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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281-290 订阅

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Cloud-based Learning for Robot Control

Cloud-based Learning for Robot Control

引用

2023 IEEE international conference on Industrial Technology, ICIT 2023

作者： Harrigan, Shane Coleman, Sonya Kerr, Dermot Quinn, Justin Lindsay, Leeanne Madden, Kyle School of Computing Engineering and Intelligent Systems Ulster University Derry United Kingdom

ISBN: (纸本)9798350336504

This paper introduces the Virtual manufacturing Platform (VMP), a cloud-complete educational platform targeting manufacturing industries providing tutorials and sandboxing. The scope of this paper focuses on the robot control architecture of the platform which allows for seamless control of both virtual and physical robots and end-effectors accompanied by supportive educational materials. The platform seeks to de-risk automation investments for small to medium enterprises by offering confirmatory environments and knowledge transfer opportunities. © 2023 IEEE.

关键词： End effectors

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Pravega: Performance impact analysis with Connection Pooling

Pravega: Performance impact analysis with Connection Pooling

引用

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

作者： Ramesh, K.V. Raju, G.T. Visvesvaraya Technological University Airspan Networks Belagavi India SJC Institute of Technology Visvesvaraya Technological University Department of Computer Science and Engineering Belagavi India

ISBN: (纸本)9798350359688

In this paper we perform analysis on Pravega distributed streaming storage with and without connection pooling. When connection pooling is enabled, there will be a restriction of number of open connections for data transfer with Pravega cluster. Storage Benchmarking Kit is used to pump data of various sizes to the Pravega cluster running as docker containers with and without enabling connection pooling. The performance numbers are captured in CSV files which are then fed to sbk-charts which will produce simple charts which can be used for detailed analysis of each tests performed. All the tests are performed on two EC2 instances of type *** on AWS Cloud infrastructure. Performance impacts are analysed with the generated charts for each test result which leads us to conclude on the performance gap when applying connection pooling during client applications integrations with Pravega server. © 2024 IEEE.

关键词： Flowcharting

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Parameter optimization of slit width for fiber laser cutting of polyethylene films 3

Parameter optimization of slit width for fiber laser cutting...

引用

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

作者： Lan, Qi Shi, Guangfeng College of Electromechanical Engineering Changchun University of Science and Technology Jilin Changchun130022 China Changchun Institute of Electronic Science and Technology School of Electromechanical Engineering Jilin Changchun130114 China

ISBN: (纸本)9781510681804

In order to study the effect of laser cutting process parameters on the slit width of garbage bags made of polyethylene (PE) material, Box-Behnken test was designed to test the cutting of polyethylene film using a 30 W fiber laser marking machine. Measurement of the slit width of polyethylene films using a Nikon microscope was used to analyze the pattern of the effect of different process parameters of the laser marking machine (amount of defocus, cutting speed, and laser power) on the slit width. Process parameters are optimized based on regression equations and actual cutting results. Based on the analysis of the experimental data and the modeling results, it can be determined that the effect of laser power and cutting speed on the slit width of polyethylene film is significant and strong, while the effect of the amount of laser defocus is relatively weak and of secondary importance. Finally, a validation test was conducted to obtain an average error of 6.167% in the model predictions. © 2024 SPIE.

关键词： Laser materials processing

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ZeitGeist: A Generic Tool Supporting the Dissemination of Time Series data Following FAIR Principles 15

ZeitGeist: A Generic Tool Supporting the Dissemination of Ti...

引用

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： Schmidt, Andreas Koubaa, Mohamad Anis Schweikert, Jan Stucky, Karl-Uwe Süß, Wolfgang Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Department of Computer Science and Business Information Systems University of Applied Sciences Karlsruhe Germany

ISBN: (纸本)9789897586712

An important point for the widespread dissemination of FAIR-data is the lowest possible entry barrier for preparing and providing data to other scientists according to the FAIR criteria. If scientists have to manually extract, transform and annotate the data according to the FAIR criteria and then export it to make it available to the public, this requires a significant investment of time that does not primarily reward the scientist who prepares and provides the data. The Energy Lab at KIT is running a large cluster of an Influx database management system with energy related time series data being stored in a variety of individual databases over periods of up to 15 years. In order to increase the willingness to make data available to the scientific public, we develop a tool that greatly supports and automates the publication and annotation process of time series data stored in Influx databases. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： database systems

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Analysis of Power Transformers Monitoring systems

Analysis of Power Transformers Monitoring Systems

引用

2024 international conference on Industrial engineering, Applications and manufacturing, ICIEAM 2024

作者： Kubarev, A.Y. Usachov, A.E. Paramonov, M.A. Kazan State Power Engineering University Department Of Electric Station Kazan Russia Grid Company Department Of New Technology Kazan Russia

