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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Rahman, Irham Izzatur Miyauchi, Hajime Takano, Hirotaka Gifu University Department of Electrical Electronic and Computer Engineering Gifu Japan Kumamoto University Department of Computer Science and Electrical Engineering Kumamoto Japan

ISBN: (纸本)9798350378573

This study proposes a decentralized frequency scheduling method to achieve SoC balancing with no communication protocol. Since the frequency scheduling method is based on modifying droop control of inverter, to make the power output of inverter to be proportional to the SoC, the frequency output of inverter would be affected. This study considers to minimize the frequency deviation within European Norm EN50160 (±1% of the nominal value of the frequency), while also able to satisfy SoC balancing. To achieve it, we propose fuzzy logic controller to weight the value of droop coefficient ( ) instead of linear scaling in conventional frequency scheduling. The proposed method is studied in several test to show the performance and compare to conventional method. From the result, the proposed method performs better in SoC balancing, and results in better system reliability. We also study the proposed battery balancing system in various condition of PV and load profile, as well "plug and play"condition of microgrid. The proposed method can perform well in both conditions. © 2024 IEEE.

关键词： State of charge

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Secure signal and image transmissions using chaotic synchronization scheme under cyber-attack in the communication channel

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Soft Computing 2025年第1期29卷 339-353页

作者： Nobakht, Shaghayegh Ahmadi, Ali-Akbar Industrial Automation Laboratory Electrical and Computer Engineering Department Kharazmi University Tehran Iran

In this paper, problem of secure message (signal and image) transmission is studied. The message is encrypted by masking it with a chaotic system state and then transmitted to receiver-side via a communication channel. It is assumed that the communication channel may be prone to a cyber-attack. The transmission system dynamics in presence of the cyber-attack in the communication channel is modeled by a singular dynamical system. Then, at the receiver-side, a novel observer is designed to recover the message together with the cyber-attack signal. Estimation of the cyber-attack is crucial for monitoring the communication channel and enable a network supervisor to mitigate the adverse consequences and make an appropriate strategy. It is assumed neither restrictions on the message nor on the cyber-attack signals. This makes the proposed scheme, the best solution in the real applications. Moreover, comparing to the existing methods which two public and private channels are required, only one communication channel is used in this paper for transmission the encrypted signal which results in a cheaper solution. Solving the optimization problem can be easily performed with the help of the existing toolboxes such as YALMIP and CVX software in MATLAB. Finally, numerical simulations for signal and image transmission are presented to justify the applicability and superior performance of the proposed scheme. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Cyber attacks

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Introduction to the Special Issue on computerModeling for Smart Cities Applications

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computer Modeling in engineering & sciences 2024年第2期138卷 1015-1017页

作者： Wenbing Zhao Chenxi Huang Yizhang Jiang Department of Electrical and Computer Engineering Cleveland State UniversityClevelandOH 44115USA School of Computer Science Xiamen UniversityXiamen36100China School of Artificial Intelligent and Computer Jiangnan UniversityWuxi224122China

A significant fraction of the world’s population is living in cities. With the rapid development ofinformation and computing technologies (ICT), cities may be made smarter by embedding ICT intotheir infrastructure. By smarter, we mean that the city operation will be more efficient, cost-effective,energy-saving, be more connected, more secure, and more environmentally friendly. As such, a smartcity is typically defined as a city that has a strong integration with ICT in all its components, includingits physical components, social components, and business components [1,2].

关键词： smart typically Smart

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Printing Stretchable Electronics With Fused Filament Fabrication

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第2期4卷 98-107页

作者： Jahan, Sharmin Lazarus, Nathan University of Delaware Electrical and Computer Engineering Department NewarkDE19716 United States

Additive manufacturing is a powerful approach forintegrating flexible and stretchable conductors into complex3-D structures, but many current printing technologies, suchas direct ink writing (DIW), are expensive and challengingto access for many potential users, such as hobbyists andsmall companies. In this work, a low-cost commercial fusedfilament fabrication (FFF) 3-D printer is used to manufacturelow-resistivity stretchable serpentine conductors. By using dualextruder heads, serpentines are printed in a highly conductivemetal-particulate-based composite on a thermoplastic elastomer(TPE) part and demonstrated to survive mechanical strains of upto 25%, sufficient for use in close contact with the human *** electroless plating is also demonstrated to metalize theprinted traces, depositing a thin layer of copper for improvedelectrical conductivity. Using this approach, we demonstrateconductivity improvements by about three orders of magnitude(∼ 103) over past FFF printed serpentines, a major milestonein making stretchable electronics cheaper and more *** resulting stretchable conductors are then demonstrated toprint devices, including stretchable inductors and stretchableinterconnect on a soft robotic actuator. © 2022 IEEE.

