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Unobstructive andsafe-to-wear watt-level wireless charger

npj Flexible Electronics 2024年第1期8卷 117-131页

作者： Sangjun Kim Jonathan Wells Sarnab Bhattacharya Hamsi Nathan Jiaming He Isabella Tubilla Heeyong Huh Pooja Kakani Ali Farshkaran Praveenkum Pasupathy Jianshi Zhou Emily Porter Nathan Lazarus Nanshu Lu Department of Mechanical Engineering The University of Texas at AustinAustinTXUSA Department of Electrical and Computer Engineering The University of Texas at AustinAustinTXUSA Department of Biomedical Engineering The University of Texas at AustinAustinTXUSA Texas Materials Institute The University of Texas at AustinAustinTXUSA Department of Aerospace Engineering and Engineering Mechanics The University of Texas at AustinAustinTXUSA Department of Electrical and Computer Engineering University of DelawareNewarkDEUSA US Army Research Laboratory AdelphiMDUSA

A wearable microgrid that centralizes and distributes harvested energy across different body regions can optimize power utilization and reduce overall battery *** setup underscores the importance of developing cable-free wireless power transfer(WPT)systems for mobile and portable devices to eliminate the risks posed by wired connections,especially in dynamic and hazardous *** introduce a thin,stretchable,and safe hand band capable of watt-level wireless charging through the widely adopted Qi protocol operating at 130 *** implementation of nonadhesive fabric encapsulation serves to protect the 50-μm-thin spiral copper antenna from mechanical strain,ensuring an overall hand band stretchability of 50%.We also create a stretchable“Ferrofabric”,characterized by a magnetic permeability of 11.3 and a tensile modulus of 75.3 kPa,that provides magnetic shielding for the antenna without compromising ***“Ferrofabric”improves the coil inductance but induces core loss in AC *** fully understanding and managing loss mechanisms such as the skin effect,proximity effect,core loss,and joule heating,we achieve a wireless charging efficiency of71%andpower delivery of 3.81Win the kHz frequency *** band is unobstructive to hand motion and can charge a handheld smartphone as fast as a desktop charger or power a battery-free chest-laminated e-tattoo sensor,with well-managed thermal and electromagnetic *** a holistic electromagnetic,structural,and thermal design,our device culminates in a safe,rugged,and versatile solution for wearable WPT systems.

关键词： antenna charge wear

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of computer Science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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Surface phase diagram of CsSnI3 from first-principles calculations

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Physical Review Materials 2024年第9期8卷 093401页

作者： Kejia Li Chadawan Khamdang Mengen Wang Department of Electrical and Computer Engineering Materials Science and Engineering Program

CsSnI3 is widely studied as an environmentally friendly Pb-free perovskite material for optoelectronic device applications. To further improve material and device performance, it is important to understand the surface structures of CsSnI3. We generate surface structures with various stoichiometries, perform density functional theory calculations to create phase diagrams of the CsSnI3 (001), (110), and (100) surfaces, and determine the most stable surfaces under a wide range of Cs, Sn, and I chemical potentials. Under I-rich conditions, surfaces with Cs vacancies are stable, which lead to partially occupied surface states above the valence band maximum. Under I-poor conditions, we find the stoichiometric (100) surface to be stable under a wide region of the phase diagram, which does not have any surface states and can contribute to long charge-carrier lifetimes. Consequently, the I-poor (Sn-rich) conditions will be more beneficial to improve the device performance.

关键词： Electronic structure Surface reconstruction Surface states Perovskites Semiconductors Density functional theory

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Greedy Optimization of Sensor Array Geometry for Magnetocardiographic Source Localization

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IEEE Transactions on biomedical engineering 2024年第6期72卷 1848-1857页

作者： Van Veen, Barry Stenroos, Matti Wakai, Ronald T. University of Wisconsin-Madison Department of Electrical and Computer Engineering MadisonWI53705 United States Aalto University Department of Neuroscience and Biomedical Engineering Espoo Finland University of Wisconsin-Madison Department of Medical Physics MadisonWI53705 United States

