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检索条件"任意字段=International Dagstuhl Workshop on Model-Based Engineering of Embedded Real-Time Systems"

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Physics-Informed Neural Networks for One-Step-Ahead Prediction of Dynamical systems 14

Physics-Informed Neural Networks for One-Step-Ahead Predicti...

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14th international workshop on Structural Health Monitoring: Designing SHM for Sustainability, Maintainability, and Reliability, IWSHM 2023

作者： Haywood-Alexander, Marcus Chatzi, Eleni Institute of Structural Engineering ETH Zürich Zürich Switzerland

ISBN: (纸本)9781605956930

During online implementation of vibration-based structural health monitoring (SHM) strategies, forward prediction of the system state may allow for improved detection speed. With adequately fast forward prediction, feedback systems can also be improved to provide in-time control plants. When using solely physics-driven models, small discrepancies between the physical system and digital model can result in significant deviation between the estimated and true output. On the opposite end, a solely data-driven model can only reasonably be applied in an identical, or sufficiently similar, scenario to that for which the data was collected. Using machine learning to combine data with known physics is a well-proved approach to overcoming this issue. One such method for this approach is the use of physics-informed neural networks (PINNs), which can be implemented as either a forward modeller, or a constrained learner, for equation solution discovery, or equation discovery. The former of these aims to provide the desired output from the governing physical equations, whereas the latter estimates the parameters in these equations. A significant advantage of PINNs is, given a suitable network architecture, the high speed and low computational-cost of their prediction step, which positions them as a useful approach for real-time estimation, given adequate training. A common assumption for PINNs is that the embedded physics is exhaustive with respect to the 'true' model. In this paper, a novel PINN-based architecture is presented to rapidly forward-predict the state of a dynamic system given an initial state. For the state estimation, the PINN acts as an equation solution discovery approach, and the novelty of the architecture here is to provide a more generalised predictor which can be applied to a wider range of instances. The PINN is intended to deliver its prediction within a prescribed time frame, which equals the sampling time of the acquisition/control system, and is here a

关键词： Network architecture

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model-based design of a Machine Learning algorithm for on-site food authenticity testing 7

Model-Based design of a Machine Learning algorithm for on-si...

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7th IEEE international workshop on Metrology for Industry 4.0 and IoT, MetroInd4.0 and IoT 2024

作者： Stighezza, Mattia Magnani, Giulia Bianchi, Valentina Cagnoni, Stefano Boni, Andrea Giliberti, Chiara Errico, Davide Fortunati, Simone Giannetto, Marco Careri, Maria De Munari, Ilaria University of Parma Dept. of Engineering and Architecture Parma Italy University of Parma Dept. of Chemistry Life Sciences and Environmental Sustainability Parma Italy

ISBN: (纸本)9798350385823

Machine Learning (ML) algorithms are commonly used for a variety of classification tasks. Among them, food quality assessment, including adulteration, is gaining particular attention. While food fraud detection is typically accomplished in specialised laboratories, there is an increasing demand for innovative sensors combined with portable devices (portable e-tongues) with Internet of Things (IoT) features for performing on-site or point-of-need (PON) measurements to be shared on clouds. Portable e-tongues combined with smart features enabled by IoT and ML techniques have expanded the panorama towards smart analytical devices of interest to the agrifood sector and brought innovation to in-process analysis interfaces. It is well known that the ML training phase requires higher computational resources than those necessary for the classification phase, thus preventing its implementation on embedded systems. A possible solution is to conduct the training offline, e.g. on a Cloud Service, performing the classification process online on an embedded device. The coding effort of complex ML techniques can be supported by model-based design enabling the high-level description of the procedures in conjunction with the automatic code generation tools. A streamlined implementation phase allows for fast prototyping on different hardware platforms resulting in reduced time-tomarket. This paper presents a case study on the use of a Linear Discriminant Analysis (LDA) model trained to discriminate tomato purées of different varieties using an IoT e-tongue device developed in our labs. The trained ML model was model-based designed in the Simulink environment with HDL compatible blocks to increase code portability to different hardware architectures, and then implemented on an STM32 Nucleo L476RG board through automatic code generation. The classification task and the code timing performance were finally evaluated. © 2024 IEEE.

