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Observer-Based Control for Networked Control systems Under Quantization and DoS Attacks 41

Observer-Based Control for Networked Control Systems Under Q...

引用

第41届中国控制会议

作者： Xiaodan Zhang Cong Zhang Feng Xiao Bo Wei State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University School of Control and Computer Engineering North China Electric Power University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper addresses an observer-based control problem in networked control systems under denial-of-service(DoS)***,an observer is constructed to estimate the unmeasurable states in the sensor side.A quantization strategy with time-varying step sizes is designed for data transmission from the encoder to the *** to the existence of DoS attacks,not all transmission attempts are *** auxiliary system with prediction ability is designed to make a reasonable estimation for observer *** estimator is introduced to estimate the observer ***,some sufficient conditions in terms of quantization levels and DoS attack parameters are given to ensure the stability of the system by multiple Lyapunov ***,simulation results are provided to verity the effectiveness of the proposed methods.

关键词： Denial-of-service attacks networked control systems observer quantization

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AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation 42

AOCC-FL: Federated Learning with Aligned Overlapping via Cal...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Wang, Haozhao Xu, Wenchao Fan, Yunfeng Li, Ruixuan Zhou, Pan Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China The Hong Kong Polytechnic University Department of Computing Hong Kong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350334142

Federated Learning enables collaboratively model training among a number of distributed devices with the coordination of a centralized server, where each device alternatively performs local gradient computation and communication to the server. FL suffers from significant performance degradation due to the excessive communication delay between the server and devices, especially when the network bandwidth of these devices is limited, which is common in edge environments. Existing methods overlap the gradient computation and communication to hide the communication latency to accelerate the FL training. However, the overlapping can also lead to an inevitable gap between the local model in each device and the global model in the server that seriously restricts the convergence rate of learning process. To address this problem, we propose a new overlapping method for FL, AOCC-FL, which aligns the local model with the global model via calibrated compensation such that the communication delay can be hidden without deteriorating the convergence performance. Theoretically, we prove that AOCC-FL admits the same convergence rate as the non-overlapping method. On both simulated and testbed experiments, we show that AOCC-FL achieves a comparable convergence rate relative to the non-overlapping method while outperforming the state-of-the-art overlapping methods. © 2023 IEEE.

关键词： Learning systems

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EdgeMove: Pipelining Device-Edge Model Training for Mobile Intelligence 23

EdgeMove: Pipelining Device-Edge Model Training for Mobile I...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Dong, Zeqian He, Qiang Chen, Feifei Jin, Hai Gu, Tao Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia School of Computing Macquarie University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Training machine learning (ML) models on mobile and Web-of-Things (WoT) has been widely acknowledged and employed as a promising solution to privacy-preserving ML. However, these end-devices often suffer from constrained resources and fail to accommodate increasingly large ML models that crave great computation power. Offloading ML models partially to the cloud for training strikes a trade-off between privacy preservation and resource requirements. However, device-cloud training creates communication overheads that delay model training tremendously. This paper presents EdgeMove, the first device-edge training scheme that enables fast pipelined model training across edge devices and edge servers. It employs probing-based mechanisms to tackle the new challenges raised by device-edge training. Before training begins, it probes nearby edge servers' training performance and bootstraps model training by constructing a training pipeline with an approximate model partitioning. During the training process, EdgeMove accommodates user mobility and system dynamics by probing nearby edge servers' training performance adaptively and adapting the training pipeline proactively. Extensive experiments are conducted with two popular DNN models trained on four datasets for three ML tasks. The results demonstrate that EdgeMove achieves a 1.3 × -2.1 × speedup over the state-of-the-art scheme. © 2023 ACM.

