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

共 37034 条记录，以下是601-610 订阅

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On Enhancing Legacy CNC Machine Tools for Process Monitoring and Cloud Integration in Industry 4.0 33rd

On Enhancing Legacy CNC Machine Tools for Process Monitoring...

引用

33rd international conference on Flexible Automation and Intelligent manufacturing

作者： Tzimas, E. Stathatos, E. Tsekos, I. Benardos, P. Vosniakos, G-C. Natl Tech Univ Athens Sch Mech Engn Mfg Technol Lab Heroon Polytehniou 9 Athens 15772 Greece

ISBN: (纸本)9783031744815;9783031744822

Industry 4.0 approaches in manufacturing rely on extensive data to create digital twins. Modern CNC machine tools can provide such data via built-in systems. However, legacy CNC machines, still prevalent in SMEs, lack these features. This paper focuses on enhancing legacy CNC machines towards process monitoring and integration into a cloud manufacturing ecosystem. The proposed methodology, demonstrated on a specific turning center, begins with instrumenting the machine and using a DAQ device to capture and synchronize analog signals e.g. for motor speed, torque and vibration. Next, a software application developed following a Service Oriented Architecture, operates on a single producer service to accumulate sensor data, group it into buffers, and distribute it in near-real time to other software components. These include a process monitoring service using a CNN to predict quality parameters, a bridge service to transmit streaming data via Server Sent Events and a web-based visualization interface.

关键词： Digital Transformation Signal Processing Instrumentation Legacy Machine Tools Cloud manufacturing

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DLT-Based Verifiable Receipt Credentials for Traceability in EV Battery Supply Chains 18

DLT-Based Verifiable Receipt Credentials for Traceability in...

引用

18th IEEE international conference on Intelligent and Service-Oriented systems engineering (CISOSE)

作者： Barman, Kaustabh Herbke, Patrick Janssen, Luca Safo, Elias Tech Univ Berlin Serv Centr Networking Berlin Germany Tech Univ Berlin Berlin Germany

ISBN: (纸本)9798331539580

In the dynamic realm of supply chain management, establishing trust and ensuring transparency are pivotal challenges, particularly in sectors like electric vehicle battery production. This paper introduces a Distributed Ledger Technology (DLT)-based framework utilizing chained Verifiable Credentials (VCs) to enhance traceability and reliability across the supply chain. By integrating smart contracts, our approach automates compliance and operational processes, facilitating real-time verification and fostering stakeholder trust. We present a scalable framework that answers three research questions about honest and accurate data entry by stakeholders, non-repudiation of asset data, and transactional event association with digital documents.

关键词： Distributed Ledger Technology Blockchain Supply Chain Management Electric Vehicle Batteries VCs IPFS Digital Traceability Escrow Smart Contracts (ESC) Decentralized Identifiers (DIDs) Sustainability in manufacturing

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Remaining useful life prediction of bearings under steady and varying operating conditions using a digital twin-based deep learning 31

Remaining useful life prediction of bearings under steady an...

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31st international conference on Noise and Vibration engineering, ISMA 2024 and 10th international conference on Uncertainty in Structural Dynamics, USD 2024

作者： Hosseinli, S.A. Ooijevaar, T. Gryllias, K. KU Leuven Department of Mechanical Engineering Celestijnenlaan 300 BOX 2420 Leuven3001 Belgium Flanders Make @ KU Leuven Belgium

ISBN: (纸本)9789082893175

Predicting the Remaining Useful Life (RUL) of rolling element bearings is crucial in condition monitoring, offering cost benefits by reducing unexpected downtimes. Digital Twins (DT) have recently gained attention for solving industrial problems. This study uses DT to address data scarcity in training deep-learning models for RUL estimation of bearings. The DT, based on a phenomenological model, mimics bearing vibration signals with defects. By calibrating DT with real measurements and simulating degradation dynamics, synthetic datasets are generated for model training. Synthetic data is used for training a deep-learning model, consisting of convolutional and fully connected layers, which incorporates speed and sequence information to improve RUL estimation under varying conditions. An unsupervised domain adaptation approach aligns synthetic and real data distributions, enhancing knowledge transfer. The proposed method's effectiveness is supported by experimental results, demonstrating the method's capability in practical applications. © 2024 Proceedings of ISMA 2024 - international conference on Noise and Vibration engineering and USD 2024 - international conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： Roller bearings

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Enhancing Misinformation Detection through Semantic Analysis and knowledge Graphs 4

Enhancing Misinformation Detection through Semantic Analysis...

