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THE TENSILE STRENGTH OF SEA ICE: REVISING ITS dep.NDENCE ON MICROSTRUCTURE AND GROWTH VELOCITY 27

THE TENSILE STRENGTH OF SEA ICE: REVISING ITS DEPENDENCE ON ...

引用

27th International Conference on Port and Ocean engineering under Arctic Conditions, POAC 2023

作者： Maus, Sonke Dep. of Civil and Transport Engineering Norwegian University for Science and Technology Trondheim Norway

The dep.ndence of most physical properties of sea ice on its microstructure is not well established. For the tensile strength, several investigators have discussed its dep.ndence on the plate spacing, also referred to as substructure or brine layer spacing. Also brine pore scales and grain size have been suggested as structural properties that influence the tensile strength. However, due to a lack in detailed microstructure observations and the limited tensile strength data, the validation and formulation of a concise model of sea ice tensile strength is still lacking. Here I revise the models once proposed, on the basis of recently available 3D micro-tomographic images of young sea ice and laboratory-grown model ice. Relationships between microstructure scales (plate spacing, characteristic brine pore width, grain size) and growth conditions (growth velocity, brine volume fraction) are compared to observations from the 3D imagery, to propose an equation for the horizontal tensile strength σ of sea ice that is consistent with the majority of published test results. The analysis suggests a strong influence of the plate spacing, and thus of the ice growth rate, as well as the grain size on the tensile strength of sea ice. © 2023 Lulea University of Technology. All rights reserved.

关键词： Microstructure

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AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention 2

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Abotaleb, Mostafa Makarovskikh, Tatiana Mijwil, Maad M. Bala, Indu Hiran, Kamal Kant Doshi, Ruchi South Ural State University Dep. of System Programming Chelyabinsk454080 Russia Baghdad College of Economic Sciences University Computer Techniques Engineering Department Baghdad Iraq Lovely Professional University Electrical and Electronics Engineering Punjab India Sir Padampat Singhania University Dep. of Computer Science Udaipur India Universidad Azteca Dep. of Computer Science and Engineering Chalco Mexico

ISBN: (纸本)9798350384277

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet's architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet's enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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A Review of Grid-Connected Inverters and Control Methods Under Unbalanced Grid Conditions 16

A Review of Grid-Connected Inverters and Control Methods Und...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Marangalu, Milad Ghavipanjeh Rehman, Fazal Ur Kurkdjian, Naser Vosoughi Computer Queen's University Dep. Of Electrical and Computer Engineering KingstonON Canada University of L'Aquila Dep. Information Engineering Computer Science and Mathematics L'Aquila Italy San Diego State University Faculty of Electrical and Computer Engineering San DiegoCA92182-0001 United States

ISBN: (纸本)9798331533946

Grid-connected inverters play a pivotal role in integrating renewable energy sources into modern power systems. However, the presence of unbalanced grid conditions poses significant challenges to the stable operation of these inverters. This review paper provides a comprehensive overview of grid-connected inverters and control methods tailored to address unbalanced grid conditions. Beginning with an introduction to the fundamentals of grid-connected inverters, the paper elucidates the impact of unbalanced grid voltages on their performance. Various control strategies, including voltage and current control methods, are examined in detail, highlighting their strengths and limitations in mitigating the effects of grid imbalance. Lastly, the review identifies emerging trends and future research directions in the field, emphasizing the need for innovative solutions to address the evolving challenges posed by unbalanced grid conditions in the context of renewable energy integration. © 2025 IEEE.

关键词： control strategy fault conditions grid-connected inverters Photovoltaic systems

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DESIGN OF TECHNOLOGY FOR SECURE FILE STORAGE BASED ON HYBRID CRYPTOGRAPHY METHODS: SHORT OVERVIEW 4

DESIGN OF TECHNOLOGY FOR SECURE FILE STORAGE BASED ON HYBRID...

