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American Control Conference (ACC)

作者： Jingxuan Zhu Yixuan Lin Alvaro Velasquez Ji Liu Department of Applied Mathematics and Statistics Stony Brook University Department of Computer Science University of Colorado Boulder Department of Electrical and Computer Engineering Stony Brook University

This paper considers a distributed optimization problem in the presence of Byzantine agents capable of introducing untrustworthy information into the communication network. A resilient distributed subgradient algorithm is proposed based on graph redundancy and objective redundancy. It is shown that the algorithm causes all non-Byzantine agents’ states to asymptotically converge to the same optimal point under appropriate assumptions. A partial convergence rate result is also provided.

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Expectation-Maximization Regularised Deep Learning for Tumour Segmentation

Expectation-Maximization Regularised Deep Learning for Tumou...

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IEEE International Symposium on Biomedical Imaging

作者： Chao Li Wenjian Huang Xi Chen Yiran Wei Lipei Zhang Jianguo Zhang Stephen Price Carola-Bibiane Schönlieb Department of Clinical Neuroscience University of Cambridge Department of Applied Mathematics and Theoretical Physics University of Cambridge Department of Computer Science and Engineering Southern University of Science and Technology Department of Computer Science University of Bath

We present an expectation-maximization (EM) regularized deep learning (EMReDL) approach for weakly supervised tumor segmentation using partially labelled MRI. The proposed framework is demonstrated on glioblastoma, characterized by diffusion infiltration. Physiological MRI provides specific information regarding infiltration over structural MRI but is hindered by its low resolution for precise labeling. To exploit partial labels, we design two components in EMReDL: 1) a physiological prior prediction model: a neural network-based binary classifier trained by the labels of core tumor and normal-appearing regions. The trained classifier generates a physiological prior map passed to 2) a segmentation model regularized under an EM framework for weakly supervised learning. We evaluate the performance on a dataset with preoperative multiparametric and recurrence MRI. Results show that EMReDL can effectively segment the infiltrated tumor from the partially labeled MRI, with an accuracy higher than the model trained without physiological MRI and other competing approaches. We will publish the code with example data soon.

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Collaborative Task Offloading with Digital Twin in Multi-Vehicle and Multi-Edge Environments

Collaborative Task Offloading with Digital Twin in Multi-Veh...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Anqi Gu Huaming Wu Yixiao Wang Ruidong Li Chaogang Tang Center for Applied Mathematics Tianjin University Tianjin China Institute of Science and Engineering Kanazawa University Kanazawa Japan School of Computer Science and Technology China University of Mining and Technology Xuzhou China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In recent years, the effective utilization of edge servers to assist vehicles in handling compute-intensive and latency-sensitive tasks has emerged as a pivotal concern in Vehicular Edge Computing (VEC). In this paper, we adopt a cooperative approach that leverages the collective capabilities of multiple edge servers. This strategy is designed to effectively manage tasks and alleviate the computational burden imposed on these servers. Specifically, Graph Neural Network (GNN) is applied to extract and classify features such as the geographical locations and communication statuses of multiple edge servers, enabling the selection of the most suitable servers for collaborative task execution. We have utilized solar energy for local computing, effectively achieving environmental protection and reducing the local energy burden on vehicles. Moreover, a novel edge attraction formula is defined to refine the rationality of clustering. In addition, Deep Reinforcement Learning (DRL) is employed to make real-time offloading decisions. To ensure experimental accuracy while mitigating costs, we establish a corresponding digital twin environment to acquire experimental data. By conducting a comparative analysis against three other baseline methods, we effectively reduce task completion time and thus meet the stringent demands of time-sensitive tasks.

关键词： Costs Simulation Collaboration Solar energy Feature extraction Graph neural networks Real-time systems Digital twins Servers Protection

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Multisequences with High Joint Nonlinear Complexity from Function Fields

Multisequences with High Joint Nonlinear Complexity from Fun...

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2022 International Conference on Electronic Information engineering and computer science, EIECS 2022

作者： Yan, Yang Wang, Qiuyan Wu, Chenghuang Zhao, Chun'e Liu, Lin School of Information Technology and Engineering Tianjin University of Technology and Education Tianjin China School of Computer Science and Technology Tiangong University Tianjin China Provincial Key Laboratory of Applied Mathematics Putian University Fujian Putian China College of Science University of Petroleum Shandong Qingdao China Tianjin LINHAITEC Ltd Tianjin China

ISBN: (纸本)9781510663312

Multisequences over finite fields play an important role in applications that related to parallelization, such as word-based stream ciphers and pseudorandom vector generators. It is interesting to study complexity measures for multisequences. In this paper, we propose three constructions of multisequences over finite fields from rational function fields and Hermitian function fields. We also analyze the joint nonlinear complexity of these multisequences. Moreover, the length and dimension of these multisequences are flexible. © 2023 SPIE.

