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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111-120 订阅

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Lean 4.0 Deployment Case Studies in UK Industrial Companies: Lessons Learned 32nd

Lean 4.0 Deployment Case Studies in UK Industrial Companies:...

引用

32nd international conference on Flexible Automation and Intelligent manufacturing

作者： Queiroz, Geandra Alves Alves Junior, Paulo Nocera Mello, Isotilia Costa Univ Fed Lavras Sao Sebastiao Do Paraiso Brazil Univ Catolica Norte UCN Coquimbo Chile Univ Lorraine CEREFIGE ICN Business Sch Paris France

ISBN: (纸本)9783031381645;9783031381652

The current paper aims to fill this gap through case-based research, understanding from empirical data the application of Lean 4.0 in four UK companies. The findings showed that Lean 4.0 represents an excellent strategy to improve the manufacturing systems' performance, mainly productivity, flexibility, real-time data accessibility, and quality control. Also, itwas possible to confirm that financial resources, adversity to change, and process complexity are the main barriers to Lean 4.0.

关键词： Lean 4.0 Lean manufacturing Industry 4.0

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data Governance to Be a data-Driven Organization 16

Data Governance to Be a Data-Driven Organization

引用

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： Bassi, Carlos Alberto Alves-Souza, Solange Nice de Souza, Luiz Sergio Escola de Artes Ciências e Humanidades Universidade de São Paulo São Paulo Brazil Departamento de Engenharia de Computação e Sistemas Digitais Escola Politécnica Universidade de São Paulo São Paulo Brazil Faculdade de Tecnologia de São Paulo São Paulo Brazil

ISBN: (纸本)9789897587160

Many organizations have been trying to become data-driven which their business decisions, their relationships with customers / suppliers, the innovation of their products / services, the improvement in their performance and their growth are based on the collection and analysis of an increasing volume of data. To reach this level, organizations need to overcome a series of challenges related to the way they govern data and creating a data-driven culture. The main challenges to be overcome are directly related to data culture and the culture of the organization itself. These paper presents the results of a survey performed among 67 professionals with experience in data Governance (DG) in which was possible to identify the main challenges to establish a DG program and data-driven culture in organizations, besides priories actions to face these challenges. The challenges and necessary actions to implement a DG program are shown and discussed. Addressing these challenges is fundamental to raising the organization culture and maturity in DG and, consequently, becoming a data-driven organization. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： data assimilation

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Conceptual knowledge Modelling for Human-AI Teaming in data-Frugal Industrial Environments 1st

Conceptual Knowledge Modelling for Human-AI Teaming in Data-...

引用

1st international Joint conference on Conceptual knowledge Structures (CONCEPTS)

作者： Galetic, Vedran Sheldon, Dylan Nottle, Alistair Airbus Cent R&T Bristol Avon England

ISBN: (纸本)9783031678677;9783031678684

When AI interacts with humans in complex environments, such as aerospace manufacturing, safety of operation is of paramount importance. Trustworthiness of AI needs to be ensured through, among other things, explainability of its behaviour and rationale, which is typically a challenge for current deep neural network-based systems. We tackle the knowledge comprehensibility aspect of intrinsic explainability by suggesting a concept-level environment awareness model combining various complementary knowledge sources - statistical learning using dedicated property detectors through publicly available software, and crowd-sourced common-sense knowledge graphs. Our approach also addresses the issue of data-frugal learning, typical for environments with highly specific purpose-built artefacts. We adopt Gardenfors's Conceptual Spaces as a cognitively-motivated knowledge representation framework and apply our typicality quantification model in a use case on interpretable classification of manufacturing artefacts.

关键词： knowledge Representation Human-AI Teaming Embodied Intelligent Agents Trustworthy AI Conceptual Spaces Neuro-Symbolic Integration knowledge Comprehensibility Explainable AI data-frugal AI

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LSTM RNN in support of manufacturing Simulation-based Digital Twin;A Case from Offshore Wind Turbine Production 19

LSTM RNN in support of Manufacturing Simulation-based Digita...