关键词： Machine learning

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Disturbance Observer based Safety-Critical model Predictive Control for Overhead Cranes

Disturbance Observer Based Safety-Critical Model Predictive ...

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18th IEEE international Conference on Advanced Motion Control, AMC 2024

作者： Wang, Jiangtong Tian, Zheng Yang, Jun Li, Shihua School of Automation Southeast University Nanjing China School of Aeronautical and Automotive Engineering Loughborough University Leicestershire United Kingdom

ISBN: (纸本)9798350342819

This paper investigates the problem of safety-critical model predictive control (MPC) for under-actuated overhead crane systems. In practical engineering, the payload swing angle of the overhead crane systems must be limited within an acceptable range to guarantee safety, and the trolley reach to the desired position accurately simultaneously. In addition, the payload may be disturbed by strong winds, which poses a certain threat to the safety of overhead crane operation. Considering this, a disturbance observer based safety-critical MPC algorithm using discrete-time control barrier function (CBF) is proposed, where the disturbance estimation value is embedded within the CBF to guarantee the swing angle constraints. Then the receding horizon optimization problem is formulated with the help of prediction model. Next, the optimal control input is obtained by solving the QP problem periodically. Subsequently, the stability analysis of the closed-loop system under the nominal control law is given. Finally, it is shown that the payload swing angle can be strictly limited within the safe range, and the trolley can reach the specified position under the proposed method through numerical simulations and physical experiments. © 2024 IEEE.

关键词： model predictive control

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A TSN-based Approach to Combine real-time CAN Network with In-Vehicle Ethernet

A TSN-Based Approach to Combine Real-Time CAN Network with I...

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Metrology for Automotive (MetroAutomotive), IEEE international workshop on

作者： Alberto Morato Elena Ferrari Stefano Vitturi Federico Tramarin Claudio Zunino Manuel Cheminod National Research Council of Italy CNR-IEIIT Padova Italy Department of Information Engineering University of Padova Padova Italy Department of Engineering “Enzo Ferrari” University of Modena and Reggio Emilia Modena Italy

ISBN: (数字)9798350384987

ISBN: (纸本)9798350384994

As in-vehicle communication networks evolve to accommodate Advanced Driver Assistance systems (ADAS), the limitations of the traditional Controller Area Network (CAN) protocol become apparent. This paper proposes a novel integration approach using time-Sensitive Networking (TSN) to bridge the real-time CAN network with in-vehicle Ethernet, addressing the challenges posed by increased data demands. Our solution leverages a TSN-enabled gateway embedded in the switch to connect CAN and Ethernet, eliminating external hardware conversion seamlessly. By avoiding CAN frame aggregation, we prioritize minimal latency, which is evaluated through real-world experiments with various traffic profiles and TSN configurations. The use of 802.1Qav within the Linux kernel ensures timely and deterministic packet delivery, simplifying the integration process on a shared TSN-enabled hardware platform. Results demonstrate the effectiveness of our approach in meeting time-critical communication requirements, preserving CAN network real-time properties.

关键词： Spectral efficiency Prototypes Ethernet Switches Logic gates real-time systems Hardware

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Can embedded real-time Linux System Effectively Support Multipath Transmission? An Experimental Study

Can Embedded Real-Time Linux System Effectively Support Mult...

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IEEE international workshop on Factory Communication systems (WFCS)

作者： Xiaojing Fan Tao Zheng Shangpeng Sun Mikael Gidlund Johan Åkerberg School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Department of Biorcsourcc Engineering Macdonald Campus McGill University QC Canada Department of Information Systems and Technology Mid Sweden University Sundsvall Sweden Division of Networked and Embedded Systems Malardalen University Vasteras Sweden

The rise of technologies such as 6G networks, edge computing, and the Industrial Internet has led to a dramatic increase in the amount of data that needs to be transmitted over heterogeneous integrated networks. The resources of embedded devices limit the ability of the Industrial Internet to transmit data. While the multipath transmission mechanism can mitigate data transmission issues of low reliability and low real-time performance from the network-level perspective. As the complexity of industry applications increases, however, the phenomenon that the high-quality data transmission is subject to the influence of the underlying layer is becoming increasingly apparent. The paper aims to explores the possibility of multipath transmission protocol running on a real-time kernel from the perspective of the operating system, as there is a lack of research and reports in this area. based on RT-Preempt, a real-time system RT-Linux suitable for the “NXP ***6Q” ARM integrated board has been proposed, which replaces the native Linux kernel to optimize and enhance its real-time performance. As described in the experiment part, the original standard Linux system OR-Linux and the new RT-Linux are tested with single-threaded and multi-threaded load experiments, respectively. The results of the analysis show that this paper provides a way of validating the trial data and ensuring its accuracy using the lognormal distribution model, which is a statistical distribution used to model variables that are positive and skewed to the right. The RT-Linux scheme has better real-time performance and is more stable than the OR-Linux scheme after real-time processing, showing the viability of the scheme.