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4th IEEE international conference on data engineering and Communication systems, ICDECS 2024

作者： Pillai, Sanjaikanth E Vadakkethil Somanathan University of North Dakota School of Electrical Engineering and Computer Science Grand Forks United States

ISBN: (纸本)9798350393354

Introducing improvements in technology has caused an exponential increase in the availability and consumption of virtual data. This has also given rise to the spread of misinformation, a problem that has significant consequences for both individuals and society. The ability to correctly locate and combat misinformation is vital in preventing its dangerous outcomes. Traditional methods of detecting misinformation, including truth-checking and source evaluation, have proven to be inadequate in the contemporary digital landscape. To address this problem, researchers have turned to advanced technology, including natural language processing and machine learning, to improve the accuracy and performance of misinformation detection. However, these techniques often depend on surface-level features, such as language patterns and metadata, which can be easily manipulated by those creating and disseminating misinformation. This quandary requires a deeper understanding of the underlying semantic meaning of the facts. Semantic analysis, a branch of natural language processing, focuses on understanding the meaning and context of language. By incorporating this method into misinformation detection, it is possible to go beyond surface-level features and analyze the underlying intent and meaning of the information. This could offer a much better and accurate approach for detecting misinformation. Moreover, the use of knowledge graphs can further enhance this approach by capturing and representing the relationships between entities, ideas, and information. © 2024 IEEE.

关键词： knowledge graph

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Recent Advances in the Application of IoT in Metallurgical Process 2

Recent Advances in the Application of IoT in Metallurgical P...

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2nd international conference on Multidisciplinary engineering and Applied Science, ICMEAS 2023

作者： Adeleke, Adekunle Akanni Anosike-Francis, Esther Nneka Ikubanni, Peter Pelumi Olosho, Adebayo Isaac Gamagira, Aisha Ahmad, Opeyemi Abdulsamad Ogedengbe, Temitayo Samson Samuel, Adeiza Avidime Nile University of Nigeria Abuja Mechanical Engineering Department F.C.T Nigeria Landmark University Mechanical Engineering Department Kwara State Omu-Aran Nigeria University of Ilorin Industrial Chemistry Department Ilorin Nigeria

ISBN: (纸本)9798350358834

This review article highlights recent advancements in applying the Internet of Things (IoT) to metallurgical processes. It focuses on the integration of Industry 4.0 technologies, adoption in emerging countries, additive manufacturing, and intelligent machining tools. IoT-enabled sensors, data analytics, and automation systems are discussed, emphasizing their real-Time monitoring and control capabilities for optimizing metallurgical operations. The article also explores the challenges and successful strategies for implementing IoT in emerging countries' metallurgical industries. In additive manufacturing, IoT facilitates real-Time monitoring, feedback control, and remote quality assurance. Additionally, IoT empowers intelligent machining tools with enhanced precision, reduced downtime, and increased productivity. This review contributes to the growing knowledge on leveraging IoT for improved productivity, efficiency, and quality in the metallurgical industry, promoting a sustainable and technologically advanced future. © 2023 IEEE.

关键词： Internet of things

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Design and development of equipment data management visualization system for intelligent KPI evaluation

Design and development of equipment data management visualiz...

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2023 international conference on Intelligent systems, Communications, and Computer Networks, ISCCN 2023

作者： Wu, Jiacheng Li, Pengzhong Department of Mechanical Engineering Tongji University Shanghai China

ISBN: (纸本)9781510666238

To address the problems of data acquisition and integration of heterogeneous equipment such as CK40 CNC lathe, KUKA industrial robot and Siemens PLC for logistics control, interconnection and interoperability between heterogeneous equipment and visual management of equipment data in the discrete manufacturing workshops, this paper proposes a data acquisition and integration method based on OPC UA and studies a data visualization and management method for equipment KPI. This study establishes an OPC UA information model based on the characteristics of discrete manufacturing heterogeneous equipment, develops an OPC UA aggregation server and client using the OPC UA open source library, and develops an equipment data management visualization system for intelligent KPI evaluation using Java. Finally, based on the field experiment platform, it is verified that the system is feasible in dealing with data acquisition and integration of heterogeneous equipment in discrete manufacturing workshops, equipment KPI evaluation, and data visualization. © 2023 SPIE. All rights reserved.

关键词： data integration

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25th international conference on Web Information systems engineering, WISE 2024

25th International Conference on Web Information Systems Eng...

