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arXiv 2021年

作者： Lin, Kaizhan Wang, Weize Wang, Lin Zhao, Chang-An School of Mathematics Sun Yat-Sen University Guangzhou510275 China Science and Technology on Communication Security Laboratory Sichuan Chengdu610041 China Guangdong Key Laboratory of Information Security Guangzhou510006 China

Currently, public-key compression of supersingular isogeny Diffie-Hellman (SIDH) and its variant, supersingular isogeny key encapsulation (SIKE) involve pairing computation and discrete logarithm computation. In this paper, we propose novel methods to compute only 3 discrete logarithms instead of 4, in exchange for computing a lookup table efficiently. The algorithms also allow us to make a trade-off between memory and efficiency. Our implementation shows that the efficiency of our algorithms is close to that of the previous work, and our algorithms perform better in some special cases. © 2021, CC BY-NC-SA.

关键词： Table lookup

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Dynamic Tracking of Ship Course with Limited Input Based on ZG control and Bivariate S-Function

Dynamic Tracking of Ship Course with Limited Input Based on ...

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Chinese control and Decision Conference, CCDC

作者： Shengmian Xie Yunong Zhang Jinjin Guo Mengling Xiao School of Information and Communication Engineering Guangzhou Maritime University Guangzhou China Guangdong Key Laboratory of Modern Control Technology Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781665478977

The nonlinear control system of ship is complex, which is a focus of research and key core of dynamic tracking. Because the input of the actual system is often restricted by the physical constraints of the actuator and energy-saving control, the feasible region of the control input is often constrained by these factors. Therefore, by combining Zhang-gradient (ZG) dynamics with S-function, in this paper, a novel controller for the limited input system is designed. Considering the fact that dynamic systems are generally nonlinear in practical applications, the nonlinear system study has more practical significance. Therefore, this paper extends the ZG control method based on gradient dynamics and the bivariate S-function to the tracking control of nonlinear ship system, and designs a ship course tracking controller with a limited input, which can control the rudder angle within a certain feasible region while successfully tracking the ship course.

关键词： Actuators control engineering Navigation Process control Nonlinear control systems Nonlinear dynamical systems Marine vehicles

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A Multi-Objective Evolutionary Algorithm Based on Adaptive Grid for Multi-Objective Optimization with Irregular Pareto Fronts

SSRN

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SSRN 2023年

作者： Han, Fei Liu, Zhe Ling, Qing-Hua Han, Henry Jiang, Jing Wang, Yue School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China Jiangsu Key Laboratory of Security Technology for industrial Cyberspace Jiangsu University Zhenjiang212013 China School of Computer Science Jiangsu University of Science and Technology Zhenjiang212100 China School of Engineering & Computer Science Baylor University Waco76798 United States School of Computer and information Anqing normal University Anqing246011 China School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China

