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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691-700 订阅

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Planetary Defense Using Machine Learning Early Warning systems for Hazardous Asteroids

Planetary Defense Using Machine Learning Early Warning Syste...

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2024 international conference on Advances in data engineering and Intelligent Computing systems, ADICS 2024

作者： Sandeep, Avala Reddy, Panta Jeevan Perkin, T. Madhu Hindustan Institute Of Technology And Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

Within the dynamic field of planetary defence, machine learning has emerged as a key component that is essential to early warning systems that are tasked with forecasting the orbits and trajectories of potentially dangerous asteroids. Through the application of a broad range of sophisticated algorithms, such as logistic regression, XGBoost, decision trees, k-nearest neighbours (KNN), and multi-layer perceptron (MLP), a group of committed scientists are hard at work developing reliable models that will be crucial in classifying asteroids into different orbit classes. These classifications, particularly the potentially dangerous asteroids and Near-Earth Objects (NEOs), provide us with important information that helps us determine risk levels and develop practical mitigation plans. Thus, the combination of astronomy and artificial intelligence has enabled a significant advancement in our ability to examine the data and features of these celestial planets, providing insight into their mysterious motions and any possible risks. With this innovative method, we may both advance our knowledge of the dynamic universe and improve our capacity to protect Earth from the impending threat of asteroidal collisions. In a time when space travel and the secrets of the cosmos entice us with both wonder and danger, this marriage of science and technology serves as a symbol of human ingenuity and our commitment to safeguarding our home planet. © 2024 IEEE.

关键词： Decision trees

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ENHANCED SUPPLY CHAIN 5.0 ADVANCED manufacturing WORKFLOWS CHARACTERISTICS FOR REGIONAL HEALTHCARE RESILIENCE 51

ENHANCED SUPPLY CHAIN 5.0 ADVANCED MANUFACTURING WORKFLOWS C...

引用

51st international conference on Computers and Industrial engineering, CIE 2024

作者： Haskell, N. Belek Fialho Teixeira, M. Chamorro-Koc, M. Loy, W. Chhikara, K. Suresh, S. Wille, M. Hughes, B. Little, P. Beatson, A. School of Design QUT Brisbane Australia School of Architecture Industrial Design and Planning Griffith University Gold Coast Australia School of Architecture & Built Environment QUT Brisbane Australia School of Mech. Medical & Process Engineering QUT Brisbane Australia School of Teacher Education and Leadership QUT Brisbane Australia School of Advertising Marketing & Public Relations QUT Brisbane Australia

The future of health care is changing led by advanced manufacturing, digital transformation technologies and workflows that allow for mass customisation and equitable access. These enabling technologies and workflows contribute to enhanced Supply Chain 5.0 eco-systems that respond to the complex needs of remote areas and regional communities. The three drivers of Supply Chain 5.0: resilience, sustainability and human centricity are central to the shifts evident in supply chains adapting to Industry 5.0. These drivers become even more important when looking at regional health care delivery and the manufacturing of mass customised solutions, as the remoteness can provide additional challenges to traditional supply chain approaches. Advanced manufacturing leaders facilitate these future-focused health care systems that are driven by digital transformation, with advanced manufacturing enabling technologies as core components of mass customisation and point of care data collection and manufacturing workflows. The Australian context is explored, to demonstrate how embedded Additive manufacturing (AM) digital workflows for high volume manufacturing within healthcare applications for personalised orthotics can enhance supply chains in regional contexts. Supply Chain 5.0 drivers of resilience (SCR), human-centricity (HCSC) and sustainability (SCS), are used to map AM capabilities within a local company to create an evidence-based approach for emphasising key characteristics for Supply Chain 5.0 viability in regional settings. © 2024 Computers and Industrial engineering. All rights reserved.

关键词： Smart manufacturing

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FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutation 36

FedQP: Towards Accurate Federated Learning using Quadratic P...

