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arXiv 2024年

作者： Moore, Samuel A. Mann, Brian P. Chen, Boyuan Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Computer Science Duke University DurhamNC United States

Dynamical systems theory has long provided a foundation for understanding evolving phenomena across scientific domains. Yet, the application of this theory to complex real-world systems remains challenging due to issues in mathematical modeling, nonlinearity, and high dimensionality. In this work, we introduce a data-driven computational framework to derive low-dimensional linear models for nonlinear dynamical systems directly from raw experimental data. This framework enables global stability analysis through interpretable linear models that capture the underlying system structure. Our approach employs time-delay embedding, physics-informed deep autoencoders, and annealing-based regularization to identify novel low-dimensional coordinate representations, unlocking insights across a variety of simulated and previously unstudied experimental dynamical systems. These new coordinate representations enable accurate long-horizon predictions and automatic identification of intricate invariant sets while providing empirical stability guarantees. Our method offers a promising pathway to analyze complex dynamical behaviors across fields such as physics, climate science, and engineering, with broad implications for understanding nonlinear systems in the real world. © 2024, CC BY.

关键词： Nonlinear dynamical systems

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Improving State of Charge Estimation for Lithium-Ion Batteries through Optimized CNN Models 5

Improving State of Charge Estimation for Lithium-Ion Batteri...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Kumar, Indradeep Dasari, Madhavi Danamaraju, Chaitanya Bindu, K.V. Mohanavel, V. Dhanraj, Joshuva Arockia Institute of Aeronautical Engineering Department of Aeronautical Engineering Dundigal Hyderabad500043 India Sapthagiri College of Engineering Department of Computer Science Karnataka Bengaluru560057 India RNS Institute of Technology Department of Electrical and Electronics Engineering Karnataka Bengaluru560098 India R.M.K. College of Engineering and Technology Department of Science And Humanities Tamil Nadu Thiruvallur601 206 India Bharath Institute of Higher Education and Research Centre for Materials Engineering and Regenerative Medicine Tamil Nadu Chennai600073 India Chandigarh University Punjab Mohali140413 India

ISBN: (纸本)9798350395235

Lithium-ion (Li-ion) batteries are the preferred choice for electric vehicles (EVs) because of their extended lifespans, low self-discharge rates, high voltage, and high energy density. A well-functioning Battery Management System (BMS) is critical to the efficient operation of an EV. The State of Charge (SoC) is an important statistic that reflects the remaining charge in the battery, and its exact assessment is essential for BMS and improving EV efficiency, which extends the battery's life and decreases the probability of catastrophic failure. However, SOC estimation is complicated and affected by numerous unknowns, such as battery age and external temperature. In this study, we estimated SOC using a Convolutional Neural Network (CNN) model. To improve the CNN architecture, this study has applied three different optimization algorithms: Particle Swarm Optimization (PSO), Elephant Search Algorithm (ESA), and Equilibrium Optimization (EO). Sensor data from lithium-ion batteries were carefully processed. The processed dataset was then supplied to the CNN and three optimized CNN models. These models were tested using error, R2, and time metrics to identify the optimal technique. CNN-ESA outperformed the other CNN models in SOC estimation, with the lowest error rates and the highest R2 value of 0.9987. This simulation result demonstrates the effectiveness of applying ESA to improve CNN architectures for better Li-ion battery SOC estimates. It enhances the efficiency and lifespan of EVs. © 2024 IEEE.

关键词： Mean square error

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Synergy of machine learning and Mie-tronics: tailoring resonant response of meta-atom on demand 28

Synergy of machine learning and Mie-tronics: tailoring reson...

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Integrated Optics: Devices, materials, and Technologies XXVIII 2024

作者： George, Renee C. Sedeh, Hooman Barati Lai, Fangxing Li, Hao Li, Wenhao Gao, Jiannan Sun, Jingbo Xiao, Shumin Kuznetsov, Arseniy Litchinitser, Natalia M. Department of Electrical and Computer Engineering Duke University DurhamNC United States Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen Shenzhen518055 China School of Materials Science and Engineering Tsinghua University Beijing100084 China A*STAR Institute Singapore Singapore

