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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

共 37034 条记录，以下是251-260 订阅

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Studying on design and manufacture of the customized cam using artificial intelligence 4

Studying on design and manufacture of the customized cam usi...

引用

4th international conference on Advanced manufacturing Technology and Electronic Information, AMTEI 2024

作者： Zhao, Yan Dong, Guangcheng Hao, Wenyue School of Information Engineering Baise University Guangxi Autonomous Region Baise533000 China School of Material Science and Engineering Baise University Guangxi Autonomous Region Baise533000 China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

A virtual product creation technology was explored, focusing on the customized cam with a rise-dwell-fall-dwell (RDFD) motion program taking high and low speeds into account using artificial intelligence which is necessary to transform the related data of the product development (represented by CAD models) into the product. The digital solution which is the manufacturing process of numbers and can completely describe the working components to eventually realize the physical shape of the product was mentioned. The database carrying different interpolation functions for designing some special curvilinear paths meeting special kinematic requirements of the two types of cams was set up. The entire process starts with Computer-Aided Process Planning (CAPP) and then there is Computer-Aided manufacturing with Numeric Control (NC). A further discussion for customized parts design and manufacturing in specialized fields has been conducted based on digital modeling technology and expert systems with the development of artificial intelligence in engineering applications. © 2025 SPIE.

关键词： Computer aided manufacturing

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An Approach for Securing IoT data using Blockchain and Proxy Re-Encryption

An Approach for Securing IoT Data using Blockchain and Proxy...

引用

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

作者： Saradha, M. Keerthivasan, B. Arunachalam, M.P.L. Gnanaprakash, K. Nivitha, K. K. S. Rangasamy College of Technology Computer Science and Engineering Tiruchengode637215 India Vellore Institute of Technology Computer Science and Engineering Vellore632014 India

ISBN: (纸本)9798350359688

The development of Internet of Things (IoT) devices has resulted in exponential data generation, creating substantial problems to guaranteeing the security and privacy of sensitive information. Traditional security mechanisms frequently fall short of meeting the unique needs of IoT contexts, demanding creative solutions. This research presents a unique way for improving IoT data security by leveraging blockchain technology and proxy re-encryption techniques. Our technique seeks to provide strong data confidentiality, integrity, and access control in IoT ecosystems by combining blockchain's immutability and decentralisation with the efficiency of proxy re-encryption. We go over the design, working principles, and implementation concerns of our proposed solution, emphasising its potential benefits and resolving any issues. Through study and testing, we demonstrate the efficacy and viability of our strategy to protecting IoT data from diverse security threats. © 2024 IEEE.

关键词： Access Control Blockchain Confidentiality data Security Integrity Internet of Things (IoT) Proxy Re-encryption

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IoT-Integrated Sensors and Actuators: Sustainable manufacturing Through Mechatronics in Industry 4.0

IoT-Integrated Sensors and Actuators: Sustainable Manufactur...

引用

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

作者： Onu, Peter Mbohwa, Charles University of Johannesburg Department of Quality and Operations Management Johannesburg South Africa

ISBN: (纸本)9798350358155

In the dynamic landscape of Industry 4.0, this research unfurls a comprehensive exploration into integrating sensors and actuators in IoT systems within the mechatronics domain. Our methodology combines qualitative and quantitative approaches with Systematic Literature Network Analysis (SLNA) and multiple case studies. Thematic analysis of diverse data sources, including literature, technical reports, operational data, and stakeholder interviews, provides qualitative depth to our exploration. This interdisciplinary investigation aims to unravel emerging trends, prospects, and implications for sustainable manufacturing. Our findings illuminate a landscape where mechatronics, augmented by IoT-enabled sensors and actuators, emerges as a transformative force in sustainable manufacturing. The synthesis of our research signifies a paradigm shift, where sustainability becomes an inherent characteristic of the manufacturing processes shaping tomorrow's industries. © 2024 IEEE.

关键词： Smart manufacturing

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Analyzing Simulink Modeling Capabilities of Real Technological Disruption in Power System Section

Analyzing Simulink Modeling Capabilities of Real Technologic...

引用

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

作者： Sorochenko, Pavel Zatsepina, Violetta Zatsenin, Yevgeny Institute Of Pfysics And Power Engineering Lipetsk State Technical University Lipetsk Russia

ISBN: (纸本)9798350395013

Modern PSP systems can record precise instrument transformer readings during failures. This helps analyze the operation of algorithms and the correct trigger settings for specific protection systems and automations. However, if the settings are incorrect, the equipment can undergo electromechanical and thermal impacts causing its damage and failure. Using the simulation modeling method can help prevent such situations by the better adjustment of settings. In this research, the authors analyzed the applicability of the developed simulation model for a transit 110 kV power line (powered from 220 kV substation buses) with a set of protection systems installed. The applicability was analyzed in comparison with the AURA data at the moment of the emergency shutdown of a 110 kV power line located in the Voronezh Oblast. © 2024 IEEE.

