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Communication-Efficient Personalized Federated Learning for Green Communications in IoMT

Communication-Efficient Personalized Federated Learning for ...

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IEEE international conference on Communications (ICC)

作者： Ziqi Chen Jun Du Chunxiao Jiang Yunlong Lu Zhu Han Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The rapid development of the Internet of Medical Things (IoMT) has brought about an enormous amount of healthcare data. Effectively and securely processing this sensitive data has become a significant challenge for green communication and privacy protection of the IoMT. As a decentralized learning framework, Federate learning (FL) enables model training without directly aggregating users' raw data, thus ensuring user privacy protection. Moreover, numerous studies have put forth various approaches to enhance the efficiency of FL by minimizing communication costs, yet they may not fully account for the unique characteristics of IoMT. Specifically, the efficiency and performance of model training are closely related to patient life and health. Meanwhile, existing research has indicated that reducing communication costs can result in a decline in training accuracy, which may be critical to patient health. Therefore, aimed at green communication and ensuring the model accuracy, we design a communication-efficient personalized federated learning framework, namely pFedCAS. Specifically, we introduce a control unit, which enables adaptive sparsity of local models, to reduce training costs. Furthermore, a selection unit based on communication quality is added into the global aggregation, which can select suitable clients for model updating. simulation results validate that the proposed method can significantly reduce communication costs while ensuring the model accuracy. Additionally, The simulation results also validate the excellent robustness of our method to non-iid healthcare data.

关键词： Training Adaptation models Data privacy Costs Accuracy Federated learning Computational modeling

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A Robust Parkinson's Disease Recognition from MRI Brain Images by Densely Multi-Dilated Densely Connected ResNet Model

A Robust Parkinson's Disease Recognition from MRI Brain Imag...

引用

Integrated Circuits, Communication, and Computing Systems (ICIC3S), international conference on

作者： M. Arogia Victor Paul Sharmila Shankar Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India

ISBN: (数字)9798350364088

ISBN: (纸本)9798350364095

Parkinson disease (PD) is a neurological ailment that can be treated that causes excruciating discomfort in the brain as well as problems with balance and dexterity. Physicians frequently employ experimental indicators, such as motorized and non-motor symptoms based on brain magnetic resonance imaging (MRI) pictures, to diagnose Parkinson's disease (PD) in a medical stage. Nevertheless, there are disparities in inter-variability because these medical evaluations heavily rely on skilled physicians. Thus, this work proposes a sophisticated deep learning (DL) method for precise brain MRI-based PD analysis. To improve the data eminence, Extended Unsharp Mask Filtering (Ex_UMF) is first used to the brain images. The suggested Multi-dilated Densely linked ResNet model (MD_DenseRNet) is then fed the preprocessed pictures to determine if the image is PD or normal. The Python platform processes and examines the generated method, and the simulation progresses using a publicly available PPMI database. A number of evaluation metrics, including computing time (CT), sensitivity, specificity, and precision, are looked at and linked to conventional research. The created MD_DenseRNet model achieved 98.87% accuracy, 97% precision, 98.1% recall, 96.9% specificity, and 0.15 ms CT in the experimental part.

关键词： Accuracy Sensitivity Parkinson's disease Computed tomography Magnetic resonance imaging Computational modeling Medical services

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A Distributed Method for State of Charge Estimation for Supercapacitor Pack

A Distributed Method for State of Charge Estimation for Supe...