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4th IEEE International Conference on Smart Information Systems and Technologies, SIST 2024

作者： Abitova, Gulnara A. Manap, Abu Said Kulniyaziva, Korlan Nikulin, Vladimir Dep. of Electrical Engineering and Computer Science Dep. of Computer Engineering State University of New York Astana IT University BinghamtonNY United States Astana Kazakhstan Astana IT University Astana Kazakhstan Dep.of System analysis and management L.N. Gumilyov Eurasian National University Astana Kazakhstan Dep. of Electrical Engineering and Computer Science Binghamton University BinghamtonNY United States

ISBN: (纸本)9798350374865

In the era of pervasive digital data, ensuring secure file storage has become a paramount concern. This paper explores the significance of hybrid cryptography for design of an advanced tools and techniques for secure file storage. Hybrid cryptography, combining symmetric and asymmetric encryption, offers robust protection against unauthorized access, tampering, and data loss. The article reviews recent cryptography literature, highlighting the importance of secure file storage in today's interconnected world and examining the benefits of hybrid cryptography. The analysis of articles on cryptography reveals emerging trends and challenges. Post-quantum cryptography addresses concerns about quantum threats, while blockchain-based cryptography enhances security in IoT data sharing. Homomorphic encryption enables computations on encrypted data, and cryptographic protocols that protect privacy enable safe multi-party computation. Machine learning's intersection with cryptanalysis introduces efficiency but raises ethical considerations. The paper further discusses advancements and trends in cryptography techniques, including post-quantum cryptography, homomorphic encryption, zero-knowledge proofs, post-quantum key exchange, secure multi-party computation, and post-quantum signature schemes. These developments aim to ensure long-term security against quantum attacks, enable privacy-preserving computations, and enhance the confidentiality, integrity, and authentication of digital communication and data storage. In conclusion, the paper advocates for the adoption of hybrid cryptography in secure file storage systems. Its combination of symmetric and asymmetric encryption, along with its adaptability to evolving security landscapes, positions hybrid cryptography as a formidable approach to data protection. By embracing hybrid cryptography and staying informed about the latest advancements, organizations can navigate the digital age with confidence, ensuring the confidentiality

关键词： Quantum cryptography

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Blockchain Enabled Secure Agri-Goods Traceability using RFID in Supply Chain Management 3

Blockchain Enabled Secure Agri-Goods Traceability using RFID...

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3rd International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2023

作者： Muthu, S. Esakki Kartheeban, K. Kalasalingam Academy of Research and Education Dep. of Computer Science and Engineering Srivilliputtur India

ISBN: (纸本)9798350306989

Supply chain is the universal link network to deliver the product from one place to another. Capability to trace and track the Agri-goods products is very important throughout various stages of the supplychain including manufacturing, processing, distribution, importation, and retail. Ensuring the safety, quality, and validation of agricultural products is very crucial. In traditional supply chain management, which often involves untrusted entities and data is centrally monitored and stored with all the transactions being processed through a centralized organization. However, this centralized system is vulnerable to hacking by third parties, which can result in data security issues and the potential for a single point failure. And the primary challenges include monitoring, the origin of agri-goods, tracing the logistics, and low level of security, trust, and transparency between the stakeholders which leads to implicit delays downstream of Agri goods, and decisions taken among different dep.rtments. In this study, we propose a consortium blockchain solution for tracing and tracking of agri-goods products. Blockchain has a rapidly evolving disruptive Technology and immutable chain structure that is linked sequentially according to chronological order. Which is achieved through the creation of smart contract algorithms between different entities and fostering communication among stakeholders in the agri-goods supply chain. The traceability of the agri-goods products, including their origin, safety, quality, and validation, is implemented through the integration of RFID (Radio frequency identifications) tags and readers. Additionally, the solution incorporates a blockchain-enabled ultra-light weight mutual authentication RFID protocol for provide the security and resist the possible vulnerabilities. © 2023 IEEE.

