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An Attack on a Proposed Construction of Small-State Stream Ciphers and Proposals for New Constructions (Nov, bxac165, 2022)

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computer JOURNAL 2024年第2期67卷 806-806页

作者： Amin-Ghafari, Vahid Attari, Mahmoud Ahmadian School of Information and Control Engineering China University of Mining and Technology Jiangsu 221116 P. R. China Electrical and Computer Engineering School K. N. Toosi University of Technology Tehran *** Iran

Small-state stream ciphers (SSCs) idea is based on using key bits not only in the initialization but also continuously in the keystream generation phase. A time-memory-data tradeoff (TMDTO) distinguishing attack was successfully applied against all SSCs in 2017 by Hamann et al. They suggested using not only key bits but also initial value (IV) bits continuously in the keystream generation phase to strengthen SSCs against TMDTO attacks. Then, Hamann and Krause proposed a construction based on using only IV bits continuously in the packet mode. They suggested an instantiation of an SSC and claimed that it is resistant to TMDTO attacks. We point out that accessing IV bits imposes an overhead on cryptosystems that might be unacceptable in some applications. More importantly, we show that the proposed SSC remains vulnerable to TMDTO attacks 1. To resolve this security threat, the current paper proposes constructions based on storing key or IV bits that are the first to provide full security against TMDTO attacks. Five constructions are proposed for different applications by considering efficiency. Designers can obtain each construction’s minimum volatile state length according to the desirable keystream, key and IV lengths.

关键词： cryptography distinguishing attack IoT security lightweight encryption stream cipher time-memory-data tradeoff attack

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Digital Twin Virtual-Real Synchronization for Aeroengine Gas Path System Based on Deep Reinforcement Learning

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IEEE Systems Journal 2025年第1期19卷 75-86页

作者： Xu, Changyi Huo, Yuming Shi, Chunkun Zhao, Ying Dalian University of Technology School of Control Science and Engineering Dalian116023 China Dalian Maritime University College of Marine Electrical Engineering Dalian116026 China

It is crucial to monitor the operational status of aeroengines by using the digital twin technology to realize virtual-real synchronization for the gas path system. The challenge is to accurately monitor deep parameters in real-time during synchronization, although existing digital twin technologies have made good progress in monitoring shallow parameters. This study proposes a virtual-real synchronization method for digital twins of an aeroengine gas path system (AGPS) based on deep reinforcement learning (RL). First, the parameters are divided into directly measurable parameters (DMP) and nondirectly measurable parameters (NDMP). Then, different algorithms are applied to different types of parameters. An unscented Kalman filtering algorithm is utilized to aid in the synchronization of the DMP. An RL approach is employed to train parameter inference models for the NDMP. By combining the two algorithms, synchronization between these two parameter classes is achieved. This method excels by integrating the NDMP into the virtual-real synchronization of the AGPS digital twin, concurrently reducing the inference time for this specific segment. Comparative experiments are conducted, and the results indicate an effective improvement in the accuracy of parameter inference with the proposed method. Simultaneously, it ensures real-time and robust parameter inference. © 2007-2012 IEEE.

关键词： Deep reinforcement learning

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of computer science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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Unpaired Synthesis of IC Scanning Electron Microscopy Images with Structural Constraints

Unpaired Synthesis of IC Scanning Electron Microscopy Images...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Li, Luyun Cheng, Deruo Shi, Yiqiong Gwee, Bah-Hwee School of Computer Science and Engineering School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350378214

The analysis of Integrated Circuit (IC) Scanning Electron Microscopy (SEM) images plays a crucial role in the reliability and authenticity investigation of modern ICs. It pertains to the task of predicting masks, which indicate the pixel-level position of different circuit elements, from IC SEM images, and it is now usually achieved through deep learning models that need to be trained or finetuned over a considerably large labeled dataset of IC SEM images. Furthermore, such labeled dataset is usually not publicly available and is costly to obtain. Recently, unpaired synthesis of IC SEM images, which aims to translate masks indicating IC circuit elements into IC SEM images with corresponding circuit elements, has made it possible for semi-supervised learning models to perform comparably well in IC image analysis but with lower dependency on labeled data. However, existing methods for unpaired synthesis of IC SEM images suffer from the low consistency of circuit element patterns between masks and synthetic images. In this paper, we propose a Structural Constrained Cycle-Consistent Adversarial Network (SC-CycleGAN), that leverages on CycleGAN and novel structural constraints to synthesize IC SEM images with improved consistency of circuit element patterns. Experiments on real masks and IC SEM images have demonstrated the superior performance of our proposed SC-CycleGAN. © 2024 IEEE.

