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A two-dimensional double layer-averaged model of hyperconcentrated turbidity currents with non-Newtonian rheology

International Journal of Sediment Research 2023年第6期38卷 794-810页

作者： Yining Sun Ji Li Zhixian Cao Alistair George Liam Borthwick State Key Laboratory of Water Resources Engineering and Management Wuhan University Zienkiewicz Centre for Computational Engineering Faculty of Science and EngineeringSwansea University Institute for Infrastructure and Environment University of Edinburgh School of Engineering Computing and MathematicsUniversity of Plymouth

Hyperconcentrated turbidity currents typically display non-Newtonian characteristics that influence sediment transport and morphological evolution in alluvial ***,hydro-sedimentmorphological processes involving hyperconcentrated turbidity currents are poorly understood,with little known about the effect of the non-Newtonian *** current paper extends a recent twodimensional double layer-averaged model to incorporate non-Newtonian constitutive *** extended model is benchmarked against experimental and numerical data for cases including subaerial mud flow,subaqueous debris flow,and reservoir turbidity *** computational results agree well with observations for the subaerial mud flow and independent numerical simulations of subaqueous debris *** between the non-Newtonian and Newtonian model results become more pronounced in terms of propagation distance and sediment transport rate as sediment concentration *** model is then applied to turbidity currents in the Guxian Reservoir planned for middle Yellow River,China,which connects to a tributary featuring hyperconcentrated sediment-laden *** non-Newtonian model predicts slower propagation of turbidity currents and more significant bed aggradation at the confluence between the tributary Wuding River and the Yellow River in the reservoir than its Newtonian *** difference in model performance could be of considerable importance when optimizing reservoir operation schemes.

关键词： Double layer-averaged model Non-Newtonian rheology Mud flow Reservoir turbidity current Yellow River

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ML and DL-based Phishing Website Detection:The Effects of Varied Size Datasets and Informative Feature Selection Techniques

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Journal of Artificial Intelligence and Technology 2024年第1期4卷 18-30页

作者： Kibreab Adane Berhanu Beyene Mohammed Abebe Faculty of Computing&Software Engineering Arba Minch UniversityEthiopia Ethiopian Cybersecurity Association Addis AbabaEthiopia

Onemust interact with a specific webpage or website in order to use the Internet for communication,teamwork,and other productive ***,because phishing websites look benign and not all website visitors have the same knowledge and skills to inspect the trustworthiness of visited websites,they are tricked into disclosing sensitive information and making them vulnerable to malicious software attacks like *** is impossible to stop attackers fromcreating phishingwebsites,which is one of the core challenges in combating ***,this threat can be alleviated by detecting a specific website as phishing and alerting online users to take the necessary precautions before handing over sensitive *** this study,five machine learning(ML)and DL algorithms—cat-boost(CATB),gradient boost(GB),random forest(RF),multilayer perceptron(MLP),and deep neural network(DNN)—were tested with three different reputable datasets and two useful feature selection techniques,to assess the scalability and consistency of each classifier’s performance on varied dataset *** experimental findings reveal that the CATB classifier achieved the best accuracy across all datasets(DS-1,DS-2,and DS-3)with respective values of 97.9%,95.73%,and 98.83%.The GB classifier achieved the second-best accuracy across all datasets(DS-1,DS-2,and DS-3)with respective values of 97.16%,95.18%,and 98.58%.MLP achieved the best computational time across all datasets(DS-1,DS-2,and DS-3)with respective values of 2,7,and 3 seconds despite scoring the lowest accuracy across all datasets.

关键词： ANOVA-F-test deep learning feature selection technique machine learning mutual information phishing website datasets phishing website detection

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Analysis of Scattering Parameters of Finite Stealthy Transmission Line 65

Analysis of Scattering Parameters of Finite Stealthy Transmi...

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65th International Symposium ELMAR, ELMAR 2023

作者： Tomic, Dubravko Zanic, Dominik University of Zagreb Faculty of Electrical Engineering and Computing Zagreb10000 Croatia

ISBN: (纸本)9798350325126

In this paper, two finite transmission lines loaded with open-ended stubs distributed in different ways are compared and studied. The first structure is a periodically loaded transmission line with open-ended stubs. For the second structure the loading is no longer periodic, but the locations of the stubs are calculated based on an optimization procedure in the reciprocal Fourier space. The locations are chosen such that the amplitude of spatial Fourier transform vanishes for frequency ranges near the first stop-band. The newly proposed transmission line has an increase in bandwidth and a reduction in ripple in the transmission coefficient, but the ripple is present throughout the whole pass-band. © 2023 IEEE.

关键词： Electric lines

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Deep Learning Based Intrusion Detection for Internet of Things and Edge Devices

Deep Learning Based Intrusion Detection for Internet of Thin...

