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2024 IAF Space Operations Symposium at the 75th International Astronautical Congress, IAC 2024

作者： Lu, Siyao Xu, Rui Gao, Ai Li, Zhaoyu Liu, Jiamou Zhang, Libo Zhao, Zhijun Zhu, Shengying Li, Yuqiong School of Aerospace Engineering Beijing Institute of Technology 5th Zhongguancun South Street Haidian Distinct Beijing Beijing100081 China Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration Beijing Institute of Technology Ministry of Industry and Information Technology Beijing100081 China School of Computer Science University of Auckland 34 Princes Street Auckland1010 New Zealand Beijing Institute of Spacecraft Engineering Beijing Beijing100094 China Institute of Mechanics Chinese Academy of Sciences 15th North Fourth Ring West Road Haidian District Beijing100190 China

ISBN: (纸本)9798331312183

The International Lunar Research Station will be established near the south pole through advanced unmanned rovers at the beginning period. The south pole of the moon has short daytime, so the efficiency of remote control is inadequate. However, the duration and power resource usage of the lunar rover moving on the lunar surface remains uncertain because of different loading weight of collection and changes of terrain in moving. What’s more, a lunar rover needs to move back to the base before nighttime without sunlight to provide energy, while the whole time of working on the moon also needs optimization. We select to solve the planning problem with reinforcement learning (RL) due to its capability in tackling uncertainty and optimization. However, traditional reinforcement learning cannot guarantee safety with time uncertainty, resource uncertainty, and constraints due to the soft constraints in optimization. Therefore, we propose a new way through safe reinforcement learning of task planning and resource collection optimization among tasks with uncertain duration and resource collection. We consider a scenario of in-situ material utilization for the lunar base, where there are tasks of moving, charging, collecting, material delivering, and material receiving, all of which have uncertain duration in execution and every task must be done during the daytime except the charging. Resource collection is related to power consumption in moving so it will be decided according to the remaining power. We further propose an architecture on reinforcement learning to let rovers decide the next step instantaneously according to the expected task duration, the remaining time, and the remaining power. Maximizing the amount of material delivered is an optimization target in training while keeping the rovers safe to work only in the daytime without an empty battery. In our experiment, we intend that our way works well in the uncertainties, and it will lead the rover to finish tasks w

关键词： Reinforcement learning

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Large-Scale Data Generation Using SWLBM on the Sunway TaihuLight Supercomputer and Subsequent Data Mining with Physics-Informed Neural Networks 12th

Large-Scale Data Generation Using SWLBM on the Sunway TaihuL...

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12th CCF Conference on BigData, BigData 2024

作者： Chu, Xuesen Guo, Wei Wu, Tianqi Cai, Shengze Yang, Guangwen China Ship Scientific Research Center Wuxi214082 China Taihu Lake Laboratory of Deep Sea Technology and Science Wuxi214082 China Department of Computer Science and Technology Tsinghua University Beijing100084 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China National Supercomputing Center in Wuxi Wuxi214072 China

ISBN: (纸本)9789819610235

Generating large-scale datasets on supercomputers is a critical component of modern research, enabling sophisticated analysis and mining applications. In the context of SUBOFF model studies for advanced submarine development, we emphasize the significance of supercomputer-generated big data and its subsequent application. Utilizing the Sunway TaihuLight supercomputer and the SWLBM software, we employ the lattice Boltzmann method (LBM) to generate a comprehensive dataset of flows over a SUBOFF model. This dataset is then used to train a physics-informed neural network (PINN) model, which aims to reconstruct flow fields from sparse velocity measurements. The PINN is designed to perform super-resolution velocity reconstruction and concurrent pressure field inference. Our results show that the reconstructed flow fields, including pressure, are in good agreement with the full-resolution LBM references, highlighting the promise of this approach for complex flow motion reconstruction. This research demonstrates the potential of supercomputer-generated big data in enhancing simulations and experiments in the study of SUBOFF and beyond. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computational fluid dynamics

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Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET 18

Lightweight Trust Model with Machine Learning scheme for sec...

