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Grid Voltage Sensorless Model Predictive Control for Single-phase Five-level ANPC-FC Rectifier 50

Grid Voltage Sensorless Model Predictive Control for Single-...

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

作者： Bayindir, Abdullah Berkay Sharida, Ali Bayhan, Sertac Abu-Rub, Haitham Dept. of Electrical & Computer Engineering Texas A&M University College StationTX77840 United States Dept. of Electrical & Computer Engineering Texas A&M University at Qatar Doha23874 Qatar Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Doha Qatar Dept. of Electrical & Computer Engineering College of Science & Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9781665464543

This paper presents a grid voltage sensorless model predictive control (MPC) technique for a single-phase five-level active neutral point clamped flying capacitor (ANPC-FC) rectifier. The control strategy utilizes MPC to regulate the grid current and ensure equilibrium in the voltages of the flying and DC side capacitors, without measuring the grid voltage. A state feedback observer (SFO) is responsible to provide the grid voltage estimation to the MPC. Achieving this involves integrating all controlled parameters into a finite control set MPC (FCS-MPC). Moreover, the reference grid current is generated to regulate the DC link voltage based on a power equilibrium technique supported by a feed-forward loop. The proposed controller is implemented and evaluated using MATLAB/Simulink. Simulation results are carried out to show that the proposed MPC technique effectively achieves control objectives, while ensuring accurate tracking, and resisting voltage distortions. © 2024 IEEE.

关键词： MATLAB

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A Novel Deep Ensemble Learning Approach to Predict Eye Diseases from OCT Images 5

A Novel Deep Ensemble Learning Approach to Predict Eye Disea...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Uddin, Nazim Mia, Md Sajib Uddin, Mohammed Nasir Mahmud, Prince Chandpur Science and Technology University Dept. of Information and Communication Technology Bangladesh Sylhet Engineering College Dept. of Computer Science and Engineering Bangladesh Jagannath University Dept. of Computer Science and Engineering Bangladesh Chandpur Science and Technology University Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

As the world advances in technology, the classification of eye diseases is thought to have clinical applications since it evaluates and analyzes the results. Optometrists research and diagnose age-related eye problems such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen using optical coherence tomography (OCT) of the human eye. Because their symptoms are similar, identifying these diseases is challenging and requires hours of expert analysis. The requirements for eye illness classification should be straightforward in nature, structure, and evaluation. Deep Learning Convolutional Neural Network Classification can create a hierarchical representation of images to differentiate between normal and diseased eye patterns for diagnostic purposes. Therefore, this paper aims to implement and define deep learning to classify and identify eye diseases. Using a four-class classification problem, this work investigates the automatic detection of Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal in Optical Coherence Tomography (OCT) pictures. Multi-transfer learning architecture was used in the suggested deep learning Ensemble classification model. The most incredible accuracy of our proposed Ensemble model for multi- class classification was 83%. © 2023 IEEE.

关键词： Optical tomography

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Synergia: Device-Edge Server Association for ISAC-assisted Mobile Edge Computing Systems

Synergia: Device-Edge Server Association for ISAC-assisted M...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Charatsaris, Panagiotis Penafiel, Arianna Santamaria Diamanti, Maria Tsiropoulou, Eirini Eleni Papavassiliou, Symeon National Technical University of Athens Institute of Communication and Computer Systems School of Electrical and Computer Engineering Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350351255

The efficient operation of the unified Integrated Sensing and Communication (ISAC) - Mobile Edge Computing (MEC) systems is important for enhancing data sensing, communication, and computation processes in next-generation wireless systems. Despite prior research focusing on these systems, little attention has been given to optimizing the device-edge server associations. This paper addresses this gap by introducing the novel two-stage device-edge server association Synergia framework. Firstly, representative utility functions capture the characteristics of the devices and MEC servers by jointly considering their sensing, communication, and computation characteristics. Secondly, the Estimated Synergia framework leverages the Matching Theory to rapidly determine an initial device-server matching by disregarding the devices' externalities, i.e., the matching decisions of other devices. Thirdly, the Accurate Synergia model refines and improves this matching by using the coalition formation games, while considering the devices' externalities in optimizing the utilities of both the devices and the MEC servers. Extensive numerical evaluations demonstrate the Synergia's operational efficiency and scalability, outperforming reinforcement learningbased approaches. Also, a real-world application involving car accident detection validates its applicability. © 2024 IEEE.

