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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4761-4770 订阅

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ICISE 2022 - 2022 7th international conference on Information systems engineering

ICISE 2022 - 2022 7th International Conference on Informatio...

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7th international conference on Information systems engineering, ICISE 2022

ISBN: (纸本)9781450397889

The proceedings contain 13 papers. The topics discussed include: a proposed approach to crowd selection in crowdsourced requirements engineering for mobile apps;swift search an open-source search engine;canvas mobile application for English language learning through corrective feedback;ant colony optimization algorithm in the design of university subject teaching platform;a systemic big data framework for the charging pile business;gain property and data analysis for diagnosing failures in a high-efficiency induction motor;crisis impact on use of technology. evidence from omnichannel restaurant sales during COVID-19 pandemic;point cloud scene reconstruction based on multi-planar fitting;research on the competency framework of management accounting profession in the age of ‘great wisdom moving cloud’ of China based on knowledge graph;and understanding intention to use Netflix in Taiwan: integrating perceived value into the technology acceptance model.

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High-Speed Deformation Prediction in Selective Laser Melting Using Context-Adaptive Neural Networks 23

High-Speed Deformation Prediction in Selective Laser Melting...

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23rd IEEE international conference on Machine Learning and Applications, ICMLA 2024

作者： Vandecasteele, Mathieu Iuso, Domenico Nasab, Milad Hamidi Verhees, Dries Sanctorum, Joaquim Nourazar, Mohsen Bey-Temsamani, Abdellatif Booth, Brian G. TELIN-IPI Ghent University - imec GentB-9000 Belgium University of Antwerp Imec-Vision Lab AntwerpenB-2610 Belgium Ku Leuven MaPS Department of Mechanical Engineering LeuvenB-3001 Belgium Flanders Make LommelB-3920 Belgium

ISBN: (纸本)9798350374889

In selective laser melting (SLM), the 3D-printed metal objects can deform, causing dimensional inaccuracies and potentially damaging the printer's recoater. While layerwise monitoring systems are available to detect these deformations, higher-speed detection would enable a controller to intervene to reduce this defect. In this work we propose the first such in-situ monitoring system that detects part deformations through the analysis of melt pool images. The system uses high-speed cameras with frame rates up to 20,000 frames per second in the visual and short-wave infrared spectrum to record the melt pool. An adaptive weight network is then used to predict deformation magnitudes based on the image data and contextual printing information such as part geometry and hatching patterns. The addition of this contextual information allows the model to normalize for within-layer printing variations when interpreting the image data. Our model was validated on three print ob-jects that display deformations resulting from different physical sources. Our model's predictions were compared to deformation measurements taken from X-ray CT scans and showed both a strong correlation with the ground truth, with average relative errors between 17 % and 22%, and generalized well to untrained deformation types. The prediction model is also able to operate at very high speeds (> 20 kHz) to facilitate high-speed intra-layer control loops. © 2024 IEEE.

关键词： Selective laser melting

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Towards data-Driven Reliability Modeling for Cyber-Physical Production systems

Towards Data-Driven Reliability Modeling for Cyber-Physical ...

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12th international conference on Ambient systems, Networks and Technologies (ANT) / 4th international conference on Emerging data and Industry 4.0 (EDI40)

作者： Friederich, Jonas Lazarova-Molnar, Sanja Univ Southern Denmark Maersk Mc Kinney Moller Inst Odense Denmark

Reliability is one of the most important performance indicators in contemporary production facilities. Increasing reliability of manufacturing systems results in their prolonged lifetimes, and reduced maintenance and repair costs. Reliability modeling is a common technique for deriving reliability measurements and illustrating relevant fault-dependencies. There is a significant body of research focusing on hardware- and software reliability models, such as Fault Trees, Petri Nets and Markov Chains. Up until now, development of reliability models has been a labor-intensive and expert-knowledge-driven process. To remedy that, through the prevalence of data stemming from the new and technologically advanced manufacturing systems, we propose that data generated in modern manufacturing lines could be used to either automate or at least to support development of reliability models. In this paper, we elaborate on the details of our proposed framework for data-driven reliability assessment of cyber-physical production systems. We, furthermore, introduce a case study that will aid the development and testing of the proposed novel data-driven approach. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Cyber-Physical Production systems Reliability Analysis data-Driven Reliability Modeling

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Anomaly Ranking of Failure Causes in manufacturing Process Using Causal Model

Anomaly Ranking of Failure Causes in Manufacturing Process U...