ISBN: (纸本)9798350395013

This article provides analysis data for Transformer Diagnostics Monitor (TDM) based Transformer and autotransformer monitoring systems manufactured by Dimrus, Russia. Some disadvantages and advantages of installing such systems on transformers and autotransformers are described. Some data on the characteristics of partial discharges and their relationships with other parameters reflecting the technical state of power transformers and autotransformers are also given. Estimates are also made of the parameters of partial discharges reflecting the insulation state of power transformers and autotransformers. In particular, a situation is periodically observed in which there is an intensive increase in the characteristics of partial discharges with a sharp increase in the humidity of the outside air. This factor can be attributed to an indirect diagnostic feature of separation of partial discharge sites. This is due to the fact that the change in the humidity of the outside air has nothing to do with the processes occurring inside the insulating structure of the transformer, and therefore has nothing to do with them. The increase in the intensity of partial discharges can be attributed to discharge processes occurring, for example, on the surface of transformer bushings. In general, it can be noted that the widespread use of monitoring systems for power transformers and autotransformers opens up prospects in obtaining a large amount of data on the technical condition of objects. © 2024 IEEE.

关键词： Partial discharges

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Synergizing Internal and External knowledge: Prompt engineering for Efficient and Effective Large Language Model Reasoning

Synergizing Internal and External Knowledge: Prompt Engineer...

引用

2024 IEEE international conference on systems, Man, and Cybernetics, SMC 2024

作者： Lu, Gewei He, Chaofan Shen, Liping Shanghai Jiao Tong University X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781665410205

Large language models (LLMs), such as ChatGPT, have demonstrated remarkable capability in question answering but face challenges when it comes to knowledge-based rea-soning, such as limited training data and hallucination. To address these challenges, integrating LLMs with knowledge graphs (KGs) has emerged as a promising solution. However, the cost associated with training and inference of LLMs is high. Our method integrates the Retrieval-Augmented Generation (RAG) paradigm, incorporating relevant information from KGs alongside the question to enhance LLMs' reasoning process without training. Moreover, we propose a novel concept of self-knowledge motivation to reduce the overhead of inference, which prompts LLMs to integrate retrieved information with their internal knowledge for reasoning before seeking additional queries to KGs. Experimental results showcase improvements in answer accuracy and a reduction in LLMs' API calls compared to the latest published state-of-the-art (SOTA) method employing an identical paradigm, underscoring the efficiency and effectiveness of our method. © 2024 IEEE.

关键词： Question answering

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Software Modeling Assistance with Large Language Models 24

Software Modeling Assistance with Large Language Models

引用

ACM/IEEE 27th international conference on Model Driven engineering Languages and systems: Companion Proceedings (MODELS)

作者： Ben Chaaben, Meriem Univ Montreal Montreal PQ Canada

ISBN: (纸本)9798400706226

Software modeling requires a challenging combination of expertise in both domain knowledge and modeling formalisms. Existing methods often fail to provide effective, general modeling assistance. This research introduces a novel approach using large language models (LLMs) to enhance software modeling. Utilizing few-shot prompt learning, our method supports various modeling activities without extensive training data. Initially focusing on static and behavioral formalisms like UML diagrams, we aim to extend this to other paradigms and integrate it into the Model-Driven engineering (MDE) pipeline. Additionally, we aim to assess productivity, model quality, and accuracy when receiving real-time, context-aware suggestions during modeling tasks.

关键词： Modeling formalisms generative AI large language models modeldriven engineering prompt learning user study

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Identifying Security Challenges in the Transition from Traditional to Smart manufacturing Through IIoT Retrofitting 24

Identifying Security Challenges in the Transition from Tradi...

引用

14th international conference on the Internet of Things, IoT 2024

作者： Comert, Mert Ahmed, Arslan Ahmed, Hafiz Nuclear AMRC The University of Sheffield Sheffield United Kingdom Nuclear AMRC The University of Sheffield Derby United Kingdom

ISBN: (纸本)9798400712852

The integration of Industrial Internet of Things (IIoT) sensors and devices into traditional manufacturing environments offers significant benefits in terms of efficiency, adaptability, and data-driven decision-making. However, the transition from traditional manufacturing systems to smart manufacturing systems, often achieved through retrofitting legacy systems, introduces new security risks that must be carefully addressed to ensure operational resilience. This paper explores the vulnerabilities and threats associated with retrofitting legacy systems with modern Industrial IoT technologies. By examining recent case studies and industry incidents, critical security gaps that emerge from the coexistence of legacy systems and contemporary IoT solutions are identified. These gaps include unauthorized access, data breaches, system manipulation. The research emphasizes the necessity of adopting a proactive cybersecurity approach that is well-suited to the specific vulnerabilities and risks associated with retrofitting traditional manufacturing systems. These measures include updated security protocols, enhanced device management practices, secure software updates, and ongoing system monitoring. By implementing these strategies, manufacturers can mitigate risks, protect their intellectual property, and maintain operational continuity. Outcome of the paper provide manufacturers with strategic insights and practical recommendations to safeguard the integrity and reliability of their smart manufacturing environments against evolving cyber threats. By addressing these security challenges proactively, organizations can realize the full potential of IIoT technologies while minimizing risks to their operations. © 2024 Copyright held by the owner/author(s).

