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Advanced Electromagnetics 2025年第1期14卷 59-64页

作者： Karami, Pouya Yahya, Salah I. Palash, Behnam Chaudhary, Muhammad Akmal Assaad, Maher Parandin, Fariborz Roshani, Saeed Hazzazi, Fawwaz Roshani, Sobhan Department of Electrical Engineering Kermanshah Branch Islamic Azad University Kermanshah Iran Department of Communication and Computer Engineering Cihan University-Erbil Erbil44001 Iraq Department of Software Engineering Faculty of Engineering Koya University KOY45 Koya Iraq Department of Electrical and Computer Engineering College of Engineering and Information Technology Ajman University 346 Ajman United Arab Emirates Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj11492 Saudi Arabia

In this paper, design and modeling of an all-optical 2×1 multiplexer based on 2D photonic crystals and artificial neural networks (ANNs) are presented. The proposed structure aims to maximize the difference between the output powers in logical states, which is critical for enhancing the system ability to distinguish between these states. In this study, an ANN model is employed to accurately predict the normalized output power of the designed photonic crystal multiplexer, providing a time-efficient alternative to conventional simulation methods for analyzing multiplexer behavior across various logical states. The results demonstrate significant improvements in signal separation and overall performance compared to previous works. Additionally, a detailed comparison of the normalized output power for different logic states is provided, highlighting the advantages of the proposed design. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Silicon photonics

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Strategic Renewable Energy Resource Selection Using a Fuzzy Decision-Making Method

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Intelligent automation & Soft Computing 2023年第2期35卷 2117-2134页

作者： Anas Quteishat M.A.A.Younis Electrical Engineering Department Faculty of Engineering TechnologyAl-Balqa Applied UniversityAl-SaltJordan Department of Electrical and Computer Engineering Sohar UniversitySultanate of Oman

Renewable energy is created by renewable natural resources such as geothermal heat,sunlight,tides,rain,and *** resources are vital for all countries in terms of their economies and *** a result,selecting the optimal option for any country is critical in terms of energy *** country is nowadays planning to increase the share of renewable energy in their universal energy sources as a result of global *** the present work,the authors suggest fuzzy multi-characteristic decision-making approaches for renew-able energy source selection,and fuzzy set theory is a valuable methodology for dealing with uncertainty in the presence of incomplete or ambiguous *** study employed a hybrid method for order of preference by resemblance to an ideal solution based on fuzzy analytical network process-technique,which agrees with professional assessment scores to be linguistic phrases,fuzzy numbers,or crisp *** hybrid methodology is based on fuzzy set ideologies,which calculate alternatives in accordance with professional functional requirements using objective or subjective *** best-suited renewable energy alternative is discovered using the approach presented.

关键词： Multi characteristic decision making framework fuzzy sets fuzzy theory renewable energy energy resource selection

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BBTM-IoT: New IoT Systems Trust Management Model Using Blockchain 19

BBTM-IoT: New IoT Systems Trust Management Model Using Block...

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19th Iranian Conference on Intelligent Systems, ICIS 2024

作者： Sharifpour, Mohammadjavad Tochae, Seyyed Amir Asghari Bohlooli, Shabnam Department of Computer Engineering Tehran Iran Kharazmi University Faculty of Engineering Department of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331507565

The Internet of Things (IoT) seeks to establish a vast network comprising billions of interconnected devices to enable seamless data exchange and intelligent interactions between people and objects. This network is characterized by openness, diversity, and dynamicity, leading to trust issues that impede the widespread adoption of IoT. Trust management plays a crucial role in identifying malicious nodes, fostering trust relationships, and bolstering system security. Conventional trust management systems face significant challenges that hinder their effectiveness in supporting IoT development. Blockchain, as a disruptive technology, shows promise in addressing these trust management challenges by leveraging its decentralized, consistent, and tamper-proof features. This study aims to enhance trust in the Internet of Things by leveraging blockchain technology. The article introduces key concepts such as blockchain technology, CTL (Certificate Trust List), IoT, and trust management, emphasizing critical research and development in trust management models. To highlight the importance of blockchain in maintaining trust, a novel trust management model is proposed, incorporating CTL to validate IoT Devices' legitimacy in blockchain transactions. Finally, the article assesses the efficiency of the proposed model, with the outcomes providing strong proof of its effectiveness. Our simulation results indicate that utilizing the PoW (Proof of Work) consensus model in an IoT system may not be the most optimal approach. The model we have presented displays a reduced memory growth rate of only 0.39% per block. This enhancement is projected to greatly improve the process of block verification. Specifically, the PoW algorithm took 4.2 seconds to incorporate 10 blocks, whereas our model completed the same task in just 0.61 seconds. © 2024 IEEE.

