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arXiv 2021年

作者： Arslan, Tunç Alper Özaslan, A. Kurt, Semih Muslu, Yavuz Saritas, Emine Ulku Department of Electrical and Electronics Engineering Bilkent University Ankara06800 Turkey Bilkent University Ankara06800 Turkey Neuroscience Program Sabuncu Brain Research Center Bilkent University Ankara06800 Turkey Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI United States Department of Radiology University of Wisconsin-Madison MadisonWI United States Department of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Science for Life Laboratory Stockholm Sweden

Magnetic particle imaging (MPI) is a rapidly developing medical imaging modality that exploits the non-linear response of magnetic nanoparticles (MNPs). Color MPI widens the functionality of MPI, empowering it with the capability to distinguish different MNPs and/or MNP environments. The system function approach for color MPI relies on extensive calibrations that capture the differences in the harmonic responses of the MNPs. An alternative calibration-free x-space-based method called TAURUS estimates a map of the relaxation time constant, τ, by recovering the underlying mirror symmetry in the MPI signal. However, TAURUS requires a back and forth scanning of a given region, restricting its usage to slow trajectories with constant or piecewise constant focus fields (FFs). In this work, we propose a novel technique to increase the performance of TAURUS and enable τ map estimation for rapid and multi-dimensional trajectories. The proposed technique is based on correcting the distortions on mirror symmetry induced by time-varying FFs. We demonstrate via simulations and experiments in our in-house MPI scanner that the proposed method successfully estimates high-fidelity τ maps for rapid trajectories that provide orders of magnitude reduction in scanning time, while preserving the calibration-free property of TAURUS. Copyright © 2021, The Authors. All rights reserved.

关键词： Trajectories

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Pale Red Dot: A Large, Robust Architecture for Human Settlements on Mars

Pale Red Dot: A Large, Robust Architecture for Human Settlem...

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Accelerating Space Commerce, Exploration, and New Discovery Conference, ASCEND 2023

作者： Lordos, George Hoying, Madelyn McKinney, Lanie Webb, Alisa Lin, Yuying Martin, Estelle Beilstein, John Miller, Duncan Villegas, David Sepulveda, Francisco AlSadah, Yousif Iyer, Divya O’Neill, Cormac Patel, Palak Garcia, Ignacio Arzuaga Azzouz, H. Chan, Wing Lam Eyre, Ezra Gleason, Dane Romero, Elizabeth Arias, Liliana Orozco, Omar Gentgen, Chloe Latyshev, Kir Hoffman, Jeffrey de Weck, Olivier Ikinci, Meltem Lordos, Alexandros Aeronautics and Astronautics Massachusetts Institute of Technology 33-409 CambridgeMA02139 United States Massachusetts Institute of Technology Harvard-MIT Health Sciences and Technology Program and MIT Aeronautics and Astronautics 33-409 CambridgeMA02139 United States Massachusetts Institute of Technology CambridgeMA02139 United States Aeronautics and Astronautics Massachusetts Institute of Technology 77 Mass Ave. 33-409 CambridgeMA02139 United States System Design and Management Massachusetts Institute of Technology 77 Mass Ave. E40-300 CambridgeMA02139 United States Mechanical Engineering Massachusetts Institute of Technology 77 Mass Ave. 33-409 CambridgeMA02139 United States Civil and Environmental Engineering Massachusetts Institute of Technology 77 Mass Ave. CambridgeMA02139 United States Electrical Engineering Massachusetts Institute of Technology 77 Mass Ave. CambridgeMA02139 United States Nuclear Science and Engineering Massachusetts Institute of Technology 77 Mass Ave. CambridgeMA02139 United States Computer Science and Engineering Massachusetts Institute of Technology 77 Mass Ave. CambridgeMA02139 United States Massachusetts Institute of Technology 77 Mass Ave. 37-219 CambridgeMA02139 United States Massachusetts Institute of Technology 77 Mass Ave. 33-410 CambridgeMA02139 United States Behavioral Economics University of Cyprus Nicosia Cyprus Department of Psychology University of Cyprus Nicosia Cyprus

