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Investigating the Underlying Dynamical Structure of Supersonic Flows Using Effective Model Reduction

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Investigating the Underlying Dynamical Structure of Superson...

American Control Conference

作者： Dan Wilson Samir Sahyoun Phil Kreth Seddik M. Djouadi Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN USA Uppsala University Uppsala Sweden Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee Space Institute Tullahoma TN USA

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

The temporal features of a cylinder-generated shock-wave/transitional boundary-layer interaction (XSWBLI) in response to supersonic flow (Mach 2) are investigated using various model reduction techniques. Experimental data are obtained using schlieren imaging at 100 kHz and image analysis is performed using proper orthogonal decomposition (POD). The POD framework is used as a starting point to define a reduced set of data-driven isostable coordinates that characterize the transient behavior of an underlying dynamical system. Observed unsteady behaviors in the shock-wave/boundary-layer interactions are well-represented as an externally forced dynamical system with a pair of complex-conjugate isostable coordinates. Results are validated against well-established reduction methodologies including POD and spectral POD. These results indicate that the isostable reduced coordinate framework can be used to provide an accurate, low-dimensional representation of the dynamical features of supersonic fluid flow, even when the relationships between underlying dynamical model and observed output are not explicitly known.

关键词： Reduced order model DYNAMICAL SYSTEMS Pods Supersonic schlieren Coordinates Transient performance fluid flow

Event cause analysis in distribution networks using synchro waveform measurements

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arXiv 2020年

作者： Niazazari, Iman Livani, Hanif Ghasemkhani, Amir Liu, Yunchuan Yang, Lei Department of Electrical and Biomedical Engineering University of Nevada RenoNV89557 United States Department of Computer Science and Engineering California State University Sanbernardino San BernardinoCA92407 United States Department of Computer Science and Engineering University of Nevada RenoNV89557 United States

This paper presents a machine learning method for event cause analysis to enhance situational awareness in distribution networks. The data streams are captured using time-synchronized high sampling rates synchro waveform measurement units (SWMU). The proposed method is formulated based on a machine learning method, the convolutional neural network (CNN). This method is capable of capturing the spatiotemporal feature of the measurements effectively and perform the event cause analysis. Several events are considered in this paper to encompass a range of possible events in real distribution networks, including capacitor bank switching, transformer energization, fault, and high impedance fault (HIF). The dataset for our study is generated using the real time digital simulator (RTDS) to simulate real-world events. The event cause analysis is performed using only one cycle of the voltage waveforms after the event is detected. The simulation results show the effectiveness of the proposed machine learning-based method compared to the state-of-the-art classifiers. Copyright © 2020, The Authors. All rights reserved.

关键词： Waveform analysis

Medical imaging with deep learning for COVID-19 diagnosis: A comprehensive review

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arXiv 2021年

作者： Bharati, Subrato Podder, Prajoy Mondal, M. Rubaiyat Hossain Prasath, V.B. Surya Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka1205 Bangladesh Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center Cincinnati45229 United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH45257 United States Department of Biomedical Informatics College of Medicine University of Cincinnati CincinnatiOH45267 United States Department of Electrical Engineering and Computer Science University of Cincinnati OH45221 United States

The outbreak of novel coronavirus disease (COVID-19) has claimed millions of lives and has affected all aspects of human life. This paper focuses on the application of deep learning (DL) models to medical imaging and drug discovery for managing COVID-19 disease. In this article, we detail various medical imaging-based studies such as X-rays and computed tomography (CT) images along with DL methods for classifying COVID-19 affected versus pneumonia. The applications of DL techniques to medical images are further described in terms of image localization, segmentation, registration, and classification leading to COVID-19 detection. The reviews of recent papers indicate that the highest classification accuracy of 99.80% is obtained when InstaCovNet-19 DL method is applied to an X-ray dataset of 361 COVID-19 patients, 362 pneumonia patients and 365 normal people. Furthermore, it can be seen that the best classification accuracy of 99.054% can be achieved when EDL_COVID DL method is applied to a CT image dataset of 7500 samples where COVID-19 patients, lung tumor patients and normal people are equal in number. Moreover, we illustrate the potential DL techniques in drug or vaccine discovery in combating the coronavirus. Finally, we address a number of problems, concerns and future research directions relevant to DL applications for COVID-19. © 2021, CC BY.

关键词： Coronavirus

Developing personalized models of blood pressure estimation from wearable sensors data using minimally-trained domain adversarial neural networks

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arXiv 2020年

作者： Zhang, Lida Hurley, Nathan C. Ibrahim, Bassem Spatz, Erica Krumholz, Harlan M. Jafari, Roozbeh Mortazavi, Bobak J. Department of Computer Science and Engineering Texas A&M University United States Department of Electrical and Computer Engineering Texas A&M University United States Yale School of Medicine Yale University United States Department of Biomedical Engineering Texas A&M University United States

