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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491-500 订阅

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Digitalizing Sustainability: Product Carbon Footprint with Green Digital Twins 29

Digitalizing Sustainability: Product Carbon Footprint with G...

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29th IEEE international conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Balduf, Florian Müller-Zhang, Zai Treichel, Tagline Kuhn, Thomas Fraunhofer Iese Embedded Systems Engineering Kaiserslautern Germany Fraunhofer Iese Digital Twin Engineering Kaiserslautern Germany Fraunhofer Iese Embedded Systems Kaiserslautern Germany

ISBN: (纸本)9798350361230

Digitalization plays an important role in improving the sustainability of manufacturing systems. Documenting and sharing the Product Carbon Footprint (PCF) improves traceability along the value chain. However, current standards do not define in detail to what extent digitalization is necessary for calculating the PCF and what information is required. To address this problem, we present a novel approach based on Green Digital Twins (GDT) to determine the product carbon footprint in the manufacturing domain. The data models of the GDT provide a modeling approach to represent the necessary activity data required for PCF calculation. The calculation models provide a practical solution for companies to calculate the PCF based on the digitalization degree of the manufacturing system. The data models are implemented using Asset Administration Shell and submodels. The evaluation demonstrates PCF calculation for batches of products with GDT during the setting process of a drying chamber. © 2024 IEEE.

关键词： Green manufacturing

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4th international conference on Intelligent systems in Production engineering and Maintenance , ISPEM 2023

4th International Conference on Intelligent Systems in Produ...

引用

4th international conference on Intelligent systems in Production engineering and Maintenance , ISPEM 2023

ISBN: (纸本)9783031450204

The proceedings contain 44 papers. The special focus in this conference is on Intelligent systems in Production engineering and Maintenance. The topics include: Information Design Management of Machining Parts on Metal Cutting Machines;Virtual Training at ELLA Platform – The Case Study;web Application for the Learning of Emotions in Children with Autism Spectrum Disorder;lean manufacturing knowledge Correlation Model;assessment of the Effectiveness of Six Sigma Methodology Implementation - A Literature Review;analysis of Basic Characteristics of Textile Yarn Using Image Processing Techniques;modeling the Impact of Leadership on Change Management Decisions Using System Dynamics Simulation;Using an ISO 9001 Based Framework as a Benchmark to Identify Best Practices Used by Sri Lankan Practitioners when Selecting Suppliers;requirements for Flow Control systems for Discrete Production of Mechanical Products;NIRF Imaging with Indocyanine Green (ICG) in a Veterinary Minimally Invasive Surgery;an Augmented System Based on Machine Learning for Boccia Assisted Gameplay;Ultrasound Tissue Differentiation (UTC) an Objective Diagnostic Tool in Prevention and Healing Monitoring Tendon Problems in Race Horses;location of Obstacles on Transport Routes in a Centralized Multi-agent System;converging data Mesh and Microservice Principles into a Unified Logical Architecture;Development and Comparative Analysis of IT Tools Supporting data Processing in Custom manufacturing of Orthoses;design of Experiments: An Overview and Future Paths;SOC Evaluation of Lithium-ion Battery Based on MFC Sensors;indicators of Hierarchical Structure Model of Supporting the Production Management Process – A Framework;gamification-Based Crowdsourcing as a Tool for New Product Development in manufacturing Companies.

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Peculiarities of ADDIE Implementation When Teaching engineering Students Professional Foreign Language Online 26th

Peculiarities of ADDIE Implementation When Teaching Engineer...

引用

26th international conference on Interactive Collaborative Learning (ICL) - Towards a Hybrid, Flexible and Socially Engaged Higher Education / 52nd IGIP international conference on engineering Pedagogy

作者： Mambetova, Roza Baltabaevna Semushina, Elena Yurievna Karakalpak State Univ Nukus Uzbekistan Kazan Fed Univ Kazan Russia

ISBN: (纸本)9783031526664;9783031526671

The purpose of the article is to present the use of instructional design ADDIE on the basis of the course "Professional foreign language" (English) worked out for the program "Translator in the sphere of professional communication" (Chemical engineers) which is operated online at technological university. The necessity of proper organization of workmakes it important to use the instructional design, which must be flexible as it can be necessary to change it quickly depending on the demands of the engineering industry and the interests of the graduates. The model of instructional design ADDIE is considered appropriate. The main peculiarity of it here is that the second stage "Design" is combined with the system of backward design, because the program has two forms of assessment: the final project and the standard exam. The final project is an example of interdisciplinary approach and can be strongly recommended as a part of ultimate assessment for advanced students who are selected and trained with the help of differentiated approach. Three learning paths (advanced, intermediate, basic) give the opportunity for students to demonstrate their skills and knowledge to potential employers from international companies when presenting the final project. Such methods of investigation are used: observation, analysis of previous experience and creative activity of the students, comparison of two forms of education, methods of data collection and accumulation, methods of control and measurement, and methods of implementing research results in pedagogical practice.