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19th Annual international IEEE conference on System of systems engineering (SoSE)

作者： Nickpasand, Mehrnoosh Jensen, Steffen W. Gaspar, Henrique M. Siemens Gamesa Renewable Energy Dept Mfg Excellence Syst Brande Denmark Norwegian Univ Sci & Technol Dept Ocean Operat & Civil Engn Alesund Norway

ISBN: (纸本)9798350365924;9798350365917

This paper describes how the application of the long short-term memory recurrent neural network (LSTM RNN) model can support the simulation-based digital twin (Sim.-based DT) of a manufacturing system in predicting the new outcome of production upon a changed parameter in the manufacturing setup. Simulation of the assembly production flow, as the only comprehensive ground-truth of the flow, represents the sequence and multi-factor dependencies of the assembly processes (associated with the spatial and environmental parameters) and renders a realistic realization of the production process. manufacturing digital twin (DT) has adopted such virtualization capability of the simulation [1] to capture, identify, and analyze the real-time abnormalities during the flow and mitigate the risk alongside the process. But simulation software systems are impotent in handling heavy detailed graphics, inflexible (by hard-coded logic) for optimization scenarios, and unresponsive to real-time changing parameters. This paper proposes a dual-layer LSTM RNN model, backed by three Dense layers, to be trained by the production simulation, so to render the generative pattern of the flow behavior and the predicted production outcome when the determinant parameters are changed in real-life. The final model will be a part of a bigger proposal for a hybrid optimization model, which is under development and not covered by the scope of this paper. However, the stand-alone proposed LSTM RNN model in this paper has capability to enhance the functionality of the Sim.-based DT in response to the real-time variations in determinant parameters. With the use of data from offshore wind turbine (OWT) generator's production line in Siemens Gamesa Renewable Energy (SGRE) Nacelle factory in Denmark and the corresponding simulation, a pilot model has managed to predict the total number of operators and their allocation to the workstations when a tact time is changed. This study denotes the logic behind the co

关键词： Long short-term memory recurrent neural network (LSTM RNN) Simulation-based digital twin (Sim.-based DT) System of systems engineering (SOSE) Offshore wind turbine (OWT) manufacturing

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Anomalous behaviors detection of IoT devices through AI-based methodologies 7

Anomalous behaviors detection of IoT devices through AI-base...

引用

7th international conference of Computer and Informatics engineering, IC2IE 2024

作者： Asghari, Ali Bergantin, Fulvio Forestiero, Agostino MacRi, Davide Shafagh Institute of Higher Education Tonekabon Iran CNR-ICAR Cosenza Italy

ISBN: (纸本)9798331505530

The rapid growth of Internet of Things (IoT) devices across sectors such as healthcare, manufacturing, and smart cities has substantially increased data volume and complexity, necessitating robust anomaly detection systems to identify critical issues, including system failures, security breaches, and inefficiencies. Traditional anomaly detection methods often struggle with the high-dimensional and dynamic nature of IoT data. This paper introduces an AI-based approach that combines deep autoencoders and transfer learning to enhance anomaly detection in IoT device behaviors. The autoencoders effectively learn detailed representations of normal operational data, enabling the identification of anomalies through significant reconstruction errors. To address the challenges posed by diverse IoT environments and limited labeled anomaly data, a transfer learning mechanism is employed to transfer knowledge from data-rich domains to those with scarce labeled data, thereby improving model generalizability and reducing the need for extensive labeled datasets. The proposed approach was evaluated using the NbaIoT dataset, demonstrating superior performance compared to traditional methods, with values of more than 90% in terms of Precision, Recall, and F-measure. These results indicate that the autoencoder-based transfer learning approach offers a scalable, adaptable, and highly accurate solution tailored to the unique characteristics of various IoT domains. © 2024 IEEE.

关键词： Smart manufacturing

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

2024 International Conference on Knowledge Engineering and C...

引用

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

ISBN: (纸本)9798350359688

The proceedings contain 289 papers. The topics discussed include: employing multifaceted bioinformatics strategies for the discovery of novel ASK1 inhibitors targeting neurodegenerative disorders;sailfish optimizer algorithm for effective toxic gas detection sensor placement in IioT;a comprehensive study on satellite-based data communication for big earth observation systems;APIs insight for phenotype classification and hive health forecasting using IoT and deep learning;the future of teaching: exploring the integration of machine learning in higher education;person recognition using ear images based on fractional gannet sparrow optimization enabled deep learning;restaurant recommendation system using machine learning algorithms;and AI-driven remote Parkinson's diagnosis with BPNN framework and cloud-based data security.

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Developing a Reference OntoUML Conceptual Model for data Management Plans: Enhancing Consistency and Interoperability 16

Developing a Reference OntoUML Conceptual Model for Data Man...

