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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Guanhua Yang, Fang Song, Jian Han, Zhu Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9798350351255

In this paper, a multi-satellite cooperation architecture for satellite-terrestrial communications is studied with the dense constellation of low Earth orbit (LEO) satellites. Moreover, beam hopping (BH) and frequency allocation enable a flexible solution for the non-uniform geographical distribution of communications, which are optimized to improve the performance and avoid interference in satellite-terrestrial communications. The total problem for maximizing the networking throughput and ensuring the delay fairness is decomposed into two sub-problems, in which the deep reinforcement learning is utilized to determine the BH pattern, and the frequency allocation among beams is implemented by the majorization-minimization algorithm. Simulation results demonstrate that the proposed method obtains a 13.0% improvement in throughput compared with the original benchmark, while the delay fairness can be ensured by the iterative solution of the two sub-problems. © 2024 IEEE.

关键词： Orbits

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Sampled-data synchronization for heterogeneous delays inertial neural networks with generally uncertain semi-Markovian jumping and its application

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Neural Computing and Applications 2024年第33期36卷 20963-20982页

作者： Wang, Junyi He, Wenyuan Xu, Hongli Cai, Haibin Chen, Xiangyong Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China Foshan Graduate School of Innovation Northeastern University Foshan528311 China College of Information Science and Engineering Northeastern University Liaoning Shenyang110819 China Department of Computer Science Loughborough University LoughboroughLE11 3TU United Kingdom School of Automation and Electrical Engineering Linyi University Linyi276000 China

This article is concerned with sampled-data synchronization problem of heterogeneous delays inertial neural networks (INNs) with generally uncertain semi-Markovian (GUSM) jumping. Different from traditional Markovian inertial neural networks (MINNs), the INNs with GUSM are investigated in this paper by fully considering the sojourn time and the lacking transition rates, which is more general and applicable for practical system. The new extended two-sided looped-functional (ETSLF) approach is adopted in this paper, and some improved less conservative criteria are derived to achieve the synchronization of the drive and response INNs. The controller gain matrices are acquired based on synchronization criteria. Finally, the viability of the method is presented through three examples. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Markov processes

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CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS 15

CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BAS...

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15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on computer Science and Information Systems, MCCSIS 2022

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. dos Coelho, Leandro S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

ISBN: (纸本)9789898704405

Early recognition of clinical deterioration (CD) has vital importance in patients’ survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network’s performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models’ performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals’ data. © 2022 15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on computer Science and Information Systems, MCCSIS 2022. All rights reserved.

关键词： Neural networks

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RoboRover - A Jetson Nano Powered Robot Car for Object Following, Obstacle Avoidance, and Wall Following 14

RoboRover - A Jetson Nano Powered Robot Car for Object Follo...

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14th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023

作者： Huynh, Trinh Walter, James Aveta, Federica University of Michigan Robotics Department Ann ArborMI48109 United States Wentworth Insitute of Technology Electrical and Computer Engineering Program BostonMA02115 United States

ISBN: (纸本)9798350304138

An intelligent and self-sufficient robot is essential across a wide range of fields, including transportation, industry, space exploration, and defense. Mobile robots possess the capability to undertake diverse tasks such as handling materials, aiding in disaster scenarios, conducting patrols, and executing rescue operations. As a result, the development of an autonomous robot that can navigate through both unchanging and ever-changing surroundings has become important. The primary objective of mobile robot navigation revolves around ensuring the seamless and secure traversal of the robot through complex environments, starting from an initial position, and reaching a designated goal position. This paper presents the design and implementation of a Jetson Nano powered robot car which uses local sensors to interact with an unknown environment. Object following, obstacle avoidance, and wall following features are built for the car to navigate to reach its desired destinations. © 2023 IEEE.

