TY - JOUR A1 - Mathias, Selvine George A1 - Schmied, Sebastian A1 - Großmann, Daniel T1 - A framework for monitoring multiple databases in industries using OPC UA JF - Journal of Ambient Intelligence and Humanized Computing N2 - Database management and monitoring is an inseparable part of any industry. A uniform scheme of monitoring relational databases without explicit user access to database servers is not much explored outside the database environment. In this paper, we present an information distribution scheme related to databases using Open Platform Communication Unified Architecture (OPC UA) servers to clients when multiple databases are involved in a factory. The aim is for external, but relevant clients, to be able to monitor this information mesh independent of explicit access to user schemas. A methodology to dispense data from, as well as check changes in databases using SQL queries and events is outlined and implemented using OPC UA servers. The structure can be used as a remote viewing application for multiple databases in one address space of an OPC UA server. UR - https://doi.org/10.1007/s12652-020-02850-x KW - OPC UA KW - Relational databases KW - Servers KW - Clients KW - Monitoring KW - Aggregation KW - SQL Y1 - 2020 UR - https://doi.org/10.1007/s12652-020-02850-x UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9464 SN - 1868-5145 VL - 12 IS - 1 SP - 47 EP - 56 PB - Springer CY - Berlin ER - TY - JOUR A1 - Schmied, Sebastian A1 - Großmann, Daniel A1 - Mathias, Selvine George A1 - Mueller, Ralph T1 - Integration of manufacturing information via dynamic information model aggregation JF - Vietnam Journal of Computer Science N2 - An important part of the industry 4.0 concept is the horizontal and vertical integration of manufacturing systems. Information exchange in traditional production environments happens through interfaces that are connections between strictly defined senders and receivers. This limits the possibility for changing and extending the manufacturing system. A possible approach to enable the information exchange between all system entities uniformly are information models. Such models are semantic descriptions of the available data. The creation of these models needs to follow the manufacturing process, but also requires certain standardization to improve efficiency. Another challenge is the actual technical integration of the information into a common address space. This paper connects an approach for information modeling with a concept for dynamic aggregation. The approach is described with the help of a continuous example that uses OPC UA as a middleware technology. UR - https://doi.org/10.1142/S219688882150010X KW - Integration KW - OPC UA KW - interoperability KW - aggregation Y1 - 2020 UR - https://doi.org/10.1142/S219688882150010X UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9495 SN - 2196-8896 VL - 8 IS - 2 SP - 245 EP - 262 PB - Singapore CY - World Scientific ER - TY - JOUR A1 - Mathias, Selvine George A1 - Großmann, Daniel A1 - Bhanja, Tapanta T1 - Exploring distance based approaches for reducing sensor data in defect related prognosis JF - Procedia Computer Science N2 - Vibration data consists of batches of time series which if accumulated over a period of time is a huge collection of numeric data. Reducing such data for use in deep learning models for computational effciency is a challenge. Combinatorial and discrete approaches, on the other hand, is not an extensively explored area when it comes to datasets. This paper aims to identify feature reduction techniques based on discrete approaches such as euclidean distance using dot products on vibration data samples from accelerometers fitted on bearings. In this limited experimentation, the procured dataset by this approach is considerably smaller in size as compared to the actual complete data, and with comparable results in prediction models, it can be used as a smaller representation of a sensor timeline. The results based on different models show that such reductions can be considered in building IoT applications in industries based on sensors. UR - https://doi.org/10.1016/j.procs.2021.03.076 KW - sensors KW - data KW - reduced distance KW - machine learning KW - accuracy scores Y1 - 2021 UR - https://doi.org/10.1016/j.procs.2021.03.076 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13111 SN - 1877-0509 VL - 2021 IS - 184 SP - 614 EP - 621 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Mathias, Selvine George A1 - Schmied, Sebastian A1 - Großmann, Daniel T1 - An investigation on database connections in OPC UA applications JF - Procedia Computer Science N2 - Open Platform Communications - Unified Architecture (OPC UA) as a communication protocol has been one of the front running IoT enablers in recent years. Many use cases in manufacturing domains demonstrate the vast inter-operability and cross-platform connectivity strengths of OPC UA. However, to consolidate its position in the IoT world, OPC UA needs to be able to bridge certain gaps, like interconnections across multiple database engines over different networks. This paper aims to investigate aggregation dynamics of OPC UA consisting of multiple database servers from a specific environment such as manufacturing. Prevailing databases over restricted networks such as production floors cannot be accessed by respective clients outside the system. In these cases, OPC UA services can provide an a selective but imperative view of data from these databases to the clients. The investigations in this paper are demonstrated with two different implementations of databases related to manufacturing with OPC UA. These applications show that using OPC UA as a means of connection and distribution of information from databases can be an effective solution to a common data view of all connections not necessarily of factory floor network. UR - https://doi.org/10.1016/j.procs.2020.03.132 KW - OPC UA KW - databases KW - implementations KW - SQL server KW - clients Y1 - 2020 UR - https://doi.org/10.1016/j.procs.2020.03.132 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-12349 SN - 1877-0509 VL - 2020 IS - 170 SP - 602 EP - 609 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Knollmeyer, Simon A1 - Caymazer, Oğuz A1 - Koval, Leonid A1 - Akmal, Muhammad Uzair A1 - Asif, Saara A1 - Mathias, Selvine George A1 - Großmann, Daniel ED - Gruenwald, Le ED - Masciari, Elio ED - Bernardino, Jorge T1 - Benchmarking of Retrieval Augmented Generation: A Comprehensive Systematic Literature Review on Evaluation Dimensions, Evaluation Metrics and Datasets T2 - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2024) - Volume 3 N2 - Despite the rapid advancements in the field of Large Language Models (LLM), traditional benchmarks have proven to be inadequate for assessing the performance of Retrieval Augmented Generation (RAG) systems. Therefore, this paper presents a comprehensive systematic literature review of evaluation dimensions, metrics, and datasets for RAG systems. This review identifies key evaluation dimensions such as context relevance, faithfulness, answer relevance, correctness, and citation quality. For each evaluation dimension, several metrics and evaluators are proposed on how to assess them. This paper synthesizes the findings from 12 relevant papers and presents a concept matrix that categorizes each evaluation approach. The results provide a foundation for the development of robust evaluation frameworks and suitable datasets that are essential for the effective implementation and deployment of RAG systems in real-world applications. UR - https://doi.org/10.5220/0013065700003838 Y1 - 2024 UR - https://doi.org/10.5220/0013065700003838 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58342 SN - 978-989-758-716-0 SP - 137 EP - 148 PB - SciTePress CY - Setúbal ER - TY - JOUR A1 - Koval, Leonid A1 - Knollmeyer, Simon A1 - Mathias, Selvine George A1 - Asif, Saara A1 - Akmal, Muhammad Uzair A1 - Großmann, Daniel A1 - Bregulla, Markus T1 - Unlocking the Potential of Information Modeling for Root Cause Analysis in a Production Environment: A Comprehensive State-of-the-Art Review Using the Kitchenham Methodology JF - IEEE Access N2 - Data from production environments