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2025 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2025

作者： Hossain, Muhammad Mubasshir Valera, Tatiana Venkatakrishnan, Satheesh B. Volakis, John L. Electrical and Computer Engineering Dept. Florida International University MiamiFL United States

ISBN: (纸本)9781946815200

High-power all-metal antenna arrays with dual-polarization and wide-angle scanning capabilities are well-suited for directed energy applications. Keeping this in consideration, a dual-polarized reconfigurable reflectarray is proposed to maximize the effective isotropic radiated power (EIRP) of a high-power phased array system. © 2025 USNC-URSI.

关键词： Reflection

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Robust Tracking Control of 6 DOF Manipulator using Artificial Neural Network based Integral Sliding Mode Control

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Transactions of the Korean Institute of electrical Engineers 2022年第1期71卷 140-156页

作者： Hwang, Junha Chwa, Dongkyoung Dept. of Electrical and Computer Engineering Ajou University Korea Republic of

This paper proposes a robust tracking control method for a six degree-of-freedom manipulator using artificial neural network based integral sliding mode control. The proposed method is designed to consider all of the actual manipulator's weight, length, and moment of inertia, while maintaining the robustness against the disturbance and the uncertainty of the system model. The trajectory tracking error can be effectively reduced by using the proposed method even in this situation. Both the simulation and experimental results are provided to verify the validity of the proposed method. © 2022 Korean Institute of electrical Engineers. All rights reserved.

关键词： Neural networks

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Incorporating Multi-Source Transfer Learning into an Unscented Kalman Filter for Object Tracking 59

Incorporating Multi-Source Transfer Learning into an Unscent...

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59th Annual Conference on Information sciences and Systems, CISS 2025

作者： Alotaibi, Omar Mark, Brian L. George Mason University Dept. of Electrical and Computer Engineering FairfaxVA United States

ISBN: (纸本)9798331513269

We develop a framework for incorporating Bayesian transfer learning into an unscented Kalman filter (UKF) to track a nonlinear dynamic motion model in a multi-source system consisting of two or more sensors. This framework extends a previous UKF-based transfer learning scheme for object tracking in a two-sensor system. Our objective is to extend the application of transfer learning to leverage knowledge gained from several source sensors. To achieve this aim, we generalize the transfer learning approach to online multi-source Bayesian transfer learning. The extended framework is approximated via a UKF, where the predicted observation densities of one or more sources are transferred to a primary sensor experiencing higher measurement noise intensity. By leveraging knowledge from multiple sources instead of a single source, the tracking performance of the primary sensor is able to achieve a higher level of estimation accuracy. The performance gain increases proportionally with the number of source sensors. Our numerical results demonstrate the effectiveness of multi-source Bayesian transfer learning in improving tracking accuracy. © 2025 IEEE.

关键词： Contrastive Learning

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A DYC system for 4WID Electric Vehicles via Fuzzy-Nonsingular Fast Terminal Sliding Mode Control

A DYC system for 4WID Electric Vehicles via Fuzzy-Nonsingula...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Eun Lee, Jung Woo Kim, Byeong Electronic and Computer Engineering University of Ulsan City of Ulsan Dept. of Electrical Korea Republic of

ISBN: (纸本)9798331533892

In this paper, we introduce a novel Direct Yaw Moment Control (DYC) system designed to bolster the stability of four-wheel independent drive (4WID) electric vehicles in diverse extreme driving conditions. Our proposed system is developed based on the design of Non-singular Fast Terminal Sliding Mode Control (NFTSMC) methods to calculate two optimal additional yaw moments, each targeting specific control objectives: the desired yaw rate and the desired sideslip angle. These calculated values are then intelligently weighted in real time using a Fuzzy Logic system to determine the final additional yaw moment applied to the vehicle. The stability of the NFTSMC methods is rigorously ensured through the Lyapunov theory. Simulation results underscore the efficacy of our proposed DYC system, particularly in navigating through extreme driving scenarios. Comparisons with no control and fixed weight-based SMC strategies demonstrated significant improvements in both yaw rate and sideslip angle tracking performance. The proposed method not only amplifies control effectiveness, ensuring swift convergence and high precision, but also substantially mitigates chattering phenomena, as evidenced by yaw moment and torque observations. © 2025 IEEE.