关键词： Internet of things

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DFIL: an Efficient Web Service Prediction Method based on Deep Feature Interactive Learning 25

DFIL: an Efficient Web Service Prediction Method based on De...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Zhu, Ke Wang, Zhixin Jiang, Tao Xiao, Yingyuan Zheng, Wenguang Hsu, Ching-Hsien Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China He'nan University of Economics and Law School of Computer and Information Engineering Zhengzhou China Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9781665405270

As a representative of nonfunctional features, the Quality-of-Service (QoS) plays an important role in recommending the best service to users. To obtain the QoS value of web services, researchers have proposed many web service QoS prediction methods. However, most existing methods, e.g., the collaborative filtering method, leverage the historical user service invocation information to predict the QoS value, and only consider the QoS information of similar users or services, and ignore the attributes and characteristics of users and services. In this paper, we propose a Deep Feature Interaction Learning method, called DFIL for short, for web service QoS prediction. Specifically, (1) DFIL constructs a feature extraction method to effectively obtain the features of users and services. (2) DFIL trains a novel neural network for QoS prediction, which can accurately find the potential relationship between users and services based on the feature information, and provide good web service recommendation. © 2022 IEEE.

关键词： Quality of service

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Explosive Synchronization in Multilayer Networks With Inter-Layer Competition and Cooperation 41

Explosive Synchronization in Multilayer Networks With Inter-...

引用

第41届中国控制会议

作者： Lingyun Xie Feng Xiao Bo Wei State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University School of Control and Computer Engineering North China Electric Power University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In recent years,the study of explosive synchronization(ES) has been extended to multilayer networks by using different *** this paper,the synchronous transition of multilayer networks with inter-layer competition and inter-layer cooperation is studied,and it is found that ES occurs in both two layers when the proportion of interlayer cooperation is large *** addition,we study ES separately in terms of oscillator dynamics and network *** experiments show that ES occurs independently of the natural frequency ***,as the average degrees of networks increase,the critical coupling strength ***,the robustness of the results is confirmed by changing the network topology of each layer.

关键词： Multilayer network explosive synchronization inter-layer competition inter-layer cooperation

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Optimization Accuracy of MRI Brain Image Based Tumor Detection Using Deep Learning LSTM Technique

Optimization Accuracy of MRI Brain Image Based Tumor Detecti...

引用

Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Aarti Chugh Ranjeet Kumar Yadav Tushar Agarwal School of Computer Science and Engineering IILM University Gurugram India Department of Computer Science and Engineering Shree Guru Gobind Singh Tricentenary University Gurugram India The Galaxy Education System Gujrat India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

Benign brain tumors (BT) are the least dangerous type and arise from brain cells or cells surrounding the brain. Benign tumors grow slowly and do not spread to other body parts. Benign brain tumors have distinct borders. They put the normal brain under stress and interfere with physical and mental function. It is also referred to as a non-cancerous BT. The acquisition of magnetic resonance images introduces noise, and removing this noise is complex. BT has tentacles and diffuse structures, making precise segmentation difficult. Another critical task is to select and extract the best features and the appropriate number of training and test samples to improve the classification. Deep learning (DL) models have received much attention because feature learning happens automatically. However, it necessitates a substantial amount of computing power and memory. As a result, lightweight models with high accuracy and fast computation times were developed. This paper is present on ML and DL model for analysis of BT detection and calculated accuracy. The ML classification i.e. K-NN achieves 78.74% accuracy, LR achieves 81.53% accuracy, VC achieves 86.9% accuracy and DL LSTM achieves 94.07% accuracy. It is clearly that the LSTM technique is provides good accuracy compared to other ML technique.

关键词： Deep learning Training Accuracy Magnetic resonance imaging Computational modeling Noise Brain tumors Feature extraction Long short term memory Medical diagnostic imaging

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Pallet Detection and Distance Estimation with YOLO and Fiducial Marker Algorithm in Industrial Forklift Robot

Pallet Detection and Distance Estimation with YOLO and Fiduc...