关键词： Labeled data

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Rotor Angle Stability Prediction Using Temporal and Topological Embedding Deep Neural Network Based on Grid-informed Adjacency Matrix

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 695-706页

作者： Peiyuan Sun Long Huo Xin Chen Siyuan Liang the School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049China the School of Electrical Engineering and the Center of Nanomaterials for Renewable EnergyState Key Laboratory of Electrical Insulation and Power EquipmentXi’an Jiaotong UniversityXi’an 710049China the Department of Computer Science and Engineering The Chinese University of Hong KongShatin999077Hong KongChina

Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power *** wide deployment of phasor measurement units(PMUs)promotes the development of data-driven methods for RAS *** paper proposes a temporal and topological embedding deep neural network(TTEDNN)model to accurately and efficiently predict RAS by extracting the temporal and topological features from the PMU *** grid-informed adjacency matrix incorporates the structural and electrical parameter information of the power *** the small-signal RAS with disturbance under initial operating conditions and the transient RAS with short circuits on transmission lines are *** studies of the IEEE 39-bus and IEEE 300-bus power systems are used to test the performance,scalability,and robustness against measurement uncertainties of the TTEDNN *** show that the TTEDNN model performs best among existing deep learning ***,the superior transfer learning ability from small-signal RAS conditions to transient RAS conditions has been proved.

关键词： Rotor angle stability prediction topological embedding deep learning graph convolution network

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Hierarchical Federated ADMM

IEEE Networking Letters

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IEEE Networking Letters 2025年第1期7卷 11-15页

作者： Azimi-Abarghouyi, Seyed Mohammad Bastianello, Nicola Johansson, Karl H. Fodor, Viktoria KTH Royal Institute of Technology School of Electrical Engineering and Computer Science and Digital Futures Stockholm10044 Sweden

In this letter, we depart from the widely-used gradient descent-based hierarchical federated learning (FL) algorithms to develop a novel hierarchical FL framework based on the alternating direction method of multipliers (ADMM), leveraging a network architecture consisting of a single cloud server and multiple edge servers, where each edge server is dedicated to a specific client set. Within this framework, we propose two novel FL algorithms, which both use ADMM in the top layer: one that employs ADMM in the lower layer and another that uses the conventional gradient descent-based approach. The proposed framework enhances privacy, and experiments demonstrate the superiority of the proposed algorithms compared to the conventional algorithms in terms of learning convergence and accuracy. Additionally, gradient descent on the lower layer performs well even if the number of local steps is very limited, while ADMM on both layers lead to better performance otherwise. © 2019 IEEE.

关键词： Federated learning

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Space/Air Covert Communications:Potentials,Scenarios,and Key Technologies

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China Communications 2024年第3期21卷 1-18页

作者： Mao Haobin Liu Yanming Zhu Lipeng Mao Tianqi Xiao Zhenyu Zhang Rui Han Zhu Xia Xianggen School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore School of Science and Engineering Shenzhen Research Institute of Big Datathe Chinese University of Hong KongShenzhenGuangdong 518172China Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoulSouth Korea446-701 Department of Electrical and Computer Engineering University of DelawareNewarkDE 19716USA

Space/air communications have been envisioned as an essential part of the next-generation mobile communication networks for providing highquality global connectivity. However, the inherent broadcasting nature of wireless propagation environment and the broad coverage pose severe threats to the protection of private data. Emerging covert communications provides a promising solution to achieve robust communication security. Aiming at facilitating the practical implementation of covert communications in space/air networks, we present a tutorial overview of its potentials, scenarios, and key technologies. Specifically, first, the commonly used covertness constraint model, covert performance metrics, and potential application scenarios are briefly introduced. Then, several efficient methods that introduce uncertainty into the covert system are thoroughly summarized, followed by several critical enabling technologies, including joint resource allocation and deployment/trajectory design, multi-antenna and beamforming techniques, reconfigurable intelligent surface(RIS), and artificial intelligence algorithms. Finally, we highlight some open issues for future investigation.

