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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 36894 条记录，以下是471-480 订阅

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Predictive Maintenance Based on embedded AI and Wireless Monitoring of Working Environment 8

Predictive Maintenance Based on Embedded AI and Wireless Mon...

引用

8th international conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2024

作者： Ma, Linyang Li, Yinchao Yue, Weiwei Song, Xiaolu Shandong Normal University School of Physics and Electronics Jinan China Yili Normal University College of Art and Design Xinjiang Yining China

ISBN: (纸本)9798331506230

Traditional fault diagnosis methods are often inefficient and costly, hindered by limited computing power that compromises accuracy. Their lack of flexibility also poses challenges in establishing effective multi-device operational monitoring systems. To address these issues, we developed a fault diagnosis and predictive maintenance system based on Lightweight-time-Delay Neural Networks (Lightweight-TDNNs). Leveraging their robust capability in processing time-series data, TDNNs excel in learning features across various time delays. By optimizing AI algorithms for lightweight deployment, we successfully implemented embedded AI for predictive maintenance of machinery and equipment faults at the edge. Utilizing a Zigbee-based IoT approach, we enabled wireless monitoring of equipment status and environmental conditions, overcoming the constraints of wired installations. Validation on the STM32F429 chip demonstrated that our model achieves over 90% accuracy on test sets, demonstrating its suitability for industrial applications. © 2024 ieee.

关键词： Predictive maintenance

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RAPID: Integrating AI and Multi-Agent systems for Enhanced Traffic Management Framework with YOLOv9

RAPID: Integrating AI and Multi-Agent Systems for Enhanced T...

引用

2024 international conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Baroud, Shadia Yahya Yahaya, Nor Adnan Department of Computing University Malaysia of Computer Science and Engineering Petaling Jaya Malaysia

ISBN: (纸本)9798331533137

Traffic accidents pose a major risk to human life and have a considerable economic impact worldwide. In this study, we present the RAPID framework, an innovative approach that integrates Artificial Intelligence (AI) and Multi-Agent systems (MAS) to enhance traffic management. The core of the RAPID framework is the YOLOv9 model, a "state-of-the-art" deep learning (DL) algorithm for real-time object detection and classification, which we employ to detect and classify traffic accidents accurately. The RAPID framework addresses key challenges in modern traffic management, including data heterogeneity, scalability and real-time processing. Our system leverages a multi-layered architecture: data is acquired from various sources, including traffic cameras and CCTV and then transmitted to a cloud data center for storage and processing. Within the cloud infrastructure, the data undergoes pre-processing and is analyzed by the YOLOv9 model. The MAS coordinates various components of the framework, distributing tasks among specialized agents responsible for data acquisition, preprocessing, model training and real-time detection. We evaluated the performance of the RAPID framework using a dataset of 3000 labeled traffic accident images. The YOLOv9 model demonstrated 95.4% accuracy, 93.8% precision, 92.6% recall and 93.2% F1-score. The experimental results indicate that the RAPID framework significantly improves traffic accident detection and response times compared to traditional systems, highlighting its potential for real-world applications. The RAPID framework contributes to the field of intelligent transportation systems (ITS) by providing a robust, scalable and efficient solution for real-time traffic management. ©2024 ieee.

关键词： Multi agent systems

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Architecting Machine Learning systems: Which Parts are the Architect's Pain? 21

Architecting Machine Learning Systems: Which Parts are the A...

引用

21st international conference on Software Architecture (ICSA)

作者： Jepsen, Sune Chung Nielsen, Anna Olgaard Kjaergaard, Mikkel Baun Univ Southern Denmark SDU Software Engn Odense Denmark

ISBN: (纸本)9798350366266;9798350366259

Designing machine learning applications can be challenging, especially software architectures for handling real-time sensor data processed by compute- and software-intensive machine learning applications. This paper answers the two research questions: "Which parts of a machine learning pipeline do novel software architectures and framework optimize?" and the associated question of "Which parts do the architect then need to focus on?". The presented experiences and experimental results suggest that novel software architectures and frameworks optimize the learning and classification part of the pipelines. Therefore, the architect in particular needs to focus on data distribution and preprocessing as these parts were observed to have an overlooked computational cost and complexity. These results are important for software architects to become better at architecting machine learning-based systems.

关键词： Architecting Machine Learning Pipeline

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Research on Electronic Information Control System Based on Computer ARM embedded Technology 3

Research on Electronic Information Control System Based on C...

引用

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

作者： Huiguo, Cao Yanxiang, Gong Taishan University School of Physics and Electronic Engineering Tai'an271000 China

ISBN: (纸本)9781665472784

This paper proposes an architecture of an ARM-based remote video electronic information monitoring system. The system converts the embedded Linux terminal system memory allocation problem into a 0-1 knapsack problem. The system solves the knapsack problem by dynamic programming method to realize the memory optimization of embedded Linux terminal system. The system is sent to the client through the sending program based on real-time Transport Protocol (RTP) to realize real-time encoding and sending. At the same time, the system realizes the CGI programming of A/D data acquisition. The experimental results show that the system has better real-time performance, clearer images and meets the system design requirements. © 2023 ieee.

