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12th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2024

作者： Yu, Xuzhou Zhu, Lei University of Macau Taipa Department of Electrical and Computer Engineering Faculty of Science and Technology 999078 China

ISBN: (纸本)9798350351019

This paper presents a square coaxial transmission line that is partially filled with 3-D printing low-cost insulator. The proposed coaxial line is composed of two metal conductors and 3D printing insulator. To reduce the dielectric loss, the space between two conductors is partially filled with 3-D printing insulator. Herein, the insulator is the resin whose cost is lower than conventional PTFE, and the relative permittivity and loss tangent are measured first. Then, the guide-wave characteristic of the proposed transmission line is studied. Finally, a transmission line with two transitions is designed and simulated to verify the analysis. © 2024 IEEE.

关键词： Coaxial cables

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Predictive Analysis of Sarajevo's AQI using Machine Learning Models for Varied Data Granularity and Prediction Windows 47

Predictive Analysis of Sarajevo's AQI using Machine Learning...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Zolota, Emina Hasić, Vahidin Mević, Amina Delić, Amra Krivić, Senka University Of Sarajevo Faculty Of Electrical Engineering Department For Computer Science And Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350382495

This study scrutinizes five years of Sarajevo's Air Quality Index (AQI) data using diverse machine learning models - Fourier autoregressive integrated moving average (Fourier ARIMA), Prophet, and Long short-term memory (LSTM) - to forecast AQI levels. Focusing on various prediction frames, we evaluate model performances and identify optimal strategies for different temporal granularities. Our research unveils subtle insights into each model's efficacy, shedding light on their strengths and limitations in predicting AQI across varied timeframes. This research presents a robust framework for automatic optimization of AQI predictions, emphasizing the influence of temporal granularity on prediction accuracy, automatically selecting the most efficient models and parameters. These insights hold significant implications for data-driven decision-making in urban air quality control, paving the way for proactive and targeted interventions to improve air quality in Sarajevo and similar urban environments. © 2024 IEEE.

关键词： Air quality

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Improving Transient Stability in Power Systems Through Integration of Large-Scale Photovoltaic Power Plants 9

Improving Transient Stability in Power Systems Through Integ...

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9th International Conference on Technology and Energy Management, ICTEM 2024

作者： Abbaspour, Hadi Yari, Siavash Khoshkhoo, Hamid Kamwa, Innocent Sahand University of Technology Sahand New Town Faculty of Electrical Engineering Tabriz Iran Laval University Department of Electrical and Computer Engineering Quebec Canada

ISBN: (纸本)9798350329797

This paper investigates the impact of integrating large-scale photovoltaic (PV) power plants (LSPVPPs) on the transient stability of power systems. As renewable energy sources, including PV and wind generation systems, become more prevalent, power systems are experiencing significant operational and control changes. The decrease in system inertia in new power grids with high renewable energy penetration raises concerns regarding transient stability during major disturbances. Using simulation studies on the IEEE 39-Bus test system, this paper focuses on assessing the effectiveness of the WECC-large-scale photovoltaic power plant in improving transient stability. Specifically, it analyzes the ability of inverter-based generators to adjust their power output and examines the influence of this capability on transient stability. Additionally, the fault ride-through capability of the WECCLSPVPP and its impact on maintaining the power supply after a short circuit event is assessed. © 2024 IEEE.

关键词： Transient stability

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Sum-Difference Analysis Based Design for Third-Order Sparse Arrays 2

Sum-Difference Analysis Based Design for Third-Order Sparse ...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Guo, Haodong Chen, Hua Liu, Wei Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong

ISBN: (纸本)9798331515669

Recently, third-order cumulants (TOCs) as a non-conventional odd-order statistics, has been introduced to direction-of-arrival (DOA) estimation with the third-order nested array (TONA), which significantly increases the number of uniform degrees-of-freedom (uDOFs) and can even be comparable to the fourth-order cumulant approach. In this paper, through analysis of the second-order sum-difference co-array, a third-order sparse linear array (SLA) design scheme suitable for TOCs is proposed, which can extend the commonly used second-order SLAs to the third-order. By selecting an appropriate generator array, the derived third-order SLA has far more uDOFs than the existing TONA, greatly improving its DOA estimation accuracy. © 2024 IEEE.