引用

IEEE international conference on Systems, Man and Cybernetics

作者： Heng Li Yulin Zhang Ren Zhu Shilong Zhuo Hui Peng Wanwan Ren Rui Zhang School of Electronic Information Central South University Changsha China School of Computer Science and Engineering Central South University Changsha China Changsha Preschool Education College Changsha China School of Electronic Information and Electrical Engineering Changsha University Changsha China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Supercapacitors, leveraging their distinctive characteristics and advantages, have evolved into efficient energy storage solutions. State of Charge (SOC) is a crucial parameter for supercapacitors, and the estimation of SOC for individual supercapacitor cells has been extensively researched. In practical applications, it is common to assemble hundreds or even thousands of cells to form a supercapacitor pack, particularly in fields like electric vehicles a nd electric b uses. Therefore, estimating the SOC for the supercapacitor pack becomes imperative. In response to the demands for supercapacitor pack SOC $(SOC_{pack})$ estimation and wireless management, this paper proposes a distributed method. After modeling the supercapacitor cells, the definition of $SOC_{pack}$ is introduced. The SOC of a cell in the definition is estimated based on Kalman filter. The proposed distributed method relies on wireless communication and computational updates between cells. Through iterative processes, it ultimately converges to the estimated $SOC_{pack}$ . Finally, we conducted simulation experiments to analyze the performance of the proposed method under various communication conditions, thereby validating its effectiveness and robustness.

关键词： Wireless communication Estimation Supercapacitors Electric vehicles Robustness Topology Delays State of charge Kalman filters Iterative methods

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Complex Double Integration Enhanced ZNN with Rejection of Linear Noise for Computing Time-Dependent Sylvester Equation

Complex Double Integration Enhanced ZNN with Rejection of Li...

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IEEE international conference on Industrial Technology (ICIT)

作者： Jinsha Xu Cheng Hua Shuai Li Bolin Liao College of Computer Science and Engineering Jishou University Jishou China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Recurrent neural networks (RNN) are highly effective in solving the inverse problem of time-dependent matrices. However, in real-world engineering applications, noise interference is inevitable. The zeroing neural network (ZNN) is a special class of RNN that exhibit strong noise tolerance in solving the time-dependent Sylvester equation (TDSE). Despite the usefulness of the previously proposed original ZNN (OZNN) model and integral enhanced ZNN (IEZNN) model, they lack the ability to suppress linear and quadratic noise when solving the Sylvester equation. In this paper, a complex double integration enhanced ZNN (CDIEZNN) model is introduced for the first time to address the problem of TDSE. The theoretical basis for the convergence and robustness of the double integral model is described in detail. Two experiments are conducted to verify the convergence and robustness of the CDIEZNN model, and the digital simulation results demonstrate that in solving the TDSE, the residual error of the CDIEZNN model rapidly and stably converges to 3.82 × 10– 6 in a linear noise environment, and even in complex quadratic noise environments, the residual error can still converge to 1.99 × 10- 2 • In contrast, the OZNN and IEZNN models have weak suppression of linear noise and the residual errors cannot converge in a quadratic noise environment and the results show divergence.

关键词： Recurrent neural networks Inverse problems Computational modeling Noise Resists Interference Mathematical models

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A novel mathematical model for optimizing energy consumption in OLSRv2 routing protocol

A novel mathematical model for optimizing energy consumption...

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Science and Information Technology in Smart Administration (ICSINTESA), international conference of

作者： Sanusi Mohammed Bunu Omar Alani Mo Saraee School of Science Engineering and Environment University of Salford Manchester United Kingdom

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

Device-to-Device (D2D) communication system is one of the fundamental elements of Fifth generation (5G) and Beyond 5G (B5G) mobile telecommunication networks. The fundamental architecture necessary for the implementation of efficient D2D communication system are the routing protocols. The initial version of routing protocol called Optimised Link State Routing Protocol (OLSR) v1 faced many challenges such as minimising energy. However, the introduction of OLSRv2 as a successor of OLSRv1 brings flexible techniques for message exchange among nodes and efficient signaling. However, the OLSRv2 routing protocol raises several difficulties, including those related to energy consumption. In this paper we therefore introduced a new model that computes the energy consumption in a static scenario in the OLSRv2 routing protocol considering various nodes. The simulation results for the energy consumption in the static scenario using our new model for $10,30,50$ and 100 nodes are obtained and compared with the energy consumption in the OLSRv2. The results show that the new model significantly outperforms OLSRv1 and OLSRv2 in terms of minimising energy consumption. The results are validated by computing the energy consumption in the static scenario using some default current values and the results show that the energy consumption is minismised compared to the OLSRv2. The energy consumption with various mobility speeds and routing overhead for both static and with various mobility speeds will be considered in our future paper.

