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3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Liu, Jing Li, Ying Liu, Yanhong Dong, Jingjing Wang, Zheng Wang, Yingyu High-tech Institute Shandong Qingzhou China

ISBN: (纸本)9798350380989

This article focuses on exploring the problems faced by 3D printing technology in the work process, and conducts in-depth research on this field by combining computer three-dimensional production technology and using modeling software as a research tool. First, this article analyzes the current status and development trends of relevant domestic knowledge. Then, through an in-depth understanding of its basic principles and characteristics, this article uses three-dimensional modeling software to generate a solid model and complete virtual prototype modeling. In addition, this article also uses CAD (Computer Aided Design) to draw two-dimensional drawings, part drawings and other files to implement the design of the 3D printing system, and verifies the final results through effect testing. The results show that the printing speed of this 3D printing manufacturing system is relatively fast and stable, and its printing resolution is also high, the adaptability of printing materials is good, with the overall adaptability being the lowest 80% and the highest 92 %. These test results ensure the printing quality and stability of the system. © 2024 IEEE.

关键词： 3D Modeling 3D Printing Intelligent Computer manufacturing System

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Deep Learning Based Automatic Porosity Detection of Laser Powder Bed Fusion Additive manufacturing 32nd

Deep Learning Based Automatic Porosity Detection of Laser Po...

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32nd international conference on Flexible Automation and Intelligent manufacturing

作者： Ibn Mohsin, Syed Farhang, Behzad Wang, Peng Yang, Yiran Shayesteh, Narges Badurdeen, Fazleena Univ Kentucky Lexington KY 40506 USA Univ Texas Arlington Arlington TX 76010 USA

ISBN: (纸本)9783031382406;9783031382413

Laser Powder Bed Fusion (LPBF) is a widely utilized additive manufacturing process. Despite its popularity, LPBF has been found to have limitations in terms of the reliability and repeatability of its parts. To address these limitations, a deep learning model based on You Only Look Once (YOLO) was adapted to automate the detection of defect areas from scanning electron microscopic images of LPBF-manufactured parts. The data on the defect areas are then integrated into an Artificial Neural Network to correlate the process parameters with defects. The results show that the development of defects is stochastic in nature with respect to the input process parameters. The high variability of defects generated from the same process parameters makes it difficult to reliably predict the quality of the parts using only a process data-driven approach. This highlights the importance of in-situ monitoring of the system for reliable prediction of part quality.

关键词： Additive manufacturing Machine Learning Powder Bed Fusion

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TRFP: A Trip Recommendation Approach for a Query with Fixed Intermediate POI

TRFP: A Trip Recommendation Approach for a Query with Fixed ...

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

作者： Luan, Wenjing Jiang, Guodong Qi, Liang Liu, Kun Guo, Xiwang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Communication Engineering Liaoning Petrochemical University Fushun113001 China

ISBN: (纸本)9781665410205

Trip recommendation aims to provide users with a sequence of points of interest (POIs) according to their interests and requirements when exploring unfamiliar cities. In contrast to prior research on trip recommendation, our research deals with such a problem: If a user is scheduled to attend an academic conference at 2:30 PM, how might he/she make a visit to the city's attractions while still managing to attend the conference? To address this problem, a trip recommendation method based on mixed graph representation learning is proposed. Firstly, a mixed graph is used to describe the spatial temporal, and transition knowledge in users' check-in data. Then, we employ the graph convolutional network to integrate knowledge matrices extracted from the mixed graph. Finally, a trip inference module, which incorporates a dual decoder, POI popularity knowledge, and positional encoding, is designed to generate a trip for a given query. Experiments are conducted on five real-world trip datasets. The results demonstrate that the proposed method outperforms several widely-used baselines when recommending a trip with an FP. © 2024 IEEE.

关键词： knowledge graph

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A Novel spatial relational features generation using encoded geometry for character recognition

A Novel spatial relational features generation using encoded...

