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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1821-1830 订阅

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5G Network: Architecture, Protocols, Challenges and Opportunities 3

5G Network: Architecture, Protocols, Challenges and Opportun...

引用

3rd ieee international conference on computing, Communication, and Intelligent systems, ICCCIS 2022

作者： Javid, Iqra Khara, Sibaram Sharda University Department of Electrical Electronics and Communication Engineering Greater Noida India

ISBN: (纸本)9781665462006

The 5th Generation system is being established to give unmatched connectivity that will connect anything anywhere. 5G networks are designed to deliver high-speed, reduced latency for increased mobile broadband, large machine-to-machine communication and real-time applications. High data rates, high coverage, high reliability, cost-effectiveness are all advantages of 5G. This article discusses architecture, protocols, enabling technologies like mmWave, massive multi-input-multi-output, heterogeneous network, application areas of 5G and challenges in the 5G network. © 2022 ieee.

关键词： 5G mobile communication systems

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Periodic Stacked Transformer-based Framework for Travel time Prediction

Periodic Stacked Transformer-based Framework for Travel Time...

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international Joint conference on Neural Networks (IJCNN)

作者： Lin, Hui-Ting Dai, Hao Tseng, Vincent S. Natl Yang Ming Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Inst Informat Management Hsinchu Taiwan Natl Chung Hsing Univ Dept Management Informat Syst Taichung Taiwan

ISBN: (纸本)9798350359329;9798350359312

Travel time analysis and prediction play critical roles in developing Intelligent Transportation systems (ITS), which have attracted significant interests from the research community. Deep learning-based methodologies have proven to be powerful tools in utilizing big data for predicting travel times. However, while most studies have focused on shortterm predictions, predicting travel times over longer periods is equally important for wide applications like traffic management and route planning. Long-term prediction, which often receives less attention due to its complexity, remains a gap in current researches. To address this challenge, we propose the Periodic Stacked Transformer (PS-Transformer), a novel Transformer-based framework designed to enhance both short and long-term traffic predictions. PS-Transformer consists of two primary modules: the Segment Encoding Integration (SEI) and the Periodic Stacked Encoder-Decoder (PSED). SEI module extracts periodic patterns from traffic data, while PSED effectively captures shortterm and long-term dependencies from temporal attributes. Additionally, PSED tackles error accumulation, a common issue in extended prediction periods, through its non-autoregressive decoder design. Our PS-Transformer is validated through a series of experiments on a real-world dataset, demonstrating its capability in multi-step predictions that provide forecasts over an extended duration. Empirical evaluation results show that PS-Transformer outperforms state-of-the-art methods in both short and long-term travel time predictions across various metrics, including MAE, RMSE, and SMAPE.

关键词： Travel time prediction Intelligent Transportation systems Long-term prediction Periodic traffic data Transformer

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Heterogeneous embedded Resource Management under Space-based Edge computing Environment with Kubernetes Device Plugin 3

Heterogeneous Embedded Resource Management under Space-based...

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3rd ieee international conference on Power, Electronics and Computer applications, ICPECA 2023

作者： Wang, Xin Yang, Xinyu Ye, Wenjie Yan, Lei Cao, Suzhi University of Chinese Academy of Sciences Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Key Laboratory of Space Utilization Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665472784

Edge devices are widely applied in space scenarios for their compact size and diminished power consumption. To avoid the collision of different applications, containerizing applications becomes a more welcomed option compared to virtual machines for its light-weightiness and short loading latency. Kubernetes, considered a de facto framework for orchestrating containers, allows automatic allocations of resources connected to nodes, but it only supports management on CPU, I/O, network, and storage by default. Supported devices can be extended with its device plugin mechanism however, official device plugins might not work on edge devices, as their functionalities are constrained due to its compactness, such as NVidia TX2 that is not supported by the official Nvidia GPU device plugin. Moreover, devices vary in type, and architecture and even some do not support operating systems, making it unintelligent to develop device plugins separately. Therefore, managing device plugins becomes an urgent problem for edge cloud providers. In this paper, we proposed and implemented a paradigm of orchestrating devices that allows Kubernetes manage edge devices, even if they do not run operating systems. As for the obstacles of being unable to report the device topology of a single node to the scheduler, we proposed an approach to hijack the scheduling process without establishing a new scheduler. We conducted experiments and the results showed that the platform we proposed can enable Ku-bernetes to manage various devices without device plugins, both on on-board embedded devices and non-OS-supported devices. We tested its performance and capacity with a simulated device, and the result shows that it only causes 6.34 % overhead, and its embedded scheduling method has an adequate performance compared to optimum method, which is only 7.7 % slower. © 2023 ieee.

