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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641-650 订阅

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knowledge-Enhanced Recommendation with User-Centric Subgraph Network 40

Knowledge-Enhanced Recommendation with User-Centric Subgraph...

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40th IEEE international conference on data engineering, ICDE 2024

作者： Liu, Guangyi Yao, Quanming Zhang, Yongqi Chen, Lei Tsinghua University Department of Electronic Engineering Beijing China Paradigm Inc. Beijing China Guangzhou China

ISBN: (纸本)9798350317152

Recommendation systems, as widely implemented nowadays on various platforms, recommend relevant items to users based on their preferences. The classical methods which rely on user-item interaction matrices has limitations, especially in scenarios where there is a lack of interaction data for new items. knowledge graph (KG)-based recommendation systems have emerged as a promising solution. However, most KG-based methods adopt node embeddings, which do not provide personal-ized recommendations for different users and cannot generalize well to the new items. To address these limitations, we propose knowledge-enhanced User-Centric subgraph Network (KUCNet), a subgraph learning approach with graph neural network (GNN) for effective recommendation. KUCN et constructs a U-I subgraph for each user-item pair that captures both the historical information of user-item interactions and the side information provided in KG. An attention-based GNN is designed to encode the U-I subgraphs for recommendation. Considering efficiency, the pruned user-centric computation graph is further introduced such that multiple U-I subgraphs can be simultaneously computed and that the size can be pruned by Personalized PageRank. Our proposed method achieves accurate, efficient, and interpretable recommendations especially for new items. Experimental results demonstrate the superiority of KUCNet over state-of-the-art KG-based and collaborative filtering (CF)-based methods. Our code and data is available in https://***/leolouis14/KUCNet. © 2024 IEEE.

关键词： Graph neural networks

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Deep Learning-Based Image Processing for Defect Detection in Electronic Components manufacturing

Deep Learning-Based Image Processing for Defect Detection in...

引用

2024 international conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Mathivanan, B. Maruthupandi, J. Lakshmi, Namala Vijaya Suvitha, S. Karthik, K. Swaroop, Anand Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore641022 India New Horizon College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560103 India Kkr & Ksr Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh 522017 India S.A. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai600077 India Velammal College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Madurai625009 India Institute of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Lucknow226021 India

ISBN: (纸本)9798331543617

In electronic factors manufacturing, icing product quality through disfigurement discovery is pivotal for maintaining trustability and performance norms. This exploration investigates the operation of deep literacy ways in image processing for disfigurement discovery. Deep literacy algorithms, particularly convolutional neural networks(CNNs), have shown remarkable success in automated image analysis tasks. This study explores their effectiveness in relating blights similar to face abnormalities, cracks, or indecorous assembly in electronic factors. By using annotated datasets and advanced neural network infrastructures, the proposed approach aims to enhance delicacy and effectiveness in disfigurement discovery compared to traditional styles. The methodology involves preprocessing raw images, training CNN models on labeled data, and validating their performance through rigorous testing and evaluation. Results demonstrate the eventuality of deep literacy to revise disfigurement discovery processes in manufacturing, offering more dependable examinations that contribute to bettered product quality and reduced product costs. This exploration contributes precious perceptivity to the integration of AI technologies for enhancing quality control in artificial settings. © 2024 IEEE.

关键词： Smart manufacturing

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15th international conference on knowledge Management and Information systems, KMIS 2023 as part of IC3K 2023 - Proceedings of the 15th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management

15th International Conference on Knowledge Management and In...

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

ISBN: (纸本)9789897586712

The proceedings contain 34 papers. The topics discussed include: enhancing leadership and management effectiveness: leveraging actor-network theory for project risk mitigation;discovering potential founders based on academic background;emotional intelligence: predictor of success and career advancement - a survey of Bulgarian digital entrepreneurs;knowledge transfer factors for internal combustion engine (ICE) industry to electric vehicle (EV) industry by artificial intelligent: machine learning;a taxonomy for autonomous LLM-powered multi-agent architectures;towards effective ecosystems: a framework for mapping knowledge governance and management activities of innovation ecosystems constituent elements;and ZeitGeist: a generic tool supporting the dissemination of time series data following FAIR principles.

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international conference on Production and Industrial engineering, CPIE 2023

International Conference on Production and Industrial Engine...