关键词： Smart contract

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Feature Selection for Robust Spoofing Detection: A Chi-Square-based Machine Learning Approach 2

Feature Selection for Robust Spoofing Detection: A Chi-Squar...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Al-Na'amneh, Qais Aljaidi, Mohammad Gharaibeh, Hasan Nasayreh, Ahmad Emhamed Al Mamlook, Rabia Almatarneh, Sattam Alzu'bi, Dalia Suliman Husien, Abla Applied Science Private University Dep. of Cyber Security Amman Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Yarmouk University Dep. Computer Science Irbid Jordan Trine University Dep. of Business Administration Indiana United States University of Zawia Department of Industrial Engineering Tripoli Libya Zarqa University Dep. of Data Science and Artificial Intelligence Zarqa Jordan Concordia University Dep. Information and Systems Engineering Montreal Canada

ISBN: (纸本)9798350373363

Nowadays, the Internet of Things (IoT) system is vulnerable to spoofing attacks that can easily where attackers can easily pose as a legal entity of the network. A 'spoofing attack' refers to a type of cyber-attack when an attacker purposefully impersonates or masquerades as someone or something else to deceive the target or obtain unauthorized access to systems, information, or resources. In such attacks, the attacker alters their name, IP address, or other attributes to fool the victim into thinking they are engaging with a legitimate entity. Spoofing attacks can take place via a variety of channels, including, ARP and DNS spoofing. Therefore. Spoofing attacks can have serious consequences. We proposed a new approach based on three machine learning models LightGBM, Gradient Boost, and XGBoost to classify attacks on spoofing, we used Chi-square to select the best features to get the highest performance, and we demonstrated that the results using Chi-square achieved higher results than without Chi-square and improved the result with a rate three percent of accuracy. In terms of results, LightGBM outperformed other models by achieving 89%, 91 %, 87 %, and 89 % for accuracy, precision, recall, and f1-score, respectively. This shows the potential and efficiency of Chi-square to achieve the best performance by selecting the best features, thus providing a secure system and identifying cyber-attacks on systems. © 2023 IEEE.

关键词： Internet of things

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Optimization of Methods for Querying Formal Ontologies in Natural Language Using a Neural Network 16

Optimization of Methods for Querying Formal Ontologies in Na...

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16th International Conference on Knowledge engineering and Ontology Development, KEOD 2024 as part of 16th International Joint Conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2024

作者： Ondo, Anicet Lepetit Capus, Laurence Bousso, Mamadou Dep. of Computer and Software Engineering Laval University QuebecQCG1V 0A6 Canada Dep. Computer Science Iba Der Thiam University Thies967 Senegal

ISBN: (纸本)9789897587160

A well-designed ontology must be capable of addressing all the needs it is intended to satisfy. This complex task involves gathering all the potential questions from future users that the ontology should answer in order to respond precisely to these requests. However, variations in the questions asked by users for the same need complicate the interrogation process. Consequently, the use of a question-answering system seems to be a more efficient option for translating user queries into the formal SPARQL language. Current methods face significant challenges, including their reliance on predefined patterns, the quality of models and training data, ontology structure, resource complexity for approaches integrating various techniques, and their sensitivity to linguistic variations for the same user need. To overcome these limitations, we propose an optimal classification approach to classify user queries into corresponding SPARQL query classes. This method uses a neural network based on Transformer encoder-decoder architectures, improving both the understanding and generation of SPARQL queries while better adapting to variations in user queries. We have developed a dataset on estate liquidation and Python programming, built from raw data collected from specialist forums and websites. Two transformer models, GPT-2 and T5, were evaluated, with the basic T5 model obtaining a satisfactory score of 97.22%. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Structured Query Language

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Logistic Regression Classification for Assessing the Risk of Kidney Tumor 2

Logistic Regression Classification for Assessing the Risk of...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Alzubi, Dalia Al Mamlook, Rabia Emhamed Nasayreh, Ahmad Aljaidi, Mohammad Alazab, Rami Gharaibeh, Hasan Al-Naamneh, Qais Concordia University Dep. Information and Systems Engineering Montreal Canada Trine University Dep. of Business Administration IN United States University Zawia Department of Industrial Engineering Tripoli Libya Yarmouk University Dep. Computer Science Irbid Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Jordan University of Science and Technology Dep. Surgery and Urology Irbid Jordan Applied Science Private University Dep. of Cyber Security Amman Jordan

