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2021 Zooming Innovation in Consumer Technologies Conference, ZINC 2021

作者： Kenjic, Dusan Antic, Marija Bjelica, Milan University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Serbia

ISBN: (纸本)9781665404174

The expansion of the automotive industry is reflected in the expansion of functionalities and services offered by a modern vehicle. In order for all of them to be executed, it is also necessary to increase the number of physical components within the vehicle. As the entire system grows, so does the problem of connecting the entities within it. The efficient simplification of the system is achieved by introducing domain controllers, and separating the physical components based on the functionality they perform, while creating the backbone communication network between domain controllers. Time-critical and other performance requirements, which are driven by the development of autonomous driving, demand the utilization of new standards and solutions for this communication network backbone. One of those solutions is Automotive Ethernet. In this paper, an implementation of the Ethernet subsystem of the domain controller for autonomous vehicles will be presented and evaluated. © 2021 IEEE.

关键词： Automotive industry

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Digital CEOs in Digital Enterprises:Automating,Augmenting,and Parallel in Metaverse/CPSS/TAOs

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 820-823页

作者： Juanjuan Li Rui Qin Sangtian Guan Xiao Xue Peng Zhu Fei-Yue Wang with State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Intelligence and Computing Tianjin UniversityTianjin 300072China School of Economics and Management Nanjing University of Science and TechnologyNanjing 210094China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

BIG models or foundation models are rapidly emerging as a key force in advancing intelligent societies[1]–[3]Their significance stems not only from their exceptional ability to process complex data and simulate advan... 详细信息

关键词： exceptional Auto advancing

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control Strategy based on Frequency Response for a Single-Phase Full- Bridge Series Active Filter 16

Control Strategy based on Frequency Response for a Single-Ph...

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16th Brazilian Power Electronics Conference, COBEP 2021

作者： Curi Busarello, Tiago Davi Automation and Computing Engineering Federal University of Santa Catarina Department of Control Blumenau Brazil

ISBN: (纸本)9781665421294

Series active filters are a mature technology and well covered in the literature. However, they are usually designed for three-phase systems employing control strategies in dq-rotating reference frame. As a result, proportional-integrator controllers are used. However, they are not suited when the system has abnormal conditions. Moreover, with the high penetration of distribution generators, single-phase distribution voltage is being deteriorated. This paper presents a control strategy based on frequency response for a single-phase full bridge series active filter. The series active filter is controlled in voltage mode through a digital lead-lag controller. The controller parameters are obtained by frequency response technique using the k-factor methodology and later discretized via Backward-Euler approximation. The series active filter is tested in a system with nonlinear loads, distortions at the distribution voltage and under disturbance at the DC-link. The proposal was verified under simulation results and under Hardware-in-Loop Typhoon HIL402 with external microcontroller TMS320F28335. Results showed the effectiveness of the proposed control strategy. © 2021 IEEE.

关键词： Active filters

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Tiruveedula Gopi Krishna Nune Sreenivas M. Ajay Kumar Chandra Sekhar Reddy Davinder Sing Rathee Teklu Urgessa Department of Computer Science and Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of Electrical and Electronics Engineering Andhra Loyala institute of Engineering and Technology Vijayawada India Department of Electrical Power and Control Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of ECE College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods.

关键词： Support vector machines Image segmentation Adaptation models Accuracy Magnetic resonance imaging Machine learning Feature extraction Brain modeling Data models Numerical models

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Fatigue Load Prediction of Large Wind Turbine by Big Data and Deep Learning

Fatigue Load Prediction of Large Wind Turbine by Big Data an...

