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Status and future development of Heating and Current Drive for the EU DEMO

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FUSION engineering AND DESIGN 2022年第0期180卷 1页

作者： Tran, M. Q. Agostinetti, P. Aiello, G. Avramidis, K. Baiocchi, B. Barbisan, M. Bobkov, V. Briefi, S. Bruschi, A. Chavan, R. Chelis, I. Day, Ch. Delogu, R. Ell, B. Fanale, F. Fassina, A. Fantz, U. Faugel, H. Figini, L. Fiorucci, D. Friedl, R. Franke, Th Gantenbein, G. Garavaglia, S. Granucci, G. Hanke, S. Hogge, J. -p. Hopf, C. Kostic, A. Illy, S. Ioannidis, Z. Jelonnek, J. Jin, J. Latsas, G. Louche, F. Maquet, V. Maggiora, R. Messiaen, A. Milanesio, D. Mimo, A. Moro, A. Ochoukov, R. Ongena, J. Pagonakis, I. G. Peponis, D. Pimazzoni, A. Ragona, R. Rispoli, N. Ruess, T. Rzesnicki, T. Scherer, T. Spaeh, P. Starnella, G. Strauss, D. Thumm, M. Tierens, W. Tigelis, I. Tsironis, C. Usoltceva, M. Van Eester, D. Veronese, F. Vincenzi, P. Wagner, F. Wu, C. Zeus, F. Zhang, W. [a]Ecole Polytechnique Fédérale de Lausanne (EPFL) Swiss Plasma Center (SPC) Station 13 Lausanne 1015 Switzerland [b]Consorzio RFX (CNR ENEA INFN Università di Padova Acciaierie Venete SpA Corso Stati Uniti 4 Padova I-35127 Italy [c]IAM-AWP Karlsruhe Institute of Technology (KIT) Kaiserstr. 12 Karlsruhe 76131 Germany [d]IHM Karlsruhe Institute of Technology (KIT) Kaiserstr. 12 Karlsruhe 76131 Germany [e]Institute for Plasma Science and Technology National Research Council (ISTP-CNR) via Cozzi 53 Milan 20125 Italy [f]Max Planck Institute for Plasma Physics Boltzmann Strasse 2 Garching 85748 Germany [g]National and Kapodistrian University of Athens Department of Physics Zografou Athens GR-157 84 Greece [h]ITEP Karlsruhe Institute of Technology (KIT) Campus North Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen 76344 Germany [i]AG Experimentelle Plasmaphysik Universitt Augsburg Augsburg 86135 Germany [j]EUROfusion Consortium Boltzmannstr. 2 Garching 85748 Germany [k]LPP-ERM/KMS Laboratory for Plasma Physics Royal Military Academy TEC Partner Renaissancelaan 30 Brussels 1000 Belgium [l]Politecnico di Torino Corso Duca degli Abruzzi 24 Torino 10129 Italy [m]National Technical University of Athens School of Electrical and Computer Engineering Zografou Athens GR-157 71 Greece [n]Max Planck Institute for Plasma Physics Wendelsteinstrasse 1 Greifswald 17491 Germany

The European DEMO is a pulsed device with pulse length of 2 hours. The functions devoted to the heating and current drive system are: plasma breakdown, plasma ramp-up to the flat-top where fusion reactions occur, the control of the plasma during the flat-top phase, and finally the plasma ramp-down. The EU-DEMO project was in a Pre-Concept Design Phase during 2014-2020, meaning that in some cases, the design values of the device and the precise requirements from the physics point of view were not yet frozen. A total of 130 MW was considered for the all phases of the plasma: in the flat top, 30 MW is required for neoclassical tearing modes (NTM) control, 30 MW for burn control, and 70 MW for the control of thermal instability (TI), without any specific functions requested from each system, Electron Cyclotron (EC), Ion Cyclotron (IC), or Neutral Beam (NB) Injection. At the beginning of 2020, a strategic decision was taken, to consider EC as the baseline for the next phase (in 2021 and beyond). R&D on IC and NB will be risk mitigation measures. In parallel with progresses in Physics modelling, a decision point on the heating strategy will be taken by 2024. This paper describes the status of the R&D development during the period 2014-2020. It assumes that the 3 systems EC, IC and NB will be needed. For integration studies, they are assumed to be implemented at a power level of at least 50 MW. This paper describes in detail the status reached by the EC, IC and NB at the end of 2020. It will be used in the future for further development of the baseline heating method EC, and serves as starting point to further develop IC and NB in areas needed for these systems to be considered for DEMO.