关键词： Blockchain

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A Review on Cybersecurity Analysis,Attack Detection,and Attack Defense Methods in Cyber-physical Power Systems

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Journal of Modern Power Systems and Clean Energy 2023年第3期11卷 727-743页

作者： Dajun Du Minggao Zhu Xue Li Minrui Fei Siqi Bu Lei Wu Kang Li Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronics Engineering and AutomationShanghai UniversityShanghaiChina Department of Electrical Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA School of Electronic and Electrical Engineering University of LeedsLeeds LS29JTUK IEEE

Potential malicious cyber-attacks to power systems which are connected to a wide range of stakeholders from the top to tail will impose significant societal risks and *** timely detection and defense are of crucial importance for safe and reliable operation of cyber-physical power systems(CPPSs).This paper presents a comprehensive review of some of the latest attack detection and defense ***,the vulnerabilities brought by some new information and communication technologies(ICTs)are analyzed,and their impacts on the security of CPPSs are *** malicious cyber-attacks on cyber and physical layers are then analyzed within CPPSs framework,and their features and negative impacts are ***,two current mainstream attack detection methods including state estimation based and machine learning based methods are analyzed,and their benefits and drawbacks are ***,two current mainstream attack defense methods including active defense and passive defense methods are comprehensively ***,the trends and challenges in attack detection and defense strategies in CPPSs are provided.

关键词： Cyber-physical power systems security threat attack detection attack defense state estimation machine learning

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RIS-Assisted Integrated Sensing and Communication Systems: Joint Reflection and Beamforming Design

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 908-927页

作者： Ismail, Mohamed I. Shaheen, Abdullah M. Fouda, Mostafa M. Alwakeel, Ahmed S. Modern Academy for Engineering and Technology Department of Electronic Engineering and Communication Technology Cairo4410242 Egypt Suez University Faculty of Engineering Department of Electrical Engineering Suez8151650 Egypt Idaho State University College of Science and Engineering Department of Electrical and Computer Engineering PocatelloID83209 United States

The Integrated Sensing and Communication (ISAC) system merged with Reconfigurable Intelligent Surface (RIS) has recently received much attention. This paper proposes an intelligent metaheuristic version of Enhanced Artificial Ecosystem Optimizer (EAEO) for a suggested beamforming optimization framework in ISAC systems with RIS. Two RIS are utilized in the presented model to enhance the received signal-to-noise ratio (SNR) for multiple-input multiple-output (MIMO) communication systems. Also, each element of each RIS scatters the incoming signal with a controllable phase-shift, without increasing its power. The signal is transmitted by a Dual-Function Base Station (DFBS) that is integrated with the RIS, which performs both communication and sensing functions simultaneously. The ISAC system is designed to optimize the signal transmission through the RIS by maximizing the SNR to increase the overall performance. In the proposed EAEO version, a Fitness-Distance-Balance Model (FDBM) is combined with the standard Artificial Ecosystem Optimizer (AEO) to improve the quality of the solutions in multidimensional and nonlinear optimization scenarios. The simulation results show that the proposed EAEO algorithm improves the SNR of different users for different numbers of RIS elements. The SNR reaches 25 dB when using 200 RIS elements. Moreover, the proposed EAEO is tested on the IEEE Congress on Evolutionary Computation 2017 (IEEE CEC'17) test suite. A comparative analysis is conducted compared to the standard AEO and several recent algorithms. The proposed EAEO derives great effectiveness and robustness over the others as it provides a higher rate of success in achieving the global optimum point. © 2020 IEEE.