关键词： Instructional design ADDIE online study professional language English

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A design method of damage effect simulation platform 3

A design method of damage effect simulation platform

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

作者： Sun, Yong Zheng, Hao Pan, Jian Wu, Jiangpeng Lu, Guangzhao Huang, Junsong Yin, Guochuan Liu, Xiaorang China Research and Development Academy of Machinery Equipment Beijing China Xi'an Modern Control Technology Research Institute Xi'an China

ISBN: (纸本)9781510681804

This paper presents a design method of damage effect simulation platform, which can simulate the process of ammunition damaging the target, including the basic model of incoming ray simulation, the basic model of projectile hitting the target, the basic model of simulation of the distance between the projectile impact point and the target during indirect aiming firing, the basic model of damage armor target simulation, the basic model of terminal guidance projectile hitting the target in indirect aiming firing It is composed of the basic simulation model of the target exposed body and other models. © 2024 SPIE.

关键词： data modeling Design Instrument modeling Computer simulations Missiles Geodesy Defense and security Injuries Fire Performance modeling

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Temperature Drift Compensation of Light Scattering PM Sensor Based on BP Neural Network 18

Temperature Drift Compensation of Light Scattering PM Sensor...

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18th international conference on Intelligent systems and knowledge engineering, ISKE 2023

作者： Shen, Zhicong Niu, Junying You, Zongzheng Midea Refrigeration Equipment Co. Ltd R&D Department Foshan China Intelligent Manufacturing School Shunde Polytechnic Foshan China China Telecom Co. Ltd Department of Wireless Network Foshan China

ISBN: (纸本)9798350318401

As the core component of light scattering particulate matter (PM) sensor, laser diode is prone to temperature increase during operation, resulting in measurement errors. Thus, a backpropagation (BP) neural network-based temperature drift compensation algorithm is proposed to reduce the error. Simulation and experimentation illustrate the effectiveness of the proposed method in temperature drift compensation. © 2023 IEEE.

关键词： Light scattering

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Survey on data Ingestion for AutoML 35

Survey on Data Ingestion for AutoML

引用

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

作者： Mac’Hamilton Renaux Alves, Gabriel Maciel, Alexandre Magno Andrade Department of Computer Engineering University of Pernambuco Recife Brazil

Automated machine learning (AutoML) is an increasingly popular approach to building machine learning (ML) models without the need for extensive human intervention. One key component of AutoML is automated data ingestion, which involves automatically collecting, cleaning, and preparing data for use in ML models. This paper aims to analyze the literature in order to identify how automated data ingestion is being developed in the literature. To achieve this goal, a survey was conducted on the state-of-the-art of automated data ingestion using a method based on a systematic literature review, in order to identify the existing practices. A total of 12 articles were initially found, however, after applying filters, only six of them were ultimately utilized in the research, showing that visual data navigation and validation as well as metadata inference are important features for automated data ingestion focused in AutoML. © 2023 knowledge systems Institute Graduate School. All rights reserved.

关键词： Machine learning

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Optimization of Methods for Querying Formal Ontologies in Natural Language Using a Neural Network 16

Optimization of Methods for Querying Formal Ontologies in Na...

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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： Ondo, Anicet Lepetit Capus, Laurence Bousso, Mamadou Dep. of Computer and Software Engineering Laval University QuebecQCG1V 0A6 Canada Dep. Computer Science Iba Der Thiam University Thies967 Senegal

ISBN: (纸本)9789897587160

A well-designed ontology must be capable of addressing all the needs it is intended to satisfy. This complex task involves gathering all the potential questions from future users that the ontology should answer in order to respond precisely to these requests. However, variations in the questions asked by users for the same need complicate the interrogation process. Consequently, the use of a question-answering system seems to be a more efficient option for translating user queries into the formal SPARQL language. Current methods face significant challenges, including their reliance on predefined patterns, the quality of models and training data, ontology structure, resource complexity for approaches integrating various techniques, and their sensitivity to linguistic variations for the same user need. To overcome these limitations, we propose an optimal classification approach to classify user queries into corresponding SPARQL query classes. This method uses a neural network based on Transformer encoder-decoder architectures, improving both the understanding and generation of SPARQL queries while better adapting to variations in user queries. We have developed a dataset on estate liquidation and Python programming, built from raw data collected from specialist forums and websites. Two transformer models, GPT-2 and T5, were evaluated, with the basic T5 model obtaining a satisfactory score of 97.22%. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Structured Query Language

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Advanced Process Control in manufacturing Using IoT Devices and Artificial Neural Networks 4

Advanced Process Control in Manufacturing Using IoT Devices ...

