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Automated Energy Monitoring System for Smart grids and Microgrids

Automated Energy Monitoring System for Smart grids and Micro...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Revathi, R. Keerthi Ragavendran, S. Dhamodharan, B. Harish, P.S. Thaiyub A, Ahamed Kpr Institute of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350308167

The technical advancements in modernizing the world in every way possible have increased the usage of power and energy. Due to this, an increase in tariff, decrease in power quality, and frequent blackouts have increased every day because of traditional methods used in transmission lines. The proposed methodology addresses this drawback by offering the usage of advanced energy meters in smart grids and microgrids for efficient transmission of power and energy across every household and industry, improving power quality and reducing the chance of blackouts [1] significantly. This allows both the user and the utility to continuously monitor the energy usage, power factor, power quality, and load usage in real-time, to maintain the load balance in the grid. © 2023 IEEE.

关键词： Automated Energy Monitoring System (AEMOS) Facebook Prophet Micro Grid (MG) Smart Grid (SG)

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Automated Deep Learning Pipeline for Accurate Segmentation of Aortic Lumen and Branches in Abdominal Aortic Aneurysm: A Two-Step Approach 2

Automated Deep Learning Pipeline for Accurate Segmentation o...

引用

2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Hassan, Fatma N. Mahmoud, Ahmed M. Elnokrashy, Ahmed F. Hassan, Ashraf Y. Benha University Benha Faculty of Engineering Department of Electrical Engineering Benha Egypt Cairo University Department of Biomedical Engineering and Systems Giza Egypt Astute Imaging KirklandWA98033 United States Nile University Department of Computer Science Technology and Computer Science Giza Egypt Benha University Benha Egypt

ISBN: (纸本)9798350362633

Abdominal Aortic Aneurysm (AAA) is a serious medical condition characterized by the abnormal enlargement of the abdominal aorta. If left untreated, AAA can have life-threatening consequences. Accurate segmentation of the aorta in Computed Tomography Angiography (CTA) images plays a vital role in treatment planning for AAA. However, manual and semi-automatic segmentation methods suffer from limitations in terms of time and accuracy. This study presents a deep learning pipeline that aims to fully automate the precise and efficient segmentation of the aorta and its branches within CTA images. A two-step approach is proposed for the segmentation of the aorta and its branches in contrast-enhanced CTA scans. The first step involves utilizing a UNet++ model to perform aortic segmentation across the entire set of CTA axial slices. In the second step, a post processing algorithm is employed to track the continuity of the segmented aorta while effectively discarding false positive (FP) objects. The assessment of the fully automated method revealed remarkable outcomes, with a mean Dice coefficient of 0.941 on a test set consisting of 10 CTA scans. The automated segmentation results are utilized to create a comprehensive 3D model. The study results indicate that the utilization of the proposed deep learning-based pipeline is highly effective in achieving accurate segmentation of the aortic lumen and its branches. The practical implications of this approach extend to pre-operative planning. This highlights the valuable contribution of the proposed method in improving the management and treatment of patients diagnosed with aortic aneurysm. © 2024 IEEE.

关键词： computerized tomography

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Structured Illumination Digital Holographic Microscopy via two integrated Mach-Zehnder interferometers

Structured Illumination Digital Holographic Microscopy via t...

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3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2024 - Part of Optica Imaging Congress

作者： Obando-Vásquez, Sofía Restrepo, René Trujillo, Carlos Doblas, Ana Applied optics group School of Applied Science and Engineering Universidad EAFIT Medellín Colombia Department of Electrical and Computer Engineering University of Massachusetts DartmouthMA United States

We propose two Match-Zehnder interferometers coupled to create a structured illumination digital holographic microscope with tunable modulation frequency capability, expanding the system's numerical aperture regar... 详细信息

关键词： Mach-Zehnder interferometers

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Point-to-Point MIMO Channel Estimation by Exploiting Array Geometry and Clustered Multipath Propagation

Point-to-Point MIMO Channel Estimation by Exploiting Array G...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Demir, Özlem Tuǧfe Björnson, Emil Tobb University of Economics and Technology Department of Electrical-Electronics Engineering Ankara Turkey Kth Royal Institute of Technology Department of Computer Science Stockholm Sweden

