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arXiv 2024年

作者： Liu, Yinqiu Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui In Kim, Dong Shen, Xuemin College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Department of Electrical and Computer Engineering University of Waterloo Canada

—In the rapidly evolving Next-Generation Networking (NGN) era, the adoption of zero-trust architectures has become increasingly crucial to protect security. However, provisioning zero-trust services in NGNs poses significant challenges, primarily due to the environmental complexity and dynamics. Motivated by these challenges, this paper explores efficient zero-trust service provisioning using hierarchical micro-segmentations. Specifically, we model zero-trust networks via hierarchical graphs, thereby jointly considering the resource- and trust-level features to optimize service efficiency. We organize such zero-trust networks through micro-segmentations, which support granular zero-trust policies efficiently. To generate the optimal micro-segmentation, we present the Large Language Model-Enhanced Graph Diffusion (LEGD) algorithm, which leverages the diffusion process to realize a high-quality generation paradigm. Additionally, we utilize policy boosting and Large Language Models (LLM) to enable LEGD to optimize the generation policy and understand complicated graphical features. Moreover, realizing the unique trustworthiness updates or service upgrades in zero-trust NGN, we further present LEGD-Adaptive Maintenance (LEGD-AM), providing an adaptive way to perform task-oriented fine-tuning on LEGD. Extensive experiments demonstrate that the proposed LEGD achieves 90% higher efficiency in provisioning services compared with other baselines. Moreover, the LEGD-AM can reduce the service outage time by over 50%. © 2024, CC BY.

关键词： Diffusion

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Obstacle avoidance and model-free tracking control for home automation using bio-inspired approach

Advanced Control for Applications: Engineering and Industria...

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Advanced Control for Applications: engineering and Industrial Systems 2022年第1期4卷

作者： Khan, Ameer Tamoor Li, Shuai Li, Zhan Department of Computing The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Electrical Engineering Swansea University Swansea United Kingdom School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

This article proposes a control algorithm for obstacle avoidance and trajectory tracking for a redundant-manipulator in smart-homes. The redundancy provides dexterity and flexibility for the applications like picking, dropping, and transporting objects, tracking predefined paths while avoiding obstacles. The obstacle avoidance is one of the critical problems that need to be addressed. Our proposed algorithm, zeroing neural network with beetle antennae search (ZNNBAS), unifies these two problems into a single constrained optimization problem, which includes a penalty function to reward the optimizer on avoiding obstacles while tracking the reference trajectory. The penalty function is based on the principle of maximizing the minimum distance between the joints of the manipulator and the obstacles. Gilbert Johnson Keerthi (GJK) algorithm is used to calculate the distance between the manipulator and the obstacle as it uses the 3D geometry of the object and manipulator. To test the working of ZNNBAS, we simulated the model of the KUKA LBR robotic manipulator. We used two reference trajectories, that is, hypotrochoid and a character "M," with an arbitrarily shaped obstacle. The simulation results show that ZNNBAS was able to trace the reference path while successfully avoiding the obstacle accurately. © 2020 John Wiley & Sons, Ltd.

关键词： Redundant manipulators

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Study on tensile stress-induced giant magnetoimpedance effects in flexible amorphous ribbon meanders

Study on tensile stress-induced giant magnetoimpedance effec...

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2023 International Conference on Electronic Materials and Information engineering, EMIE 2023

作者： Yang, Zhen Liu, Mengyu Meng, Ziqin Wang, Zhenbao Sun, Xuecheng Guo, Yongbin Huang, Yong School of Electronic and Information Engineering School of Integrated Circuits Guangxi Normal University Guangxi Guilin541004 China Education Department of Guangxi Zhuang Autonomous Region Guangxi Guilin541004 China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin541004 China Microelectronic Research & Development Center School of Mechatronics Engineering and Automation Shanghai University Shanghai200884 China Key Laboratory of UWB & THz of Shandong Academy of Sciences Institute of Automation Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Xidian-Wuhu Research Institute Anhui Wuhu241000 China

