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

共 37040 条记录，以下是451-460 订阅

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Content-based Art Recommendation Using Multimodal Graph Neural Networks 15

Content-based Art Recommendation Using Multimodal Graph Neur...

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15th IEEE international conference on knowledge Graph, ICKG 2024

作者： Dutta, Haimonti Tiwari, Anushka The State University of New York at Buffalo School of Management and Institute for Artificial Intelligence and Data Science Department of Management Science and Systems BuffaloNY14260 United States The State University of New York at Buffalo Institute for Artificial Intelligence and Data Science BuffaloNY14260 United States

ISBN: (纸本)9798331508821

Graph Neural Network (GNN)-based recommendation systems have become very popular in recent years. Their popularity stems from the fact that nodes can access higher-order neighbor information and there are well-designed algorithms for embedding, message passing, and propagation. In this paper, we present the design of a GNN-based recommendation system on a novel data set collected from field research. Designed for an endangered performing art form, the recommendation system uses multimodal text and image data to suggest similar panels (paintings) to the end-user of the system. We show that multimodal data is particularly helpful in graph representation learning, message passing, and propagation and the panels recommended to users using content-based features are similar to those obtained from systems using user-item bipartite graphs such as MMGCN and GRCN. © 2024 IEEE.

关键词： Graph neural networks

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36th international conference on Advanced Information systems engineering, CAiSE 2024

36th International Conference on Advanced Information System...

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36th international conference on Advanced Information systems engineering, CAiSE 2024

ISBN: (纸本)9783031610561

The proceedings contain 18 papers. The special focus in this conference is on Advanced Information systems engineering. The topics include: Incorporating Behavioral Recommendations Mined from Event Logs into AI Planning;trustworthy Collaborative Business Intelligence Using Zero-knowledge Proofs and Blockchains;Towards Intelligent systems to Improve IEC 62559 Use Cases and Smart Grid Architecture Models Quality;pricing4SaaS: Towards a Pricing Model to Drive the Operation of SaaS;Validity at the Forefront: Investigating Threats in Green AI Research;requirement-Based Methodological Steps to Identify Ontologies for Reuse;Toward Ontology-Guided IFRS Standard-Setting;towards an Explorable Conceptual Map of Large Language Models;proReco: A Process Discovery Recommender System;recPro: A User-Centric Recommendation Tool for Business Process Execution;predictive Maintenance in a Fleet Management System: The Navarchos Case;CDMiA: Revealing Impacts of data Migrations on Schemas in Multi-model systems;MApp-KG: Mobile App knowledge Graph for Document-Based Feature knowledge Generation;CAKE: Sharing Slices of Confidential data on Blockchain;PADI-web for Plant Health Surveillance;PROMISE: A Framework for Model-Driven Stateful Prompt Orchestration.

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Topological Analysis to Enhance the Understanding of Transdisciplinary engineering 31

Topological Analysis to Enhance the Understanding of Transdi...

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31st ISTE international conference on Transdisciplinary engineering, TE 2024

作者： Thomas-Seale, Lauren E.J. Hawthorn, Barnaby Kombrink, Sabrina Samuel, Tony Bryson, John R. Thomson, Harriet Montenegro-Johnson, Thomas D. Department of Mechanical Engineering School of Engineering University of Birmingham United Kingdom CDT Topological Design School of Physics and Astronomy University of Birmingham United Kingdom School of Mathematics University of Birmingham United Kingdom Department of Mathematics and Statistics University of Exeter United Kingdom Department of International Business and Strategy Birmingham Business School University of Birmingham Birmingham United Kingdom School of Social & Political Sciences University of Glasgow United Kingdom Mathematics Institute University of Warwick Coventry United Kingdom

