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2nd Electric Power and Renewable Energy Conference, EPREC 2021

作者： Chenchevoi, Volodymyr Kuznetsov, Valeriy Zachepa, Iurii Chornyi, Oleksii Chencheva, Olga Kuznetsov, Vitaliy Yatsiuk, Rostyslav Luhova, Olha Department of Systems of Automatic Control and Electric Drive Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Kremenchuk Ukraine Electric Power Department Railway Research Institute Chlopickiego str. 50 Warsaw Poland Manufacturing Engineering Department Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Kremenchuk Ukraine Department of the Electrical Engineering and Electromechanic National Metallurgical Academy of Ukraine Gagarina ave. 4 Dnipro Ukraine Department of Automation and Computer-Integrated Technologies Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Kremenchuk Ukraine Department of Electric Machines and Devices Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Kremenchuk Ukraine

ISBN: (纸本)9789811692383

The article defines the conditions of self-excitation of an inductive generator with capacitor excitation as part of an autonomous power source. Recommendations for the design of an autonomous power source based on an asynchronous machine in terms of the implementation of self-excitation conditions and optimal operating modes are formulated. A simulation model of an autonomous inductive generator and a solar battery for the analysis of transient, static and dynamic modes of operation has been developed. The conducted theoretical studies of capacitive self-excitation of inductive machines have created a good basis for solving problems related to the practical use of autonomous sources based on an inductive generator. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Asynchronous generators

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ROCFL: A Robust Clustered Federated Learning Framework towards Heterogeneous Data

ROCFL: A Robust Clustered Federated Learning Framework towar...

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Intelligent Communication and Networking (ICN), International Conference on

作者： Zihan Li Zhitao Guan Shuai Yuan Ningyu An Xiao Liang School of Control and Computer Engineering North China Electric Power University Beijing China Department of Finance Operations and Information Systems (FOIS) Brock University Canada State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Beijing China

As a decentralized machine learning approach, federated learning has become a key solution for numerous applications. However, one primary challenge lies in the data heterogeneity issue when developing effective federated learning algorithms. While many existing frameworks address the concern of non-independent and identically distributed data(non-IID), they have not considered heterogeneity of data quality. Therefore, we propose ROCFL, a robust clustered federated learning method in this study, which can amplify the disparity in weight allocation between models trained on different quality data. We first develop an optimal clustering matching mechanism that groups clients with similar data distributions. This allows the optimal clustering model to be derived without specifying a predetermined number of clusters. Next, we introduce a personalized weight allocation strategy, which assigns a weight benchmark to each cluster based on its cluster importance index. This strategy mitigates the negative impacts of low-quality data during model aggregation. Finally, we design a federated aggregation strategy grounded in a sampling approach. It not only ensures unbiased sampling but also significantly reduces both computational and communication overheads. Experiments have been carried out and the results demonstrate that higher accuracy and robustness can be achieved by ROCFL in scenarios wherein data distribution and quality heterogeneity coexist, in comparison with the benchmarks.

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Modeling and Analysis of a Coupled SIS Bi-Virus Model

arXiv

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arXiv 2022年

作者： Gracy, Sebin Paré, Philip E. Liu, Ji Sandberg, Henrik Beck, Carolyn L. Johansson, Karl Henrik Başar, Tamer Department of Electrical Engineering and Computer Science South Dakota School of Mines and Technology SD United States School of Electrical and Computer Engineering Purdue University IN United States Department of Electrical and Computer Engineering Stony Brook University United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Coordinated Science Laboratory University of Illinois at Urbana-Champaign United States

