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2022 Solid-State Sensors, Actuators and Microsystems Workshop, Hilton Head 2022

作者： Del-Rio-Ruiz, Ruben Asci, Cihan Owyeung, Rachel Widmer, Giovanni Sonkusale, Sameer Nano Lab. Advanced Technology Laboratory Tufts University United States Department of Electrical and Computer Engineering Tufts University United States Cummings School of Veterinary Medicine Tufts University United States

ISBN: (纸本)9781940470047

Sampling of gut microbiome upstream of the colon is challenging and expensive. Research has shown that ingestible devices are effective in sampling microbiome, however existing designs are complex and suffer from high levels of contamination from untargeted regions. This paper aims to present a simple sampling design concept with a self-closing mechanism relying on swellable polyacrylate beads that close the inlet to prevent contamination. The pill is fully elastic, lightweight, biocompatible, battery-less realized using stereo-lithography based 3D-printer. Herein, it is believed that such an ingestible device will increase the understanding of spatial diversity of gut microbiome and its response to medical conditions and treatments, using a non-invasive and simple technique. © 2022 TRF.

关键词： Actuators

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Unsupervised SAR Images for Submesoscale Oceanic Eddy Detection

Unsupervised SAR Images for Submesoscale Oceanic Eddy Detect...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Grace Vincent Kai Pak Diego Martinez Edwin Goh Jinbo Wang Brian Bue Ben Holt Brian Wilson Electrical and Computer Engineering North Carolina State University Raleigh NC California Institute of Technology Jet Propulsion Laboratory Pasadena CA

Accurate and efficient identification of submesoscale ocean eddies is crucial for understanding ocean circulation, tracer mixing, and energy transfer, especially in coastal regions. However, current methodologies face challenges due to their reliance on extremely large datasets typically requiring specialized domain expertise. To address these limitations, we propose a novel semi-supervised framework in this study. Leveraging self-supervised contrastive learning, we extract meaningful features from unlabeled SAR images and fine-tune them using a small set of labeled images. By employing SimCLR and MoCo algorithms, we achieve promising outcomes and superior performance in SAR-based submesoscale eddy detection, surpassing supervised techniques. The proposed approach yields a top F1-Macro of 0.85 and 0.80-0.83 for evaluating Mediterranean and California SAR patches, respectively.

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Constrained Reinforcement Learning for Predictive Control in Real-Time Stochastic Dynamic Optimal Power Flow

arXiv

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arXiv 2023年

作者： Wu, Tong Scaglione, Anna Arnold, Daniel Department of Electrical and Computer Engineering Cornell Tech Cornell University 10044 United States Lawrence Berkeley National Laboratory United States

Deep Reinforcement Learning (DRL) has become a popular method for solving control problems in power systems. Conventional DRL encourages the agent to explore various policies encoded in a neural network (NN) with the goal of maximizing the reward function. However, this approach can lead to infeasible solutions that violate physical constraints such as power flow equations, voltage limits, and dynamic constraints. Ensuring these constraints are met is crucial in power systems, as they are a safety critical infrastructure. To address this issue, existing DRL algorithms remedy the problem by projecting the actions onto the feasible set, which can result in sub-optimal solutions. This paper presents a novel primal-dual approach for learning optimal constrained DRL policies for dynamic optimal power flow problems, with the aim of controlling power generations and battery outputs. We also prove the convergence of the critic and actor networks. Our case studies on IEEE standard systems demonstrate the superiority of the proposed approach in dynamically adapting to the environment while maintaining safety constraints. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Generic Load Flow Solution for Droop-Controlled AC Microgrids Considering Electric Vehicle Loads

A Generic Load Flow Solution for Droop-Controlled AC Microgr...

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International Conference on Power Electronic Drives

作者： Ghanshyam Meena Akhilesh Mathur V. P. Singh Krishna Murari Sukumar Kamalasadan Department of Electrical Engineering Malaviya National Institute of Technology Jaipur Rajasthan India Electrical Engineering and Computer Science Department The University of Toledo Toledo Ohio USA Department of Electrical Engineering Power Energy and Intelligent Systems Laboratory EPIC University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

Microgrids (MGs) have become more unpredictable due to integration of renewable generation sources. Several methods are used to solve load flow problems in MGs. Load flow analysis is a complex problem for islanded MGs due to the absence of slack bus and dependency of active power generation of droop-controlled DGs on frequency. In this paper, a Modified Newton Raphson (MNR) approach is used to perform load flow analysis of isolated MGs considering various types of electric vehicle (EV) loads. The presented method has been validated on 6-bus and 38-bus test systems of the islanded microgrid to give an accurate and simple approach for computing the load flow solutions.