Most existing multi-objective optimization algorithms suffer from performance degradation when solving problems with irregular Pareto fronts due to the ineffective diversity preservation strategies. To address this issue, a multiobjective evolutionary algorithm based on adaptive grid is proposed by improving the grid-based diversity preservation approach. The main idea of the proposed algorithm is to use an adaptive grid to divide the objective space into a series of subspace and guide the distribution of solutions to make them well-distributed, where the number of grid divisions can be adaptively adjusted to keep the most appropriate interval between solutions for problems with any shape of Pareto front. Besides, a grid nadir point stabilization strategy is presented to create a relatively stable environment for diversity preservation, and an internal competition strategy is designed to select solutions in each subspace to ensure convergence and diversity. Experimental results on the benchmark test suites with various types of Pareto fronts verify that our proposed algorithm outperforms seven state-of-the-art multi-objective evolutionary algorithms for multi-objective optimization problems with irregular Pareto fronts. © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Computing Power in the Sky: Digital Twin-assisted Collaborative Computing with Multi-UAV Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Wang, Chao Han, Yu Zhang, Long Jia, Ziye Zhang, Hongliang Hong, Choong Seon Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing211111 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Kyung Hee University Department of Computer Science and Engineering Gyeonggi-do Yongin-si17104 Korea Republic of University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The collaboration of computing powers (CPs) among unmanned aerial vehicles (UAVs)-mounted edge servers is essential to handle data-intensive tasks of user equipments (UEs). This paper presents a multi-UAV computing power network (CPN) that orchestrates the CP resources of edge servers to cooperatively process tasks through collaborative computing. Due to the frequent interactions among edge servers and the increased complexities for large-scale task executions, we propose a digital twin (DT)-driven multi-UAV CPN framework, where the aerial DT network is created as a digital replica of the multi-UAV network to assist collaborative computing. A joint optimization problem of the service association, offloading power control, task partitions, CP allocation, and UAV trajectory design is formulated to minimize the long-term average delay of UEs. We use the Lyapunov technique to tackle the long-term energy constraint, forming a tractable optimization problem, which is modeled as a Markov decision process to balance the UAV's deficit queue stability and the UE's task completion delay. A hybrid soft actor-critic based deep reinforcement learning algorithm is developed to optimize the joint design by sampling the hybrid action from the Gumbel and Gaussian distributions. Simulation results verify that the multi-UAV CPN contributes to significant performance gains when supported by DTs. © 2025 IEEE.

关键词： Markov processes

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Research on Improved Multiple Users Detection Algorithm in Pattern Division Multiple Access System

SSRN

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SSRN 2022年

作者： Tang, Wanwei Wang, Lixia Wang, Zedai Han, Guofeng Zhao, Jinlong Yue, Xinwei The Key Laboratory of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Tangshan063000 China The School of Engineering Hong Kong University of Science and Technology Hong Kong999077 Hong Kong The Guofeng Han is with the Tangshan employment service center Tangshan human resources and Social Security Bureau Tangshan063000 China The School of Electronic and Information Engineering Beihang University Beijing100191 China The School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China

Receiver detection algorithm has always been a research hotspot in communication system and the key point to determine the performance of 5G system. Both industry and academia have been committed to finding detection algorithms with lower complexity and higher detection performance. This paper will study how to solve the problems of low detection performance and high complexity of classical algorithms. Taking Pattern Division Multiple Access (PDMA) as an example, this paper improves the existing receiver detection algorithms such as Minimum Mean Square Error-Serial Interference Cancellation(MMSE-SIC), Message Passing Algorithm (MPA) and Expectation Propagation Algorithm (EPA), and puts forward corresponding improved algorithms with specific formulas, i.e. Enhanced-MMSE-SIC, Turbo-like MPA (MPA-Turbo) and Turbo-like EPA with hybrid parallel interference cancellation (EPA-Turbo-PIC). Link-level simulation results and analysis show that the proposed improved algorithms have performance advantages over the corresponding traditional algorithms. © 2022, The Authors. All rights reserved.

关键词： Mean square error

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GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network

GraphEye: A Novel Solution for Detecting Vulnerable Function...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Li Zhou Minhuan Huang Yujun Li Yuanping Nie Jin Li Yiwei Liu School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China National Key Laboratory of Science and Technology on Information System Security BeijingChina School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665418164

With the continuous extension of the Industrial Internet, cyber incidents caused by software vulnerabilities have been increasing in recent years. However, software vulnerabilities detection is still heavily relying on code review done by experts, and how to automatically detect software vulnerabilities is an open problem so far. In this paper, we propose a novel solution named GraphEye to identify whether a function of C/C++ code has vulnerabilities, which can greatly alleviate the burden of code auditors. GraphEye is originated from the observation that the code property graph of a non-vulnerable function naturally differs from the code property graph of a vulnerable function with the same functionality. Hence, detecting vulnerable functions is attributed to the graph classification problem. GraphEye is comprised of VecCPG and GcGAT. VecCPG is a vectorization for the code property graph, which is proposed to characterize the key syntax and semantic features of the corresponding source code. GcGAT is a deep learning model based on the graph attention networks, which is proposed to solve the graph classification problem according to VecCPG. Finally, GraphEye is verified by the SARD Stack-based Buffer Overflow, Divide-Zero, Null Pointer Deference, Buffer Error, and Resource Error datasets, the corresponding F1 scores are 95.6%, 95.6%, 96.1%, 92.6%, and 96.1% respectively, which validate the effectiveness of the proposed solution.

