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arXiv 2024年

作者： Yu, Jingru Yu, Yi Wang, Xuhong Lin, Yilun Yang, Manzhi Qiao, Yu Wang, Fei-Yue Shanghai Artificial Intelligence Laboratory Shanghai200240 China The Macau Institute of Systems Engineering Macau University of Science and Technology China Eversec Technology Company Ltd. Beijing100080 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Social engineering (SE) attacks remain a significant threat to both individuals and organizations. The advancement of Artificial Intelligence (AI), including diffusion models and large language models (LLMs), has potentially intensified these threats by enabling more personalized and convincing attacks. This survey paper categorizes SE attack mechanisms, analyzes their evolution, and explores methods for measuring these threats. It highlights the challenges in raising awareness about the risks of AI-enhanced SE attacks and offers insights into developing proactive and adaptable defense strategies. Additionally, we introduce a categorization of the evolving nature of AI-powered social engineering attacks into"3E phases": Enlarging, wherein the magnitude of attacks expands through the leverage of digital media;Enriching, introducing novel attack vectors and techniques;and Emerging, signifying the advent of novel threats and methods. Moreover, we emphasize the necessity for a robust framework to assess the risk of AI-powered SE attacks. By identifying and addressing gaps in existing research, we aim to guide future studies and encourage the development of more effective defenses against the growing threat of AI-powered social engineering. Copyright © 2024, The Authors. All rights reserved.

关键词： Artificial intelligence

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Multiscale forming simulation of carbon fiber reinforced thermoplastics: an application of supercomputer 23

Multiscale forming simulation of carbon fiber reinforced the...

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23rd International Conference on Composite Materials, ICCM 2023

作者： Wu, Q. Ogasawara, T. Yoshikawa, N. Zhai, H. Morita, N. State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and Astronautics Yudao Street 29 Nanjing210016 China Institute of Industrial Science The University of Tokyo 4-6-1 Komaba Meguro-Ku Tokyo153-8505 Japan Faculty of Engineering Information and Systems University of Tsukuba 1-1-1 Tennodai Tsukuba305-8573 Japan

(1) Theoretical research on asymptotic homogenization for unsteady-state thermal conduction, linear viscoelasticity and thermal expansion in the thermo-viscoelastic composite is respectively studied and analytical methods for effective density, specific heat capacity, numerical implementation methods for effective thermal conductivity, linear viscoelasticity and thermal expansion coefficient are presented. (2) Creating unidirectional-RVE and woven fabric-RVE through experiment method, calculating effective material moduli of GF/PEI composite and investigating the temperature gradient influence on the stress, strain and residual deformation during the forming process through experimental validation and numerical simulation. (3) Proposing a multi-step forming technique to fabricate GF/PEI composite wing leading edge and simulating the forming process of a GF/PEI composite wing leading edge to study temperature gradient and tool-part impacts on the residual deformation, as well as the mechanism of warpage during the demolding process on the macroscale. © 2023 International Committee on Composite Materials. All rights reserved.

关键词： Thermal gradients

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control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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Data-Driven Learning and control with Event-Triggered Measurements

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IEEE Transactions on Automatic control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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OceanCL-IDS: A Continual Learning-based Intrusion Detection System for Ocean-going Ship-Satellite Communication Network 7

OceanCL-IDS: A Continual Learning-based Intrusion Detection ...

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7th IEEE International Conference on Transportation Information and Safety, ICTIS 2023

作者： Zhang, Chenyun Liu, Jin Zhi, Jiamao Zhang, Xiliang Wang, Junxiang Wu, Zhongdai Shanghai Maritime University College of Information Engineering Shanghai China Shanghai Ship and Shipping Research Institute China Shipping Telecommunication Co. Ltd. Shanghai China National Engineering Research Center of Ship Transportation Control Systems Cosco Shipping Technology Co. Ltd Shanghai China Shanghai Ship and Shipping Research Institute State Key Laboratory of Maritime Technology and Safety Shanghai China