关键词： Containers

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Implementation of Plant Data Security using embedded Hardware 5

Implementation of Plant Data Security using Embedded Hardwar...

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5th ieee international conference on Advances in Science and Technology, ICAST 2022

作者： Prasath, J.S. Kannan, G. Narayana Engineering College Department of Ece Gudur India Amrita College of Engineering and Technology Department of Ece Nagercoil India

ISBN: (纸本)9781665492638

embedded systems are utilized in variety of fields. embedded chips are used in smart mobiles, home appliances, processing the real-time information etc. embedded systems are susceptible to different attacks, energy constraints and greater complexness. The objective of this proposed system is to preserve the plant data from intruders in real-time environment. The process parameters are detected by the sensors and performs encoding using the embedded system. The decoding is done to obtain the information in readable text. The Graphical User Interface (GUI) is utilized to monitor the parameters. The methodology is required for securing the plant parameters and to prevent the alteration of data over internet from attackers. This proposed system is applicable for protecting the parameters across wireless medium. © 2022 ieee.

关键词： embedded systems

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Auto-tuning of Large-Scale Parallel computing applications Based on Bayesian Optimization 4

Auto-tuning of Large-Scale Parallel Computing Applications B...

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4th international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Zhang, Jinhu Sun, Bin Sun, Jianpeng Yu, Hao Xie, Xiguo Zhengzhou University Zhengzhou China Co. Ltd. Beijing China

ISBN: (纸本)9798331531225

The performance of large-scale parallel computing applications is highly dependent on the parameter settings within complex systems. Due to the high-dimensional and nonlinear nature of kernel environment parameter spaces, traditional manual tuning methods are both labor-intensive and inefficient when exploring optimal parameter combinations, often leading to suboptimal performance. To address this issue, this paper proposes an automated parameter tuning framework based on Bayesian optimization, aimed at efficiently exploring complex high-dimensional parameter spaces and predicting better-performing parameter combinations. This framework builds a surrogate model, iteratively selects potentially efficient parameter configurations, and integrates SHAP to analyze the influence of each parameter on performance optimization. We validated the effectiveness of this framework in several typical large-scale parallel computing application scenarios, including NAS Parallel Benchmarks (NPB), quantum chemistry simulation software Quantum ESPRESSO and VASP, and the Weather Research and Forecasting model (WRF). Experimental results show that Bayesian optimization significantly shortens the overall tuning time and performs traditional methods such as genetic algorithms in terms of performance optimization. Experimental results show that Bayesian optimization significantly shortens the overall tuning time by 50% and improves application performance by up to 11%, outperforming traditional methods such as genetic algorithms in terms of performance optimization. Furthermore, SHAP analysis reveals the importance of key kernel parameters in enhancing application performance. © 2024 ieee.

关键词： Bayesian networks

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On Multivariate Linear Regression applications 11

On Multivariate Linear Regression Applications

引用

11th international conference on Electrical, Electronic and computing Engineering, IcETRAN 2024

作者： Jovanović, Aleksa Krstić, Aleksandra Vujnović, Sanja Durović, Željko University of Belgrade School of Electrical Engineering The Department of Signals and Systems Belgrade Serbia

ISBN: (纸本)9798350386998

Linear regression is a very simple machine learning model that is supposed to find linear relations between input and output data. Its use is limited since real-world random variables are almost never linearly correlated, but when there is limited amount of data for training, using other larger and more sophisticated models might be impossible. Even though simple linear relations are hard to come by in nature, more complex mathematical dependencies can often be linearized i.e. approximated using linear models. Additionally, by preprocessing the input data using nonlinear mathematical operations linear regression can artificially account for nonlinear relations. This methodology was tested on real data representing measurements in thermal power plant while the goal was predicting the specific energy of the coal that was being burnt in the plant. Estimating coals caloric value is of tremendous value since using this information power plant can be configured to run more efficiently. © 2024 ieee.

关键词： Multiple linear regression

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Analysis, Trends, and Utilization of Security Information and Event Management (SIEM) in Critical Infrastructures 10

Analysis, Trends, and Utilization of Security Information an...

