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Mobility-aware Decentralized Federated Learning for Autonomous Underwater Vehicles

IEEE Transactions on Wireless Communications 2025年

作者： He, Hongyi Du, Jun Jiang, Chunxiao Wang, Jintao Song, Jian Han, Zhu Tsinghua University Department of Electronic Engineering Beijing100084 China Tsinghua University Department of Electronic Engineering State Key laboratory of Space Network and Communication Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology and State Key laboratory of Space Network and Communication Beijing100084 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China 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 underwater Internet of Things (UIoT) is crucial in developing marine resources. However, due to the low data rate of underwater channels, it is difficult to have a central server to process data from numerous devices as using terrestrial communications. Therefore, decentralized federated learning (DFL) with communication-efficient modifications is a promising alternative to empower UIoT with artificial intelligence and collaborative training. However, existing DFL strategies rely on a carefully designed small aggregation weight when aggregating parameters from neighbor nodes to mitigate the compression error, resulting in a slow convergence rate. In addition, the effect of data compression under time-varying topologies is not considered in current DFL algorithms. In response to these problems, this work studies a DFL framework with underwater acoustic channel and time-varying topology. Firstly, considering the low data rate and dynamics of the acoustic channel, we propose a practical scheme for adaptive compression and device connectivity. Moreover, we combine data compression and the error-compensation technique with time-varying topology and propose a DFL algorithm with aggregation weights decaying over time to achieve fast convergence under non-independent and identically distributed (non-IID) data. We derive a convergence bound for the proposed algorithm with respect to compression and time-varying topology and demonstrate that it achieves the same asymptotic convergence rate as centralized FL with perfect communication. Simulation results show that, compared with DFL algorithms without decaying aggregation weights and centralized FL schemes, the proposed algorithm exhibits higher accuracy and faster convergence rate in underwater environments. © 2002-2012 IEEE.

关键词： Autonomous underwater vehicles

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Intelligent Bayesian regularization-based solution predictive procedure for hybrid nanoparticles of AA7072-AA7075 oxide movement across a porous medium

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ZAMM Zeitschrift fur Angewandte Mathematik und Mechanik 2023年第10期103卷

作者： Awan, Saeed Ehsan Ali, Faizan Awais, Muhammad Shoaib, Muhammad Raja, Muhammad Asif Zahoor Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Pakistan Department of Mathematics COMSATS University Islamabad Attock Pakistan Yuan Ze University Taoyuan City Taiwan Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan

The research community has shown great interest for investigation in the nanofluids models involving Aluminum Alloys AA7072 and AA7072 +AA7075 due to their advantageous impact on heat transfer, physical and mechanical characteristic exploiting in broad engineering applications such as manufacturing of spacecraft, aircraft parts and building testing. The hybrid nanomaterial AA7072-AA7075 based fluidic system is investigated in this paper using an artificial neural network with Bayesian regularization scheme (ANNs-BRS). The derived partial differential equations (PDEs) are transformed into ordinary differential equations system ODEs and obtained the reference datasets for the estimated solution dynamics of hybrid nanofluidic system. For prominent parameters, the influence of flow on the temperature distribution and velocity plot are investigated. The performance on 80% training samples, 5% testing and 15% validation dataset for ANNs-BRS is well-established in terms of error histogram plots, regression analysis, and MSE based statistics. The results for entropy generation, Eckert number Ec, magnetic interaction parameter M, suction parameter S, and heat generation parameter Q are also discussed. The results show that the Eckert number Ec has the effect of slowing down the rate of heat transfer while increasing the temperature and increases in suction parameter causes decrease in temperature while increase in temperature profile due to enhancement of suction parameter. © 2023 Wiley-VCH GmbH.

关键词： Ordinary differential equations

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An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools

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Sensors 2024年第8期24卷 2531页

作者： Kristiani, Endah Wang, Lu-Yan Liu, Jung-Chun Huang, Cheng-Kai Wei, Shih-Jie Yang, Chao-Tung Department of Computer Science Tunghai University Taichung City407224 Taiwan Department of Informatics Krida Wacana Christian University Jakarta11470 Indonesia Industrial Technology Research Institute Hsinchu310401 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung City411030 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung City407224 Taiwan

This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi. © 2024 by the authors.

关键词： Edge computing

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Fault Tolerance and Cost Effectiveness Analysis of Low-Powered 3D-TESH Network

Fault Tolerance and Cost Effectiveness Analysis of Low-Power...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Faiz Al Faisal M.M. Hafizur Rahman Yasushi Inoguchi Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Networks & Communications CCSIT King Faisal University Saudi Arabia Research Center for Advanced Computing Infrastructure JAIST Nomi Ishikawa Japan

Exa-scale supercomputing is the prime target for the supercomputers. To reach at that point, supercomputers require redesigning the interconnection networks to support high network scalability, suitable network performance, low cost-performance ratio, low power usage, high fault-tolerance, low network congestion and high throughput with low latency. Among the thousand of interconnection networks, one of the most capable one is 3D-TESH, is a hierarchical interconnection network (HIN) interconnected with multiple basic modules (basic modules are basically treated as single on-chip network), where each on-chip network is similar to a 3Dmesh network and off-chip network are built using the 2Dtorus connections. Some static network parameters as well as dynamic communication performance (on-chip level) had been studied earlier for 3DTESH, which ensured the high significance of this network in comparing with others. Moreover, in case of power consumption, it requires about 41% less router power than 5Dtorus at the on-chip level. In this paper, we like to evaluate some other important static parameters like- packing density, fault tolerance, wiring complexity, cost effectiveness factor and etc. Here, we found that 3D-TESH yields much better results than network like - 3Dmesh, 2Dmesh, 2Dtorus, TESH and even the 3Dtorus.

