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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Fengl, Minxi Li, Wei Qin, Boyu Zomaya, Albert Y. Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Xi'an Jiaotong University State Key Laboratory of Electrical Insulation and Power Equipment Department of Electrical Engineering China

ISBN: (纸本)9798350304602

With the deep integration of cyber tools, control algorithms are increasingly employed in cyber-physical energy systems to enhance management, cost efficiency, and robustness. Effective demand load management is crucial in cyber-physical energy systems as it directly impacts operational costs. Failure to adequately manage spiky or seasonal demand loads can lead to significant expenses on monthly utility bills. In this study, we propose AMPAMOD, a randomized online algorithm with machine-learned advice, to optimize battery operations and mitigate highly dynamic peak loads. AMPAMOD utilizes limited advice from machine learning algorithms to guide our online algorithm and ensure cost-effective peak load management. The theoretical analysis shows that our solution has minimal advice complexity, a linear computational cost, and an improved competitive ratio. Finally, we conduct extensive trace-driven experiments on real-world datasets. AMPAMOD achieves a peak shaving success rate of over 90%, outperforming baselines by at least 50%. These experimental results confirm theoretical findings and demonstrate the potential of AMPAMOD. © 2023 IEEE.

关键词： Cyber Physical System

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NOMA-Empowered Federated Learning for Enhancing Uplink Efficiency in Wireless Networks 18

NOMA-Empowered Federated Learning for Enhancing Uplink Effic...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Le, Hung Hoang Nguyen, Toan-Van Van, Tung Dinh Nguyen, Tien Hoa Choo, Hyunseung School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi100000 Viet Nam School of Computer Science and Engineering International University HCM Viet Nam Vietnam National University HCM Viet Nam Sungkyunkwan University Superintelligence Laboratory Suwon440-746 Korea Republic of

ISBN: (纸本)9798350331011

This paper proposes an uplink non-orthogonal multiple access (NOMA)-assisted federated learning (FL) framework to enhance the stable and accurate convergence in wireless networks. In this system, clients upload their local models securely to the base station through uplink NOMA to improve the uplink transmissions efficiency. We mathematically formulate the total latency optimization problem, taking into account the quality-of-service (QoS) requirements for clients, the power allocation at the base station (BS), and the frequency considerations. Simulation results show the that of the proposed FL framework achieves an 11% higher test accuracy and reduces latency by 25% compared to FedAvg and FedProx. © 2024 IEEE.

关键词： Wireless networks

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A Self-Powered Tactile Sensing Interface For Human-Machine Interaction 22

A Self-Powered Tactile Sensing Interface For Human-Machine I...

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22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Sun, Yuyang Li, Hanyang Wang, Kaiyao Feng, Xiaowei Hou, Cheng Chen, Tao Liu, Huicong Soochow University School Of Mechanical And Electrical Engineering Jiangsu Provincial Key Laboratory Of Advanced Robot Suzhou215123 China Soochow University School Of Computer Science And Technology Suzhou215123 China

ISBN: (纸本)9784886864352

Human-machine interfaces (HMIs) are the critical platforms for achieving effective and intuitive operations and control tasks between the user and machine. In this work, we propose a self-powered tactile sensing interface (STI) based on an array of bilayer single-electrode triboelectric nanogenerator (TENG) units. For each TENG unit, a pair of innovative, flexible materials, i.e., PET-PDMS and PTFE-PDMS, are arranged in a mosaic pattern, which can serialize the signal peaks and valleys, thus realizing the encoding of tactile sensing information and optimization of the wiring layout. This self-powered, signal-serialized and neatly wired STI can demonstrate the control of intelligent vehicles by detecting touch events. © 2023 IEEJ.

关键词： Human-machine interface self-powered signal-serializing triboelectric nanogenerator

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Keeping Drivers Alert: A Solution for Monitoring Driver Attention in Assisted-Driving Vehicles

Keeping Drivers Alert: A Solution for Monitoring Driver Atte...