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2023 IEEE International Conference on Electrical, Automation and Computer engineering, ICEACE 2023

作者： Wu, Jiayue Yang, Qinmin Jin, Nanlin Xxi'An Jiaotong-Liverpool University Low Carbon Electrical Power and Energy Technology Suzhou China College of Control Science and Engineering Zhejiang University Hangzhou China Xi'An Jiaotong-Liverpool University Department of Computing Suzhou China

ISBN: (纸本)9798350309614

This paper introduces a fatigue load prediction framework for the nacelle of wind turbines utilizing operational data sourced from the supervisory control and data acquisition (SCADA) system of a Wind Farm. The primary objective revolves around achieving precise fatigue load predictions for the nacelle, forecasting 15 minutes in advance. Firstly, a series of data preprocessing steps were implemented on the wind turbine data procured from the SCADA system to enhance the model's generalization capability and robustness, including 3-Sigma outlier detection, sample point selection, as well as filtering and de-noising techniques. Then, the gate recurrent unit (GRU) was tuned and trained as the optimal model for long time-series forecasting of SCADA parameter channels. Subsequently, the support vector regression (SVR) model, refined with grid search methodology, was employed to establish the intricate relationship between SCADA parameters and fatigue loads. Finally, the predicted SCADA parameter values were fed into the optimized SVR model to derive fatigue load prediction results for wind turbines. Experimental results indicated that the proposed model framework can effectively predict fatigue loads on the nacelle of wind turbines utilizing the SCADA data in real scenarios. © 2023 IEEE.

关键词： GRU SCADA system.fatigue loads SVR wind turbine

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Programmable logic controller memory management vulnerability analysis 3

Programmable logic controller memory management vulnerabilit...

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2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022

作者： Geng, Yangyang Ma, Rongkuan Wei, Qiang Wang, Wenhai State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310000 China

Programmable logic controller (PLC) is omnipresent in industrial control system (ICS), which plays an imperative part in critical infrastructures. However, once a PLC is compromised by an attacker, it will create destructive effects on critical infrastructures. The memory systems in PLCs are considered significant targets for attackers to disrupt real-world physical processes. In this paper, we proposed five types of attacks on the PLC memory management mechanism. Furthermore, we evaluated the feasibility of our memory attack generation approach on six PLCs from three leading controller vendors. A total of 22 memory attack payloads are implemented with several vulnerabilities on the target PLCs. © Published under licence by IOP Publishing Ltd.

关键词： Programmable logic controllers

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A Loop Optimization Method for Dataflow Architecture 24

A Loop Optimization Method for Dataflow Architecture

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24th IEEE International Conference on High Performance computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Fan, Zhihua Li, Wenming Liu, Tianyu Tang, Shengzhong Wang, Zhen An, Xuejun Ye, Xiaochun Fan, Dongrui State Key Lab of Processors Institute of Computing Technology Cas Beijing100049 China School of Computer and Control Engineering Ucas Beijing Beijing100190 China

ISBN: (纸本)9798350319934

Dataflow architecture is a promising parallel computing platform that provides high performance, high efficiency and flexibility. Since the execution of a loop is usually the most time-consuming in a program, extracting sufficient loop-level parallelism is an essential task of dataflow architecture. However, existing methods often result in low execution performance and poor hardware utilization due to the imbalance among dataflow graph nodes and the non-preemptive mechanism. To tackle these problems, this paper makes three contributions: 1) A highly effective dataflow graph balancing method is developed to improve the utilization. 2) An enhanced dataflow execution model is proposed, which adapts a novel preemptive mechanism to further improve the hardware utilization and performance. 3) A decoupled dataflow architecture is designed to efficiently support our proposed dataflow execution model. Experiment shows that our methods can achieve an average of 2.61 ×performance improvement and 2.5 ×utilization improvement compared to the state-of-the-art methods with acceptable overhead. © 2022 IEEE.

关键词： Computer architecture

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An Entropy-Integrated Adaptive Coding and Scheduling Framework for Optimized Data Transmission in Fog-Cloud IoT Architectures

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IEEE Internet of Things Journal 2025年

作者： Lu, Yang Zang, Yuting Bian, Ziyi Zhao, Shuai Zheng, Yan Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Normal University College of Computer and Information Engineering Harbin China La Trobe University School of Computing Engineering and Mathematical Sciences MelbourneVIC Australia