关键词： Wiring Fault tolerance Costs Multiprocessor interconnection Scalability Heuristic algorithms Fault tolerant systems

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Developing U-Net Enhanced Depth Sensing Detection on Earthwork Management

SSRN

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SSRN 2024年

作者： Chen, Jieh-Haur Su, Mu-Chun Chen, Chen-Hau Lin, Wei-Jen Chien, Kuan-Yu Department of Civil Engineering Research Center of Smart Construction National Central University Taoyuan Jhongli320317 Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan Jhongli320317 Taiwan Department of Civil Engineering National Central University Taoyuan Jhongli320317 Taiwan

Efficiently managing the substantial annual earthwork volume of over 30 million cubic meters in Taiwan demands considerable resources and human effort. Practitioners in this field seek a streamlined solution for effective earthwork management. This study aims to enhance earthwork management through computational intelligence by employing U-Net for depth sensing detection. The literature review encompasses earthwork calculation, the feasibility of Unmanned Aerial Vehicle (UAV) detection, object detection principles, and U-Net configuration. Employing a UAV, aerial depth sensing is executed at a predetermined earthwork site spanning 43,939 square meters. The UAV maintains a steady altitude of 60 meters above sea level, traveling at a speed of 4.7 m/s, resulting in a total airborne time of 10 minutes and 51 seconds. Capturing 276 high-resolution images with 80% overlap and 70% photo overlap, the U-Net model achieves an average detection accuracy of 92.49% for earthwork site identification. A comparison with actual earth imported to the site over 10 months reveals a 13.84% increment in detected volume by the proposed model. These findings, aligned with empirical operations, offer practitioners an efficient approach to earthwork calculation and management. © 2024, The Authors. All rights reserved.

关键词： Object detection

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A new approach to delegate signing rights to proxy signers using isogeny-based cryptography

arXiv

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arXiv 2024年

作者： Dey, Kunal Kumar, Somnath Srivastava, Vikas Debnath, Sumit Kumar Das, Ashok Kumar Department of Computer Science University of Calgary Alberta Canada Department of Mathematics National Institute of Technology Jamshedpur Jamshedpur India Department of Mathematics Indian Institute of Technology Madras Chennai India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500 032 India

E-governance is a two-way protocol through which one can use government services, share data and request information. It refers to the use of communication and information technologies to provide government services to public in an efficient and fast manner. In addition, any document submitted to the e-Government system must be authenticated by a government officer using a digital signature scheme. In the context of digital signatures, the proxy signature is an important cryptographic primitive that allows the original signer to delegate signing authority to another signer (proxy signer). The proxy signature has a number of important applications in the e-government system. There are now a large amount of proxy signature schemes. The security of most of them relies on the following hard problems: the discrete logarithm problem and the factorization of integers problem. However, a large-scale quantum computers can solve them in polynomial time due to Shor’s algorithm. As a consequence, there is a need for a quantum computer-resistant proxy signature to secure e-governance system from quantum adversaries. In this work, we propose the first post-quantum isogeny based proxy signature scheme CSI-PS (commutative supersingular isogeny proxy signature). Our construction is proven to be uf-cma secure under the hardness of the group action inverse problem (GAIP) based on isogeny. © 2024, CC BY.

关键词： Quantum computers

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Design and Implementation of Distributed Image Recognition App with Federal Learning Techniques 5th

Design and Implementation of Distributed Image Recognition A...

引用

5th EAI International Conference on Smart Grid and Internet of Things, SGIoT 2021

作者： Chan, Yu-Wei Wu, Bo-You Huang, Yi-Ming Yang, Chao-Tung Department of Information Management Providence University Taichung Taiwan Department of Computer Science Tunghai University No. 1727 Sec. 4 Taiwan Boulevard Taichung407224 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University No. 1727 Sec. 4 Taiwan Boulevard Taichung407224 Taiwan

ISBN: (纸本)9783031203978

In recent years, machine learning technology has been widely used in many fields, such as smart transportation, smart healthcare, smart finance and smart cities. Although machine learning technology has brought people a lot of convenience, the privacy problem of user data has also emerged [1]. Considering that users are not necessarily willing to upload personal privacy data to the cloud for deep learning training, therefore, instead of consuming a lot of bandwidth to upload data to the cloud, it is better to train on the local device and then use the model parameters obtained after training. (For example: weights and bias, etc.) upload to the server for aggregation. This emerging machine learning technology is called federated learning. In this way, the privacy and security of data can be guaranteed, and the purpose of decentralized learning can be achieved through aggregation. This study uses the architecture of federated learning technology and convolutional neural network algorithms to implement distributed image recognition mobile applications. This application allows users to use their mobile devices and the central servers for repeated training. After multiple rounds of repeated training, the convergence will be stabilized, and the accuracy will be significantly improved. At the same time, it can take into account privacy and achieve the machine the purpose of learning. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Image recognition