关键词： Data acquisition

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Nonlinear Output Feedback Control for Double-Pendulum Aerial Transportation systems with Variable-Length Cable

Nonlinear Output Feedback Control for Double-Pendulum Aerial...

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2024 ieee international conference on real-time computing and Robotics, RCAR 2024

作者： Yu, Hai Wang, Yang Liu, Bingbing Han, Jianda Fang, Yongchun Liang, Xiao Nankai University The Institute of Robotics and Automatic Information System College of Artificial Intelligence The Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China

ISBN: (纸本)9798350372601

Implementing aerial transportation systems in real-world scenarios is becoming feasible. Due to the ability to dynamically adjust the distance between quadrotor and cargo, the aerial transportation system with variable-length cable (ATSVC) holds significant value in applications such as tunnel traversal and obstacle avoidance. However, existing studies often overlook the hook, which means that the double-pendulum effect is ignored. In practical systems, there exists complex dynamic coupling among the motion of quadrotor, variations in cable length, and the swing motion of hook and cargo. Besides, the velocity information in real-world environments cannot be accurately measured, which makes precise control extremely challenging. To address the aforementioned challenges, this paper introduces an output feedback control method for the double-pendulum ATSVC. Specifically, the proposed method operates without the need for velocity information and effectively constrains the quadrotor's overshoot within a prescribed scope. Lyapunov techniques and LaSalle's Invariance Theorem are employed to prove the stability of the closed-loop system. Numerical simulations are implemented to verify the proposed method's performance. © 2024 ieee.

关键词： Pendulums

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time Machine: Generative real-time Model For Failure (and Lead time) Prediction in HPC systems 53

Time Machine: Generative Real-Time Model For Failure (and Le...

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53rd Annual ieee/IFIP international conference on Dependable systems and Networks (DSN)

作者： Alharthi, Khalid Ayed Jhumka, Arshad Di, Sheng Gui, Lin Cappello, Franck McIntosh-Smith, Simon Univ Warwick Coventry England Univ Chicago Argonne Natl Lab Chicago IL USA Kings Coll London London England Univ Bristol Bristol England Univ Bisha Bisha Saudi Arabia Alan Turing Inst London England Univ Illinois Champaign IL USA

ISBN: (纸本)9798350347937

High Performance computing (HPC) systems generate a large amount of unstructured/alphanumeric log messages that capture the health state of their components. Due to their design complexity, HPC systems often undergo failures that halt applications (e.g., weather prediction, aerodynamics simulation) execution. However, existing failure prediction methods, which typically seek to extract some information theoretic features, fail to scale both in terms of accuracy and prediction speed, limiting their adoption in real-time production systems. In this paper, differently from existing work and inspired by current transformer-based neural networks which have revolutionized the sequential learning in the natural language processing (NLP) tasks, we propose a novel scalable log-based, self-supervised model (i.e., no need for manual labels), called time Machine1, that predicts (i) forthcoming log events (ii) the upcoming failure and its location and (iii) the expected lead time to failure. time Machine is designed by combining two stacks of transformer-decoders, each employing the self-attention mechanism. The first stack addresses the failure location by predicting the sequence of log events and then identifying if a failure event is part of that sequence. The lead time to predicted failure is addressed by the second stack. We evaluate time Machine on four realworld HPC log datasets and compare it against three state-of-the-art failure prediction approaches. Results show that time Machine significantly outperforms the related works on Bleu, Rouge, MCC, and F1-score in predicting forthcoming events, failure location, failure lead-time, with higher prediction speed.

关键词： Transformer decoder, LSTM, failure prediction, logs, HPC systems, deep learning, time Machine, Generative Model, Lead time Prediction, supercomputer, large scale systems, anomaly prediction

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Malaysia Traffic Sign Recognition for Autonomous Vehicles with Textual Information using Computer Vision 8

Malaysia Traffic Sign Recognition for Autonomous Vehicles wi...

引用

ieee 8th international conference on Signal and Image Processing applications (ieee ICSIPA)

作者： Xian, Chear Li Sheikh, Usman Ullah Abu Bakar, Syed Abdul Rahman Syed Univ Teknol Malaysia Fac Elect Engn Johor Baharu Malaysia

ISBN: (纸本)9798350352368

Traffic sign recognition is crucial for the safe and efficient operation of autonomous vehicles. While previous research has primarily focused on traffic sign recognition in foreign countries, these studies often face limitations such as differing traffic sign designs, language barriers in textual information, and varying environmental conditions. In this paper, we propose a traffic sign detection and recognition system tailored for Malaysia, utilizing Convolutional Neural Networks (CNNs) and Optical Character Recognition (OCR). In this paper, we propose a traffic sign detection and recognition system utilizing You Only Look Once (YOLO) V8 for object detection and EasyOCR to process textual information on selected traffic signs. Our system achieves a mean Average Precision (mAP) of 0.824 and an average processing time of 1.2 seconds per frame, which is comparable to existing literature. Furthermore, the complexity of our method is significantly reduced, enhancing its potential for real-time processing applications, as evidenced by its efficient processing time.