关键词： Smart manufacturing

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data EFFICIENT MODELING FOR PREDICTION AND FORECASTING OF WAAM PROCESS CONDITIONS 19

DATA EFFICIENT MODELING FOR PREDICTION AND FORECASTING OF WA...

引用

ASME 19th international manufacturing Science and engineering conference (MSEC) held jointly with the 52nd North American Research conference (NAMRC)

作者： Thien, Austen Van Handel, Nicole Hu, Helen Saleeby, Kyle Saldana, Christopher Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA

ISBN: (纸本)9780791888100

Wire arc additive manufacturing (WAAM) utilizes conventional welding processes and wire-feedstock to deposit material layer by layer until a near net-shape geometry has been achieved. This approach gives WAAM great potential among metal additive manufacturing processes for its ability to produce larger scale components much faster than powder-based processes. The selection of certain initial WAAM process parameters, such as input power and interlayer dwell time, can often impact the stability of the contact-tip-to-workpiece distance (CTWD), current, voltage, and linear energy density during deposition. The resulting process stochasticity can cause variations in part geometry, microstructure, and mechanical properties. Therefore, it is desirable to create process models that can predict these WAAM process condition values for a variety of initial process parameter settings. These predictions can then be used to optimize initial WAAM process parameter settings or forecast when process condition instability is expected to occur. In the present work, single- bead multi-layer walls are deposited using a variety of input power and interlayer dwell time settings. Two different modeling approaches are then investigated: multilayer perceptron (MLP) regression and time series forecasting. An artificial neural network (ANN) is trained to predict process condition values based on the initial WAAM process parameters and a given time in the deposition process. Autoregressive integrated moving average (ARIMA) and longshort term memory (LSTM) time series models are trained to forecast subsequent process condition values based solely on the previous process condition values with no prior knowledge of the initial WAAM process parameters. The models are evaluated and compared based on their accuracy in predicting data streams of CTWD, current, voltage, and linear energy density. Additionally, the effect of training data quantity on model accuracy is examined for each model approach. F

关键词： wire arc additive manufacturing machine learning time series forecasting

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Validation of Digital Twins in Labor-intensive manufacturing:Significance and Challenges 15

Validation of Digital Twins in Labor-intensive Manufacturing...

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15th international conference on Ambient systems, Networks and Technologies Networks, ANT 2024 / The 7th international conference on Emerging data and Industry 4.0, EDI40 2024

作者： Zare, Ashkan Lazarova-Molnar, Sanja The Maersk Mc-Kinney Moller Institute University of Southern Denmark Campusvej 55 Odense5230 Denmark Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Kaiserstr. 89 Karlsruhe76133 Germany

Digital Twins allow for designing, operating, and optimizing running systems by replicating the operations of the physical components in a digital environment. Interconnectivity between Digital Twin models and corresponding real-world counterparts facilitates continuous examining of the actual system, resulting in nearly real-time analysis and decision-making support. Validation is an integral part of Digital Twins as underlying models must accurately reflect the corresponding physical systems according to predefined objectives. The near real-time nature of Digital Twins demands a continuous validation process to ensure models' accuracy. Labor-intensive manufacturing, where humans are at the heart of manufacturing processes, is a sector that encompasses a wide range of industries, from toys and apparel to medical devices and automotive components. This sector continues to play a vital role in emerging economies, offering employment opportunities that mitigate poverty and enhance social stability. Enabling Digital Twins for labor-intensive manufacturing systems opens many opportunities towards humancentricity and improvement of well-being of human operators. In these systems, however, human data must also be considered for Digital Twin development and the corresponding validation processes. Handling human data further complicates the creation and validation of Digital Twins. To the best of our knowledge, there has not been a comprehensive study on the validation of Digital Twins in labor-intensive manufacturing. In this paper, we review Digital Twin validation in manufacturing, focusing on systems that feature data from human operations. As a result, we outline the current challenges of validation of Digital Twins in labor-intensive manufacturing environments and suggest future research directions. © 2024 Elsevier B.V.. All rights reserved.

关键词： Decision making

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