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International Conference on Data Science and Advanced Analytics (DSAA)

作者： Antonina Krajewska Ewa Niewiadomska-Szynkiewicz Research and Academic Computer Network (NASK) Warsaw Poland Institute of Control and Computation Engineering Warsaw University of Technology Warsaw Poland

ISBN: (数字)9798350364941

ISBN: (纸本)9798350364958

Fast data completion and augmentation methods with provable theoretical guarantees enable efficient and reliable data analysis. Many datasets can be represented as real-valued matrices and filled using matrix completion (MC) methods. This paper proposes a novel two-step approach for matrix recovery involving the sequential solution of convex optimization tasks. Firstly, a subset of columns is randomly chosen, and the matrix is completed using a nuclear norm minimization algorithm. Secondly, we solve the least squares problem by incorporating the known elements and the completed columns. We introduce three algorithms for implementing our Columns Selected Matrix Completion (CSMC) method, tailored to different problem sizes. Provided necessary assumptions and the probability of achieving accurate solutions are validated through extensive numerical experiments. Synthetic data experiments assess the method's performance regarding matrix size, rank, and missing element proportion. Evaluation in image completion and graph link prediction demonstrates that CSMC provides comparable solution quality to state-of-the-art convex optimization-based MC algortthms while significantly reduciny computation time.

关键词： Accuracy Reliability theory Data science Minimization Convex functions Optimization Synthetic data

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Baseline Results for Selected Nonlinear System Identification Benchmarks

arXiv

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arXiv 2024年

作者： Champneys, Max D. Beintema, Gerben I. Tóth, Roland Schoukens, Maarten Rogers, Timothy J. Dynamics Research Group University of Sheffield Sheffield United Kingdom Control System Group Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Nonlinear system identification remains an important open challenge across research and academia. Large numbers of novel approaches are seen published each year, each presenting improvements or extensions to existing methods. It is natural, therefore, to consider how one might choose between these competing models. Benchmark datasets provide one clear way to approach this question. However, to make meaningful inference based on benchmark performance it is important to understand how well a new method performs comparatively to results available with well-established methods. This paper presents a set of ten baseline techniques and their relative performances on five popular benchmarks. The aim of this contribution is to stimulate thought and discussion regarding objective comparison of identification methodologies. © 2024, CC BY.

关键词： Nonlinear simulations

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Decoupling parameter variation from noise: Biquadratic Lyapunov forms in data-driven LPV control

arXiv

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arXiv 2024年

作者： Verhoek, Chris Eising, Jaap Dörfler, Florian Tóth, Roland Control Systems Group Eindhoven University of Technology Netherlands Institut für Automatik ETH Zürich Switzerland Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Hungary

A promising step from linear towards nonlinear data-driven control is via the design of controllers for linear parameter-varying (LPV) systems, which are linear systems whose parameters are varying along a measurable scheduling signal. However, the interplay between uncertainty arising from corrupted data and the parameter-varying nature of these systems impacts the stability analysis and limits the generalization of well-understood data-driven methods available for linear time-invariant systems. In this work, we decouple this interplay using a recently developed variant of the Fundamental Lemma for LPV systems and the concept of data-informativity, in combination with biquadratic Lyapunov forms. Together, these allow us to develop novel linear matrix inequality conditions for the existence of scheduling-dependent Lyapunov functions, incorporating the intrinsic nonlinearity. Appealingly, these results are stated purely in terms of the collected data and bounds on the noise, and they are computationally favorable to check. © 2024, CC BY-NC-SA.