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IEEE international conference on systems, Man, and Cybernetics (SMC)

作者： Hattori, Reiko Ota, Yuya Fujii, Toru Nakajima, Hiroshi OMRON Corp Technol & Intellectual Property HQ Kyoto Japan

ISBN: (纸本)9781665442077

Identifying causes of unknown failures during the manufacturing process is quite difficult because of the uncertainty nature of the failures. As the manufacturing process becomes more complicated, failure-cause identification becomes more time consuming due to the increased number of causal candidates to be checked when addressing anomalies, and the complicatedly intertwined factors are difficult to discern. To solve these problems, various methods of anomaly detection and ranking have been developed using sensor data gathered during the manufacturing process. However, once an anomaly occurs at any point in the process, it propagates to other steps. Conventional anomaly detection methods are not sufficient against the complicated and unpredictable anomalies. In this study, we developed a novel anomaly ranking method based on a causal model. It employs a fusion model of human knowledge and sensor data to acquire a suitable causal model for accurate ranking. Experiments were conducted on a real packaging machine, the results of which showed a roughly 30% reduction of the ranking average compared with that of the conventional methods.

关键词： Anomaly detection

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ML-Quadrat & Driotdata: A Model-Driven engineering Tool and a Low-Code Platform for Smart loT Services 22

ML-Quadrat & DriotData: A Model-Driven Engineering Tool and ...

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44th ACM/IEEE international conference on Software engineering - New Ideas and Emerging Results (ICSE-NIER)

作者： Moin, Armin Mituca, Andrei Challenger, Moharram Badii, Atta Gunnemann, Stephan Tech Univ Munich TUM Dept Informat Munich Germany DriotData UG Munich Germany Univ Antwerp Dept Comp Sci Antwerp Belgium Flanders Make Lommel Belgium Univ Reading Dept Comp Sci Reading Berks England TUM Dept Informat Munich Germany TUM Munich Data Sci Inst Munich Germany

ISBN: (纸本)9781665495981

In this paper, we present ML-Quadrat, an open-source research prototype that is based on the Eclipse Modeling Framework (EMF) and the state of the art in the literature of Model-Driven Software engineering (MDSE) for smart Cyber-Physical systems (CPS) and the Internet of Things (IoT). Its envisioned users are mostly software developers who might not have deep knowledge and skills in the heterogeneous IoT platforms and the diverse Artificial Intelligence (AI) technologies, specifically regarding Machine Learning (ML). ML-Quadrat is released under the terms of the Apache 2.0 license on Github(l). Additionally, we demonstrate an early tool prototype of Driotdata, a web-based Low-Code platform targeting citizen data scientists and citizen/end-user software developers. Driotdata exploits and adopts ML-Quadrat in the industry by offering an extended version of it as a subscription-based service to companies, mainly Small- and Medium-Sized Enterprises (SME). The current preliminary version of Driotdata has three web-based model editors: text-based, tree-/form-based and diagram-based. The latter is designed for domain experts in the problem or use case domains (namely the IoT vertical domains) who might not have knowledge and skills in the field of IT. Finally, a short video demonstrating the tools is available on YouTube: https://***/VAuz25w0a5k.