ISBN: (纸本)9798350373363

The kidneys have the important function of filtering waste products and toxins from the blood in the human body. Any problems affecting the kidneys can potentially impact their efficiency and overall function. Kidney Tumor (KT) is a disease that specifically affects kidney cells and causes the abnormal growth of tissue in one or both kidneys. Assessing the risk of developing a kidney tumor and identifying the key factors that contribute to this risk are crucial for ensuring patient well-being. Additionally, this information assists doctors and specialists in making faster and more accurate diagnoses, determining appropriate treatment methods, and potentially influencing the course of the disease to minimize its severity and impact. Machine learning algorithms have recently been introduced to evaluate disease risks, and in this study, we specifically focus on examining the risk of kidney tumor development and investigating the influencing factors. We employed a logistic regression model to predict if a patient is at risk of developing a kidney tumor or not, and further categorized the samples into high-risk and low-risk groups. Our model was trained and tested using a unique dataset obtained from the (KAUH) hospital in Jordan, consisting of well-balanced metadata from 120 patients with kidney issues. Our work demonstrated accuracy results ranging from 90% to 98%. Ultimately, specialists can utilize our model as an additional tool to enhance the speed and accuracy of patient diagnoses © 2023 IEEE.

关键词： Tumors

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A SENSITIVITY ANALYSIS ON THE EFFECT OF HYPERPARAMETERS IN DEEP NEURAL OPERATORS APPLIED TO SOUND PROPAGATION 10

A SENSITIVITY ANALYSIS ON THE EFFECT OF HYPERPARAMETERS IN D...

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10th Convention of the European Acoustics Association, EAA 2023

作者： Borrel-Jensen, Nikolas Engsig-Karup, Allan P. Jeong, Cheol-Ho Technical University of Denmark Dep. of Electrical and Photonics Engineering Denmark Technical University of Denmark Dep. of Applied Mathematics and Computer Science Denmark

ISBN: (纸本)9788888942674

Deep neural operators have seen much attention in the scientific machine learning community over the last couple of years due to their capability of efficiently learning the nonlinear operators mapping from input function spaces to output function spaces showing good generalization properties. This work will show how to set up a per-formant DeepONet architecture in acoustics for predicting 2-D sound fields with parameterized moving sources for real-time applications. A sensitivity analysis is carried out with a focus on the choice of network architectures, activation functions, Fourier feature expansions, and data fidelity to gain insight into how to tune these models. Specifically, a default feed-forward neural network (FNN), a modified FNN, and a convolutional neural network will be compared. This work will de-mystify the DeepONet and provide helpful knowledge from an acoustical point of view. © 2023 Nikolas Borrel-Jensen 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： Sensitivity analysis

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Deep Feature Extraction Framework Based on DNN for Enhancing Mirai Attachment Classification in Machine Learning 2

Deep Feature Extraction Framework Based on DNN for Enhancing...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Gharaibeh, Hasan Aljaidi, Mohammad Nasayreh, Ahmad Al-Na'amneh, Qais Jaradat, Ameera S. Samara, Ghassan Emhamed Al Mamlook, Rabia Yarmouk University Dep. Computer Science Irbid Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Applied Science Private University Dep. of Cyber Security Amman Jordan Dep. of Business Administration Trine University IN United States Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libya

ISBN: (纸本)9798350373363

Nowadays, the rapid growth of Internet of Things (IoT) devices has changed many parts of our lives by providing seamless connectivity and automation. However, this expansion has created new challenges and vulnerabilities, especially with regard to security. Mirai botnet, a strain of malware that has severely damaged the IoT ecosystem, is one of the prominent risks to IoT security. This research examines the impact of the Mirai botnet on IoT devices. In this study, we present a hybrid model to detect Mirai attacks. Deep neural networks (DNNs) are used to extract significant deep features, which are subsequently passed to the (Light Gradient Boosting Machine) LGBM algorithm for classification. A dataset from the Canadian Institute 2023 that included several kinds of Internet of Things assaults was used to assess the model. The model achieved excellent feature extraction and promising results with accuracy, recall, precision, and F1-score scoring up to 95 % for all. These accuracy results demonstrated the superior performance of the suggested model over competing techniques, which had scores of 71 %, 85%, and 52%, respectively, for Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), and Stochastic Gradient Descent (SGD). Furthermore, the model outperforms the DNN model, which received a score of 65%. © 2023 IEEE.

关键词： Deep neural networks

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