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36th international conference on Software engineering and knowledge engineering, SEKE 2024

作者： Weng, Jiawen Xia, Zeke Li, Ran Hu, Ming Chen, Mingsong Software/Hardware Co-Design Engineering Research Center Ministry of Education East China Normal University Shanghai China School of Software Nanjing University of Information Science and Technology Nanjing China Research Lab for Intelligent Software Engineering Singapore Management University Singapore

ISBN: (纸本)1891706594

Due to the advantages of privacy-preserving, Federated Learning (FL) is widely used in distributed machine learning systems. However, existing FL methods suffer from low-inference performance caused by data heterogeneity. Specifically, due to heterogeneous data, the optimization directions of different local models vary greatly, making it difficult for the traditional FL method to get a generalized global model that performs well on all clients. As one of the state-of-the-art FL methods, the mutation-based FL method attempts to adopt a stochastic mutation strategy to guide the model training towards a well-generalized area (i.e., flat area in the loss landscape). Specifically, mutation allows the model to shift within the solution space, providing an opportunity to escape areas with poor generalization (i.e., sharp area). However, the stochastic mutation strategy easily results in diverse optimal directions of mutated models, which limits the performance of the existing mutation-based FL method. To achieve higher performance, this paper proposes a novel mutation-based FL approach named FedQP, utilizing a quadratic programming strategy to regulate the mutation directions wisely. By biasing the model mutation towards the direction of gradient update rather than traditional random mutation, FedQP can effectively guide the model to optimize towards a well-generalized area (i.e., flat area). Experiments on multiple well-known datasets show that our quadratic programming-guided mutation strategy effectively improves the inference accuracy of the global model in various heterogeneous data scenarios. © 2024 knowledge systems Institute Graduate School. All rights reserved.

关键词： Federated learning

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AN INDUSRY 4.0 PROJECT WITH data INTEGRATION - FROM PART DESIGN TO CHIP FLOW AND PHYSICS-BASED MACHINE LEARNING FOR ALMG5 19

AN INDUSRY 4.0 PROJECT WITH DATA INTEGRATION - FROM PART DES...

引用

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

作者： Lortz, W. Univ Appl Sci Trier Germany

ISBN: (纸本)9780791888117

For decades scientists have leveraged computers to solve the complex chip-formation problem - the hope is that one day the computer will be able to solve every technological problem such as modeling of large plastic deformation occurring under friction conditions. Despite the significant number of modeling and simulation studies, there is still limited knowledge about the real nature of the chip formation process. Until today, better agreement with practical results is still being sought-after. Analyzing this situation, some scientists founded the industry 4.0 project to create new opportunities and benefits for industry. In manufacturing, this initiative requires data integration from CAD to CAM with associated Quality Management (QM), and leveraging advanced analytics such as artificial intelligence (AI) with machine learning (ML) to solve complicated problems. In metal cutting, one such complicated problem is modeling of chip formation. Prior to leveraging AI, or rather to create the premise for a physics-based approach, the phenomena occurring during chip formation need better and more realistic modeling. This paper is presenting advances in physics-based modeling of chip formation with particularization for AlMg5. Adequate process mechanics will be developed resulting in modeling of chip-formation and simulation of chip flow. Moreover, it could be shown that the theoretically developed cutting result is in good agreement with the existing experimental result. There is only one disadvantage during production in the machining cell - the generated chip is very long and it is blocking the production process. Further investigation was necessary. A chip breaker was developed as a function of the existing production spectrum with individual geometry and kinematics. For different cutting conditions and tool geometry considerations for a physics-based machine learning process are proposed.

关键词： Industry 4.0 project cutting AlMg5 contact and friction condition temperatures during process chip breaker machine learning ML

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GRAPH NEURAL NETWORK-BASED LINK PREDICTION FOR HIGHLY IMBALANCED NETWORK data

GRAPH NEURAL NETWORK-BASED LINK PREDICTION FOR HIGHLY IMBALA...

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ASME 2024 international Design engineering Technical conferences and Computers and Information in engineering conference, IDETC-CIE 2024

作者： Xiao, Yinshuang Sha, Zhenghui Walker Dept. of Mechanical Engineering The University of Texas at Austin AustinTX78712-1591 United States

ISBN: (纸本)9780791888377

The rapid advancement of graph neural networks (GNN) has revolutionized the integration of complex network and neural network models, fostering diverse engineering applications such as the classification of assembly parts and the prediction of customer preferences. GNN-based link prediction (LP), as a representative study, has shown utility in resource allocation and system performance evaluation, as evidenced in the existing literature. However, the inherent challenge of imbalanced network data (i.e., sparse network) from engineering domains poses obstacles to accurate link predictions due to the lack of training data of positive links, leading to biases and reduced performance. In contrast to existing studies that focus on enhancing model architecture, this paper concentrates on exploring preprocessing methods, e.g., the data undersampling strategies, and two post-processing methods for accuracy calculation based on 1) probability threshold-based labeling and 2) probability rank-based labeling, and studies their impacts on the prediction results with a given GNN-based LP model architecture. Real-world network data sourced from shared mobility systems and product market systems, which exhibit various levels of imbalance, are used as test cases. This study contributes to a better understanding of effective data undersampling strategies and post-processing methods for handling imbalanced network data when applying GNN-based LP to real-world complex systems. Copyright © 2024 by ASME.

