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21st OITS International Conference on Information Technology, OCIT 2023

作者： Kethineni, Kiran K. Mohanty, Saraju P. Kougianos, Elias University of North Texas Department of Computer Science and Engineering United States University of North Texas Department of Electrical Engineering United States

ISBN: (纸本)9798350358230

Plant diseases are a major factor affecting agriculture yield by accounting for approximately 20% of the total produce and pose a constant threat to global food security. Accordingly, many solutions have been proposed to detect the presence of disease and estimate severity using AI and ML. These techniques estimate the severity as the proportion of area that was affected by the disease. Instead, this article estimates the severity of disease as the harmonic mean of the percentage of area affected by the disease and the percentage of area the disease has spread to. It represents the data of diseased locations in farmland as a graph and performs estimation with the help of GNN. These insights can help a disease management A-CPS to automate its downstream tasks like resource allocation and targeted spraying to control the disease efficiently. The proposed method has been validated by training a GNN on images of apple leaves in which diseased parts have been identified and annotated. When presented with a new image, the trained model produced results resembling human visual estimation. © 2023 IEEE.

关键词： Cyber Physical System

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Density-Based Clustering and Performance Enhancement of Aeronautical Ad Hoc Networks

Density-Based Clustering and Performance Enhancement of Aero...

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2022 International Balkan Conference on Communications and Networking, BalkanCom 2022

作者： Shahbazi, Mohsen Simsek, Murat Kantarci, Burak University of Ottawa School of Electrical Engineering and Computer Science OttawaON Canada

ISBN: (数字)9781665487641

ISBN: (纸本)9781665487641

In-Flight Entertainment and Connectivity (IFEC) is becoming a key trend and an essential need. A grand challenge is to provide in-flight connectivity in high altitudes, and particularly in isolated locations, such as the oceans, where establishing an air-to-ground link is not possible. Aeronautical Ad-Hoc Networking (AANET) intends to cope with this challenge by forming a network of airplanes having air-to-air (A2A) connections. However, the dynamic nature of such a network is likely to lead to unstable connections. The primary cause of the majority of these stability issues is known to be poor clustering of aircrafts. Consequently, concentrating on aircraft clustering and making them more stable can improve connection. This paper aims to unveil the benefits of density-based clustering to improve the AANET performance. To do so, the paper employs a multi-feature DBSCAN algorithm for the clustering problem that exploits several features of real flight datasets, including latitude, longitude, altitude, direction, and velocity. Instead of a typical distance metric such as Euclidean or Haversine, the technique produces a precomputed distance matrix and feeds it to DBSCAN. This method also includes a weighted scheme to reflect the relative importance of each component of the distance calculation. Simulations under OMNET++ by using real-time flight data point out that packet delivery ratio and end-to-end latency of the state of the art clustering-based AANET solutions can be improved by 40 % and 30 %, respectively. Furthermore, the proposed method achieves a 20% reduction in cluster changes and the number of clusters. © 2022 IEEE.

关键词： Clustering algorithms

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Unsupervised Knowledge Graph Generation Using Semantic Similarity Matching 3

Unsupervised Knowledge Graph Generation Using Semantic Simil...

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3rd Workshop on Deep Learning Approaches for Low-Resource NLP, DeepLo 2022

作者： Liu, Lixian Omidvar, Amin Ma, Zongyang Agrawal, Ameeta An, Aijun Department of Electrical Engineering and Computer Science York University Canada Department of Computer Science Portland State University United States

ISBN: (纸本)9781955917971

Knowledge Graphs (KGs) are directed labeled graphs representing entities and the relationships between them. Most prior work focuses on supervised or semi-supervised approaches which require large amounts of annotated data. While unsupervised approaches do not need labeled training data, most existing methods either generate too many redundant relations or require manual mapping of the extracted relations to a known schema. To address these limitations, we propose an unsupervised method for KG generation that requires neither labeled data nor manual mapping to the predefined relation schema. Instead, our method leverages sentence-level semantic similarity for automatically generating relations between pairs of entities. Our proposed method outperforms two baseline systems when evaluated over four datasets. © 2022 Association for Computational Linguistics.

关键词： Knowledge graph

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The Impact of the 2024 Total Solar Eclipse on the Power System Frequency of US Interconnections: A Case Study

The Impact of the 2024 Total Solar Eclipse on the Power Syst...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Dulal, Saurav Wen, Yan Liu, Yilu The University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN United States Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798331504847

With the deployment of wide-area monitoring systems, power systems can be continuously monitored and analyzed, particularly during unusual events. Frequency fluctuations in the United States (US) interconnected power grid are influenced by various factors, including weather conditions and variability in solar power generation. This paper investigates the impact of the solar eclipse on April 8, 2024, on the frequency stability of the US power grid. Phasor measurement data from multiple interconnections are collected and analyzed. Methods of approximate frequency estimation for each interconnection are employed. The study involves comparing the frequency distribution, probability density, and variance on the eclipse day with those of non-eclipse days. The results provide insight into the grid's response to reduced solar generation and underscore the importance of predictive measures to maintain stability during solar eclipses and similar disruptions. The study shows that the Electric Reliability Council of Texas (ERCOT) experienced a dramatic increase in frequency fluctuations - 198% higher compared to periods without eclipse - highlighting the grid's vulnerability to reduced solar irradiance. Conversely, Eastern Interconnection (EI) demonstrated remarkable stability during the eclipse with minimal frequency fluctuations. © 2025 IEEE.