ISBN: (数字)9781624107054

ISBN: (纸本)9781624107054

We propose a ten-yearMars mission to establish Earth-independence and support efficient, safe, and sustainable science and exploration goals. The campaign begins with a series of precursor missions, starting with the Artemis program, followed by robotic missions to performsite surveys and selection, de-risk technologies, and to start stockpiling in-situ water and propellant. Robotic Cargo Starships would depart first in 2035 to deliver and deploy approximately 5,800 tons of habitats, nuclear microreactors, farmmodules, manufacturing and ISRU systems. In 2040, two Crew Starships carrying 36 persons would land onMars to establish two close-proximity villages. The lifecycle cost is estimated at $81 billion, with a peak annual cost of $6.6 billion. To meet RASC-AL competition requirements, intended to motivate the exploration of highly self-reliant architecture concepts, resupply from Earth was constrained to only 5 metric tons every two years and minimum surface endurance was set at 7 years. The risks to human life and to critical systems increase exponentially over a minimally-resupplied, 7-10 year mission toMars. Our approach focused on identifying capabilities to mitigate known and unknown risks. Using a high-fidelity crew time model, we found that large architectures, with their economies of scale and productivity gains from specialization, are essential to robustly support the necessary capabilities. The model also shows that small 7-year missions of 12 persons or less cannot provide value in terms of free surface traverse time for science and exploration. Given the necessity of a large, complex architecture, we then sought out efficiencies and synergies (value engineering) in order to make the mission feasible and affordable. In the process, we identified more benefits that are enabled only by large missions, including the creation of a true community on Mars with sufficient social complexity for humans to thrive, and the geopolitically compelling option to inclu

关键词： Value engineering

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Machine learning approaches for fault detection and diagnosis of induction motors

Machine learning approaches for fault detection and diagnosi...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Lamia Belguesmi Mansour Hajji Majdi Mansouri Mohamed-Faouzi Harkat Abdelmalek Kouadri Hazem Nounou Mohamed Nounou Higher institute of Applied Science and Technology of Kasserine University of Kairouan Kasserine Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728110806

ISBN: (纸本)9781728110813

This paper deals with the problem of monitoring of induction motors (IM) through the development of fault detection and diagnosis (FDD) approach. The developed FDD technique is addressed such that, the principal component analysis (PCA) technique is used for features extraction purposes and the machine learning (ML) classifiers are applied for fault diagnosis. In the proposed FDD approach the most efficient features are extracted and selected through PCA scheme using induction motor data. Besides, their statistical characteristics (mean and variance) are also included. The ML classifiers are applied using the extracted and selected features to perform the FDD problem. The obtained results indicate that the proposed techniques have a wide application area, fast fault detection and diagnosis, making them more reliable for induction motors monitoring.

关键词： Induction motors Machine learning algorithms Fault detection Machine learning Feature extraction Monitoring Principal component analysis

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Efficient fault detection and diagnosis of wind energy converter systems

Efficient fault detection and diagnosis of wind energy conve...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Zahra Yahyaoui Mansour Hajji Majdi Mansouri Mohamed-Faouzi Harkat Abdelmalek Kouadri Hazem Nounou Mohamed Nounou Higher institute of Applied Science and Technology of Kasserine University of Kairouan Kasserine Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728110806

ISBN: (纸本)9781728110813

Fault detection and diagnosis for modern wind turbines converter (WTC) systems have been received an important measure for improving the operation of these systems, in such a way to increase their reliability, availability and required safety. Therefore, this paper deals with the problem of fault detection and diagnosis (FDD) in WTC systems. The developed FDD approach uses feature extraction and selection, and fault classification tools for monitoring WTC system under different operating conditions. The developed FDD approach is addressed such that, the principal component analysis (PCA) technique is used for feature extraction purposes and the machine learning (ML) classifiers are applied for fault diagnosis. In the proposed FDD approach, an efficient features in PCA subspace that extract and select the most informative features from WTC data are provided. Besides, their statistical characteristics are also included. The ML classifiers are applied to the extracted and selected features to perform the fault diagnosis problem. The effectiveness and higher classification accuracy of the developed approach are demonstrated using simulated data extracted from different operating conditions of the wind turbine.

