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Digital Twin-Assisted Robust and Adaptive Resource Slicing in LEO Satellite Networks

Digital Twin-Assisted Robust and Adaptive Resource Slicing i...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： He, Mingcheng Wu, Huaqing Zhou, Conghao Hu, Shisheng Tang, Zhixuan Zhuang, Weihua University of Waterloo Department of Electrical and Computer Engineering Canada University of Calgary Department of Electrical and Software Engineering Canada

ISBN: (纸本)9798350351255

Resource slicing in low Earth orbit satellite networks (LSN) is essential to support diversified services. In this paper, we investigate a resource slicing problem in LSN to reserve resources in satellites to achieve efficient resource provisioning. To address the challenges of non-stationary service demands, inaccurate prediction, and satellite mobility, we propose an adaptive digital twin (DT)-assisted resource slicing scheme for robust and adaptive resource management in LSN. Specifically, a slice DT, being able to capture the service demand prediction uncertainty through collected service demand data, is constructed to enhance the robustness of resource slicing decisions for dynamic service demands. In addition, the constructed DT can emulate resource slicing decisions for evaluating their performance, enabling adaptive slicing decision updates to efficiently reserve resources in LSN. Simulation results demonstrate that the proposed scheme outperforms benchmark methods, achieving low service demand violations with efficient resource consumption. © 2024 IEEE.

关键词： Resource allocation

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The Behavior of DC Microgrid Connected Solid State Transformer During Internal Short Circuits 25

The Behavior of DC Microgrid Connected Solid State Transform...

引用

25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Lulbadda, Kushan Tharuka De Seram, Ruvini Sidhu, Tarlochan Williamson, Sheldon S. Ontario Tech University Department Of Electrical Computer And Software Engineering Oshawa Canada

ISBN: (纸本)9798350340266

Solid State Transformer (SST) enabled DC micro-grids are a modern solution for replacing the conventional Low-Frequency Transformer (LFT) by enhancing the system power density. Further, using SSTs in power systems has several additional advantages which are impossible with LFTs. As the SST is made out of sensitive power electronic components, safety must be guaranteed successfully to maintain a reliable operation. Also, the existing system will operate differently fundamentally with the integration of SST and the Low Voltage (LV) DC network will experience new LV fault profiles. SST-enabled power systems can be vulnerable to several kinds of faults such as overcurrent, overvoltage, and switching failures which will be discussed in this research. Therefore, it is more important to conduct a fault analysis based on different types of faults in the SST-enabled DC microgrid. The manuscript is mainly focused on three areas faults in the Medium Voltage (MV) side of the SST, faults within the SST, and faults in the Low Voltage (LV) side of the SST. The main objective of this research is to study the behavior of the SST in faulty conditions mentioned above. Different types of faults in a DC microgrid based on SST are simulated and the results are discussed and analyzed in this paper. Further, it has been found that the SST has a moderated capability for fault-tolerant operation during the faults. © 2024 IEEE.

关键词： Timing circuits

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A Global-Local Probsparse Self-Attention Transformer for LEO Satellite Orbit Prediction 23

A Global-Local Probsparse Self-Attention Transformer for LEO...

引用

23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Huang, Guan Shu, Tao Auburn University Department of Computer Science and Software Engineering Auburn United States

ISBN: (纸本)9798350374889

In recent years, the proliferation of LEO (Low-Earth Orbit) satellites and the accumulation of space debris have made Near-Earth space more and more crowded, and hence significantly increased the risk of collisions in this space. As a result, precise orbit prediction becomes essential for LEO satellites to avoid collision, maintain the right constellation, and perform normal space operations. Although machine learning(ML)-based satellite orbit prediction methods have been extensively explored, most existing methods are trained on simulated/synthetic orbit data, which essentially assumes a stationary orbit process and cannot reflect the non-stationary dynamic orbit changes in real-world LEO satellite constellations that are caused by satellite flight status adjustment (e.g., for the purpose of collision avoidance). In this study, we propose a novel multi-range (global-local) self-attention transformer-based ML model, the GloLoSAT (Global-Local Probsparse Self-Attention Transformer), and train the model over real-world LEO satellite orbit data crawled from *** to give more precise orbit prediction in case of a series of orbit adjustments. Theoretical analysis demonstrates our Global-Local Probsparse Self-Attention mechanism can achieve O(Llog(L)) computational complexity with respect to the input sequence length. Extensive experiments conducted on real satellite orbit tracking datasets demonstrate the efficacy of GloLoSAT in achieving consistent performance improvements across various prediction scenarios compared to their counterparts. © 2024 IEEE.

关键词： Space debris

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Motor Imagery EEG Classification Using Fine-Tuned Deep Convolutional EfficientNetB0 Model 3

Motor Imagery EEG Classification Using Fine-Tuned Deep Convo...