ISBN: (纸本)9781510671331

Micropatterned Co-based amorphous ribbon meanders were prepared by MEMS technology on the flexible PET substrate. Different holders with varying arch heights were fabricated through 3D printing technology and used for carrying out tensile stresses. The giant magnetoimpedance (GMI) ratio was investigated under different tensile stress inductions (0~88 MPa). The results indicate that the GMI ratio of the flexible ribbon meanders is significantly impacted due to presence of tensile stress. As tensile stress increases, the GMI ratio first increases and then decreases. The maximum GMI ratio of 28.38% is achieved at a tensile stress level of 11 MPa. Additionally, the peak field gradually approaches zero as stress increases. When tensile stress increases from 0 MPa to 88 MPa, the peak field is shifted by 10.59 Oe. © 2023 SPIE · 0277-786X ·

关键词： Tensile stress

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Drives and Impulses: Shaping Motivation and Procedural Learning for Humanoid Robots

Drives and Impulses: Shaping Motivation and Procedural Learn...

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IEEE International Conference on Development and Learning, ICDL

作者： Leonardo L. Rossi Letícia Berto Paula D. P. Costa Ricardo Gudwin Esther Colombini Alexandre Simões School of Electrical and Computer Engineering University of Campinas Campinas Brazil Hub for Artificial Intelligence and Cognitive Architectures (H.IAAC) Campinas Brazil Institute of Computing University of Campinas Campinas Brazil Department of Control and Automation Engineering São Paulo State University Sorocaba Brazil

ISBN: (数字)9798350348552

ISBN: (纸本)9798350348569

In the emerging field of robotics, the initial development of robots handling basic tasks represented a revolutionary breakthrough. With ongoing advancements in science and technology, we anticipate the integration of artificial entities into diverse environments, performing a range of tasks autonomously. This necessitates the cultivation of intelligence and adaptability in these agents. To achieve this, agents must make informed decisions, considering environmental consequences. Motivation becomes pivotal, guiding decision-making and behavior toward fulfilling the agent's needs, drawing inspiration from human adeptness driven by motivation. This research endeavors to adapt two distinct motivation theories from human literature for the learning process of artificial agents. Employing a cognitive architecture, our investigation involves the learning process of a simulated humanoid robot, incorporating two distinct motivation systems: (i) behaviors driven by enduring needs and (ii) behaviors arising from impulses linked to temporary needs. The primary aim is to enhance comprehension of behaviors under diverse motivational frameworks within artificial cognitive agents and give insights into the direction of autonomous robots.

关键词： Affordances Scalability Decision making Humanoid robots External stimuli Learning (artificial intelligence) Drives

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Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

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Computers, Materials & Continua 2021年第9期68卷 3147-3165页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Lee Ching Kwang Rizaludin Kaspin Bhawani Shankar Chowdhry Rajkumar Buyya Satya Prasad Majumder Manoj Gupta Shuaib Memon Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation CentreCyberjaya63000SelangorMalaysia National Center of Robotics and Automation Mehran University of Engineering&TechnologyJamshoroPakistan Department of Computer Science and Engineering Hanyang UniversitySeoul04763South Korea School of Electrical and Electronic Engineering Nanyang Technological University639798Singapore Cloud Computing and Distributed Systems Laboratory The University of MelbourneMelbourneVIC 3053Australia Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology(BUET)Dhaka1205Bangladesh Department of Electronics and Communication Engineering JECRC UniversityVidhaniJaipur303905India Auckland Institute of Studies Mt AlbertAucklandNew Zealand

In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication *** research has concentrated largely on factors such as reliability,latency,controller capacity,propagation delay,and energy ***,SDNs are vulnerable to distributed denial of service(DDoS)attacks that interfere with legitimate use of the *** ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design,especially in critical applications such as military,health care,and financial services networks requiring high *** propose a mathematical model for planning the deployment of SDN smart backup controllers(SBCs)to preserve service in the presence of DDoS *** a number of input parameters,our model has two distinct ***,it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating ***,it recommends an optimal number of smart backup controllers for use with different levels of DDoS *** goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the *** simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.