关键词： Lyapunov functions

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Throughput-oriented associated transaction assignment in sharding blockchains for IoT social data storage

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Digital Communications and Networks 2022年第6期8卷 885-899页

作者： Liping Tao Yang Lu Xu Ding Yuqi Fan Jung Yoon Kim School of Computer Science and Information Engineering Hefei University of TechnologyHefei230009China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of EducationHefei University of TechnologyHefei230009China Institute of Industry&Equipment Technology Hefei University of TechnologyHefei230009China Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei230009China Graduate School of Game Gachon UniversitySeongnam-Si461-701South Korea

Blockchain is a viable solution to provide data integrity for the enormous volume of 5G IoT social data, while we need to break through the throughput bottleneck of blockchain. Sharding is a promising technology to solve the problem of low throughput in blockchains. However, cross-shard communication hinders the effective improvement of blockchain throughput. Therefore, it is critical to reasonably allocate transactions to different shards to improve blockchain throughput. Existing research on blockchain sharding mainly focuses on shards formation, configuration, and consensus, while ignoring the negative impact of cross-shard communication on blockchain throughput. Aiming to maximize the throughput of transaction processing, we study how to allocate blockchain transactions to shards in this paper. We propose an Associated Transaction assignment algorithm based on Closest Fit (ATCF). ATCF classifies associated transactions into transaction groups which are then assigned to different shards in the non-ascending order of transaction group sizes periodically. Within each epoch, ATCF tries to select a shard that can handle all the transactions for each transaction group. If there are multiple such shards, ATCF selects the shard with the remaining processing capacity closest to the number of transactions in the transaction group. When no such shard exists, ATCF chooses the shard with the largest remaining processing capacity for the transaction group. The transaction groups that cannot be completely processed within the current epoch will be allocated in the subsequent epochs. We prove that ATCF is a 2-approximation algorithm for the associated transaction assignment problem. Simulation results show that ATCF can effectively improve the blockchain throughput and reduce the number of cross-shard transactions.

关键词： IoT social data Blockchain Shar ding Associated transactions Transaction assi gnment

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Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

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computers, Materials & Continua 2022年第10期73卷 749-765页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Ali E.Takieldeen Tarek M.Hassan Ehab A.Hegazy Elsayed Abdel Fattah Eid Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Communications and Computers Engineering Department Faculty of EngineeringDelta University for Science and TechnologyInternational Coastal RoadGamasa CityDakhliyaEgypt Faculty of Applied Health Science Technology Delta University for Science and TechnologyMansoura35712Egypt Faculty of Medicine Head of Internal Medicine DepartmentDelta University for Science and TechnologyMansouraEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

One of the most common kinds of cancer is breast *** early detection of it may help lower its overall rates of *** this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal *** proposed approach starts with data preprocessing the input images and segmenting the significant regions of *** addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling *** the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are *** resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification *** selected features are used to train a neural network to finally classify the thermal images of breast *** achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization *** results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the ***,several experiments were conducted to compare the performance of the proposed approach with the other *** results of these experiments emphasized the superiority of the proposed approach.

关键词： Breast cancer image segmentation dipper throated optimization feature selection meta-heuristics

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Decoupling parameter variation from noise: Biquadratic Lyapunov forms in data-driven LPV control

Decoupling parameter variation from noise: Biquadratic Lyapu...

引用

IEEE Conference on Decision and control

作者： Chris Verhoek Jaap Eising Florian Dörfler Roland Tóth Control Systems Group Eindhoven University of Technology The Netherlands Institut für Automatik ETH Zürich Switzerland Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Hungary

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

关键词： Linear systems Uncertainty Noise Stability analysis Robustness Trajectory Linear matrix inequalities Noise measurement Output feedback Lyapunov methods

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Resilient distributed optimization under mobile malicious attacks

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IFAC-PapersOnLine 2023年第2期56卷 997-1002页

作者： Yuan Wang Changxin Liu Hideaki Ishii Karl Henrik Johansson Department of Robotics Hunan University Changsha China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Computer Science Tokyo Institute of Technology Yokohama Japan

This article addresses the distributed optimization problem in the presence of malicious adversaries that can move within the network and induce faulty behaviors in the attacked nodes. We first investigate the vulnerabilities of a consensus-based secure distributed optimization protocol under mobile adversaries. Then, a modified resilient distributed optimization algorithm is proposed. We develop conditions on the network structure for both complete and non-complete directed graph cases, under which the proposed algorithm guarantees that the estimates by regular nodes converge to the convex combination of the minimizers of their local functions. Simulations are carried out to verify the effectiveness of our approach.