关键词： Mobile edge computing

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Performance Comparison of Overlapping Sound Event Detection Methods 14

Performance Comparison of Overlapping Sound Event Detection ...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Tyagi, Akshay Singh, Kartikeya S Jaswanth Kumar, P.V. Garg, Prof. Rakesh Yousef Abdullah, Mohammed Edres Amity University Dept. of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350344837

Sound event detection is pivotal in various applications, including environmental monitoring and surveillance systems, enhancing situational awareness and response strategies. This paper investigates the intricacies of overlapping sound events, where multiple events occur concurrently, creating complexities in accurate detection. Traditional methods falter when confronted with this challenge, necessitating advanced approaches. This research pursues two primary objectives: comprehensive review of existing overlapping sound event detection methodologies and exploration of innovative solutions to enhance accuracy and robustness. Through meticulous analysis of prior research, this paper offers insights into field progress and identifies unexplored areas. Subsequent sections dissect existing methodologies, evaluating their strengths and limitations, and addressing challenges in common evaluation metrics and datasets. This paper contributes to the advancement of sound event detection technology, particularly in complex audio environments, promoting better solutions for real-world applications. © 2024 IEEE.

关键词： Pattern recognition

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Simulating City-Scale Aerial Data Collection Using Unreal Engine

Simulating City-Scale Aerial Data Collection Using Unreal En...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Agarwal, Dhruv Kucukpinar, Taci Fraser, Joshua Kerley, Jeffrey Buck, Andrew R. Anderson, Derek T. Palaniappan, Kannappan University of Missouri Dept. of Electrical Engineering and Computer Science ColumbiaMO United States

ISBN: (纸本)9798350359527

The rendering quality of game engines has significantly improved in realism. Physically-based rendering techniques started to overtake traditional rasterization-based graphics rendering pipelines in interactive rendering applications. We leverage these advancements to replicate our real-world city-scale aerial image data collections in the Unreal Engine. The use of city-scale synthetic data has many benefits in computer vision and remote sensing research. We can generate ground truth data for many applications, such as feature extraction, bundle adjustment, 3D reconstruction, object tracking, shadow detection, and shadow removal. This is crucial since sufficient ground truth data is not always publicly available for these use cases. In this paper, we introduce an easy-To-use Unreal Engine Blueprint pipeline that replicates drone and plane flight data collections in a circular orbit. Then, we highlight the issues arising from the approximate algorithms used by game engines as proxies for real-world data, and offer solutions to these challenges. Finally, we present our use cases, showing how we use the synthetic data generated by this approach in various research applications. © 2023 IEEE.

关键词： Antennas

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Design and Analysis of Digital Twin Models for Dual Active Bridge 50

Design and Analysis of Digital Twin Models for Dual Active B...

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

作者： Bayindir, Abdullah Berkay Al-Khateeb, Ahmad Sharida, Ali Alnuweiri, Hussein Bayhan, Sertac Abu-Rub, Haitham Dept. of Electrical & Computer Engineering Texas A&M University College StationTX77840 United States Dept. of Electrical & Computer Engineering Texas A&M University at Qatar Doha23874 Qatar Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Doha Qatar Dept. of Electrical & Computer Engineering College of Science & Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9781665464543

Digital twins (DTs) are emerging as effective tools for power electronic converters, addressing important objectives such as real-time monitoring, fault detection and predictive maintenance. Among these power converters, the dual active bridge (DAB) stands out for DC-DC conversion applications. However, the non-linear dynamics of DABs pose considerable challenges to their accurate modeling. To address these challenges, this paper investigates the use of machine learning (ML) and deep learning (DL) models for DT implementation in DABs. Specifically, XGBoost and dense neural network (DNN) models are employed to construct DTs compatible with low-cost microcontrollers. These DTs are operated in parallel with the physical DAB converter to predict its output voltage in real-time. Experimental results demonstrate that both of the models perform well in terms of accuracy and applicability to low-cost microcontroller. However, the DNN is more reliable and the XGBoost is simpler. © 2024 IEEE.

关键词： Microcontrollers

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TRUSTCACHE: Trust-based Content Caching in Information-Centric Networks

TRUSTCACHE: Trust-based Content Caching in Information-Centr...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Tsikteris, Sean Rahman, Aisha B. Siraj, Md Sadman Charatsaris, Panagiotis Tsiropoulou, Eirini Eleni Papavassiliou, Symeon National Technical University of Athens Institute of Communication and Computer Systems School of Electrical and Computer Engineering Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350351255

The Information-Centric Networking (ICN) paradigm has reshaped the modern network architectures and promises efficient content delivery to the end-users. This paper introduces TRUSTCACHE, a novel framework enabling the content caching within ICNs and focusing on the trust-based ICN selection and optimal cache memory allocation to the Content Providers (CPs). The TRUSTCACHE framework incorporates the Optimistic Q-learning with Upper Confidence Bound reinforcement learning algorithm that enables the CPs to autonomously select ICNs based on their cache memory availability and trust levels. Also, TRUSTCACHE enables the CPs to jointly consider the reliability of the ICNs and their cache memory availability by integrating a novel trust model. Furthermore, TRUSTCACHE leverages the multilateral bargaining principles in order to ensure the optimal cache memory allocation among the CPs, in terms of aligning with their profit margin characteristics. Simulation-based experiments validate TRUSTCACHE's operational efficiency across diverse CP profit margin profiles and highlight its superiority over alternative models lacking trust-based ICN selection or employing proportional fairness strategies for cache memory allocation. © 2024 IEEE.