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

作者： Soora, Narasimha Reddy Thakare, Atul Vodithala, Swathy Kakatiya Institute of Technology and Science Computer Science and Engineering Warangal India Narsee Monjee Institute of Management Studies School of Technology Management & Engineering Navi Mumbai India

ISBN: (纸本)9798350359688

This paper proposes two different feature extraction techniques for small-size datasets in the context of character recognition. The aim is to achieve high accuracy even with limited training data. The proposed feature extraction techniques are contour-based feature vector (CFV) and tracing line-contour intersection count (TCIC). The proposed algorithms were trained using a small data set containing English alphabets and numerals. The feature extraction techniques were used to extract relevant features from the characters in the training set, and the resulting feature vectors are used to classify the input character by the Levenshtein edit distance classifier. The trained model was then tested on the test set to evaluate its accuracy. Experimental results shows that the proposed approach achieved encouraging results on publicly available MediaLab data set with an overall accuracy of 97%. The best performance was obtained using a combination of proposed algorithm and crossing count feature vector. The results demonstrate that even with a small datasets, high accuracy can be achieved using appropriate feature extraction techniques and a well-designed classifier. In addition, the proposed methods were able to accurately classify broken and distorted characters, which are often challenging to recognize using conventional approaches. These findings can be useful for developing offline character recognition systems for embedded devices and mobile devices with very limited computing power. © 2024 IEEE.

关键词： Feature extraction

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Empowering Personalized Health data Queries with knowledge Graph and GPT-Enhanced Voice Assistant 25

Empowering Personalized Health Data Queries with Knowledge G...

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on data Science and systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wang, Xin Ul Hasan, Wordh Li, Juan Hendawi, Rasha Du, Yang Xie, Bo Tao, Cui North Dakota State University Department of Computer Science Fargo United States Valley City State University Department of Computer Systems and Software Engineering Valley City United States School of Nursing The University of Texas at Austin Austin United States School of Biomedical Informatics University of Texas Health Science Center at Houston Houston United States

ISBN: (纸本)9798350330014

The proliferation of digital health technologies has led to an abundance of personal health data. However, querying and retrieving specific health-related information from disparate sources can be challenging and inconvenient, particularly for older individuals unfamiliar with technology. While ChatGPT offers a conversational interface, it lacks domain-specific knowledge, including personalized health information. To address this limitation, we present a novel approach that combines a knowledge graph and GPT to enable personalized health queries. Our solution utilizes a personal knowledge graph as a comprehensive knowledge source and fine-tunes GPT to provide accurate responses. We have implemented a voice assistant mobile app incorporating this knowledge graph-assisted GPT model and conducted initial feasibility testing. © 2023 IEEE.

关键词： Query processing

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Remote manufacturing in Untrusted Domains 7

Remote Manufacturing in Untrusted Domains

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7th IEEE international conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Kecskeméti, Károly Györgvi, Csaba Szabó, Géza Vörös, Peter Laki, Sándor Andó, Mátyás Komlósi, István Pepó, Tamás Ericsson Hungary Ltd. Budapest Hungary ELTE Eötvös Loránd University Budapest Hungary KUKA Hungary Ltd Hungary

ISBN: (纸本)9798331531119

This paper addresses the challenge of maintaining the security of manufacturing systems in untrusted environments by proposing a novel approach that separates the physical production equipment from its control logic. The proposed solution ensures that corporate know-how is protected even if the physical factory is compromised. This is achieved by remotely controlling the factory from a secure and trusted location while obfuscating the communication between the control systems and production equipment. The methodology includes random seed generation and exchange, masking status and control messages, introducing random shifts in trajectory positions, and utilizing alternate trajectories to obscure the manufacturing logic. The viability of the proposed concept is validated through a simulated scenario, demonstrating its potential to enhance cybersecurity in cyber-physical systems. © 2024 IEEE.

关键词： Smart manufacturing

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Asset Administration Shells and GAIA-X Enabled Shared Production Scenario 32nd

Asset Administration Shells and GAIA-X Enabled Shared Produc...

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32nd international conference on Flexible Automation and Intelligent manufacturing

作者： Volkmann, Magnus Wagner, Andreas Hermann, Jesko Ruskowski, Martin Technol Initiat SmartFactory KL eV Kaiserslautern Germany Univ Kaiserslautern Landau RPTU Inst Machine Tools & Control Syst WSKL Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI Kaiserslautern Germany

