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arXiv 2024年

作者： Liu, Guangyuan Van Huynh, Nguyen Du, Hongyang Hoang, Dinh Thai Niyato, Dusit Zhu, Kun Kang, Jiawen Xiong, Zehui Jamalipour, Abbas In Kim, Dong The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The Department of Electrical Engineering and Electronics University of Liverpool Liverpool L69 3GJ United Kingdom The School of Electrical and Data Engineering University of Technology Sydney Australia The School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney Australia The College of Information and Communication Engineering Sungkyunkwan University Korea Republic of

With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans. However, learning and coordinating movements and actions for a large number of unmanned vehicles in complex and dynamic environments introduce significant challenges to conventional AI methods. Generative AI (GAI), with its capabilities in complex data feature extraction, transformation, and enhancement, offers great potential in solving these challenges of unmanned vehicle swarms. For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms. Specifically, we first present an overview of unmanned vehicles and unmanned vehicle swarms as well as their use cases and existing issues. Then, an in-depth background of various GAI techniques together with their capabilities in enhancing unmanned vehicle swarms are provided. After that, we present a comprehensive review on the applications and challenges of GAI in unmanned vehicle swarms with various insights and discussions. Finally, we highlight open issues of GAI in unmanned vehicle swarms and discuss potential research directions. © 2024, CC BY.

关键词： Metadata

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IEEE Transactions on Instrumentation and Measurement 2024年 1-1页

作者： Wu, Jin Wang, Chaoqun Li, Chong Jiang, Yi Zhang, Chengxi Huang, Yulong Qi, Yuhua Xue, Bohuan Jiao, Jianhao Fan, Rui Zhang, Wei Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China School of Control Science and Engineering Shandong University Jinan China Department of Automation and Measurement Ocean University of China Qingdao China Department of Electrical Engineering City University of Hong Kong Hong Kong China School of Internet of Things Jiangnan University Wuxi China Department of Automation Harbin Engineering University Harbin China School of Systems Science and Engineering Sun Yat-Sen University China College of Electronics and Information Engineering Tongji University Shanghai China

Similarity transformation problems are important in robotic instrumentation and computer vision based measurements since in many cases the information of visually observed scene scale is unknown and must be restored for accurate 3-dimensional reconstruction. In existing solvers, the scale is often considered as a scalar, i.e., isotropic, which may be invalid for anisotropic-scale setups. This paper exploits some mathematical coincidences that will lead to efficient solution to these problems. Possible further applications also include hand-eye calibration and structure-from-motion. We revisit pose estimation problems within the framework of similarity transformation, the one that considers scale-stretching, rotation and translation simultaneously. Two major problems are taken into account, i.e., the scale-stretching point-cloud registration and perspective-n-points (PnP). It has been found out that these two problems are quite similar. Moreover, we solve the anisotropic-scale registration problem that is important and is a remaining unsolved one in previous literatures. To compute the globally optimal solution of these non-convex problems, algebraic solution is obtained to compute all local minima using computationally efficient methods. The designed algorithm is deployed for robotic-arm pose estimation. We also extend the algorithm for solving the problem of robust magnetometer calibration. Visual pose experiments verify the superiority of the proposed method compared with representatives, including P3P, Lambda-Twist P3P and EPnP, which can be reproduced by repository in https://***/zarathustr/APnP. IEEE

关键词： Calibration

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Optimal AoI-based Block Propagation and Incentive Mechanism for Blockchain Networks in Web 3.0

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Wen, Jinbo Kang, Jiawen Xiong, Zehui Du, Hongyang Yang, Zhaohui Niyato, Dusit Shen, Meng Jiao, Yutao Zhang, Yang Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Guangdong University of Technology School of Automation China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Hong Kong University Department of Electrical and Electronic Engineering Hong Kong Zhejiang University College of Information Science and Electronic Engineering China Nanyang Technological University College of Computing and Data Science Singapore Beijing Institute of Technology School of Cyberspace Science and Technology China Army Engineering University of PLA College of Communications Engineering China

Web 3.0 is regarded as a revolutionary paradigm that enables users to securely manage data without a centralized authority. Blockchains, which enable data to be managed in a decentralized and transparent manner, are key technologies for achieving Web 3.0 goals. However, blockchain-based Web 3.0 is still in its infancy, such as ensuring block freshness and optimizing block propagation to improve blockchain performance. In this paper, we develop a freshness-aware block propagation optimization framework for Web 3.0. We first propose a novel metric called Age of Block information (AoBI) based on the concept of age of information to quantify block freshness. AoBI measures the time elapsed from the freshest transaction generation to the completion of block consensus. To make block propagation optimization tractable, we classify miners into five different states and propose a block propagation model for public blockchains inspired by epidemic models. Moreover, considering that the miners are bounded rational, we propose an incentive mechanism based on the evolutionary game for block propagation to improve block propagation efficiency. Numerical results demonstrate that compared with other block propagation mechanisms in public blockchains, the proposed scheme has a higher block forwarding probability, which improves block propagation efficiency and decreases the minimum value of average AoBI. © 2015 IEEE.