ISBN: (纸本)9798350351255

A large-scale MIMO (multiple-input multiple-output) system offers significant advantages in wireless communication, including potential spatial multiplexing and beamforming capabilities. However, channel estimation becomes challenging with multiple antennas at both the transmitter and receiver ends. The minimum mean-squared error (MMSE) estimator, for instance, requires a spatial correlation matrix whose dimensions scale with the square of the product of the number of antennas on the transmitter and receiver sides. This scaling presents a substantial challenge, particularly as antenna counts increase in line with current technological trends. Traditional MIMO literature offers alternative channel estimators that mitigate the need to fully acquire the spatial correlation matrix. In this paper, we revisit point-to-point MIMO channel estimation and introduce a reduced-subspace least squares (RS-LS) channel estimator designed to eliminate physically impossible channel dimensions inherent in uniform planar arrays. Additionally, we propose a cluster-aware RS-LS estimator that leverages both reduced and cluster-specific subspace properties, significantly enhancing performance over the conventional RS-LS approach. Notably, both proposed methods obviate the need for fully/partial knowledge of the spatial correlation matrix. © 2024 IEEE.

关键词： Mean square error

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Quantum Annealing-Based Machine Learning for Battery Health Monitoring Robust to Adversarial Attacks

Quantum Annealing-Based Machine Learning for Battery Health ...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Akash, Alve Rahman Khot, Amey Kim, Taesic Texas A&m University-Kingsville Department of Electrical Engineering and Computer Science KingsvilleTX78363 United States

ISBN: (纸本)9798350316445

Battery health monitoring methods including machine learning (ML) models rely on trustworthiness of battery sensor data and features. As more battery systems require network connectivity for intelligent health monitoring, cyberattacks become a serious threat. A notable attack is an adversarial attack that generates stealthier input data misleading the ML results. This paper introduces an adversarial attack targeting battery health monitoring system and explores a quantum annealing (QA)-based machine learning method to mitigate the adversarial attacks. In this paper, a Quantum Boost (QBoost)-based state of health (SOH) estimation algorithm for electric vehicles is developed and trained by D-Wave QA processor and the performance is compared with a state-of the art random forest (RF)-based SOH estimation method. The experimental results show that the proposed QBoost method is more robust to the adversarial attack than the RF method, while the RF method provides better accuracy during normal operation. © 2023 IEEE.

关键词： Adversarial attack battery health monitoring quantum annealing processor quantum boost quantum machine learning

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Design of Control System for Simulation Training Device of Urban Rail Trains 5

Design of Control System for Simulation Training Device of U...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： Chen, Xiaoxia Xue, Xu Wang, Lin Chaudhry, Shehzad Ashraf School of Automation and Electrical Engineering Dalian Jiaotong University Dalian China Department of Computer Science and Information Technology College of Engineering Abu Dhabi University United Arab Emirates

ISBN: (纸本)9781643685243

With the rapid development of urban rail transit across the country, the industry has put forward higher requirements for quickly cultivating high-quality train drivers, maintenance personnel, and drivers. Therefore, the development of a highly simulated urban rail train simulation training device is of great significance. Compared to traditional dedicated industrial networks, industrial Ethernet has larger bandwidth, lower cost, and better scalability. This has made industrial Ethernet widely used in various industrial fields, further improving production efficiency and reducing operating costs. This article uses industrial Ethernet PROFINET to replace the twisted train bus WTB and multi-functional vehicle bus MVB communication of actual trains, and designs a cost-effective control system for urban rail train simulation training equipment. Detailed introduction was provided on equipment selection, hardware configuration, PROFINET master slave station control program, Ethernet communication program between master station and other CPUs, human-machine interaction interface, etc. Provide efficient and economical solutions for training and simulation operations of rail vehicles. © 2024 The Authors.

关键词： Operating costs

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EuclideanPose: Novel Boolean Filtering Approach for Accurate Multiview 3D Human Pose Estimation Based on Euclidean Distance 12

EuclideanPose: Novel Boolean Filtering Approach for Accurate...

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12th electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2024

作者： Suprapto, Sena Sukmananda Pramono, Sholeh Hadi Mudjirahardjo, Panca Utaminingrum, Fitri Universitas Brawijaya Department of Electrical Engineering Malang Indonesia Institut Teknologi Kalimantan Balikpapan Indonesia Universitas Brawijaya Department of Computer Science Malang Indonesia

ISBN: (纸本)9798350379914

Motion capture technology has advanced significantly, capturing real-time motion imagery in 2D and 3D. While marker-based systems have achieved sub-millimeter accuracy, their setup complexity and interference with natural movement drive the search for alternative methods. Wearable devices like IMUs overcome some limitations but still disrupt natural movement and suffer from drift. Marker-less motion capture., particularly 3D human pose estimation using multi-view 2D estimations, presents a promising alternative. However, methods like Weighted Direct Linear Transform (WDLT) depend heavily on visibility weights from 2D pose estimation, which can be unreliable. This study introduces the EuclideanPose method, a novel approach using boolean filtering based on Euclidean distance to eliminate less accurate 2D estimations, enhancing 3D reconstruction accuracy. Experimental results demonstrate that EuclideanPose achieves an average error reduction in hip and knee angles in gait cycle to 1.68° and 2.32° respectively, compared to WDLT's 2.25° and 3.31°. © 2024 IEEE.