is now available in unprecedented volumes, making the problem-solving of incidents through root cause analysis straightforward. However, the root cause analysis process remains time-consuming. This study employs the Kitchenham standard systematic literature review methodology to explore how information models and deep learning can streamline this process. By conducting a comprehensive search across four major databases, we evaluate the current technological advancements and their application in root cause analysis. The aim of this study is to assesses the impact of information models for root cause analysis in a production environment. Our findings reveal that integrating knowledge graphs, association rule mining, and deep learning algorithms significantly improves the speed and depth of root cause analysis compared to traditional methods. Specifically, the use of neural networks in recent literature shows substantial advancements in analyzing complex datasets, facilitating large-scale data integration, and enabling automated learning capabilities. Comparing our findings with other recent studies highlights the advantages of using information modeling and deep learning technologies in root cause analysis. This comparison underscores the superior accuracy and efficiency of these advanced methodologies over traditional manual interpretation methods. The effective implementation of these technologies requires a robust foundation of clean, standardized data, giving rise to the concept of “Production IT.” Furthermore, it is crucial for this data to be openly available to facilitate academic research, thereby enabling the development of new methods for more efficient and effective root cause analysis. UR - https://doi.org/10.1109/ACCESS.2024.3406020 Y1 - 2024 UR - https://doi.org/10.1109/ACCESS.2024.3406020 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-49240 SN - 2169-3536 VL - 12 SP - 80266 EP - 80282 PB - IEEE CY - New York ER - TY - CHAP A1 - Mueller, Ralph A1 - Großmann, Daniel A1 - Schmied, Sebastian A1 - Mathias, Selvine George T1 - Architecture of a model in the middle approach for virtual commissioning and integration of production entities T2 - Proceedings: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA45728.2021.9613619 KW - virtual commissioning KW - information model KW - information model management KW - cyber physical system KW - digital twin KW - machine communication KW - aggregation Y1 - 2021 UR - https://doi.org/10.1109/ETFA45728.2021.9613619 SN - 978-1-7281-2989-1 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schmied, Sebastian A1 - Großmann, Daniel A1 - Mathias, Selvine George A1 - Mueller, Ralph T1 - A concept towards the evolution and versioning of aggregated information models T2 - Proceedings: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA45728.2021.9613193 KW - information modelling KW - versioning KW - evolution of models Y1 - 2021 UR - https://doi.org/10.1109/ETFA45728.2021.9613193 SN - 978-1-7281-2989-1 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Schmied, Sebastian A1 - Mathias, Selvine George A1 - Großmann, Daniel A1 - Jumar, Ulrich T1 - Integration of existing cyber-physical manufacturing systems into a common information model JF - IFAC-PapersOnLine N2 - In order to be able to serve constantly new customer requirements, manufacturing systems must be able to adapt to frequent changes. In addition, repeatedly objects are removed or added to the network. To control and monitor such a constantly changing system a mapping of existing manufacturing systems into a common information model is necessary. This model describes information that is produced and stored in different entities of the complete system. To create a common address space and expose the relations between the devices an aggregation of every element in the system is needed. This paper describes a methodology for the creation of an information model for a complete manufacturing environment, followed by an approach for the aggregation of the singular system entities. The concept of this paper is illustrated with a demonstrator. The results of