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2023 IEEE Asia-Pacific Conference on Computer Science and Data engineering, CSDE 2023

作者： Moawad, Mohamad Tze, Vong Wan Hang Hui, Patrick Then Hsien Ming, Jason Yong Swinburne University of Technology Faculty of Engineering Computing and Science Sarawak Malaysia

ISBN: (纸本)9798350341072

The rapid expansion of Internet of Things (IoT) and Edge devices has introduced a variety of security challenges, particularly within resource-constrained computing environments. This paper aims to address these challenges by proposing a resource-efficient anomaly-based intrusion detection system tailored for IoT and Edge Devices, leveraging the capabilities of Binary Neural Networks (BNNs). Anomaly-based systems have demonstrated their efficacy in identifying potential cyber-attacks, but their conventional development methods often demand substantial storage and computational resources. Therefore, this research investigates the viability of BNNs for anomaly detection, offering a compelling alternative to full-precision approaches. Three distinct models are developed for this study using TensorFlow and Keras: Full-binary, Binary-weighted, and Full-precision. The models are trained and evaluated on the NSL-KDD dataset and achieved accuracy rates of 82%,87%, and 97% for these respective models. While binary networks experience a minor performance reduction due to aggressive quantization, they maintain notable levels of accuracy and precision. This observation takes into consideration the anticipated reduction in resource requirements, affirming the feasibility of BNNs' utilization for efficient anomaly detection. © 2023 IEEE.

关键词： Intrusion detection

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Triple-Band Circular Ring Monopole Antenna for RF Power Harvesting Applications

Advanced Electromagnetics

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Advanced Electromagnetics 2025年第1期14卷 39-51页

作者： Mbida, Joseph Mari Mebara Eyebe, Guy Ayissi Singh, Ghanshyam Mbinack, Clément Yende, Mohamed Njianga Department of Physics Faculty of Sciences University of Yaounde I Yaounde Cameroon Electrical and Computing Engineering Department Université du Québec à Trois-Rivieres Canada Department of Electronics and Communication Engineering Feroze Gandhi Institute of Engineering and Technology Raebareli India

This paper presents a simple and efficient approach for an antenna design and construction based on an I-shaped slot-loaded triple wideband microstrip monopole antenna with novel radio frequency (RF) power/energy harvesting capability. The proposed antenna takes advantage of circular ring shape properties, loaded with an I-shaped slot with degraded ground structure (DGS) to improve the input performances of the conventional planar circular microstrip patch antenna (C-patch). Input impedance and magnetic field radiation pattern are calculated using the electromagnetic theory of transmission line in traveling waves and resonant cavity TE11 field mode, respectively. The proposed antenna is configured on thin lossy substrate material RO4003C of volume 64.800×57.000×1.524 (in mm3), relative dielectric permittivity Εr = 3.55, and numerically investigated using CST MWS. The prototype of the proposed antenna has been fabricated and tested. The experimental results show that the fabricated antenna exhibits an impressive 142% (2.98-11.11 GHz), 21.3% (11.54-14.42 GHz), and 19.6% (15.78-19.44 GHz) fractional bandwidth (FBW) for the resonant frequency of 5.73, 13.55, and 18.69 GHz respectively and peak gain of 4.2 dBi at the frequency of 5.8 GHz. Experimentally, the proposed antenna can be used in multiple applications like 2.4 GHz (2.401-2.495 GHz) Wi-Fi band/5 GHz (5.170-5.875 GHz) WLAN band/(3.1-10.6 GHz) US UWB at the view of the achieved lower band, FR3 6G operation regarding the obtained middle/intermediate band, and the future wireless and millimeter-wave communication systems according to the obtained upper band. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Directional patterns (antenna)

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Cooperative Formation and Path Following Control of Multiple USVs Along Curved Paths 32

Cooperative Formation and Path Following Control of Multiple...

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32nd Telecommunications Forum, TELFOR 2024

作者： Zecevic, Zarko Martinovic, Luka Bibuli, Marco Caccia, Massimo Batos, Matko Nad, Dula University of Montenegro Faculty of Electrical Engineering Podgorica Montenegro National Research Council Institute of Marine Engineering Genoa Italy University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9798350391053

In this paper, a novel cooperative formation and path following control method for multiple underactuated unmanned surface vehicles (USVs) is proposed. First, a distributed path variable update law is constructed to achieve coordination among the vehicles. Subsequently, a surge-heading guidance law is designed for each vehicle to ensure convergence to the desired path and to compensate for sideslip. The effectiveness of the proposed method is verified through numerical simulations. © 2024 IEEE.

关键词： Unmanned surface vehicles

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MEASUREMENT UNCERTAINTIES FOR AIRBORNE AND IMPACT SOUND INSULATION BUILDING ACOUSTIC PARAMETERS FROM INTERLABORATORY COMPARISON 10

MEASUREMENT UNCERTAINTIES FOR AIRBORNE AND IMPACT SOUND INSU...