关键词： Signal to noise ratio

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Practical autoencoder based anomaly detection by using vector reconstruction error

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Cybersecurity 2023年第2期6卷 1-13页

作者： Hasan Torabi Seyedeh Leili Mirtaheri Sergio Greco Department of Electrical and Computer Engineering Faculty of EngineeringKharazmi UniversityTehranIran Department of Informatics ModelingElectronics and System EngineeringUniversity of CalabriaArcavacataItaly.

Nowadays,cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the *** computing services are available through common internet protocols and network standards.n addition to the unique benefits of cloud computing,insecure communication and attacks on cloud networks cannot be *** are several techniques for dealing with network *** this end,network anomaly detection systems are widely used as an effective countermeasure against network *** anomaly-based approach generally learns normal traffic patterns in various ways and identifies patterns of *** anomaly detection systems have gained much attention in intelligently monitoring network traffic using machine learning *** paper presents an efficient model based on autoencoders for anomaly detection in cloud computing *** autoencoder learns a basic representation of the normal data and its reconstruction with minimum ***,the reconstruction error is used as an anomaly or classification *** addition,to detecting anomaly data from normal data,the classification of anomaly types has also been *** have proposed a new approach by examining an autoencoder's anomaly detection method based on data reconstruction *** the existing autoencoder-based anomaly detection techniques that consider the reconstruction error of all input features as a single value,we assume that the reconstruction error is a *** enables our model to use the reconstruction error of every input feature as an anomaly or classification *** further propose a multi-class classification structure to classify the *** use the CIDDS-001 dataset as a commonly accepted dataset in the *** evaluations show that the performance of the proposed method has improved considerably compared to the existing ones in terms of accuracy,recall,false-positive rate,and F1-score

关键词： Cloud Practical Anomaly detection Autoencoder Reconstruction error Machine learning

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Resilient Platooning Control of Connected Automated Vehicles in the Presence of Cyber-attacks 63

Resilient Platooning Control of Connected Automated Vehicles...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Gusrialdi, Azwirman Iqbal, Muhammad Qu, Zhihua Unit Tampere University Automation and Mechanical Engineering Finland Aalto University Department of Electrical Engineering and Automation Finland University of Central Department of Electrical and Computer Engineering FL United States

ISBN: (纸本)9798350316339

This paper considers the problem of platooning control of connected automated vehicles under cyber-attacks. Specifically, the attacker aims to prevent the follower vehicle from maintaining a pre-defined safe distance from its immediate predecessor by manipulating the measurement of the on-board radar and inserting bounded injections into the communication links and actuators of the follower vehicles. A novel distributed resilient control is proposed which does not require any assumptions on the number of attacks. It is shown that by appropriately designing the information being exchanged between the vehicles, the resilient control ensures that the follower vehicles converge to the leader vehicle's velocity and constant distance between the vehicles in presence of any number of attacks. Furthermore, the proposed resilient control also enables attack detection and identification in real-time and distributed manner. A Numerical example demonstrates the effectiveness of the proposed strategy. © 2024 IEEE.

关键词： Cyber attacks

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Brain tumors segmentation using a hybrid filtering with U-Net architecture in multimodal MRI volumes

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International Journal of Information Technology (Singapore) 2024年第2期16卷 1033-1042页

作者： Esmaeilzadeh Asl, Sima Chehel Amirani, Mehdi Seyedarabi, Hadi Department of Electrical Engineering Urmia University Urmia Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