关键词： Fault-tolerant distributed optimization multi-agent systems network security mobile adversary agents

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Stabilizing Manipulator Trajectory via Collision-Aware Optimization 9th

Stabilizing Manipulator Trajectory via Collision-Aware Optim...

引用

9th International Conference on Interactive Collaborative Robotics, ICR 2024

作者： Rubleva, Elena Mironov, Konstantin Panov, Aleksandr Moscow Institute of Physics and Technology 9 Institutsky lane Moscow Dolgoprudny141701 Russia AIRI Artificial Intelligence Research Institute 6/2 Presnenskaya Emb. Moscow Russia Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences 44/2 Vavilova St. Moscow119333 Russia

ISBN: (纸本)9783031713590

In this work, we develop an optimization-based solution for a manipulation planning among obstacles. This task is particularly challenging for collaborative manipulators when the operations include movements through the singular configurations. Trajectory optimization for this case requires a collision model, which allows converging to a valid solution from an invalid initial guess. By improving the initial trajectory approximation and optimizing collisions with advanced obstacle representation, it significantly enhances trajectory planning accuracy and efficiency. In our work, we propose a method based on the combining of obstacles. We use Octomap as the baseline (obstacles are represented as a set of cubes). This creates the need to calculate each cube when calculating the trajectory. Enlarging cubes to parallelepipeds allows one to obtain a collision-free trajectory faster. We have conducted a set of experiments with Octomap and with the representation of obstacles in the form of enlarged cubes. Experiments have shown that enlarging cubes to parallelepipeds reduces planning time and increases the success rate. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Trajectories

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Discrete memristive neuron model and its interspike interval-encoded application in image encryption

引用

Science China(Technological Sciences) 2021年第10期64卷 2281-2291页

作者： BAO Han HUA ZhongYun LIU WenBo BAO BoCheng College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhen 518055China School of Microelectronics and Control Engineering Changzhou UniversityChangzhou 213164China

Bursting is a diverse and common phenomenon in neuronal activation patterns and it indicates that fast action voltage spiking periods are followed by resting *** interspike interval(ISI)is the time between successive action voltage spikes of neuron and it is a key indicator used to characterize the ***,a three-dimensional memristive Hindmarsh-Rose(mHR)neuron model was constructed to generate hidden chaotic ***,the properties of the discrete mHR neuron model have not been investigated,*** this article,we first construct a discrete mHR neuron model and then acquire different hidden chaotic bursting sequences under four typical sets of *** make these sequences more suitable for the application,we further encode these hidden chaotic sequences using their ISIs and the performance comparative results show that the ISI-encoded chaotic sequences have much more complex chaos properties than the original *** addition,we apply these ISI-encoded chaotic sequences to the application of image *** image encryption scheme has a symmetric key structure and contains plain-text permutation and bidirectional diffusion *** results and security analyses prove that it has excellent robustness against various possible attacks.

关键词： chaos complexity chaotic bursting sequence memristive neuron model interspike interval(ISI) image encryption

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Al-Biruni Earth Radius(BER)Metaheuristic Search Optimization Algorithm

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computer Systems Science & Engineering 2023年第5期45卷 1917-1934页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Seyedali Mirjalili Nima Khodadad Mona A.Al duailij Amel Ali Alhussan Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and *** metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization *** novel optimization methodologies are still *** Al-Biruni earth radius(BER)search optimization algorithm is proposed in this *** proposed algorithm was motivated by the behavior of swarm members in achieving their global *** search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization *** results show that BER can both explore and avoid local *** has also been tested on an engineering design optimization *** results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

关键词： Metaheuristics evolutionary optimization exploration exploitation mutation Al-biruni earth radius

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