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

作者： Umam, Khairul Purnama, I. Ketut Eddy Rachmadi, Reza Fuad Wulandari, Sari Ayu Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Surabaya Indonesia Universitas Dian Nuswantoro Department of Biomedical Engineering Semarang Indonesia

ISBN: (纸本)9798350378573

Tuberculosis is a severe disease caused by Mycobacterium tuberculosis (MT). In handling it, the sputum examination method was used because it is relatively cheap and easy. The importance of MT detection in medical personnel analysis. We propose detection of Mycobacterium tuberculosis (MT) with a 1-stage detection object using two models: Detection Transformer (DETR) and Real-time Detection Transformer (RT-DETR) with different backbone variations to determine the performance of both models and conduct experiments using other models, such as yolov5l and Faster-RCNN, to compare their evaluation results with those of the DETR and RT-DETR models. In this study, MT detection using DETR with the Backbone ResNet 101 variation obtained AP:50 = 0.681 and AP:50:95 = 0.276. The RT-DETR model with HGNetv2l backbone variations yielded AP:50 = 0.837 and AP:50:95 = 0.147. The Yolov5l model produced AP:50 = 0.75 and AP:50:95 = 0.326 values. while the Faster-RCNN model produced AP:50 = 0.696 and AP:50:95 = 0.30. The results of RT-DETR with the HGNetv2-l backbone demonstrated good performance in the detection of Mycobacterium tuberculosis. © 2024 IEEE.

关键词： detection detection transformer mycobacterium tuberculosis transformer tuberculosis

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A Cyclic Vernier Digital-to-Time Converter for Time-Mode Successive Approximation TDC 66

A Cyclic Vernier Digital-to-Time Converter for Time-Mode Suc...

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Lee, Daniel Junehee Yuan, Fei Zhou, Yushi Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering TorontoON Canada Lakehead University Department of Electrical and Computer Engineering Thunder BayON Canada

ISBN: (纸本)9798350302103

This paper presents an 8-bit cyclic Vernier digital-to-time converter (DTC) for time-mode successive approximation register time-to-digital converters (SAR TDCs). The DTC offers a low degree of mismatch-induced nonlinearity hence a small lower bound of the dynamic range, a virtually unlimited upper bound of the dynamic range, and a small silicon area. The principle, operation, design, and calibration of the DTC are presented. The DTC is designed in TSMC 130 nm 1.2 V technology and analyzed using Spectre with BSIM3v3 device models. Simulation results show that the DTC achieves a dynamic range of 2.31 591.4 ps, a DNL of 0.37 LSB, an INL of 0.66 LSB, and consumes 1.49 mW. © 2023 IEEE.

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Exploring Biological Features of Electroencephalography Signals for Explanation-Guided Learning

Exploring Biological Features of Electroencephalography Sign...

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2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Joo, Hyosung Kim, Dongseok Trang, Le Thi Quan, Luong Do Anh Woo, Jihwan University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan Korea Republic of University of Ulsan Department of Biomedical Engineering Ulsan Korea Republic of

ISBN: (纸本)9798350371888

Explanation-guided learning (EGL) has gained prominence for improving both the explainability and performance of deep neural networks by integrating additional supervision signals based on the elucidation of the model. However, its applicability in analyzing electroencephalography (EEG) signals remains underexplored. This study attempted to identify and evaluate biological features for EGL-based EEG analysis using compact convolutional neural networks. The identified features included anatomical location, dipolarity, relative peak power, and event-related desynchronization of the cortical source. The feasibility of the features was evaluated with performance prediction tests, which predict model performance based on the interpreted features. All features except dipolarity possessed a significant relationship with model performance. These findings indicate the possibility of utilizing these features as biological supervision signals in future EGL-based EEG analysis. © 2024 IEEE.

关键词： Electroencephalography

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Unobstructive andsafe-to-wear watt-level wireless charger

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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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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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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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Enhanced healing of critical-sized bone defects using degradable scaffolds with tailored composition through immunomodulation and angiogenesis

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Bioactive Materials 2025年第2期44卷 371-388页

作者： Juncen Zhou Negar Akrami Hanbo Wang Liang Fang Jie Shen Cunjiang Yu Ben Zhang Donghui Zhu Department of Biomedical Engineering University of Stony Brook100 Nicolls RdStony BrookNY11794USA Department of Orthopaedic Surgery Washington UniversitySt.LouisMO63110USA Department of Electrical&Computer Engineering University of Illinois Urbana-ChampaignUrbanaIL61801USA

The impact of orthopedic scaffolds on bone defect healing,particularly the late-stage bone remodeling process,is pivotal for the therapeutic *** study applies fadditively manufactured scaffolds composed of hydroxyapatite-doped poly(lactide-co-glycolide)-b-poly(ethylene glycol)-b-poly(lactide-co-glycolide)(HAPELGA)with varying properties to treat rat calvarial defects,elucidating their significant role in bone remodeling by modulating physiological *** engineered two scaffolds with different polylactic acid(PLA)to polyglycolic acid(PGA)ratio(9/1 and 18/1)to vary in hydrophobicity,degradation rate,mechanical properties,and structural *** variations influenced physiological responses,including osteogenesis,angiogen-esis,and immune reactions,thereby guiding bone *** findings show that the HA-PELGA(18/1)scaffold,with a slower degradation rate,supported bulk bone formation due to a stable ***,the HA-PELGA(9/1)scaffold,with a faster degradation rate and more active interfaces,facilitated the formation of a thin bone layer and higher bone *** study demonstrates these degradable scaffolds help to promote bone healing and reveals how scaffold properties influence the bone remodeling process,offering a potential strategy to optimize scaffold design aiming at late-stage bone defect healing.

关键词： Additive manufacturing Polymer scaffolds Biodegradable Bone defect healing Bone remodeling

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