Romuald Jupiter; Hopfengärtner, Michael; Bürner, Johannes; Braun, Thomas; Schäfer, Franziska; Wittmann, Maximilian; Fehrle, Adrian; Maußner, Benedikt; Lechler, Tobias; Meiners, Moritz; Konrad, Christoph; et al. (Smart Public Building 2018 Proceedings, 2018)

Strategic energy management in industry 4.0 environment - Javied, Tallal; Bakakeu, Jupiter; Gessinger, Dennis; Franke, Jörg (12th Annual IEEE International Systems Conference, SysCon 2018 - Proceedings, 2018)
One of the latest innovations in the industrial environment was the emergence of Industry 4.0. Along with numerous challenges, there are many chances for companies in it. Besides automation, energy efficiency and energy flexibility is becoming increasing important. Because of the planned phasing-out of nuclear power and high prices for energy, it is important for companies to reduce their energy consumption in order to reduce cost and stay compatible in market place. Therefore strategic energy management plays an important role in overcoming these obstacles. Implementation of energy management is often based on standards and norms with the DIN EN ISO 50001 being the most important. Energy monitoring is a key factor for the successful execution of energy management. For energy monitoring of Industry 4.0 production plants, a cloud based energy monitoring and management system was developed. This solution allows users to monitor their production in real time enabling them a possibility of condition monitoring, energy flexible planning of production based on historic data and energy management along with load management. The detailed concept of the cloud based totally integrated energy management system, beginning with data acquisition to data analysis and visualization is presented in this paper.
CRIS-Team Scopus Importer:2023-03-07
Keywords: Energy management; Energy monitoring; Industry 4.0