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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4781-4790 订阅

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Decentralized Intelligence: The Convergence of AI and Edge Computing for Industry 4.0

Decentralized Intelligence: The Convergence of AI and Edge C...

引用

Cognitive Computing in engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), international conference on

作者： Saundarya Yadav Bhawna Mallick School of Computer Science And Engineering(SSET) Sharda University Greater Noida India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

By combining edge computing with AI, technology is now experiencing a revolution - and data is being processed and decisions are being made where the data “lives” instead of having to rely on distant servers. This research explores how this trend is completely remaking industries like those in healthcare, manufacturing, and autonomous vehicles. The closer to the user's service is the information processing, the better the experience for the user in critical situations, and it also improves data security and bandwidth consumption. Edge based AI devices are improving patient monitoring and diagnostics while shortening critical care responses in healthcare. It is helping to streamline production in manufacturing through real time analysis and predictive maintenance. Edge computing is key to fast decisions in autonomous systems for vehicles and drones without continuous internet. In this study, we explore the state of art on using AI on the edge, current and future practical applications, challenges and future trends and their impact on industries undergoing digital transformation.

关键词： Industries data privacy Decision making Medical services Edge AI Market research Real-time systems Fourth Industrial Revolution Internet of Things Artificial intelligence

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Examining the Advantages of Dynamic Communications Software Design in Networks 5th

Examining the Advantages of Dynamic Communications Software ...

引用

5th international conference on data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Raghavendra, R. Das, Neeraj Inzimam Kurmi, Raviraj Singh Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

This paper examines the blessings of dynamic communications software program design in networks. By leveraging advances in generation, dynamic communications provide an ever-evolving method of connecting, communicating, and sharing facts inside networks without relying on static, often outdated, hardware. The principle blessings related to this community layout are scalability, flexibility, and excellent reliability. As networks extend, dynamic communications software can auto-regulate accordingly to ensure the overall capability of the general gadget. Moreover, users can count on operational performance upgrades and extended capability to address protection and privacy worries. Moreover, using dynamic software allows for more green aid usage, which facilitates lessening expenses related to the control and operation of the network. Moreover, dynamic communications have also helped reduce downtime and sell extra resilience in the face of surprising changes or unanticipated threats. Standard, this paper provides an in-depth knowledge of the advantages of dynamic communications software layout in networks. Dynamic Communications software program design and its various benefits in networking have been gaining substantial attention in recent years. Dynamic Communications software program layout additionally allows protocols to be modified on an as-needed foundation. That is useful for networks that might be constantly changing and want to adapt, which will take care of the converting environment. New protocols may be introduced or eliminated as wished, and the gadget will adjust to make the most efficient performance. In addition, using Dynamic Communications software design allows for a more straightforward integration of different systems. It is useful for networks that require a couple of systems to work together in order for them to function well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Software design

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Domain knowledge-Driven Multi-Source data Fusion Method for Power Grid Dispatching Business

Domain Knowledge-Driven Multi-Source Data Fusion Method for ...

引用

Energy engineering and Power systems (EEPS), international conference on

作者： Zhenlin Huang Jiaming Hu Xuxian Wang Kaihang Li Juhao Wu Junbo Zhang Electric Power Research Institute Guangdong Power Grid Co. Ltd. Guangzhou China School of Electric Power Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350375138

ISBN: (纸本)9798350375145

Power grid dispatching business critically require the fusion of multi-source data, but the data involved often exhibit significant specialized characteristics, making general data fusion methods unsuitable for direct application to power grid dispatching. This paper proposes a domain knowledge-driven multi-source data fusion method, which realizes dynamic weighted multidimensional similarity calculation, implements synonym replacement via domain knowledge graphs, and verifies data matching results through attributes value similarity calculations. Additionally, the method is applied to fuse various data files related to power grid dispatching. Experimental results indicate that this method significantly improves the accuracy of data fusion in power grid dispatching. Without considering domain knowledge, it can increase accuracy by 38.3-58.5% compared to single-metric similarity calculation methods. When domain knowledge is taken into account, the accuracy can be further improved by 53.8% under certain conditions, achieving precise matching and accurate fusion.

关键词： Accuracy Terminology Fuses Power system dynamics data integration Machine learning knowledge graphs Power grids Dispatching Business

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Research on the behaviour of Bluetooth Low Energy protocol in the heart rate monitoring application 27

Research on the behaviour of Bluetooth Low Energy protocol i...