关键词： SDN automated controller placement SBC ILP DDoS attack

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Cybersecurity in Industrial Networks: Artificial Intelligence Techniques Applied to Intrusion Detection Systems

Cybersecurity in Industrial Networks: Artificial Intelligenc...

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Computer Science, Computer engineering, & Applied computing (CSCE), Congress in

作者： Ralf Luis de Moura Virginia N. L. Franqueira Gustavo Pessin Operational Technology Architecture Vale S.A. Vitória Brazil School of Computing University of Kent Canterbury UK Department of Automation Engineering Instituto de Tecnologia Vale Ouro Preto Brazil

Industrial control systems (ICS) operate on serial-based networks which lack proper security safeguards by design. They are also becoming more integrated to corporate networks, creating new vulnerabilities which expose ICS networks to in-creasing levels of risk with potentially significant impact. Despite those risks, only a few mechanisms have been suggested and are available in practice as cybersecurity safeguards for the ICS network layer, maybe because they might not be commercially viable. Intrusion detection systems (IDS) are typically deployed in the corporate networks to protect against attacks since they are based on TCP/IP. However, IDS are not used in serial-based ICS networks yet. This study examines and compares modern Artificial Intelligence (AI) techniques applied in IDS that are potentially useful for serial-based ICS networks. The results showed that current AI-based IDS methods are viable in such networks. A mix of AI techniques would be the best way forward to detect known attacks via rules and novel attacks, not previously mapped, via supervised and unsupervised techniques. Despite these strategies' limited use in serial-based networks, their adoption could significantly strengthen cybersecurity of ICS networks.

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Hwamei: A Learning-Based Synchronization Scheme for Hierarchical Federated Learning

Hwamei: A Learning-Based Synchronization Scheme for Hierarch...

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International Conference on Distributed computing Systems

作者： Tianyu Qi Yufeng Zhan Peng Li Jingcai Guo Yuanqing Xia School of Automation Beijing Institute of Technology Beijing China School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan Department of Computing The Hong Kong Polytechnic University Hong Kong SAR China

Federated learning (FL) enables collaborative model training among distributed devices without data sharing, but existing FL suffers from poor scalability because of global model synchronization. To address this issue, hierarchical federated learning (HFL) has been recently proposed to let edge servers aggregate models of devices in proximity, while synchronizing via the cloud periodically. However, a critical open challenge about how to design a good synchronization scheme (when devices and edges should be synchronized) is still unsolved. Devices are heterogeneous in computing and communication capability, and their data could be non-IID. No existing work can well synchronize various roles (e.g., devices and edge) in HFL to guarantee high learning efficiency and accuracy. In this paper, we propose a learning-based synchronization scheme for HFL systems. By collecting data such as edge models, CPU usage, communication time, etc., we design a deep reinforcement learning-based approach to decide the frequencies of cloud aggregation and edge aggregation, respectively. The proposed scheme well considers device heterogeneity, non-IID data and device mobility, to maximize the training model accuracy while minimizing the energy overhead. We build an HFL testbed and conduct experiments using real data obtained from Raspberry Pi and Alibaba Cloud. Extensive experimental results have confirmed the effectiveness of Hwamei.

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Real-time Semantic Segmentation of Solar Photovoltaic Arrays for Autonomous UAV Flights

Real-time Semantic Segmentation of Solar Photovoltaic Arrays...

