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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

共 36972 条记录，以下是91-100 订阅

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Utilizing ISA-95 in an Industrial knowledge Graph for Material Flow Simulation - Semantic Model Extensions and Efficient data Integration 56

Utilizing ISA-95 in an Industrial Knowledge Graph for Materi...

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56th CIRP international conference on manufacturing systems, CIRP CMS 2023

作者： Listl, Franz Georg Fischer, Jan Sohr, Annelie Dittler, Daniel Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Pfaffenwaldring 47 Stuttgart70550 Germany Technology Department Siemens AG Otto-Hahn-Ring 6 Munich81739 Germany

Despite access to emerging technologies for effective data representation and interoperability between different types of data, many enterprises struggle to use the information and knowledge available in manufacturing data to increase the efficiency and quality of their production. This is especially true for knowledge graphs and semantic web technologies. Nevertheless, breaking down the rigid and historical manufacturing data systems and transferring them to knowledge graphs often involves a lot of manual work for cleaning, mapping, and transforming the data. To address this issue, we have developed a methodology to efficiently utilize ISA-95 in knowledge graphs for manufacturing. The methodology is based on two main building blocks. First, we built an ontology based on the ISA-95 standard and extended it with additional semantic concepts. The second building block is an extract, transform, and load pipeline, to allow an efficient mapping of different data formats into the concepts specified by the ontology, and consequently a fast data integration into the knowledge graph. The created knowledge graph serves as basis for efficient management of material flow simulations. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 56th CIRP international conference on manufacturing systems 2023.

关键词： data Integration Industry 4.0 ISA-95 knowledge Graph Ontology Semantic Mappin

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Utilizing data Analysis for Optimized Determination of the Current Operational State of Heating systems 16

Utilizing Data Analysis for Optimized Determination of the C...

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16th international conference on knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Qarqour, Ahmed Arora, Sahil-Jai Heisenberg, Gernot Rabe, Markus Kleinert, Tobias Bosch Thermotechnik GmbH Junkersstraße 20-24 Neckar Wernau73243 Germany Department of Information Management TH Cologne University Claudiusstraße 1 Köln50678 Germany Department IT in Production and Logistics TU Dortmund University Dortmund44221 Germany Department of Information and Automation Systems for Process and Material Technology RWTH Aachen University Turmstraße 46 Aachen52064 Germany

ISBN: (纸本)9789897587160

In response to the pressing global challenge of climate change, the emphasis on sustainable energy technologies has escalated, spotlighting the critical role of heat pump systems as eco-friendly alternatives for heating and cooling. These systems stand at the forefront of efforts to reduce greenhouse gas emissions and improve energy efficiency. The advent of Internet of Things (IoT) technology has unlocked the potential for comprehensive data collection on the operational intricacies of heat pump systems in real-world settings, offering precious insights into their performance and guiding technological advancements. This paper introduces an analytical approach to optimize air-to-water heat pump systems using time series data from Bosch Home Comfort Group's systems. Utilizing Fayyad's data-driven analysis model and the Random Forest algorithm, the study tackles system behavior complexities. Characterized by interpretability crucial for application, it achieves a 97.6% fault detection accuracy. The method encounters difficulties in accurately predicting compressor control faults due to limited data quality and a lack of comprehensive system information. The findings highlight IoT's potential to enhance system efficiency and availability, but also point to the limitations of relying solely on data-driven models for fault prediction in field systems. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Network security

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Lenses on data: Toward an Application Perspective on data in manufacturing 43rd

Lenses on Data: Toward an Application Perspective on Data in...

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43rd IFIP WG 5.7 international conference on Advances in Production Management systems (APMS)

作者： Albers, Alexander Netland, Torbjorn Swiss Fed Inst Technol Weinbergstr 56-58 CH-8092 Zurich Switzerland

ISBN: (纸本)9783031716355;9783031716331;9783031716324

data is becoming increasingly important in manufacturing, enabling visions like cyber-physical production systems and digital twins. However, companies still struggle to effectively leverage data to drive process improvements and productivity gains. One reason is that there are many implicit assumptions and perspectives on what data fundamentally represents. This paper explores five distinct "lenses" through which data can be viewed, drawing from the fields of information theory, information systems, accounting, management theory, and economics. While each lens offers valuable insights, we argue that manufacturing requires a more application-oriented understanding of data that accounts for the socio-technical nature of factories. We discuss six key attributes of data from a manufacturing perspective, including cost, coverage, fidelity, timeliness, accuracy, and accessibility. We argue that rather than pursuing an all-encompassing digital replica, the focus of manufacturers should be on collecting just enough relevant data to enable specific manufacturing applications while prioritizing coverage of the entire shop floor.