关键词： Mobile robots

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Occupancy-level-aware Indoor Terahertz Channel Prediction: A Robust Deep Learning Approach 100

Occupancy-level-aware Indoor Terahertz Channel Prediction: A...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Hasan, Eslam Mahalal, Elmahedi Ismail, Muhammad Wu, Zi-Yang Fouda, Mostafa M. Kato, Nei Tennessee Technological University Department of Computer Science CookevilleTN United States Northeastern University College of Information Science and Engineering Shenyang China Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Idaho FallsID United States Tohoku University Graduate School of Information Sciences Sendai Japan

ISBN: (纸本)9798331517786

Accurate channel prediction using deep learning (DL) algorithms can address the challenges of terahertz (THz) propagation, such as atmospheric absorption and object scattering, by enabling proactive handover and beamforming. However, indoor environments are inherently dynamic, with factors like occupancy level variations causing the channel characteristics to change over time. This phenomenon, known as concept drift, can severely degrade the DL model performance used in channel prediction. This paper investigates the impact of indoor occupancy level variations on the generalization ability of state-of-the-art DL models for THz channel prediction. We identify three distinct occupancy levels (low, medium, and high) within the THz indoor channel. Our results demonstrate that the state-of-the-art DL models exhibit limited generalization capabilities, with performance deterioration in prediction accuracy ranging from 4-62%. We propose a robust two-stage framework to mitigate concept drift in THz channel prediction. The first stage predicts the indoor occupancy level from the THz wireless signal, which is a multi-class classification problem. Due to the reoccurring concept of occupancy levels, the second stage contains a pool of models in a sleeping mode based on a hybrid convolutional neural network (CNN) long-short-term memory (LSTM) architecture. One of these DL expert models is activated for channel prediction based on the occupancy level predicted from the previous stage. Our framework demonstrates superior generalization by limiting the performance deterioration from 62% due to concept drift to ≤ 9%. This represents an 85% reduction in performance deterioration compared to the existing state-of-the-art DL models. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis of Loss Tangent in Current Transformers Using the Finite Element Method 23

Analysis of Loss Tangent in Current Transformers Using the F...

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23rd International Symposium on High Voltage engineering, ISH 2023

作者： Leite Neto, Antonio F. Guedes, Edson G. Vilar, Pablo B. Lira, George R.S. Andrade, Filipe L.M. Melo, João Victor J. Junior, Almir C.S. Marotti, Ana C.F. Costa, André I. Souza, João Paulo A. Electrical Engineering Graduate Program Federal University of Campina Grande Campina Grande Brazil Department of Electrical Engineering Federal University of Campina Grande Campina Grande Brazil Department of Electrical Engineering Federal Institute of Paraiba Patos Brazil Eletrobras Furnas Electric Power Furnas Rio de Janeiro Brazil Concert Concert Technologies Belo Horizonte Brazil

ISBN: (纸本)9781839539954

Failures in Current Transformers (CTs) can have a significant financial impact on utilities, causing revenue loss, service interruptions, and even damage to nearby equipment. To minimize the effects of CT degradation on the electrical system, predictive techniques and continuous monitoring are essential. One such tool that can aid in the supervision and diagnosis of equipment in service is the loss tangent. This parameter is directly related to the aging and degradation of electrical insulation, making it a useful indicator of possible failures in CTs. Therefore, this work aims to perform computer simulations using the finite element method (FEM) to simulate the effect of oil and paper degradation on the loss tangent values of a 230 kV current transformer. To achieve this, the COMSOL Multiphysics® software was used to verify the loss tangent as a function of the conductivity of parameters correlated to the CT dielectric. The results of the simulations show that the degradation of oil and paper substantially increases the value of the loss tangent. Additionally, it was found that the simultaneous or individual degradation of oil and paper have different effects on the loss tangent values obtained. The findings of this study demonstrate the usefulness of loss tangent as a tool for the diagnosis and supervision of electrical equipment in service. © The Institution of engineering & Technology 2023.

关键词： Distribution transformers

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Deep-Learning-Based Magnetic Resonance Simultaneous Multislice Imaging Using Holographic Image Decoding

Deep-Learning-Based Magnetic Resonance Simultaneous Multisli...