this approach have been discussed in the following sections along with the proposal for further directions. UR - https://doi.org/10.1016/j.ifacol.2020.12.2827 KW - Information Model KW - Aggregation KW - OPC UA KW - Cyber-physical manufacturing systems Y1 - 2021 UR - https://doi.org/10.1016/j.ifacol.2020.12.2827 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13309 SN - 2405-8963 VL - 53 IS - 2 SP - 10905 EP - 10910 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Mathias, Selvine George A1 - Großmann, Daniel A1 - Schmied, Sebastian A1 - Mueller, Ralph T1 - Decentralized Integration Approach of Disjoint Modules in Factory Environments T2 - IEIM 2022: 2022 The 3rd International Conference on Industrial Engineering and Industrial Management UR - https://doi.org/10.1145/3524338.3524368 KW - Data Acquisition KW - OPC UA KW - Comunication KW - Instruments Y1 - 2022 UR - https://doi.org/10.1145/3524338.3524368 SN - 978-1-4503-9569-4 SP - 195 EP - 199 PB - ACM CY - New York ER - TY - CHAP A1 - Mathias, Selvine George A1 - Großmann, Daniel A1 - Sequeira, Gerald Joy T1 - A Comparison of Clustering Measures on Raw Signals of Welding Production Data T2 - Proceedings 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) UR - https://doi.org/10.1109/Deep-ML.2019.00019 KW - clustering KW - k-means KW - kmeans KW - hierarchical KW - metric evaluation Y1 - 2019 UR - https://doi.org/10.1109/Deep-ML.2019.00019 SN - 978-1-7281-2914-3 SP - 55 EP - 60 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Mathias, Selvine George A1 - Schmied, Sebastian A1 - Großmann, Daniel T1 - Monitoring of discrete electrical signals from welding processes using data mining and IIoT approaches T2 - Proceedings, IEEE 32nd International Conference on Tools with Artificial Intelligence, (ICTAI 2020) UR - https://doi.org/10.1109/ICTAI50040.2020.00142 KW - electrical signals KW - clustering KW - kmeans KW - hierarchical KW - time series KW - OPC UA KW - multi-label classification KW - metrics Y1 - 2020 UR - https://doi.org/10.1109/ICTAI50040.2020.00142 SN - 978-1-7281-9228-4 SP - 911 EP - 916 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schmied, Sebastian A1 - Großmann, Daniel A1 - Mathias, Selvine George A1 - Mueller, Ralph T1 - An approach for aggregation and historicization of production entities in the graph T2 - Proceedings 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA46521.2020.9211907 KW - information modelling KW - aggregation KW - graph database Y1 - 2020 UR - https://doi.org/10.1109/ETFA46521.2020.9211907 SN - 978-1-7281-8956-7 SP - 1107 EP - 1110 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Mueller, Ralph A1 - Großmann, Daniel A1 - Schmied, Sebastian A1 - Mathias, Selvine George T1 - Validation of dynamic interoperability and virtual commissioning of production equipment in early development stages T2 - Proceedings 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA46521.2020.9211988 KW - virtual commissioning KW - information model KW - cyber physical system KW - digital twin KW - machine communication KW - design engineering process Y1 - 2020 UR - https://doi.org/10.1109/ETFA46521.2020.9211988 SN - 978-1-7281-8956-7 SP - 1159 EP - 1162 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Mathias, Selvine George A1 - Schmied, Sebastian A1 - Großmann, Daniel A1 - Mueller, Ralph A1 - Mroß, Björn T1 - A Compliance Testing Structure for Implementation of Industry Standards through OPC UA T2 - Proceedings 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA46521.2020.9212119 KW - OPC UA KW - compliance testing KW - information models KW - servers KW - clients Y1 - 2020 UR - https://doi.org/10.1109/ETFA46521.2020.9212119 SN - 978-1-7281-8956-7 SP - 1091 EP - 1094 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schmied, Sebastian A1 - Großmann, Daniel A1 - Mathias, Selvine George A1 - Banerjee, Suprateek T1 - Vertical integration via dynamic aggregation of information in OPC UA T2 - Intelligent Information and Database Systems UR - https://doi.org/10.1007/978-981-15-3380-8_18 KW - OPC UA KW - interoperability KW - aggregation Y1 - 2020 UR - https://doi.org/10.1007/978-981-15-3380-8_18 SN - 978-981-15-3379-2 SN - 978-981-15-3380-8 SN - 1865-0929 SN - 1865-0937 N1 - Access to this content is enabled by Nationallizenz Ebooks Medicine SP - 204 EP - 215 PB - Springer CY - Singapur ER - TY - CHAP A1 - Mathias, Selvine George A1 - Mancha, Mathew John A1 - Großmann, Daniel A1 - Kujat, Bernd A1 - Schiebold, Kay T1 - Investigations on numerical techniques for detecting variations in acoustic emissions T2 - IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society UR - https://doi.org/10.1109/IECON48115.2021.9589074 KW - acoustic emissions KW - Gaussian mixture model KW - self-organizing map KW - comparison Y1 - 2021 UR - https://doi.org/10.1109/IECON48115.2021.9589074 SN - 978-1-6654-3554-3 PB - IEEE CY - Piscataway (NJ) ER - TY - CHAP A1 - Schmied, Sebastian A1 - Großmann, Daniel A1 - Mathias, Selvine George A1 - Mueller, Ralph T1 - An approach for an industrial information model management T2 - Proceedings, 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS) UR - https://doi.org/10.1109/ICPS48405.2020.9274751 KW - information modelling KW - cyber physical system KW - OPC UA Y1 - 2020 UR - https://doi.org/10.1109/ICPS48405.2020.9274751 SN - 978-1-7281-6389-5 SP - 402 EP - 405 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Mathias, Selvine George A1 - Großmann, Daniel T1 - Efficacy of Statistical Formulations on Acoustic Emission Signals for Tool Wear Predictions T2 - Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics - IN4PL N2 - Acoustic emission (AE) signals obtained during machining processes can be used to detect, locate and assess flaws in structures made of metal, concrete or composites. This paper aims to characterize AE signals using derived parameters from raw signatures along with statistical feature extractions to correlate with tool wear readings. Missing tool wear values are imputed using domain knowledge rules and compared to AE signals using machine learning models. The amount of effect on tool wear is formulated using Bayesian Inferences on derived parameters such as areas under the raw signal curve in addition to comparisons with the supervised models for predictions. Using the constructed models and formulation, the presented study also includes a trace-back pseudo-algorithm for determining the stage in process where tool wear values begin to approach the wear limits. UR - https://doi.org/10.5220/0010676400003062 KW - acoustic emission KW - tool wear KW - data imputations KW - statistical approach Y1 - 2021 UR - https://doi.org/10.5220/0010676400003062 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-18695 SN - 978-989-758-535-7 SP - 108 EP - 115 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Akmal, Muhammad Uzair A1 - Asif, Saara A1 - Koval, Leonid A1 - Mathias, Selvine George A1 - Knollmeyer, Simon A1 - Großmann, Daniel ED - Koprinkova-Hristova, Petia ED - Kasabov, Nikola T1 - Layered Data-Centric AI to Streamline Data Quality Practices for Enhanced Automation T2 - Artificial Intelligence: Methodology, Systems, and Applications: 19th International Conference, AIMSA 2024, Varna, Bulgaria, September 18–20, 2024, Proceedings UR - https://doi.org/10.1007/978-3-031-81542-3_11 Y1 - 2025 UR - https://doi.org/10.1007/978-3-031-81542-3_11 SN - 978-3-031-81542-3 SP - 128 EP - 142 PB - Springer CY - Cham ER - TY - CHAP A1 - Mathias, Selvine George A1 - Asif, Saara A1 - Akmal, Muhammad Uzair A1 - Knollmeyer, Simon A1 - Koval, Leonid A1 - Großmann, Daniel ED - Rocha, Ana Paula ED - Steels, Luc ED - van den Herik, Jaap T1 - Industrial Image Grouping Through Pre-Trained CNN Encoder-Based Feature Extraction and Sub-Clustering T2 - Proceedings of the 17th International Conference on Agents and Artificial Intelligence (ICAART 2025) - Volume 2 N2 - A common challenge faced by many industries today is the classification of unlabeled image data from production processes into meaningful groups or patterns for better documentation and analysis. This paper presents a sequential approach for leveraging industrial image data to identify patterns in products or processes for plant floor operators. The