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检索条件"任意字段=IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

共 37219 条记录，以下是1801-1810 订阅

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Deep Learning Segmentation Models Evaluation for Deforestation Monitoring embedded systems 4

Deep Learning Segmentation Models Evaluation for Deforestati...

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4th international conference on Intelligent Cybernetics Technology and applications, ICICyTA 2024

作者： Careli, Álvaro S. Boas, Evandro C. Vilas Teixeira, Eduardo H. Silva, Elaine C. C. Aquino, Guilherme P. Figueiredo, Felipe A. P. National Institute of Telecommunication Inatel Cyber Security and IoT Laboratory Cs&i Lab. Santa Rita do Sapucaí Brazil National Institute of Telecommunication Inatel IoT Research Group Laboratory Santa Rita do Sapucaí Brazil National Institute of Telecommunication Inatel Wireless and Artificial Intelligence Laboratory Wai Lab. Santa Rita do Sapucaí Brazil

ISBN: (纸本)9798331506490

This work evaluates deep learning segmentation models to propose a deforestation monitoring embedded system. The approach stands for environmental monitoring using remote sensing imagery, edge computing, and a deep learning segmentation model. Thus, the performance of you only look once architecture version 8 (YOLOv8) and Mask Region-based convolutional neural networks (Mask R-CNN) embedded in Raspberry Pi Model 4 regarding Intersection over Union (IoU), mean Average Precision (mAP), and time per image processing metrics is compared. The models are combined with a pixel-based algorithm that analyzes the temporal segmented images to define their forest area percentage for deforestation monitoring and detection. The results demonstrate YOLOv8x model achieved an IoU of 0.762, with a time per image of 0.4777 seconds, while Mask R-CNN R101 FPN 3x obtained an IoU of 0.763, with a time per image of 0.2669 seconds. The average times for YOLOv8 ranged from 0.0434 to 0.4777 seconds, and for Mask R-CNN from 0.1969 to 0.2669 seconds. Finally, this work proposes evaluating the model's performance when working with generative AI models Dall-e, Craiyon, and Tess-AI to create a synthetic dataset to augment the initial one with synthetic samples and improve the model's training with a large dataset. The Dall-e has been shown to outperform the others regarding the IoU metric, which was suggested to augment datasets with synthetic samples. © 2024 ieee.

关键词： Deforestation Environmental Monitoring Mask R-CNN Segmentation YOLOv8

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A Model for Data Processing on Warehouse-Scale Computers

A Model for Data Processing on Warehouse-Scale Computers

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2024 ieee international conference on Big Data, BigData 2024

作者： Exton, Kevin Read, Maria The University of Melbourne School of Computing and Information Systems Melbourne Australia

ISBN: (纸本)9798350362480

Modern data processing workloads often have highly unpredictable end-to-end latency characteristics that are caused by heterogeneity, time-variation, and parallelized processing. The increase in unpredictability is in part attributable to job "straggling", and is symptomatic of a new class of stochastic scheduling challenges that will degrade the performance of current and future applications at scale. While the job scheduling literature for data processing frameworks is rich with ideas;there is little coordination between research groups on methodology and presentation, stunting the ability for designers to survey a collection of results and draw generalizable conclusions about good design patterns. We introduce an abstract system model for data processing on warehouse scale machines that aids in eliminating ambiguity in the job scheduling research by categorizing schedulers based on where they act on the system in the job processing data pathway. Furthermore, we demonstrate that although the scheduling problem is NP-Hard in the general case, it is still possible to derive scheduler design principles using bounds and asymptotics. © 2024 ieee.

关键词： Data warehouses

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Adaptive Computation Offloading for Heterogeneous IoT applications: An Experimental Study 20

Adaptive Computation Offloading for Heterogeneous IoT Applic...

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20th international Multi-conference on systems, Signals and Devices, SSD 2023

作者： Aidaros, Othman Al Bouida, Zied Ibnkahla, Mohamed Carleton University Systems and Computer Engineering Department OttawaON Canada

ISBN: (纸本)9798350332568

Internet of Things (IoT) applications are heterogeneous in terms of the deployed hardware, developed protocols, and requirements of each of these applications. The decision whether to process such applications at the sensor nodes or at a higher layer (i.e., Edge, Fog, or Cloud) heavily depends on those heterogeneity features. In this paper, we propose a number of adaptive schemes to address the local processing vs. offloading of IoT applications based on their energy consumption and execution time. To this end, we deploy six IoT applications on real sensor nodes, measure their energy consumption and execution time, and then decide where to process the application based on the measurements. We study the performance of the proposed schemes in terms of energy efficiency and execution time. We also compare their performances to selected non-adaptive schemes. © 2023 ieee.

关键词： Energy efficiency

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A real-time cyber-physical testbed to assess protection system traffic over 5G networks

A real-time cyber-physical testbed to assess protection syst...