ISBN: (纸本)9783031381645;9783031381652

Increasing the flexibility of production environments and the resilience of value chains are major challenges of Industry 4.0. Multi-enterprise manufacturing networks can be a solution to create dynamic supply chains and to make product manufacturing more flexible. To enable the concept of manufacturing networks, vendor-independent and data-secure solutions are required. This includes automated bidding procedures and cross-company interpretation of information. Existing approaches to data exchange between companies are based on proprietary concepts. An industrial data space should support vendor-independent solutions to facilitate data exchange, participation in multiple data spaces, and offers the freedom not to be dependent on a single software company or marketplace provider. The paper focuses on existing technologies and describes a Shared Production scenario based on the decentralized GAIA-X architecture, the Asset Administration Shell and the Industry 4.0 language. In this way, uniform semantics can be achieved, the Industry 4.0 language defines the structure of text messages as well as the communication flow, and GAIA-X provides technologies for secure and data-sovereign information exchange. The focus is on the possible identification of supply chains in the machining industry.

关键词： Cloud manufacturing Shared Production Asset Administration Shell Industry 4.0 GAIA-X capabilities skills

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Deploying Optical Character Recognition to Improve Material Handling and Processing 32nd

Deploying Optical Character Recognition to Improve Material ...

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32nd international conference on Flexible Automation and Intelligent manufacturing

作者： Shahin, Mohammad Hosseinzadeh, Ali Chen, F. Frank Davis, Marvin Rashidifar, Rasoul Shahin, Awni Univ Texas San Antonio Mech Engn Dept San Antonio TX 78249 USA Mutah Univ Fac Educ Al Karak Jordan

ISBN: (纸本)9783031381645;9783031381652

This paper assesses the supporting function of a Machine-based Identification system (MBID) via Optical Character Recognition (OCR) in a Lean manufacturing paradigm. The objective of this paper is to also explore the use of MBID to enable a competitive manufacturing process in a Lean 4.0 environment. Furthermore, a MBID via OCR model is proposed to extract the printed identification number of packages from images captured by a fixed camera in an industrial environment. The method considers different digital image processing techniques to deal with the significant lighting and printing variation observed, followed by a segmentation process that extracts and aligns the characters. Experiments were carried out on a data set consisting of 200 images and achieved an overall detection accuracy of 95% with a very low Character Error Rate (CER) value of 0.0041, clearly supporting the validity and effectiveness of the proposed method.

关键词： Lean manufacturing Big data Waste Reduction Machine Learning Industry 4.0 Optical Character Recognition

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Securing the smart revolution: Navigating security and privacy concerns in Industry 4.0 vs. Industry 5.0 1st

Securing the smart revolution: Navigating security and priva...

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1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Uprety, Deepak Chandra Rastogi, Ritesh Jain, Garima Ahamad, Shakeel Yadav, Chandra Shekhar Banarjee, Dyuti Dwivedi, Rashmi Department of CS NIET Engineering College Knowledge Park-II Gr UP Noida India Department of CSBS NIET Engineering College Knowledge Park-II Gr UP Noida India Department of DS NIET Engineering College Knowledge Park-II Gr UP Noida India Department of MCA NIET Engineering College Knowledge Park-II Gr UP Noida India India Department of Economics NIMS University Rajasthan Jaipur India

ISBN: (纸本)9781032874036

Industry 5.0 is defined by the manufacturing sector’s adoption of cutting-edge technology, including robotics, AI, and the IOT. Industry 5.0 substantially improves productivity, efficiency, and customization, but it also presents new security and privacy issues that must be addressed for its successful adoption. This study compares Industry 4.0 with Industry 5.0 on the basis of security and privacy concerns and suggests ways to reduce the risks. One major concern is the increased connectivity of industrial systems and the corresponding rise in the attack surface. This article proposes a complete approach that stresses the integration of security measures from the design phase and the application of privacy-by-design principles for security and privacy issues in Industry 4.0 vs. Industry 5.0. By proactively addressing these challenges, Industry 5.0 can unlock its full potential while fostering trust among stakeholders, enabling sustainable growth, and safeguarding societal well-being. © 2025 the Author(s).

关键词： Differential privacy

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15th CIRP international conference on Intelligent Computation in manufacturing engineering CIRP ICME 21 Editorial

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Procedia CIRP 2022年 112卷 607-608页

作者： Teti, Roberto Department of Chemical Materials and Industrial Production Engineering University of Naples Federico II P.le Tecchio 80 Naples80125 Italy Fraunhofer Joint Laboratory of Excellence on Advanced Production Technology Fh J_LEAPT UniNaples P.le Tecchio 80 Naples80125 Italy

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