关键词： Energy consumption 5G mobile communication Computational modeling simulation computer architecture Routing Routing protocols Mathematical models Device-to-device communication Information technology

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Design and simulation of Elevator Emergency System Based on TIA Portal V15.1 7

Design and Simulation of Elevator Emergency System Based on ...

引用

2020 7th international conference on computer-Aided Design, Manufacturing, modeling and simulation, CDMMS 2020

作者： He, Huajin Long, Yinxin Yu, Wei Wuhan University of Technology Wuhan China

The main control system of the elevator can issue different instructions for the operation of the elevator according to the different needs of users, so as to achieve the purpose of transporting people and goods efficiently. With the continuous development of China's elevator industry and the significant increase of elevator installed capacity[1], In practical engineering, higher requirements are put forward for elevator safety. In this regard, based on Siemens Portal platform, this paper designs an automatic control emergency system including overweight, over travel protection, door opening and closing protection, to assist the main control system to maintain the normal operation of the elevator. The proposed scheme is simulated in the Elevator simulation platform, and the results show that the emergency system can effectively prevent the wrong operation during the operation of the elevator, and greatly improve the safety of the elevator. At the same time, because the system is written in LAD language and has good readability and portability, it can be widely used in the elevator master control system with Siemens PLC as the main controller. © Published under licence by IOP Publishing Ltd.

关键词： Elevators

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Using Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Reconstruct Mixture Fraction Statistics of Turbulent Jet Flows

Using Physics-Informed Enhanced Super-Resolution Generative ...

引用

35th international ISC High Performance conference

作者： Gauding, Michael Bode, Mathis Univ Rouen Rouen France Rhein Westfal TH Aachen Inst Combust Technol Aachen Germany

ISBN: (纸本)9783030905392;9783030905385

This work presents the full reconstruction of coarse-grained turbulence fields in a planar turbulent jet flow by a deep learning framework for large-eddy simulations (LES). Turbulent jet flows are characterized by complex phenomena such as intermittency and external interfaces. These phenomena are strictly non-universal and conventional LES models have shown only limited success in modeling turbulent mixing in such configurations. Therefore, a deep learning approach based on physics-informed enhanced super-resolution generative adversarial networks (Bode et al., Proceedings of the Combustion Institute, 2021) is utilized to reconstruct turbulence and mixture fraction fields from coarsegrained data. The usability of the deep learning model is validated by applying it to data obtained from direct numerical simulations (DNS) with more than 78 Billion degrees of freedom. It is shown that statistics of the mixture fraction field can be recovered from coarse-grained data with good accuracy.

关键词： Large eddy simulation

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Generating Assembly Instructions Using Reinforcement Learning in Combination with Large Language Models

Generating Assembly Instructions Using Reinforcement Learnin...

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IEEE international conference on Industrial Informatics (INDIN)

作者： Niklas Widulle Frederic Meyer Oliver Niggemann Computer Science in Mechanical Engineering Helmut Schmidt University Hamburg Germany Industrial Engineering Laboratory OWL University of Applied Sciences and Arts Lemgo Germany