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Proceedings of the International Convention MIPRO

作者： Robert Jutreša Peter Peer Žiga Emeršič Jihun Kim Faculty of Computer and Information Science/Computer Vision Laboratory Ljubljana Slovenia School of Electronic and Electrical Engineering Kyungpook National University Daegu South Korea

The advent of the new industrial revolution has led to a surge in the number of self-driving and assisted-driving vehicles on the roads. Although this development enhances the overall quality of life, it also poses new challenges and risks. One such difficulty is the need for driver attention in the event of system errors. To overcome this issue, a driver monitoring system is necessary to ensure that the driver remains focused and can take control if necessary, while also sending alerts in case of driver distraction. To address this challenge, we propose a method that employs a convolutional neural network based on the ResNet architecture. This system can recognize selected types of driver distractions in captured infrared images. We experimented with different depths of the ResNet architecture to determine the most optimal results. The best model achieved an accuracy rate of 94% in detecting various forms of driver distraction.

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Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks 38

Model LEGO: Creating Models Like Disassembling and Assemblin...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Jiacong Gao, Jing Ye, Jingwen Gao, Yang Wang, Xingen Feng, Zunlei Song, Mingli College of Computer Science and Technology Zhejiang University China Robotics Institute School of Computer Science Carnegie Mellon University United States Electrical and Computer Engineering National University of Singapore Singapore School of Software Technology Zhejiang University China State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China Bangsheng Technology Co. Ltd. China

With the rapid development of deep learning, the increasing complexity and scale of parameters make training a new model increasingly resource-intensive. In this paper, we start from the classic convolutional neural network (CNN) and explore a paradigm that does not require training to obtain new models. Similar to the birth of CNN inspired by receptive fields in the biological visual system, we draw inspiration from the information subsystem pathways in the biological visual system and propose Model Disassembling and Assembling (MDA). During model disassembling, we introduce the concept of relative contribution and propose a component locating technique to extract task-aware components from trained CNN classifiers. For model assembling, we present the alignment padding strategy and parameter scaling strategy to construct a new model tailored for a specific task, utilizing the disassembled task-aware components. The entire process is akin to playing with LEGO bricks, enabling arbitrary assembly of new models, and providing a novel perspective for model creation and reuse. Extensive experiments showcase that task-aware components disassembled from CNN classifiers or new models assembled using these components closely match or even surpass the performance of the baseline, demonstrating its promising results for model reuse. Furthermore, MDA exhibits diverse potential applications, with comprehensive experiments exploring model decision route analysis, model compression, knowledge distillation, and more. For more information, please visit https://***/. © 2024 Neural information processing systems foundation. All rights reserved.

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Behavior of multiple modes before and during minor disruption with the external resonant magnetic perturbations on J-TEXT tokamak

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Plasma science and Technology 2022年第6期24卷 88-95页

作者： Mingxiang HUANG Zhengkang REN Feiyue MAO Zhoujun YANG Yuan GAO Zhichao ZHANG Shunfan HE Guoliang LI Jinrong FAN Wei TIAN Nengchao WANG Zhipeng CHEN Yonghua DING Yuan PAN Zhongyong CHEN College of Computer Science South-Central Minzu UniversityWuhan 430074People's Republic of China International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074People's Republic of China

The behavior of multiple modes before and during minor disruption with the external resonant magnetic perturbations(RMPs)has been studied on a J-TEXT *** main component of RMPs is m/n=2/1,where m and n are the numbers of the poloidal and toroidal modes,*** the mode-locking caused by RMPs,it is found that before a minor or a major disruption(if there is no minor disruption),strong oscillations in both electron temperature and density occur if the edge safety factor q_(a)>*** analysis shows that the oscillations are caused by the m/n=3/1 *** addition,using the ECE,Mirnov coils and 2D electron cyclotron emission imaging diagnostic systems,it is found that a thermal collapse occurs on the inner side of the 2/1magnetic island during the minor disruption,and before the collapse,a 3/2 island increases,after the collapse,the 3/2 island may ***,the study also shows that these 3/1,2/1 and 3/2modes play roles in the thermal collapse of disruptions.