Objective: Until recently, magnetocardiography (MCG) studies were performed using SQUID systems, consisting of a planar array of sensors with uniform spacing. The introduction of optically-pumped magnetometers (OPMs) now enables the deployment of large, conformal arrays, in which the sensors can be mounted on a wearable vest at nearly any location. The objective of this study was to optimize the sensor array geometry of an OPM system for MCG imaging applications. Methods: We devised a new optimization criterion for spatial resolution based on sensitivity to localization error. We also implemented a greedy optimization technique to overcome the difficulty of combinatoric optimization over an extremely large number of possible sensor configurations. Simulations were performed to compare the localization accuracy of the optimized arrays to that of conventional arrays with a regular geometry over the front of the torso. The number of sensors and the signal-tonoise ratio were varied. Results: Optimization resulted in non-planar, irregular geometries biased toward the left half of the torso. Arrays optimized for posterior cardiac sources showed the best overall performance. The localization accuracy was shown to be significantly improved by optimization for a given number of sensors and signal-to-noise ratio. Conclusion: The results of this study can serve as a guide for designing MCG arrays for a given number of sensors and/or determining the required number of sensors for a given level of performance. Significance: Sensor array optimization can improve the performance of OPM-based MCG imaging systems for applications, such as non-invasive localization of arrhythmogenic foci. © 1964-2012 IEEE.

关键词： Magnetocardiography

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Assessing Biological Responses of Saccharomyces Cerevisiae to Low-Frequency Electromagnetic Fields: Insights and Implications 15

Assessing Biological Responses of Saccharomyces Cerevisiae t...

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15th International Conference Elektro, ELEKTRO 2024

作者： Bajtos, Marek Radil, Roman Janousek, Ladislav Dang, Nhat University of Zilina Faculty of Electrical Engineering and Information Technology Department of Electromagnetic and Biomedical Engineering Zilina Slovakia University of Colorado Boulder Department of Electrical Computer and Energy Engineering BoulderCO United States

ISBN: (纸本)9798350372359

This study investigates the biological effects of Low-Frequency Electromagnetic Fields (LF-EMF) on Saccharomyces cerevisiae yeast cells, an essential model organism in biological research. To achieve controlled exposure, the exposure system ensuring uniform exposition while maintaining identical cultivation conditions for both exposed and control samples is used. To evaluate the impact of LF-EMF, yeast cell growth and proliferation were quantified using a Bürker chamber before and after exposure. Our findings reveal statistically significant differences between the exposed and control samples, suggesting a potential biological response to LF-EMF. The present research offers valuable insights into the effects of LF-EMF on biological cells, contributing to the broader understanding of electromagnetic field's impact on living organisms. Furthermore, the proposed exposure system provides a reliable platform for future bioelectromagnetic research, facilitating health risk assessments and investigations into the therapeutic potential of LF-EMF exposure. © 2024 IEEE.

关键词： Electromagnetic fields

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Dynamic Environment-Aware Lifetime Prediction of SiC MOSFET Modules Through LSTM 14

Dynamic Environment-Aware Lifetime Prediction of SiC MOSFET ...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Hasan, Md Zakir Choi, Seungdeog Aider, Youssef Singh, Prashant Liu, Chun-Hung Mississippi State University Department of Electrical and Computer Engineering Starkville United States University of Tennessee Department of Mechanical Aerospace & Biomedical Engineering Knoxville United States

ISBN: (纸本)9798331516116

The state-of-the-art determines the remaining useful lifetime (RUL) through a steady-state, fixed power cycling tests (PCT) without considering the impact of dynamically changing environmental conditions. It has resulted in considerable RUL prediction errors in the real world. However, the dynamic changing conditions (e.g., large temperature swings) may affect the degradation evolution of SiC MOSFET, which could eventually result in RUL changes. Thus, it must be integrated to make accurate predictions. To precisely understand the RUL variation complexity, the junction temperature (Tj) has been measured with a Negative Thermal Coefficient (NTC) thermistor, Temperature Sensitive electrical Parameter (TSEP), and these profiles have been modeled through the thermal model RC foster network using Extended Kalman Filter (EKF). Then, the on-state resistance (Rds,on) variations and Degradation Acceleration Factor (DAF) under the dynamic environment conditions are integrated into a lifetime prediction model to accurately predict the RUL through the Long Short-Term Memory (LSTM) machine learning algorithm. © 2025 IEEE.

关键词： Silicon carbide

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Enhanced battery life prediction with reduced data demand via semi-supervised representation learning

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Journal of Energy Chemistry 2025年第2期101卷 524-534,I0011页

作者： Liang Ma Jinpeng Tian Tieling Zhang Qinghua Guo Chi Yung Chung School of Mechanical MaterialsMechatronic and Biomedical EngineeringUniversity of WollongongNSW 2522Australia Department of Electrical and Electronic Engineering and Research Centre for Grid Modernisation The Hong Kong Polytechnic UniversityKowloonHong Kong 999077China Photonics Research Institute The Hong Kong Polytechnic UniversityKowloonHong Kong 999077China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia

Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion *** various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for *** of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for ***,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL *** approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL *** approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge *** method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional *** also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder *** projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled *** approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.