引用

3rd IEEE International Conference on Artificial Intelligence, ICAI 2023

作者： Ali, Muhammad Shahroze Hassan, Ali Rahim, Aqsa Ashraf, Muhammad Hashir Rahim, Amna Saghir, Shayaan CEME Department of Computer & Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350322125

This work proposes a new way to apply the deep convolutional EfficientN etB0 model for the classification to learn various electroencephalogram (EEG) signal properties on BCI competition IV dataset 2b. Several deep convolutional neural networks (DCNN)-based techniques have been applied to enhance the accuracy of motor imagery-based brain-computer interfaces (BCIs). The time-varying nature of various frequency bands makes extracting informative features from EEG signals difficult, causing the loss of DCNN classification accuracy. The EfficientNetB0 baseline model's feature extraction capability is used to overcome these limitations by first transforming EEG 1D signals into 2D images using the feature extraction technique of the Short-Time Fourier Transform (STFT) algorithm to train and evaluate the EfficientNetB0 model. The transfer learning approach is used to expand the initial feature sets for efficient model training in a short period. After learning from the dataset, the entire model is retrained and fine-tuned to work with the proposed layers. Our evaluated results demonstrated that the highest average Accuracy, Precision, Recall, F1-Score and MCC with the STFT method is 86.46%,88.2%, 91.2%,89.78% and 0.815 respectively over 10 epochs. According to the results, the proposed methodology outperforms other state-of-the-art DCNN models for feature extraction and classification of two-class motor imagery, namely right-hand and left-hand movements for the given dataset. © 2023 IEEE.

关键词： Brain computer interface

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Real-time CAN Data Acquisition and Visualization: Synerging Physical-to-Virtual (P2V) Twinning of Automotive Battery Management Systems 50

Real-time CAN Data Acquisition and Visualization: Synerging ...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Sharma, Mohit Samanta, Akash Chetri, Chandan Williamson, Sheldon Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (纸本)9781665464543

Controller area network (CAN) is widely used in automotive applications and has become the standard communication protocol to enable efficient communication primarily between electronic control units (ECUs) to reduce the complexity and cost of electrical wiring in automobiles through multiplexing. Towards developing the cloud-based electric vehicle battery data monitoring and digital-twinning of a battery management system (BMS), this paper introduced an online CAN data acquisition and visualization technique from an automotive grade BMS of NXP®®. Python-based CAN data processing tool is developed to process the raw data from the NXP® BMS and an open-source platform Grafana® is utilized together with the InfluxDB for visualization of the time-series data in real-time from a battery module containing 14 SAMSUNG 21700 lithium-ion battery cells. Each of those elements is implemented through the Docker container platform to become a standardized unit called a container. Besides presenting the detailed architecture of the data acquisition and visualization platform and the python-based data processing tool, this paper demonstrated the capability of the proposed architecture through examples of visualizing individual cell voltage, current, and temperature in real-time and their applications and utility in implementing cloud-based BMS. © 2024 IEEE.

关键词： Problem oriented languages

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A Quad-Active Bridge (QAB)-Based Solid-State Transformer for Fast Charging of Light/Medium and Heavy Electric Vehicles 25

A Quad-Active Bridge (QAB)-Based Solid-State Transformer for...

引用

25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： De Seram, Ruvini Lulbadda, Kushan Tharuka Sidhu, Tarlochan Williamson, Sheldon S. Ontario Tech University Department Of Electrical Computer And Software Engineering Oshawa Canada

ISBN: (纸本)9798350340266

Integrating electric vehicle (EV) fast charging stations with the medium voltage (MV) grid using a solid-state transformer (SST) has attracted more interest from researchers due to the reduced size and the smart functionalities it offers for maintaining the stability and power quality of the grid. However, the modular structure of SST requires many power converters and high-frequency transformers in the isolated DC/DC converter stage which again increases the space of the charging station. This paper proposes a design of an SST-based EV charging station using a quadruple-active-bridge (QAB) as the isolated DC/DC converter and investigates the power density enhancement compared to the dual-active-bridge (DAB)-based system. The proposed charging station can charge different EVs with different battery voltages (800V, 400V, and 200V) and capacities. The performance is validated by simulating a 1MVA, 10kV EV charging station using MATLAB. The control architecture and a detailed analysis of the system are included in the paper. © 2024 IEEE.

关键词： Bridges

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Classification of ECG Signals Encrypted with CNN Based Autoencoder with LSTM 12

Classification of ECG Signals Encrypted with CNN Based Autoe...