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4th international conference on Sustainable Expert systems, ICSES 2024

作者： Sumathi, V. Ramesh, S. Basavaraddi, Chethan Chandra S. Visumathi, J. Ishwarya, M.V. Srinivasan, S. Sri Sairam Engineering College Department of Mathematics Tamil Nadu Chennai India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India R&d Don Bosco Institute of Technology Vtu Department of Artificial Intelligence and Machine Learning Karnataka Belagavi India Veltech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Agni College of Technology Artificial intelligence and Data science Department Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Optimization of industrial activities is significantly helped by Advanced Process Control (APC), which increases efficiency, decreases costs, and improves product quality. Artificial Neural Networks (ANNs) and the Internet of Things (IoT) have greatly improved APC in manufacturing. It examines how APC systems may include ANNs and IoT devices in industrial processes. By connecting to a wide range of sensors placed strategically throughout a production facility, IoT devices may gather data in real time, allowing for managing and monitoring crucial parameters. To APC systems, ANNs are invaluable for predictive modeling and control due to their adaptability to changing process conditions and capacity to represent complicated nonlinear interactions. Manufacturers may gain better control over their operations, improving productivity and product quality, by using data from the IoT to train and update ANN models. Recent advances in APC using IoT devices and ANNs are reviewed in this study. Their applications in various industrial industries are discussed, and case examples are shown to demonstrate their usefulness. Further improvements in APC for manufacturing using IoT devices and ANNs may be guided by highlighting the field's problems and future research goals. Challenges with data security, integration challenges, and reliability impact the accuracy of predictive modeling, scalability, and performance of IoT systems. © 2024 IEEE.

关键词： Adaptive control systems

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Research on english distance education network resource management system based on big data mining technology 3

Research on english distance education network resource mana...

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3rd international conference on Electronic Information engineering and data Processing, EIEDP 2024

作者： Huang, Huang Zhengzhou University of Science and Technology Henan Zhengzhou China

ISBN: (纸本)9781510680531

In recent years, the rise of distance education has transformed the traditional learning paradigm, with English learning being particularly emphasized as a crucial tool for global communication. However, effective management of online educational resources has become a bottleneck in enhancing the quality of remote English teaching. Leveraging big data mining technology, this study proposes a novel system for managing remote English education resources. Initially, a deep directed graph knowledge tracking model is introduced to analyze and predict learners' grasp of knowledge;this model classifies learners scientifically based on acquired knowledge and recommends corresponding learning paths. Subsequently, the study constructs a model centered around reinforcement learning neural networks, which dynamically adjusts educational content through real-time interaction with learners. This model integrates academic data, learning behaviors, and personal feedback to optimize the learning experience and effectiveness through training. Moreover, the designed system combines resource collection, analysis, and personalized recommendations to ensure the smoothness and efficiency of the management process. Quantitative experiments demonstrate that the proposed system significantly enhances learning outcomes and satisfaction, validating the effectiveness and potential of applying big data mining technology in the management of remote English education resources. The outcomes of this study foreshadow the immense potential and expansion room for data-driven management systems in the field of educational technology, particularly in remote English teaching. © 2024 SPIE.

关键词： Big data

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Multimodal knowledge Graph Completion Using CLIP-Enhanced Hyper-Node Relational Graph Attention Networks 5

Multimodal Knowledge Graph Completion Using CLIP-Enhanced Hy...

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5th international conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Shashank, R.B. Swami, Saurabh B. Pai, Akanksha A Mady, Pranamya Thomas, Sharon Moharir, Minal Rv College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350379945

knowledge graph (KG) completion is a critical task in Artificial Intelligence (AI) focused on deducing absent connections between entities. The diverse nature of data, encompassing text, images, and numerical information, introduces considerable difficulties in precisely forecasting these missing links. Multi-modal KGs are necessary to fully capture the diverse and rich information associated with entities, enhancing applications in search, recommendation, and AI. This study presents a new framework for completing multi-modal knowledge graphs that integrates textual, visual, and numerical data. Pre-trained Contrastive Language-Image Pre-training (CLIP) embeddings are leveraged for textual and visual information and a dense layer is utilized for numerical data, creating a unified entity representation through a low-rank multi-modal fusion approach. The proposed model employs a Hyper-node Relational Graph Attention Network (HRGAT) to effectively aggregate and process multi-modal information. The research shows that this new approach performs significantly better than current models in certain metrics, such as Mean Rank (MR), particularly excelling in lowering the mean rank of head entities, showcasing the enhanced capability and accuracy of the method proposed in completing multi-modal KGs. © 2024 IEEE.

关键词： knowledge graph

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