ISBN: (纸本)9781510670389

Optical metasurfaces are engineered 2D electromagnetic structures enabling flat optical elements with properties not readily found in nature. Their unit cells, meta-atoms, usually are represented by a set of electric and magnetic multipoles. All-dielectric-based metasurfaces have recently attracted significant attention owing to their virtually lossless transmission properties at optical frequencies. A majority of reported dielectric metamaterials are composed of relatively simple meta-atoms such as spheres, cubes, and cylinders, whose electromagnetic response is dominated by the electric dipole. However, magnetic dipoles and higher-order multipoles may enable new optical properties and functionalities, including directional scattering, beam steering, and new frequency generation. Despite impressive progress in the field of optical metamaterials and nanofabrication technologies, engineering meta-atoms that support such higher-order resonances is still challenging. Here, we demonstrate that designed titanium dioxide meta-atoms can enable dominant magnetic dipole response. We apply a machine-learning model to predict a meta-atom shape with a strong magnetic dipole resonant mode at the operating wavelength of 750 nm. Using finite-element-based numerical simulations implemented in COMSOL Multiphysics, we found that the optimized meta-atom is robust against experimental variations and conditions such as a non-perfectly collimated incident beam, nanofabrication inaccuracies, and an added substrate. The meta-atoms have been fabricated using two approaches, focused ion beam lithography and an electron beam lithography followed by reactive ion etching, and characterized using white light spectroscopy. To the best of our knowledge, this is the first experimental realization of a machine-learning-based optimization of a magnetic dipole mode at optical frequencies. © 2024 SPIE.

关键词： Atoms

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Sum-Rate Maximization for Intelligent Reflecting Surface Assisted MIMO SWIPT Systems 6

Sum-Rate Maximization for Intelligent Reflecting Surface Ass...

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6th IEEE Symposium on Future Telecommunication Technologies, SOFTT 2022

作者： Liong, H.J. Chiong, C.W.R. Gopal, Lenin Juwono, Filbert H. Curtin University Malaysia Department of Electrical and Computer Engineering Miri Sarawak98009 Malaysia University of Southampton Malaysia Eko Galleria Department of Electrical and Electronic Engineering Nusajaya Johor79100 Malaysia University of Southampton Malaysia Eko Galleria Computer Science Program Nusajaya Johor79100 Malaysia

ISBN: (纸本)9781665455978

Intelligent reflecting surface (IRS) has recently stimulated an upsurge of research interest due to its capability of enhancing the spectral and energy efficiencies for future sixth generation (6G) wireless communication systems. This motivates the integration of IRS into simultaneous wireless information and power transfer (SWIPT) systems to improve the performance of energy harvesting and information de-coding. To unleash the full potential of IRS and achieve high sum-rate, the optimal joint transmitter precoding and reflective beamforming design is crucial yet challenging to solve. The issue has not been well investigated and the solution remains elusive. Hence, this research aims to jointly optimize the transmitter precoding and reflective beamforming matrices for IRS-Assisted multi-user multiple-input multiple-output (MIMO) SWIPT systems under quality of service (QoS) constraints. Simulation results demonstrate the effectiveness of IRS in enhancing the system performance. © 2022 IEEE.

关键词： Beamforming

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Universal hydrogel adhesives with robust chain entanglement for bridging soft electronic materials

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npj Flexible Electronics 2024年第1期8卷 509-522页

作者： Yejin Jo Yurim Lee Jeong Hyun Heo Yeonzu Son Tae Young Kim Kijun Park Soye Kim Seo Jung Kim Yoonhee Jin Seongjun Park Jungmok Seo School of Electrical and Electronic Engineering Yonsei UniversitySeoul03722Republic of Korea Department of Physiology Yonsei University College of MedicineSeoul03722Republic of Korea Program of Brain and Cognitive Engineering Korea Advanced Institute of Science and Technology(KAIST)291 Daehak-roYuseong-guDaejeon34141Republic of Korea Department of Pediatrics Severance Children’s HospitalYonsei University College of MedicineSeoul03722Republic of Korea Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology(KAIST)291 Daehak-roYuseong-guDaejeon34141Republic of Korea Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology(KAIST)291 Daehak-roYuseong-guDaejeon34141Republic of Korea KAIST Institute for Nano Century(KINC) 291 Daehak-roYuseong-guDaejeon34141Republic of Korea

Ensuring stable integration of diverse soft electronic components for reliable operation under dynamic conditions is ***,integrating soft electronics,comprising various materials like polymers,metals,and hydrogels,poses challenges due to their different mechanical and chemical *** study introduces a dried-hydrogel adhesive made of poly(vinyl alcohol)and tannic acid multilayers(d-HAPT),which integrates soft electronic materials through moisture-derived chain entanglement.d-HAPT is a thin(~1μm)and highly transparent(over 85%transmittance in the visible light region)adhesive,showing robust bonding(up to 3.6 MPa)within a short time(<1 min).d-HAPT demonstrates practical application in wearable devices,including a hydrogel touch panel and strain ***,the potential of d-HAPT for use in implantable electronics is demonstrated through in vivo neuromodulation and electrocardiographic recording experiments while confirming its biocompatibility both in vitro and in *** is expected that d-HAPT will provide a reliable platform for integrating soft electronic applications.

