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Global Sensitivity Methods for Design of Experiments in Lithium-ion Battery Context ⁎

IFAC-PapersOnLine 2020年第2期53卷 7248-7255页

作者： A. Pozzi X. Xie D.M. Raimondo R. Schenkendorf Institute of Energy and Process Systems Engineering TU Braunschweig 38106 Braunschweig Germany Department of Electrical Computer and Biomedical Engineering University of Pavia 27100 Pavia Italy

Battery management systems may rely on mathematical models to provide higher performance than standard charging protocols. Electrochemical models allow us to capture the phenomena occurring inside a lithium-ion cell and therefore, could be the best model choice. However, to be of practical value, they require reliable model parameters. Uncertainty quantification and optimal experimental design concepts are essential tools for identifying systems and estimating parameters precisely. Approximation errors in uncertainty quantification result in sub-optimal experimental designs and consequently, less-informative data, and higher parameter unreliability. In this work, we propose a highly efficient design of experiment method based on global parameter sensitivities. This novel concept is applied to the single-particle model with electrolyte and thermal dynamics (SPMeT), a well-known electrochemical model for lithium-ion cells. The proposed method avoids the simplifying assumption of output-parameter linearization (i.e., local parameter sensitivities) used in conventional Fisher information matrix-based experimental design strategies. Thus, the optimized current input profile results in experimental data of higher information content and in turn, in more precise parameter estimates.

关键词： parameter identification global parameter sensitivities uncertainty quantification design of experiments lithium-ion batteries

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Enhanced nonlinearity in a functionalized whispering gallery mode microcavity

Enhanced nonlinearity in a functionalized whispering gallery...

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2020 Frontiers in Optics Conference, FiO 2020

作者： Pampel, Steven K. Bae, Kyuyoung Horning, Thomas M. Zohrabi, Mo Grayson, Michael Park, Wounjhang Gopinath, Juliet T. Department of Physics University of Boulder Colorado BoulderCO80309 United States Department of Electrical Computer and Energy Engineering University of Boulder Colorado BoulderCO80309 United States Materials Science and Engineering Program University of Colorado BoulderCO80309 United States

We report a ~3x increase in third-harmonic generation conversion efficiency using indium tin oxide (ITO) nanoparticles on the surface of a silica microsphere. This establishes ITO-microresonators as a promising tool f... 详细信息

ISBN: (纸本)9781943580804

关键词： Microresonators

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Automated radiomic-based prognostication for pancreatic adenocarcinoma

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Pancreatology 2024年 24卷 e55-e56页

作者： Vezakis, Ioannis Skreka, Androniki-Maria Chardalias, Leonidas Vezakis, Antreas Polydorou, Antreas Vezakis, Antonios Matsopoulos, George Biomedical Engineering Laboratory School of Electrical and Computer Engineering National and Technical University of Athens Athens Attiki Greece 2nd Department of Surgery Aretaieion Hospital National and Kapodistrian University of Athens Athens Attiki Greece

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Motion artifact reduction in quantitative susceptibility mapping using deep neural network

arXiv

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arXiv 2021年

作者： Li, Chao Zhang, Hang Zhang, Jinwei Spincemaille, Pascal Nguyen, Thanh D. Wang, Yi Department of Applied and Engineering Physics Cornell University IthacaNY United States Department of Radiology Weill Medical College of Cornell University New YorkNY United States Department of Electrical and Computer Engineering Cornell University IthacaNY United States Department of Biomedical Engineering Cornell University IthacaNY United States

An approach to reduce motion artifacts in Quantitative Susceptibility Mapping using deep learning is proposed. We use an affine motion model with randomly created motion profiles to simulate motion-corrupted QSM images. The simulated QSM image is paired with its motion-free reference to train a neural network using supervised learning. The trained network is tested on unseen simulated motion-corrupted QSM images, in healthy volunteers and in Parkinson's disease patients. The results show that motion artifacts, such as ringing and ghosting, were successfully suppressed. © 2021, CC BY.

关键词： Deep neural networks

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Depression Severity Identification Based on Shallow 2d Self-Attention-Cnn Using Eeg Functional Connectivity Network

SSRN

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SSRN 2024年

作者： Zhou, Yihan Yu, Xiaokang Lin, Huiping Li, Rihui Liang, Jiuxing Shi, Xue Luo, Yuxi School of Biomedical Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518100 China Center for Cognitive and Brain Sciences Department of Electrical and Computer Engineering University of Macau Macau S.A.R Taipa China Key Laboratory of Brain Cognition and Education Science Ministry of Education Institute for Brain Research and Rehabilitation Guangdong Key Laboratory of Mental Health and Cognitive Science South China Normal University China Department of Neurology Shenzhen People's Hospital The Second Clinical Medical College Jinan University The First Affiliated Hospital Southern University of Science and Technology Shenzhen China Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province Sun Yat-sen University Guangdong Shenzhen518100 China