关键词： Direction of arrival

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A Measurement Study of the Ethereum Underlying P2P Network

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computers, Materials & Continua 2024年第1期78卷 515-532页

作者： Mohammad ZMasoud Yousef Jaradat Ahmad Manasrah Mohammad Alia Khaled Suwais Sally Almanasra Electrical Engineering Department Al-Zaytoonah University of JordanAmman11733Jordan Mechanical Engineering Department Al-Zaytoonah University of JordanAmman11733Jordan Computer Science Department Al-Zaytoonah University of JordanAmman11733Jordan Faculty of Computer Studies Arab Open UniversityRiyadh11681Saudi Arabia

This work carried out a measurement study of the Ethereum Peer-to-Peer(P2P)network to gain a better understanding of the underlying *** was applied because it pioneered distributed applications,smart contracts,and ***,its application layer language“Solidity”is widely used in smart contracts across different public and private *** this end,we wrote a new Ethereum client based on Geth to collect Ethereum node ***,various web scrapers have been written to collect nodes’historical data fromthe Internet Archive and the Wayback Machine *** collected data has been compared with two other services that harvest the number of *** has collectedmore than 30% more than the other *** data trained a neural network model regarding time series to predict the number of online nodes in the *** findings show that there are less than 20% of the same nodes daily,indicating thatmost nodes in the network change *** poses a question of the stability of the ***,historical data shows that the top ten countries with Ethereum clients have not changed since *** popular operating system of the underlying nodes has shifted from Windows to Linux over time,increasing node *** results have also shown that the number of Middle East and North Africa(MENA)Ethereum nodes is neglected compared with nodes recorded from other *** opens the door for developing new mechanisms to encourage users from these regions to contribute to this ***,the model has been trained and demonstrated an accuracy of 92% in predicting the future number of nodes in the Ethereum network.

关键词： Ethereum measurement ethereum client neural network time series forecasting web-scarping wayback machine blockchain

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A Comprehensive Two-Step Methodology for Automated Segmentation of Architectural Distortion in Mammograms 6

A Comprehensive Two-Step Methodology for Automated Segmentat...

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6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Ibrahim, Sameh E. Elnokrashy, Ahmed F. Mohamed, Wael A. Benha University Benha Faculty of Engineering Department of Electrical Engineering Benha Egypt Benha University Electrical Engineering Benha Egypt Nile University Computer Science Giza Egypt

ISBN: (纸本)9798350378511

Breast cancer is one of the most prevalent forms of cancer worldwide and a leading cause of mortality among women. Early detection of breast cancer is crucial for effective treatment. Architectural distortion (AD) is an early sign of breast cancer, characterized by a subtle contraction of breast tissue that often goes unnoticed. Traditional methods of detection heavily rely on the expertise of radiologists, making the process more difficult. To address this, we propose a deep learning approach to automate precise and efficient AD segmentation. Our approach involves a two-step process. In the first step, we utilize a Mask R-CNN Detectron2 model to perform AD segmentation across the entire set of mammography images. This initial segmentation provides a baseline for identifying AD regions. The ResNet-18 patch model is incorporated into the Mask R-CNN model's segmentation pipeline in the second stage. The purpose of this combination is to improve AD area localization and precision. By combining the strengths of both models, we achieve improved accuracy in AD segmentation. The evaluation of our fully automated method yielded remarkable outcomes on a diverse test set consisting of private datasets, including the Baheya dataset and the NCI dataset, as well as the publicly available Digital Database for Screening Mammography (DDSM). The results showed a Segmentation Accuracy of 0.852, Classification Accuracy of 0.915, and Mean Average Precision (mAP) of 0.894. These findings demonstrate potential to enhance the efficiency and accuracy of AD detection and segmentation in mammogram images, contributing to early diagnosis and treatment planning for patients at risk of breast cancer. © 2024 IEEE.

关键词： Mammography

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A learning artificial visual system for motion direction detection

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Neural Computing and Applications 2024年第27期36卷 17181-17197页

作者： Chen, Tianqi Kobayashi, Yuki Todo, Yuki Tang, Zheng Faculty of Electrical and Computer Engineering Kanazawa University Kakuma Ishikawa Kanazawa9201162 Japan Department of Intelligence Information Systems University of Toyama Toyama9308555 Japan