关键词： Simulink

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Predicting Post Myocardial Infarction Complication: A Study Using Dual-Modality and Imbalanced Flow Cytometry data 16

Predicting Post Myocardial Infarction Complication: A Study ...

引用

16th international conference on knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Aldausari, Nada Coenen, Frans Nguyen, Anh Shantsila, Eduard Department of Computer Science The University of Liverpool Liverpool United Kingdom Institute of Population Health The University of Liverpool Liverpool United Kingdom

ISBN: (纸本)9789897587160

Previous research indicated that white blood cell counts and phenotypes can predict complications after Myocardial Infarction (MI). However, progress is hindered by the need to consider complex interactions among different cell types and their characteristics and manual adjustments of flow cytometry data. This study aims to improve MI complication prediction by applying deep learning techniques to white blood cell test data obtained via flow cytometry. Using data from a cohort study of 246 patients with acute MI, we focused on Major Adverse Cardiovascular Events as the primary outcome. Flow cytometry data, available in tabular and image formats, underwent data normalisation and class imbalance adjustments. We built two classification models: a neural network for tabular data and a convolutional neural network for image data. Combining outputs from these models using a voting mechanism enhanced the detection of post-MI complications, improving the average F1 score to 51 compared to individual models. These findings demonstrate the potential of integrating diverse data handling and analytical methods to advance medical diagnostics and patient care. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Convolutional neural networks

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Unmasking data Secrets: An Empirical Investigation into data Smells and Their Impact on data Quality 24

Unmasking Data Secrets: An Empirical Investigation into Data...

引用

IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Recupito, Gilberto Rapacciuolo, Raimondo Di Nucci, Dario Palomba, Fabio Univ Salerno Sesa Lab Salerno Italy Univ Salerno Salerno Italy

ISBN: (纸本)9798400705915

Artificial Intelligence (AI) is rapidly advancing with a data-centered approach suitable for various domains. Nevertheless, AI faces significant challenges, particularly in data quality. data collection from diverse sources can introduce quality issues that may threaten the development of AI-enabled systems. A growing concern in this context is the emergence of data smells - issues specific to the data used in building AI models, which can have long-term consequences. In this paper, we aim at enlarging the current body of knowledge on data smells, by proposing a two-step investigation into the matter. First, we updated an existing literature review in an effort of cataloguing the currently existing data smells and the tools to detect them. Afterward, we assess the prevalence of data smells and their correlation with data quality metrics. We identify a novel set composed of 12 data smells distributed across three additional categories. Secondly, we observe that the correlation between data smells and data quality is notably impactful, exhibiting a pronounced and substantial effect, especially in highly diffused data smell instances. This research sheds light on the complex relationship between data smells and data quality, providing valuable insights into the challenges of maintaining AI-enabled systems.

关键词： AI Technical Debt data Smells data Quality Software engineering for Artificial Intelligence Empirical Software engineering

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Short Paper: SuperArchitecture: A New Approach for Reconnaissance over data Centers Considering Architectural Alternatives 24

Short Paper: SuperArchitecture: A New Approach for Reconnais...

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11th international conference on Networking, systems and Security, NSysS 2024

作者： Sheikh, Iftikharun Nisa Sakib, Md. Nazmus Hoque, Md Mozammal Hamim, Imdadul Hasan Noor, Jannatun Al Islam, A.B.M. Alim BUET CSE Department Dhaka Bangladesh University of Houston Houston United States Brac University Dhaka Bangladesh Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798400711589

Recently, we have been experiencing ever-increasing deployment of data centers, which unavoidably comes up with possibilities of associated security breaches. Reconnaissance, the initial stage of a security breach targeting a data center, mostly depends on relevant data collection tools, mostly ignoring the plethora of knowledge built with potential architectural alternatives of the data center under consideration. In this regard, for the first time in the literature to the best of our knowledge, we introduce a new model, SuperArchitecture, which realizes key elements from existing pragmatic data center architectures. Leveraging SuperArchitecture for on-boarding the knowledge inbuilt with potential architectural alternatives, we develop a more comprehensive approach for reconnaissance over a data center. We explore the approach in the process of performing reconnaissance over two websites open for security testing. Our comparative analysis of the findings from the exploration demonstrates how our SuperArchitecture-driven reconnaissance can provide more comprehensive outcomes compared to the traditional technique realizing no architectural knowledge. © 2024 Copyright held by the owner/author(s).