关键词： Heating Current Drive Electron Cyclotron Ion Cyclotron Neutral Beam DEMO

Sensing the local magnetic environment through optically active defects in a layered magnetic semiconductor

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arXiv 2022年

作者： Klein, Julian Song, Zhigang Pingault, Benjamin Dirnberger, Florian Chi, Hang Curtis, Jonathan B. Dana, Rami Bushati, Rezlind Quan, Jiamin Dekanovsky, Lukas Sofer, Zdenek Alù, Andrea Menon, Vinod M. Moodera, Jagadeesh S. Lončar, Marko Narang, Prineha Ross, Frances M. Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States College of Letters and Sciences UCLA Los AngelesCA90095 United States QuTech Delft University of Technology Delft2600 GA Netherlands Department of Physics City College of New York New YorkNY10031 United States Francis Bitter Magnet Laboratory Plasma Science and Fusion Center Massachusetts Institute of Technology CambridgeMA02139 United States U.S. Army CCDC Army Research Laboratory AdelphiMD20783 United States Department of Physics The Graduate Center City University of New York New YorkNY10016 United States Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Photonics Initiative CUNY Advanced Science Research Center New YorkNY10031 United States Department of Electrical Engineering City College of the City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10026 United States Department of Inorganic Chemistry University of Chemistry and Technology Prague Technická 5 Prague 6166 28 Czech Republic Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States

Atomic-level defects in van der Waals (vdW) materials are essential building blocks for quantum technologies and quantum sensing applications. The layered magnetic semiconductor CrSBr is an outstanding candidate for exploring optically active defects owing to a direct gap in addition to a rich magnetic phase diagram including a recently hypothesized defect-induced magnetic order at low temperature. Here, we show optically active defects in CrSBr that are probes of the local magnetic environment. We observe spectrally narrow (1 meV) defect emission in CrSBr that is correlated with both the bulk magnetic order and an additional low temperature defect-induced magnetic order. We elucidate the origin of this magnetic order in the context of local and non-local exchange coupling effects. Our work establishes vdW magnets like CrSBr as an exceptional platform to optically study defects that are correlated with the magnetic lattice. We anticipate that controlled defect creation allows for tailor-made complex magnetic textures and phases with the unique ingredient of direct optical access. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic semiconductors

Multistage model for robust face alignment using deep neural networks

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arXiv 2020年

作者： Wang, Huabin Cheng, Rui Zhou, Jian Tao, Liang Kwan, Hon Keung MOE Key Laboratory of Intelligent Computing and Signal Processing School of Computer Science and Technology Anhui University Hefei Anhui230601 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

An ability to generalize unconstrained conditions such as severe occlusions and large pose variations remains a challenging goal to achieve in face alignment. In this paper, a multistage model based on deep neural networks is proposed which takes advantage of spatial transformer networks, hourglass networks and exemplar-based shape constraints. First, a spatial transformer - generative adversarial network which consists of convolutional layers and residual units is utilized to solve the initialization issues caused by face detectors, such as rotation and scale variations, to obtain improved face bounding boxes for face alignment. Then, stacked hourglass network is employed to obtain preliminary locations of landmarks as well as their corresponding scores. In addition, an exemplar-based shape dictionary is designed to determine landmarks with low scores based on those with high scores. By incorporating face shape constraints, misaligned landmarks caused by occlusions or cluttered backgrounds can be considerably improved. Extensive experiments based on challenging benchmark datasets are performed to demonstrate the superior performance of the proposed method over other state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

A Parallel Cell Impedance Measurement System for Real-time Cell Growth Detection 2

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A Parallel Cell Impedance Measurement System for Real-time C...

2nd IEEE International Conference on Information Systems and computer Aided Education, ICISCAE 2019

作者： Han, Yibo Lei, Kin Fong Pun, Sio Hang Mak, Peng Un Vai, Mang I. University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China Chang Gung University Graduate Institute of Medical Mechatronics Taoyuan City Taiwan University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI AMSV Faculty of Science and Technology China University of Cambridge Department of Electrical and Computer Engineering Faculty of Science and Technology CB3 0AS United Kingdom

ISBN: (纸本)9781728130668

This paper presents a new automatic cell bio-impedance measurement system for multiple cell condition detections. To make sure this system can work in an easy and efficient way, an effective impedance detection method based on on-board cell number-impedance theory was put forward. The method aimed to measure cell impedance by detecting the changes of interfaces between cells and electrodes. Cell impedance can be detected, recorded and processed by an embedded electronic circuit system. Some carefully designed tests proved that this new system remained high sensitivity, high efficiency and good precision in real-time cell impedance monitoring. It has the potential for further application in other cell sensing fields. © 2019 IEEE.