关键词： model-driven software engineering low-code domain-specific modeling machine learning iot

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Sign Language to Text Classification using One-Shot Learning 2

Sign Language to Text Classification using One-Shot Learning

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2nd international conference on Sustainable Computing and data Communication systems, ICSCDS 2023

作者： Pal, Sourav Ruhella, Sourabh Sinha, Sparsh Goel, Anurag Delhi Technological University Dept. of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781665491990

Sign language is utilized by deaf individuals to communicate with each other. Sign languages, simply put are signs and gestures made by hand and are currently being used by approximately 80 million people around the world. The limited knowledge of sign languages in the hearing-abled community makes it difficult for them to interact with the deaf or mute community leading to the marginalization of the latter. Sign-to-text classification is a field of research that aims to convert sign language into written text, providing a communication bridge between the deaf-mute communities and the hearing population. However, there are some limitations in the current state of the field, such as the absence of datasets that are publicly available for sign language in many languages, and most of the existing models are trained in American Sign Language (ASL) which makes it difficult for researchers to train and evaluate their methods on other sign languages. Additionally, the ASL-trained models may generalize poorly to other sign languages, as they are heavily tuned to the specific dataset of ASL signs. The current models are also not best suited to incorporate any new additions to the sign language as it requires to train the model all over again. The proposed model makes use of one-shot learning, a new-age machine learning technique that allows the model to learn and recognize using limited training data, to identify and classify hand signs and convert them into corresponding text. The ability to learn from a very small dataset makes the model easily trainable and expandable in any sign language of choice. The proposed model has been trained on ASL and Indian Sign Language (ISL) datasets and achieved accuracy of 90.03% and 91.25% respectively. © 2023 IEEE.

关键词： Image classification

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Cost-minimized and Multi-plant Scheduling in Distributed Industrial systems

Cost-minimized and Multi-plant Scheduling in Distributed Ind...

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2022 IEEE international conference on systems, Man, and Cybernetics, SMC 2022

作者： Yuan, Haitao Hu, Qinglong Bi, Jing Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beijing University of Technology Faculty of Information Technology Beijing100124 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

As a new paradigm, industrial Internet provides information sharing of various elements and resources in a whole industrial production process. It makes industrial production processes intelligent and provides low-cost and efficient scheduling. manufacturing planning for multi-plant enterprises in industrial Internet brings many big challenges due to numerous optimization variables and limits of manufacturing capacities of plants, production resources, etc. Current studies fail to jointly consider the cost of different products in multiple heterogeneous plants, and ignore machine-level scheduling of manufacturing tasks. This work designs an improved framework for multi-plant enterprises, based on which a constrained non-linear integer program for reducing the total cost including production cost and transportation one is formulated. It jointly considers many complex nonlinear constraints, e.g., limits of replacement times, storage space, substitution and pairing production. It investigates machine-level task scheduling where different machines have heterogeneous manufacturing capacities. To solve it, this work proposes an algorithm named Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO). Realistic data-based experiments demonstrate GSPSO reduces the cost of a multi-plant system by at least 23% than its typical peers. © 2022 IEEE.

关键词： Simulated annealing

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CellFormer: A DaViT Transformer-Based Method for Nuclei Segmentation

CellFormer: A DaViT Transformer-Based Method for Nuclei Segm...

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2023 RIVF international conference on Computing and Communication Technologies, RIVF 2023

作者： Pham, Viet-Tien Vu, Ngoc-Tu Pham, Van-Truong Tran, Thi-Thao School of Electrical and Electronic Engineering Hanoi University of Science and Technology Department of Automation Engineering Viet Nam

ISBN: (纸本)9798350315844

Automated analysis of microscopic pictures in computer-aided clinical support systems has difficulty iden- tifying cell nuclei inside nuclei. Models based on the U- Net network, its derivatives or transformers, and their combinations have been proposed to segment cell nuclei, with positive results. However, these techniques omitted a number of details vital to simulating the regional appearance of the cell nucleus or missing a high-level representation of features for location dependence during decoding. For overcoming these limitations, the CellFormer network has been recommended in this research. With CellFormer, an encoder-decoder architecture, both the encoder and the decoder branches may mimic a long-range knowledge of semantics. The encoder is a compact architecture built on transformers that describe global semantic linkages at different data levels. An AAMC module that we suggested along with the design to learn advanced features to enhance the feature representation is used by the decoder, which is an extended network topology, to convey data from the decoder. In order to improve the boundaries of nuclear items on the global map for precise segmentation, an MSFB block with a novel bypass connection mechanism has been introduced. Through cell segmentation datasets such as GlaS and data Science Bowl 2018, we assess the performance of this suggested architecture. The findings were far better than we had anticipated in the area of biomedical image segmentation. © 2023 IEEE.

