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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4631-4640 订阅

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Automated data-driven creation of the Digital Twin of a brownfield plant 26

Automated data-driven creation of the Digital Twin of a brow...

引用

26th IEEE international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Braun, Dominik Schloegl, Wolfgang Weyrich, Michael Univ Stuttgart Grad Sch Excellence Adv Mfg Engn GSaME Stuttgart Germany Siemens AG Digital Engn Nurnberg Germany Univ Stuttgart Inst Ind Automat & Software Engn Stuttgart Germany

ISBN: (纸本)9781728129891

The success of the reconfiguration of existing manufacturing systems, so called brownfield systems, heavily relies on the knowledge about the system. Reconfiguration can be planned, supported and simplified with the Digital Twin of the system providing this knowledge. However, digital models as the basis of a Digital Twin are usually missing for these plants. This article presents a data-driven approach to gain knowledge about a brownfield system to create the digital models of a Digital Twin and their relations. Finally, a proof of concept shows that process data and position data as data sources deliver the relations between the models of the Digital Twin.

关键词： Digital Twin brownfield manufacturing system ARENA2036 PLC data analytics RTLS

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Reinforcement Learning for Dependency-Aware Tasks Offloading in Digital Twin Edge Networks

Reinforcement Learning for Dependency-Aware Tasks Offloading...

引用

2023 international conference on Artificial Intelligence of Things and systems, AIoTSys 2023

作者： Xiong, Jiaxin Chen, Lingxiao Zhao, Liang Chang, Wei China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology Yichang China Saint Joseph's University Computer Science Department United States

ISBN: (纸本)9798350312270

Mobile Edge Computing (MEC) and Digital Twin (DT) technologies are crucial to meet the quality of service (QoS) of emerging applications. MEC sinks the processing power of the cloud to the edge and responds to users' requests in real time. DT can collect and analyze a large amount of data to make the optimal decision. However, the combination of MEC and DT is always ignored. Furthermore, it is more challenging to offload dependency-aware tasks efficiently. Therefore, this paper aims to present a more comprehensive survey of DT edge networks (DTEN). We first discuss prior knowledge about DTEN, such as its basic concepts, advantages, and application scenarios. Then, we highlight the research challenges for dependency-aware task offloading in recent years, including computation offloading, resource allocation, service caching, and edge collaboration. Furthermore, we introduce Reinforcement Learning (RL) for networking systems in detail. Finally, we conduct a typical dependency-aware task offloading case study to understand DTEN more deeply. © 2023 IEEE.

关键词： Reinforcement learning

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Causal knowledge in data Fusion Subject to Latent Confounding and Measurement Error

Causal Knowledge in Data Fusion Subject to Latent Confoundin...

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international conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Jingyi Yu Tim Pychynski Marco F. Huber Institute of Industrial Manufacturing and Management IFF University of Stuttgart and with Bosch Center for Artificial Intelligence Bosch Center for Artificial Intelligence Institute of Industrial Manufacturing and Management IFF University of Stuttgart and with the Fraunhofer IPA

ISBN: (数字)9798350368031

ISBN: (纸本)9798350368048

data fusion is the process of integrating data from multiple sources to produce more accurate and reliable information. It is often the case that data are subject to latent confounding and measurement error in real-world scenarios. In this paper, we evaluate fusion strategies based on different levels of contained causal knowledge to solve quality prediction under varied conditions of latent confounding and measurement error. We show that the machine learning-based fusion strategy achieves the best prediction quality when data are independent and identically distributed (i.i.d.). However, in the presence of latent confounding, the causality-based fusion strategy makes prediction models more robust against severe distribution shifts. Moreover, the out-of-distribution (OOD) generalizability of prediction models is also affected by measurement error in the data. If causal knowledge needs to be inferred from data by applying causal discovery methods, we demonstrate that measurement error can adversely impair causal discovery. We advocate that caution needs to be exercised when using standard causal discovery methods if the circumstances under which the data were generated are unknown.

关键词： Measurement errors Noise Closed box data integration Predictive models data models Sensors Reliability Standards Optimization

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A Comprehensive Analysis of knowledge Graph-based Social Recommendation System

A Comprehensive Analysis of Knowledge Graph-based Social Rec...