引用

27th international conference on knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Balas, Zuzanna Tokarz, Krzysztof Zielinski, Bartlomiej Guzniczak, Tomasz Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland

In the paper, the authors check the behaviour of Bluetooth Low Energy protocol in a popular smart wristband and a microcontroller in the heart rate monitoring application. The measurements were collected using a development board with the ESP32 System on Chip. The authors tested measurement period stability and measurement reliability in various conditions. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Bluetooth Low Energy Health monitoring Internet of Things Smart wristband Wireless data transmission

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Blockchain-data Mining Fusion for Financial Anomaly Detection: A Brief Review 2

Blockchain-Data Mining Fusion for Financial Anomaly Detectio...

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2nd international conference on Machine Learning and data engineering, ICMLDE 2023

作者： Tien, Huy Tran Tran-Trung, Kiet Hoang, Vinh Truong VSB Technical University of Ostrava Poruba Ostrava Czech Republic Ho Chi Minh City Open University Ho Chi Minh City Viet Nam

Financial anomalies must be detected in order for financial institutions and regulatory bodies to manage risks and avoid fraudulent behavior. Financial anomaly detection is the practice of identifying unexpected or irregular financial transactions or patterns that may indicate fraudulent behavior or errors. It is crucial in today's digital era to prevent fraud, limit financial losses, and maintain secure financial systems. Various types of financial anomalies, such as credit card fraud, money laundering, financial statement fraud, and cryptocurrency fraud, pose significant risks to individuals and organizations. This review critically evaluates a selected research article on the use of blockchain technology in conjunction with data mining techniques to detect financial anomalies. The paper employs the case study method to demonstrate how well the suggested integrated system works in spotting financial anomalies. This review evaluates the article's methodology and conclusions and discusses its implications for practice. The report claims that merging data mining methods with blockchain technology can increase the precision and effectiveness of financial anomaly identification. This research advances knowledge about how block-chain technology and data mining techniques can be used to find financial abnormalities while also offering suggestions for further study and use. © 2024 Elsevier B.V.. All rights reserved.

关键词： Anomaly detection

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Methodology for Characterizing Spectrum data by Combining Quantitative and Qualitative Information 16th

Methodology for Characterizing Spectrum Data by Combining Qu...

引用

16th European-Alliance-for-Innovation (EAI) international conference on Cognitive Radio Oriented Wireless Networks (EAI CROWNCOM)

作者： Nagpure, Vaishali Das, Udayan Hood, Cynthia IIT Chicago IL 60616 USA St Marys Coll Calif Moraga CA 94575 USA

ISBN: (纸本)9783030980023;9783030980016

Wideband spectrum data can provide information on how large portions of the spectrum are being used. Spectrograms are typically used to visualize this data. The interpretation of the spectrogram (e.g., identification of bands and patterns) is left to the user, requires significant domain knowledge and is extremely time consuming. In this paper, we present a methodology for combining quantitative and qualitative information to identify channels and changes in spectrum occupancy. Channel identification and change detection algorithms are applied to real spectrum data collected over several years on two different measurement systems in Chicago. These analyses were then used to formulate queries to a knowledge graph implemented on a neo4j graph database. The results of the queries validated the channel identification and provided validation and explanation of the changes detected. This methodology was tested on measurement data from 470-698 MHz.

关键词： Spectrum measurements knowledge graph Graph database Change detection

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Classification of Arrhythmia using Electrocardiogram based Image Features with Deep Learning 1

Classification of Arrhythmia using Electrocardiogram based I...

引用

1st international conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Reddy, K. Naga Sankar Coumar, S. Oudaya Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Electrical and Communication Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350342796

Digital health technology leverages artificial intelligence for real-time monitoring of patients' arrhythmia conditions. Heartbeat data are captured via an electrocardiogram (ECG) and processed to extract pivotal features of each heartbeat. These extracted characteristics serve as the foundation for the training of data-driven models, facilitating the detection and measurement of arrhythmias. The success and reliability of these algorithmic decision-making systems heavily depend on the quality of features extracted, which typically necessitate significant empirical and domain-specific knowledge. To address these challenges, the current work introduces a novel method for arrhythmia classification that hinges on image features, derived using deep learning techniques, with the aim of augmenting the accuracy of the decision-making algorithms. Denoised one-dimensional (1D) ECG signals corresponding to various arrhythmia conditions are transformed into scalograms via the continuous wavelet transform (CWT) process. These scalograms are then classified with the help of a convolutional neural network (CNN), a powerful deep learning model. The proposed methodology exhibits an impressive classification accuracy of 91.52%, underscoring its potential effectiveness. This approach opens avenues for the establishment of trustworthy healthcare systems, enabling precise monitoring of patients' cardiac health, thereby facilitating timely and appropriate intervention. © 2023 IEEE.