关键词： data centers

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Applied data Analytics approach for defect root causes analysis in manufacturing: the case of multi-product assembly lines 48

Applied data Analytics approach for defect root causes analy...

引用

48th Annual IEEE international Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Puech, Louis Dupuis, Ambre Dadouchi, Camelia Pellerin, Robert Polytech Montreal Montreal PQ Canada

ISBN: (纸本)9798350376975;9798350376968

Multi-product assembly lines are commonly used for their flexibility in mass-customized production, but their complexity makes identifying the root causes of defects difficult. This research addresses the limitations of traditional methods for analyzing the root causes of defects in this context. It introduces a four-step methodology for preparing data and conducting descriptive analysis of defect root causes. Product families are created by segmenting production data from the company's information systems using AHC algorithms or company knowledge. Defect rates are then calculated for each product group and the transitions between product sequences. Finally, a CART decision tree is used to generate rules that lead to defective clusters. These rules are used for descriptive analysis of defect root causes and are seen as improvement opportunities for multi-product assembly lines. The methodology was applied to two case studies using real production data. This led to the identification and validation of the root causes of defects by the partner companies. Nevertheless, limitations must be taken into account, e.g., the reliance on expert judgment for the identification of root causes, the sensitivity of the data used, and the necessity of regenerating the decision tree for each new analysis.

关键词： multi-product assembly line root cause analysis defect machine learning manufacturing

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AssemAI: Interpretable Image-Based Anomaly Detection for manufacturing Pipelines 23

AssemAI: Interpretable Image-Based Anomaly Detection for Man...

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

作者： Prasad, Renjith Shyalika, Chathurangi El Kalach, Fadi Venkataramanan, Revathy Zand, Ramtin Harik, Ramy Sheth, Amit Artificial Intelligence Institute College of Engineering and Computing University of South Carolina ColumbiaSC29208 United States College of Engineering and Computing University of South Carolina McNair Center for Aerospace Innovation and Research Department of Mechanical Engineering ColumbiaSC29208 United States College of Engineering and Computing University of South Carolina Intelligent Circuits Architectures and Systems Lab ColumbiaSC29208 United States CU-ICAR Clemson University College of Engineering and Computing GreenvilleSC29607 United States

ISBN: (纸本)9798350374889

Anomaly detection in manufacturing pipelines remains a critical challenge, intensified by the complexity and variability of industrial environments. This paper introduces AssemAI, an interpretable image-based anomaly detection system tailored for smart manufacturing pipelines. Utilizing a curated image dataset from an industry-focused rocket assembly pipeline, we address the challenge of imbalanced image data and demonstrate the importance of image-based methods in anomaly detection. Our primary contributions include deriving an image dataset, fine-tuning an object detection model YOLO-FF, and implementing a custom anomaly detection model for assembly pipelines. The proposed approach leverages domain knowledge in data preparation, model development and reasoning. We implement several anomaly detection models on the derived image dataset, including a Convolutional Neural Network, Vision Transformer (ViT), and pretrained versions of these models. Additionally, we incorporate explainability techniques at both user and model levels, utilizing ontology for user-level explanations and SCORE-CAM for indepth feature and model analysis. Finally, the best-performing anomaly detection model and YOLO-FF are deployed in a real-time setting. Our results include ablation studies on the baselines and a comprehensive evaluation of the proposed system. This work highlights the broader impact of advanced image-based anomaly detection in enhancing the reliability and efficiency of smart manufacturing processes. The image dataset, codes to reproduce the results and additional experiments are available at https:/***/renjithk4/AssemAI. © 2024 IEEE.

关键词： Smart manufacturing

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Proceedings - 2024 22nd international conference on ICT and knowledge engineering, ICT and KE 2024

Proceedings - 2024 22nd International Conference on ICT and ...

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22nd international conference on ICT and knowledge engineering, ICT and KE 2024

ISBN: (纸本)9798350391411

The proceedings contain 40 papers. The topics discussed include: development of a web-based intervention application for high school teachers’ intervention efforts;ADOMATH: an android mobile game application for preschoolers;calculation method of winning percentage in handball games and its application to player evaluation;strategic prioritization of industry 4.0 adoption in Indonesian manufacturing SMEs: a best-worst method analysis;predicting motorcycle tire failure with deep learning;GoCart: an IoT-based cart system with promotional feature in mobile application;process mining techniques utilizing ChatGPT: a comparative analysis with disco software on call center log data;and siltation modeling in Laguna Lake: a case study of Brgy. Palingon, Calamba City.

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