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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4741-4750 订阅

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A Sensor-Based data Analytics for Patient Monitoring in Connected Healthcare Applications 8

A Sensor-Based Data Analytics for Patient Monitoring in Conn...

引用

8th international conference on Advanced Computing and Communication systems, ICACCS 2022

作者： Shanthi, S. Mithun, S. Kavi Prakash, P. Maharajan, K. Nandha Kishore, C. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665408165

Nowadays, Remote sensor networks have been broadly utilized in medical services applications, like emergency clinic and home patient checking. Compared to wired organizations, Remote clinical sensor networks are more vulnerable to spying, modification, manipulation, and playback attacks. To get remote clinical sensor organizations, a lot of work has been done. The current methods can protect patient information during transmission, but they can't preclude the director of the patient data set from uncovering sensitive patient information during an attack. In this research, we offer a practical method for preventing internal attacks by storing patient data on multiple information servers. The main goal of this work is to properly disseminate patient information across several information servers while using the Paillier and elgamal cryptosystems to conduct measurement investigations on the data without jeopardizing the patients' safety. Remote clinical sensor networks definitely lead to an improved knowledge of the nature of care without affecting patients' comfort. However, there are numerous potential security risks to the patient's sensitive physiological information transferred over public networks and stored in back-end frameworks. Snooping creates a security risk to the privacy of patient information. © 2022 IEEE.

关键词： Sensor networks

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Chinese Machine Reading Comprehension Based on Language Model Containing knowledge 22

Chinese Machine Reading Comprehension Based on Language Mode...

引用

6th international conference on Computer Science and Application engineering, CSAE 2022

作者： Chen, Wentong Fan, Chunxiao Wu, Yuexin Wang, Yitong School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781450396004

Machine reading comprehension (MRC) is a task that requires machines to answer relevant questions based on a given context. In recent years, it has attracted extensive attention with the development of deep learning and big data. Considering that human beings will associate some external relevant knowledge when understanding the text, researchers have proposed a method of introducing knowledge outside the given context to assist reading and this method is called knowledge-Based Machine Reading Comprehension (KBMRC). However, the current research on this method is still scattered, and the retrieval and fusion of relevant knowledge are still two challenges in application, especially in Chinese MRC. The contribution of this paper mainly on the following three points: Firstly, in order to resolve the problem of related knowledge retrieval, we build up a related knowledge set. Secondly, in order to resolve the problem of related knowledge fusion, we propose a negative sample generation strategy and train a language model containing knowledge. Finally, a twin-tower fusion model is constructed based on this model. The experiments on Chinese reading comprehension dataset CMRC2018 show that our method has a certain improvement compared with the baseline method without external knowledge. © 2022 Association for Computing Machinery.

关键词： knowledge based systems

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Literature Review on Advanced Cloud Robotics for Enhanced Industrial Automation

Literature Review on Advanced Cloud Robotics for Enhanced In...

引用

Electronics and Renewable systems (ICEARS), international conference on

作者： A. Manonmani S. Akash A. Aswinraj A. Manikandan Department of Electronics and Instrumentation Engineering Saveetha Engineering College Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Cloud robotics is transforming industrial automation by combining cloud computing with robotic systems. This paper reviews recent advancements in cloud robotics and their industrial applications, highlighting the synergy between cloud infrastructure and robotic platforms. Key technologies like artificial intelligence, machine learning, and the Internet of Things are explored for their roles in enhancing scalability, computational efficiency, and real-time data processing. The paper also addresses challenges such as latency and data security, proposing solutions to overcome them. The findings suggest that cloud robotics can significantly improve productivity, cost-efficiency, and flexibility in manufacturing and other sectors, with future research directions aimed at promoting its broader adoption.

关键词： Technological innovation Automation Service robots Scalability data security Machine learning Real-time systems Computer security Cloud robotics Edge computing

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Harnessing AI Mining Methodologies to Anticipate the Probability of Gestational Diabetes Mellitus Occurrence 3rd

Harnessing AI Mining Methodologies to Anticipate the Probabi...

引用

3rd international conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Hemavathy, P. Priya, M. Sangeetha Sundaravadivel, P. Vinothkumar, E.S. Kumar, K. Suresh Saveetha Engineering College Chennai Thandalam India

ISBN: (纸本)9789819739905

The effective diagnosis of medical conditions can be achieved through the application of knowledge discovery techniques in medical databases. Utilizing AI-based mining proves to be a valuable method for extracting insights and uncovering hidden patterns within stored information. This research focuses on utilizing healthcare data from gestational women during the first trimester to predict the risk of gestational diabetes, marking a novel approach in the field. data preprocessing involves employing an equal width binning interval technique to discretize continuous attributes, with input from medical experts to determine the desired interval width. The discretized values are then used as inputs for the model generation process, which occurs in two phases. Phase 1 involves converting numerical attributes into categorical values through discretization techniques, while Phase 2 applies and compares KNN and CNN algorithms to generate association rules. These rules reveal connections among measured attributes and provide insights into the risk level of gestational diabetes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Risk assessment

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CHARACTERISATION OF DEFECTS IN STRAND-BASED THERMOPLASTIC COMPOSITE PARTS USING ULTRASONIC INSPECTION

CHARACTERISATION OF DEFECTS IN STRAND-BASED THERMOPLASTIC CO...