关键词： Reinforced plastics

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Fabrication of LCE Microactuator Arrays Through Soft Lithography With Surface Alignment

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Journal of Microelectromechanical Systems 2025年第3期34卷 241-243页

作者： Li, Ke Kim, Jae-Jun Fernandes, Jayer Jiang, Hongrui University of Wisconsin–Madison Department of Electrical and Computer Engineering MadisonWI53705 United States University of Wisconsin–Madison Department of Electrical and Computer Engineering Department of Biomedical Engineering Department of Materials Science and Engineering Department of Ophthalmology and Visual Sciences McPherson Eye Research Institute MadisonWI53706 United States

Liquid crystal elastomers (LCEs) offer potentially programmable actuation through precise molecular alignment, making them ideal for microactuators in soft robotics and optical systems. However, achieving precise microscale alignment for LCE actuator arrays through scalable microfabrication approaches has been challenging. This letter introduces a low-cost surface alignment method to fabricate LCE microactuator arrays, reducing the dependency on expensive equipment, improving accessibility and manufactuarability compared to existing studies using field-assisted alignment methods. Thermal actuation tests demonstrated strong thermal responsiveness and stability. Our method offers a superior approach to integrating LCE microactuator arrays with modern microfabrication processes, promising multitudinous applications in MEMS and beyond. © 2025 IEEE. All rights reserved,.

关键词： Microfabrication

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Entanglement-Controlled Quantum Federated Learning

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IEEE Internet of Things Journal 2025年第11期12卷 18318-18330页

作者： Park, Soohyun Lee, Hyunsoo Jung, Soyi Park, Jihong Bennis, Mehdi Kim, Joongheon The Division of Computer Science Sookmyung Women’s University Seoul04310 Korea Republic of The Department of Electrical and Computer Engineering Korea University Seoul02841 Korea Republic of The Department of Electrical and Computer Engineering Ajou University Suwon16499 Korea Republic of Singapore The Centre for Wireless Communications University of Oulu Oulu90014 Finland

According to the advances in quantum computing and distributed learning, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. To successfully opeate QFL under various and dynamic channel conditions in Internet of Things (IoT) environments, this paper develops a novel depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs), and thus, proposes an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eSQNNs. Even though the proposed eSQNN-based eSQFL is superior, training the depth-controllable eSQNN architecture is challenging due to high entanglement entropy and inter-depth interference. Therefore, the proposed method in this paper mitigates the interference using entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing inter-depth quantum state differences, respectively. Furthermore, the proposed method optimizes the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. The novelty of this work is evaluated via extensive simulations in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions. © 2014 IEEE.

关键词： Federated learning

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Exploring Hyperdimensional Computing Robustness Against Hardware Errors

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IEEE Transactions on computers 2025年第6期74卷 1963-1977页

作者： Zhang, Sizhe Juretus, Kyle Jiao, Xun Villanova University Department of Electrical and Computer Engineering VillanovaPA19085 United States

Brain-inspired hyperdimensional computing (HDC) is an emerging machine learning paradigm leveraging high-dimensional spaces for efficient tasks like pattern recognition and medical diagnostics. As a lightweight alternative to deep neural networks, HDC offers smaller model sizes, reduced computation, and memory-centric processing. However, deploying HDC in safety-critical applications, such as healthcare and robotics, is challenged by hardware-induced errors. This paper investigates HDC's robustness to memory errors via extensive bit-flip injection experiments on item and associative memories. Results reveal that certain bit-flips severely degrade accuracy. To address this, we introduce the Hyperdimensional Bit-Flip Search (HD-BFS), a similarity-guided method for identifying vulnerabilities and crafting efficient attacks, where flipping just 6 critical bits—3.9% of random bit-flips—reduces accuracy to chance levels. We further propose Hyperdimensional Accelerated Bit-Flip Search (HD-ABFS), which narrows the search space by targeting critical dimensions and most significant bits (MSBs), achieving up to 282x speedup over HD-BFS. Finally, we develop an effective protection mechanism to enhance model safety. These insights highlight HDC's resilience to random errors, offer robust defenses against targeted attacks, and advance the security and reliability of HDC systems. © 1968-2012 IEEE.