The paper deals with the setting where two viruses (say virus 1 and virus 2) coexist in a population, and they are not necessarily mutually exclusive, in the sense that infection due to one virus does not preclude the possibility of simultaneous infection due to the other. We develop a coupled bi-virus susceptible-infected-susceptible (SIS) model from a 4n-state Markov process, where n is the number of agents (i.e., individuals or subpopulation) in the population. We identify a sufficient condition for both viruses to eventually die out, and a sufficient condition for the existence, uniqueness and asymptotic stability of the endemic equilibrium of each virus. We establish a sufficient condition and multiple necessary conditions for local exponential convergence to the boundary equilibrium (i.e., one virus persists, the other one dies out) of each virus. Under mild assumptions on the healing rate, we show that there cannot exist a coexisting equilibrium where for each node there is a nonzero fraction infected only by virus 1;a nonzero fraction infected only by virus 2;but no fraction that is infected by both viruses 1 and 2. Likewise, assuming that healing rates are strictly positive, a coexisting equilibrium where for each node there is a nonzero fraction infected by both viruses 1 and 2, but no fraction is infected only by virus 1 (resp. virus 2) does not exist. Further, we provide a necessary condition for the existence of certain other kinds of coexisting equilibria. We show that, unlike the competitive bivirus model, the coupled bivirus model is not monotone. Finally, we illustrate our theoretical findings using an extensive set of simulations. © 2022, CC0.

关键词： Coronavirus

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A Trustless Federated Framework for Decentralized and Confidential Deep Learning

A Trustless Federated Framework for Decentralized and Confid...

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Global Emerging Technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE

作者： Chao Li Qiuyu Shen Cheng Xiang Bharath Ramesh Department of Electrical and Computer Engineering Advanced Control Technology Lab National University of Singapore Singapore International Center for Neuromorphic Systems The MARCS Institute for Brain Behaviour and Development Western Sydney University Sydney Australia

Nowadays, deep learning models can be trained on large amounts of web data on power hungry servers and be deployment-ready for specific real-world applications. With a state-of-the-art model architecture and a large publicly available dataset for pre-training, convolutional neural network models can be further fine-tuned via transfer learning for a related task. Nonetheless, the training process required of privacy-sensitive applications needs to protect data confidentiality and simultaneously boost performance using limited training data. For such data-deprived and privacy-centric learning, we introduce a trustless federated learning framework that seamlessly integrates deep learning models from different edge nodes using a blockchain-based architecture. Our framework performs federated learning without the need of a central server by leveraging a smart contract blockchain platform with a distributed file system for model storage. Users can locally train on their data while routinely benefiting from an enhanced model obtained by merging the models from all users in a decentralized fashion. Most importantly, this framework is free of the potential single point of failure in centralized federated learning. Besides, our framework has in-built incentive mechanisms to prevent model corruption and temper bad actors. We tested our framework on various computer vision datasets. The experimental results show that the merged model accuracy is on-par compared to a centralized federated training setup. To the best of our knowledge, this work represents the first systematic attempt at building a blockchain-based federated deep learning framework for computer vision. The code is publicly made available at: https://***/s-elo/DNN-Blockchain.

关键词： Deep learning Training Data privacy Federated learning Computational modeling Smart contracts Data models

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PDMATLAB2D: A Peridynamics MATLAB Two-dimensional Code

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Journal of Peridynamics and Nonlocal Modeling 2024年第1期6卷 149-205页

作者： Seleson, Pablo Pasetto, Marco John, Yohan Trageser, Jeremy Reeve, Samuel Temple Computer Science and Mathematics Division Oak Ridge National Laboratory P.O. Box 2008 MS-6013 Oak Ridge 37831-6013 TN United States Department of Mechanical and Aerospace Engineering University of California San Diego 9500 Gilman Drive La Jolla 92093 CA United States George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology 801 Ferst Dr Atlanta 30332 GA United States Center for Control Dynamical Systems and Computation University of California at Santa Barbara Harold Frank Hall Room 5119 Santa Barbara 93106 CA United States Engineering Sciences Center Sandia National Laboratories P.O. Box 5800 MS-1322 Albuquerque 87185-1322 NM United States Computational Sciences and Engineering Division Oak Ridge National Laboratory P.O. Box 2008 MS-6085 Oak Ridge 37831-6085 TN United States

PDMATLAB2D is a meshfree peridynamics implementation in MATLAB suitable for simulation of two-dimensional fracture problems. The purpose of this code is twofold. First, it provides an entry-level peridynamics computational tool for educational and training purposes. Second, it serves as an accessible and easily modifiable computational tool for peridynamics researchers who would like to adapt the code for a multitude of peridynamics simulation scenarios. The current version of the code implements a bond-based brittle elastic peridynamic model and a critical stretch criterion for bond breaking. However, the code is designed to be extendable for other peridynamic models and computational features. In this paper, we provide an overview of the code structure and functions with illustrative examples. Due to the integrated computation and postprocessing MATLAB capabilities, PDMATLAB2D can serve as an effective testbed for testing new constitutive models and advanced numerical features for peridynamics computations. © UT-Battelle, LLC, under exclusive licence to Springer Nature Switzerland AG 2024. corrected publication 2024.

