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A Novel Construction Method for 1-Dof Multi-Loop Kirigami Mechanisms

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SSRN

SSRN 2024年

作者： Lv, Weilin Nie, Wansui Wei, Jun Zhang, Jianjun Meng, Chuizhou Liu, Teng Sui, Jin Guo, Shijie Engineering Research Center The Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies Hebei University of Technology Tianjin300401 China Hebei Provincial Key Laboratory of Robot Perception and Human-Machine Fusion Hebei University of Technology Tianjin300401 China School of Mechanical Engineering Hebei University of Technology Tianjin300401 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

Kirigami has been utilized in designing deployable mechanisms for various engineering applications. This paper presents a novel construction method for one-degree-of-freedom (1-DOF) multi-loop kirigami mechanisms whose folding motions are kinematically equivalent to their original degree-4 rigid origami. Firstly, we introduce a family of single-loop kirigami (SLK) as the main loop of 1-DOF multi-loop kirigami. Secondly, the coupling-constraint properties of multi-DOF SLKs are clarified, and the structural characteristics of the 1-DOF multi-loop kirigami are defined, based on which, the construction method for the 1-DOF multi-loop kirigami is proposed. Thirdly, the representative models are selected and their kinematics are presented to demonstrate their capability to achieve folding motion equivalent to their origami prototypes, thus verifying the validity of our proposed methodology. Fourthly, the non-constraint condition of the 1-DOF multi-loop kirigami is presented. Finally, we propose extended configurations of 1-DOF multi-loop kirigami and provide the forward and inverse design methods for constructing kirigami meshes. The proposed methodology facilitates the design of large-scale deployable structures. © 2024, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

Large Depth Range Binary-Focusing Projection 3d Shape Reconstruction Via Unpaired Data Learning

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SSRN

SSRN 2023年

作者： Tan, Ji Wang, Xu He, Zhaoshui Su, Wenqing Huang, Tao Xie, Shengli School of Automation Guangdong University of Technology Guangzhou510006 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China

The binary defocusing technique (BDT) shows prospects in high-speed 3D reconstruction. However, the defocusing projection mechanism results in low accuracy at inappropriate defocus levels due to the high-order harmonics, thereby constraining the achievable depth range. To overcome the problems, this paper proposes a large depth range binary-focusing projection technique, aimed at fundamentally solving the compromise of the reconstructed depth range. Based on the focused-projection strategy, we develop a cycle generative adversarial framework for the binary-to-sinusoidal domain translation to obtain high-quality phase information in both the focused and improperly defocused regions. Additionally, a dedicated unpaired dataset is built to perform self-supervised learning through the utilization of a cycle consistency strategy. Compared with the traditional BDT, the proposed method is not limited by the defocusing degree but inherits the advantages of high-speed projection, thus showing substantial potential to achieve super-fast and large depth range dynamic reconstruction and measurement. Experiments verify the performance of the proposed method. © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

IR Net: information restriction and information recovery for accurate binary neural networks

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Neural Computing and Applications 2023年第19期35卷 14449-14464页

作者： Xue, Ping Lu, Yang Chang, Jingfei Wei, Xing Wei, Zhen School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Mine IOT and Security Monitoring Technology Key Laboratory Hefei China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei University of Technology Hefei China Intelligent Manufacturing Institute of Hefei University of Technology Hefei China

Weight and activation binarization can efficiently compress deep neural networks and accelerate model inference, but they cause severe accuracy degradation. Existing optimization methods for binary neural networks (BNNs) focus on fitting full-precision networks to reduce quantization errors and suffer from a tradeoff between accuracy and efficiency. In contrast, considering information loss and the mismatch between model capacity and input information quantity caused by network binarization, we propose Information Restriction and Information Recovery Network (IR $$^2$$ Net) to stimulate the potential of BNNs and achieve improved network accuracy by restricting the input information and recovering feature information. The proposed approach includes (1) information restriction, which evaluates the feature information extracted from the input by a BNN, discards some of the information it cannot focus on, and reduces the amount of the input information to match the model capacity; and (2) information recovery: due to the information loss incurred during forward propagation, the extracted feature information of the network is not sufficient for supporting accurate classification. Shallow feature maps with richer information are selected, and these feature maps are fused with the final feature maps to recover the extracted feature information and further enhance the model capacity to match the amount of input information. In addition, the computational cost is reduced by streamlining the information recovery method to strike a better tradeoff between accuracy and efficiency. Experimental results demonstrate that our approach still achieves comparable accuracy even with a $$\sim$$ 10x floating-point operations (FLOPs) reduction for ResNet-18. The models and code are available at https://***/pingxue-hfut/IR2Net .