ISBN: (纸本)9781643685502

In engineering, the design of a product relies heavily on a design specification;a co-creation of customer and engineer which captures the requirements. Subjectivity is intrinsic to this process. Whilst engineers typically have a high appreciation of the technical aspects of design, the detailed knowledge of environmental and socioeconomic (ESE) implications are often held elsewhere. As such, efficient and effective design is critically dependent on the processes underpinning knowledge transfer. However, the information interfaces between engineering and the requirements of our swiftly changing civilization remain indirect and suboptimal, and the unintended consequences of design choices are becoming increasingly serious. Transdisciplinary engineering bridges knowledge boundaries interfacing with engineering (e.g. social science). This paper explores whether topology (a branch of pure mathematics) presents an opportunity to analyse the complex interdependency of transdisciplinary engineering information. Topology and geometry describe the structure of objects such as connectedness or the number of holes and have recently provided a suite of powerful and robust tools for analysing high-dimensional data sets. However, the real-world implementation of the term topology is still evolving. Interviews with engineering organisations, revealed that topology is predominately interpreted as ‘Topology Optimisation’ in the context of advanced design and manufacturing. To date, mathematical processes for critically and systematically examining the topology of systems have not been transferred through to the engineering industry. This paper contrasts how topology is interpreted by the engineering industry compared to academic literature and reflects on the opportunities of applying the mathematical theory of topological analysis to transdisciplinary engineering data. © 2024 The Authors.

关键词： Product design

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PBF-AMP-Onto: an ontology for powder bed fusion additive manufacturing processes 1

PBF-AMP-Onto: an ontology for powder bed fusion additive man...

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1st international Workshop on Semantic Materials Science: Harnessing the Power of Semantic Web Technologies in Materials Science, SeMatS 2024

作者： Pour, Mina Abd Nikooie Tarafder, Prithwish Wiberg, Anton Li, Huanyu Moverare, Johan Lambrix, Patrick Department of Computer and Information Science Linköping University Linköping Sweden Swedish e-Science Research Centre Linköping University Linköping Sweden Department of Management and Engineering Linköping University Linköping Sweden Wallenberg Initiative Materials Science for Sustainability Linköping University Linköping Sweden Wallenberg AI Autonomous Systems and Software Program Linköping University Linköping Sweden

Additive manufacturing is an innovative production approach aimed at creating products that traditional techniques cannot produce with the desired quality and requirements. Throughout the additive manufacturing process, data is either used (such as materials properties, printer characteristics and settings) or generated (such as monitoring data during printing, slicing strategies setting parameters). However, managing such data with complex relationships remains a significant challenge in both research and industry in the additive manufacturing field. To address this issue, we developed a modular ontology that can be used as the basis for a framework that supports decision-making systems, facilitate semanticsaware data management, and enhance the understanding and optimization of additive manufacturing processes. In this paper we focus on one of the state-of-the-art additive manufacturing approaches, i.e., powder bed fusion. To show the use and the feasibility of our approach, we created a knowledge graph for an actual additive manufacturing experiment based on our ontology, and show how queries relevant to domain experts can be answered using this knowledge graph. © 2024 CEUR-WS. All rights reserved.

关键词： Ontology

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Machine Learning Techniques for Credit Card Risk Prediction: An Experimental Study 36

Machine Learning Techniques for Credit Card Risk Prediction:...

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36th international conference on Software engineering and knowledge engineering, SEKE 2024

作者： Jin, Haoying School of Telecommunications Engineering Xidian University Xi’An China

ISBN: (纸本)1891706594

Nowadays, with the rapid development of mobile Internet and smartphones, credit card fraud has become more and more serious, causing significant credit and financial damage to cardholders and economics. Many previous studies proposed using machine learning techniques to predict credit card risks. However, it is still unclear what machine learning models are most suitable for credit card risk prediction. In this paper, we perform an experimental study on real credit card transaction data to evaluate existing machine learning techniques on credit card risk prediction. The unique contributions of the paper are two-fold. First, we present a systematic experimental study to compare eight machine learning techniques on credit card risk prediction. These techniques cover both supervised models and deep learning approaches, providing a relatively complete framework for revealing the performance of different machine learning techniques. Second, we use both imbalanced and balanced datasets and employ various evaluation metrics to measure the performance of machine-learning models. The results show that all models perform better on the balanced dataset than on the imbalanced data, and the Logistic Regression model performs best in most experiments. In addition, deep learning models do not perform as well as supervised models. Finally, we present some helpful suggestions and new insights for future research on credit card risk prediction. © 2024 knowledge systems Institute Graduate School. All rights reserved.