Blood pressure monitoring is an essential component of hypertension management and in the prediction of associated comorbidities. Blood pressure is a dynamic vital sign with frequent changes throughout a given day. Capturing blood pressure remotely and frequently (also known as ambulatory blood pressure monitoring) has traditionally been achieved by measuring blood pressure at discrete intervals using an inflatable cuff. However, there is growing interest in developing a cuffless ambulatory blood pressure monitoring system to measure blood pressure continuously. One such approach is by utilizing bioimpedance sensors to build regression models. A practical problem with this approach is that the amount of data required to confidently train such a regression model can be prohibitive. In this paper, we propose the application of the domain-adversarial training neural network (DANN) method on our multitask learning (MTL) blood pressure estimation model, allowing for knowledge transfer between subjects. Our proposed model obtains average root mean square error (RMSE) of 4.80 ± 0.74 mmHg for diastolic blood pressure and 7.34 ± 1.88 mmHg for systolic blood pressure when using three minutes of training data, 4.64 ± 0.60 mmHg and 7.10 ± 1.79 respectively when using four minutes of training data, and 4.48±0.57 mmHg and 6.79±1.70 respectively when using five minutes of training data. DANN improves training with minimal data in comparison to both directly training and to training with a pretrained model from another subject, decreasing RMSE by 0.19 to 0.26 mmHg (diastolic) and by 0.46 to 0.67 mmHg (systolic) in comparison to the best baseline models. We observe that four minutes of training data is the minimum requirement for our framework to exceed ISO standards within this cohort of patients. Copyright © 2020, The Authors. All rights reserved.

关键词： Blood pressure

Predicting extreme events from data using deep machine learning: when and where

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arXiv 2022年

作者： Jiang, Junjie Huang, Zi-Gang Grebogi, Celso Lai, Ying-Cheng The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Research Center for Brain-inspired Intelligence Xi’an Jiaotong University The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Shaanxi Xi’an China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences King’s College University of Aberdeen AB24 3UE United Kingdom Department of Physics Arizona State University TempeAZ85287 United States

We develop a deep convolutional neural network (DCNN) based framework for model-free prediction of the occurrence of extreme events both in time ("when") and in space ("where") in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the 2D complex Ginzburg-Landau equation and empirical wind speed data of the North Atlantic ocean to demonstrate and validate our machine-learning based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme event on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world. © 2022, CC BY.

关键词： Forecasting

From 3-D Light Microscopic Images to Quantitative Insight

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Microscopy and Microanalysis 2020年第S2期5卷 524-525页

作者： B. Roysam A. Can H. Shen K. Al-Kofahi J.N. Turner Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY12180 Biomedical Engineering Rensselaer Polytechnic Institute Troy NY12180 Wadsworth Center New York State Department of Health Albany NY12201

This presentation will describe a common core set of widely applicable image analysis techniques for automated quantitative analysis of volumetric microscope image data. Volumetric (as distinct from stereoscopic) three-dimensional (3-D) Microscopy is a rapidly maturing field offering the ability to image thick (compared to the depth of field) specimens using a variety of instrumentation techniques, and producing arrays of brightness values in three spatial dimensions. Also well developed are methods to correct the acquired images for a variety of physical effects including blur and ***, what is of interest is the best-possible visualization of thick specimens. The next step, increasingly being considered in view of growing computational resources, and progress in image analysis techniques, seeks to quantify many of the processes and effects being studied. In some mainstream fields, such quantitation is essential. For instance, various assays for substance testing in pharmaceutical and chemical industries involve quantitative end points. As an illustration, the Draize assay for ocular irritancy testing of drugs and biochemical products for human use requires counting of live and dead cells that stain differently. Another example is the mouse lymphoma test that requires a 3-D counting of bacterial colonies. Neurobiological assays require morphometry, as well as quantification of changes in neurons as a function of time and various applied stimuli such as drugs, heat, and radiation. Angiogenesis assays require quantification of changes in vascular morphometry. computerized image analysis is a powerful tool for extracting quantitative data from 3-D images for statistical analysis.

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SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

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arXiv 2023年

作者： Luo, Xiangde Fu, Jia Zhong, Yunxin Liu, Shuolin Han, Bing Astaraki, Mehdi Bendazzoli, Simone Toma-Dasu, Iuliana Ye, Yiwen Chen, Ziyang Xia, Yong Su, Yanzhou Ye, Jin He, Junjun Xing, Zhaohu Wang, Hongqiu Zhu, Lei Yang, Kaixiang Fang, Xin Wang, Zhiwei Lee, Chan Woong Park, Sang Joon Chun, Jaehee Ulrich, Constantin Maier-Hein, Klaus H. Ndipenoch, Nchongmaje Miron, Alina Li, Yongmin Zhang, Yimeng Chen, Yu Bai, Lu Huang, Jinlong An, Chengyang Wang, Lisheng Huang, Kaiwen Gu, Yunqi Zhou, Tao Zhou, Mu Zhang, Shichuan Liao, Wenjun Wang, Guotai Zhang, Shaoting School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Department of Radiation Oncology Sichuan Cancer Hospital & Institute Chengdu China Shanghai AI Lab Shanghai China Co. Ltd. Beijing China Department of Medical Radiation Physics Stockholm University Solna Sweden Department of Biomedical Engineering and Health Systems KTH Huddinge Sweden Northwestern Polytechnical University Xi’an China Guangzhou China Wuhan National Laboratory for Optoelectronics with MoE Key Laboratory for Biomedical Photonics China Yonsei University College of Medicine Seoul Korea Republic of Oncosoft Inc. Seoul Korea Republic of Heidelberg Germany Department of Computer Science Brunel University London Uxbridge United Kingdom MedMind Technology Co. Ltd. Beijing China Shanghai Jiao Tong University Shanghai China Nanjing University of Science and Technology Nanjing China

Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. Previously, the delineation of GTVs and OARs was performed by experienced radiation oncologists. Recently, deep learning has achieved promising results in many medical image segmentation tasks. However, for NPC OARs and GTVs segmentation, few public datasets are available for model development and evaluation. To alleviate this problem, the SegRap2023 challenge was organized in conjunction with MICCAI2023 and presented a large-scale benchmark for OAR and GTV segmentation with 400 Computed Tomography (CT) scans from 200 NPC patients, each with a pair of pre-aligned non-contrast and contrast-enhanced CT scans. The challenge’s goal was to segment 45 OARs and 2 GTVs from the paired CT scans. In this paper, we detail the challenge and analyze the solutions of all participants. The average Dice similarity coefficient scores for all submissions ranged from 76.68% to 86.70%, and 70.42% to 73.44% for OARs and GTVs, respectively. We conclude that the segmentation of large-size OARs is well-addressed, and more efforts are needed for GTVs and small-size or thin-structure OARs. The benchmark will remain publicly available here: https://***. © 2023, CC BY-NC-ND.

关键词： Radiotherapy

Graph-based Approaches for the Interval Scheduling Problem

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Graph-based Approaches for the Interval Scheduling Problem

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Panagiotis Oikonomou Nikos Tziritas Georgios Theodoropoulos Maria Koziri Thanasis Loukopoulos Samee U. Khan Computer Science and Engineering Southern Univ. of Science and Technology Shenzhen China Computer Science and Telecomm University of Thessaly Lamia Greece Comp. Science and Biomedical Informatics University of Thessaly Lamia Greece Electrical and Computer Engineering Mississippi State University Mississippi USA

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

One of the fundamental problems encountered by large-scale computing systems, such as clusters and cloud, is to schedule a set of jobs submitted by the users. Each job is characterized by resource demands, as well as start and completion time. Each job must be scheduled to execute on a machine having the required capacity between the start and completion time (referred as interval) of the job. Each machine is defined by a parallelism parameter g that indicates the maximum number of jobs that can be processed by the machine, in parallel. The above problem is referred to as the interval scheduling problem with bounded parallelism. The objective is to minimize the total busy time of all machines. Majority of the solutions proposed in the literature consider homogeneous set of jobs and machines that is a simplified assumption as in practice, heterogeneous jobs and machines are frequently encountered. In this article, we tackle the aforesaid problem with a set of heterogeneous jobs and machines. A major contribution of our work is that the problem is addressed in a novel way by combining a graph-based approach and a dynamic programming approach which is based on a variation of bin packing problem. A greedy algorithm is also proposed by employing only a graph-based approach at the aim to reduce the computational complexity. Experimental results show that the proposed algorithms can significantly reduce the cumulative busy interval over all machines compared with state-of-the-art algorithms proposed in the literature.

关键词： Greedy algorithms Schedules Processor scheduling Heuristic algorithms Conferences Parallel processing Dynamic programming

Perfusable cell-laden micropatterned hydrogels for delivery of spatiotemporal vascular-like cues to tissues

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Organs-on-a-chip 2022年 4卷 100017页

作者： Walter B Varhue Aditya Rane Ramon Castellanos-Sanchez Shayn M Peirce George Christ Nathan S Swami Electrical and Computer Engineering University of Virginia Charlottesville VA 22904 USA. Chemistry University of Virginia Charlottesville VA 22904 USA. Biomedical Engineering University of Virginia Charlottesville VA 22904 USA.

The integration of vasculature at physiological scales within 3D cultures of cell-laden hydrogels for the delivery of spatiotemporal mass transport, chemical and mechanical cues, is a stepping-stone towards building tissue models that recapitulate cues. To address this challenge, we present a versatile method to micropattern adjoining hydrogel shells with a perfusable channel or lumen core, for enabling facile integration with fluidic control systems, on one hand, and to cell-laden biomaterial interfaces, on the other hand. This microfluidic imprint lithography methodology utilizes the high tolerance and reversible nature of the bond alignment process to lithographically position multiple layers of imprints within a microfluidic device for sequential filling and patterning of hydrogel lumen structures with single or multiple shells. Through fluidic interfacing of the structures, the ability to deliver physiologically relevant mechanical cues for recapitulating cyclical stretch on the hydrogel shell and shear stress on endothelial cells in the lumen are validated. We envision application of this platform for recapitulation of the bio-functionality and topology of micro-vasculatures, alongside the ability to deliver transport and mechanical cues, as needed for 3D culture to construct tissue models.

关键词： 3D cell culture Biofabrication In vitro vasculature models Microfluidics Organ-on-a-Chip