关键词： Rational functions

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Coordination of Coupled Electrified Road Systems and Active Power Distribution Networks with Flexibility Integration

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SSRN 2024年

作者： Najafi, Arsalan Tsaousoglou, Georgios Gao, Kun Department of Architecture and Civil Engineering Chalmers University of Technology Goteburg SE-412 96 Sweden Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark

Electric Road Systems (ERS) offer a promising solution for mobile charging, reducing the need for mandatory stops to recharge electric vehicles. However, the operational efficiency of ERS is constrained by the limitations of the Power Distribution Network (PDN) responsible for providing electricity. This study proposes an integrated optimization approach for a coupled ERS-PDN system, encompassing traffic assignment and power flow modeling. The optimization considers the presence of self-interested electric vehicle drivers, diverse flexibility resources, and uncertainties in energy supplies, including those arising from renewable sources. To ensure the security of the PDN while supporting ERS, active and flexible energy storage, along with flexible power loads, is employed. The traffic assignment is modeled using a semi-dynamic approach. The proposed optimization framework utilizes a stochastic bi-level optimization based on the Stackelberg game under uncertainty. This approach addresses the joint optimization problem, aiming to minimize the overall cost of the coupled ERS-PDN system and maximize the profit of the energy flexibility provider. The application of Karush Kuhn Tucker conditions transforms the bi-level model into an equivalent single-level model. The results obtained through numerical experiments highlight the effectiveness and benefits of the proposed framework. Specifically, the optimization strategy demonstrates a 3.66% reduction in the burden of ERS on the underlying PDN, emphasizing the positive impact of considering diverse sources of flexibility in the overall system. © 2024, The Authors. All rights reserved.

关键词： Electric power distribution

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An easy-to-implement construction for (k,n)-threshold progressive visual secret sharing schemes

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Journal of Information Security and Applications 2024年 83卷

作者： Chen, Hong-Bin Hsu, Hsiang-Chun Huang, Cang-Wei Juan, Justie Su-Tzu Department of Applied Mathematics National Chung Hsing University Taichung40249 Taiwan Department of Mathematics Tamkang University New Taipei City25137 Taiwan Department of Computer Science and Information Engineering National Chi Nan University Nantou54561 Taiwan

Visual cryptography encrypts the secret image into n shares (transparency) so that only stacking a qualified number of shares can recover the secret image by the human visual system while no information can be revealed without a large enough number of shares. This paper investigates the (k,n)-threshold Visual Secret Sharing (VSS) model, where one can decrypt the original image by stacking at least k shares and get nothing with less than k shares. There are two main approaches, which have been showed equivalent in some sense, in the literature: codebook-based schemes and random-grid-based schemes;the former is the case of this paper. In general, given any positive integers k and n, it is not easy to design a valid scheme for the (k,n)-threshold VSS model. In this paper, we propose a simple strategy to construct an efficient scheme for the (k,n)-threshold VSS model for any positive integers 2≤k≤n. The crucial idea is to establish a seemingly unrelated connection between the (k,n)-threshold VSS scheme and a mathematical structure — the generalized Pascal's triangle. This paper improves and extends previous results in four aspects: • Our construction offers a unified viewpoint and covers several known results. • The resulting scheme has a progressive-viewing property that means the more shares being stacked together the clearer the secret image would be revealed. • The proposed scheme can be constructed explicitly and efficiently based on the generalized Pascal's triangle without a computer. • Performance of the proposed scheme is comparable with known results. © 2024 Elsevier Ltd

关键词： Cryptography

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Automatic Traffic Control of Autonomous Vessels in Coastal Waters

Automatic Traffic Control of Autonomous Vessels in Coastal W...

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2023 International Conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS 2023

作者： Sedova, Nelly A. Sedov, Viktor A. Bazhenov, Ruslan I. Alutina, Elena F. Gorbunova, Tatiana N. Ledovskikh, Irina A. Maritime State University named after G.I. Nevelskoy Automatic and Information Systems Vladivostok Russia Maritime State University named after G.I. Nevelskoy Electrician Theoretical Bases Vladivostok Russia Sholom-Aleichem Priamursky State University Information Systems and Mathematics Birobidzhan Russia Blagoveschensk State Pedagogical University Computer Science and Methods of Teaching Computer Science Blagoveschensk Russia Moscow State University of Civil Engineering Applied Mathematics Moscow Russia Mathematics and Information Technology Pacific National University Khabarovsk Russia