关键词： engineering systems and industry applications Complex systems Resource allocation manufacturing Performance evaluation Graph neural networks Preferences Probability link prediction Neural network models imbalanced binary classification networked engineering systems Mechanical admittance graph neural network

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Collaborating LLMs and PLMs for Medical Tasks 15

Collaborating LLMs and PLMs for Medical Tasks

引用

15th IEEE international conference on knowledge Graph, ICKG 2024

作者： Xu, Gaofeng Meng, Yuan Wang, Runzhe Qi, Guilin Southeast University School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798331508821

The intelligentization of the medical field not only enhances service quality and efficiency but also reduces costs and meets the growing demand for healthcare. Although various AI models have been developed to address specific medical tasks, they struggle to independently manage complex tasks. Given the strengths of large language models (LLMs) in problem understanding and logical reasoning, we propose MedicalGPT, which leverages fine-tuned LLMs to connect and orchestrate different medical AI models to solve complex medical tasks. Specifically, upon receiving a patient request, MedicalGPT employs LLMs for task planning, selects appropriate models to handle each subtask, and integrates the results to generate the final output provided to the patient, thereby advancing the intelligentization of healthcare. © 2024 IEEE.

关键词： Multi agent systems

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A Novel Approach to Design Secured and Privacy Enabled Health data Linkage System based on Cyber Security Principles

A Novel Approach to Design Secured and Privacy Enabled Healt...

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2024 IEEE international conference on Big data and Machine Learning, ICBDML 2024

作者： Gayathri, S. Jaya Mabel Rani, A. Anushalin, P.S. Ravi, C.N. Jeyalaxmi, M. Samuthira Pandi, V. R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Dr.Sakunthala Engineering College Department of Biomedical Engineering Vel Tech Multi Tech Dr.Rangarajan Avadi Chennai India CMR Engineering College Department of Computer Science and Engineering Medchal Hyderabad India Prince Shri Venkateshwara Padmavathy Engineering College Department of Physics Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350374100

Medical records from a variety of sources must be integrated into healthcare data repositories for knowledge discovery to be possible. An essential area of study is how to keep records linked when merging medical data. Researchers have proposed many solutions to this issue, all of which are relevant to industrialized nations that use patient identification numbers such as social security numbers, health insurance numbers, universal patient identifiers (UPIs), etc., to keep track of electronic health records. Because of issues with missing data, unclear patient identification, and excessive noise in patient information, these systems are not suitable for record linking of health data in underdeveloped nations. There is a serious concern that patients' personally identifiable information stored in electronic health records poses a threat to their privacy and leaves health information systems open to intrusion. We have examined the real-world challenges of healthcare data collection and integration for a national health data warehouse in this article. To accommodate the limited health data of poor nations like Bangladesh, we have developed privacy-preserving secured record linkage architecture we call Secured Health data Handler (SHDH). For the purpose of facilitating the knowledge discovery process, our approach may anonymize identifying private data of patients while preserving record linkage in integrated health repositories. Even without a standard ID, our suggested strategy was able to connect records with sufficient accuracy for noisy data, according to the experimental results. © 2024 IEEE.

关键词： data warehouses

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Improving Radar-Camera Fusion Network for Distance Estimation 16

Improving Radar-Camera Fusion Network for Distance Estimatio...