关键词： Lyapunov methods

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Denoising Task-Based Functional Magnetic Resonance Imaging Data Using Hybrid Deep Neural Network Model

Denoising Task-Based Functional Magnetic Resonance Imaging D...

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2023 International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2023

作者： Kashfolayat, Sahar Shiri, Hamid Baniasadi, Amirali Univ. of Victoria Dept. of Electrical and Computer Engineering Victoria Canada Univ. of Tabriz Dept. of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798350339291

We present a hybrid deep neural network model for Denoising task-based fMRI data. This model is based on the Deep Neural Network (DNN) model [1] and improves its performance by utilizing different layers for Grey Matter and non-Grey Matter related data. The preprocessed data passes through GM and non-GM masks. Then it is fed to the network and passes through temporal filters along with an LSTM layer. The result will be classified using time-distributed fully-connected layers -since the input data is a time-series of voxel values-which have different structures for GM and non-GM input data. This hybrid mechanism helps the model to reduce the correlation between non-GM processed data and the design matrix significantly. We also increase the correlation of GM output, which is equivalent to better cost function and less noise. This new self-supervised model is tested using simulated data and the HCP database [2]. We succeed in denoising task better than the DNN model by an average of at least 4%. © 2023 IEEE.

关键词： Neural network models

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Cybersecurity for Industrial IoT: A Review on Vulnerabilities and Attacks 8

Cybersecurity for Industrial IoT: A Review on Vulnerabilitie...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kumari, Reetu Kumar, Sunil Amandeep Dutta, Kamlesh Kumari, Aparna Guru Jambheshwar University of Science and Technology Dept. of Computer Science & Engineering Haryana Hisar India Guru Jambheshwar University of Science and Technology Dept. of AI & Data Science Haryana Hisar India National Institute of Technology Hamirpur Dept. of Computer Science & Engineering Himachal Pardesh India Institute of Technology Nirma University Dept. of Computer Science & Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798331520762

Internet has evolved from a network of connecting people to a network of connecting things, leading to a more complex and sophisticated network of Industrial Things, known as Industrial IoT (IIoT) today. This evolution has revolutionized the traditional Industrial system into real-time smarter systems that enhanced overall efficiency and productivity. Despite its benefits, the interconnected nature of IIoT, expanded the attack surface, exposing the systems to new crucial and complex cybersecurity threats. Several real time attacks on various industries as mentioned in this paper and the increasing cybersecurity cost over the years, clearly states the need of robust security solutions. To deeply understand the cybersecurity aspect, we provided a comprehensive study of attacks and vulnerabilities in this paper. In this paper, we did a indept. study of various IIoT related severe attacks and aspects related to these attacks, like vulnerabilities, impacts, potential solutions etc. We also provided future research directions to help the research community, to work upon and develop more robust and efficient cybersecurity solutions. © 2025 IEEE.

关键词： attacks cybersecurity etc IIoT Industrial IoT IoT vulnerabilities

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Enhancing Security in Communication Networks through Quantum Key Distribution 2

Enhancing Security in Communication Networks through Quantum...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Purushottam, Vaishnavi V Meena, S. Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Bengaluru India Buddha Institute of Technology Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Quantum Key Distribution or QKD revolutionizes secure communication using quantum mechanics. Unlike traditional cryptographic methods vulnerable to advances in computing power, QKD guarantees unconditional security by detecting any eavesdropping attempt. Quantum Key Distribution (QKD) constitutes a through development in the realm of secure communications by utilizing the ideas of quantum mechanics to provide potentially unbreakable encryption. This study examines the most current advancements in QKD, including the recently announced Measurement-Device-Independent QKD (MDI-QKD) and protocols like BB84 and E91. We analyze the real-world implementation, including satellite-based QKD and quantum repeaters. Furthermore, we discuss the challenges and opportunities of integrating QKD into present communication infrastructure. The significance of QKD in attaining secure communication networks for the next generation is underscored by our findings, which also have ramifications for forthcoming research and applications. Ultimately, this research aims to contribute to the development of robust and impenetrable communication networks in an increasingly interconnected world. © 2024 IEEE.