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Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Madhavilatha Navuluri Hemanta Kumar Bhuyan Department of Computer Science and Engineering RGUKT-IIIT Ongole India Department of Information Technology and Computer Applications Vignan's Foundation for Science Technology & Research (Deemed to be University) Guntur India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Recommendation systems are crucial due to their high relevance in terms of interpretability and performance. A Social Recommendation system explores how social relations influence user choices and how users select items. In this system, user-embeddings are learned from knowledge graphs to enhance the recommendations. The social recommendation system can be categorized into three main areas: user recommendation, movie recommendation, and explainable recommendation. User recommendation focusses on providing users with personalized services, movie recommendation enhances the movie-watching experience, and explainable recommendation aims to provide the reasoning behind the suggested items. data sparsity and the cold start problem are significant challenges faced by RS. The Social RS model addresses these issues by leveraging both user-item interactions and user-user social relationships, thereby improving the overall quality of social network-based recommendations. Methods such as Light Graph Convolutional Network (LGCN), Hyperbolic Recommender (HSR), and knowledge Enhanced Graph Neural Networks (KEGNN) have been proposed to enhance social recommendation performance. Evaluation metrics such as recall @N, precision @N are commonly used to assess and estimate the effectiveness of recommendation systems.

关键词： Measurement knowledge engineering Social networking (online) Graph convolutional networks Noise knowledge graphs Motion pictures data models Intelligent systems Recommender systems

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Anomaly detection for sensor data of semiconductor manufacturing equipment using a GAN 25

Anomaly detection for sensor data of semiconductor manufactu...

引用

25th KES international conference on knowledge-Based and Intelligent Information & engineering systems (KES)

作者： Hashimoto, Miki Ide, Yusuke Aritsugi, Masayoshi Sony Semicond Mfg Corp Data Sci Technol Sect 4000-1 Haramizu Kikuyo Kumamoto 8691102 Japan Kumamoto Univ Grad Sch Sci & Technol Chuo Ku 2-39-1 Kurokami Kumamoto 8608555 Japan Kumamoto Univ Fac Adv Sci & Technol Chuo Ku 2-39-1 Kurokami Kumamoto 8608555 Japan

In semiconductor manufacturing, it is required to detect anomalies which cause expensive defects. In recent years, Generative Adversarial Networks (GANs) have played a big role in anomaly detection. This study aims to detect anomalies by analyzing sensor data using a GAN when multivariate time series of sensor data are given. Our GAN could detect anomalies which cannot be detected visually. Experimental results indicated that an attention mechanism could tell us important sensors in detecting anomalies. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES international.

关键词： GAN anomaly detection sensor data semiconductor factory automation

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MOSE: A Monotonic Selectivity Estimator Using Learned CDF (Extended abstract)

MOSE: A Monotonic Selectivity Estimator Using Learned CDF (E...

引用

38th IEEE international conference on data engineering (ICDE)

作者： Sun, Luming Li, Cuiping Ji, Tao Chen, Hong Renmin Univ China Sch Informat Key Lab Data Engn & Knowledge Engn Minist Educ Beijing Peoples R China

The accuracy of selectivity estimation is of vital importance to create good query plans in database management systems. We propose MOSE, a learning-based MOnotonic Selectivity Estimator, to provide accurate, reliable... 详细信息

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

关键词： Adaptation models Costs Query processing Design methodology conferences Estimation Lattices

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A Critical Analysis of the Block Chain in manufacturing System Implementation

A Critical Analysis of the Block Chain in Manufacturing Syst...

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international conference on Advanced Computing and Communication systems (ICACCS)

作者： G Gokilakrishnan D Prabha S Srinivasan S Thirumurugaveerakumar Vigneshkumar M Anandakumar H Department of Mechanical Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu India Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamilnadu India Agriculture Engineering Nehru Institute of Technology Coimbatore Tamilnadu India Department of Mechanical Engineering Kumaraguru College of Technology Coimbatore Tamilnadu India Department of Mechanical Engineering Sri Krishna College of Engineering and Technology Coimbatore Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu India

As component of the industry 4.0 project, a novel concept of Cyber-Physical systems (CPS), fog computing, big data analytics, cloud manufacturing, the Internet of Things (IoT), and other technologies have been brought to the manufacturing sector. Some of the projected benefits and possibilities that these innovations might provide include self-prediction, self-maintenance, self-comparison, self-configuration, and self-awareness. In these concepts, the centralised corporate system and third-party trust operations are still in use. However, contemporary manufacturing has a slew of problems, including data security and reliability, adaptability, security, trust, and privacy. This paper provides an analysis of the Blockchain Technology (BT), reviews the application of blockchain in Artificial Intelligence (AI), proposes a model for blockchain-enabled CPS and discusses the potential challenges that face the implementation of blockchain in manufacturing systems.