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pyCEPS: A cross-platform Electroanatomic Mapping Data to Computational Model Conversion Platform for the Calibration of Digital Twin Models of Cardiac Electrophysiology

arXiv

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arXiv 2024年

作者： Arnold, Robert Prassl, Anton J. Neic, Aurel Thaler, Franz Augustin, Christoph M. Gsell, Matthias A.F. Gillette, Karli Manninger, Martin Scherr, Daniel Plank, Gernot Gottfried Schatz Research Center Division of Medical Physics and Biophysics Medical University of Graz Graz Austria NumeriCor GmbH Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria BioTechMed-Graz Graz Austria Division of Cardiology Department of Medicine Medical University of Graz Graz Austria

Background and Objective: Data from electro-anatomical mapping (EAM) systems are playing an increasingly important role in computational modeling studies for the patient-specific calibration of digital twin models. However, data exported from commercial EAM systems are challenging to access and parse. Converting to data formats that are easily amenable to be viewed and analyzed with commonly used cardiac simulation software tools such as openCARP remains challenging. We therefore developed an open-source platform, pyCEPS, for parsing and converting clinical EAM data conveniently to standard formats widely adopted within the cardiac modeling community. Methods and Results: pyCEPS is an open-source Python-based platform providing the following functions: (i) access and interrogate the EAM data exported from clinical mapping systems;(ii) efficient browsing of EAM data to preview mapping procedures, electrograms (EGMs), and electro-cardiograms (ECGs);(iii) conversion to modeling formats according to the openCARP standard, to be amenable to analysis with standard tools and advanced workflows as used for in silico EAM data. Documentation and training material to facilitate access to this complementary research tool for new users is provided. We describe the technological underpinnings and demonstrate the capabilities of pyCEPS first, and showcase its use in an exemplary modeling application where we use clinical imaging data to build a patient-specific anatomical model. Conclusion: With pyCEPS we offer an open-source framework for accessing EAM data, and converting these to cardiac modeling standard formats. pyCEPS provides the core functionality needed to integrate EAM data in cardiac modeling research. We detail how pyCEPS could be integrated into model calibration workflows facilitating the calibration of a computational model based on EAM data. © 2024, CC BY-NC-ND.

关键词： Mapping

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Category Guided Attention Network for Brain Tumor Segmentation in MRI

arXiv

引用

arXiv 2022年

作者： Li, Jiangyun Yu, Hong Chen, Chen Ding, Meng Zha, Sen School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Center for Research in Computer Vision University of Central Florida Orlando United States Scoop Medical Houston United States

Objective: Magnetic resonance imaging (MRI) has been widely used for the analysis and diagnosis of brain diseases. Accurate and automatic brain tumor segmentation is of paramount importance for radiation treatment. However, low tissue contrast in tumor regions makes it a challenging task. Approach: We propose a novel segmentation network named Category Guided Attention U-Net (CGA U-Net). In this model, we design a Supervised Attention Module (SAM) based on the attention mechanism, which can capture more accurate and stable long-range dependency in feature maps without introducing much computational cost. Moreover, we propose an intra-class update approach to reconstruct feature maps by aggregating pixels of the same category. Main results: Experimental results on the BraTS 2019 datasets show that the proposed method outperformers the state-of-the-art algorithms in both segmentation performance and computational complexity. Significance: The CGA U-Net can effectively capture the global semantic information in the MRI image by using the SAM module, while significantly reducing the computational cost. Code is available at https://***/delugewalker/CGA-U-Net. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Dynamic and Distributed Optimization for the Allocation of Aerial Swarm Vehicles

arXiv

引用

arXiv 2022年

作者： Hughes, Jason Larkin, Dominic O’Donnell, Charles Korpela, Christopher The Robotics Research Center Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States

Optimal transport (OT) is a framework that can guide the design of efficient resource allocation strategies in a network of multiple sources and targets. This paper applies discrete OT to a swarm of UAVs in a novel way to achieve appropriate task allocation and execution. Drone swarm deployments already operate in multiple domains where sensors are used to gain knowledge of an environment [1]. Use cases such as, chemical and radiation detection, and thermal and RGB imaging create a specific need for an algorithm that considers parameters on both the UAV and waypoint side and allows for updating the matching scheme as the swarm gains information from the environment. Additionally, the need for a centralized planner can be removed by using a distributed algorithm that can dynamically update based on changes in the swarm network or parameters. To this end, we develop a dynamic and distributed OT algorithm that matches a UAV to the optimal waypoint based on one parameter at the UAV and another parameter at the waypoint. We show the convergence and allocation of the algorithm through a case study and test the algorithm’s effectiveness against a greedy assignment algorithm in simulation. © 2022, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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