引用

SAMPE 2024 conference and Exhibition

作者： Pattery, Hanae Smith, Adam Pik, Arthur Canart, Jean-Philippe Tabiai, Ilyass Dubé, Martine Department of Mechanical Engineering École de technologie supérieure Montréal Canada Québec Canada Polytech Orléans Orléans France Teijin Carbon America Inc Rockwood United States

ISBN: (纸本)9781934551455

Strand-based compression moulding compounds are increasingly used in both the aerospace and automotive industries, as they offer an interesting compromise between processability and performance. Non-destructive inspection via ultrasonic scan is often used to assess the internal quality of composite structures, revealing the size and location of defects such as voids, delamination and foreign body contamination. However, the heterogeneous structure of strand-based composite components makes it difficult to obtain meaningful results using this method. Therefore, ultrasonic inspection is complemented using optical microscopy and photographs. The aim of this paper is to correlate c-scan data to manufacturing defects. During the production of pre-impregnated thermoplastic composite textiles known as prepregs, the edges exhibit unacceptable variations in thickness and fibre content. These edges are trimmed and these scraps of prepregs carbon fibre with polyetheretherketone are landfilled. This waste, referred to here as tape edge trim (TET), resembles traditional strands but comes in a variety of shapes and lengths. Compression-moulded panels were made from TET strands of similar length obtained through sieving. The panels underwent the analyses mentioned above. It appears that resin concentration areas and out-of-plane TET strands orientation prevail in panels made with smaller strands. © 2024 Soc. for the Advancement of Material and Process engineering. All rights reserved.

关键词： Automotive industry

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Maintenance 4.0 Model Development for Production Lines in Industry 4.0 Using a Deep Learning Approach and IoT data in Real-Time: an Experimental Case Study 12

Maintenance 4.0 Model Development for Production Lines in In...

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12th IEEE international conference on Intelligent data Acquisition and Advanced Computing systems: Technology and Applications, IDAACS 2023

作者： Bouyahrouzi, El Mahdi Kihel, Ali El Embarki, Soufiane Kihel, Bachir El National School of Applied Sciences Mohammed First University Industrial Engineering Laboratory Oujda6000 Morocco

ISBN: (纸本)9798350358056

The current buzz in research and industry circles revolves around digitalization and digitization. By incorporating new technologies, companies can now collect data in real-time, which can be processed at a later stage. The manufacturing sector, in particular, is showing a keen interest in adopting maintenance 4.0 to enhance energy efficiency, optimize machine availability and utilization rate, extend equipment life, and forecast equipment failures before they occur. Such proactive measures help prevent breakdowns and minimize downtime, thereby improving productivity. This study focuses on a data-driven predictive maintenance system developed to implement effective maintenance 4.0. The system aims to first acquire real-time data from various sensors using an IoT-based system, second to detect failures before they occur, and finally to estimate the remaining useful life (RUL) using artificial neural networks of the LSTM type. Therefore, our methodology facilitates the prediction of defects in rotating machines, enabling timely and preventive interventions to address issues and prevent production interruptions. We conducted a case study evaluation to assess the efficacy of our proposed approach. The outcomes demonstrated the system's proficiency in recognizing failure indicators and its role in anticipating production halts resulting from defects. Moreover, experimental results highlighted its superior accuracy in Remaining Useful Life (RUL) estimation and reduced prediction errors in comparison to various algorithms explored in the literature, including RNN models, XGBoost, and boosting techniques. © 2023 IEEE.

关键词： Artificial Intelligence (AI) Big data Industry 4.0 Internet of Things (IoT) LSTM Maintenenace 4.0 Monitoring Prognosis Real-Time

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Differential Analysis for Networks Obeying Conservation Laws 48

Differential Analysis for Networks Obeying Conservation Laws

引用

48th IEEE international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Rayas, Anirudh Anguluri, Rajasekhar Cheng, Jiajun Dasarathy, Gautam School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States School of Computing and Augmented Intelligence Arizona State University TempeAZ85281 United States

ISBN: (纸本)9781728163277

Networked systems that occur in various domains, such as electric networks, the brain, and opinion networks, are known to obey conservation laws. For instance, electric networks obey Kirchoff's laws, and social networks obey opinion consensus. Conservation laws are often modeled as balance equations that relate appropriate injected flows and potentials at the nodes of the networks. A recent line of work considers the problem of estimating the unknown structure of such networked systems from observations of node potentials (and only the knowledge of the statistics of injected flows). Given the dynamic nature of the systems under consideration, an equally important task is estimating the change in the structure of the network from data - the so called differential network analysis problem. That is, given two sets of node potential observations, the goal is to estimate the structural differences between the underlying networks. We formulate this novel differential network analysis problem for systems obeying conservation laws and devise a convex estimator to learn the edge changes directly from node potentials. We derive conditions under which the estimate is unique in the highdimensional regime and devise an efficient ADMM-based approach to perform the estimation. Finally, we demonstrate the performance of our approach on synthetic and benchmark power network data. © 2023 IEEE.