关键词： Electric power transmission networks

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Development of a Face Recognition Door Lock System with OpenCV Integration for Securing Residential Properties 15

Development of a Face Recognition Door Lock System with Open...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Haque, Samirul Sarker, Biplob Mawla, Atm Rafiqul Reza, Md Naeem Riya, Aparna Sarker American International University-Bangladesh Electrical and Electronics Engineering Dhaka Bangladesh Brac University Electrical and Electronic Engineering Dhaka Bangladesh International Islamic University Chittagong Electrical and Electronic Engineering Chittagong Bangladesh Eastern University Electrical and Electronics Engineering Dhaka Bangladesh Brac University Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350370249

The Face Recognition Door Lock System project presents a novel strategy for enhancing home security by combining face recognition technology with automated access control features. This technique is intended to be used in order to implement the system. It is the intention of this system to enhance security standards while simultaneously providing a means of property entrance that is both user-friendly and efficient. Through the use of face recognition algorithms, other hardware components such as a camera sensor and an Arduino Uno microcontroller, and software programs such as OpenCV and Python programming, the system is able to reliably identify authorized persons and manage door access based on facial features. The project has an emphasis on the development of security, convenience, and system operability in order to adapt to the changing expectations of modern homeowners who are looking for sophisticated access management solutions. © 2024 IEEE.

关键词： Locks (fasteners)

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Filtering Rectangular Dielectric Antennas for Large Frequency Ratio Applications 2

Filtering Rectangular Dielectric Antennas for Large Frequenc...

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2nd International Conference on Microwave, Antennas and Circuits, ICMAC 2025

作者： Asadullah Khan, Muhammad U. School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad 44000 Pakistan

ISBN: (纸本)9798331518424

This paper presents the design and performance analysis of a novel antenna aimed for 5G Distributed Antenna Systems (DAS), WLAN and millimeter-wave (mm-wave) 5G applications. Antenna consists of two rectangular dielectric resonator antennas (DRAs). The antenna is multi-band and covers the Citizens Broadband Radio Service band (2.75-3.8 GHz), WLAN band (4.4-5.47 GHz) as well as the mm-wave band (23.1- 24.99 GHz) making it applicable to indoor deployments. Antenna is fed through slot coupled feeding mechanism. The feeding method implements a filtering strip-line which filters the mm- wave band from and prohibits excitation of low-band DRA at mm- waveband. Simulation was performed using a full-wave simulator, and the results show that the antenna has peak gains of 6.37 dBi, 5.7 dBi, and 7.5 dBi in the respective frequency bands, which shows robust performance in terms of gain and bandwidth. Furthermore, radiation patterns at both azimuth and elevation planes verify that only the small DRA is excited over the mm-wave frequency range. These observations, hence, clearly illustrate the potential of the antenna to realize low as well as high-frequency applications and the progress made towards realizing such technologies for future wireless communication applications under 5G and beyond. © 2025 IEEE.

关键词： CBRS Dielectric Resonator Antenna filtering antenna millimeter-wave WLAN

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An Intelligent Thyroid Diagnosis System Utilizing Multiple Ensemble and Explainable Algorithms with Medical Supported Attributes

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2840-2855页

作者： Sutradhar, Ananda Al Rafi, Mustahsin Ghosh, Pronab Shamrat, F M Javed Mehedi Moniruzzaman, Md. Ahmed, Kawsar Azad, A.K.M. Bui, Francis M. Chen, Li Moni, Mohammad Ali Daffodil International University Department of Computer Science and Engineering Dhaka1216 Bangladesh Lakehead University Department of Computer Science Thunder BayONP7B 5E1 Canada University of Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Lakehead University Department of Software Engineering Thunder BayONP7B 5E1 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSK S7N 5A9 Canada Imam Mohammad Ibn Saud Islamic University Faculty of Science Department of Mathematics and Statistics Riyadh13318 Saudi Arabia Charles Stuart University AI and Cyber Futures Institute BathurstNSW2795 Australia

The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ML) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including feature importance (FIS), information gain selections (IGS), and least absolute shrinkage and selection operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as high-risk factors (HRF). Second, the models, including the three stage hybrid classifier (3SHC) and the three stage hybrid artificial neural network (3SHANN), are used as classifiers on the training data set. Third, a local interpretable model-agnostic explanations (LIME) to the 3SHC with the HRF samples was applied to individually explain the predictions. Then, the overall behaviors of both gender and age categories were explored with the help of a partial dependence plot (PDP). Finally, the proposed system is validated with extensive experiments, where the 3SHC achieves an accuracy (ACC) of 99.29%, which can play a crucial role in preventing thyroid disease and alleviating stress in the healthcare sector. © 2020 IEEE.