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16th international conference on Computer and Automation engineering (ICCAE)

作者： Samuktha, V. Shukla, Hershita Kumar, Nitish Tejasri, N. Reddy, D. Santhosh Rajalakshmi, P. IIT Hyderabad Dept Artificial Intelligence Hyderabad India IIT Hyderabad Ctr Interdisciplinary Programs Integrated Sensor Syst Hyderabad India IIT Hyderabad Dept Elect Engn Hyderabad India IIT Hyderabad Dept Elect Engn NM ICPS TiHAN Hyderabad India

ISBN: (纸本)9798350370058;9798350370164

In today's world, a sensor-based autonomous industry needs a range of sensors to understand its environment and decide how to function safely and productively. The precise and trustworthy assessment of the surroundings around a vehicle has made a variety of autonomous or assisted driving approaches possible. Hence, we provide a novel radar camera fusion technique based on deep learning that greatly enhances object identification performance. The combination of radar and camera data in particular has emerged as a promising strategy for improving the precision and dependability of object identification systems. However, the performance of current fusion algorithms is frequently constrained by issues with occlusion, variable illumination, and sensor noise. With enhancing capabilities of autonomous vehicles through different algorithms and perception methodologies, it has become important to look into the importance of computationally lighter algorithms which work efficiently as well. Based on the knowledge gleaned from analyzing several deep learning models, we came up with a modified fusion algorithm by adding a number of new operations. The processes of this algorithm consist of improved object identification techniques, feature extraction, and feature fusion. Our approach successfully addresses the issues posed by occlusion, varying illumination, and sensor noise by utilizing the complementary nature of radar and camera technologies, resulting in enhanced object detection performance. In conclusion, this study offers a cutting-edge deep learning-based radar camera fusion technique that surpasses current methods for object detection. Our approach provides improved accuracy, robustness, and real-time performance by using insights from several deep-learning models and including extra processes. The suggested approach provides a sizable improvement in sensor fusion methods, opening the door for more improvements to perception system enhancement.

关键词： Radar signal processing Deep Learning Object detection Error Estimation Convolution Neural Networks Multi-sensor Calibration data Annotation, and Multi-modal fusion of sensors

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AirMobilize: IoT-Powered Air Quality Monitoring on the Go

AirMobilize: IoT-Powered Air Quality Monitoring on the Go

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2023 IEEE international conference on Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Rajitha Jasmine, R. Preethi, S. Swetha, P. Priyanka, K. Valliammai, V. Information Technology Dept RMK Engineering College Chennai India

ISBN: (纸本)9798350305708

This project investigates the use of Internet of Things (IoT) technologies for the present monitoring of air pollution. We construct and use sensor nodes to collect data on various air contaminants, allowing for remote access and analysis. The system provides a cost-effective and efficient substitute for monitoring air quality, assisting in combating pollution and public health management. © 2023 IEEE.

关键词： Air pollution monitoring system Cloud Distributed systems GPS sensors

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An Artificial Intelligence-based Integrated Framework for Lean and Smart manufacturing: A Case Study of the Rail Industry

An Artificial Intelligence-based Integrated Framework for Le...

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2024 international conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ale, Felix Daniyan, Ilesanmi Aderoba, Olugbenga Adediran, Adeolu A. National Space Research & Development Agency Engineering & Space Systems Abuja Nigeria Achievers University Department of Mechanical & Mechatronics Engineering Owo Nigeria Landmark University Department of Mechanical Engineering Omu-Aran Nigeria Tshwane University of Technology Department of Industrial Engineering Pretoria South Africa University of Johannesburg Department of Mechanical Engineering Science South Africa

ISBN: (纸本)9798350358155

This study deals with the development of an Artificial Intelligence-based integrated digital technology framework for lean and smart manufacturing implementation. Some technologies and techniques driving Industry 4.0 that are considered in this study include Additive manufacturing (AM), Automatic Train Control (ATC), smart sensors, Internet of Things (IoT), big data, cloud computing, Total Productive Maintenance, Predictive Maintenance (PM), and Artificial Intelligence (AI). Using the rail industry as a case study, an overview of the enabling technologies was presented to acquaint the manufacturing industries with the potentials and applications of these technologies to manufacturing. The study also develops a conceptual framework which incorporates the main driving technologies of lean and smart manufacturing for adoption by the rail industry. The validation of the proposed integrated framework involves the use of AI technology for total productive and predictive maintenance using the rail industry as a case study was conducted in the MATLAB 2020 b environment. The outcome of the demonstration shows the Remaining Useful Life (RUL) of a bearing component of a railcar can be predicted using AI. The predicted RUL of the wheel bearing indicates 500 hours over the next 40 days before failure occurs. Thus, this study adds to the understanding of AI in the lean and smart manufacturing domain. The implementation of the integrated framework can promote efficiency and effectively service delivery in industries that employs the concept of lean and smart manufacturing. © 2024 IEEE.

关键词： Smart manufacturing

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