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10th international conference on Advanced computing and Communication systems, ICACCS 2024

作者： Sashwin, K. Nithika, K.S. Priyadharshini, S. Maduvant, S.P. Saranya Sri Eshwar College of Engineering Department of Computer Science and Business Systems Computer Science and Business Systems Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

Security Information and Event Management (SIEM) systems have become essential assets in the realm of cybersecurity. They fulfill a central role in the prevention, detection, and response to cyber threats. Over time, SIEM solutions have matured into comprehensive platforms that offer extensive visibility, enabling organizations to pinpoint high-risk areas and take proactive measures to mitigate potential threats. This proactive approach not only bolsters security but also streamlines incident response, reducing both costs and response times. Recent developments in the field have witnessed the convergence of SIEM systems with big data analytics tools, opening doors to more sophisticated threat detection and response capabilities. This survey delves into the core features of widely adopted SIEM platforms and evaluates the external factors influencing the trajectory of the SIEM landscape over the medium and long term. Furthermore, the study presents a catalog of potential enhancements for the next generation of SIEMs, offering a thorough analysis of their advantages and practical applications within critical infrastructure settings. © 2024 ieee.

关键词： Critical infrastructures

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realtime Object Recognition Method Inspired by Multimodal Information Processing in the Brain for Distributed Digital Twin systems 18

Realtime Object Recognition Method Inspired by Multimodal In...

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18th ieee international Wireless Communications and Mobile computing conference (IWCMC)

作者： Seki, Ryoga Kominami, Daichi Shimonishi, Hideyuki Murata, Masayuki Fujiwaka, Masaya Osaka Univ Grad Sch Informat Sci & Technol Osaka Japan NEC Corp Ltd Tokyo Japan NEC Corp Ltd Syst Platform Res Labs Yokohama Kanagawa Japan

ISBN: (纸本)9781665467490

Recently, digital twins have been paid much attention as a major application towards Beyond 5G/6G network, and real-time object recognition methods are key technology to digitize the real world as a digital twin. However, it is challenging to make a fast and accurate decision on what the object is from real-time streaming information such as video because accurate object recognition algorithms require a huge computation. To satisfy delay requirement of digital twin applications, such computations have to be moved from cloud to edges or even small terminal devices, where computing capacity is very limited. Thus, recognition mechanisms have to be simplified for small devices but they would result in degraded accuracy. In this paper, we focus on the multimodal information processing mechanism of the brain, which makes decisions based on multiple types of uncertain observed information, to improve accuracy of simplified recognition mechanisms. We first propose a unimodal object recognition mechanism based on the Bayesian attractor model, which continuously recognizes objects from noisy streaming media data. Then, we extend the mechanism with Bayesian causal inference to fuse the results of unimodal media recognition. Through computer simulations, we show that our proposed method identifies an object accurately and quickly from uncertain observed information.

关键词： Mobile AR digital twin multimodal recognition Bayesian attractor model Bayesian causal inference

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Method for Finding the Maximum Dynamic Flow in Periodic Fuzzy Graphs

Method for Finding the Maximum Dynamic Flow in Periodic Fuzz...

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2024 international conference on Industrial Engineering, applications and Manufacturing, ICIEAM 2024

作者： Bozhenyuk, Alexander Gerasimenko, Evgeniya Nikashina, Polina Southern Federal University Department Of Information Security Systems Taganrog Russia

ISBN: (纸本)9798350395013

The article presents a method for determining the maximum flow value in dynamic networks using periodic graphs. These networks are generalized networks that have a wide range of practical applications, such as water distribution, currency exchange, and transportation of perishable or valuable goods. Due to the variation in bandwidth values in different areas of the network, dynamic networks are used. These parameters are presented in a fuzzified form, accounting for environmental factors and human activities. The selection of periodic graphs is based on the presence of cycles and frequency of time intervals. These types of networks can be applied in real-world transportation scenarios. © 2024 ieee.

关键词： Graphic methods

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Monitor and Recover: A Paradigm for Future Research on Distribution Shift in Learning-Enabled Cyber-Physical systems 25

Monitor and Recover: A Paradigm for Future Research on Distr...

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16th international conference on Cyber Physical systems-ICCPS-Annual

作者： Lin, Vivian Lee, Insup Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9798400714986

With the known vulnerability of neural networks to distribution shift, maintaining reliability in learning-enabled cyber-physical systems poses a salient challenge. In response, many existing methods adopt a detect and abstain methodology, aiming to detect distribution shift at inference time so that the learning-enabled component can abstain from decision-making. This approach, however, has limited use in real-world applications. We instead propose a monitor and recover paradigm as a promising direction for future research. This philosophy emphasizes 1) robust safety monitoring instead of distribution shift detection and 2) distribution shift recovery instead of abstention. We discuss two examples from our recent work.

关键词： Distribution Shift Learning-Enabled Cyber-Physical systems

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