关键词： age of information block propagation evolutionary game Web 3.0 wireless networks

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Semantic Communications for Wireless Sensing: RIS-aided Encoding and Self-supervised Decoding

arXiv

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arXiv 2022年

作者： Du, Hongyang Wang, Jiacheng Niyato, Dusit Kang, Jiawen Xiong, Zehui Zhang, Junshan Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of California Davis United States The Department of Electrical and Computer Engineering University of Waterloo Canada

Semantic communications can reduce the resource consumption by transmitting task-related semantic information extracted from source messages. However, when the source messages are utilized for various tasks, e.g., wireless sensing data for localization and activities detection, semantic communication technique is difficult to be implemented because of the increased processing complexity. In this paper, we propose the inverse semantic communications as a new paradigm. Instead of extracting semantic information from messages, we aim to encode the task-related source messages into a hyper-source message for data transmission or storage. Following this paradigm, we design an inverse semantic-aware wireless sensing framework with three algorithms for data sampling, reconfigurable intelligent surface (RIS)-aided encoding, and self-supervised decoding, respectively. Specifically, on the one hand, we propose a novel RIS hardware design for encoding several signal spectrums into one MetaSpectrum. To select the task-related signal spectrums for achieving efficient encoding, a semantic hash sampling method is introduced. On the other hand, we propose a self-supervised learning method for decoding the MetaSpectrums to obtain the original signal spectrums. Using the sensing data collected from real-world, we show that our framework can reduce the data volume by 95% compared to that before encoding, without affecting the accomplishment of sensing tasks. Moreover, compared with the typically used uniform sampling scheme, the proposed semantic hash sampling scheme can achieve 67% lower mean squared error in recovering the sensing parameters. In addition, experiment results demonstrate that the amplitude response matrix of the RIS enables the encryption of the sensing data. © 2022, CC BY.

关键词： Semantics

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Hybrid Data-Free Knowledge Distillation

arXiv

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arXiv 2024年

作者： Tang, Jialiang Chen, Shuo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called Hybrid Data-Free Distillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, i.e., the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Code is available at https://***/tangjialiang97/HiDFD. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks

arXiv

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arXiv 2024年

作者： He, Jiayi Luo, Xiaofeng Kang, Jiawen Du, Hongyang Xiong, Zehui Chen, Ci Niyato, Dusit Shen, Xuemin School of Automation Guangdong University of Technology Guangzhou510006 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Semantic Communication (SemCom) plays a pivotal role in 6G networks, offering a viable solution for future efficient communication. Deep Learning (DL)-based semantic codecs further enhance this efficiency. However, the vulnerability of DL models to security threats, such as adversarial attacks, poses significant challenges for practical applications of SemCom systems. These vulnerabilities enable attackers to tamper with messages and eavesdrop on private information, especially in wireless communication scenarios. Although existing defenses attempt to address specific threats, they often fail to simultaneously handle multiple heterogeneous attacks. To overcome this limitation, we introduce a novel Mixture-of-Experts (MoE)based SemCom system. This system comprises a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. Multiple experts collaborate to accomplish semantic communication tasks while meeting the security requirements of users. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system. Simulation results show that the proposed MoE-based SemCom system effectively mitigates concurrent heterogeneous attacks, with minimal impact on downstream task accuracy. © 2024, CC BY.

关键词： Latent semantic analysis

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Training of Professionals on PV systems in Bulgaria

Training of Professionals on PV Systems in Bulgaria

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Energy Efficiency and Agricultural Engineering (EE&AE), International Conference on

作者： Boris I. Evstatiev Nikolay Valov Tsvetelina Mladenova Vladislav Hinkov Nicolay Mihailov Department of Electronics University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Automation and Mechatronics University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Computer Systems and Technologies University of Ruse “Angel Kanchev” Ruse Bulgaria Computing and Information Services Center University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Electrical Power Engineering University of Ruse “Angel Kanchev” Ruse Bulgaria

ISBN: (数字)9798350369380

ISBN: (纸本)9798350369397

The article presents the experience of a team from Ruse University “Angel Kanchev” (RUAK) in organizing and conducting training for installers of photovoltaic systems. These trainings are a response to the rapidly increased growth of photovoltaic (PV) plants in the country in recent years. Taking into account the qualification frameworks of the National Agency for Training and Education, a curriculum, theoretical part, and practical exercises have been developed. The offered trainings are met with interest by the business, and their results are positive.