关键词： Semantics

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Usability Study of a Virtual Reality-Based Ecological Battery for Memory Assessment: A Pilot Study 3

Usability Study of a Virtual Reality-Based Ecological Batter...

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Mancuso, Valentina Arlati, Sara Saritas, Selen Naz Borghesi, Francesca Di Santo, Simona Gabriella Cavallo, Marco Pedroli, Elisa Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing - Stiima National Research Council of Italy - Cnr Italy University of Turin Department of Psychology Turin Italy Irccs Fondazione Santa Lucia Department of Clinical and Behavioral Neurology Roma Italy ECampus University Faculty of Psychology Novedrate Italy Irccs Istituto Auxologico Italiano Department of Geriatrics and Cardiovascular Medicine Milan Italy

ISBN: (纸本)9798350378009

The reliability and validity of conventional neuropsychological assessments have been called into question because they have a narrow ecological perspective. Traditional paper and pencil approaches frequently fail to accurately simulate real-world situations, which can result in possible misunderstandings of cognitive abilities. Virtual Reality and 360° videos offer potential remedies to these restrictions by offering immersive, lifelike environments for cognitive evaluations. The objective of this study is to assess the usability of the ecological and automated memory battery (V-MEB) which incorporates 360° movies and head-mounted displays. Eight participants were subjected to one memory tasks in a virtual city setting. Their feedback on usability was gathered using the System Usability Scale, Senior Technology Acceptance Model, Thinking Aloud Protocol, and the Independent Television Commission Sense of Presence Inventory (ITC-SOPI). The quantitative data demonstrated a high score for usability, however the qualitative data from TAP identified particular usability difficulties pertaining to interface design and interaction style. It is crucial to address these difficulties in order to enhance the user experience and effectiveness of cognitive evaluations that rely on virtual reality technology. © 2024 IEEE.

关键词： Virtual environments

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The development of the smart factory during the Industry 4.0 period under the study of system thinking

The development of the smart factory during the Industry 4.0...

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2023 international conference on Artificial Intelligence, systems and Network Security, AISNS 2023

作者： Zhou, Zhenquan Wang, Lanxin Tian, Chenxin Syamsunur, Deprizon School of Architecture and Engineering Tianfu College of SWUFE Mianyang621050 China Department of Civil Engineering Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur56000 Malaysia School of Electronical and Information Engineering Chengdu Aeronautic Polytechnic Chengdu610100 China Department of Civil and Environmental Engineering Faculty of Engineering The University of Auckland Auckland1010 New Zealand

ISBN: (纸本)9798400716966

Following introducing the Industrial Revolution 4.0 framework in 2013, many industrialised nations have implemented this new industrial paradigm. The smart factory is a fundamental concept imperative for successfully implementing the Fourth Industrial Revolution. Many scholars and specialists contend that the ongoing advancement of smart factories will transform the industrial structure. This study aims to examine the status of smart factories, exploring the development and management of the smart factory during the Industry 4.0 period under the study of system thinking. This work extensively explores the distinctive notion and presents individual anecdotes regarding the development, execution, arrangement, and administration of intelligent factories by individuals. The research employs multiple methodologies, such as system thinking of relevant literature and systematic analysis of the proposed complex project. The findings suggest that incorporating intricate system thinking, appropriate technology integration, efficient simulation and machine learning employment, and a responsive monitoring and feedback mechanism are crucial in establishing and managing intelligent manufacturing facilities. External factors and governing constraints need to be considered during the lifecycle of treating complexity. This report offers a thorough and analytical evaluation of developing the smart factory during the Fourth Industrial Revolution, serving as a scholarly resource for further exploration of related subject areas. A further investigation plans to consider deep data collection. © 2023 ACM.

关键词： Project management

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