关键词： Renewable energy sources Microgrids Drives Electric vehicles Software Power electronics Distributed power generation Planning MATLAB Load flow analysis

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Towards Redefining the Reproducibility in Quantum Computing: A Data Analysis Approach on NISQ Devices

Towards Redefining the Reproducibility in Quantum Computing:...

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Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Priyabrata Senapati Zhepeng Wang Weiwen Jiang Travis S Humble Bo Fang Shuai Xu Qiang Guan Department of Computer Science Kent State University Department of Electrical and Computer Engineering George Mason University Quantum Science and Engineering Center George Mason University Quantum Science Center Oak Ridge National Laboratory Pacific Northwest National Laboratory HPC Group Department of Computer and Data Sciences Case Western Reserve University

Although the building of quantum computers has kept making rapid progress in recent years, noise is still the main challenge for any application to leverage the power of quantum computing. Existing works addressing noise in quantum devices proposed noise reduction when deploying a quantum algorithm to a specified quantum computer. The reproducibility issue of quantum algorithms has been raised since the noise levels vary on different quantum computers. Importantly, existing works largely ignore the fact that the noise of quantum devices varies as time goes by. Therefore, reproducing the results on the same hardware will even become a problem. We analyze the reproducibility of quantum machine learning (QML) algorithms based on daily model training and execution data collection. Our analysis shows a correlation between our QML models' test accuracy and quantum computer hardware's calibration features. We also demonstrate that noisy simulators for quantum computers are not a reliable tool for quantum machine learning applications.

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Med-Tuning: A New Parameter-Efficient Tuning Framework for Medical Volumetric Segmentation

arXiv

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arXiv 2023年

作者： Shen, Jiachen Wang, Wenxuan Chen, Chen Jiao, Jianbo Liu, Jing Zhang, Yan Song, Shanshan Li, Jiangyun School of Automation and Electrical Engineering University of Science and Technology Beijing China Center for Research in Computer Vision University of Central Florida United States School of Computer Science University of Birmingham United Kingdom Institute of Automation Chinese Academy of Sciences China

The "pre-training then fine-tuning (FT)" paradigm is widely adopted to boost the model performance of deep learning-based methods for medical volumetric segmentation. However, conventional full FT incurs high computational and memory costs. Thus, it is of increasing importance to fine-tune pre-trained models for medical volumetric segmentation tasks in a both effective and parameter-efficient manner. In this paper, we introduce a new framework named Med-Tuning to realize parameter-efficient tuning (PET) for medical volumetric segmentation task and an efficient plug-and-play module named Med-Adapter for task-specific feature extraction. With a small number of tuned parameters, our framework enhances the 2D baselines’s precision on segmentation tasks, which are pre-trained on natural images. Extensive experiments on three benchmark datasets (CT and MRI modalities) show that our method achieves better results than previous PET methods on volumetric segmentation tasks. Compared to full FT, Med-Tuning reduces the fine-tuned model parameters by up to 4×, with even better segmentation performance. Our project webpage is at https://***/Med-Tuning/. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Physics guided machine learning for multi-material decomposition of tissues from dual-energy CT scans of simulated breast models with calcifications

Physics guided machine learning for multi-material decomposi...

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IS and T International Symposium on Electronic Imaging: High Performance Computing for Imaging, HPCI 2023

作者： Meena, Muralikrishnan Gopalakrishnan Ziabari, Amir K. Venkatakrishnan, Singanallur V. Lyngaas, Isaac R. Norman, Matthew R. Joo, Balint Beck, Thomas L. Bouman, Charles A. Kapadia, Anuj J. Wang, Xiao National Center for Computational Sciences Oak RidgeTN United States Electrification and Energy Infrastructure Division Oak RidgeTN United States School of Electrical and Computer Engineering Purdue University West LafayetteIN United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States

We introduce a physics guided data-driven method for image-based multi-material decomposition for dual-energy computed tomography (CT) scans. The method is demonstrated for CT scans of virtual human phantoms containing more than two types of tissues. The method is a physics-driven supervised learning technique. We take advantage of the mass attenuation coefficient of dense materials compared to that of muscle tissues to perform a preliminary extraction of the dense material from the images using unsupervised methods. We then perform supervised deep learning on the images processed by the extracted dense material to obtain the final multi-material tissue map. The method is demonstrated on simulated breast models with calcifications as the dense material placed amongst the muscle tissues. The physics-guided machine learning method accurately decomposes the various tissues from input images, achieving a normalized root-mean-squared error of 2.75%. © 2023, Society for Imaging Science and Technology.