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2022 ieee international conference on Communications, Control, and computing Technologies for Smart Grids, SmartGridComm 2022

作者： Adrah, Charles M. Katoulaei, Mohammad K. Amare, Tesfaye Palma, David Norwegian University of Science and Technology Ntnu Department of Information Security and Communication Technology Trondheim Norway Ntnu Department of Electric Power Systems Trondheim Norway Sintef Energy As Trondheim Norway

ISBN: (数字)9781665432542

ISBN: (纸本)9781665432542

The fifth-generation (5G) mobile network promises to offer low latency services. Hence, there is interest in assessing various power distribution grid applications that can be deployed with a 5G infrastructure. This paper presents a smart grid cyber-physical testbed for protection systems. It consists of power system applications deployed on OPAL-RT, a real-time platform, and a 5G communication network modeled in ns-3. The testbed is used to assess the performance of a power system protection application (Permissive Underreaching Transfer Trip (PUTT) protection scheme) deployed over a 5G communication network. The proposed approach enables real-time protection traffic to be analyzed in an emulated 5G network and gives insights into how such a testbed can be used to assess the performance of protection traffic in 5G networks and beyond. © 2022 ieee.

关键词： 5G mobile communication systems

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Quantum Machine Learning for Network Intrusion Detection systems, a Systematic Literature Review 2

Quantum Machine Learning for Network Intrusion Detection Sys...

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2nd ieee international conference on AI in Cybersecurity, ICAIC 2023

作者： Nicesio, Otavio Kiyatake Leal, Adriano Galindo Gava, Vagner Luiz Institute for Technological Research Artificial Intelligence and Analytics Department São Paulo Brazil

ISBN: (纸本)9798350396966

Quantum computing presents potential advantages over classical computing in terms of computational complexity. Therefore, it is expected for quantum machine learning applications to have improvements in capacity and learning efficiency over classical machine learning methods. This paper aims to present a Systematic Literature Review of articles published between 2017 and 2022, identifying, analyzing, and comparing different proposals of quantum machine learning applications for network intrusion detection systems (IDS). This study focused on identifying papers that implemented quantum machine learning algorithms in the context of intrusion detection systems. The main algorithms found were variational hybrid quantum-classical, with models based on quantum support vector machines and quantum neural networks. Benefits compared to classical models were observed and described, such as reduced training time and improved classification accuracy for attacking traffic. © 2023 ieee.

关键词： Support vector machines

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A Heuristic Approach for Resolution of Issues in Power System applications in SCADA/ EMS System at National Load Despatch Centre 10

A Heuristic Approach for Resolution of Issues in Power Syste...

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10th ieee international conference on Power systems, ICPS 2023

作者： Saxena, S.C. Singh, Pramod Shukla, Rohit Kumar, Deepak Singh, Bhavneet Grid Controller of India Limited New Delhi India

ISBN: (纸本)9798350318739

Supervisory Control and Data Acquisition & Energy Management systems (SCADA/EMS) System are extensively used at load despatch Centres worldwide for real time grid operation in Power System. These systems are equipped with several Power System applications (PSA) features as required for operators to make informed decisions. These applications are generic to a great extent and are part of the overall functionality of the SCADA/EMS system. State Estimation (SE), Contingency Analysis (CA), Optimal power flow (OPF), etc. are some of the widely and commonly used applications in SCADA/EMS system. The smooth running of these applications is of prime importance to ensure safe and economic grid operation. Besides providing situational awareness to system operators it also gives meaningful full information about violations, vulnerabilities and operational issues which may result in potential problems in real time grid operation. Solution and results obtained from these applications primarily depends on SCADA data and correct modeling of the power system network. The source of SCADA data may be directly reporting from sub stations or from other control Centres through Inter Control Centre Protocol (ICCP) data. Further regular tuning of key parameters also plays an important role in the successful running of PSA application. This paper provides the issues and their mitigation in heuristic manner which are encountered during the running of these applications at national level i.e. National Load Despatch Centre (NLDC) in India. © 2023 ieee.

关键词： Energy management systems

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In-Situ Techniques on GPU-Accelerated Data-Intensive applications 19

In-Situ Techniques on GPU-Accelerated Data-Intensive Applica...

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19th ieee international conference on e-Science, e-Science 2023

作者： Ju, Yi Li, Mingshuai Perez, Adalberto Bellentani, Laura Jansson, Niclas Markidis, Stefano Schlatter, Philipp Laure, Erwin Max Planck Computing and Data Facility Germany Kth Royal Institute of Technology Sweden Cineca Italy Technical University of Munich Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

ISBN: (纸本)9798350322231

The computational power of High-Performance computing (HPC) systems is constantly increasing, however, their input/output (IO) performance grows relatively slowly, and their storage capacity is also limited. This unbalance presents significant challenges for applications such as Molecular Dynamics (MD) and Computational Fluid Dynamics (CFD), which generate massive amounts of data for further visualization or analysis. At the same time, checkpointing is crucial for long runs on HPC clusters, due to limited walltimes and/or failures of system components, and typically requires the storage of large amount of data. Thus, restricted IO performance and storage capacity can lead to bottlenecks for the performance of full application workflows (as compared to computational kernels without IO). In-situ techniques, where data is further processed while still in memory rather to write it out over the I/O subsystem, can help to tackle these problems. In contrast to traditional post-processing methods, in-situ techniques can reduce or avoid the need to write or read data via the IO subsystem. They offer a promising approach for applications aiming to leverage the full power of large scale HPC systems. In-situ techniques can also be applied to hybrid computational nodes on HPC systems consisting of graphics processing units (GPUs) and central processing units (CPUs). On one node, the GPUs would have significant performance advantages over the CPUs. Therefore, current approaches for GPU-accelerated applications often focus on maximizing GPU usage, leaving CPUs underutilized. In-situ tasks using CPUs to perform data analysis or preprocess data concurrently to the running simulation, offer a possibility to improve this underutilization. © 2023 ieee.