ISBN: (纸本)9798350340532

The paper examines an approach to arrange marine autonomous surface ships (both with and without crew) traffic surveillance illustrated with the Amur Bay and the Golden Horn Bay water area of the city of Vladivostok. The approach involves real-time application of a computer alliance based on General Packet Radio Service (GPRS) and Global Positioning System (GPS) satellite navigation technologies. The research presents the diagram of the system to observe autonomous surface ships located in the coastal area, as well as the assessment algorithm of the current navigation. Machine learning methods applied for analyzing the obtained information contribute to the fast and quite accurate review of the surface navigation, as well as hazard evaluation of vehicles caught in the area of heading in a sea autonomous surface vessel assessment. © 2023 IEEE.

关键词： Global positioning system

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An Optimal Framework for SDN Based on Deep Neural Network

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computers, Materials & Continua 2022年第10期73卷 1125-1140页

作者： Abdallah Abdallah Mohamad Khairi Ishak Nor Samsiah Sani Imran Khan Fahad RAlbogamy Hirofumi Amano Samih M.Mostafa Department of Industrial Engineering School of Applied Technical Sciences German Jordanian UniversityAmman35247Jordan School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyThe National University of Malaysia(UKM)Bangi43600SelangorMalaysia Department of Electrical Engineering University of Engineering and TechnologyPeshawar814Pakistan Turabah University College Computer Sciences ProgramTaif UniversityTaif21944Saudi Arabia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network ***,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in *** address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is *** approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection *** initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on *** assaults were found when suspected irregular traffic was *** reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy *** false alarm rate(FAR)is much lower than that of the information entropy-based detection ***,the proposed framework can shorten the detection time and improve the resource utilization efficiency.

关键词： Deep neural network computer networks data security optimization

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对流水平板上二次混合的三重扩散纳米流体的流体力学和流体磁学对比分析

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Journal of Central South University 2023年第8期30卷 2616-2626页

作者： KHALID Abdulkhaliq M-alharbi HINA Gul MUHAMMAD Ramzan SEIFEDINE Kadry ABDULKAFI Mohammed-saeed Mechanical Engineering Department College of EngineeringUmm Al-Qura UniversityMakkah 24382Sandi Arabia Department of Computer Science Bahria UniversityIslamabad 44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman 346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Mathematics College of ScienceQassim UniversityBuraydah 51452Saudi Arabia

当两种不同密度和扩散速率的纳米流体混合时,会发生三重扩散和对流现象。本文主要目的是比较对流水平板上二次混合的三重扩散纳米流体的流体力学和流体磁学。文中建立的模型其他新特征考虑了可变导热系数和非线性热辐射热通量。该问题... 详细信息

当两种不同密度和扩散速率的纳米流体混合时,会发生三重扩散和对流现象。本文主要目的是比较对流水平板上二次混合的三重扩散纳米流体的流体力学和流体磁学。文中建立的模型其他新特征考虑了可变导热系数和非线性热辐射热通量。该问题由一组方程组成,借助MATLAB bvp4c包进行求解。为了使参数与相关剖面的影响可视化,给出了图形说明,并利用图表来评估关键参数变化是如何影响相关场及其相应的物理量。通过对盐1和盐2的Dufour参数的多次估计,可以推断出流体流速的波动。验证了流体动力流相对于流体磁流的优势。此外,还将特定情景下已发表的结果与当前研究结果进行了比较。

关键词：三重扩散纳米流体二次混合对流可变导热系数非线性热辐射

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Fuzzy Electricity Management System with Anomaly Detection and Fuzzy Q-Learning

Fuzzy Electricity Management System with Anomaly Detection a...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Jia-Hao Syu Jerry Chun-Wei Lin Gautam Srivastava Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Mathematics & Computer Science Brandon University Brandon Canada

Smart grid management is an emerging research topic that recently has adopted artificial intelligence algorithms to assist in the task. However, as more and more data is used, data insecurity and cyber-physical attacks hinder the performance of intelligent systems. In this paper, we propose a fuzzy electricity management system (FEMS) consisting of an attention-based anomaly detection module for attack classification and a fuzzy Q-learning decision module for grid management. Experimental results show that the proposed anomaly detection module achieves high accuracy and F1 scores, significantly outperforming state-of-the-art systems. As for the management evaluation, FEMS achieves extremely low convergence days and mean absolute error (MAE) of supply distribution, which proves the effectiveness of the proposed FEMS in shaping supply distribution. Moreover, FEMS achieves the lowest failure rate (highest stability) but a slightly higher MAE of operating reserve rate due to the unavoidable trade-off between grid stability and energy efficiency.

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