dataset used is sourced from steel production, and the model architecture integrates feature reduction through convolutional neural networks (CNNs) like VGG, EfficientNet, and ResNet, followed by clustering algorithms to assign appropriate labels to the observed data. The model’s selection criteria combine clustering metrics, including entropy minimization and silhouette score maximization. Once primary clusters are identified, sub-clustering is performed using near-labels, which are pre-assigned to images with initial distinctions. A novel metric, C-Score, is introduced to assess cluster convergence and grouping accuracy. Experimental re sults demonstrate that this method can address challenges in detecting variations across images, improving pattern recognition and classification. UR - https://doi.org/10.5220/0013189000003890 Y1 - 2025 UR - https://doi.org/10.5220/0013189000003890 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58004 SN - 978-989-758-737-5 SP - 496 EP - 506 PB - SciTePress CY - Setúbal ER - TY - JOUR A1 - Mathias, Selvine George A1 - Akmal, Muhammad Uzair A1 - Asif, Saara A1 - Koval, Leonid A1 - Knollmeyer, Simon A1 - Großmann, Daniel T1 - Pattern Identifications in Transformed Acoustic Signals Using Classification Models JF - Procedia CIRP N2 - Pattern identifications in signals is necessary to discern variations from approved normal values in different scenarios. With machine learning algorithms, it is possible to use hybrid methods of pattern identifications such as feature extractions followed by classifications and/or clustering. This paper presents a pattern identification approach of acoustic signals using their transformations as inputs to classification algorithms. The analysis is carried out on two transformed versions of acoustic emission (AE) hits such as log transformations of peak hits and binary sequencing based on threshold crossing. A comparative analysis using custom data loss metrics is made to determine which inputs provide the best information in predictive methods for identifying commonly occurring patterns while acknowledging significant data loss. The methodology is conducted on transformed versions of a public dataset and the results show that patterns can be discerned to above 90% accuracy with the transformed datasets. The experimental results yield that actual source signals need not be utilized depending on which transformations suit the practical application. UR - https://doi.org/10.1016/j.procir.2024.10.061 Y1 - 2024 UR - https://doi.org/10.1016/j.procir.2024.10.061 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-56233 SN - 2212-8271 VL - 2024 IS - 130 SP - 93 EP - 99 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Knollmeyer, Simon A1 - Akmal, Muhammad Uzair A1 - Koval, Leonid A1 - Asif, Saara A1 - Mathias, Selvine George A1 - Großmann, Daniel ED - Facchinetti, Tullio ED - Cenedese, Angelo ED - Lo Bello, Lucia ED - Vitturi, Stefano ED - Sauter, Thilo ED - Tramarin, Federico T1 - Document Knowledge Graph to Enhance Question Answering with Retrieval Augmented Generation T2 - 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA61755.2024.10711054 Y1 - 2024 UR - https://doi.org/10.1109/ETFA61755.2024.10711054 SN - 979-8-3503-6123-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Asif, Saara A1 - Akmal, Muhammad Uzair A1 - Koval, Leonid A1 - Knollmeyer, Simon A1 - Mathias, Selvine George A1 - Großmann, Daniel ED - Facchinetti, Tullio ED - Cenedese, Angelo ED - Lo Bello, Lucia ED - Vitturi, Stefano ED - Sauter, Thilo ED - Tramarin, Federico T1 - Supervised Anomaly Detection for Production Line Images using Data Augmentation and Convolutional Neural Network T2 - 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA61755.2024.10710718 Y1 - 2024 UR - https://doi.org/10.1109/ETFA61755.2024.10710718 SN - 979-8-3503-6123-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Koval, Leonid A1 - Akmal, Muhammad Uzair A1 - Asif, Saara A1 - Mathias, Selvine George A1 - Knollmeyer, Simon A1 - Großmann, Daniel T1 - Optimizing AI-Driven Production in Industry 4.0: A Morphological Box and Taxonomy Approach T2 - 2025 