关键词： Deep neural networks

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Deep Reinforcement Learning Data Collection for Bayesian Inference of Hidden Markov Models

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1217-1232页

作者： Alali, Mohammad Imani, Mahdi Northeastern University Department of Electrical and Computer Engineering BostonMA02115 United States

Hidden Markov models (HMMs) are a powerful class of dynamical models for representing complex systems that are partially observed through sensory data. Existing data collection methods for HMMs, typically based on active learning or heuristic approaches, face challenges in terms of efficiency in stochastic domains with costly data. This article introduces a Bayesian lookahead data collection method for inferring HMMs with finite state and parameter spaces. The method optimizes data collection under uncertainty using a belief state that captures the joint distribution of system states and models. Unlike traditional approaches that prioritize short-term gains, this policy accounts for the long-term impact of data collection decisions to improve inference performance over time. We develop a deep reinforcement learning policy that approximates the optimal Bayesian solution by simulating system trajectories offline. This pretrained policy can be executed in real time, dynamically adapting to new conditions as data is collected. The proposed framework supports a wide range of inference objectives, including point based, distribution based, and causal inference. Experimental results across three distinct systems demonstrate significant improvements in inference accuracy and robustness, showcasing the effectiveness of the approach in uncertain and data-limited environments. © 2020 IEEE.

关键词： Deep reinforcement learning

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Noninvasive estimation of blood potassium concentration using ECG and FCM-ANFIS model

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Research on Biomedical engineering 2024年第3-4期40卷 647-658页

作者： Samandari, Zeynab Molaeezadeh, Seyyedeh Fatemeh Department of Electrical and Computer Engineering Jundi-Shapur University of Technology Dezful Iran

Purpose: Potassium imbalance, often symptomless but potentially fatal, is prevalent in patients with kidney or heart conditions. Traditional laboratory tests for potassium measurement are costly and require skilled technicians. Additionally, phlebotomy is challenging and painful for high-risk patients. To address these issues, this study proposes a noninvasive and bloodless approach that leverages the electrocardiogram (ECG) signal, which may be sensitive to potassium changes. The research introduces a data-driven framework for ECG-based potassium measurement. Methods: The proposed framework comprises five main components: Cohort Selection—Among 461,178 patients in the ECG-ViEW II database, those with simultaneous ECG, Age-adjusted Charlson Comorbidity Index (ACCI), and potassium measurements within a 5-min interval are chosen;Data Labeling—Three labels—hypokalemia, normal, and hyperkalemia—are defined;Feature Extraction—Relevant features such as RR interval, PR interval, QRS duration, QT interval, QTc interval, P axis, QRS axis, T axis, and ACCI are extracted;Feature Selection—The Kruskal–Wallis technique evaluates feature importance and selects discriminative ones;and Model Design—An ANFIS model based on FCM clustering (FCM-ANFIS) is developed using the selected features. Results: Among the 42 patients selected during cohort filtering, T axis demonstrated significant association with potassium levels (P © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2024.

关键词： Electrocardiograms

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Theoretical Analysis of Detection of Flying Vehicles Based on the Passive Radiometric Detection of Microwave-Millimeter-Terahertz Wavelength Electromagnetic Emissions from Exhaust Plasma Gases

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Progress in Electromagnetics Research C 2025年 154卷 267-275页

作者： Chatziathanasiou, Thomas N. Uzunoglu, Nikolaos Microwave Laboratory Department of Electrical and Computer Engineering National Technical University of Athens Greece

The feasibility of using passive radiometric detection of chaotic electromagnetic signals emanating from low density plasma plumes of the jet exhaust gases to detect low radar cross section aircrafts is analyzed for the frequency band 3.108–3.1012 Hz. The aircraft exhaust plume gas formation is modelled with a number of discrete different dimensions ellipsoids, with each having different electron densities and temperatures. Electromagnetic radiation analysis of emitting signals is computed applying fluctuation-dissipation theorem and geometric considerations. The detection range of characteristic military jet aircraft is computed numerically for the whole frequency band, from UHF to 3 THz. It is shown that high range detection can be achieved at upper microwave frequencies. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Terahertz waves

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