Background and objectives: Depression inflicts significant harm on both society and family. Previous studies have indicated that the functional network of EEG signals worked well in recognizing major depression. This study aims to further identify depression severities and characterize their EEG functional network difference by designing a deep learning strategy and a corresponding visualization ***: Rest state EEG recordings from 30 healthy controls, 35 mild depressed and 26 severe depressed patients were included. Weighted phase lag indexes were computed across four frequency sub-bands to delineate the functional connectivity of EEG networks, serving as the input matrix. To better adapt volume conduction effects, a shallow CNN-based incorporated with 2D Self-Attention architecture was designed, enabling the model to capture information across diverse spans and scales within the functional connectivity (FC) matrix. Leveraging the Grad-CAM algorithm, the model highlighted crucial FCs and corresponding EEG pairs for classification. Finally, the changes of EEG FC network across depression severities were statistically analyzed and ***: An accuracy of 89.2% was achieved in tri-classification of 10-second EEG segments using 52 EEG channels, remaining high at 84.1% with 30 selected channels. Notably, the investigation revealed that significant FC changes from mild to severe depression did not exhibit a simple or monotonous ***: This research presented a directly measured methodology to identify depression severity, vital for informing prevention and therapeutic interventions. Furthermore, the findings shed light on the evolving patterns of brain function as depression progresses. The proposed deep learning model and channel selection algorithm offer potential applications beyond this study, promising broader utility in EEG-based research endeavors. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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AI-enabled CT-guided end-to-end quantification of total cardiac activity in 18FDG cardiac PET/CT for detection of cardiac sarcoidosis

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Journal of Nuclear Cardiology 2025年 48卷 102195页

作者： Miller, Robert JH. Shanbhag, Aakash Marcinkiewicz, Anna M. Struble, Helen Gransar, Heidi Hijazi, Waseem Fujito, Hidesato Kransdorf, Evan Kavanagh, Paul Liang, Joanna X. Builoff, Valerie Dey, Damini Berman, Daniel S. Slomka, Piotr J. Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA United States Department of Cardiac Sciences University of Calgary Calgary AB Canada Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States Center of Radiological Diagnostics National Medical Institute of the Ministry of the Interior and Administration Warsaw Poland

Background: [18F]-fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) plays a central role in diagnosing and managing cardiac sarcoidosis. We propose a fully automated pipeline for quantification of [18F]FDG PET activity using deep learning (DL) segmentation of cardiac chambers on computed tomography (CT) attenuation maps and evaluate quantitative approaches based on this framework. Methods: We included consecutive patients undergoing [18F]FDG PET/CT for suspected cardiac sarcoidosis. DL segmented left atrium, left ventricle (LV), right atrium, right ventricle, aorta, LV myocardium, and lungs from CT attenuation scans. CT-defined anatomical regions were applied to [18F]FDG PET images automatically to quantify target to background ratio (TBR), volume of inflammation (VOI) and cardiometabolic activity (CMA) using full sized and shrunk segmentations. Results: A total of 69 patients were included, with mean age of 56.1 ± 13.4 and cardiac sarcoidosis present in 29 (42 %). CMA had highest prediction performance (area under the receiver operating characteristic curve [AUC] .919, 95 % confidence interval [CI] .858 – .980) followed by VOI (AUC .903, 95 % CI .834 – .971), TBR (AUC .891, 95 % CI .819 – .964), and maximum standardized uptake value (AUC .812, 95 % CI .701 – .923). Abnormal CMA (≥1) had a sensitivity of 100 % and specificity 65 % for cardiac sarcoidosis. Lung quantification was able to identify patients with pulmonary abnormalities. Conclusion: We demonstrate that fully automated volumetric quantification of [18F]FDG PET for cardiac sarcoidosis based on CT attenuation map-derived volumetry is feasible, rapid, and has high prediction performance. This approach provides objective measurements of cardiac inflammation with consistent definition of myocardium and background region. © 2025 American Society of Nuclear Cardiology

关键词： Cardiac sarcoidosis Deep learning Hypermetabolism Positron emission tomography Quantification

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Polarized reflectance texture distinguishes superficial damage of bovine articular cartilage explants

Polarized reflectance texture distinguishes superficial dama...