Brain MRI makes it possible to evaluate brain tumor diagnosis and treatment. There are, however, many challenges for automated brain tumor segmentation, and these challenges have become tougher as deep learning has progressed. Gliomas tumors, which can be proliferated quickly, can develop anywhere and in any shape in the brain. This article presents a new method for automated brain tumor segmentation using hybrid filters and employing 3D medical images. The U-Net model is employed for semantic segmentation. 2D MRIs are taken from 3D MRI, which allows us to view the tumor in two dimensions in three levels (axial, sagittal, and coronal). This ensures that there are not any tumor points that have been overlooked for detection, and the tumor volume images are available in two dimensions. In addition, three types of MRI are used to segment this tumor from each patient. In these three types of MRI, the Glioma tumor is more visible than the fourth type (T2). To improve the tumor segmentation, a hybrid filter, including the bilateral filter and blacktop hat, is used for pre-processing. The intersection of these images is used to evaluate the model. Experimental results showed that the proposed approach demonstrates better tumor segmentation compared to similar studies. In addition, the computational and memory used is lower than what has been done in recent research. The Dice coefficient and Hausdorff distance are estimated at 91.34% and 3.74 for the whole tumor image in the present work, respectively. Experiments show that our method is an optimal method for the segmentation of brain images. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023.

关键词： Brain tumor Deep learning Medical image segmentation U-Net

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Close Quarters Permittivity Detection Based on Tagging Antenna Sensor for Solid Material Characterization

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Progress in Electromagnetics Research C 2025年 154卷 21-29页

作者： Alam, Syah Surjati, Indra Mardian, Raden D. Sari, Lydia Daffin, Ghatfan Iznih Zakaria, Zahriladha Asrar, Leni D. Firmansyah, Teguh Department of Electrical Engineering Universitas Trisakti West Jakarta11440 Indonesia Faculty of Electronic and Computer Engineering and Technology Universiti Teknikal Malaysia Melaka 76100 Malaysia Department of Electrical Engineering Universitas Sultan Ageng Tirtayasa Banten 42117 Indonesia

This research proposes a tagging antenna sensor for permittivity detection of solid materials based on a close quarter approach. The sensor is proposed to operate at a frequency of 2.53 GHz using a single port resonator with a reflection coefficient (S11) ≤ −10 dB. The sample is placed directly in the sensing area of the antenna sensor based on the concentration of the electric field. Permittivity detection is proposed based on the resonant frequency shift of the transmission coefficient (S21) using interrogator antennas separated by a distance of (d) = 100 mm determined using the Fresnel region. Based on the measurement results, the antenna sensor has a high accuracy of 96% while the sensitivity and ∆F are 0.39% and 0.012 GHz, respectively. Moreover, the sensitivity ofthe proposed sensor is still low due the low concentration of the electric field. Therefore, increasing the sensitivity of the antenna sensor can be recommended as further work such as combining the structure of single port resonator with another structure such as interdigital capacitor and artificial magnetic conductor (AMC). Finally, this research makes a significant contribution to the permittivity detection of solid materials with a close quarter approach to support real time and flexible measurements and can be recommended for several applications for the biomedical, pharmaceutical, and material quality control industries. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Permittivity measurement

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Autonomous embodied navigation task generation from natural language dialogues

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Science China(Information Sciences) 2025年第5期68卷 119-132页

作者： Haifeng XU Yongchang LI Lumeng MA Chunwen LI Yanzhi DONG Xiaohu YUAN Huaping LIU Department of Automation Tsinghua University School of Physics and Electronic Information Yantai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Computer Science and Technology Tsinghua University

Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks often require multiple instructions and prolonged monitoring, which can be time-consuming and demanding for users. Despite this, there is limited research on enabling robots to autonomously generate tasks based on real-life scenarios. Advanced intelligence necessitates robots to autonomously observe and analyze their environment and then generate tasks autonomously to fulfill human requirements without explicit commands. To address this gap, we propose the autonomous generation of navigation tasks using natural language dialogues. Specifically, a robot autonomously generates tasks by analyzing dialogues involving multiple persons in a real office environment to facilitate the completion of item transportation between various *** propose the leveraging of a large language model(LLM) through chain-of-thought prompting to generate a navigation sequence for a robot from dialogues. We also construct a benchmark dataset consisting of 625 multiperson dialogues using the generation capability of LLMs. Evaluation results and real-world experiments in an office building demonstrate the effectiveness of the proposed method.

关键词： proactive robot robot navigation service robot large language model

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