关键词： Adversarial machine learning

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knowledge GRAPH CONSTRUCTION FOR CROSS ENTERPRISE PRODUCT SUPPLY RELATIONSHIP OF INTELLIGENT INSTRUMENTS 10

KNOWLEDGE GRAPH CONSTRUCTION FOR CROSS ENTERPRISE PRODUCT SU...

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10th international Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Xu, Min Huang, Min Li, Yiming School of Mechanical Electrical Engineering Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781837242672

The massive resources of intelligent instruments provide data support for cross enterprise regional network collaborative manufacturing. However, the discrete distribution of data, a large amount of redundancy, and weak correlation limit system knowledge learning. This paper proposes an automatic construction method of a knowledge graph for the supply and demand matching of products and the relationship between upstream and downstream enterprises. Firstly, Stanford CoreNLP tool is used to achieve enterprise entity recognition. Secondly, considering the potential relationship between boundary words and product keywords, an improved CRF model is proposed to achieve product entity recognition. Then, a method for extracting enterprise products supply relationships based on relationship indicator lexicon and dependency syntax analysis is proposed to obtain the upstream and downstream relationships of enterprise products. Finally, the Neo4j database is used to store the knowledge graph of cross enterprise product supply relationship of intelligent instruments, and it is published on a third-party service platform to promote efficient resource sharing and collaborative manufacturing of intelligent instruments across enterprises. © The Institution of engineering & Technology 2024.

关键词： knowledge graph

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Application of Enterprise Master data Management System in the Informatization of Electric Power Enterprises

Application of Enterprise Master Data Management System in t...

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2024 international conference on Electrical Drives, Power Electronics and engineering, EDPEE 2024

作者： Hu, Liu Jianhong, Pan Zhuang, Cui Zhonglong, Zheng Lu, Shu State Grid Corporation Of China Hebei056000 China State Grid Jilin Electric Power Co. Ltd. Jilin132400 China State Grid Xintong Yili Technology Co. Ltd Liaoning 121200 China

ISBN: (纸本)9798350395631

In order to improve efficiency and reduce costs, large organizations are building key systems, such as enterprise resource planning (ERP), manufacturing execution system, human resource management (HRM) and customer relationship management (CRM). These core systems pose new challenges in terms of data consistency, integrity, accuracy, and auditability across departments, systems, and fields, and even in terms of organizational consistency requirements. In the early stages of enterprise IT (Information Technology) development, companies often lacked comprehensive planning for data standards and data exchange between enterprise systems, so it was necessary to implement Master data Management (MDM) systems. This article explored the application of enterprise level master data management system in the informatization of power enterprises. Firstly, the enterprise level master data management system and power enterprise informatization were introduced, and then the improvement of enterprise level master data management system in enterprise database operation efficiency was verified through experiments (the reading and calling efficiency of experimental databases was generally higher than 80%, higher than traditional data reading efficiency). © 2024 IEEE.

关键词： Informatization

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Design and Implementation of Industrial Temperature Control System 2

Design and Implementation of Industrial Temperature Control ...