关键词：

Multi-view data classification with a label-driven auto-weighted strategy

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arXiv 2022年

作者： Yu, Yuyuan Zhou, Guoxu Huang, Haonan Xie, Shengli Zhao, Qibin School Of Automation Guangdong University Of Technology Guangzhou510006 China Key Laboratory Of Intelligent Detection And The Internet Of Things In Manufacturing Ministry Of Education Guangzhou510006 China Jt. Intl. Research Laboratory Of Intelligent Information Processing And System Integration Of IoT Ministry Of Education Guangdong University Of Technology Guangzhou510006 China Guangdong-Hong Kong-Macao Joint Laboratory For Smart Discrete Manufacturing Guangdong University Of Technology Guangzhou510006 China RIKEN Tokyo103-0027 Japan

Distinguishing the importance of views has proven to be quite helpful for semi-supervised multi-view learning models. However, existing strategies cannot take advantage of semi-supervised information, only distinguishing the importance of views from a data feature perspective, which is often influenced by low-quality views then leading to poor performance. In this paper, by establishing a link between labeled data and the importance of different views, we propose an auto-weighted strategy to evaluate the importance of views from a label perspective to avoid the negative impact of unimportant or low-quality views. Based on this strategy, we propose a transductive semi-supervised auto-weighted multi-view classification model. The initialization of the proposed model can be effectively determined by labeled data, which is practical. The model is decoupled into three small-scale sub-problems that can efficiently be optimized with a local convergence guarantee. The experimental results on classification tasks show that the proposed method achieves optimal or sub-optimal classification accuracy at the lowest computational cost compared to other related methods, and the weight change experiments show that our proposed strategy can distinguish view importance more accurately than other related strategies on multi-view datasets with low-quality views. Copyright © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

Generative AI-driven Cross-layer Covert Communication: Fundamentals, Framework and Case Study

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arXiv 2025年

作者： Liu, Tianhao Liu, Jiqiang Zhang, Tao Wang, Jian Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100045 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Ensuring end-to-end cross-layer communication security in military networks by selecting covert schemes between nodes is a key solution for military communication security. With the development of communication technology, covert communication has expanded from the physical layer to the network and application layers, utilizing methods such as artificial noise, private networks, and semantic coding to transmit secret messages. However, as adversaries continuously eavesdrop on specific communication channels, the accumulation of sufficient data may reveal underlying patterns that influence concealment, and establishing a cross-layer covert communication mechanism emerges as an effective strategy to mitigate these regulatory challenges. In this article, we first survey the communication security solution based on covert communication, specifically targeting three typical scenarios: device-to-device, private network communication, and public network communication, and analyze their application scopes. Furthermore, we propose an end-to-end cross-layer covert communication scheme driven by Generative Artificial Intelligence (GenAI), highlighting challenges and their solutions. Additionally, a case study is conducted using diffusion reinforcement learning to sovle cloud edge internet of things cross-layer secure communication. © 2025, CC BY.