ISBN: (纸本)9798350308532

Deep learning-based intrusion detection has become an effective approach to ensure the security of communication network traffic in scenarios such as the Internet of Things (IoT) and the Internet of Vehicles (IoV). However, there is relatively little research on intrusion detection for ship-satellite communication network in marine scenarios. In addition, due to the inherent catastrophic forgetting issue of deep learning models, vanilla intrusion detection methods can only recognize previously learnt attack types, but cannot learn new attack types in real time. In this paper, an ocean-going ship-satellite communication network (OSSCN) intrusion detection system (OceanCL-IDS) is proposed, which can effectively alleviate the communication interference caused by the harsh maritime communication environment. Furthermore, a continual learning algorithm GEM based on replay strategy is introduced into the system, which is capable of continual learning new attack features while retaining the capacity to detect previously learnt attack. Finally, to validate the efficacy of the proposed methods, a dataset with attack labels and maritime communication features is constructed by collecting long-term satellite network traffic data of real ocean-going ships. Extensive experimental evidence demonstrates that our method successfully beats existing continual learning algorithms on a variety of metrics. © 2023 IEEE.

关键词： Ships

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Quantifying the impacts of COVID-19 on Sustainable Development Goals using machine learning models

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Fundamental Research 2024年第4期4卷 890-897页

作者： Chenyang Shuai Bu Zhao Xi Chen Jianguo Liu Chunmiao Zheng Shen Qu Jian-Ping Zou Ming Xu School of Management Science and Real Estate Chongqing UniversityChongqing 400044China bSchool for Environment and Sustainability University of MichiganAnn ArborMI 489United Stats Michigan Institute for Computational Discovery&Engineering University of MichiganAnn ArborMI 48109United States College of Economics and Management Southwest UniversityChongqing 400044China Center for Systems Integration and Sustainability Department of Fisheries and WildlifeMichigan State UniversityEast LansingMI 48824United States Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control School of Environmental Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China School of Management and Economics Beijing Institute of TechnologyBeijing 100081China Center for Energy&Environmental Policy Research Beijing Institute of TechnologyBejing 100081China Key Laboratory of Jiangxi Province for Persistent Plutants Control and Resources Recycle Nanchang Hangkong UniversityNanchang 330063China Department of Civil and Environmental Engineering University of MichiganAnn ArborMI 48109United States

The COvID-19 pandemic has posed severe threats to global sustainable ***,a comprehensive quantitative assessment of the impacts of COVID-19 on Sustainable Development Goals(SDGs)is still *** research quantified the post-COVID-19 SDG progress from 2020 to 2024 using projected GDP growth and population and machine learning models including support vector machine,random forest,and extreme gradient *** results show that the overall SDG performance declined by 7.7%in 2020 at the global scale,with 12 socioeconomic SDG performance decreasing by 3.0%-22.3%and 4 environmental SDG performance increasing by 1.6%-9.2%.By 2024,the progress of 12 SDGs will lag behind for one to eight years compared to their pre-COVID-19 trajectories,while extra time will be gained for 4 environment-related ***,the pandemic will cause more impacts on countries in emerging markets and developing economies than those on advanced economies,and the latter will recover more quickly to be closer to their pre-covID-19 trajectories by ***-COVID-19 economic recovery should emphasize in areas that can help decouple economic growth from negative environmental *** results can help government and non-state stakeholders identify critical areas for targeted policy to resume and speed up the progress to achieve SDGs by 2030.

关键词： Sustainable development goals COVID-19 Machine learning Global development Sustainable development assessment

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Deep Learning Enabled Semantic Communication systems for Video Transmission

Deep Learning Enabled Semantic Communication Systems for Vid...

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IEEE Conference on Vehicular technology (VTC)

作者： Zhenguo Zhang Qianqian Yang Shibo He Jiming Chen College of Control Science and Engineering Zhejiang University Hangzhou China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China The State Key Laboratory of Industrial Control Technology Hangzhou China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Zhejiang University Hangzhou China

Semantic communication has emerged as a promising approach for improving efficient transmission in the next generation of wireless networks. Inspired by the success of semantic communication in different areas, we aim to provide a new semantic communication scheme from the semantic level. In this paper, we propose a novel DL-based semantic communication system for video transmission, which compacts semantic-related information to improve transmission efficiency. In particular, we utilize the Bi-optical flow to estimate residual information of inter-frame details. We also propose a feature choice module and a feature fusion module to drop semantically redundant features while paying more attention to the important semantic-related content. We employ a frame prediction module to reconstruct semantic features of the prediction frame from the received signal at the receiver. To enhance the system’s robustness, we propose a noise attention module that assigns different importance weights to the extracted features. Simulation results indicate that our proposed method outperforms existing approaches in terms of transmission efficiency, achieving about 33.3% reduction in the number of transmitted symbols while improving the peak signal-to-noise ratio (PSNR) performance by an average of 0.56dB.