Research on the visual system and its development is not only crucial for understanding how we see and interpret the environment from basic visual processing to complex perception but also aids in the development of technologies like virtual reality, augmented reality, and computer vision. In our previous paper, we have proposed a novel biological motion direction detection mechanism. Based on this mechanism, the authors of this paper proposed a learning artificial visual system (AVS) and use it to achieve motion direction detection. The learning AVS consists of two layers: the local visual feature detective neuron layer and the global feature detective neuron layer. For local motion direction detection, we use dendritic neurons to implement local motion direction-detective neurons and use them to extract local motion direction information. Global feature detective neurons are simply implemented by summing the local motion direction-detective neurons in each direction of possible motion. We trained the learning AVS by eliminating unnecessary branches or synapses and keeping only those that are strongly connected to the function of motion direction detection. computer simulations demonstrate that AVS can learn the correct motion direction of objects with different shapes, size and positions. We compare the performance of AVS with support vector machine (SVM) and traditional convolutional neural networks (CNNs)—LeNet-5 and EfficientNet-B0 (EfN)—in motion direction detection and find that our learning AVS has better recognition accuracy, noise immunity and less demands on size of dataset and learning costs. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neurons

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A Novel Determination of Soluble NPK Nutrient Measurement System Using K-means and SOM Methods Compared with UV Visible Spectrophotometer

A Novel Determination of Soluble NPK Nutrient Measurement Sy...

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2023 International electrical engineering Congress, iEECON 2023

作者： Tipwong, Wallop Sirikham, Adisorn Konpang, Jessada Kirdpipat, Putthiphong Chongjarearn, Yutana Preutisrunyanont, Oradee Rajamangala University of Technology Krungthep Department of Electrical and Telecommunication Engineering Bangkok Thailand Thammasat School of Engineering Thammasat University Faculty of Engineering Department of Electrical and Computer Engineering Pathum Thani Thailand Dhurakij Pundit University Department of Robotic Engineering and Automation System Bangkok Thailand

ISBN: (纸本)9798350332360

Technologies are currently being used in smart farms to improve productivity. Agricultural innovation is also widespread. Therefore, the development of Internet of Things (IoTs) technologies and devices to collect data is important. Finding essential nutrients for growth such as Nitrogen (N), Phosphorus (P) and Potassium (K) are important factors. Competitiveness is the important key for growing vertically or indoors. However, soil testing can be expensive, complicated and time consuming. The development of devices that can effectively measure nutrient NPK levels could be used over time. This paper presents a suitable clustering using the K-means method and a forecasting method using the Self-Organizing Map (SOM) method to enhance the device's ability to analyze nutrients in real time. A total of 50,688 samples were tested using the K-Means clustering algorithm, and the sample data were also analyzed. A SOM was prepared to predict the accuracy of the analysis. A total of 11,486 samples were tested and analyzed, then the results of the soluble NPK nutrient monitoring system were taken. It has an efficiency of 84.15% compared to using a UV Visible spectrophotometer, allowing data to be predicted during nutrient solubility changes and to operate according to the proposed system. Therefore, the unsupervised machine learning will improve the measurement system of future cultivation systems. © 2023 IEEE.

关键词： Nutrients

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of computer Science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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Secure Cancelable Template Based on Double Random Phase Encoding and Entropy Segmentation

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computers, Materials & Continua 2022年第11期73卷 4067-4085页

作者： Ahmed M.Ayoup Ashraf A.M.Khalaf Fathi E.Abd El-Samie Fahad Alraddady Salwa M.Serag Eldin Faculty of Engineering Electrical Engineering DepartmentMinia UniversityMinia61111Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif 21944Saudi Arabia

In this paper,a proposed cancellable biometric scheme is based on multiple biometric image identifiers,Arnold’s cat map and double random phase encoding(DRPE)to obtain cancellable biometric *** proposed segmentation scheme that is used to select the region of interest for generating cancelable templates is based on chaos entropy low correlation statistical *** objective of segmentation is to reduce the computational cost and reliability of template *** left and right biometric(iris,fingerprint,palm print and face)are divided into non-overlapping blocks of the same *** define the region of interest(ROI),we select the block with the highest *** shorten the registration process time and achieve a high level of security,we select 25%of the image volume of the biometric *** addition,the low-cost security requirement lies in the use of selective encryption(SE)*** step of selecting the maximum entropy is executed on all biometric *** maximum right and left multi-biometric blocks are arranged in descending order from the entropy perspective and select 50%of each biometric couple and store the single *** obtained matrix is scrambled with a certain number of iterations using Arnold’s Cat Map(ACM).The obtained scrambled matrix is encrypted with the DRPE to generate the cancellable biometric templates,which are further *** simulation results display better performance of the suggested cancellable biometric system in noise scenarios using the area under the receiver operating characteristic(AROC).The strength of the suggested technique is examined with correlation,irregular deviation,maximum difference and maximum *** recommended proposed approach shows that the ability to distinguish the authentic and imposter biometrics of user seven in different levels of the noise environment.

关键词： Image identifier computation segmentation ACM (DRPE)

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