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18th International Learning and technology Conference, L and T 2021

作者： Junejo, Muhammad Haleem Ab Rahman, Ab Al-Hadi Shaikh, Riaz Ahmed Yusof, Kamaludin Mohamad Kumar, Dileep Memon, Imran Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai81310 Malaysia Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Department of Computer Science Bahria University Karachi campus sindh Pakistan

A vehicular ad hoc network (VANETs) is transforming public transport into a safer wireless network, increasing its safety and efficiency. The VANET consists of several nodes which include RSU (Roadside Units), vehicles, traffic signals, and other wireless communication devices that are communicating sensitive information in a network. Nevertheless, security threats are increasing day by day because of dependency on network infrastructure, dynamic nature, and control technologies used in VANET. The security threats could be addressed widely by using machine learning and artificial intelligence on the road transport nodes. In this paper, a comparison of trust and cryptography was presented based on applications and security requirements of VANET. © 2021 The Authors. Published by Elsevier B.V.

关键词： Machine learning

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Multi-modal Speech Emotion Recognition via Feature Distribution Adaptation Network

arXiv

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arXiv 2024年

作者： Li, Shaokai Ji, Yixuan Song, Peng Sun, Haoqin Zheng, Wenming School of Computer and Control Engineering Yantai University Yantai264005 China Business School Xuzhou University of Technology Xuzhou221018 China College of Computer Science Nankai University Tianjin300350 China Key Laboratory of Child Development and Learning Science of Ministry of Education Southeast University Nanjing210096 China

In this paper, we propose a novel deep inductive transfer learning framework, named feature distribution adaptation network, to tackle the challenging multi-modal speech emotion recognition problem. Our method aims to use deep transfer learning strategies to align visual and audio feature distributions to obtain consistent representation of emotion, thereby improving the performance of speech emotion recognition. In our model, the pre-trained ResNet-34 is utilized for feature extraction for facial expression images and acoustic Mel spectrograms, respectively. Then, the cross-attention mechanism is introduced to model the intrinsic similarity relationships of multi-modal features. Finally, the multi-modal feature distribution adaptation is performed efficiently with feed-forward network, which is extended using the local maximum mean discrepancy loss. Experiments are carried out on two benchmark datasets, and the results demonstrate that our model can achieve excellent performance compared with existing ones. Our code is available at https://***/shaokai1209/FDAN. Copyright © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on control of Network Systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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A Multiple-Image Authentication Method Based on Phase-Only Hologram and Logistic Map

SSRN

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SSRN 2024年

作者： Liansheng, Sui Zishan, Jiang Haiwei, Lu Zhaolin, Xiao Tianyu, Zeng School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China Hebei Normal University Shijiazhuang050024 China Hebei Provincial Key Laboratory of Information Fusion and Intelligent Control Shijiazhuang050024 China

An interesting security method for multiple-image authentication scheme is proposed based on computer-generated holograms and logistic map. First, each original image is encoded as the complex-valued hologram under the point light source model. The resulting hologram is then converted to a phase-only hologram using the Floyd-Steinberg dithering algorithm. Second, each phase-only hologram is randomly sampled by the aid of a binary mask. Through the catenation of all selected pixels, a phase-only pixel sequence is formed. Finally, a non-periodic and non-converging sequence generated with logistic map is used to scramble this sequence. After only preserving the phase data of the scrambled sequence, the real-valued ciphertext carrying the information of all original images is obtained. In the process of authentication, although no valid information can be discerned from noisy reconstructed images at a small sampling rate, the verification of original images can be efficiently accomplished using the nonlinear correlation maps. Besides binary masks, the parameters of logistic map are served as secret keys. Due to their high sensitivity, the security of the proposed method is greatly enhanced. The proposed authentication mechanism has been demonstrated to be effective and robust through experiments. To our best knowledge, it is first time to implement multiple-image authentication using phase-only holograms, which can provide a new perspective for optical information security. © 2024, The Authors. All rights reserved.

关键词： Holograms

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Stealthy Attacks on Sampled-Data control and Encryption-Based Countermeasures

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IEEE Transactions on automatic control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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Research on Black-Box Adversarial Attack Based on Transformer

Research on Black-Box Adversarial Attack Based on Transforme...