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第43届中国控制会议

作者： Nanyan Li Hao Chen Zhigang Sun Jing Gao Dewei Yi Cunjia Liu Jinya Su School of Automation Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education Southeast University State Grid Jiangsu Integrated Energy Service Co. Ltd. Department of Computing Science University of Aberdeen Department of Aeronautical and Automotive Engineering Loughborough University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper considers the real-time semantic segmentation of solar PhotoVoltaic(PV) arrays using down-facing cameras onboard Unmanned Aerial Vehicles(UAVs) to support autonomous navigation for ultra-low altitude industrial inspection. Various semantic segmentation algorithms, including SegNet, UNet, Deeplabv3+, and their respective variants, are compared to identify the most suitable one that strikes a balance between segmentation and real-time performance. Subsequently, each video frame is processed by the selected segmentation network to create a solar array mask. This mask is then optimally fitted to diverse solar array shapes to determine angular and horizontal deviations between the image center and the solar array center-line. The algorithms are validated on a self-constructed dataset of 594 solar PV array UAV images, encompassing varied environmental conditions and solar farm layouts. The top-performing network, lightweight vgg16bn-Unet, achieves 99.4% overall accuracy and 98.2% Intersection over Union(IoU), while the most balanced network, mobilev3-Deeplabv3+, attains 98.9% accuracy and96.5% IoU, with a 27.59 fps(using an NVIDIA GeForce MX150 GPU) suitable for real-time UAV flights.

关键词： CNN Machine Vision Solar Inspection UAV Navigation Real-time and Embedded systems

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Soccer match broadcast video analysis method based on detection and tracking

Soccer match broadcast video analysis method based on detect...

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作者： Li, Hongyu Yang, Meng Yang, Chao Kang, Jianglang Suo, Xiang Meng, Weiliang Li, Zhen Mao, Lijuan Sheng, Bin Qi, Jun School of Information science and Technology Beijing Forestry University Beijing China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration Beijing China School of Athletic Performance Shanghai University of Sport Shanghai China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong Univerity Shanghai China Department of Computing Xi'an JiaoTong-Liverpool University Suzhou China

We propose a comprehensive soccer match video analysis pipeline tailored for broadcast footage, which encompasses three pivotal stages: soccer field localization, player tracking, and soccer ball detection. Firstly, we introduce sports camera calibration to seamlessly map soccer field images from match videos onto a standardized two-dimensional soccer field template. This addresses the challenge of consistent analysis across video frames amid continuous camera angle changes. Secondly, given challenges such as occlusions, high-speed movements, and dynamic camera perspectives, obtaining accurate position data for players and the soccer ball is non-trivial. To mitigate this, we curate a large-scale, high-precision soccer ball detection dataset and devise a robust detection model, which achieved the (Formula presented.) of 80.9%. Additionally, we develop a high-speed, efficient, and lightweight tracking model to ensure precise player tracking. Through the integration of these modules, our pipeline focuses on real-time analysis of the current camera lens content during matches, facilitating rapid and accurate computation and analysis while offering intuitive visualizations. © 2024 John Wiley & Sons Ltd.

关键词： Visualization

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A New Federated Learning Framework Against Gradient Inversion Attacks

arXiv

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arXiv 2024年

作者： Guo, Pengxin Zeng, Shuang Chen, Wenhao Zhang, Xiaodan Ren, Weihong Zhou, Yuyin Qu, Liangqiong School of Computing and Data Science The University of Hong Kong Hong Kong Department of Mathematics The University of Hong Kong Hong Kong College of Computer Science Beijing University of Technology China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering UC Santa Cruz United States

Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demonstrate that information exchanged during FL is subject to Gradient Inversion Attacks (GIA) and, consequently, a variety of privacy-preserving methods have been integrated into FL to thwart such attacks, such as Secure Multi-party computing (SMC), Homomorphic Encryption (HE), and Differential Privacy (DP). Despite their ability to protect data privacy, these approaches inherently involve substantial privacy-utility trade-offs. By revisiting the key to privacy exposure in FL under GIA, which lies in the frequent sharing of model gradients that contain private data, we take a new perspective by designing a novel privacy preserve FL framework that effectively "breaks the direct connection" between the shared parameters and the local private data to defend against GIA. Specifically, we propose a Hypernetwork Federated Learning (HyperFL) framework that utilizes hypernetworks to generate the parameters of the local model and only the hypernetwork parameters are uploaded to the server for aggregation. Theoretical analyses demonstrate the convergence rate of the proposed HyperFL, while extensive experimental results show the privacy-preserving capability and comparable performance of HyperFL. Code is available at https://***/Pengxin-Guo/HyperFL. © 2024, CC BY.

关键词： Federated learning

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