关键词： Business Analytics knowledge Management Digital Twin & Industrial Metaverse

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Research on point cloud simplification method for retaining sharp features of turbine blades 3

Research on point cloud simplification method for retaining ...

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3rd international conference on Advanced manufacturing Technology and manufacturing systems, ICAMTMS 2024

作者： Zhao, Lixiang Cheng, Yunyong Ye, Wenguang Xue, Chaojun Zhang, Wenmiao School of Mechanical Engineering Northwestern Polytechnical University Shaanxi Xi'an China

ISBN: (纸本)9781510681804

To address the issue of large data volume and information redundancy in turbine blade point cloud data acquired through industrial computerized tomography scanning, which leads to difficulties in subsequent data processing, this paper proposes a point cloud simplification method that preserves sharp features. This method segments feature regions based on curvature meshes, calculates curvature feature values, identifies and retains feature points, and conducts a secondary reduction on non-feature points. By merging the feature points with the simplified non-feature points, the method achieves simplification of high-density point cloud data while preserving sharp features. Experiments indicate that this approach can effectively remove redundant points from turbine blades, retain sharp features of the point cloud, and ensure the precision requirements of the point cloud are met. © 2024 SPIE.

关键词： Turbomachine blades

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Design and Development of a Provenance Capture Platform for data Science 40

Design and Development of a Provenance Capture Platform for ...

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40th international conference on data engineering Workshop (ICDEW)

作者： Gregori, Luca Missier, Paolo Stidolph, Matthew Torlone, Riccardo Wood, Alessandro Roma Tre Univ Dept Engn Rome Italy Univ Birmingham Sch Comp Sci Birmingham W Midlands England Newcastle Univ Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9798350384048;9798350384031

As machine learning and AI systems become more prevalent, understanding how their decisions are made is key to maintaining their trust. To solve this problem, it is widely accepted that fundamental support can be provided by the knowledge of how data are altered in the pre-processing phase, using data provenance to track such changes. This paper focuses on the design and development of a system for collecting and managing data provenance of data preparation pipelines in data science. An investigation of publicly available machine learning pipelines is conducted to identify the most important features required for the tool to achieve impact on a broad selection of preprocessing data manipulation. This reveals that the operations that are used in practice can be implemented by combining a rather limited set of basic operators. We then illustrate and test implementation choices aimed at supporting the provenance capture for those operations efficiently and with minimal effort for data scientists.

关键词： data preparation data provenance data science

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ML System engineering Supported by a Body of knowledge 16

ML System Engineering Supported by a Body of Knowledge

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16th international conference on knowledge Management and Information systems, KMIS 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Mattioli, Juliette Tachet, Dominique Tschirhart, Fabien Sohier, Henri Cantat, Loic Robert, Boris Thales France IRT SystemmX France IRT Saint Exupéry France

ISBN: (纸本)9789897587160

A body of knowledge (BoK) can be defined as the comprehensive set of concepts, terminology, standards, and activities that facilitate the dissemination of knowledge about a specific field, providing guidance for practice or work. This paper presents a methodology for the construction of a body of knowledge (BoK) based on knowledge-based artificial intelligence. The process begins with the identification of relevant documents and data, which are then used to capture concepts, standards, best practices, and state-of-the-art. These knowledge items are then fused into a knowledge graph, and finally, query capacities are provided. The overall process of knowledge collection, storage, and retrieval is implemented with the objective of supporting a trustworthy machine learning (ML) end-to-end engineering methodology, through the ML engineering BoK. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： knowledge graph

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15th international conference on Axiomatic Design, ICAD 2023

15th International Conference on Axiomatic Design, ICAD 2023

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15th international conference on Axiomatic Design, ICAD 2023

ISBN: (纸本)9783031499197

The proceedings contain 20 papers. The special focus in this conference is on Axiomatic Design. The topics include: On Dynamic Axiomatic Design or Projections of System Control Theory on Axiomatic Design;axiomatic Design as a Tool to Develop a Global Production Strategy in Transportation Industry;enabling Vocational Students to Develop Axiomatic Design Application knowledge;connecting the V-Model and Axiomatic Design;An Analysis How systems engineering Methodologies Relate;A Combined Axiomatic Design-MCDA Method for Selecting Medical systems Operating on a Common Telemedicine Platform;application of Axiomatic Design in engineering: Designing a Smart Medical Cast: Increasing Robustness by Decreasing Information;applying Axiomatic Design to Risk Assessment;axiomatic Design and The Diamond Model: Towards Defining the Role of Axiomatic Design in the Age of data Driven Development;Axiomatic Design Meets Model-Based systems engineering (MBSE);smart Mobile Factory Design Decomposition Using Model-Based systems engineering;model-Based systems engineering in Smart manufacturing - Future Trends Toward Sustainability;optimizing Automated manufacturing Processes Using Axiomatic Design Methods;identifying a Device for Tracking the Evolution of Thermal Transfer in 3D Printed Parts Using Principles from Axiomatic Design;sustainable Manufacturer engineering for Industry 6.0;education Design Requirements Towards a Sustainable and Autonomous Europe;axiomatic Design Facilitating Integrated Building Design and Operation;designing an Ergonomic Geothermally Heated Pinecone Seed Extractor;axiomatic Design Using Multi-criteria Decision Making for Material Selection in Mechanical Design: Application in Different Scenarios.