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Frontiers in Optics, FIO 2019 - Part of Frontiers in Optics + Laser Science APS/DLS

作者： Huynh, Ruby Pesante, Benjamin Nehmetallah, George Raub, Christopher Department of Biomedical Engineering Catholic University of America 620 Michigan Ave NE. WashingtonDC20064 United States Electrical Engineering and Computer Science Catholic University of America 620 Michigan Ave NE. WashingtonDC20064 United States

Polarized reflectance maps of bovine articular cartilage are rich in texture content. Image texture analysis extracts quantitative parameters useful to assess intact cartilage versus cartilage with mechanical surface ... 详细信息

ISBN: (纸本)9781943580675

关键词： Cartilage

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Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach

arXiv

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arXiv 2023年

作者： Azad, Reza Jia, Yiwei Aghdam, Ehsan Khodapanah Cohen-Adad, Julien Merhof, Dorit The Institute of Imaging and Computer Vision RWTH Aachen University Aachen52074 Germany The Department of Electrical Engineering Shahid Beheshti University Tehran*** Iran The Mila Quebec AI Institute Canada The Functional Neuroimaging Unit CRIUGM University of Montreal Canada The NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal MontrealH3T 1J4 Canada The Institute of Image Analysis and Computer Vision Faculty of Informatics and Data Science University of Regensburg Regensburg93053 Germany The Fraunhofer Institute for Digital Medicine MEVIS Bremen28359 Germany

While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness, they suffer from limitations in capturing long-range dependencies. Transformer-based approaches are currently prevailing since they enlarge the reception field to model global contextual correlation. To further extract rich representations, some extensions of the U-Net employ multi-scale feature extraction and fusion modules and obtain improved performance. Inspired by this idea, we propose TransCeption for medical image segmentation, a pure transformer-based U-shape network featured by incorporating the inception-like module into the encoder and adopting a contextual bridge for better feature fusion. The design proposed in this work is based on three core principles: (1) The patch merging module in the encoder is redesigned with ResInception Patch Merging (RIPM). Multi-branch transformer (MB transformer) adopts the same number of branches as the outputs of RIPM. Combining the two modules enables the model to capture a multi-scale representation within a single stage. (2) We construct an Intra-stage Feature Fusion (IFF) module following the MB transformer to enhance the aggregation of feature maps from all the branches and particularly focus on the interaction between the different channels of all the scales. (3) In contrast to a bridge that only contains token-wise self-attention, we propose a Dual Transformer Bridge that also includes channel-wise self-attention to exploit correlations between scales at different stages from a dual perspective. Extensive experiments on multi-organ and skin lesion segmentation tasks present the superior performance of TransCeption compared to previous work. The code is publicly available on GitHub. © 2023, CC BY.

关键词： Merging

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ZETA: a library for Zonotope-based EsTimation and fAult diagnosis of discrete-time systems

arXiv

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arXiv 2025年

作者： Rego, Brenner S. Scott, Joseph K. Raimondo, Davide M. Terra, Marco H. Raffo, Guilherme V. Department of Electrical and Computer Engineering University of São Paulo SP São Carlos13566-590 Brazil Department of Chemical and Biomolecular Engineering Georgia Institute of Technology 311 Ferst Dr. AtlantaGA30318 United States Department of Engineering and Architecture University of Trieste Trieste34127 Italy Department of Electronics Engineering Graduate Program in Electrical Engineering Federal University of Minas Gerais MG Belo Horizonte31270-901 Brazil

This paper introduces ZETA, a new MATLAB library for Zonotope-based EsTimation and fAult diagnosis of discrete-time systems. It features user-friendly implementations of set representations based on zonotopes, namely zonotopes, constrained zonotopes, and line zonotopes, in addition to a basic implementation of interval arithmetic. This library has capabilities starting from the basic set operations with these sets, including propagations through nonlinear functions using various approximation methods. The features of ZETA allow for reachability analysis and state estimation of discrete-time linear, nonlinear, and descriptor systems, in addition to active fault diagnosis of linear systems. Efficient order reduction methods are also implemented for the respective set representations. Some examples are presented in order to illustrate the functionalities of the new library. © 2025, CC BY.

关键词： Digital arithmetic

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Event-triggered Multi-agent Optimal Regulation Using Adaptive Dynamic programming

Event-triggered Multi-agent Optimal Regulation Using Adaptiv...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Xiangnan Zhong Haibo He Department of Computer & Electrical Engineering and Computer Science Florida Atlantic University Boca Raton FL USA Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI USA

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

This paper develops an event-triggered multi-agent control method based on adaptive dynamic programming (ADP) techniques. Different from the traditional ADP-based multi-agent control with fixed sampling period, our method designs an adaptive controller only based on the efficiently reduced samples. The sampling instants are decided by an adaptive triggering condition to guarantee the stability of the event-triggered learning process. The theoretical analysis of the proposed method is also provided in this paper. It is proved that the designed event-triggered ADP controller can make all the agents synchronize to the leader's dynamics with reduced sampled data, and also reach Nash equilibrium at the same time. Therefore, the proposed method can save the computational resources in the learning process. Finally, the simulation results verify the theoretical analysis and also demonstrate the performance of the developed method.

关键词： Multi-agent systems Stability analysis Nash equilibrium Dynamic programming Synchronization Task analysis Nickel

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