关键词： Photovoltaic systems Renewable energy sources Companies Market research Problem-solving Qualifications

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Learning Novel Skills from Language-Generated Demonstrations

arXiv

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arXiv 2024年

作者： Jin, Ao-Qun Xiang, Tian-Yu Zhou, Xiao-Hu Gui, Mei-Jiang Xie, Xiao-Liang Liu, Shi-Qi Wang, Shuang-Yi Cao, Yue Duan, Sheng-Bin Xie, Fu-Chao Hou, Zeng-Guang The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Hubei Normal University Huangshi435002 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Information Shanxi University of Finance and Economics Taiyuan030006 China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa China

Current robot learning algorithms for acquiring novel skills often rely on demonstration datasets or environment interactions, resulting in high labor costs and potential safety risks. To address these challenges, this study proposes a skill-learning framework that enables robots to acquire novel skills from natural language instructions. The proposed pipeline leverages vision-language models to generate demonstration videos of novel skills, which are processed by an inverse dynamics model to extract actions from the unlabeled demonstrations. These actions are subsequently mapped to environmental contexts via imitation learning, enabling robots to learn new skills effectively. Experimental evaluations in the MetaWorld simulation environments demonstrate the pipeline’s capability to generate high-fidelity and reliable demonstrations. Using the generated demonstrations, various skill learning algorithms achieve an accomplishment rate three times the original on novel tasks. These results highlight a novel approach to robot learning, offering a foundation for the intuitive and intelligent acquisition of novel robotic skills. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Motion Planning Integrated with Vehicle-Terrain Interactions for Off-Road Autonomous Ground Vehicles 27

Motion Planning Integrated with Vehicle-Terrain Interactions...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Yin, Jianhua Zhan, Xianyuan Ji, Tianying Xu, Bingrong He, Yi Zhang, Daqing Li, Lingxi Wuhan University of Technology Intelligent Transportation Systems Research Center Wuhan430063 China Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan430063 China Chongqing Research Institute Wuhan University of Technology Chongqing China Institute for Ai Industry Research Tsinghua University Beijing100084 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Tsinghua University Department of Computer Science and Technology Beijing100084 China School of Automation Wuhan University of Technology Wuhan430070 China College of Mechanical and Electrical Engineering Hunan Agricultural University Changsha410128 China Sunward Intelligent Equipment Co. Ltd. Changsha410100 China Elmore Family School of Electrical and Computer Engineering College of Engineering Purdue University IN46202 United States

ISBN: (纸本)9798331505929

Off-road autonomous ground vehicles (AGVs) work in unstructured environments with varied elevation and deformable ground, where vehicle-terrain interactions are more complex resulting in unpredictable dynamics behavior. Current sensor-based hazards/obstacle identification methods mainly focus on geometric hazards/obstacles with explicit geometry, which might be adequate for vehicles in on-road conditions. However, in the off-road environment, vehicle-terrain interactions could introduce non-geometric hazards that cannot be detected by current geometric hazards-oriented sensing techniques, e.g. vehicle stuck in mud. To enable AGVs to traverse the target terrain and meet the goal while avoiding non-geometric hazards, this work proposes a motion planning method integrated with coupled vehicle-terrain dynamics response. Specifically, terramechanics-based modeling and simulation (M&S) is utilized to characterize vehicle-terrain interactions. Based on the dynamics response, a reinforcement learning (RL) algorithm with a specialized reward function is used to navigate the AGV to find the goal with high mobility and safety. We demonstrate the proposed method with two case studies, 1) high mobility is the foremost concern with less safety requirement, and 2) both high mobility and safety are required. The results show that the proposed motion planning method is effective for navigating the AGV in off-road terrains. © 2024 IEEE.

关键词： Off road vehicles

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Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs

arXiv

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arXiv 2024年

作者： Zhang, Tianyu Hou, Chengbin Jiang, Rui Zhang, Xuegong Zhou, Chenghu Tang, Ke Lv, Hairong The Ministry of Education Key Laboratory of Bioinformatics Bioinformatics Division Beijing National Research Center for Information Science and Technology Department of Automation Tsinghua University Beijing100084 China The School of Computer Science and Engineering Fuyao Institute of Technology Fuzhou350109 China The State Key Laboratory of Resources and Environmental Information Systems Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Fuzhou Institute of Data Technology Fuzhou350200 China

Node Importance Estimation (NIE) is a task of inferring importance scores of the nodes in a graph. Due to the availability of richer data and knowledge, recent research interests of NIE have been dedicating to knowledge graphs for predicting future or missing node importance scores. Existing state-of-the-art NIE methods train the model by available labels, and they consider every interested node equally before training. However, the nodes with higher importance often require or receive more attention in real-world scenarios, e.g., people may care more about the movies or webpages with higher importance. To this end, we introduce Label Informed ContrAstive Pretraining (LICAP) to the NIE problem for being better aware of the nodes with high importance scores. Specifically, LICAP is a novel type of contrastive learning framework that aims to fully utilize the continuous labels to generate contrastive samples for pretraining embeddings. Considering the NIE problem, LICAP adopts a novel sampling strategy called top nodes preferred hierarchical sampling to first group all interested nodes into a top bin and a non-top bin based on node importance scores, and then divide the nodes within top bin into several finer bins also based on the scores. The contrastive samples are generated from those bins, and are then used to pretrain node embeddings of knowledge graphs via a newly proposed Predicate-aware Graph Attention Networks (PreGAT), so as to better separate the top nodes from non-top nodes, and distinguish the top nodes within top bin by keeping the relative order among finer bins. Extensive experiments demonstrate that the LICAP pretrained embeddings can further boost the performance of existing NIE methods and achieve the new state-of-the-art performance regarding both regression and ranking metrics. The source code for reproducibility is available at https://***/zhangtia16/LICAP Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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