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international conference on Production and Industrial engineering, CPIE 2023

ISBN: (纸本)9789819960934

The proceedings contain 72 papers. The special focus in this conference is on Production and Industrial engineering. The topics include: A Comprehensive Overview on Additive manufacturing Processes: Materials, Applications, and Challenges;an Overview of the Untapped Potential of Soft Robotic Arms with Integration of Machining Tools;life Cycle Assessment of Abrasive Flow Machining of 3D Printed Parts: A Comparative Analysis;Modelling and Simulation of Wire DED Additive manufacturing Process;an Overview of the Recent Advances in Additive manufacturing, and Its Scope for the Benefit of Research and Development in Commercial Vehicle Industries;design and Development of Customized Helmet for Military Personnel;additive manufacturing and Sustainability: The Mediating Role of Supply Chain;leverage of Metal 3D Printing Technology in the Automotive Industry;digital Twins of Hybrid Additive and Subtractive manufacturing systems–A Review;exploring Sustainable manufacturing: A Comprehensive Review of Literature and Practices;performance Analysis of 3D Printed Impellers for Portable Vacuum Cleaner;3D Printed Electronics: Role of Materials and Processes;electrochemical Micromachining: A Review on Principles, Processes, and Applications;friction and Wear Characteristics of Engine Oil Through Four-Ball Tester;recent Trends in 4th Industrial Revolution for A Sustainable Future–A Review;AI Control of EMG Sensor data for 3D Printed Prosthesis Hand.

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Prediction Of Machining Conditions Using Machine Learning

Prediction Of Machining Conditions Using Machine Learning

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2024 international conference on Smart Devices, ICSD 2024

作者： Pande, Sagar Dhanraj Jang Bahadur Saini, DilipKumar Chhabra, Gunjan Kaushik, Keshav Meshram, Kamlesh Arun Narayan Nandgaonkar, Vikas Pimpri Chinchwad University School of Engineering and Technology Department of Computer Science and Engineering Maharashtra Pune India Graphic Era Hill University Department of CSE India Graphic Era Deemed to be University Uttarakhand Dehradun India University of Petroleum and Energy Studies School of Computer Science Uttarakhand Dehradun India Pimpri Chinchwad University School of Engineering and Technology Maharashtra Pune India Indira College of Engineering and Management Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350394412

Improving additive manufacturing (AM) process modelling is necessary to better understand the relationship between process parameters and material properties, and current developments in AI and ML offer promising avenues for this. Although the proliferation of labelled datasets from experiments or high-fidelity simulations has been the main driver behind the development of traditional AI tools in data science, it is becoming more and more necessary to use AI in AM metal processes while reducing reliance on big datasets. Our solution to this is the Improved Neural Network (INN) framework, which improves the modelling process by incorporating knowledge of basic physics concepts like momentum, energy, and mass conservation into the neural network design. In order to effectively enforce boundary conditions and speed up the modelling process, we include compressed-type approaches into the Dirichlet boundary condition regulation using a Heaviside function. The INN framework is used to two typical metal manufacturing challenges, one of which is the NIST AM-Benchmark series test. We thoroughly examine the effectiveness of the INN framework employing a finite element variational multi-processing training method by comparing its predictions with available experimental data and high-fidelity simulation findings. By utilising additional physical data, the results show that the INN can consistently forecast temperature and melt pool dynamics in metal AM processes, even with a limited quantity of labelled datasets. © 2024 IEEE.

关键词： Smart manufacturing

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Towards Test-Driven Performance Validation of a Flexible Cyber-Physical Production System 28

Towards Test-Driven Performance Validation of a Flexible Cyb...

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28th IEEE international conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Biffl, Stefan Meixner, Kristof Hoffmann, David Winkler, Dietmar Lüder, Arndt TU Wien Christian Doppler Laboratory SQI Austria TU Wien Inst. of Information Systems Engineering Austria CDP Austria Otto-von-Guericke U. Institute of Ergonomics Manufacturing Systems and Automation Germany SBA Austria

ISBN: (纸本)9798350339918

Fast changes in production require flexibility regarding products, production processes, and production resources (PPR), moving towards a flexible production system that consists of independent systems. However, production systems engineering traditionally considers mainly static systems and requires advanced capabilities to effectively and efficiently validate performance indicators specified in test scenarios for a flexible production system. For validating production process and system variants, this vision paper explores the Test-Driven Performance Validation (TPV) approach for knowledge representation of (1) a family of production variants (2) with test scenarios on production effects and conditions, and (3) a Production Asset Network that defines PPR configuration dependencies and production data sources as a foundation for calculating a production variant's performance. We conclude with a research agenda towards coordinating performance validation of flexible production with human and machine agents. © 2023 IEEE.

关键词： knowledge representation

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international conference on Production and Industrial engineering, CPIE 2023

International Conference on Production and Industrial Engine...