关键词： Fracture Meshfree Peridynamics Two-dimensional

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Real-time Seamless Image Stitching in Autonomous Driving

Real-time Seamless Image Stitching in Autonomous Driving

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International Conference on Information Fusion

作者： Christian Kinzig Irene Corté s Carlos Ferná ndez Martin Lauer Institute of Measurement and Control Systems Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Department of Computer Science and Engineering Universidad Carlos III de Madrid (UC3M) Madrid Spain

ISBN: (数字)9781737749721

ISBN: (纸本)9781665489416

Autonomous vehicles depend on an accurate perception of their surroundings. For this purpose, different approaches are used to detect traffic participants such as cars, cyclists, and pedestrians, as well as static objects. A commonly used method is object detection and classification in camera images. However, due to the limited field of view of camera images, detecting in the entire environment of the ego-vehicle is an additional challenge. Some solutions include the use of catadioptric cameras or clustered surround view camera systems that require a large installation height. In multi-camera setups, an additional step is required to merge objects from overlapping areas between cameras. As an alternative to these systems, we present a real-time capable image stitching method to improve the horizontal field of view for object detection in autonomous driving. To do this, we use a spherical camera model and determine the overlapping area of the neighboring images based on the calibration. Furthermore, lidar measurements are used to improve image alignment. Finally, seam carving is applied to optimize the transition between the images. We tested our approach on a modular redundant sensor platform and on the publicly available nuScenes dataset. In addition to qualitative results, we evaluated the stitched images using an object detection network. Moreover, the real-time capability of our image stitching method is shown in a runtime analysis.

关键词： Runtime Laser radar Pipelines Merging Object detection Cameras Real-time systems

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Evolving Neuromorphic systems on the Ethereum Smart Contract Platform

Evolving Neuromorphic Systems on the Ethereum Smart Contract...

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Global Emerging Technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE

作者： Hongchi Wu Binhao Fang Cheng Xiang Gregory Cohen André van Schaik Bharath Ramesh Department of Electrical and Computer Engineering Advanced Control Technology Lab National University of Singapore Singapore International Center for Neuromorphic Systems The MARCS Institute for Brain Behaviour and Development Western Sydney University Sydney Australia

Neuromorphic intelligent systems are motivated by the observation that biological organisms - from algae to primates - excel in swiftly sensing their environment, reacting promptly to its perils and opportunities. Furthermore, biological organisms function more resiliently than our most advanced machines, with a fraction of their power requirements. Taking inspiration from how primates and humans have successfully evolved higher cognitive intelligence within social constructs, this paper proposes neuromorphic systems to be built and governed on a public distributed ledger platform. However, following in the footsteps of generic AI research, neuromorphic benchmarks and algorithms are developed in isolation. Furthermore, as a relatively niche research field, there is limited access to the actual neuromorphic sensors and large publicly available curated data, exacerbating the slow research progress. Nonetheless, centralized neuromorphic datasets and algorithms pose a threat to secure closed-loop behavior and learning outcomes, both commonly modulated in biological organisms via social interactions. This paper makes the case for early adoption of distributed ledger technology by neuromorphic systems and benchmarks to avoid the pitfalls endured by AI research – showcasing competing event-based gesture recognition systems on the Ethereum smart contract platform. This shift towards real-world and dynamic systems on a distributed ledger platform will improve collaboration among neuromorphic researchers while enabling healthy competition via incentives. Smart contract protocols allow model behavior monitoring, setting new learning tasks and increase in baseline performance, and naturally provides a governance framework for evolving neuromorphic systems. The code is publicly made available at: https://***/BruceFang123.