关键词： Generative adversarial networks

Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition

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arXiv 2022年

作者： Xie, Kan Zhang, Zhe Li, Bo Kang, Jiawen Niyato, Dusit Xie, Shengli Wu, Yi The School of Automation Guangdong University of Technology Guangzhou510006 China The Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China The Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China The Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Data Science and Technology Heilongjiang University Harbin150080 China The Institute for Cryptology & Network Security Heilongjiang University Harbin150080 China School of Computer Science and Engineering Nanyang Technological University Singapore

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information. To address this issue, we first exploit privacy-preserving federated learning to perform collaborative training for accurate recognition models without sharing raw traffic sign data. Nevertheless, due to the limited computing and energy resources of most devices, it is hard for vehicles to continuously undertake complex artificial intelligence tasks. Therefore, we introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training, which is the next generation of neural networks and is practical and well-fitted to IoV scenarios. Furthermore, we design a novel encoding scheme for SNNs based on neuron receptive fields to extract information from the pixel and spatial dimensions of traffic signs to achieve high-accuracy training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well. Copyright © 2022, The Authors. All rights reserved.

关键词： Traffic signs

Global Consensus for Heterogeneous Saturated Multi-Agent Systems via Sampled-Data Control

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Global Consensus for Heterogeneous Saturated Multi-Agent Sys...

IEEE Conference on Decision and Control

作者： Juan Qian Xiaoling Wang Daniele Astolfi Housheng Su Guo-Ping Jiang College of Automation and the College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Engineering Center for IOT Intelligent Robots(IOTRobot) Nanjing China CNRS LAGEPP UMR 5007 43 boulevard du 11 novembre 1918 Université Claude Bernard Lyon 1 Villeurbanne France Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper addresses the global consensus problem for multi-input multi-output saturated systems within a sampled-data framework, aiming to advance global consensus, manage heterogeneous actuator saturation across components, and preserve distributed characteristics by using sampled-data feedback. We propose a distributed control algorithm that incorporates a redesigned saturation function, represented as decentralized dynamic saturation levels. These levels for each agent’s dimensions are autonomously updated through an adaptive strategy, which mitigates heterogeneous saturation by carefully selecting constant and time-varying saturation parameters in it. Lyapunov analysis proves that global consensus can be achieved under the proposed control law, provided the sampling periods of all agents remain below a calculated threshold. An example is given to demonstrate the effectiveness of this approach.

关键词： Actuators Upper bound Heuristic algorithms Decentralized control Multi-agent systems

Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses

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arXiv 2023年

作者： Kang, Jiawen He, Jiayi Du, Hongyang Xiong, Zehui Yang, Zhaohui Huang, Xumin Xie, Shengli The School of Automation Guangdong University of Technology Guangzhou510006 China The Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China The Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Computer Science and Engineering Nanyang Technological University Singapore The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310007 China Hangzhou310007 China Zhejiang Laboratory Hangzhou31121 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore

For vehicular metaverses, one of the ultimate user-centric goals is to optimize the immersive experience and Quality of Service (QoS) for users on board. Semantic Communication (SemCom) has been introduced as a revolutionary paradigm that significantly eases communication resource pressure for vehicular metaverse applications to achieve this goal. SemCom enables high-quality and ultra-efficient vehicular communication, even with explosively increasing data traffic among vehicles. In this article, we propose a hierarchical SemCom-enabled vehicular metaverses framework consisting of the global metaverse, local metaverses, SemCom module, and resource pool. The global and local metaverse are brand-new concepts from the metaverse’s distribution standpoint. Considering the QoS of users, this article explores the potential security vulnerabilities of the proposed framework. To that purpose, this study highlights a specific security risk to the framework’s SemCom module and offers a viable defense solution, so encouraging community researchers to focus more on vehicular metaverse security. Finally, we provide an overview of the open issues of secure SemCom in the vehicular metaverses, notably pointing out potential future research directions. © 2023, CC BY.

关键词： Quality of service

Mixture of Experts for intelligent Networks: A Large Language Model-enabled Approach

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Mixture of Experts for Intelligent Networks: A Large Languag...

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Hongyang Du Guangyuan Liu Yijing Lin Dusit Niyato Jiawen Kang Zehui Xiong Dong In Kim School of Computer Science and Engineering Nanyang Technological University Singapore Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Automation Guangdong University of Technology and Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLMenabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems.

关键词： Wireless communication Energy consumption Navigation Computational modeling Logic gates Probability Throughput