引用

12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Akkus, Merve Karabatak, Murat Tekin, Ramazan Batman University Department of Computer Engineering Batman Turkey Firat University Department of Software Engineering Elaziǧ Turkey

ISBN: (纸本)9798350330366

The electrocardiogram (ECG) plays a critical role in the early prevention and diagnosis of cardiovascular diseases. The signals obtained from the electrocardiogram device used in the diagnosis of heart diseases are obtained by healthcare professionals and used for arrhythmia detection. Many cardiac abnormalities show up on the ECG, including arrhythmia, which means abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification and correct classification of normal and abnormal heartbeats based on ECG morphology. Autoencoder can compress ECG signals to obtain time series signal features, which can be used for classification purposes. This work is realized using a layered network architecture that includes a convolutional autoencoder for ECG signal compression and a long short-term memory (LSTM) that drives the encoder output for classification. In this architecture, ECG signals are used after preprocessing to achieve higher arrhythmia classification accuracy. The open access MitBih arrhythmia dataset from PhysioNet was used in the study. The results showed that high accuracy, precision and recall were achieved. Compared to traditional methods, this method has better performance in arrhythmia classification. © 2024 IEEE.

关键词： Electrocardiograms

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Analyzing the Behavior of Solid State Protection System for Dual Active Bridge in Solid State Transformers Under Short Circuit Faults

Analyzing the Behavior of Solid State Protection System for ...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Lulbadda, Kushan Tharuka De Seram, Ruvini Sidhu, Tarlochan Williamson, Sheldon S. Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798350376067

DC microgrids equipped with solid-state transformers (SST) offer a contemporary alternative to traditional low-frequency transformers (LFT) by increasing the system's power density. The SST consists of a front-end AC/DC converter and a Dual Active Bridge (DAB) as the two major components. However, the protection of the SSTs is still under minor development as they need a fast and reliable protection system. Solid-state circuit breakers (SSCBs) are becoming more popular in DC power systems due to the need for protection at ultrafast speed. The main objective of this article is to analyze the performance of the SSCB-based protection system for DAB during short circuit faults. PLECS simulation environment has been used to simulate and analyze the protection system's reaction to short circuit failures. A 1kW SSCB integrated DAB has been implemented in testing with actual hardware to validate the simulation results. The SSCB-based protection system achieves a remarkably high speed tripping time of 340μs. For a better understanding of the system, the behavior of the SSCB during a short circuit has been demonstrated at 50V by achieving a response time of 55.4μs. Moreover, the system is tested for different voltage levels to demonstrate the response time. This illustrates how the SSCB can safeguard SST quickly and effectively, improving safety and reliability. © 2024 IEEE.

关键词： Transformer protection

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Towards Enhancing Aviation Safety Through Advanced Incident Analysis Using Large Language Models 34

Towards Enhancing Aviation Safety Through Advanced Incident ...

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34th Annual International Conference on Collaborative Advances in software and Computing, CASCON 2024

作者： Siddeshwar, Vaishali Azim, Akramul Alwidian, Sanaa Makrehchi, Masoud University of Ontario Institute of Technology Department of Electrical Computer and Software Engineering Canada

ISBN: (纸本)9798331504830

Aerospace incident reports, crucial for identifying abnormal aircraft occurrences, are submitted to regulatory authorities like FAA or EASA. These reports, often in natural language, offer clarity and context. Existing literature proposes incident-analysis tools utilizing manual rules and NLP techniques like Part-of-Speech Tagging and dependency parsers to extract entities from logs. However, these approaches may miss nuances, leading to incomplete or inaccurate extractions and potential mis-interpretations. This study aims to automate initial extraction using Large Language Models, extracting Aircraft Number/Model, contributing and human factors, primary root cause, and incident summaries. Automating this phase can expedite investigations and improve expert efficiency during review processes. © 2024 IEEE.

关键词： Aircraft accidents

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Transient Stability Improvement of Grid-Tied Photovoltaics using Deep Reinforcement Learning 22

Transient Stability Improvement of Grid-Tied Photovoltaics u...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Dewantoro, Gunawan Swain, Akshya Patel, Nitish Department of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand

ISBN: (纸本)9798331527471

The imperative for global transformation towards energy sustainability leads to the increasing incorporation of photovoltaic generation into the power systems networks. The increase of photovoltaic (PV) capacity in power system, however, would reduce the system inertial response and may negatively affect the transient stability of the generators. This paper, therefore, proposes a deep reinforcement learning (DRL)-based controller to ensure the transient stability of the power grid with PV penetration. The agent of the proposed Deep Q-Network optimally learns the control actions and adjusts the excitation voltage to maintain the voltage and synchronism in the event of faults. The performance of the controller under different levels of PV penetration and LVRT capabilities are also investigated. The results are compared with that of power systems stabiliser (PSS) to verify the effectiveness. The results show that Deep Q-Network is superior to PSS in maintaining transient stability. Furthermore, the DQN is able to quickly damp out the transient oscillations at various levels of PV penetration and tripping conditions. © 2024 IEEE.

关键词： Photovoltaics

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