关键词： Deep learning Codes Conferences Semantics Cyberspace Syntactics Data science

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Outlier Detection for Sensor Data Streams Based on Maximum Frequent and Minimum Rare Patterns 15th

Outlier Detection for Sensor Data Streams Based on Maximum F...

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15th CCF Conference on Computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020

作者： Shi, Xiaochen Cai, Saihua Sun, Ruizhi College of Information and Electrical Engineering China Agricultural University Beijing100083 China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu University Zhenjiang Jiangsu212013 China The Ministry of Agriculture Beijing100083 China

ISBN: (纸本)9789811625398

Identifying outliers in data is an essential assignment in data mining. It aims to find the data that is significantly different from other data in the data streams, and scholars have proposed many outlier detection methods in recent years. Because pattern-based outlier recognition technology clearly illustrates the causes of anomalies, they have received extensive attention. However, the existing pattern-based outlier identification techniques main use frequent patterns or rare patterns with perform the outlier identification operations, which will affect the precision from outlier identification. To improve the exactness of outlier detection on data streams, in this paper, the maximum frequent patterns and minimum rare patterns are combined together and regarded as a whole as the pattern used in the process of outlier detection, and then it redefines the deviation index, so as to perfectly measure the abnormal degrees of outlier in the data streams. Dependent upon those search patterns and characterized deviation index, Novel maximum frequent and minimum rare pattern-based outlier detection method are put forward, that is, specific MFaMRP-OD, which can faultlessly recognize those possibility outliers. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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Electroencephalogram and Event-Related Potential in Mild Cognitive Impairment: Recent Developments in Signal Processing, Machine Learning, and Deep Learning

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 162-184页

作者： Azami, Hamed Mirjalili, Mina Rajji, Tarek K. Wu, Chien-Te Humeau-Heurtier, Anne Jung, Tzyy-Ping Wei, Chun-Shu Trinh, Thanh-Tung Liu, Yi-Hung University of Toronto Centre for Addiction and Mental Health TorontoONM5S 1A1 Canada University of Texas Southwestern Medical Center DallasTX75390 United States University of Florida Department of Occupational Therapy GainesvilleFL32611 United States University of Florida Center for Cognitive Aging and Memory McKnight Brain Institute GainesvilleFL32611 United States University of Angers LARIS SFR MATHSTIC AngersF-49000 France University of California Institute for Neural Computation Institute of Engineering in Medicine La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu City30010 Taiwan National Taipei University of Technology Graduate Institute of Manufacturing Technology College of Mechanical and Electrical Engineering Taipei10608 Taiwan Vietnam National University Department of Information and Communication Technology Faculty of Nontraditional Security HSB Hanoi720325 Viet Nam National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu City30010 Taiwan