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Incorporating knowledge Graph Embeddings into Graph Neural Networks for Sequential Recommender systems 9

Incorporating Knowledge Graph Embeddings into Graph Neural N...

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9th international conference on Computer Science and engineering, UBMK 2024

作者： Yüksel, Kazim Emre Üsküdarli, Susan Bogazici University Department of Computer Engineering Istanbul Turkey

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365887

Traditional recommendation methods often struggle to capture the evolving nature of user interactions and the rich contextual information available in external data sources. This paper presents an innovative approach to sequential recommendations by integrating knowledge graph embeddings into a graph neural network (GNN) architecture. We utilize the MovieLens dataset as our primary data source and enrich it with additional contextual information from Wikidata, an open-source comprehensive knowledge base by incorporating the translation-based knowledge graph embeddings. Experimental results demonstrate that our approach improves recommendation accuracy, particularly in terms of Hit@K metrics, by leveraging the enriched movie embeddings. This study highlights the potential of utilizing open-source knowledge bases to enhance the predictive capabilities of recommendation systems, providing a more nuanced and comprehensive understanding of user preferences. © 2024 IEEE.

关键词： knowledge graph

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Metadata Model Construction and Annotation Framework to Build Product data Repository for Cloud manufacturing 32nd

Metadata Model Construction and Annotation Framework to Buil...

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32nd international conference on Flexible Automation and Intelligent manufacturing

作者： Lim, Eunchae Kim, Changyeong Lin, Zeyue Shen, Yinfeng Liu, Shengyu Kim, Kyoung-Yun Yang, Hyung-Jeong Chonnam Natl Univ Dept Artificial Intelligence Convergence Gwangju 61186 South Korea Wayne State Univ Dept Ind & Syst Engn Detroit MI 48202 USA

ISBN: (纸本)9783031381645;9783031381652

This paper presents a framework for automatically building a product data repository, overcoming the limitations of machine understanding and the time-consuming, costly nature of manual human annotation. In this study, we focus on Personal Protective Equipment (PPE), specifically respirator mask product categories, as a case study. First, we extract product specifications from the details and sub-details sections of product pages and build < attribute, attribute-values > metadata in dictionary form. Second, to process the data, we represent numerical data with a special token [NUM] and use hierarchical clustering based on Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity for categorical data. Third, we propose an algorithm-based method to annotate product attributes and attribute values in product descriptions using the metadata, while minimizing human efforts. Additionally, we employ BIO tagging to extract location information for attribute values, streamlining the overall annotation process. This paper contributes to the field by presenting a method for automatically generating product-related datasets through an algorithm that enables the construction of metadata without any human intervention. This approach is a crucial step toward implementing a knowledge repository in aCloud manufacturing (CM) environment. Particularly, by addressing time and effort concerns in the dataset construction process, this method can significantly contribute to the creation of large-scale datasets.

关键词： Attribute values extraction Product data data integration Graph database knowledge graph Cloud manufacturing Metadata model

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An Intelligent manufacturing Models Recommendation Model Based on knowledge Graph and Recommendation Algorithms

An Intelligent Manufacturing Models Recommendation Model Bas...

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2023 international conference on Big data, Information and Intelligent engineering, BDIIE 2023

作者： Zhang, Liwei Hou, Kaixuan College of Management and Economics Tianjin University Tianjin China

With the concept of intelligent manufacturing, a variety of intelligent manufacturing models emerge. But companies don't have enough knowledge to choose the suitable intelligent manufacturing model. To solve this problem, based on lean design principles, combined with expert knowledge and expertise, we propose an Intelligent manufacturing Models Recommendation Model (referred to as IMMR Model). First, we categorize customer requirements into real need, customer preference and customer expectation to accurately identify customer requirements. Then, we use knowledge graph to build a professional knowledge base for intelligent manufacturing, and form customized intelligent manufacturing models based on lean design. Finally, a recommendation system is constructed by combining an ontology rule-based recommendation algorithm and a similarity-based recommendation algorithm to recommend suitable intelligent manufacturing models for different customer requirements. © Published under licence by IOP Publishing Ltd.

关键词： Recommender systems

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