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25th international conference on Web Information systems engineering, WISE 2024

ISBN: (纸本)9789819605699

The proceedings contain 132 papers. The special focus in this conference is on Web Information systems engineering. The topics include: TAKE: Tracing Associative Empathy Keywords for Generating Empathetic Responses Based on Graph Attention;intent Identification Using Few-Shot and Active Learning with User Feedback;CLIMB: Imbalanced data Modelling Using Contrastive Learning with Limited Labels;equivariant Diffusion-Based Sequential Hypergraph Neural Networks with Co-attention Fusion for Information Diffusion Prediction;CL3: A Collaborative Learning Framework for the Medical data Ensuring data Privacy in the Hyperconnected Environment;selectivity Estimation for Spatial Filters Using Optimizer Feedback: A Machine Learning Perspective;on Adversarial Training with Incorrect Labels;model Lake : A New Alternative for Machine Learning Models Management and Governance;a Benchmark Test Suite for Multiple Traveling Salesmen Problem with Pivot Cities;Deconfounded Causality-Aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs;Regularized Multi-LLMs Collaboration for Enhanced Score-Based Causal Discovery;Combining Uncensored and Censored LLMs for Ransomware Generation;Therapying Outside the Box: Innovating the Implementation and Evaulation of CBT in Therapeutic Artificial Agents;iText2KG: Incremental knowledge Graphs Construction Using Large Language Models;"Is this Site Legit?": LLMs for Scam Website Detection;Towards Enhancing Linked data Retrieval in Conversational UIs Using Large Language Models;BioLinkerAI: Capturing knowledge Using LLMs to Enhance Biomedical Entity Linking;Enhancing LLMs Contextual knowledge with Ontologies for Personalised Food Recommendation;ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources;Web-Based AI Assistant for Medical Imaging: A Case Study on Predicting Spontaneous Preterm Birth via Ultrasound Images;satellite-Driven Deep Learning Algorithm for Bathymetry Extraction;Would You Trust an AI Doc

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Longitudinal Analysis of Student Progress in AI-Integrated English Language Courses

Longitudinal Analysis of Student Progress in AI-Integrated E...

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

作者： Adlin Jerusha, J. Rajakumari, R. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of English Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350359688

This study offers a thorough longitudinal examination of student growth inside AI-infused English language programs, with an emphasis on students' viewpoints and experiences. The research follows students for a long time using a quantitative technique and an online poll to see how their views on AI integration and its effects on language learning results change. This study analyzes collected data to provide light on how students' views on AI tools have changed over time and how well they work to improve language skills. This research seeks to optimize the integration of AI technologies in language instruction by examining the junction of AI technology and language education. Practical implications for educators, curriculum creators, and legislators are offered by the results, which add to understanding the function of AI in facilitating language development. The overarching goal of this project is to improve language teaching approaches by shedding light on the most effective ways to use AI technology in language education. © 2024 IEEE.

关键词： Students

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knowledge Sharing Between Higher Educational Institutions: Evaluation of a Transfer Platform 15

Knowledge Sharing Between Higher Educational Institutions: E...

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15th international conference on knowledge Management and Information systems, KMIS 2023 as part of the 15th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2023

作者： Doering, Claudia Timinger, Holger Wolff, Christian Institute of Data and Process Science University of Applied Sciences Landshut Germany Faculty for Informatics and Data Science Media Informatics Group University of Regensburg Germany

ISBN: (纸本)9789897586712

The paper presents a platform for knowledge and technology transfer processes and relevant information for higher educational institutions (HEIs). Nowadays knowledge transfer or third-mission activities are daily business for universities. Nevertheless, they often lack internal standardized processes and procedures. Every HEI tends to establish their own way of efficiently handling transfer activities, without sharing their knowledge with other HEIs. The presented platform outlines standardized processes with knowledge from several HEIs, presented in digitized form. The processes and the platform were evaluated with use cases and a standardized user experience questionnaire (UEQ). The results presented in this contribution indicate a high need for and positive perception of the processes and the platform. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： knowledge management

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ASMix: An Attention-based Smooth data Augmentation Approach 35

ASMix: An Attention-based Smooth Data Augmentation Approach

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35th international conference on Software engineering and knowledge engineering, SEKE 2023

作者： Guo, Rongxi Jiang, Wanrong Zhao, Xi Cao, Ding Liu, Guiquan University of Science and Technology of China Hefei China

data augmentation through linearly interpolating inputs and modeling targets of random samples has significantly improved predictive performance. However, data augmentation based on linear interpolation generates semantically cluttered and ambiguous text, resulting in ineffective augmentation. To address these issues, in this paper, we propose a novel data augmentation approach called Attention-based Smooth data Augmentation (ASMix). ASMix accepts the smoothed embeddings of pairwise data predicted by a masked language model (MLM) instead of one-hot embeddings, which makes the inputs more informative and context-rich. We employ the attention mechanism to select discriminative and more-attentioned parts of text hidden representations and mix up the parts containing key semantics in the hidden representations of pairwise data through a multi-token replacement strategy to augment the data of the minority class, which greatly reduces redundant information in the representations that hurts the performance of the model. On several public imbalanced text classification benchmarks, ASMix outperforms state-of-the-art data augmentation methods. In minority classes, the performance improvement of ASMix is particularly prominent. © 2023 knowledge systems Institute Graduate School. All rights reserved.

关键词： Embeddings

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