International Conference on Computer Technology Applications (ICCTA) UR - https://doi.org/10.1109/ICCTA65425.2025.11166106 Y1 - 2025 UR - https://doi.org/10.1109/ICCTA65425.2025.11166106 SN - 979-8-3315-1265-1 SP - 272 EP - 278 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Koval, Leonid A1 - Akmal, Muhammad Uzair A1 - Asif, Saara A1 - Mathias, Selvine George A1 - Knollmeyer, Simon A1 - Großmann, Daniel ED - Facchinetti, Tullio ED - Cenedese, Angelo ED - Lo Bello, Lucia ED - Vitturi, Stefano ED - Sauter, Thilo ED - Tramarin, Federico T1 - Addressing the complexity of AI Integration in Manufacturing: A Morphological Analysis T2 - 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA61755.2024.10711011 Y1 - 2024 UR - https://doi.org/10.1109/ETFA61755.2024.10711011 SN - 979-8-3503-6123-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Asif, Saara A1 - Akmal, Muhammad Uzair A1 - Koval, Leonid A1 - Mathias, Selvine George A1 - Knollmeyer, Simon A1 - Großmann, Daniel ED - Sgurev, Vassil ED - Jotsov, Vladimir ED - Piuri, Vincenzo ED - Doukovska, Luybka ED - Yoshinov, Radoslav T1 - A Conceptual Framework for Addressing Class Imbalance in Image Data: Challenges and Strategies T2 - 2024 IEEE 12th International Conference on Intelligent Systems (IS): Proceedings UR - https://doi.org/10.1109/IS61756.2024.10705251 Y1 - 2024 UR - https://doi.org/10.1109/IS61756.2024.10705251 SN - 979-8-3503-5098-2 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Mathias, Selvine George A1 - Großmann, Daniel T1 - Use Cases of Data Reduction to Time Series Data in Sensor Monitoring JF - Journal of Ubiquitous Systems and Pervasive Networks UR - https://doi.org/10.5383/JUSPN.16.02.005 Y1 - 2022 UR - https://doi.org/10.5383/JUSPN.16.02.005 SN - 1923-7332 VL - 16 IS - 2 SP - 87 EP - 92 PB - IASKS CY - [s. l.] ER - TY - JOUR A1 - Schmied, Sebastian A1 - Mathias, Selvine George A1 - Großmann, Daniel A1 - Mueller, Ralph A1 - Jumar, Ulrich T1 - Information modelling with focus on existing manufacturing systems JF - Annual Reviews in Control UR - https://doi.org/10.1016/j.arcontrol.2021.04.010 KW - information model KW - aggregation KW - OPC UA KW - cyber–physical manufacturing systems Y1 - 2021 UR - https://doi.org/10.1016/j.arcontrol.2021.04.010 SN - 1367-5788 VL - 2021 IS - 51 SP - 392 EP - 400 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schmied, Sebastian A1 - Großmann, Daniel A1 - Mueller, Ralph A1 - Mathias, Selvine George A1 - Jumar, Ulrich T1 - Erstellung und Management von Informationsmodellen für bestehende Produktionssysteme T1 - Creation and management of Information Models for existing production systems JF - at - Automatisierungstechnik UR - https://doi.org/10.1515/auto-2020-0021 KW - OPC UA KW - Informationsmodellierung KW - Produktionssystem KW - Prozessdaten-Management KW - OPC UA KW - Information Modeling KW - production system KW - process data management Y1 - 2020 UR - https://doi.org/10.1515/auto-2020-0021 SN - 2196-677X VL - 68 IS - 5 SP - 325 EP - 336 PB - De Gruyter CY - Berlin ER - TY - CHAP A1 - Mathias, Selvine George A1 - Akmal, Muhammad Uzair A1 - Asif, Saara A1 - Knollmeyer, Simon A1 - Koval, Leonid A1 - Grossmann, Daniel ED - Maglogiannis, Ilias ED - Iliadis, Lazaros ED - Andreou, Andreas ED - Papaleonidas, Antonios T1 - Handling Anonymized Non-numerical Features in Data Using Transformations for Regression Models T2 - Artificial Intelligence Applications and Innovations: 21st IFIP WG 12.5 International Conference, AIAI 2025, Limassol, Cyprus, June 26–29, 2025, Proceedings, Part III UR - https://doi.org/10.1007/978-3-031-96231-8_8 Y1 - 2025 UR - https://doi.org/10.1007/978-3-031-96231-8_8 SN - 978-3-031-96231-8 SP - 99 EP - 111 PB - Springer CY - Cham ER - TY - CHAP A1 - Akmal, Muhammad Uzair A1 - Mathias, Selvine George A1 - Asif, Saara A1 - Koval, Leonid A1 - Knollmeyer, Simon A1 - Großmann, Daniel T1 - Humanizing AI: A Human-Centered Architecture to Developing Trustworthy Intelligent Systems T2 - CS & IT Conference Proceedings Y1 - 2025 UR - https://www.csitcp.com/abstract/15/158csit03 SN - 978-1-923107-57-1 SP - 27 EP - 44 PB - AIRCC CY - [s. l.] ER -