关键词： Lithium-ion batteries Battery degradation Remaining useful life Semi-supervised learning

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Evasive attacks against autoencoder-based cyberattack detection systems in power systems

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Energy and AI 2024年第3期17卷 126-135页

作者： Yew Meng Khaw Amir Abiri Jahromi Mohammadreza F.M.Arani Deepa Kundur Department of Electrical and Computer Engineering University of TorontoTorontoON M5S 3G4Canada School of Electronic and Electrical Engineering University of LeedsLeedsLS29JTUnited Kingdom Department of Electrical Computer&Biomedical EngineeringToronto Metropolitan University(formerly Ryerson University)TorontoON M5B 2K3Canada

The digital transformation process of power systems towards smart grids is resulting in improved reliability, efficiency and situational awareness at the expense of increased cybersecurity vulnerabilities. Given the availability of large volumes of smart grid data, machine learning-based methods are considered an effective way to improve cybersecurity posture. Despite the unquestionable merits of machine learning approaches for cybersecurity enhancement, they represent a component of the cyberattack surface that is vulnerable, in particular, to adversarial attacks. In this paper, we examine the robustness of autoencoder-based cyberattack detection systems in smart grids to adversarial attacks. A novel iterative-based method is first proposed to craft adversarial attack samples. Then, it is demonstrated that an attacker with white-box access to the autoencoder-based cyberattack detection systems can successfully craft evasive samples using the proposed method. The results indicate that naive initial adversarial seeds cannot be employed to craft successful adversarial attacks shedding insight on the complexity of designing adversarial attacks against autoencoder-based cyberattack detection systems in smart grids.

关键词： Cybersecurity Adversarial attacks Anomaly detection Iterative-based methods Substati on automati on

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Intelligent ADHD Detection Using Explainable Multimodal Fusion Model 4

Intelligent ADHD Detection Using Explainable Multimodal Fusi...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Tsai, Ho-Jung Wu, Hsiao-Kuang Chen, Chun-Chuan Yeh, Chen-Hsin Chu, Tzu-Yueh Yeh, Shih-Ching National Central University Department of Computer Science and Information Engineering Taiwan National Central University Department of Biomedical Sciences and Engineering Taiwan Hunan University College of Computer Science and Electrical Engineering China

ISBN: (纸本)9798350363586

Deep learning has been proved to diagnose Attention Deficit/Hyperactivity Disorder (ADHD) accurately, but it has raised concerns about trustworthiness because of the lack of explainability. Fortunately, the development of explainable artificial intelligence (XAI) offers a solution to this problem. In this study, we employed a VR-based GO/NOGO task with distractions, capturing participants' eye movement, head movement, and electroencephalography (EEG) data. We used the collected data to train an explainable multimodal fusion model. Besides classifying between ADHD and normal children, the proposed model also generates explanation heatmaps. The heatmaps provide the importance of specific variables and timestamps in the EEG data to help us analyze the patterns captured by the model. According to our observations, the model identified specific time intervals that related to specific event-related potentials (ERPs) components. The heatmaps also demonstrate that the impacts of distractions vary between not only the GO and NOGO events but also ADHD and normal children. © 2024 IEEE.

关键词： Eye movements

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Role of Deep Learning Models in Intelligent Classification of Various Date Fruit Bunches 17

Role of Deep Learning Models in Intelligent Classification o...

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17th International Conference on Developments in eSystems engineering, DeSE 2024

作者： Abuowda, Zainab Salam, Nour Ramadan, Shorouk Hassan, Taimur Ghazal, Mohammed Almajali, Eqab Hussain, Abir Jafar Yousaf, Jawad Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350368697

This study presents a performance comparison of different deep machine learning models for the intelligent segregation of date fruit bunches of numerous varieties. The shape, size, and color of the various types of dates with different maturity conditions require experienced farmers to estimate the accrual class. This study has used a transfer learning approach on various machine learning models (VGG-19, MobileNetV2, DenseNet, and NASNet) to classify five different kinds of date bunches (Barhi, Khalas, Menifi, Nabout saif, and Sullaj). Each model is trained on a dataset of 11,944 images of five different date classes. The comparison deduces that DenseNet produces the best accuracy, precision, and recall when compared with the performance of other models. The maximum achieved accuracy of the trained DenseNet model is 100% for the validation dataset and 98.4% for the test dataset. © 2024 IEEE.

关键词： Contrastive Learning

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