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A comprehensive survey on object detection YOLO 5

A comprehensive survey on object detection YOLO

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5th international Symposium on Advanced Technologies and Applications in the Internet of Things, ATAIT 2023

作者： Wang, Xiangheng Li, Hengyi Yue, Xuebin Meng, Lin Graduate School of Science and Engineering Ritsumeikan University 1-1-1 Noji-higashi Kusatsu Shiga525-8577 Japan Research Organization of Science and Technology Ritsumeikan University 1-1-1 Noji-higashi Kusatsu Shiga525-8577 Japan College of Science and Engineering Ritsumeikan University 1-1-1 Noji-higashi Kusatsu Shiga525-8577 Japan

As a single-stage object detection framework, the YOLO (You Only Look Once) technique has emerged as a prominent technique for various object detection tasks owing to its impressive balance between speed and precision. This research article presents a comprehensive review of the YOLO family of algorithms. This review covers the evolutionary journey of YOLO from its initial release to the latest versions, encompassing an in-depth analysis of the performance and critical characteristics exhibited by each iteration. Particular emphasis is given to exploring the applications of YOLO in diverse domains, focusing on its role in real-time object detection on embedded systems. Furthermore, the paper delves into the latest advancements in compressing algorithms for optimizing the cumbersome YOLO models and practical implementation examples. The potential of deploying YOLO on resource-constrained devices is further unlocked by addressing the challenge of model size reduction. Finally, this study outlines potential research trends and improvements for the YOLO family of algorithms, including novel architectural designs and innovative training strategies. Overall, the thorough investigation presented in this review is a valuable reference for researchers seeking to explore the YOLO framework and its evolving landscape in object detection. © 2023 CEUR-WS. All rights reserved.

关键词： Object detection

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Student Perceptions of AI-Enhanced Adaptive Learning systems: A Pilot Survey

Student Perceptions of AI-Enhanced Adaptive Learning Systems...

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international IEEE Conference and workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Klara Ida Katonane Gyonyoru Jozsef Katona Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

Artificial Intelligence (AI) significantly enhances adaptive learning by personalizing and tailoring instruction to individual student needs. AI analyzes data in real-time to create personalized learning paths based on students' strengths, weaknesses, and preferences, which keeps students engaged and motivated. A major benefit of AI in adaptive learning is the provision of real-time feedback and assessment, allowing students to correct mistakes promptly and understand concepts more thoroughly. AI-based intelligent tutoring systems are primarily intended to simulate personalized tutoring processes that guide students in complex problem-solving and answering questions. It is convenient in teaching mathematics, sciences, and languages. AI also supports inclusive education, dealing with diversified learning requirements and styles, such as those of learners with disabilities. For the teacher, AI acts as a reflector of student performance so that one can intervene early and make adjustments in the method of instruction by creating effective learning environments. AI technology is a field in constant development and harbors the potential to change the face of adaptive learning, bringing an upswing in educational outcomes. This article will summarize the advantages and features that merit improvement of the AI-embedded adaptive learning systems with student feedback.

关键词： Surveys Adaptive learning Power engineering Data security real-time systems Mathematics Problem-solving Artificial intelligence Faces

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Intelligent Manufacturing Principles for Structural Health Monitoring in Advance Composite Structures: A Machine Learning-based Driven Placement Approach 14

Intelligent Manufacturing Principles for Structural Health M...