关键词： Road Sign Detection Autonomous Vehicles Intelligent Transportation systems (ITS) Convolutional neural network (CNN) Traffic Sign Recognition (TSR)

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FlameNet: A real-time, Lightweight Fire & Smoke Detection Solution for Internet of Vehicles 19

FlameNet: A Real-Time, Lightweight Fire & Smoke Detection So...

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19th ieee international Wireless Communications and Mobile computing (ieee IWCMC)

作者： Mondal, Koustav Kumar Das, Debasis Indian Inst Technol IIT Inter Disciplinary Res Div IoT & Applicat Jodhpur India Indian Inst Technol IIT Dept Comp Sci Engn Jodhpur India

ISBN: (纸本)9798350333398

The growth of the Internet of Vehicles (IoV) has introduced new challenges and opportunities. Among the most crucial considerations is ensuring the safety of passengers and the environment. Connected vehicles offer numerous benefits, but they are also at risk of fire incidents caused by various factors such as electrical failures, fuel leaks, and collisions. These events can result in devastating outcomes, including property loss, injury, and even loss of life. The conventional fire detection systems employed in vehicles are large, expensive, and consume a considerable amount of power, making them incompatible with the resource-limited environment of the IoV. The present work overcomes these limitations with the introduction of FlameNet, a custom-designed neural network for fire detection. FlameNet not only outperforms existing solutions but also boasts a lightweight design, which contributes to its high computational efficiency, allowing it to run smoothly on low-cost embedded devices with a frame rate of 28 frames per second. Accuracy, recall, precision, and F-measure were used to assess the model's efficiency on both industry-standard fire datasets and a custom-built test set. The results showed that FlameNet performed well on both datasets, with its performance being better on the standard fire test dataset due to its limited image diversity. The performance of the model is encouraging, and the IoT functionality allows immediate visual feedback and a fire alarm in the event of an emergency.

关键词： Internet of Vehicles Neural Network Smoke Detection Convolution Neural Network embedded systems Internet of Things

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real-time Implementation of Phased Array Radar Tracking Algorithm Based on embedded GPU 2

Real-time Implementation of Phased Array Radar Tracking Algo...

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2nd ieee international conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Fang, Delin Lin, Ruiqi Chang, Ningyuan Chen, Youwang Tian, Weiming Deng, Yunkai Beijing Institute of Technology Radar Technology Research Institute Beijing China Beijing Key Laboratory of Embedded Realtime Information Processing Technology Radar Technology Beijing China Beijing Institute of Technology School of Information and Electronic Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China Beijing Institute of Technology Advanced Technology Research Institute Jinan China

ISBN: (纸本)9798331515669

The embedded Graphics Processing Unit (GPU) module, which includes both Central Processing Unit (CPU) and GPU processors, can be easily integrated into radar systems, offering high performance and flexibility. Phased array radar tracking algorithm involves numerous modules with poor parallelism between them, leading to significant data blocking issues during sequential execution. Traditional implementations using CPUs or DSPs are time-consuming and inefficient. To address this problem, this paper proposes a real-time implementation solution based on embedded GPU. The solution decouples logic-intensive and computation-intensive tasks in the algorithm and maps them separately to the CPU and GPU within the embedded GPU module. Then three-level hybrid parallel processing method is adopted, which is based on grid-stride-based intra-kernel thread parallelism, CUDA stream asynchronous parallelism and CPU thread parallelism in order to realize real-time processing of radar echo data. Verification experiment using real measured data indicates that the proposed real-time implementation solution achieves significant acceleration with no loss of accuracy on the Jetson AGX Xavier embedded GPU platform, markedly improving tracking efficiency. © 2024 ieee.

关键词： Digital storage

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N-Stack Execution of real-time Ethernet Protocols 29

N-Stack Execution of Real-Time Ethernet Protocols

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29th ieee international conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Doran, Hans Dermot Leibundgut, Prosper Institute of Embedded Systems Zurich University of Applied Sciences Winterthur Switzerland

ISBN: (纸本)9798350361230

We propose fitting a distributed real time Ethernet (RTE) device with more than one RTE communication stack. We target this proposal for increasing flexibility for the manufacturing and system commissioning phases as well as facilitating more innovative network topologies and use-cases. We discuss these potential use-cases and the ramifications of replacing the protocol-specific layer-2 bandwidth management protocol with a universal TSN protocol. We also develop a methodology for porting and characterizing the likely performance of the stack on embedded platforms. © 2024 ieee.

关键词： Ethernet

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