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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331-340 订阅

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The Impact of Six Sigma Approach on Improvement Productivity in manufacturing Company 3rd

The Impact of Six Sigma Approach on Improvement Productivity...

引用

3rd international conference on Mechanical and Energy Technologies, ICMET 2023

作者： Agrawal, Rahul Kumar, Dilip Khan, Nausad Department of Mechanical Engineering Sanskriti University Uttar Pradesh Mathura281401 India Department of Mechanical Engineering Galgotias College of Engineering and Technology Knowledge Park—II Uttar Pradesh Greater Noida201013 India

ISBN: (纸本)9789819727155

Numerous factories worldwide have embraced Lean Six Sigma as a successful strategy to reduce waste and faults throughout production operations. The advantages and efficacy of implementing Lean Six Sigma project administration in the industrial field, taking into account its execution approaches. The Greater Emphasis on quality is one of the primary changes that have occurred in the sector as a result of the adoption of Methodical approaches. Previously, the sector frequently valued speed and efficiency over quality, resulting in a high percentage of defects and consumer complaints. Cycle time and lead time are two crucial measures used to monitor and control the effectiveness of the production process in the manufacturing sector. Six sigma methodology affected the reduction of lead and cycle times in a manufacturing process. The technique consisted of five basic steps: identifying the issue and the research scope, measuring the existing process, analysing the data, improving the process and managing the new process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cost reduction

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knowledge Graph Creation and Management Made Easy with KGraphX 35th

Knowledge Graph Creation and Management Made Easy with KGrap...

引用

35th international conference on database and Expert systems Applications (DEXA)

作者： Hemid, Ahmad Khiat, Abderrahmane Jayakumar, Megha Lange, Christoph Quix, Christoph Decker, Stefan Fraunhofer FIT Data Sci & Artificial Intelligence St Augustin Germany Fraunhofer IAIS Enterprise Informat Syst St Augustin Germany Univ Bonn Bonn Germany Hsch Niederrhein Krefeld Germany Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9783031683114;9783031683121

Tools for creating and managing knowledge graphs are a crucial ingredient of data management in various fields such as healthcare or manufacturing. knowledge graphs comprise entities, attributes, and relationships, which help machines understand the meaning of data and facilitate data sharing. Although knowledge graphs are a key concept for many applications, their creation remains complex and challenging, especially for novice users. To address this limitation, we have developed KGraphX, an easy-to-use visual editor to make knowledge graph creation for users with limited knowledge of Semantic Web technology more engaging. KGraphX uses visual elements, predictive typing, and augmented information from existing knowledge repositories to create knowledge graphs easily. In a task-based evaluation, we have proven the usability and quality of the created knowledge graphs using KGraphX. Compared to other tools, our tool significantly enhanced knowledge graph creation by 80%, while also improving its clarity by 91%.

关键词： RDF RDFS SPARQL Visualization knowledge Graph

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Enhanced Structural Image Classification using Hybrid CNN-GNN Model

Enhanced Structural Image Classification using Hybrid CNN-GN...

引用

2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Thangamariappan, P. Singh, Manish Arthi, M. Kannan, T. Saraswathy, S. K. S. Rangasamy College of Technology Computer Science and Engineering Tiruchengode637 215 India Sri Sai Ram Engineering College Computer Science and Business Systems Chennai600044 India

ISBN: (纸本)9798350359688

The availability of structural image data has significantly increased in recent years due to the widespread use of improved imaging techniques. The increase in the amount of data has required the creation of strong and effective classification algorithms to extract valuable information from these photos. Convolutional Neural Networks (CNNs) have exhibited exceptional efficacy in image classification tasks by capturing hierarchical characteristics. Graph Neural Networks (GNNs) have demonstrated potential in representing intricate connections within graph-structured data. This research presents a new hybrid model that combines the advantages of both CNNs and GNNs to improve the categorization of structural images. Our model combines the spatial locality acquired by CNNs with the relational information stored by GNNs to obtain better classification performance. We perform extensive experiments on standard structural picture datasets to showcase the efficacy of our suggested methodology. The findings demonstrate that our hybrid CNN-GNN model surpasses current approaches, underscoring its capacity to enhance structural picture categorization tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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An online customization platform for headphones based on additive manufacturing 3

An online customization platform for headphones based on add...