关键词： artificial intelligence(AI) sixth generation(6G) space-air-ground integrated networks(SAGINs) space/air covert communications

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Exploring Dynamics and Hardware Implementation of an Enhanced 5D Hyperchaotic Memristive System Inspired by Sprott-C System

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Complex System Modeling and Simulation 2025年第1期5卷 34-45页

作者： Mokbel, Abdulmajeed Abdullah Mohammed Yu, Fei Gracia, Yumba Musoya Tan, Bohong Lin, Hairong Iu, Herbert Ho-Ching School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China School of Electronic Information Central South University Changsha410083 China School of Electrical Electronic and Computer Engineering University of Western Australia Perth6009 Australia

This paper proposes a novel 5D hyperchaotic memristive system based on the Sprott-C system configuration, which greatly improves the complexity of the system to be used for secure communication and signal processing. A critical aspect of this research work is the introduction of a flux-controlled memristor that exhibits chaotic behavior and dynamic responses of the system. To this respect, detailed mathematical modeling and numerical simulations about the stability of the system's equilibria, bifurcations, and hyperchaotic dynamics were conducted and showed a very wide variety of behaviors of great potential in cryptographic applications and secure data transmission. Then, the flexibility and efficiency of the real-time operating environment were demonstrated, and the system was actually implemented on a field-programmable gate array (FPGA) hardware platform. A prototype that confirms the theoretical framework was presented, providing new insights for chaotic systems with practical significance. Finally, we conducted National Institute of Standards and Technology (NIST) testing on the proposed 5D hyperchaotic memristive system, and the results showed that the system has good randomness. © 2021 TUP.

关键词： Digital storage

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Enhanced Multi-Scale Object Detection Algorithm for Foggy Traffic Scenarios

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computers, Materials & Continua 2025年第2期82卷 2451-2474页

作者： Honglin Wang Zitong Shi Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Electrical&Computer Engineering University of Illinois at Urbana ChampaignUrbanaIL 61801USA

In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different layers. Thirdly, a new decoupled detection head is proposed by redesigning the original network head based on the Diverse Branch Block module to improve detection accuracy and reduce missed and false detections. Finally, the Minimum Point Distance based Intersection-over-Union (MPDIoU) is used to replace the original YOLOv8 Complete Intersection-over-Union (CIoU) to accelerate the network’s training convergence. Comparative experiments and dehazing pre-processing tests were conducted on the RTTS and VOC-Fog datasets. Compared to the baseline YOLOv8 model, the improved algorithm achieved mean Average Precision (mAP) improvements of 4.6% and 3.8%, respectively. After defogging pre-processing, the mAP increased by 5.3% and 4.4%, respectively. The experimental results demonstrate that the improved algorithm exhibits high practicality and effectiveness in foggy traffic scenarios.

关键词： Deep learning object detection foggy scenes traffic detection YOLOv8

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Translation and rotation of particles in different flow pattern areas of a silo

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Particuology 2023年第2期73卷 68-77页

作者： Ran Li Quan Chen Hongze Li Wenzheng Xiu Vladimir Zivkovic Hui Yang School of Medical Instrument and Food Engineering University of Shanghai for Science and TechnologyShanghai200093China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China School of Engineering Newcastle UniversityNE17RUUnited Kingdom

The present paper reports the results obtained for translational and rotational velocity profiles of spherical particles for the mixed flow in a conical *** discrete element method(DEM)based on Hertz-Mindlin(no slip)with RVD rolling friction contact model is used for *** correlations are found between translational and rotational velocities in different flow areas of the *** particular,the abrasion caused by rotation is dominant in the funnel flow *** addition,increase of the mass flow rate of silo can effectively reduce the abrasion induced by *** highlights that understanding of dynamic characteristics of particles is helpful for optimization of silos and reduction of granular material abrasion.

关键词： Translational velocity Rotational velocity Rolling contribution rate Mass flow index

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