关键词： oscillation multiple modes disruption

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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios engineering

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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vision transformer-convolution for breast cancer classification using mammography images: A comparative study

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International Journal of Hybrid Intelligent Systems 2024年第2期20卷 67-83页

作者： Abimouloud, Mouhamed Laid Bensid, Khaled Elleuch, Mohamed Aiadi, Oussama Kherallah, Monji National Engineering School of Sfax University of Sfax Laboratory of Electrical Engineering (LAGE) University of KASDI Merbah Ouargla National School of Computer Science (ENSI) University of Manouba Artifcial Intelligence and Information Technology Laboratory (LINATI) Kasdi Merbah University Faculty of Sciences University of Sfax Advanced Technologies for Environment and Smart Cities (ATES Unit) University of Sfax

Breast cancer is a significant global health concern, highlighting the critical importance of early detection for effective treatment of women's health. While convolutional networks (CNNs) have been the best for analysing medical images, recent interest has emerged in leveraging vision transformers (ViTs) for medical data analysis. This study aimed to conduct a comprehensive comparison of three systems a self-attention transformer (VIT), a compact convolution transformer (CCT), and a tokenlearner (TVIT) for binary classification of mammography images into benign and cancerous tissue. Thorough experiments were performed using the DDSM dataset, which consists of 5970 benign and 7158 malignant images. The performance accuracy of the proposed models was evaluated, yielding results of 99.81% for VIT, 99.92% for CCT, and 99.05% for TVIT. Additionally, the study compared these results with the current state-of-the-art performance metrics. The findings demonstrate how convolution-attention mechanisms can effectively contribute to the development of robust computer-aided systems for diagnosing breast cancer. Notably, the proposed approach achieves high-performance results while also minimizing the computational resources required and reducing decision time. © 2024 - IOS Press. All rights reserved.

关键词： Breast cancer (BC) computer-aided diagnosis (CAD) convolutional networks (CNN) DDSM mammography vision transformers (VIT)

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Temporal Up-Sampling for Asynchronous Events

Temporal Up-Sampling for Asynchronous Events

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Xiang, Xijie Zhu, Lin Li, Jianing Tian, Yonghong Huang, Tiejun Shanghai Jiao Tong University Department of Electrical and Computer Engineering China School of Computer Science Peking University China Peng Cheng National Laboratory China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

The event camera is a novel bio-inspired vision sensor. When the brightness change exceeds the preset threshold, the sensor generates events asynchronously. The number of valid events directly affects the performance of event-based tasks, such as reconstruction, detection, and recognition. However, when in low-brightness or slow-moving scenes, events are often sparse and accompanied by noise, which poses challenges for event-based tasks. To solve these challenges, we propose an event temporal up-sampling algorithm 11Code: https://***/XIJIE-XIANG/Event-Temporal-Up-sampling to generate more effective and reliable events. The main idea of our algorithm is to generate up-sampling events on the event motion trajectory. First, we estimate the event motion trajectory by contrast maximization algorithm and then up-sampling the events by temporal point processes. Experimental results show that up-sampling events can provide more effective information and improve the performance of downstream tasks, such as improving the quality of reconstructed images and increasing the accuracy of object detection. © 2022 IEEE.

关键词： Cameras

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Zero-Shot Video Restoration and Enhancement Using Pre-Trained Image Diffusion Model

arXiv

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arXiv 2024年

作者： Cao, Cong Yue, Huanjing Liu, Xin Yang, Jingyu School of Electrical and Information Engineering Tianjin University Tianjin China Computer Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland

Diffusion-based zero-shot image restoration and enhancement models have achieved great success in various tasks of image restoration and enhancement. However, directly applying them to video restoration and enhancement results in severe temporal flickering artifacts. In this paper, we propose the first framework for zero-shot video restoration and enhancement based on the pre-trained image diffusion model. By replacing the spatial self-attention layer with the proposed short-long-range (SLR) temporal attention layer, the pre-trained image diffusion model can take advantage of the temporal correlation between frames. We further propose temporal consistency guidance, spatial-temporal noise sharing, and an early stopping sampling strategy to improve temporally consistent sampling. Our method is a plug-and-play module that can be inserted into any diffusion-based image restoration or enhancement methods to further improve their performance. Experimental results demonstrate the superiority of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Restoration

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