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

The efficiency of manual assembly can be significantly improved by utilizing assistance systems that display as-sembly instructions. However, generating and maintaining these instructions require substantial effort, especially for low-volume or highly customized products. If neglected, this can lead to outdated instructions, frustrated operators, and the abandonment of the assistance platform. Recent advancements in Large Language Models (LLMs) have made it possible to generate high-quality assembly instructions given the right input. However, relying solely on LLMs to plan the assembly process risks producing unfeasible assembly sequences due to potential hallucinations by the models. To address this, Reinforcement Learning (RL) can be used in simulations to plan the assembly process, imposing restrictions on impractical movements. We propose a framework that integrates RL and LLMs to generate practical and accurate assembly instructions. In our framework, RL is used within a simulation to generate feasible assembly sequences. These sequences are then transformed into detailed assembly instructions by an LLM. We evaluate our framework on real-world product assemblies, generating comprehensive assembly instructions from corresponding CAD files. Our results demonstrate the potential of combining RL and LLMs to automate the generation of assembly instructions, thereby overcoming the limitations of current assistance systems and enhancing the efficiency of manual assembly processes.

关键词： Solid modeling Accuracy Large language models Reinforcement learning Manuals Informatics Assembly

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Sim-to-Real Transfer for Quadrupedal Locomotion via Terrain Transformer

Sim-to-Real Transfer for Quadrupedal Locomotion via Terrain ...

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IEEE international conference on Robotics and Automation (ICRA)

作者： Hang Lai Weinan Zhang Xialin He Chen Yu Zheng Tian Yong Yu Jun Wang Dept. of Computer Sci. and Eng. Shanghai Jiao Tong University China Digital Brain Lab Shanghai China School of Info. Sci. and Tech. ShanghaiTech University China School of Creativity and Art ShanghaiTech University China Centre for Artificial Intelligence University College London UK

Deep reinforcement learning has recently emerged as an appealing alternative for legged locomotion over multiple terrains by training a policy in physical simulation and then transferring it to the real world (i.e., sim-to-real transfer). Despite considerable progress, the capacity and scalability of traditional neural networks are still limited, which may hinder their applications in more complex environments. In contrast, the Transformer architecture has shown its superiority in a wide range of large-scale sequence modeling tasks, including natural language processing and decision-making problems. In this paper, we propose Terrain Transformer (TERT), a high-capacity Transformer model for quadrupedal locomotion control on various terrains. Furthermore, to better leverage Transformer in sim-to-real scenarios, we present a novel two-stage training framework consisting of an offline pretraining stage and an online correction stage, which can naturally integrate Transformer with privileged training. Extensive experiments in simulation demonstrate that TERT outperforms state-of-the-art baselines on different terrains in terms of return, energy consumption and control smoothness. In further real-world validation, TERT successfully traverses nine challenging terrains, including sand pit and stair down, which can not be accomplished by strong baselines.

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Motion simulation and Force Analysis in Built Car Dynamic Model 2

Motion Simulation and Force Analysis in Built Car Dynamic Mo...

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2nd international conference for Information Systems and Design (ICID)

作者： Shi, Yuan Xu, Wentao Wuhan Univ Technol Sch Int Educ Wuhan 430000 Hubei Peoples R China Jiangsu Univ Technol Sch Automot & Traff Engn Changzhou 213100 Jiangsu Peoples R China

ISBN: (纸本)9781665420655

Modern car engineering relies more heavily on mathematical models and computer simulations than on experiments. A digitally designed car can let any of the parts be tested, amended, or replaced without any of the costs of actually making them. Because of this general trend, engineers are expected to receive solid training in mathematical modeling of complex physical systems and learn how to solve the resulting equations on a computer. In this paper, we use python to build two different cars (BMW and Chevrolet Cavalier) in linear and non-linear force models to observe how their motion and force change in given turn profiles. The study aims to model the car by python and find out how it turns. First, we develop a simple model of a car. Then we use python to define two kinds of cars (BMW and Chevrolet Cavalier), two kinds of turn profiles (U-turn and lane change) and two forces models (linear and nonlinear). Finally, we use the parameters we define and some physical equations to find the force and solve the car's motion.

关键词： BMW Chevrolet Cavalier Lane change U-turn Linear Nonlinear

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