关键词： arrhythmia classification continuous wavelet transform convolutional neural networks image features

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Anomaly Ranking of Failure Causes in manufacturing Process Using Causal Model

Anomaly Ranking of Failure Causes in Manufacturing Process U...

引用

IEEE international conference on systems, Man, and Cybernetics (SMC)

作者： Hattori, Reiko Ota, Yuya Fujii, Toru Nakajima, Hiroshi OMRON Corp Technol & Intellectual Property HQ Kyoto Japan

ISBN: (纸本)9781665442077

Identifying causes of unknown failures during the manufacturing process is quite difficult because of the uncertainty nature of the failures. As the manufacturing process becomes more complicated, failure-cause identification becomes more time consuming due to the increased number of causal candidates to be checked when addressing anomalies, and the complicatedly intertwined factors are difficult to discern. To solve these problems, various methods of anomaly detection and ranking have been developed using sensor data gathered during the manufacturing process. However, once an anomaly occurs at any point in the process, it propagates to other steps. Conventional anomaly detection methods are not sufficient against the complicated and unpredictable anomalies. In this study, we developed a novel anomaly ranking method based on a causal model. It employs a fusion model of human knowledge and sensor data to acquire a suitable causal model for accurate ranking. Experiments were conducted on a real packaging machine, the results of which showed a roughly 30% reduction of the ranking average compared with that of the conventional methods.

关键词： Anomaly detection

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Application of AutomationML/OPC UA in Robot Interoperability

Application of AutomationML/OPC UA in Robot Interoperability

引用

Artificial Intelligence and Computer engineering (ICAICE), international conference on

作者： Siyuan Chang Hualiang Zhang Software Engineering Institute Shenyang University of Technology Shenyang China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

This paper focuses on the application of AutomationML (AML) and Open Platform Communications Unified Architecture (OPC UA) in robot interoperability, aiming to promote the intelligence and interoperability of production systems in the era of Industry 4.0. By analyzing the current challenges of industrial robot interoperability, especially in terms of data standardization, a robot system architecture is proposed to improve the compatibility and information exchange efficiency between different types of robots. Research methods include system architecture design, integration of AML and OPCUA methods, and their empirical application in real-world manufacturing environments. The results confirm the feasibility and practicability of the method in the application of industrial robots, and give different devices higher interoperability and flexible response ability. It provides a theoretical basis and practical guidance for the development of intelligent manufacturing systems in the future, and has important practical significance and application value in improving the level of industrial digitalization and intelligence.

关键词： Systematics Service robots Computational modeling systems architecture Standardization Industrial robots data models Servers Interoperability Testing

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Survey on IoT-based Big data Analytics 13

Survey on IoT-based Big Data Analytics

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13th international conference on Electrical engineering, ICEENG 2022

作者： Shouaib, Mohamed Metwally, Khaled Badran, Khaled Military Technical College Computer Engineering And Ai Cairo Egypt

ISBN: (纸本)9781665435093

Nowadays, the fast development of the internet and 5G technology encouraged the emergence of many technologies that enhanced the human lifestyle. Moreover, the wide spread of Internet of Things (IoT) gadgets contributed significantly to this technological transformation and provided individuals with pleasure and simplified their lives. Many systems have been developed using sensor-based devices, i.e., IoT devices, e.g., smart healthcare, smart transportation, smart agriculture..etc. Consequently, it became the main source of huge amount of data that are collected frequently, named Big data. However, such collected data is useless without applying analytics that discover new insights. This data is massive, collected at a rapid speed, and in different formats, constituted the big data main characteristics;volume, velocity, and variety (3Vs). Performing analytics on big data, i.e., Big data Analytics (BDA) has many challenges that are most probably related to its characteristics. This paper will undertake a survey on Big data technologies covering various IoT domains. This survey discusses overview of IoT as the main source of big data, big data analytics mechanisms, methodologies, architecture, use cases, challenges and finally the existing frameworks and tools for analytics. © 2022 IEEE.

关键词： Electrical engineering Smart cities 5G mobile communication Loading Memory Medical services Big data

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