Entropy-driven network coding has been used as a basis for optimizing data transmission and enhancing resource utilization in Fog-Cloud IoT architectures, including applications in smart cities, industrial automation, environmental monitoring, and healthcare. In Fog-Cloud IoT architectures, conventional data transmission protocols are inefficient because they cannot adapt to dynamic entropy levels, resulting in underutilized bandwidth, increased latency, and higher energy consumption. In this paper, we propose an Enhanced Entropy-Driven Network Coding (E-EDNC) framework to address these problems. Our framework integrates real-time entropy estimation with adaptive coding strategies and employs a hybrid evolutionary-reinforcement learning (HE-RL) algorithm to dynamically optimize coding parameters and scheduling decisions. Experimental results demonstrate that E-EDNC improves bandwidth utilization by 25%, reduces latency by 25%, and decreases energy consumption by 12%, thereby enhancing overall data transmission efficiency and reliability in Fog-Cloud IoT environments. © 2014 IEEE.

关键词： Network coding

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Inside Bridges: Autonomous Crack Inspection with Nano UAVs in GNSS-Denied Environments

Inside Bridges: Autonomous Crack Inspection with Nano UAVs i...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： David Müller Patrick Herbers Raphael Dyrska Firdes Çelik Markus König Martin Mönnigmann Automatic Control and Systems Theory Ruhr-Universität Bochum Germany Computing in Engineering Ruhr-Universität Bochum Germany

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Detecting damage to bridges is essential for safety and financial reasons. We focus on inspections inside bridges and claim Unmanned Aerial Vehicles (UAVs) are ideally suited to assist inspections of hard-to-reach areas. Using UAVs in narrow indoor environments imposes constraints on the size of the UAV. While small UAVs are attractive for their agility and reduced risks associated with them, the need to carry cameras and sensors results in a lower bound on thrust and thus UAV and rotor size. Moreover, position and orientation control is particularly demanding inside bridges because satellite navigation is not available. To address these challenges, we implement a nano UAV that is capable of analyzing camera data for cracks with a machine learning model. UAV position is tracked with inertial measurement units, an optical flow, and a laser-based range sensor.

关键词： Bridges Satellites Robot vision systems Rotors Position control Inspection Autonomous aerial vehicles Cameras Satellite navigation systems Safety

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Vision Transformers(ViT) Pretraining on 3D ABUS Image and Dual-CapsViT: Enhancing ViT Decoding via Dual-Channel Dynamic Routing

Vision Transformers(ViT) Pretraining on 3D ABUS Image and Du...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Xu, Mingwang Wang, Wei Wang, Kuanquan Dong, Suyu Sun, Pengzhong Sun, Jinwei Luo, Gongning Harbin Institute of Technology Faculty of Computing Harbin China Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology School of Instrumentation Science and Engineering Harbin China

ISBN: (纸本)9798350337488

Breast cancer continues to be a pressing global health concern, emphasizing the essential need for effective diagnostic techniques. Automated Breast Ultrasound Systems (ABUS) provide a promising advance in breast tumor detection, yet they require significant expertise in interpreting 3D ABUS images, a task fraught with distinctive challenges. Although Vision Transformers (ViT) display remarkable potential for image processing, their low inductive bias and significant data requirements pose obstacles, particularly in the data-constrained medical field. To mitigate these issues, we introduce a Mask-Recover strategy for pretraining Transformer models on 3D ABUS images, enhancing model adaptability and reducing the data demands of the ViT model. Moreover, recognizing the risk that ViTs' average pooling approach may unintentionally mask small but vital features, we propose Dual-CapsViT, an inventive model combining Transformers and Capsule Networks. This integration affords efficient token routing while preserving fine-grained details. To reconcile potential inconsistencies between capsules and tokens, we engineer a novel dual-channel routing algorithm, strengthening the decoder's performance. We benchmarked our models against well-known standards such as ResNet and ViT for classifying breast tumors in ABUS images. Our models exhibited superior performance, as evidenced by improved accuracy, specificity, and Area Under the Receiver Operating Characteristic Curve (AUC) metrics, thereby affirming Dual-CapsViT's potential to enhance breast cancer diagnostics. © 2023 IEEE.

关键词： abus breast cancer capsule network classification vit

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