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international conference on Production and Industrial engineering, CPIE 2023

ISBN: (纸本)9789819966004

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Development of stationary expert systems for improving energy efficiency in manufacturing 17

Development of stationary expert systems for improving energ...

引用

17th CIRP conference on Intelligent Computation in manufacturing engineering, CIRP ICME 2023

作者： Ioshchikhes, Borys Weigold, Matthias Technical University of Darmstadt Institute for Production Management Otto-Berndt-Str. 2 Darmstadt64287 Germany

Increasing energy prices as well as the transition towards the goal of climate neutrality are major challenges for manufacturing companies. Expert systems offer the possibility to address these challenges by aggregating expert knowledge, processing energy data and thus identifying energy efficiency potentials to deduce energy efficiency measures. In this paper, approaches for mobile preliminary and stationary detailed energy analysis are compared. Additionally, a systematic development method for stationary expert systems is presented. The proposed method is demonstrated through the implementation for a chamber cleaning machine to quantify energy efficiency potentials. © 2024 Elsevier B.V.. All rights reserved.

关键词： Smart manufacturing

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Distributed Cloud Simulation Architecture Combining Virtual and Real Environment

Distributed Cloud Simulation Architecture Combining Virtual ...

引用

2024 international conference on Interactive Intelligent systems and Techniques, IIST 2024

作者： Hu, Jiangang Mao, Yukun Li, Junjie Ye, Mengya Chen, Li Wan, Zhangbo Peng, Yuhuai Zhiyuan Research Institute Strategic Research Department Hangzhou China Northeastern University School of Computer Science and Engineering Shenyang China

ISBN: (纸本)9798350374421

With the deep integration of advanced manufacturing technology and the new generation of information technology, human-machine collaborative intelligent manufacturing has emerged as the core technology driving the development of the industrial sector. However, the human-machine collaborative intelligent manufacturing equipment exhibits high complexity and uncertainty in faults. The data silos that exist between equipment, personnel, systems, and platforms contribute to decreased productivity. Furthermore, the prolonged and costly technician training cycles add further obstacles. Therefore, a virtual-real integrated cloud simulation architecture is constructed using LVC (Live, Virtual, Constructive) simulation technology combined with a distributed computing framework. This architecture aims to achieve intelligent human-machine collaboration and real-time virtual-real interaction in industrial production. Real time monitoring of equipment and the entire production process through cloud simulation technology to optimize production processes and efficiency. The architecture supports simulation services such as intelligent task scheduling, human-machine collaborative manufacturing, adaptive flexible manufacturing, and production equipment precision and performance evaluation. The current architecture has just been proposed as a theoretical innovation for intelligent manufacturing cloud simulation solutions, but it has yet to be put into practice. © 2024 IEEE.

关键词： Smart manufacturing

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From Pixels to Payouts -A Satellite Based Solution

From Pixels to Payouts -A Satellite Based Solution

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

作者： Deepthi, R.S.S.V. Priya, A Leela Revanth, D. Ajay, K. Madhavi, M. Velagapudi Ramakrishna Siddhartha Engineering College Depratment of Artificial Intelligence and Data Science Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Depratment of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350359688

The Pradhan Mantri Fasal Bima Yojana (PMFBY) stands as a crucial initiative for farmers' protection, yet grapples with challenges like delayed claims, inaccurate loss assessments, and uneven aid distribution [1]. This project proposes an innovative solution that addresses these issues by introducing a precision agriculture and risk management framework. The current crop insurance policies rely on an area-based approach and limited weather indices, resulting in an inadequate assessment of individual crop health and losses [2]. This shortfall leads to unjust aid distribution, leaving deserving farmers underserved [3]. The proposed solution advocates for a paradigm shift, incorporating a personalized and technology-driven approach. By integrating remote sensing technology, machine learning algorithms, and comprehensive data analysis, the system optimizes insurance resource allocation, assessing crop and regional risk profiles with precision. Furthermore, the project implements advanced image processing, segmentation, and machine learning models, such as SVM and CNN, for crop detection and yield estimation. The Google Maps API enhances decision support through geo-mapping, providing a transparent and data-driven basis for aid distribution. This innovative approach fosters equity, transparency, and active farmer participation, overcoming the limitations of existing methodologies. With a focus on precision, transparency, and equity, the proposed solution aims to revolutionize the effectiveness of PMFBY, ensuring accurate assessment and targeted aid distribution for farmers facing crop stress and losses. © 2024 IEEE.

关键词： Risk management

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