Although declarative data transformation functions defined in RML-FNML were implemented in KG materialization systems, they have not yet been studied or implemented in virtual knowledge graph systems. In this work we ...
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ISBN:
(纸本)9783031623615;9783031623622
Although declarative data transformation functions defined in RML-FNML were implemented in KG materialization systems, they have not yet been studied or implemented in virtual knowledge graph systems. In this work we propose to translate RML-FNML mapping to RML Views, which can be transparently used by virtual knowledge graph systems without modifying them. We implemented a research prototype of RML-FNML to RML Views unfolding and applied it to GTFS data to show the feasibility of the approach.
The integration of Internet of Things (IoT) technologies into manufacturingsystems enables the remote monitoring and control of equipment, significantly enhancing operational efficiency, reducing downtime, and improv...
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Extension theory introduces new aspects in the modern technological environment with the use and development of artificial intelligence, ChatGPT, and data mining technologies. This paper summarizes the intelligent ext...
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The integration of human knowledge and experience with artificial intelligence, especially in the context of Industry5.0, holds the promise of advanced capabilities for manufacturing that may facilitate reduced waste ...
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ISBN:
(纸本)9798350362442;9798350362435
The integration of human knowledge and experience with artificial intelligence, especially in the context of Industry5.0, holds the promise of advanced capabilities for manufacturing that may facilitate reduced waste and increased efficiency. However, there is a gap between the two. This work discusses the critical role of Explainable AI (XAI) within this paradigm, fostering a collaborative environment where human operators can leverage AI-driven insights. A framework for data-driven proactive quality control is coupled with XAI and human-centric approaches to enable a path towards zero-defect manufacturing processes, improved operational efficiency, and enhanced workforce empowerment. Furthermore, practical implications, the impact of XAI and recommendations for upskilling and reskilling the manufacturing personnel are discussed with a focus on small and medium-sized enterprises.
This paper introduces an approach that integrates Natural Language Processing (NLP) and knowledge graphs with Reconfigurable manufacturingsystems (RMS) to enhance flexibility and adaptability. We utilize a chatbot in...
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ISBN:
(数字)9783031744853
ISBN:
(纸本)9783031744846;9783031744853
This paper introduces an approach that integrates Natural Language Processing (NLP) and knowledge graphs with Reconfigurable manufacturingsystems (RMS) to enhance flexibility and adaptability. We utilize a chatbot interface powered by GPT-4 and a structured knowledge base to simplify the complexities of manufacturing reconfiguration. This system not only boosts reconfiguration efficiency but also broadens accessibility to advanced manufacturing technologies. We demonstrate our methodology through an application in capability matching, showcasing how it facilitates the identification of assets for new product requirements. Our results indicate that this integrated solution offers a scalable and user-friendly approach to overcoming adaptability challenges in modern manufacturing environments.
In the context of the highly competitive and digital transformation of the manufacturing industry, the Internet of Things (IoT) plays a crucial role in enhancing production efficiency in smart manufacturing. This pape...
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ISBN:
(纸本)9798350375084;9798350375077
In the context of the highly competitive and digital transformation of the manufacturing industry, the Internet of Things (IoT) plays a crucial role in enhancing production efficiency in smart manufacturing. This paper delves into how IoT improves production efficiency in smart manufacturing through real-time data collection, monitoring, automated decision-making, remote maintenance and services, and supply chain optimization. At the same time, we explore various challenges encountered in this process, including technical, economic, and regulatory and policy challenges. Despite these challenges, we firmly believe that by deepening the research on the application of IoT in smart manufacturing, we can overcome these difficulties, further improve the production efficiency of smart manufacturing, and promote the continuous development of the manufacturing industry.
Quality assurance in manufacturing companies is an essential process for ensuring that products meet established standards. It contributes to customer satisfaction, as well as the reduction of the costs associated wit...
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Improving the overall equipment effectiveness (OEE) of machines on the shop floor is crucial to ensure the productivity and efficiency of manufacturingsystems. To achieve the goal of increased OEE, there is a need to...
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Access to accurate manufacturing capability information is necessary for efficient supplier discovery and agile supply chain formation. However, manufacturing capability data, particularly for small and medium-sized m...
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Access to accurate manufacturing capability information is necessary for efficient supplier discovery and agile supply chain formation. However, manufacturing capability data, particularly for small and medium-sized manufacturers, is often unavailable or, if accessible, lacks essential qualities such as correctness, completeness, interoperability, and openness. The objective of the research presented in this paper is to develop an open manufacturing Capability Network (MCN) that represents various manufacturers' capabilities as an interconnected and formal knowledge graph. This capability graph is part of a larger graph referred to as the Supply and Demand Open knowledge Network (SUDOKN). The ontologies that provide the semantics of the knowledge graph comply with the Basic Formal Ontology (BFO). A proof-of-concept knowledge graph, based on 1700 manufacturers, is presented in this work. The graph's validity was assessed by submitting queries related to supplier discovery use cases. SUDOKN, once fully deployed, serves as a shared, canonical, and consensus-driven knowledge backbone, that supports supply chain analytics solutions with AI-ready data.
Traffic data analysis and forecasting is a multidimensional challenge that extracts details from sources such as social media and vehicle sensor data. This study proposes a three-stage framework using Deep Learning (D...
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