关键词： Signal detection

Observation of the out-of-plane polarized spin current from CVD grown WTe2

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arXiv 2021年

作者： Shi, Shuyuan Li, Jie Hsu, Chuang-Han Lee, Kyusup Wang, Yi Yang, Li Wang, Junyong Wang, Qisheng Wu, Hao Zhang, Wenfeng Eda, Goki Liang, Gengchiau Chang, Haixin Yang, Hyunsoo Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore Quantum-Nano Matter and Device Lab State Key Laboratory of Material Processing and Die & Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Institute of Physics Academia Sinica Taipei11529 Taiwan Key Laboratory of Materials Modification by Laser Ion and Electron Beams Ministry of Education Dalian University of Technology Dalian116024 China Centre for Advanced 2D Materials National University of Singapore 6 Science Drive 2 117546 Singapore Department of Physics National University of Singapore 117542 Singapore

Weyl semimetal Td-phase WTe2 possesses the spin-resolved band structure with strong spin-orbit coupling, holding promises as a useful spin source material. The noncentrosymmetric crystalline structure of Td-WTe2 endows the generation of the out-of-plane polarized spin, which is of great interest in magnetic memory applications. Previously, WTe2 was explored in spin devices based on mechanically exfoliated single crystal flakes with a size of micrometers. For practical spintronics applications, it is highly desirable to implement wafer-scale thin films. In this work, we utilize centimeter-scale chemical vapor deposition (CVD)-grown Td-WTe2 thin films and study the spin current generation by the spin torque ferromagnetic resonance technique. We find the in-plane and out-of-plane spin conductivities of 7.36 × 103 (ħ/2e) (Ωm)–1 and 1.76 × 103 (ħ/2e) (Ωm)–1, respectively, in CVD-growth 5 nm-WTe2. We further demonstrate the current induced magnetization switching in WTe2/NiFe at room temperature in the domain wall motion regime, which may invigorate potential spintronic device innovations based on Weyl semimetals. Copyright © 2021, The Authors. All rights reserved.

关键词： Thin films

Quasi-linear theory of forced magnetic reconnection for the transition from linear to Rutherford regime

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arXiv 2020年

作者： Huang, Wenlong Zhu, Ping School of Computer Science and Technology Anhui Engineering Laboratory for Industrial Internet Intelligent Applications Security Anhui University of Technology Anhui Ma’anshan243002 China International Joint Research Laboratory of Magnetic Confinement Fusion Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering Technology School of Electrical and Electronic Engineering Huazhong University ofScience and Technology Hubei Wuhan430074 China Department of Engineering Physics University of Wisconsin-Madison MadisonWI53706 United States

Using the in-viscid two-field reduced MHD model, a new analytical theory is developed to unify the Hahm-Kulsrud-Taylor (HKT) linear solution and the Rutherford quasi-linear regime. Adopting a quasi-linear approach, we obtain a closed system of equations for plasma response in a static plasma in slab geometry. An integral form of analytical solution is obtained for the forced magnetic reconnection, uniformly valid throughout the entire regimes from the HKT linear solution to the Rutherford quasi-linear solution. In particular, the quasi-linear effect can be described by a single coefficient Ks ∝ S8/5ψc2, where S = ττAR and ψc are the Lunquist number and amplitude of external magnetic perturbation, respectively. The HKT linear solution for response can be recovered when the index Ks → 0. On the other hand, the quasilinear effect plays a key role in the island growth when Ks ∼ 1. Our new analytical solution has also been compared with reduced MHD simulations with agreement. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetoplasma

CMR×Recon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

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arXiv 2023年

作者： Wang, Chengyan Lyu, Jun Wang, Shuo Qin, Chen Guo, Kunyuan Zhang, Xinyu Yu, Xiaotong Li, Yan Wang, Fanwen Jin, Jianhua Shi, Zhang Xu, Ziqiang Tian, Yapeng Hua, Sha Chen, Zhensen Liu, Meng Sun, Mengting Kuang, Xutong Wang, Kang Wang, Haoran Li, Hao Chu, Yinghua Yang, Guang Bai, Wenjia Zhuang, Xiahai Wang, He Qin, Jing Qu, Xiaobo Human Phenome Institute Fudan University Shanghai China School of Nursing The Hong Kong Polytechnic University Hong Kong Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Bioengineering/˜Imperial-X Imperial College London United Kingdom School of Data Science Fudan University Shanghai China Department of Radiology Zhongshan Hospital Fudan University Shanghai China School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China Department of Computer Science The University of Texas Dallas United States Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Simens Healthineers Ltd. China Department of Brain Sciences Imperial College London London United Kingdom Department of Computing Imperial College London London United Kingdom