引用

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

作者： Xie, Liangjie Wang, Xubao Faculty of Materials and Manufacturing Beijing University of Technology Beijing China Beijing Engineering Research Center of 3D Printing for Digital Medical Health Beijing China Key Laboratory of Trans-scale Laser Manufacturing Technology Ministry of Education Beijing China

ISBN: (纸本)9781510681804

This article proposes a customized online production platform for headphones to solve the problems of discomfort and easy dropping caused by consumers wearing headphones in large quantities using traditional methods. Reverse modeling technology is used to obtain the scanning data of the ear nail cavity, and based on this, the ear machine online customization platform is used to achieve personalized customization of the headphones, and ultimately 3D printing technology is used to complete the production of the headphones. Use Python for secondary development on Geomagic Wrap, and combine it with SceneBuilder to draw a platform page to obtain a user-operable headphone customization platform. Customized headphone production mainly includes three parts: ear nail cavity scanning, parameterized platform modeling, and headphone printing. Using a 3D scanner Artec Space Spider scans the external ear to obtain 3D data of the ear cavity, model repair is performed on a customized platform, and customized earphone models are quickly generated. After conducting finite element simulation analysis on the ear earphone model, we studied the contact pressure between the earphone and the auricle and optimized the earphone parameters. We objectively evaluate the comfort of headphones based on the uniformity of pressure distribution and use this indicator as the goal of simulation optimization. © 2024 SPIE.

关键词： Earphones

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Battalogy: Empowering Battery data Management through Ontology-driven knowledge Graph 3

Battalogy: Empowering Battery Data Management through Ontolo...

引用

Joint of the 3rd international Workshop One knowledge Graph Generation from Text and data Quality Meets Machine Learning and knowledge Graphs, TEXT2KG 2024 and DQMLKG 2024

作者： Ismail, Muhamamd Fareedi, Abid Ali Nazir, Hammad Faculty of Computing Engineering and Sciences University of South Wales Treforest PontypriddCF37 1DL United Kingdom Halmstad University Halmstad Sweden

Developing a battery ontology to represent battery management knowledge is crucial in the new sustainable and green energy era. As battery production revenue is projected to exceed 300 billion US dollars annually by 2030, researchers are exploring new battery materials, models, standards, and manufacturing processes. AI and ML methods are being employed to manage battery manufacturing and enhance performance. data representation techniques and formats are important for enhancing the expressiveness of battery data and improving battery quality. This paper presents an ontology for creating a battery knowledge graph to address data interoperability challenges and share battery data among different actors. The battery ontology includes various types of knowledge, such as domain knowledge, battery applications, and core battery-specific knowledge. The ontology was evaluated through competency questions and usability tests. It aims to enhance battery production and design by facilitating efficient communication and data exchange between battery management systems and applications. This research has significant societal, economic, and environmental impacts as it contributes to developing more efficient and sustainable batteries. © 2024 Author.

关键词： knowledge graph

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Decoding AI’s Evolution Using Big data: A Methodological Approach 16

Decoding AI’s Evolution Using Big Data: A Methodological Ap...

引用

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： Gvasalia, Sophie Pelucchi, Mauro Perego, Simone Porcelli, Rita Global Data Science Lightcast Italy INAPP – Istituto per l’Analisi delle Politiche Pubbliche Italy

ISBN: (纸本)9789897587160

This study presents a novel approach to measuring the impact of Artificial Intelligence on occupations through an analysis of the Atlante del Lavoro dataset and web job postings. By focusing on data preparation and model selection, we provide real-time insights into how AI is reshaping job roles and required skills. Our methodological framework enables a detailed examination of specific labour market segments, emphasizing the dynamic nature of occupational demands. Through a rigorous mixed-method approach, the study highlights the AI impact on sectors such as ICT, telecommunications, and mechatronic, revealing distinct skill clusters and their significance. This innovative analysis not only delineates the convergence of digital, soft, and hard skills but also offers a multidimensional view of future workforce competencies. The findings serve as a valuable resource for educators, policymakers, and industry stakeholders, guiding workforce development in line with emerging AI-driven demands. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Artificial Intelligence Impact Big data Methodology Job Market Analysis

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Iterative LLM Prompting for Intelligent Cockpit knowledge Graph Construction 4

Iterative LLM Prompting for Intelligent Cockpit Knowledge Gr...

引用

4th international conference on Computer systems, ICCS 2024

作者： Dong, Haomin Wang, Wenbin Sun, Zhenjiang Kang, Ziyi Ge, Xiaojun School of Mechanical and Aerospace Engineering Jilin University Research Institute China FAW Group Co. Ltd. Changchun China Research Institute China FAW Group Co. Ltd. Changchun China School of Automotive Engineering Jilin University Changchun China