Mild cognitive impairment (MCI) is an early stage of non-age-related cognitive decline with an increased risk of progressing to dementia. Early detection of MCI is essential for implementing preventative strategies that can delay or prevent the onset of dementia, ultimately improving patient outcomes and reducing healthcare costs. Electroencephalograms (EEGs) and event-related potentials (ERPs) have shown significant promise in detecting MCI due to their affordability, real-time monitoring capabilities, and noninvasiveness. EEG provides continuous brain activity data, while ERPs offer insights into specific cognitive processes by analyzing brain responses to stimuli. These methods can complement each other in MCI diagnosis by providing a comprehensive view of overall brain function and detailed information on specific cognitive processes. However, EEG and ERP are susceptible to noise and interindividual variability, which can hinder their reliability. In addition, applying machine learning models on EEG or ERP for MCI detection presents challenges such as the risk of overfitting and difficulties in interpreting the underlying decision-making process. This review emphasizes recent advancements in signal processing and feature extraction methods applied to EEG and ERP data and explores the use of machine learning and deep learning techniques to enhance diagnostic accuracy and interpretative depth. By integrating these methodologies, the review highlights how EEG and ERP can contribute to a more effective understanding and monitoring of cognitive changes associated with MCI, underscoring the importance of early diagnosis for timely intervention and improved patient care. Finally, the review focuses on future research directions, including the development of advanced analytical techniques and multimodal integration approaches involving EEG and ERP to further improve diagnostic accuracy and clinical application. © 2024 IEEE.

关键词： Deep learning

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Mn3O4-g-C3N4 Composite to Activate Peroxymonosulfate for Organic Pollutants Degradation: Electron Transfer and Structure-Dependence

SSRN

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SSRN 2022年

作者： Fan, Jinhong Wang, Qiaoqiao Yan, Wei Chen, Jiabin Zhou, Xuefei Xie, Haijiao State Key Laboratory of Pollution Control and Resources Reuse College of Environmental Science & Engineering Tongji University Shanghai200092 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China Hangzhou Yanqu Information Technology Co. Ltd. China

A novel heterogeneous manganese/graphitic carbon nitride (Mn3O4-CN) catalyst for activating peroxymonosulfate (PMS) was successfully assembled by alkali precipitation. The g-C3N4 improved the surface morphology, microporous structure, surface area and particle size distribution of the composite, and an electron rich center with Mn site was created. The Mn3O4-CN/PMS system exhibited high efficacy and stability when the solution pH varied from 3.0 to 9.0, with more than 90% p-acetaminophen (ACT) removal in 30 min under experimental conditions. A possible reaction mechanism was proposed that mainly involved electron transfer from Mn (II) and Mn (III) to PMS along with the generation of ·O2- and 1O2, and the degradation of ACT was attributed to the 1O2. Specifically, it was found that the degradation rate of phenolic compounds varied with their molecular structure, as followed ACT > bisphenol A (BPA) > p-cresol (MP) > p-chlorophenol (CP) > phenol (Ph) > p-nitrophenol (NP). Further, density functional theory (DFT) calculation indicated that the degradation efficiency of phenols was related to their adsorption energy and Bader charge value. All these results improved our understanding of the Manganese-based PMS non-radical dominated process and could predict the degradation of phenols. © 2022, The Authors. All rights reserved.

关键词： Phenols

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Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Yishen Deng Weisheng Li Xin Lu Frederic Dufaux Bo Hang Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et systèmes The Computer School Hubei University of Arts and Science School of Information and Communication Engineering University of Electronic Science and Technology of China

Versatile Video Coding (VVC) now supports Screen Content Coding (SCC) by integrating two efficient coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the numerous modes and the Quad-Tree Plus Multi-Type Tree (QTMT) structure inherent to VVC contribute to a very high coding complexity. To effectively reduce the computational complexity of VVC SCC, we propose a fast Intra mode prediction algorithm for VVC SCC. More specifically, we first use the difference of minimum Sum of Absolute Transformed Differences (SATD) value of four Directional Modes (DMs) of Intra and the SATD value of the IBC-merge mode to determine whether to early skip Intra checking. Subsequently, we use a decision tree to determine whether to early terminate the checking after block differential pulse coded modulation (BDPCM). Finally, we employ a decision tree to determine whether to early skip multiple transform selection (MTS) and low frequency non-separable transform (LFNST) checking. The results demonstrate that our algorithm achieves an average encoding time reduction of 34.34% with a negligible Bjøntegaard delta bitrate increase of 0.46%.

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