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have not been publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. Manual segmentations of the myocardium and chambers of all the subjects are also provided within the dataset. Scripts of state-of-the-art reconstruction algorithms were also provided as a point of reference. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community. Researchers can access the dataset at https://***/#!Synapse:syn51471091/wiki/. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

Boost AI power: Data augmentation strategies with unlabelled data and conformal prediction, a case in alternative herbal medicine discrimination with electronic nose

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arXiv 2021年

作者： Liu, Li Zhan, Xianghao Wu, Rumeng Guan, Xiaoqing Wang, Zhan Zhang, Wei Pilanci, Mert Wang, You Luo, Zhiyuan Li, Guang The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Department of Bioengineering Stanford University StanfordCA94305 United States The Computer Learning Research Center Royal Holloway University of London Egham Hill Surrey EghamTW20 0EX United Kingdom Department of Electrical Engineering Stanford University StanfordCA94305 United States

Electronic nose has been proven effective in alternative herbal medicine classification, but due to the nature of supervised learning, previous research heavily relies on the labelled training data, which are time-costly and labor-intensive to collect. To alleviate the critical dependency on the training data in real-world applications, this study aims to improve classification accuracy via data augmentation strategies. The effectiveness of five data augmentation strategies under different training data inadequacy are investigated in two scenarios: the noise-free scenario where different availabilities of unlabelled data were considered, and the noisy scenario where different levels of Gaussian noises and translational shift were added to represent sensor drifts. The five augmentation strategies, namely noise-adding data augmentation, semi-supervised learning, classifier-based online learning, Inductive Conformal Prediction (ICP) online learning and our novel ensemble ICP online learning (EICP) proposed in this study, are compared against supervised learning baselines, with Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) as the classifiers. This study provides a systematic analysis of different augmentation strategies. Our novel strategy, EICP, outperforms the others by showing non-decreasing classification accuracy on all tasks and a significant improvement on most simulated tasks (25 out of 36 tasks, p ≤ 0.05), which demonstrated both effectiveness and robustness in boosting the classification model generalizability. This strategy can be employed in other machine learning applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Electronic nose

Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance

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arXiv 2021年

作者： Dong, Yi Huang, Wei Bharti, Vibhav Cox, Victoria Banks, Alec Wang, Sen Zhao, Xingyu Schewe, Sven Huang, Xiaowei Department of Computer Science University of Liverpool Ashton Building Ashton Street LiverpoolL69 3BX United Kingdom School of Engineering & Physical Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom Defence Science and Technology Laboratory SalisburySP4 0JQX United Kingdom Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2BX United Kingdom

The increasing use of Machine Learning (ML) components embedded in autonomous systems—so-called Learning-Enabled Systems (LESs)—has resulted in the pressing need to assure their functional safety. As for traditional functional safety, the emerging consensus within both, industry and academia, is to use assurance cases for this purpose. Typically assurance cases support claims of reliability in support of safety, and can be viewed as a structured way of organising arguments and evidence generated from safety analysis and reliability modelling activities. While such assurance activities are traditionally guided by consensus-based standards developed from vast engineering experience, LESs pose new challenges in safety-critical application due to the characteristics and design of ML models. In this article, we first present an overall assurance framework for LESs with an emphasis on quantitative aspects, e.g., breaking down system-level safety targets to component-level requirements and supporting claims stated in reliability metrics. We then introduce a novel model-agnostic Reliability Assessment Model (RAM) for ML classifiers that utilises the operational profile and robustness verification evidence. We discuss the model assumptions and the inherent challenges of assessing ML reliability uncovered by our RAM and propose solutions to practical use. Probabilistic safety argument templates at the lower ML component-level are also developed based on the RAM. Finally, to evaluate and demonstrate our methods, we not only conduct experiments on synthetic/benchmark datasets but also scope our methods with case studies on simulated Autonomous Underwater Vehicles and physical Unmanned Ground Vehicles. © 2021, CC BY.

关键词： Software reliability