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Frontiers technology of Information and computer (ICFTIC), IEEE International Conference on

作者： Wenjuan Zheng Hanxu Luo YuXin Ji Haipeng Li Longjian Cong Master of Computer Vision National Key Laboratory of Aerospace Intelligent Control Technology Beijing China Beijing Aerospace Automatic Control Institute Beijing China Department of Information Engineering Wuhan University of Technology Wuhan Hubei Province China

The classic adversarial samples of black-box attacks are all aimed at the models of Convolutional neural networks (CNNs), but they do not perform well on the new recognition networks based on Transformer. In this paper, we propose an adversarial sample generation algorithm based on Vision transformers (VITs)‘ self-attention mechanism and patch partition. We noticed that different blocks in the VIT have uneven attention distribution, so we first generated a patch-based attention map and performed threshold segmentation, which is used as a mask to perform data enhancement operations based on patches with high weight and patches with low weight, and then exchanged information between patches to generate adversarial samples. The experiment of simulating black-box attack shows that the adversarial samples generated by the algorithm in this paper have a high success rate in all kinds of models based on Transformer of attacks, and also perform well on CNNs.

关键词： Closed box Benchmark testing Transformers Partitioning algorithms Convolutional neural networks Convergence

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A Material Identification Approach Based on Wi-Fi Signal

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computers, Materials & Continua 2021年第12期69卷 3383-3397页

作者： Chao Li Fan Li Wei Du Lihua Yin Bin Wang Chonghua Wang Tianjie Luo Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510700China PCL Research Center of Cyberspace Security Peng Cheng LaboratoryShenzhen518052China Department of Computer Science and Computer Engineering University of ArkansasFayetteville72701USA College of Electrical Engineering Zhejiang UniversityHangzhou310058China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Material identification is a technology that can help to identify the type of target *** approaches depend on expensive instruments,complicated pre-treatments and professional *** is difficult to find a substantial yet effective material identification method to meet the daily use *** this paper,we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier,which can significantly reduce the cost and guarantee a high level *** practical measurement of WiFi based material identification,these two features are commonly interrupted by the software/hardware noise of the channel state information(CSI).To eliminate the inherent noise of CSI,we design a denoising method based on the antenna array of the commercial off-the-shelf(COTS)Wi-Fi *** that,the amplitude ratios and phase differences can be more stably utilized to classify the *** implement our system and evaluate its ability to identify materials in indoor *** result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%.It can also identify similar liquids with an overall accuracy higher than 95%,such as various concentrations of salt water.

关键词： Internet of Things Wi-Fi signal channel state information material identification noise elimination

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TSGN: Transaction Subgraph Networks Assisting Phishing Detection in Ethereum

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Jinhuan Chen, Pengtao Xu, Xinyao Wu, Jiajing Shen, Meng Xuan, Qi Yang, Xiaoniu Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou310023 China ZJUT Binjiang Institute of Artificial Intelligence Hangzhou310056 China Fudan University Adaptive Networks and Control Lab Department of Electronic Engineering School of Information Science and Technology Shanghai200433 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Shenzhen518066 China National Key Laboratory of Electromagnetic Space Security Jiaxing314033 China

Due to the decentralized and public nature of the blockchain ecosystem, malicious activities on the Ethereum platform impose immeasurable losses on users. At the same time, the transparency of cryptocurrency transactions provides a unique opportunity to analyze illegal activities, such as phishing scams, from a network perspective. Most existing phishing scam detection methods focus primarily on analyzing account interaction networks, which limits their ability to uncover transaction behavior patterns embedded within transaction interactions. To address this, we construct the Transaction SubGraph Network (TSGN) by using transaction subgraphs as basic elements and further propose a novel framework for Ethereum phishing account detection. Specifically, we rebuild the graph structures via three well-designed mapping mechanisms, yielding TSGN and its two variants, i.e., Directed-TSGN and Temporal-TSGN, to obtain direction-aware and time-aware transfer flow features. By further incorporating the mapping strategy into transaction multidigraphs, we develop the Multiple-TSGN, which could preserve more transaction flow features while concurrently reducing the time consumption of modeling large-scale networks. TSGN models based on transaction subgraph interactions can capture complex higher-order dependencies, which lay beyond the reach of models that exclusively capture pairwise account interactions. As a general framework, our model can incorporate various feature extraction methods to improve the performance of phishing detection. Extensive experimental results on Ethereum datasets show that our method achieves superior performance in phishing detection, yielding 3.27%∼6.71% relative improvement over previous state-of-the-art. © 2004-2012 IEEE.

关键词： Phishing

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