关键词： Fault diagnosis Fault detection Machine learning Feature extraction Wind turbines Monitoring Principal component analysis

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Knee Injury Diagnostic Device

Knee Injury Diagnostic Device

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Julie E. McKenna Tyler R. Hopkins Lucas T. Lavallee Douglas E. Dow Program in Engineering conentration in Computer Eng. Wentworth Institute of Technology Boston Massachussetts USA Program in Electrical Eng. Wentworth Institute of Technology Boston Massachussetts USA Program in Computer Eng. Wentworth Institute of Technology Boston Massachussetts USA Wentworth Institute of Technology Boston Massachussetts USA

ISBN: (数字)9781728196565

ISBN: (纸本)9781728196572

Knee injuries are difficult to accurately diagnose. The manual evaluation relies on many subjective factors such as physician experience, swelling, patient guarding, and the severity of the injury. These factors can lead to an inaccurate or incomplete diagnosis, resulting in less than optimal treatment and recovery. Knee injuries are very common among athletes and can occur during the day to day activities, with many resulting in tears to one or more of the four ligaments. For evaluation, a physician manually manipulates the knee with a series of standard tests. Even though these standard manual tests are considered best practice, they are known to lead to some inaccuracies with upwards of 1 in 8 patients being misdiagnosed due to testing deficiencies. Imaging by MRI is used to support the diagnosis if available, though not available to all patients due to cost and time requirements. This purpose of this project was to develop and test a wearable diagnostic system contained within a sleeve over the knee. Incorporated sensors were used to monitor movement and electromyographic activity to determine quantitative measurements toward a diagnosis. The movement and displacement monitoring subsystems were tested on a constructed model of the lower leg and knee. Preliminary results have shown accurate readings with an average percent error of 1% for range of motion testing and 3% (0.1 to 0.2 mm) for laxity testing. This measurement determined by this system could be reported to a physician who could use when making a diagnosis. Improved diagnosis would guide appropriate treatment and contribute to improved recovery.

关键词： Sensors Knee Injuries Muscles Electromyography Flexible printed circuits Ligaments

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Fault detection in photovoltaic systems using machine learning technique

Fault detection in photovoltaic systems using machine learni...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Khadija Attouri Mansour Hajji Majdi Mansouri Mohamed-Faouzi Harkat Abdelmalek Kouadri Hazem Nounou Mohamed Nounou Higher institute of Applied Science and Technology of Kasserine University of Kairouan Kasserine Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728110806

ISBN: (纸本)9781728110813

To ensure high reliability of the Grid-Connected Photovoltaic (GCPV) systems, promptly faults detection, diagnosis and automatic process monitoring are essential tools to keep the PV and the grid network under optimal functioning. Regardless of fault types, incipient faults are usually more difficult to detect and accurately isolate. As an alternative and effective method, the principal components analysis (PCA) is proposed to extract and select more relevant features and support vector machines (SVM) technique is applied to quickly detect the faults that occur in a GCPV system. The T2 and squared weighted errors (SWE) statistics, generally used as fault detection indices, are appropriately extracted and selected within the PCA framework. Both of these features are fed to a SVM classifier handling the incipient fault detection. This task is carried out on a simulated GCPV operating under maximum power point trackers (MPPT) and matching realistic outdoor to demonstrate the effectiveness and robustness of the proposed technique.

关键词： Support vector machines Photovoltaic systems Maximum power point trackers Fault detection Feature extraction Robustness Principal component analysis

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Universality of Winning Tickets: A Renormalization Group Perspective

arXiv

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arXiv 2021年

作者： Redman, William T. Chen, Tianlong Wang, Zhangyang Dogra, Akshunna S. Interdepartmental Graduate Program in Dynamical Neuroscience University of California Santa Barbara United States Department of Electrical and Computer Engineering University of Texas at Austin United States Department of Mathematics Imperial College London United Kingdom EPSRC CDT in Mathematics of Random Systems: Analysis Modelling and Simulation United Kingdom

