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检索条件"机构=Dep. of Electrical Engineering and Computer Science"

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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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An EV On-Board Charger Based on Dual-Boost AC-DC Converter

An EV On-Board Charger Based on Dual-Boost AC-DC Converter

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Amer, Omar Ashraf Abou Abdelsalam, Ibrahim Marei, Mostafa I. Madkour Company For Engineering Projects New Cairo Egypt College of Engineering and Technology Arab Academy for Science Technology & Maritime Transport Electrical & Control Engineering Dep. Cairo Egypt Ain Shams University Faculty of Engineering Electrical Power and Machines Dep. Cairo Egypt

ISBN: (纸本)9798350351408

This paper evaluates the performance of a double-stage converter for electric vehicles onboard charger applications. The dual-boost AC-DC converter is chosen as a front-end converter since it has higher efficiency, small passive element size, and lower EMI interference than the other AC-DC converters. A coupled inductor-based design for the AC-DC converter is chosen to overcome issues related to reserve current flowing problems for the rectifier body diodes and achieve zero-current switching (ZCS). Based on an LLC resonant network, the back-end converter provides galvanic isolation. To achieve ZVS, the LLC resonant converter frequency is higher than the resonance frequency. The suggested LLC resonant converter differs from the traditional LLC resonant in using two identical tiny transformers instead of utilizing one large transformer. This approach reduces the current stress on the secondary winding and decreases converter diode conduction losses. © 2024 IEEE.

关键词： Transformer windings

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Integrated Battery Energy Storage with Modular Multilevel Converter for Grid-Connected Photovoltaic System: Design and Control 13

Integrated Battery Energy Storage with Modular Multilevel Co...

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13th Smart Grid Conference, SGC 2023

作者： Akbari, Ehsan Shadlu, Milad Samady Mazandaran University of Science and Technology Dep. of Electrical Engineering Babol Iran Islamic Azad University Young Researchers and Elite Club Bojnourd Branch Dep. of Electrical Engineering Bojnourd Iran

ISBN: (纸本)9798350396065

Modular multilevel converters with integrated battery energy storage system (MMC-BESS) is proposed for voltage regulation in high-voltage applications and integration with renewable energy sources. Since balancing the voltage of the capacitors between the upper and lower arms of the converter and controlling the current in the rectifier mode for charging the battery are not possible by conventional control methods, a new control model is proposed in this paper. In the proposed control model, a block based on the model predictive control (MPC) is implemented to control the MMC side, and the improved state of charge (SOC) equalization is also used to control the battery side converter. Despite the excess power flowing on the battery side in MMC rectifier operation mode, the proposed controller can successfully control the AC grid and battery currents. In order to design the integrated MMC-BESS structure, full-bridge modules are used. The MMC-BESS structure and the proposed control model are simulated in MATLAB/Simulink, and the results are evaluated. The results confirm the superior performance of the proposed control model under the reference power variation and external load addition conditions on the grid side. © 2023 IEEE.

关键词： Model predictive control

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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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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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Backflashover Analysis of Transmission Lines Considering Variation of Soil electrical Parameters with Frequency and Water Content 15

Backflashover Analysis of Transmission Lines Considering Var...

引用

15th IEEE International Conference on Industry Applications, INDUSCON 2023

作者： de Azevedo, Walter Luiz Manzi Colqui, Jaimis Sajid Leon de Araújo, Anderson Ricardo Justo Filho, José Pissolato Kordi, Behzad School of Electrical and Computer Eng University of Campinas-UNICAMP Campinas Brazil Dep. of Electrical and Computer Engineering University of Manitoba MB Winnipeg Canada

ISBN: (纸本)9798350314182

This paper investigates the lightning performance of three soil models considering their electrical parameters dep.ndent on the frequency and water content. These soil models are Smith-Longmire, Scott, and Messier models. First, the harmonic grounding impedance of a 30-m rod buried in stratified soil is analyzed using the full-wave electromagnetic software Altair FEKO for a frequency range of 100 Hz to 10MHz. Then, their impact on the ground potential rise (GPR) waveforms for different lightning currents is assessed. Finally, a backflashover analysis is carried out for a transmission line on stratified soil. Results indicated a notable variation in the harmonic impedance above 1 MHz. This variation has a pronounced impact on the GPR waveforms for different currents, especially in the peak values when the three soil models are used compared with the frequency-constant soil model. Finally, transient analysis for the transmission line demonstrates that dep.nding on the lightning current waveform and soil model, a backflashover occurs and may lead to an outage in the power system. © 2023 IEEE.

关键词： backflashover electromagnetic transient lightning performance soil modeling water content

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Enhancing Freezing of Gait Detection in Parkinson’s Through Fine-Tuned Deep Learning Models

Enhancing Freezing of Gait Detection in Parkinson’s Through...

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Digital Health (ICDH), IEEE International Conference on

作者： Michele Tebaldi Graziano Pravadelli Florenc Demrozi Rosalba Giugno Cristian Turetta Dep. of Computer Science University of Verona Italy Dep. of Engineering for Innovation Medicine University of Verona Italy Dep. of Electrical Engineering and Computer Science University of Stavanger Norway

ISBN: (数字)9798350368574

ISBN: (纸本)9798350368581

Freezing of Gait (FoG) is a common and disabling symptom in Parkinson’s Disease (PD), characterized by a sudden and temporary inability to initiate or continue walking. FoG arises from various factors such as environmental triggers, or physiological status of people with Parkinson’s. Traditional methods for preventing or alleviating FoG have limitations, prompting exploration into new technologies, such as the combination of sensing technologies and Deep Learning (DL) and Machine Learning (ML) algorithms. However, recognizing FoG with sensors and ML/DL poses challenges, such as the generalizability of the FoG recognition models over different individuals. Moreover, current approaches often require extensive time and effort to personalize the FoG recognition models. To mitigate these challenges, we propose a system that reduces the workload for creating personalized models through a fine-tuning approach. Our methodology has undergone rigorous testing in a subject-indep.ndent setup on a self-collected dataset of 22 subjects. Through the fine-tuning phase, we observed a remarkable average increase of up to $20.9 \%$ in F1-score performance compared to the training and testing approach without fine-tuning.

关键词： Training Deep learning Radio frequency Measurement Machine learning algorithms Refining Physiology

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A Region-Wide Cost-Benefit Assessment of Residential V2G Participation in Demand Response

A Region-Wide Cost-Benefit Assessment of Residential V2G Par...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： Khunsha Nasr Hany Farag Shivam Saxena Dep. Of Electrical Engineering and Computer Science York University Toronto Canada Dep. Of Electrical Engineering and Computer Engineering University of New Brunswick Fredericton Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Participation of Electric Vehicles (EVs) in providing grid services is becoming popular for the economic benefits to their stakeholders, i.e., Local Distribution Companies (LDCs), ratepayers, and EV owners. Battery Electric Vehicles can be effectively used for power system applications such as demand response and peak shaving. However, there is a lack of region-wide cost-benefit analysis of Vehicle to Grid (V2G) participation in providing grid services. Thus, this paper introduces a model that could be utilized to perform residential V2G mainly to evaluate its effect on non-participating ratepayers as a measure of fairness. The developed model yields the benefit-to-cost ratios (BCRs) for LDCs (administrators), ratepayers (those who don’t participate in V2G), and EV owners (participants).

关键词： Vehicle-to-grid Sensitivity analysis Particle measurements Demand response US dep.rtment of Defense Power systems Stakeholders

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