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A Novel Heterogeneous Actor-critic Algorithm with Recent Emphasizing Replay Memory

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International Journal of Automation and computing 2021年第4期18卷 619-631页

作者： Bao Xi Rui Wang Ying-Hao Cai Tao Lu Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China

Reinforcement learning(RL) algorithms have been demonstrated to solve a variety of continuous control tasks. However,the training efficiency and performance of such methods limit further applications. In this paper, we propose an off-policy heterogeneous actor-critic(HAC) algorithm, which contains soft Q-function and ordinary Q-function. The soft Q-function encourages the exploration of a Gaussian policy, and the ordinary Q-function optimizes the mean of the Gaussian policy to improve the training efficiency. Experience replay memory is another vital component of off-policy RL methods. We propose a new sampling technique that emphasizes recently experienced transitions to boost the policy training. Besides, we integrate HAC with hindsight experience replay(HER) to deal with sparse reward tasks, which are common in the robotic manipulation domain. Finally, we evaluate our methods on a series of continuous control benchmark tasks and robotic manipulation tasks. The experimental results show that our method outperforms prior state-of-the-art methods in terms of training efficiency and performance, which validates the effectiveness of our method.

关键词： Reinforcement learning(RL) actor-critic experience replay training efficiency manipulation skill learning

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Quasi-synchronization of bounded confidence opinion dynamics with a stochastic asynchronous rule

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Science China(Information Sciences) 2022年第1期65卷 285-286页

作者： Wei SU Xue-Qiao WANG Ge CHEN Kai SHEN School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing Key Laboratory of Information Service Engineering Beijing Union University Key Laboratory of Systems and Control Chinese Academy of Sciences School of Automation Beijing Institute of Technology

Dear editor,One of the interesting topics in opinion dynamics is the noise-induced synchronization of opinion systems. Su et al. [1–3] conducted a rigorous theoretical analysis of this phenomenon based on the Hegselmann-Krause(HK) *** HK model is a typical bounded confidence opinion model with a synchronous opinion updating rule, i.e., all agents continuously check and decide whether or not they will update their opinions at each time [4].

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TLS-Net: A Hybrid Time Series Prediction Model Combining TCN and LSTM for Ship-Satellite Network Traffic 7

TLS-Net: A Hybrid Time Series Prediction Model Combining TCN...

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7th IEEE International Conference on Transportation Information and Safety, ICTIS 2023

作者： Cao, Mengxiang Liu, Jin Zhi, Jiamao Gong, Peizhu Wang, Junxiang Wu, Zhongdai Shanghai Maritime University College of Information Engineering Shanghai China China Shipping Telecommunication Co. Ltd. Shanghai Ship and Shipping Research Institute Shanghai China National Engineering Research Center of Ship Transportation Control Systems Cosco Shipping Technology Co. Ltd Shanghai China State Key Laboratory of Maritime Technology and Safety Shanghai Ship and Shipping Research Institute Shanghai China

ISBN: (纸本)9798350308532

Accurate prediction of ocean-going ship satellite network traffic (OSSNT) can optimize communication resource allocation and improve communication efficiency. But ordinary statistical learning models cannot capture the non-linear relationships between data well. Standalone single deep learning prediction models cannot extract short-term features and capture long-term dependence in time series data simultaneously. In addition, traffic forecasting studies for ship satellite networks have not been investigated due to the unavailability of data. In this paper, a real-world situation-based ship-satellite network traffic dataset is constructed. Furthermore, a hybrid time series prediction neural network model (TLS-Net), which is built on Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM), is proposed to forecast OSSNT. This model learns short-term local feature in the time series from TCN, and then captures the long-term dependence of relevant data by LSTM, which extracts multi-source temporal feature and high and low-frequency information. Considering the high noise and non-linear in the raw data, we use Savitzky-Golay filters to eliminate potential noise. Experimental results demonstrate that the proposed model predicts network traffic more accurately than other vanilla prediction models. © 2023 IEEE.

关键词： Long short-term memory

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