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IEEE International Conference on Image Processing

作者： Satoshi Ito Yuki Sato Naoya Endo Shohei Ouchi Electrical and Electronic Systems Engineering Graduate Program in Information Utsunomiya University Department of Innovative Electrical and Electronic Engineering National Institute of Technology Oyama

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

We propose a new magnetic resonance simultaneous multislice imaging (SMS) by developing our previously proposed imaging method based on the principle of holography. Different amplitude modulations for each slice are applied to separate the slice images. The slice image separation is performed using a data-driven science in which U-Nets separates the slice images using the given amplitude modulation as a cue. Simulation experiments using a real-valued and an phase-varied image model revealed the relationship between the coefficients of amplitude modulation, the number of simultaneously excited slices, and image quality. In addition, we performed image separation experiments on the holographic imaging method by approximating the excited volume region as five slice images with no gaps, and showed the feasibility of image separation. This method has the potential to be a new type of SMS.

关键词： Image quality Deep learning Phase modulation Neural networks Magnetic resonance Holography Amplitude modulation

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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Prognostics and System Health Management Conference (PHM)

作者： Congrui Cai Zhonghai Lu Embedded Systems Master Program School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Department of Electrical Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms.

关键词： Performance evaluation Semiconductor device modeling Recurrent neural networks Computational modeling Estimation Power electronics Data models

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Observing Generator Performance by Finite Element Analysis Considering Coil Numbers 5

Observing Generator Performance by Finite Element Analysis C...

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5th International Conference on Technology and Policy in Energy and Electric Power, ICT-PEP 2023

作者： Habibi, Muhammad Afnan Ali Irfani, Mochamad Farhan Gumilar, Langlang Kusumawardana, Arya Aripriharta Baskoro, Angger Universitas Negeri Malang Study Program of Power Plant Engineering Technology Malang Indonesia Universitas Negeri Malang Department of Electrical Engineering and Informatics Malang Indonesia National Yang Ming Chiao Tung University Dept. Electrical Engineering and Computer Sciences Hsinchu Taiwan

ISBN: (纸本)9798350369717

This paper conducts a comprehensive analysis of electrical generator performance using Finite Element Analysis (FEA), with a specific emphasis on the role of coil numbers in influencing generator efficiency and functionality. In the context of modern energy systems, where efficient power generation is paramount, this research aims to elucidate the relationship between the number of coils within a generator and its overall performance, including power output and electromagnetic behavior. Through systematic FEA simulations that vary coil numbers while keeping other parameters constant, this study provides valuable insights into the trade-offs associated with increased coil numbers and enhanced efficiency. These findings have significant implications for optimizing generator designs across various applications, from renewable energy systems to industrial power generation, ultimately advancing our understanding of generator dynamics and contributing to more sustainable and efficient power generation technologies. © 2023 IEEE.

关键词： Finite element method

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The Impact of Equipment Specifications on the Efficiency of Visual Inspection using Unmanned Aerial Vehicles

The Impact of Equipment Specifications on the Efficiency of ...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Weitong Wu Yuki Funabora Shinji Doki Kae Doki Department of Electrical Engineering and Computer Science Graduate School of Engineering Nagoya University Aichi Japan Department of Electrical and Electronics Engineering Aichi Institute of Technology Aichi Japan

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This manuscript investigates the impact of unmanned aerial vehicles (UAVs) and their cameras’ key specifications on the efficiency of infrastructure visual inspections using UAVs. The challenges of UAV-based inspection data collection include reducing necessary waypoints to improve inspection efficiency while collecting desired data for sure. Existing studies generally concentrated on optimizing the efficiency of UAVs’ inspection trajectories and waypoints when equipment is given. However, the impacts of equipment specifications and their selections have not been investigated, yet they also significantly affect inspection efficiency. This manuscript surveys the key specifications of current equipment available for inspection and investigates the variation in efficiency through inspection simulations with different equipment specification combinations. Simulation results reveal that with the highest pose accuracy achievable by existing UAV platforms, it is possible to achieve efficient inspection. However, such high pose accuracy may be difficult to achieve, underscoring the importance of targeted selection of camera focal lengths according to UAV specifications and the types of pose errors. Additionally, using higher-resolution cameras may substantially improve the efficiency of high-resolution data collection tasks, which is a more practical solution for enhancing efficiency than further improvements in UAV pose accuracy. The conclusions drawn from this research provide actionable guidelines for equipment selection and development and lay the foundations of efficiency modeling, ultimately contributing to the advancement of UAV-based inspection methodologies.

关键词： Surveys Visualization Accuracy Inspection Data collection Autonomous aerial vehicles Cameras Trajectory Standards Guidelines

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