关键词： Neural networks

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Prediction of Downhole Pressure while Tripping 14

Prediction of Downhole Pressure while Tripping

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14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

作者： Mohammad, Amir Karunakaran, Subankan Panchalingam, Mithushankar Davidrajuh, Reggie University of Stavanger Department of Electrical Engineering and Computer Science Stavanger Norway

ISBN: (纸本)9781665487719

During drilling operations for oil and gas, swab and surge pressure occur while tripping in and out of a wellbore. High tripping speed can lead to fracturing the well's formation, whereas low tripping speed can increase non-productive time and cost. Hence, there is a need to predict surge/swab pressure accurately. Several analytical and machine learning models have already been developed to predict surge/swab pressure. However, these existing models use numerical calculations to generate the data. This paper explored four supervised machine learning models, i.e., Linear Regression, XGBoost, Feedforward Neural Network (FFNN), and Long-Short-Term Memory (LSTM). In this study, actual field data from the Norwegian Continental Shelf provided by an Exploration & Production company is utilized to develop the four machine learning models. The results indicated that XGBoost was the best-performing model with an R2-score of 0.99073. Therefore, this trained model can be applied during a tripping operation to regulate tripping speed where repetitive surge/swab pressure calculation is needed. © 2022 IEEE.

关键词： Forecasting

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Investigation of Effective Electrode Structure for Metal Removal from Composite Materials Using Pulsed Discharge 10

Investigation of Effective Electrode Structure for Metal Rem...

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10th Euro-Asian Pulsed Power Conference, 25th International Conference on High-Power Particle Beams and 20th International Symposium on Electromagnetic Launch Technology, EAPPC-BEAMS-EML 2024

作者： Doi, Takuto Oishi, Shota Sakugawa, Takashi Nishimura, Kaisei Shobako, Shinichiro Shimasaki, Shinichi Yamashita, Tomohiko Kumamoto University Graduate School of Science and Technology Department of Computer Science and Electrical Engineering Kumamoto Japan Kagawa College National Institute of Technology Department of Electro-Mechanical Systems Engineering Takamatsu Japan

ISBN: (纸本)9789038661353

The authors investigate the potential of pulsed power technology in recycling of E-waste. Applying the pulsed discharge can separate composite materials into plastic and metal. In this study, pulsed discharge was applied to indium tin oxide (ITO) coated plastic films. We used two electrode types: rod-To-rod electrode and a pair of flat plate electrodes to investigate the effect of electrode structure on the metal removal area in detail. A series of single pulse discharges were applied at various electrode distances. As a result, it was revealed that metal can be removed over a wide range by the pulsed discharge using a pair of flat plate electrodes. When the gap between the electrodes was 30 mm, the removal area by the flat plate electrodes was approximately 3.4 times that by the rod electrodes. Analysis of the current density also revealed that the metal removal area was greatly affected by the current density. © 2024 Eindhoven University of Technology.

关键词： Tin oxides

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Exploring public perception and willingness to pay for a renewable source of energy:evidence from an emerging economy of renewable-energy development

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Clean Energy 2024年第2期8卷 177-188页

作者： Mohammed J.K.Bashir Senadjki Abdelhak Wael A.Salah Mai Abuhelwa School of Engineering&Technology Central Queensland University120 Spencer St.MelbourneQLD 3000Australia Faculty of Business and Finance Universiti Tunku Abdul RahmanKamparPerakMalaysia Department of Electrical Engineering College of Engineering and TechnologyPalestine Technical University-KadooriePO Box 7Yafa StreetTulkarmPalestine Department of Electrical Engineering and Computer Science University of MissouriColumbiaMO 65211USA

The study focuses on the transition from conventional energy sources to sustainable renewable energy(RE)in Palestine,particularly in the West Bank and Gaza *** research aims to understand the relationship between key factors and residents’willingness to invest in the RE sector,with a specific focus on the impact of *** the unified theory of acceptance and use of technology,the study analyzes data from 400 residents through a questionnaire and employs ordinary least-squares regression analysis with Eviews *** key findings indicate that residents’inclination to invest in RE is significantly influenced by factors such as resource availability,knowledge level and power ***,the study suggests that educated residents are more likely to invest in RE when factors such as knowledge,effort expectation,enabling conditions and resource availability are relatively *** conclusion emphasizes the importance of policymakers’allocating sufficient funds to research and development to promote technological innovation in RE sources,thereby enhancing the population’s energy efficiency.

关键词： renewable energy technology energy security public perceptions sustainable development

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