关键词： properties hydrogel electronic

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厚度变化不敏感的近红外量子点发光二极管

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science Bulletin 2023年第23期68卷 2954-2961,M0005页

作者：沈万姗刘洋 Luke Grater So Min Park Haoyue Wan 俞燕君潘嘉琳孔繁诚田起生周东营刘泽柯马万里孙宝全 Sjoerd Hoogland 王亚坤廖良生 Institute of Functional Nano&Soft Materials(FUNSOM) Jiangsu Key Laboratory for Carbon-Based Functional Materials&DevicesSoochow UniversitySuzhou 215123China Department of Electrical and Computer Engineering University of TorontoToronto M5S 3G4Canada Macao Institute of Materials Science and Engineering Macao University of Science and TechnologyMacao 999078China

近红外发光二极管在通信、传感和成像等领域的应用日益广泛.量子点作为一种有潜力的发光材料,在近红外发光范围内具有独特的优势.然而,现有的近红外量子点发光二极管存在一个严重的问题,即它们对发射层厚度非常敏感.为了解决这个问题,... 详细信息

近红外发光二极管在通信、传感和成像等领域的应用日益广泛.量子点作为一种有潜力的发光材料,在近红外发光范围内具有独特的优势.然而,现有的近红外量子点发光二极管存在一个严重的问题,即它们对发射层厚度非常敏感.为了解决这个问题,本文采用了一系列创新方法.通过引入核壳结构和表面处理,使光致发光量子效率达到80%,成功提高了载流子的传输性能并且增加了量子点的辐射复合速率,从而获得具有对发光层厚度不敏感的高效近红外量子点发光二极管.最终作者制备得到了在40-220 nm的范围内表现出性能稳定的近红外量子点发光二极管,该发光器件的发光峰波长位于1025 nm处,最高外量子效率达到11.5%.这项研究为近红外发光领域的发展提供了有力的支持,为未来光电子学和光通信技术的进步铺平了道路.

关键词： Quantum dots Near-infrared Light-emitting diodes Electroluminescence

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A novel image-based approach for interactive characterization of rock fracture spacing in a tunnel face

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Journal of Rock Mechanics and Geotechnical engineering 2022年第4期14卷 1077-1088页

作者： Jiayao Chen Yifeng Chen Anthony G.Cohn Hongwei Huang Jianhong Man Lijun Wei Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education Department of Geotechnical EngineeringTongji UniversityShanghai200092China School of Computing University of LeedsLeedsLS29JTUK Department of Computer Science and Technology Tongji UniversityShanghai211985China School of Civil Engineering Shandong UniversityJinan250061China Luzhong Institute of Safety Environmental Protection Engineering and Materialsand School of Mechanical and Electrical EngineeringQingdao University of Science and TechnologyQingdao255000China

This paper presents a novel integrated method for interactive characterization of fracture spacing in rock tunnel *** main procedure includes four steps:(1)Automatic extraction of fracture traces,(2)digitization of trace maps,(3)disconnection and grouping of traces,and(4)interactive measurement of fracture set spacing,total spacing,and surface rock quality designation(S-RQD)*** evaluate the performance of the proposed method,sample images were obtained by employing a photogrammetrybased scheme in tunnel *** were then conducted to determine the optimal parameter values(*** threshold,angle threshold,and number of fracture trace grouping)for characterizing rock fracture *** applying the identified optimal parameters involved in the model,the proposed method could lead to excellent qualitative results to a new tunnel *** perform a quantitative analysis,three methods(***,straightening,and the proposed method)were employed in the same study and comparisons were *** proposed method agrees well with the field measurement in terms of the maximum and average values of measured spacing ***,the proposed method has reasonably good accuracy and interactive advantage for estimating the ultimate fracture spacing and *** can be a possible extension of existing methods for fracture spacing characterization for two-dimensional(2D)rock tunnel faces.