Foundational work on the Lottery Ticket Hypothesis has suggested an exciting corollary: winning tickets found in the context of one task can be transferred to similar tasks, possibly even across different architectures. This has generated broad interest, but methods to study this universality are lacking. We make use of renormalization group theory, a powerful tool from theoretical physics, to address this need. We find that iterative magnitude pruning, the principal algorithm used for discovering winning tickets, is a renormalization group scheme, and can be viewed as inducing a flow in parameter space. We demonstrate that ResNet-50 models with transferable winning tickets have flows with common properties, as would be expected from the theory. Similar observations are made for BERT models, with evidence that their flows are near fixed points. Additionally, we leverage our framework to study winning tickets transferred across ResNet architectures, observing that smaller models have flows with more uniform properties than larger models, complicating transfer between them. © 2021, CC BY.

关键词： Statistical mechanics

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Ultrastrong Spin-Photon Coupling in Gadolinium Gallium Garnet

Ultrastrong Spin-Photon Coupling in Gadolinium Gallium Garne...

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Conference on Lasers and Electro-Optics (CLEO)

作者： T. Elijah Kritzell Junzhe Bao Jacques Doumani Jae Joon Lee Hongjing Xu Fuyang Tay Hiroyuki Nojiri Motoaki Bamba Andrey Baydin Junichiro Kono Department of Electrical and Computer Engineering Rice University Houston Texas USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas USA Department of Physics and Astronomy Rice University Houston Texas USA Institute for Materials Research Tohoku University Senai Japan The Hakubi Center for Advanced Research Kyoto University Kyoto Japan Department of Physics I Kyoto University Kyoto Japan Smalley-Curl Institute Rice University Houston Texas USA Department of Material Science and NanoEngineering Rice University Houston Texas USA

We examine ultrastrong coupling of an ensemble of paramagnetic spins in Gd 3 Ga 5 O 12 with terahertz photons in the presence of a high external magnetic field both in the bulk and thin-film limits.

ISBN: (纸本)9781957171258

We examine ultrastrong coupling of an ensemble of paramagnetic spins in Gd 3 Ga 5 O 12 with terahertz photons in the presence of a high external magnetic field both in the bulk and thin-film limits.

关键词： CCD cameras Condensed matter Magnetic fields Photons Quantum optics Terahertz spectroscopy

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Retraction notice to "Soliton dynamics in optical fiber based on nonlinear Schrödinger equation" [Heliyon 9 (2023) e14235]

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Heliyon 2025年

作者： Harish Abdillah Mardi Nasaruddin Nasaruddin Muhammad Ikhwan Nurmaulidar Nurmaulidar Marwan Ramli Mathematics Graduate Program Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Banda Aceh 23111 Indonesia Electrical and Computer Engineering Department Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Banda Aceh 23111 Indonesia

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Mini shufflenet for efficient parking space classification

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AIP Conference Proceedings 2023年第1期2480卷

作者： Sayuti Rahman Marwan Ramli Fitri Arnia Rusdha Muharar Haida Dafitri Dharmawati Mahyus Ihsan Doctoral Program School of Engineering Syiah Kuala University Banda Aceh Indonesia Department of Mathematics Syiah Kuala University Banda Aceh Indonesia Department of Electrical and Computer Engineering Syiah Kuala University Banda Aceh Indonesia Department of Informatics Technology Universitas Harapan Medan Medan Indonesia

Motorists driving around looking for parking spaces has negative impacts such as increasing traffic jams, noise, air pollution, wasting fuel, and panic. A smart parking system providing information on the availability of parking spaces is needed. This study aims to develop a parking system by providing information on the availability of car parking spaces. Classification of car parking spaces using Sufflenet V2 is by trimming it. Based on the tests carried out, pruning was on the number of Sufflenet V2 output channels. This trimming produces the smallest Mini Shufflenet size with almost half the parameter number of mAlexnet and less than 1/10 times the number of FLOPs of mAlexnet. Mini Shufflenet has better accuracy than mAlexnet in processing time per image that is almost the same as mAlexnet. Mini Shufflenet implemented is on low-end computing devices such as the Raspberry Pi for real-time data classification. So this architecture can be implemented in a low-cost parking system.

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