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Auflistung nach Autor:in "Plociennik, Christiane"

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  • Zeitschriftenartikel
    A Jumpstart Framework for Semantically Enhanced OPC-UA
    (KI - Künstliche Intelligenz: Vol. 33, No. 2, 2019) Katti, Badarinath; Plociennik, Christiane; Schweitzer, Michael
    Decentralization is the norm of future smart production as it assists in contextual dynamic decision-making and thereby increases the flexibility required to produce highly customized products. When manufacturing business software is operated as a cloud based solution, it experiences network latency and connectivity issues. To overcome these problems, the production control should be delegated to the manufacturing edge layer and hence, the argument of decentralization is even more applicable to this narrative. In order to accomplish the assigned manufacturing task effectively, the edge layer is required to possess contextual awareness to make run-time decisions in production. Semantic technologies, on the other hand, assist in discerning the meaning, reasoning and drawing inferences from the data. There are several specifications and frameworks to automate the discovery, orchestration and invocation of web services; the prominent are OWL-S, SAWSDL and WSMO. This paper derives a hybrid approach that integrates OWL-S and SAWSDL specifications to overcome the downsides, yet retain the benefits of both approaches to the OPC-UA application methods. Consequently, the proposed semantically enriched OPC-UA concept enables the edge layer to create flexible production orchestration plans in a manufacturing scenario controlled by cloud MES. Furthermore, the derived hybrid approach is applied to a real use case to demonstrate its feasibility in industrial environments.
  • Konferenzbeitrag
    Autonomous mobile robot search strategy for automated compressed air leakage detection
    (INFORMATIK 2024, 2024) Richard, Philipp; Dudhagara, Satyam Uttamkumar; Kunz, Leonhard; Plociennik, Christiane; Ruskowski, Martin
    Compressed air is an important work medium for transfer of energy in many industrial processes. The inefficient physical processes used to produce it make compressed air one of the most expensive energy sources in supply systems. Even small leakages can over time result in high energy losses and costs if not detected and fixed timely. In addition, finding leakages in such systems is very time-consuming and expensive as the whole network of pipes must be examined to localize defects. This paper presents the concept of a targeted detection approach for effectively detecting and locating Compressed Air Leakages semi-autonomously through the usage of an Autonomous Mobile Robot as a mobile sensor. It describes how the detection process in an unknown environment can be rapidly accelerated by constraining the search space for the detection of leakages. The process utilizes expert knowledge, object detection and scene interpretation techniques to constrain the search space. The results obtained are integrated into an existing map of the environment and enable targeted repair actions. The evaluation includes an analysis of the individual phases of the approach, proposes further evaluation scenarios, and compares the efficiency of the approach with conventional manual leakage detection.
  • Zeitschriftenartikel
    Correction to: A Jumpstart Framework for Semantically Enhanced OPC-UA
    (KI - Künstliche Intelligenz: Vol. 33, No. 2, 2019) Katti, Badarinath; Plociennik, Christiane; Schweitzer, Michael
    The original article can be found.
  • Konferenzbeitrag
    Energy Load Profile Analysis and Application for Production Simulation and Scheduling using Energy Load Disaggregation
    (INFORMATIK 2024, 2024) Klostermeier, Mario; Kunz, Leonhard; Motsch, William; Ioshchikhes, Borys; Plociennik, Christiane; Ruskowski, Martin
    Rising energy prices and an increasing share of volatile energy supply from renewable energy are leading to greater interest in detailed modeling of energy consumption in manufacturing. Nevertheless, energy measurements and energy load profiling at the machine level as well as the application of energy-related data for production scheduling is challenging. To provide detailed information for applications like scheduling models, degrees of freedom to adapt to energy consumption must be considered even at the component level. Since metering hardware and energy load profiles are often not available for machine components, the methodical application of energy load disaggregation can contribute to these topics. The paper introduces a concept for incorporating event-based load disaggregation to create energy load profiles for production machines. It also explores and discusses potential applications for simulation and scheduling in manufacturing environments.
  • Konferenzbeitrag
    A Novel Approach for Sensor Fusion Object Detection in Waste Sorting: The Case of WEEE
    (EnviroInfo 2023, 2023) Nazeri, Ali; Plociennik, Christiane; Vogelgesang, Malte; Li, Chanchan; Ruskowski, Martin
    This paper investigates the application of AI-based methods for characterizing waste materials in sorting processes. With the increasing use of sensors in waste sorting systems, there is an opportunity to integrate data and improve accuracy. AI methods, such as deep object detection models, have the potential to optimize waste management processes and promote sustainability. This research examines the utilization of Sensor Fusion Object Detection in a multi-sensor sorting system, focusing on two different data fusion methods: concatenation and image mirroring. In the first approach, image data is concatenated with data from a hyperspectral near-infrared camera (NIR) and an inductive sensor, where dimensionality reduction techniques are applied to the data from both sensors. The second approach relies on a specific combination of NIR and inductive sensor data to simulate the format of image data. A Siamese Object Detection architecture is developed to train the model. The real-world testing results show that both approaches improve waste characterization accuracy and reliability by augmenting the models’ mean average precision (mAP). These findings demonstrate the potential for AI-based methods to transform the waste separation and management process, leading to more sustainable practices and resource efficiency.
  • Konferenzbeitrag
    What influences user acceptance of ad-hoc assistance systems? – A quantitative study
    (Mobile und Ubiquitäre Informationssysteme, 2010) Plociennik, Christiane; Wandke, Hartmut; Kirste, Thomas
    Which factors influence user acceptance of Ambient Intelligence applications is an interesting question worth to be studied comprehensively. We describe a quantitative user study that investigates how experience, stress, and system behavior influence user acceptance of an ad-hoc assistance system. We find that stressed users perceive the assistance system as more useful than relaxed users. Furthermore, system behavior influences how useful people perceive the system, and experience influences how easy to use people find the system. Perceived usefulness also depends on how technophile a person is. Following our findings, we develop a scheme of user acceptance and performance.