关键词： Training data privacy Technological innovation Training data Cyber-physical systems Throughput Blockchains

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Dynamic data Management for Continuous Retraining 22

Dynamic Data Management for Continuous Retraining

引用

ACM/IEEE 25th international conference on Model Driven engineering Languages and systems (MODELS)

作者： Baumann, Nils Kusmenko, Evgeny Ritz, Jonas Rumpe, Bernhard Weber, Moritz Benedikt Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9781450394673

Managing dynamic datasets intended to serve as training data for a Machine Learning (ML) model often emerges as very challenging, especially when data is often altered iteratively and already existing ML models should pertain to the data. For example, this applies when new data versions arise from either a generated or aggregated extension of an existing dataset a model has already been trained on. In this work, it is investigated on how a model-based approach for these training data concerns can be provided as well as how the complete process, including the resulting training and retraining process of the ML model, can therein be integrated. Hence, model-based concepts and the implementation are devised to cope with the complexity of iterative data management as an enabler for the integration of continuous retraining routines. With Deep Learning techniques becoming technically feasible and massively being developed further over the last decade, MLOps, aiming to establish DevOps tailored to ML projects, gained crucial relevance. Unfortunately, data-management concepts for iteratively growing datasets with retraining capabilities embedded in a model-driven ML development methodology are unexplored to the best of our knowledge. To fill in this gap, this contribution provides such agile data management concepts and integrates them and continuous retraining into the model-driven ML Framework MontiAnna [18]. The new functionality is evaluated in the context of a research project where ML is exploited for the optimal design of lattice structures for crash applications.

关键词： model-driven engineering artificial intelligence data management retraining

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Development of an Automatic Shade Generation Through Canopy Deployment 15

Development of an Automatic Shade Generation Through Canopy ...

引用

15th IEEE international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Boncodin, Carl Patrick Q. Concepcion, Edwin S. Evaristo, Gier Bryant J. Ong, Bryce Erwin D. Del Rosario, Jay Robert B. Bandala, Argel A. De la Salle University Department of Electronics and Computer Engineering 2401 Taft Ave Metro Manila Metro Manila1004 Philippines

ISBN: (纸本)9798350381177

Sunlight and rain are mostly a blessing but sometimes, they can be a curse. There are objects that are intentionally left out in the Sun but there are some that should not be exposed to it, that goes the same way with rainwater. The system proposed in this study aims to deal with the aforementioned problem by implementing proper exposure to sunlight and rain or lack thereof using a PIC16F877A and variety of sensors such as LM35, HL-83 and LDR sensors to determine the current weather condition. The data from the sensors would be interpreted by the microcontroller using conditional statements and it dictates whether to rotate the DC motors connected to the canopy generating the shade may it be closed, half-open or fully open. The system proposed can also be overridden if wanted by the user through a series of switches. © 2023 IEEE.

关键词： Embedded systems

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Integrating Multiple knowledge-based Automation Methodologies into the A/MS IC Design Flow 20

Integrating Multiple Knowledge-based Automation Methodologie...

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20th international conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2024

作者： Prautsch, Benjamin Sommer, Ralf Scheible, Jürgen Eichler, Uwe Renner, Lorenz Moldenhauer, Till Schweikardt, Matthias Uhlmann, Yannick Institute for Integrated Circuits Fraunhofer IIS/EAS Division Engineering of Adaptive Systems Dresden Germany Ilmenau University of Technology IMMS GmbH Ilmenau Germany Reutlingen University Electronics & Drives Reutlingen Germany

ISBN: (纸本)9798350351927

Analog and mixed-signal (A/MS) IC design is still a largely manual process. It lags far behind its digital counterpart were synthesis methods automate many key design steps. This advantage in digital roots from the possibility of a high degree of abstraction with manageable information loss from which a highly systematic digital flow arose. Not only does it incorporate automation throughout, but it also links the individual flow steps by well-defined interfaces. In analog, to the contrary, both a comparable flow approach and automation is largely missing. Even though key design steps, such as schematic and layout, are meanwhile linked well, other important steps, like breaking down a specification to a suitable topology, are not yet in place. Therefore, design teams develop individual design approaches in order to handle the complexity of analog design all the way from specification to layout. A major challenge is the lack of reproducibility, which roots from the lack of expert knowledge stored in the design data. Thus, in this tutorial paper, we address the shortcomings of the A/MS design flow and discuss how knowledge-based automaton methods can both systematize and automate key design steps. Also, we discuss how the steps can be linked together in order to form a systematic design flow. Our proposed A/MS IC design design flow considers automation from specification over schematic level to the layout level. © 2024 IEEE.

关键词： Analog integrated circuits

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