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2nd international conference on Advanced manufacturing Technology and manufacturing systems, ICAMTMS 2023

作者： Tong, Yulong Wang, Chenyu Shen, Hai College of Communication Engineering Jilin University Changchun130022 China College of Physics Science and Technology Shenyang Normal University Shenyang110034 China

ISBN: (纸本)9781510667327

The technical realization of temperature control system plays a key role in the development of industrial automation. This paper designs a temperature control system with the heating furnace model as the controlled object, the heating furnace water temperature as the data acquisition object and controlled parameters, and the Siemens S7-1500 PLC as the lower computer controller. The PLC ladder diagram programming language in the software carries out the programming design of the lower computer, and combines the configuration and monitoring screen of the upper computer to design the configuration monitoring screen. At the same time, the function expansion and operation debugging are carried out to realize the monitoring and automatic control of the temperature data. © 2023 SPIE.

关键词： Computer control systems

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Adapting Industrial engineering Techniques to Optimising Productivity in manufacturing Operations 33rd

Adapting Industrial Engineering Techniques to Optimising Pro...

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33rd international conference on Flexible Automation and Intelligent manufacturing

作者： Musa, P. S. Ramdass, K. R. Phuluwa, H. S. UNISA Johannesburg South Africa

ISBN: (纸本)9783031744815;9783031744822

The conversion department is in desperate need of increasing production while reducing input resources. The conversion department is a recently formed department inside company XYZ in South Africa, and it is currently moving from the introduction phase to the growth phase of its life cycle. The current productivity in the Converting department is a significant obstacle that impedes the growth of the South African-based factory and is unsuitable for long-term operations. Lead times that are a fraction of the takt time, staff and equipment utilization ranging from 40% to 70%, no standardised work, and the absence of standard operating procedures all have a detrimental impact on productivity and need the adoption of industrial engineering approaches. As a result, the study will use industrial engineering techniques such aswork-study, and ECRS (remove, combine, reorganize, simplify) to analyze the reasons that contribute to overall low productivity and strategies to improve it. To answer research questions, researchers used a pragmatic research philosophy to examine data based on these components. The researcher also conducted a literature review to validate current knowledge of manufacturing department productivity. The study's goal is to boost overall productivity and help the department navigate the growth-to-maturity phase of its life cycle.

关键词： Productivity Lean Six Sigma Work Study

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Leveraging GANs to Generate Synthetic Log Files for Smart-Troubleshooting in Industry 4.0 50

Leveraging GANs to Generate Synthetic Log Files for Smart-Tr...

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50th Euromicro conference on Software engineering and Advanced Applications

作者： Partovian, Sania Flammini, Francesco Bucaioni, Alessio Malardalen Univ Sch Innovat Design & Engn Vasteras Sweden Sigma Technol Informat Stockholm Sweden

ISBN: (纸本)9798350380279;9798350380262

In this paper, we tackle the challenge of generating synthetic log files using generative adversarial networks to support smart-troubleshooting experimentation. Log files are critical for implementing monitoring systems for smart-troubleshooting, as they capture valuable information about the activities and events occurring within the monitored system. Analyzing these logs is crucial for effective smart-troubleshooting, enhancing the overall efficiency, reliability, and security of smart manufacturing processes. However, accessing public log data is difficult due to privacy concerns and the need to protect sensitive information. Moreover, for the purpose of effective troubleshooting, it is essential to have datasets that include fault, error, and failure logs as well as standard logs. In recent years, synthetic log files have emerged as a promising solution to augment limited real-world datasets and facilitate the development and evaluation of anomaly detection techniques. Building on this concept of synthetic data, we have developed a specific log generation technique and dataset tailored for testing smart-troubleshooting techniques in heterogeneous connected systems environments, such as industrial cyber-physical systems and the internet of things. First, we propose a methodology that generates synthetic log files based on generative adversarial networks. Later, we instantiate this methodology using different Generative Adversarial Network implementations and present a validation and a comprehensive comparative analysis of their performance. Eventually, we provide a robust dataset for anomaly detection and threat analysis in cyberspace security. Based on the results of our comparison, CTGAN has shown superior performance in generating high-quality synthetic log files.

关键词： Generative Adversarial Network Synthetic data Log Files Industry 4.0 Smart-Troubleshooting

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