ISBN: (纸本)9798350366341

With the development of digital and intelligent cockpits, the data within these environments exhibit complexity and diversity, leading to issues such as inefficient data processing and difficulty in information extraction. Consequently, effectively capturing and representing the hidden associative knowledge in cockpits is crucial. Against this backdrop, knowledge graphs serve as an effective tool capable of retrieving and organizing a vast amount of information within a connected and interpretable structure. By systematically representing complex data relationships in the cockpit, they help enhance the prediction of precise interaction intentions and provide richer and more relevant knowledge support for personalized recommendations. However, rapidly and flexibly generating domain-specific knowledge graphs still poses certain challenges. This paper introduces an innovative method of knowledge graph construction using generative large language models, implementing a novel iterative zero-shot and domain-agnostic strategy. We propose an innovative strategy of iteratively prompting large language models to extract relevant triples for constructing knowledge graphs, effectively addressing key challenges such as entity recognition ambiguity and relationship extraction complexity in cockpit data. Experiments conducted in a domain-specific dataset demonstrate the feasibility of this method. © 2024 IEEE.

关键词： knowledge graph

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A Unified Framework to Classify Business Activities into international Standard Industrial Classification through Large Language Models for Circular Economy

A Unified Framework to Classify Business Activities into Int...

引用

2024 IEEE international conference on Industrial engineering and engineering Management, IEEM 2024

作者： Li, Xiang Zhao, Lan Ren, Junhao Sun, Yajuan Tan, Chuan Fu Yeo, Zhiquan Xiao, Gaoxi School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Singapore Singapore

ISBN: (纸本)9798350386097

Effective information gathering and knowledge codification are pivotal for developing recommendation systems that promote circular economy practices. One promising approach involves the creation of a centralized knowledge repository cataloging historical waste-to-resource transactions, which subsequently enables the generation of recommendations based on past successes. However, a significant barrier to constructing such a knowledge repository lies in the absence of a universally standardized framework for representing business activities across disparate geographical regions. To address this challenge, this paper leverages Large Language Models (LLMs) to classify textual data describing economic activities into the international Standard Industrial Classification (ISIC), a globally recognized economic activity classification framework. This approach enables any economic activity descriptions provided by businesses worldwide to be categorized into the unified ISIC standard, facilitating the creation of a centralized knowledge repository. Our approach achieves a 95% accuracy rate on a 182-label test dataset with fine-tuned GPT-2 model. This research contributes to the global endeavour of fostering sustainable circular economy practices by providing a standardized foundation for knowledge codification and recommendation systems deployable across regions. © 2024 IEEE.

关键词： Recommender systems

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Digital Twin System for River Network Based on Graph Neural Network 36

Digital Twin System for River Network Based on Graph Neural ...

引用

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

作者： Jin, Zishuo Ye, Feng Zhang, Peng Hohai University NanJing China Jiangsu water Conservancy Engineering Planning Office NanJing China

ISBN: (纸本)1891706594

River network management presents a significant challenge in the field of water conservancy, and the subsequent advancements in digital technology offer a promising avenue for addressing this challenge. However, the utilization of cutting-edge technologies like digital twin and deep learning within the water conservancy domain remains largely experimental, hampered by the absence of applicable models, authentic scenarios, and viable solutions. To address this, we propose a river network prediction and response system based on digital twin. This system leverages an improved graph attention network to discern spatial correlations within the detection data of individual sites, integrating bidirectional long short-term memory neural network for predictive analysis. Concurrently, we have developed a suite of dynamic visualization tools designed to streamline real-time situation analysis for users. Through the establishment of an interactive digital twin river network system, we furnish a fresh strategic framework for addressing water conservancy challenges. © 2024 knowledge systems Institute Graduate School. All rights reserved.

关键词： Rivers

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Information Modeling for Digitalized Sustainability Assessment in manufacturing

Information Modeling for Digitalized Sustainability Assessme...

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2024 IEEE international conference on Industrial engineering and engineering Management, IEEM 2024

作者： Schneider, D. Macanás Azcona, C. Woerle, M. Reinhart, G. Boltzmannstrasse 15 Garching near Munich85748 Germany

ISBN: (纸本)9798350386097

Amid growing pressures for sustainable operations, the manufacturing industry faces methodological, knowledge-related, and organizational challenges in employing existing Life Cycle Assessment (LCA) tools effectively. Addressing LCA’s limitations related to static data and complex system boundaries, this paper presents an information modeling framework designed to enhance LCA applications. The study adopts a systematic approach using Unified Modeling Language (UML) to organize and visualize LCA data efficiently, based on the ecoinvent database. This framework is prototypically implemented and tested in an industrial use case involving the assembly of video surveillance cameras, demonstrating its capability to support dynamical assessments of sustainability performance. Aiming at bridging LCA with advanced digital technologies that are based on information models and interfaces, this framework proposes a concept for more accurate and adaptive sustainability evaluations in manufacturing, offering a pathway towards more informed and responsive environmental management. © 2024 IEEE.

关键词： Life cycle assessment

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