引用

16th international conference on knowledge engineering and Ontology Development, KEOD 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Martínková, Jana Suchánek, Marek Pergl, Robert Faculty of Information Technology CTU in Prague Prague Czech Republic

ISBN: (纸本)9789897587160

The growing significance of data Management Plans (DMPs) has highlighted the need for standardized and accurate data management practices. Current DMPs often suffer from inconsistent terminology, leading to misunderstandings and reducing their effectiveness. This study proposes the development of a DMP OntoUML conceptual model to address these issues. The model aims to clearly define all relevant concepts and their relationships, ensuring consistency and interoperability, particularly by connecting with the FAIR principles OntoUML model. The research follows a structured approach: specifying necessary concepts using existing templates and ontologies, defining terms and their relationships within the OntoUML model, and verifying the model’s syntax. The resulting conceptual model will standardize terminology, promote interoperability, and support future DMP development and education. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： data consistency

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TYPICAL FAULT DIAGNOSIS MODEL OF NUCLEAR POWER PLANT COMBINED WITH knowledge DRIVEN AND data DRIVEN 31

TYPICAL FAULT DIAGNOSIS MODEL OF NUCLEAR POWER PLANT COMBINE...

引用

31st international conference on Nuclear engineering (ICONE) - 24/7 Clean Nuclear Energy - Solution for Global Warming

作者： Wang, Xin Peng, Minjun Wang, Hang Li, Zikang Harbin Engn Univ Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)9780791888308

In order to prevent potential radioactive leaks and maintain personnel safety, fault diagnosis in nuclear power plants is crucial. knowledge-driven and data-driven approaches are two predominant directions in nuclear power plant fault diagnosis. knowledge-driven approach relies on prior professional knowledge, rules and model for state monitoring and diagnosis, offering strong logic and interpretability. However, it often yields poor results in complex systems. On the other hand, data-driven approach utilizes black-box inference to establish the mapping relationship between complex system characteristics and fault modes. However, this approach lacks interpretability in its operation mechanism, and it is deemed unacceptable for high-security industries such as nuclear power. To address these challenges, this paper introduces the concept of graph neural network and proposes an innovative idea of integrating knowledge-driven and data-driven approaches for fault diagnosis in nuclear power systems. The proposed method embeds the fault propagation path into the data-driven inference process by constructing a graph based on feature nodes and fault type nodes. Furthermore, we analyze how different methods of sampling features and aggregating data influence the diagnostic results during the process of inferring fault paths using graphs with corresponding fault labels. We implement and verify our scheme using a comprehensive simulation simulator for nuclear power devices. The results demonstrate that our method effectively carries out fault diagnosis by combining both interpretable graph-based inference with powerful nonlinear fitting capabilities offered by data-driven approaches.

关键词： knowledge driven data driven nuclear power plant fault diagnosis

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Framework for a knowledge-Based Course Recommender System Focused on IT Career Needs 16

Framework for a Knowledge-Based Course Recommender System Fo...

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作者： Xuan, Hien Pham Thi Nam, Le Nguyen Hoai Pham-Nguyen, Cuong Faculty of Information Technology University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789897587160

This paper presents an approach for a knowledge-based recommender system that provides relevant courses based on learners’ profiles, requirements, and career needs. The framework integrates an automatic data collection process, ensuring that the knowledge base reflects the latest job market and course information. The recommendation method relies on a set of rules that combine various matching techniques, incorporating user requirements, skill and knowledge gaps, contextual information, and the course weight indicating its relevance to the career or market. An experiment was conducted to measure the satisfaction of the approach through a survey of users who used the system. The results reveal that the approach is deemed acceptable. This framework contributes to ongoing discussions surrounding the application of technology in building recommender systems for education. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Course Recommendation Method data Collection Process knowledge Base

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An Index Bucketing Framework to Support data Manipulation and Extraction of Nested data Structures 16

An Index Bucketing Framework to Support Data Manipulation an...

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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： Myers, Jeffrey Mowafi, Yaser School of Engineering and Applied Sciences Western Kentucky University Bowling GreenKY United States

ISBN: (纸本)9789897587160

Handling nested data collections in large-scale distributed data structures poses considerable challenges in query processing, often resulting in substantial costs and error susceptibility. These challenges are exacerbated in scenarios involving skewed, nested data with irregular inner data collections. Processing such data demands costly operations, leading to extensive data duplication and imposing challenges in ensuring balanced distribution across partitions—consequently impeding performance and scalability. This work introduces an index bucketing framework that amalgamates upfront computations with data manipulation techniques, specifically focusing on flattening procedures. The framework resembles principles from the bucket spreading strategy, a parallel hash join method that aims to mitigate adverse implications of data duplication and information loss, while effectively addressing both skewed and irregularly nested structures. The efficacy of the proposed framework is assessed through evaluations conducted on prominent question-answering datasets such as QuAC and NewsQA, comparing its performance against the Pandas Python API and recursive, iterative flattening implementations. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： data collection

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