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14th international workshop on Structural Health Monitoring: Designing SHM for Sustainability, Maintainability, and Reliability, IWSHM 2023

作者： Psulkowski, Sean Rodriguez, Bryant Noman, Abdullah Torino, Sergio Dickens, Tarik Department of Industrial and Manufacturing Engineering Florida State University TallahasseeFL United States

ISBN: (纸本)9781605956930

This paper presents a fundamental investigation into the composition and placement of mechanoluminescence smart material ink to develop a predictive model framework for machine learning-based lead placement in structural health monitoring of next-generation fabrication methods. To address the need for multifunctionality and structural health monitoring capabilities in next-generation composite structures, this study proposes a closed-loop integration of material-driven design, multi-modal additive processing, and intelligent manufacturing principles. The proposed method utilizes mechanical simulations and in-situ sensing to establish a priori knowledge to optimize the placement, orientation, and manufacturing conditions for smart sensor embedment to maximize sensing efficiency and part performance. Experimental validation involved the hybrid machining of compact tension samples to produce a crack front in additively manufactured material. Microfluidic deposition facilitated mechanoluminescent sensor embodiment near the crack front, and the sample was then subjected to a fracture mechanic analysis, whereby the crack growth rate and direction was monitored. The resulting light emission from the sensor was captured using a mixture of high-speed video and optical sensors to determine the mechanical energy transferred during crack propagation. The resulting dataset informed an algorithmic approach to govern the placement and process parameters of the embedded mechanoluminescent sensors throughout the polymeric structure. This approach enriches the knowledge pool of digital design discovery, showcasing the capabilities of material-driven learning schema for multifunctional composite fabrication. Our investigation provides important insights into the design and development of next-generation materials with enhanced structural health monitoring capabilities. This approach provides a valuable tool for the real-time monitoring of crack propagation and material failure, which

关键词： Structural health monitoring

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Improved Object Detection Algorithm for workshop Environments based on YOLOv5

Improved Object Detection Algorithm for Workshop Environment...

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international Conference on Computer and Communications (ICCC)

作者： Jinting Tan Hongliang Wang Xuerao Li Xiaoxing Zhang Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang Ligong University Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China

In the context of workshop-restricted environments, this paper presents an improved lightweight detection algorithm named YOLO-M3C to achieve safety helmet wearing detection on embedded devices in real-time while handling multiple video streams efficiently. YOLO-M3C introduces two key enhancements. Firstly, it replaces the backbone network of YOLOv5 with the more lightweight MobileNetV3 network for feature extraction. This improvement significantly reduces the network's parameter count, resulting in enhanced computational speed and a substantial decrease in computational resource requirements. Secondly, the attention mechanism CBAM is introduced to increase the model's focus on important features, leading to further improvements in detection accuracy. CBAM helps the model better comprehend crucial information in the images, thereby enhancing detection algorithm performance. Experimental results demonstrate that YOLO-M3C achieves increased average precision while shortening the training time by 15%. Additionally, the improved algorithm reduces the parameter count of the YOLOv5 model by 52%, effectively reducing model size and training time, while enhancing detection efficiency. The improved YOLOv5 algorithm can be effectively deployed on embedded devices in workshop environments, enabling efficient real-time processing of multiple video streams and reliable safety helmet wearing detection, thus meeting the real-time detection requirements in workshop settings.

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A Comparative Analysis of Deep Learning-based Object Detectors for embedded systems

A Comparative Analysis of Deep Learning-Based Object Detecto...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications

作者： Yehor Boltov Inna Skarga-Bandurova Maryna Derkach G.E. Pukhov Institute for Modelling in Energy Engineering Kyiv Ukraine Oxford Brookes University Oxford UK Ternopil Ivan Puluj National Technical University Ternopil Ukraine

The paper presents a comparative analysis of popular deep learning-based object detectors, focusing on evaluating their real-time performance on embedded platforms. The objective was to identify the optimal algorithm that can efficiently operate within limited computational resources and memory constraints of embedded systems while minimizing energy consumption. We tested a set of popular detectors, such as Faster R-CNN, YOLO, and SSD MobileNet and evaluated their performance on the testing platform with CUDA cores. A faster variant of bounding box suppression, blazing fast Non-Maximum Suppression (NMS), was employed to enhance efficiency. Additionally, the generalized intersection over union (GIoU) metric was adopted as the optimal testing method, allowing for more accurate object localization and detection. Results demonstrate that the SSD MobileNet-V2 and Tiny YOLO detectors exhibit promising real-time performance on embedded platforms, although their accuracy is diminished compared to larger models.

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