关键词： Neuromorphics Distributed ledger Smart contracts Biological systems Benchmark testing Blockchains Sensors

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Solution of a Decision Making Problem Under Risk with Z-Information 14th

Solution of a Decision Making Problem Under Risk with Z-Info...

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14th International Conference on Applications of Fuzzy systems, Soft Computing, and Artificial Intelligence Tools, ICAFS 2020

作者： Jabbarova, Konul Huseynov, O.H. Department of Computer Engineering Azerbaijan State Oil and Industry University 20 Azadlig Ave. BakuAZ1010 Azerbaijan Research Laboratory of Intelligent Control and Decision Making Systems in Industry and Economics Azerbaijan State Oil and Industry University Baku Azerbaijan

ISBN: (纸本)9783030640576

In this paper expected utility under Z-information methodology is surveyed in order to select one of a fixed number of alternative actions in the context of risk, as many real-world solution taking problems are defined by uncertainty and partial reliability. In the final part, we will apply the Fuzzy Pareto Optimality (FPO) principle based on the comparison of Z-numbers. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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A New Algorithm Watermark for Improving Objects Based on Clusters and Perceptions

A New Algorithm Watermark for Improving Objects Based on Clu...

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2020 Ural Symposium on Biomedical engineering, Radioelectronics and Information Technology, USBEREIT 2020

作者： Kholov, Fozil Tolibovich Shelupanov, Aleksandr Aleksandrovich Shonazarov, Parviz Mahmadnazarovich Tomsk State University of Control Systems and Radioelectronics Department of Complex Information Security of Computer Systems Tomsk Russia Tomsk State University of Control Systems and Radioelectronics Doctor of Engineering Tusur Tomsk Russia South Ural State University Department of Information and Communication Technologies Chelyabinsk Russia

ISBN: (数字)9781728131658

ISBN: (纸本)9781728131658

The Internet of Things is the concept of connecting a large number of devices connected to a common network. This data communicates with each other via the Internet. Most connected devices in a single network create a complete picture of what is happening around, increase comfort and improve the quality of life of people. This article proposes a layout based on marking and improved clustering that resolves the conflict between security and perceptual resource levels. The Internet of Things, or IOT, refers to the billions of physical devices around the world that are now connected to the Internet that collect and exchange data. Thanks to low-cost processors and wireless networks, IOT can be partly turned into anything - from simple devices to an airplane. This adds a level of digital intelligence to devices, allowing them to communicate without human intervention and the fusion of the digital and physical worlds. To improve security, we are developing a stochastic positioning strategy based on a clustering algorithm to calculate the position embedded in the temporal dynamics of data measurement. This security vulnerability created by a stationary built-in situational situation can not only be effectively eliminated, but also lead to zero data breach. The results of our studies show that the proposed algorithm can effectively provide the integration of low-cost data, as well as reduce energy consumption and increase the lifetime of the network. © 2020 IEEE.

关键词： Internet of things

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A scalable distributed dynamical systems approach to compute the strongly connected components and diameter of networks

arXiv

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arXiv 2021年

作者： Reed, Emily A. Ramos, Guilherme Bogdan, Paul Pequito, Sérgio Ming Hsieh Electrical and Computer Engineering Department University of Southern California United States Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Portugal Delft University of Technology Delft Center for Systems and Control

Finding strongly connected components (SCCs) and the diameter of a directed network play a key role in a variety of discrete optimization problems, and subsequently, machine learning and control theory problems. On the one hand, SCCs are used in solving the 2-satisfiability problem, which has applications in clustering, scheduling, and visualization. On the other hand, the diameter has applications in network learning and discovery problems enabling efficient internet routing and searches, as well as identifying faults in the power grid. In this paper, we leverage consensus-based principles to find the SCCs in a scalable and distributed fashion with a computational complexity of O (Ddmax in-degree ) , where D is the (finite) diameter of the network and dmax in-degree is the maximum in-degree of the network. Additionally, we prove that our algorithm terminates in D + 1 iterations, which allows us to retrieve the diameter of the network. We illustrate the performance of our algorithm on several random networks, including Erdos-Rényi, Barabási- Albert, and Watts-Strogatz networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Optimization

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