关键词： Reinforcement learning

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A Single-Carrier Modulation Strategy for the Reduction of the DC-Link Voltage Ripple of a 5-Level Multilevel DC-Link Inverter 50

A Single-Carrier Modulation Strategy for the Reduction of th...

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

作者： Kahwa, Almachius Ebot, Besong John Fujimoto, Yasutaka Mwasilu, Francis Ojo, Joseph Olorunfemi Dept. of Electrical Engineering University of Dar es Salaam Dar es Salaam Tanzania United Republic of Inst. of Multidisciplinary Science Yokohama National University Yokohama Japan Dept. of Physics Electrical and Computer Engineering Yokohama National University Yokohama Japan Dept. of Electrical and Computer Engineering Tennessee Technological University TN United States

ISBN: (纸本)9781665464543

This paper presents a simple single-carrier PWM (SC-PWM) method based on the multi-modulation technique and using the redundant switching states of the 5-level hybrid multilevel dc-link inverter to balance the dc-link voltage. The proposed SC-PWM reduces the dc-link capacitor-voltage ripple, thereby reducing the voltage stress on the switches and improving the reliability of the inverter by eliminating all the low-fundamental frequency voltage oscillations in the capacitor voltage in the full range of power factors. Additionally, the proposed modulation strategy achieves a 2-fold reduction in the size of the output filter while reducing the total harmonic distortion of the output voltage and current. The feasibility of the proposed scheme over other modulation techniques is confirmed in a comparative analysis. Finally, the steady-state performances of the SC-PWM strategy are demonstrated in simulations and experiments. © 2024 IEEE.

关键词： Capacitor bank

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Effect of Dimensionality Reduction on Uncertainty Quantification in Trustworthy Machine Learning

Effect of Dimensionality Reduction on Uncertainty Quantifica...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Li, Yen-Chen Zhan, Justin University of Cincinanti Department of Electrical and Computer Engineering CincinnatiOH United States University of Cincinanti Department of Computer Science CincinnatiOH United States

ISBN: (纸本)9798350303780

Machine learning (ML) is a commonly employed computer-assisted tool for ECG diagnosis with above 85 % correct. However, the interpretability of the prediction has become a barrier for the clinical use. To enhance the trustworthiness of ML systems, this paper integrates the trust score framework with the existing ECG classification. At present, the trust score framework is effective for low-dimensional data, but it does not offer significant enhancement over the model confidence for feature spaces with high dimensions. Moreover, the ECG data is a time series data with duration about one or half minute with sampling time about 3 ms. In other words, each signal usually contains several thousand or more sample points. As a result, this paper investigated effect of six popular dimensionality reduction (DR) techniques, including linear and kernel-based principal component analysis (KPCA), independent component analysis (ICA), Autoencoder, uniform manifold Approximation and projection (UMAP), and local linear embedded (LLE). The experimental results showed that UMAP with 2 dimensions can achieve result as well as ICA with 25 dimensions in tracking the model tendency and the other four DR methods has better performance with 25 dimensions. © 2023 IEEE.

关键词： Independent component analysis

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Optimization of New Radio Physical Uplink Shared Channel Resources Allocation with Efficient Demodulated Referenced Signals and PT-RE Deployment

Optimization of New Radio Physical Uplink Shared Channel Res...

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2024 International Conference on electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Upadhyay, Deepak Gupta, Mridul Sharma, Kunj Bihari Upadhyay, Abhay Yadav, Satya Prakash Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350378092

This paper explores the resource allocation optimisation of New Radio Physical Uplink Shared Channels with an emphasis on the effective implementation of Phase Tracking Reference Signals and Demodulation Reference Signals in 5G networks. Understanding how important these reference signals are to dependable communication, we created a Python framework to model and display their distribution on a time-frequency grid. By carefully modifying important factors including mapping type, PRB allocation, and symbol allocation, our method greatly increased resource efficiency, decreased overhead, and improved signal quality. The results show how sophisticated resource allocation techniques may be used to maximise system performance, providing important information for the creation of more reliable and effective 5G NR communication systems © 2024 IEEE.

关键词： 5G mobile communication systems

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