关键词： Fracture spacing Rock tunnel Image processing Fracture set grouping

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Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning

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npj Computational materials 2023年第1期9卷 1402-1410页

作者： Ying Huang David Perlmutter Andrea Fei-Huei Su Jerome Quenum Pavel Shevchenko Dilworth Y.Parkinson Iryna V.Zenyuk Daniela Ushizima Department of Materials Science&Engineering University of California IrvineIrvineCaliforniaUSA National Fuel Cell Research Center University of California IrvineIrvineCaliforniaUSA Applied Math and Computational Research Division Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Department of Chemical&Biomolecular Engineering University of California IrvineIrvineCaliforniaUSA Department of Electrical Engineering and Computer Sciences College of EngineeringUniversity of California BerkeleyBerkeleyCaliforniaUSA Advanced Photon Source Argonne National Laboratory9700 South Cass AvenueLemontIllinoisUSA Advanced Light Source Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Berkeley Institute for Data Science University of California BerkeleyBerkeleyCaliforniaUSA

Operando X-ray micro-computed tomography(µCT)provides an opportunity to observe the evolution of Li structures inside pouch *** is an essential step to quantitatively analyzingµCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the ***,we report a computational approach,batteryNET,to train an Iterative Residual U-Net-based network to detect Li *** resulting semantic segmentation shows singular Li-related component changes,addressing diverse morphologies in the *** addition,visualizations of the dead Li are provided,including calculations about the volume and effective thickness of electrodes,deposited Li,and redeposited *** also report discoveries about the spatial relationships between these *** approach focuses on a method for analyzing battery performance,which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.

关键词： battery lithium state

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Analysis of Gait Data Based on Deep Learning to Predict Multiple Balance Scale Scores

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IEEE Sensors Journal 2024年第1期24卷 920-930页

作者： Lin, Chueh-Ho Peng, Chih-Wei Lee, I-Jung Wu, Chi-Ming Lin, Bor-Shing Taipei Medical University International Ph.D. Program in Gerontology and Long-Term Care Taipei City110 Taiwan Taipei Medical University Research Center in Nursing Clinical Practice Wan Fang Hospital Taipei City110 Taiwan Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering Taipei City110 Taiwan Taipei Medical University School of Gerontology and Long-Term Care College of Nursing Taipei City110 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237 Taiwan National Taipei University College of Electrical Engineering and Computer Science New Taipei City237 Taiwan

Unbalanced walking is increasingly common among older adults;therefore, routinely assessing the balance of older adults is crucial. The traditional method of assessing balance uses scales, requires the supervision of a physical therapist (PT), and is time-consuming. The present study, therefore, proposes a deep learning (DL) model architecture that combines a convolutional neural network (CNN) and a long short-term memory (LSTM) network model to predict the scores of three scales, namely the Berg balance scale (BBS), timed up and go (TUG), and single-leg stance (SLS). The gait data of 15 m of walking were collected from seven inertial measurement units (IMUs) and input into the CNN-LSTM model for evaluation. The BBS and TUG only require the participants to wear two IMUs on the left and right thighs, respectively, for accurate predictions. The mean absolute errors (MAEs) of predicting the scores of the BBS and TUG are 1.2562 and 1.4016 s, respectively;however, the MAE of score predictions of SLS is higher than that of the BBS and TUG, indicating that gait data cannot be used for assessing SLS;moreover, participants only wear one IMU on the right calf for BBS and TUG evaluations, which yield MAEs of 1.4334 and 1.5229 s, respectively. The proposed system can quickly and accurately predict the scores of the BBS and TUG. The proposed model can assist PTs with making clinical decisions. © 2001-2012 IEEE.

关键词： Long short-term memory

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Opto2P-FCM: A MEMS Based Miniature Two-Photon Microscope with Patterned Optogenetic Stimulation

Opto2P-FCM: A MEMS Based Miniature Two-Photon Microscope wit...

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Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Futia, Gregory L. Zohrabi, Mo McCullough, Connor Teel, Alec de Souza, Fabio Simoes Oroke, Ryan Bright, Victor M. Restrepo, Diego Gopinath, Juliet T. Gibson, Emily A. Department of Bioengineering University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Cell and Developmental Biology University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Department of Mechanical Engineering University of Colorado BoulderCO80309 United States Department of Physics University of Colorado BoulderCO80309 United States Materials Science and Engineering Program University of Colorado BoulderCO80309 United States

Miniaturized microscopes for monitoring neural activity are an indispensable tool for neuroscience research. We present a novel MEMS based miniature microscope with patterned optogenetic stimulation capabilities enabl... 详细信息

ISBN: (纸本)9781957171340

关键词： Microscopes

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