关键词： Extraction

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Field-free approaches for deterministic spin–orbit torque switching of the perpendicular magnet

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Materials Futures 2022年第2期1卷 51-63页

作者： Hao Wu Jing Zhang Baoshan Cui Seyed Armin Razavi Xiaoyu Che Quanjun Pan Di Wu Guoqiang Yu Xiufeng Han Kang L Wang Songshan Lake Materials Laboratory DongguanGuangdong 523808People’s Republic of China Department of Electrical and Computer Engineering and Department of Physics and AstronomyUniversity of CaliforniaLos AngelesCA 90095United States of America Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsChinese Academy of SciencesBeijing 100190People’s Republic of China

All-electrical driven magnetization switching attracts much attention in next-generation spintronic memory and logic devices,particularly in magnetic random-access memory(MRAM)based on the spin–orbit torque(SOT),***-MRAM,due to its advantages of low power consumption,fast write/read speed,and improved endurance,*** conventional SOT-driven switching of the magnet with perpendicular magnetic anisotropy,an external assisted magnetic field is necessary to break the inversion symmetry of the magnet,which not only induces the additional power consumption but also makes the circuit more *** the last decade,significant effort has been devoted to field-free magnetization manipulation by using *** this review,we introduce the basic concepts of *** that,we mainly focus on several approaches to realize the field-free deterministic SOT switching of the perpendicular *** mechanisms mainly include mirror symmetry breaking,chiral symmetry breaking,exchange bias,and interlayer exchange ***,we show the recent progress in the study of SOT with unconventional origin and *** final section is devoted to the industrial-level approach for potential applications of field-free SOT switching in SOT-MRAM technology.

关键词： spin-orbit torque perpendicular magnetic anisotropy field-free switching symmetry breaking magnetic memory

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Design and Implementation of a GaN-based Capacitively-Isolated Hybrid Dickson Switched-Capacitor DC-DC Converter for Space Applications

Design and Implementation of a GaN-based Capacitively-Isolat...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Samantha Coday Elisa Krause Margaret E. Blackwell Nathan M. Ellis Ansel Barchowsky Robert C.N. Pilawa-Podgurski Department of Electrical Engineering and Computer Sciences University of California Berkeley Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA

To enable next generation robotic space exploration, innovations in the area of high step-down voltage converters is necessary. This work explores the use of multilevel and hybrid switched capacitor power converters which have unique advantages in mass-critical and harsh radiation space environments. A capacitively-isolated Dickson hybrid switched capacitor converter designed for implementation in a partial power pro-cessing high-voltage step-down converter is presented. The converter operation and design are detailed, with particular attention paid to minimizing the total passive component mass. Finally, a 12:1 hardware prototype is presented and experimental results verify operation up to 600 V, with 150 V isolation and a peak efficiency of over 96%.

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Lossless Compression of Synchro-Waveform Measurements for Smart Grid Monitoring

Lossless Compression of Synchro-Waveform Measurements for Sm...

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IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East)

作者： Wei Qiu He Yin Yuru Wu Chang Chen Chujie Zeng Lingwei Zhan Yilu Liu Department of Electrical Engineering & Computer Science The University of Tennessee Knoxville TN USA Oak Ridge National Laboratory ORNL TN USA

Widespread implementations of high-frequency elec-tronic equipment increase the demand for high-frequency synchronized measurement data. However, high-speed data streaming challenges the quality of communication and data server storage. To address this problem, this paper proposes an online lossless compression of synchro-waveform measurement for smart grid monitoring. The lossless compression will highly reduce the data transmission and storage burden while reserving all the critical information for both online and offline analysis. To this end, the proposed method is designed based on the delta-delta, Simple-8b, and Lempel-Ziv-Markov chain algorithm technologies, where the delta-delta consists of a two-order delta and one periodical delta. Then, the simulation experiments based on various noise levels, time windows, and sampling rates are presented and compared with some state-of-art methods. Moreover, the laboratory experiment is conducted to verify the validity of the synchro-waveform measurement compression method, where 53.1% to 77.3% of data space can be saved under 60dB to 120dB noise scenarios.

关键词： Cutoff frequency Low-pass filters Extraterrestrial measurements Loss measurement Smart grids Noise measurement DSL

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