引用

International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Eric Sean Kesuma Pranoto Hidaya Rusmin Devira Anggi Maharani Control and Computer System Research Group School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia

Utilising technology such as artificial intelligence and robotics potentially improves E-Commerce in efficiency. In this trends, the usage of autonomous forklifts in the warehouse to lift and arrange things should be implemented. The picking system in the warehouse needs pallet detection and tracking to carry out the things. This research will find the best performance of the YOLOv5 model and correct the distance estimation model to the fiducial marker. In this paper, we used the ArUco fiducial marker to mark the pallet target and estimate the pose and distance in real time. The insertion points of the pallet were also detected using the YOLOv5 algorithm to validate the pallet and get the coordinate variables of the holes. The YOLOv5n gives the best performance at 24 fps in real-time detection. Distance measurement from the marker detection had an average error of 2.28 cm with linear regression.

关键词： Robot kinematics Linear regression Transportation Cameras Fiducial markers Real-time systems Distance measurement

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Distributed Asynchronous Event-Triggered Secondary Control of Microgrids with Time-Varying Communication Delays 41

Distributed Asynchronous Event-Triggered Secondary Control o...

引用

第41届中国控制会议

作者： Kai Zhao Feng Xiao Bo Wei State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University School of Control and Computer Engineering North China Electric Power University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper focuses on the secondary control of the inverter based microgrid,which is essential to overcome the influence of load *** reduce unnecessary communication,a distributed asynchronous event-triggered control algorithm is proposed to recover frequency and voltage,and the time-varying delays in communication channels are *** controllers in energy generation units are designed to work asynchronously,which means that the controllers do not need to check event-triggering conditions *** stability of the proposed algorithm is proved rigorously by the Lyapunov method and the micro grid is simulated in the MATLAB/Simulink environment.

关键词： Secondary control asynchronous event-triggered control time-varying delays

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Unlearnable 3D point clouds: class-wise transformation is all you need 24

Unlearnable 3D point clouds: class-wise transformation is al...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Xianlong Wang Minghui Li Wei Liu Hangtao Zhang Shengshan Hu Yechao Zhang Ziqi Zhou Hai Jin National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (纸本)9798331314385

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples; (ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC.

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Abnormal Image Classification Using Vision Transformer for Smart Agriculture 8

Abnormal Image Classification Using Vision Transformer for S...

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8th IEEE International Conference on engineering Technologies and Applied Sciences, ICETAS 2023

作者： Hafeez, Talha Shahid, Muhammad Farrukh Khanzada, Tariq J.S. Fast School of Computing FAST-NUCES Karachi Pakistan King Abdulaziz University Faculty of Computing and Information Technology Department of Information System Jeddah21589 Saudi Arabia Mehran Uet Computer Systems Engineering Departments Jamshoro Pakistan

ISBN: (纸本)9798350327090

Smart agriculture is an emerging innovative sector that harnesses technological advancements like sensor monitoring, image processing, soil quality evaluation, and automated water systems, all of which contribute towards enhanced crop yields and superior quality of produce. With the progression towards automated systems in smart agriculture, the need for a robust data infrastructure that assures data integrity is critical. This demands an integrated approach with anomaly detection algorithms and a robust classification technique to ensure an error-free data pipeline. This study employs two distinct datasets to achieve abnormal image classification using a vision transformer (ViT) and a convolutional neural network (CNN). The primary dataset, obtained through direct collection from cotton fields in Karachi and Balochistan, Pakistan, comprises high-resolution agricultural images captured using specialised cameras and Unmanned Aerial Vehicles (UAVs). The secondary dataset was sourced from a publicly available database. The two classification models, ViT and CNN, were applied to both datasets. These supervised models are primarily used to classify images as normal or abnormal. The results of the two classification models were also compared, with the ViT model surpassing the CNN model in performance and achieving the highest accuracy on both datasets. The Vision Transformer model proved to be robust for supervised data, showing remarkable accuracy of 96.00% on the public dataset, 99.18% on both camera based images and 97.56% on UAV images of collected dataset as compared to the CNN model's accuracy that are 82.00% on the public dataset and 98.36% on camera and 95.12% on UAV images of collected dataset. © 2023 IEEE.

关键词： Classification (of information)

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