关键词： Graphics processing unit

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Revolutionizing Education through Digital Twin Technology: Key Techniques and applications 2

Revolutionizing Education through Digital Twin Technology: K...

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2nd ieee international conference on Emerging Research in Computational Science, ICERCS 2024

作者： Valarmathie, P. Guruvarshni, G. Rajalakshmi Engineering College Department of Information Technology Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Digital Twin technology, which was first employed in industry, is now providing exciting opportunities for education. Imagine kids learning through interaction with virtual representations of real-world systems experimenting, making mistakes, and comprehending complex processes in a secure environment. However, today's Digital Twin educational applications often fall lacking when it comes of flexibility and differentiation. The study explores the key techniques used in the digital twin technology in education, includes real-time Data Integration, AI-Driver Feedback Mechanisms, Simulation and Interactive Learning, Personalization, Performance analytics and Learning Insights. The essential aspects of the framework, such as the data collection for gathering raw data, the data processing for preparing, the data transformation for formatting data for the Virtual Learning Environment (VLE), dashboard and data Visualization to provide feedback and insights for educators. Our platform is designed to be adaptable and scalable, making it easy to fit into various educational settings and adapt to different learning goals. This survey aims to make these powerful learning tools accessible to more students, enhancing their education in meaningful ways. © 2024 ieee.

关键词： Federated learning

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Data-Driven Graph Construction of Power Flow Graphs for Electric Power Transmission Networks 23

Data-Driven Graph Construction of Power Flow Graphs for Elec...

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23rd ieee international conference on Machine Learning and applications, ICMLA 2024

作者： Poole, Benjamin Ratnakumar, Rajan Madurasinghe, Dulip Tharaka Kummerle, Christian Venayagamoorthy, Ganesh Kumar Lee, Minwoo University of North Carolina Department of Computer Science Charlotte United States University of Pretoria Department of Electrical Electronic and Computer Engineering South Africa Clemson University Real-Time Power and Intelligent Systems Laboratory United States

ISBN: (纸本)9798350374889

A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specific methods, it lacks data-driven approaches that have proven effective in modeling the topology of complex systems. To address this gap, this paper explores the potential of data-driven methods for more accurate and adaptive solutions to uncover the true underlying topology of EPTNs. First, this paper examines Gaussian Graphical Models (GGM) to create an EPTN network graph (i.e., undirected simple graph). Second, to further refine and validate this estimated network graph, a physics-based, domain-specific refinement algorithm is proposed to prune false edges and construct the corresponding electric power flow network graph (i.e., directed multi-graph). The proposed method is tested using a synchrophasor dataset collected from a two-area, four-machine power system simulated on the real-time digital simulator (RTDS) platform. Experimental results show both the network and flow graphs can be reconstructed using various operating conditions and topologies with limited failure cases. © 2024 ieee.

关键词： Undirected graphs

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Digital Twin-Based Transfer Learning for Collaborative Robot systems: A Proof of Concept 38

Digital Twin-Based Transfer Learning for Collaborative Robot...

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38th international conference on Information Networking (ICOIN)

作者： Roongpraiwan, Supat Li, Zongdian Pourghasemian, Mohsen Gacanin, Haris Sakaguchi, Kei Tokyo Inst Technol Dept Elect & Elect Engn Tokyo Japan Rhein Westfal TH Aachen Chair Distributed Signal Proc Aachen Germany

ISBN: (纸本)9798350330946;9798350330953

Nowadays, the increasing trend toward digitalization has driven the extensive adoption of collaborative robotic automation across industries, yet a significant limitation is the robots' adaptability to unexpected and dynamic environments. This research introduces a Digital Twin (DT)-based Transfer Learning (TL) approach that combines DTs and Machine Learning (ML) to enhance adaptability in collaborative robot systems. The proposed system uses DT cyberspace for pre-training ML algorithms and leverages TL to apply this knowledge to real-world applications. This innovative approach efficiently trains state-of-the-art ML models, delivering exceptional performance while reducing the required time and data resources. The proof-of-concept experiments, employing the proposed DT-based TL to control soccer robots, demonstrate a remarkable 96% reduction in training time while maintaining a high level of adaptability, achieving a 70% goal accuracy rate in dynamic scenarios.

关键词： digital twin collaborative robot system machine learning transfer learning proof of concept

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