关键词： ADMM convex optimization differential network analysis sparsity structure learning

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Unravelling the Potential of knowledge Graphs and Graph Neural Networks: Techniques and Applications

Unravelling the Potential of Knowledge Graphs and Graph Neur...

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Signal Processing and Advance Research in Computing (SPARC), international conference on

作者： Shreyas Om Anika Dogra Ananya Singh Ashwani Kumar Dubey Puneet Sharma Computer Science and Engineering ASETAmity University Uttar Pradesh Noida UP India Electronics and Communication Engineering ASETAmity University Uttar Pradesh Noida UP India School of Computer Science & Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

In today’s digital economy, organisations must manage an excessive volume of unstructured data. Traditional data warehouses are frequently underutilised due to inconsistencies in how to successfully evaluate such massive amounts of information. knowledge graphs have developed as an effective solution, giving a structured representation of interrelated facts to improve data organisation, retrieval, and inference across several disciplines. knowledge graphs, which capture interactions between elements such as people, locations, and concepts, provide a complete framework for interpreting and identifying hidden patterns in data. This study investigates the evolution of knowledge graphs, from simple data integration tools to complex systems that support semantic searches. These searches go beyond standard keyword searches, providing context-aware results that increase the relevance and accuracy of information retrieval. In addition, the study investigates the use of knowledge graphs in recommendation systems, emphasising their capacity to personalise user experiences through personalised suggestions. This research highlights the potential of knowledge graph technology to improve data management and utilisation, promoting innovation and efficiency across a variety of sectors.

关键词： Technological innovation Semantic search Signal processing algorithms knowledge graphs Machine learning Signal processing Graph neural networks Question answering (information retrieval) Recommender systems Standards

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Application of Computer data Algorithms in Enterprise Intelligent manufacturing Operation Management System

Application of Computer Data Algorithms in Enterprise Intell...

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data Science and Network Security (ICDSNS), international conference on

作者： Jingyan Yuan Feng Yuan School of Arts and Sciences University of Rochester Rochester New York USA School of Economics and Management Jilin Engineering Normal University Changchun Jilin China

With the development of the times, the use of intelligent manufacturing operation management systems has gradually emerged in enterprises. Nowadays, intelligent manufacturing operation management systems have achieved significant results, but the system is not perfect and there are still some problems in operation. Therefore, this article studied the application of computer (AC) data algorithms in enterprise intelligent manufacturing operation management systems, which optimized intelligent manufacturing operation management systems through computer data algorithms. This article tested the improvement rate of manufacturing output of enterprises after using computer data algorithms through experiments. The highest improvement rate in the experimental data was 24%, and the lowest was 16%. From this experimental data, it can be seen that computer data algorithms have played a good role in the intelligent manufacturing operation management system of enterprises, in order to improve the manufacturing output of enterprises.

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Self-Balancing System and Control Design for Two-Wheeled Single-Track Vehicles 20

Self-Balancing System and Control Design for Two-Wheeled Sin...

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20th international Learning and Technology conference, L and T 2023

作者： Alzaydi, Ammar King Fahd University of Petroleum and Minerals Department of Mechanical Engineering Dhahran Saudi Arabia King Fahd University of Petroleum and Minerals Interdeciplinary Research Center for Intelligent Manufacturing and Robotics Dhahran Saudi Arabia

ISBN: (纸本)9798350300307

This study presents a novel approach to achieve self-balance by employing a proportional integral derivative (PID) control system. Simply put, the innovative design consists of two electric ducted fans (EDF) that propel air against the direction of fall and thereby maintain balance. If these motors are allowed to move in two degrees of freedom, the EDF motors will propel and reduce weight of the two-wheeler while maintaining stability. To the best of our knowledge, no study has proposed a system that simultaneously provides propulsion and weight reduction along with achieving self-balance. The working mechanism of the utilized PID arducopter controller is elucidated which utilizes an IMU sensor and employs a nonlinear complementary filter on the special orthogonal group to determine the lean angles at any instant of time and a feedback loop to maintain the system's position at the desired upright zero degrees lean angle. Next, the proposed PID controller is first tested on a small-scale model to validate the developed concept to achieve self-balance by employing EDF motors. After achieving successful results on the small-scale model and thereby attaining the step of validation, the proposed concept is tested against a full-scale model (motorbike) by designing the mechanical and electrical parts. The methodology is divided into three major steps - mechanical parts design and manufacture, electrical components design and control system design. Furthermore, three mechanisms are designed to control steering, braking and throttle via a remote transmitter receiver control and autonomous control. © 2023 IEEE.

关键词： Proportional control systems

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