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10th Convention of the European Acoustics Association, EAA 2023

作者： Petošić, Antonio Marinković, Toni Stošić, Domagoj Franček, Petar Department of Electroacoustics University of Zagreb Faculty of Electrical Engineering and Computing Croatia

ISBN: (纸本)9788888942674

This paper represents the results of the interlaboratory comparisons (ILC) conducted in the purpose to test proficiency of accredited laboratories (23) according to in-situ measurement methods described in ISO 16283-1:2014/A1:2017, ISO 16283-2:2020 with ISO 717-1:2020 and ISO 717-2:2020. The comparison of all measurement parameters and single number building acoustic parameters are presented with obtained measurement uncertainties. The overall influence of all uncertainty components on the final results are analyzed and discussed. The measurement uncertainties are obtained by doing one independent measurement from each lab and they are determined at the end for all results in reproducibility conditions. The obtained values for standard deviations are compared with values given in standard ISO 12999-1:2020 and with previous ILC-s results for airborne and impact sound insulation parameters. © 2023 First author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Uncertainty analysis

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Brain Tumor Classification Using MRI Images and Deep Learning Methods 1

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13th National Technical Symposium on Unmanned System Technology, NUSYS 2023

作者： Joudaki, Atra Meshgini, Saeed Makouei, Somayeh Hassanlou, Leila Farzamnia, Ali Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Faculty of Engineering Universiti Malaysia Sabah Sabah Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

A brain tumor is an abnormal mass in the brain that can be benign or malignant depending on the nature of the constituent cells. The origin of the tumor may be from the brain tissue, or it may spread to the brain from another place, or it may metastasize. If not diagnosed in the early stages, it can be life-threatening. Therefore, accurate prognosis of brain tumor in the early stages is very important for its diagnosis and treatment. The traditional methods used to diagnose brain tumors are sampling and examining Magnetic resonance imaging (MRI) or CT scans by humans. However, examining the large number of MRI images by humans that require expertise is tedious and impractical, so there is a basic need to automatically classify a brain tumor image. In this paper, an algorithm for the diagnosis and classification of brain tumors is presented using transfer learning. Basically, instead of building a new model, we used a pre-trained model (ResNet 50) to extract the feature and classification. We have also presented another method based on the combination of transfer learning (TF) and support vector machine (SVM) algorithm. Finally, these two methods were compared and it was found that the use of machine learning algorithms can have a significant effect on classification accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Magnetic resonance imaging

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Vision-Based Automated Parking Monitoring Using Geo-Tagged UAV Images

Vision-Based Automated Parking Monitoring Using Geo-Tagged U...

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2024 International Conference on Green Energy, computing and Sustainable Technology, GECOST 2024

作者： Lai, Chung Yi Jo, Hudyjaya Siswoyo Lai, Chean Hung Dunn, Michelle Swinburne University of Technology Sarawak Campus Faculty of Engineering Computing and Science Kuching Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn Campus Melbourne Australia

ISBN: (纸本)9798350357905

This paper proposes an automated solution for parking monitoring using geo-tagged UAV (Unmanned Aerial Vehicle) images. The parking monitoring system utilizes a UAV to patrol the parking lot. The UAV captures video footage that contains geolocation information. Geo-tagged images are extracted from the video footage and processed using the YOLOv8 object detection algorithm to recognize occupied and vacant parking spaces. The license plates of the parked vehicles in the occupied parking space are extracted using EasyOCR and stored in a database. Each parking space is labeled with a unique ID with its date, time, geolocation data, and occupancy status to facilitate integrated digital parking solutions for both the users and parking operators. © 2024 IEEE.

关键词： License plates (automobile)

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ERBM: A Machine Learning-Driven Rule-Based Model for Intrusion Detection in IoT Environments

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Computers, Materials and Continua 2025年第3期83卷 5155-5179页

作者： Mehmmod, Arshad Batool, Komal Sajid, Ahthsham Alam, Muhammad Mansoor Su'ud, Mazliham MohD Khan, Inam Ullah Department of Information Security and Data Science Riphah Institute of Systems Engineering Riphah International University Islamabad46000 Pakistan Faculty of Computing Riphah International University Islamabad46000 Pakistan Faculty of Computing and Informatics Multimedia University Cyberjaya63100 Malaysia

Traditional rule-based Intrusion Detection Systems (IDS) are commonly employed owing to their simple design and ability to detect known threats. Nevertheless, as dynamic network traffic and a new degree of threats exist in IoT environments, these systems do not perform well and have elevated false positive rates—consequently decreasing detection accuracy. In this study, we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model (ERBM) to classify the packets better and identify intrusions. The ERBM developed for this approach improves data preprocessing and feature selections by utilizing fuzzy logic, where three membership functions are created to classify all the network traffic features as low, medium, or high to remain situationally aware of the environment. Such fuzzy logic sets produce adaptive detection rules by reducing data uncertainty. Also, for further classification, machine learning classifiers such as Decision Tree (DT), Random Forest (RF), and Neural Networks (NN) learn complex ways of attacks and make the detection process more precise. A thorough performance evaluation using different metrics, including accuracy, precision, recall, F1 Score, detection rate, and false-positive rate, verifies the supremacy of ERBM over classical IDS. Under extensive experiments, the ERBM enables a remarkable detection rate of 99% with considerably fewer false positives than the conventional models. Integrating the ability for uncertain reasoning with fuzzy logic and an adaptable component via machine learning solutions, the ERBM system provides a unique, scalable, data-driven approach to IoT intrusion detection. This research presents a major enhancement initiative in the context of rule-based IDS, introducing improvements in accuracy to evolving IoT threats. Copyright © 2025 The Authors.

关键词： Membership functions

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