关键词： Motion capture

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A Measurement based Predictive Maintenance Algorithm for Rigid-Body Dynamical Systems using Radial Basis Function Approximation and Information Theory 27

A Measurement based Predictive Maintenance Algorithm for Rig...

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27th International Conference on System Theory, Control and Computing, ICSTCC 2023

作者： Ortmann, Jan Ortmann, Mathis Department of Electrical Engineering and Computer Science University of Siegen Germany Department of Mathematics Justus Liebig University of Giessen Germany

ISBN: (纸本)9798350337983

This paper presents new viewpoints to solve predicative maintenance problems for arbitrarily rigid-body mechanical systems. Reliable predictions for changes in physical parameters are highly dependent on system model accuracy. Hence, costs for physical system modeling are expensive and time-consuming. To overcome this problem, we replaced the modeling process with a full measurement based procedure in conjunction with radial basis functions (RBF) approximation to create the system model. Subsequently, information theory is employed to generate a meaningful metric quantity that reports about the changes in physical parameters. A rigid-body mechanical system is used as an example to verify the presented theory. © 2023 IEEE.

关键词： Radial basis function networks

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Industrial Worker Safety Device with Proactive Gas Leak and Fire Protection System 8th

Industrial Worker Safety Device with Proactive Gas Leak and ...

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8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Kesanapalli, Sri Kaushik Yarramallu, V. S. N. Lokesh Abbaraju, Sree Tharun Raju Gangireddy, Rahul Jogi Suresh, Kavya Valsan, Vipina Pandi, V. Ravikumar Sathyan, Soumya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819994854

Gas leaks and fires present dangers that require detection and decisive action to protect lives and property. This paper specifically proposes cutting-edge sensor technologies with intelligent threshold algorithms and real-time communication capabilities to enhance the safety and well-being of industrial workers by reducing the risk of accidents and injuries, which addresses Sustainable Development Goal (SDG) 3 (good health and well-being). It includes MQ5 and MQ135 gas sensors, a DHT11 temperature sensor, and an IR flame sensor to provide monitoring. The real-time data from the gas sensors is carefully analyzed to trigger alerts at two threshold levels directly linked to gas concentrations, ensuring warnings in case of any risks. If the second threshold is exceeded, indicating the presence of gas, the device activates a safety protocol. An exhaust fan swiftly dissipates any accumulated gas, while a servo motor promptly shuts off the gas supply, effectively preventing any escalation of the situation. An integrated Wi-Fi module will enable communication and notifications through a dedicated smartphone app or web interface to enhance functionality, which enables remote device control and real-time access to sensor data for users. This combination of sensor technologies, intelligent algorithms, and remote accessibility showcases how technology-driven solutions can significantly enhance safety measures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gases

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Microscopic Fungi Classification Using Vision Transformer Guided by Transfer Learning Approach 26

Microscopic Fungi Classification Using Vision Transformer Gu...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ahmed, Sheikh Iftekhar Haque, A. H. M. Osama Bangladesh University of Engineering and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh Bangladesh University of Engineering and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

The diagnosis of fungal infections is quite challenging because of their wide-ranging symptoms and characteristics. Different fungi species are typically categorized using manual microscopical techniques, which are ostensibly prone to some degree of subjectivity. Deep learning models have been employed in computer-assisted diagnostic systems in recent years for quick and precise mycological diagnosis. This study represents an automatic detection of fungi species in microscopic images using the vision transformer (ViT) architecture and transfer learning-based technique. The dataset in this study includes microscopic image samples of five distinct fungal species. First, a vision transformer network is trained and validated on the dataset, achieving 83.12% accuracy with decent precision, recall, and F1 score. The concept of transfer learning is then utilized by adding pre-trained models - VGG16, InceptionV3, and ResNet50, separately in parallel with the ViT network, where features extracted using the transfer learning technique fine-tune and improve the overall performance of the ViT network. Our study finds the ViT with ResNet50 model as the top-performing model, with an enhanced performance of 90.13% accuracy on the same dataset with 91.22% precision, 89.70% recall, and 90.91% F1 Score. These findings demonstrate the effectiveness of our approach in precisely categorizing fungi species. © 2023 IEEE.

关键词： AdamW Categorical Cross-entropy (CCE) Transfer Learning Vision Transformer (ViT)

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