关键词： Quantum communication

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Diversity Seeking Techniques for Red-Teaming Large Language Models

Diversity Seeking Techniques for Red-Teaming Large Language ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lee, Seokhan Ku, Bonhwa Ko, Hanseok Dept. Computer Science and Engineering Korea University Seoul Korea Republic of Machine Learning Big Data Institute Korea University Seoul Korea Republic of Dept. Electrical and Computer Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798350368741

In this paper, we present new techniques for increasing the diversity of red-teaming prompts generated by automated machine learning-based methods, thereby enabling the discovery of more vulnerabilities in large language models. Using reinforcement learning to train models to output effective prompts for this task results in the models converging deterministically to a single output. Our first technique, which we term Defender, acts by blocking the reward signal for prompts that have already been discovered, thus making what was a stationary problem into a non-stationary problem that compels the reward maximizing algorithm to continually seek new prompts. Our second technique, Teamplay, trains two prompt generation models in tandem and adds the KL divergence between them to the reward in order to make them search in disparate regions of the space of prompts. Our techniques are shown experimentally to increase the effectiveness and diversity of prompts generated by existing reinforcement learning baselines. © 2025 IEEE.

关键词： AI safety large language models red-teaming reinforcement learning toxicity

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Line Graph is the Key: An Exploration of Line Graph on Link Prediction with Social Networks 11

Line Graph is the Key: An Exploration of Line Graph on Link ...

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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Xing, Chen Makrehchi, Masoud Ontario Tech University Dept. of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798331531904

Network data plays a crucial role in various real-world applications, as connections between entities can be represented and analyzed through graphs. These include various types, such as social, information, and technical networks. However, the complex topologies of these networks present challenges in converting graph data into machine-readable vector formats. Existing models, such as Graph Neural Networks, Graph Attention Networks, and node2vec, have made strides in graph embeddings. For edge-related tasks, models such as node2vec typically use indirect methods like concatenating node vectors to represent edges. This approach is useful for tasks like link prediction. In this paper, we present LineDi2vec, a novel approach that improves the Node2vec embedding method by utilizing a line graph. The proposed LineDi2vec not only generalizes the original graphs, transforming the relationships between edges and nodes but also maintains the original graphs' topological integrity for effective node embedding by Node2vec. We evaluated LineDi2vec on four real-world datasets, focusing on link prediction. The results demonstrate that LineDi2vec outperforms traditional node concatenation methods. © 2024 IEEE.

关键词： Graph embeddings

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Comprehensive Women's Health Risk Assessment Platform 2

Comprehensive Women's Health Risk Assessment Platform

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Navya, S. Saha, Sharmistha Nagarajan, Deepa Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349221

The goal of this research is to develop a machine learning-based method for predicting several diseases in women, with an emphasis on polycystic ovarian syndrome (PCOs), diabetes, heart disease, and breast cancer. Timely and accurate prediction of these disorders is essential for early intervention and prevention due to their impact on women's health and prevalence. Predictive models are trained using a wide variety of medical data, including genetic markers, lifestyle factors, demographic data, and clinical test findings. To find patterns and connections between these predictors and the incidence of diseases, machine learning methods are used, such as decision trees, random forests, and support vector machines. The models are able to forecast with accuracy the probability that a woman would develop one or more of these diseases by efficiently obtaining and evaluating data from these datasets. Metrics including accuracy, precision, recall, and F1-score are used to assess the models' performance;the outcomes demonstrate the models' promising levels of predictive power. The creation of a system like this can facilitate targeted screening and preventive measures, assist medical practitioners in identifying high-risk individuals, and eventually lead to better patient outcomes. © 2024 IEEE.

关键词： Diseases

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