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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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RF chain reduction for MIMO systems: A hardware prototype

arXiv

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arXiv 2019年

作者： Gong, Tierui Shlezinger, Nir Ioushua, Shahar Stein Namer, Moshe Yang, Zhijia Eldar, Yonina C. State Key Lab of Robotics Key Lab of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel School of Electrical Engineering Tel Aviv University Tel Aviv Israel Faculty of Electrical Engineering Technion Haifa Israel

RF chain circuits play a major role in digital receiver architectures, allowing passband communication signals to be processed in baseband. When operating at high frequencies, these circuits tend to be costly. This increased cost imposes a major limitation on future multiple-input multiple-output (MIMO) communication technologies. A common approach to mitigate the increased cost is to utilize hybrid architectures, in which the received signal is combined in analog into a lower dimension, thus reducing the number of RF chains. In this work we present a hardware prototype implementing analog combining for RF chain reduction. The prototype consists of a specially designed configurable combining board as well as a dedicated experimental setup. Our hardware prototype allows to evaluate the effect of analog combining in MIMO systems using actual communication signals. The experimental study, which focuses on channel estimation accuracy in MIMO channels, demonstrates that using the proposed prototype, the achievable channel estimation performance is within a small gap in a statistical sense from that obtained using a costly receiver in which each antenna is connected to a dedicated RF chain. Furthermore, in the considered scenarios, this gap becomes negligible when the reduction rate, i.e., the ratio of the number of RF chains to the number of antennas, is above 62.5%. Copyright © 2019, The Authors. All rights reserved.

关键词： Channel estimation

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Constraining Lens Masses in Moderately to Highly Magnified Microlensing Events from Gaia

arXiv

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arXiv 2025年

作者： Pylypenko, U. Wyrzykowski, L. Mikolajczyk, P.J. Kotysz, K. Zieliński, P. Ihanec, N. Wicker, M. Ratajczak, M. Sitek, M. Howil, K. Jablońska, M. Kaczmarek, Z. Kruszyńska, K. Udalski, A. Damljanovic, G. Stojanovic, M. Jovanovic, M.D. Kvernadze, T. Kvaratskhelia, O. Zejmo, M. Brincat, S.M. Qvam, J.K.T. Güver, T. Bachelet, E. Rybicki, K.A. Garofalo, A. Hardy, L. Zdanavicius, J. Pakstiene, E. Zola, S. Kurowski, S. Reichart, D.E. Davidson, J.W. Burgaz, U. Rivet, J.P. Jelinek, M. Popowicz, A. Esenoglu, H.H. Sonbas, E. Carrasco, J.M. Awiphan, S. Tasuya, O. Godunova, V. Simon, A. Fukui, A. Galdies, C. Bakowska, K. Hofbauer, P. Gurgul, A. Joachimczyk, B. Dominik, M. Tsapras, Y. Street, R.A. Hundertmark, M. Bozza, V. Rota, P. Cassan, A. Wambsganss, J. Figuera Jaimes, R. Astronomical Observatory University of Warsaw Al. Ujazdowskie 4 Warsaw00-478 Poland Astrophysics Division National Centre for Nuclear Research Pasteura 7 Warsaw02-093 Poland Astronomical Institute University of Wroclaw ul. Mikolaja Kopernika 11 Wroclaw51-622 Poland Institute of Astronomy Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University in Toruń Grudziadzka 5 Toruń87-100 Poland Faculty of Mathematics and Computer Science Jagiellonian University Lojasiewicza 6 Kraków30-348 Poland Research School of Astronomy and Astrophysics Australian National University Mount Stromlo Observatory Cotter Road Weston CreekACT2611 Australia Zentrum für Astronomie der Universität Heidelberg Astronomisches Rechen-Institut Mönchhofstr. 12-14 Heidelberg69120 Germany Las Cumbres Observatory Global Telescope Network 6740 Cortona Drive suite 102 GoletaCA93117 United States Astronomical Observatory Volgina 7 Belgrade11060 Serbia E.Kharadze Georgian National Astrophysical Observatory Abastumani0301 Georgia Janusz Gil Institute of Astronomy University of Zielona Góra Lubuska 2 Zielona GóraPL-65-265 Poland Flarestar Obsevatory Fl.5 Ent.B Silver Jubilee Apt George Tayar Street San GwannSGN 3160 Malta Horten Videregaende Skole Strandpromenaden 33 Horten3183 Norway Istanbul University Faculty of Science Department of Astronomy and Spaces Sciences Istanbul34119 Turkey Istanbul University Observatory Research and Application Center Istanbul University Istanbul34119 Turkey Department of Physics Adıyaman University Adıyaman02040 Turkey Department of Physics The George Washington University WashingtonDC20052 United States IPAC Mail Code 100-22 Caltech 1200 E. California Blvd. PasadenaCA91125 United States Department of Particle Physics and Astrophysics Weizmann Institute of Science Rehovot76100 Israel INAF-Osservatorio di Astrofisica e Scienza dello Spazio Via Gobetti 93/3 BolognaI-40129 Italy Department of Physics and Astronom

Context. Microlensing events provide a unique way to detect and measure the masses of isolated, non-luminous objects, particularly dark stellar remnants. Under certain conditions, it is possible to measure the mass of these objects using photometry alone, specifically when a microlensing light curve displays a finite-source (FS) effect. This effect generally occurs in highly magnified light curves, i.e. when the source and the lens are very well aligned. Aims. In this study, we analyse Gaia Alerts and Gaia Data Release 3 datasets, identifying four moderate-to-high-magnification microlensing events without a discernible FS effect. The absence of this effect suggests a large Einstein radius, implying substantial lens masses. Methods. In each event, we constrain the FS effect and therefore establish lower limits for angular Einstein radius and lens mass. Additionally, we use the DarkLensCode software to obtain mass, distance, and brightness distribution for the lens based on the Galactic model. Results. Our analysis established lower mass limits of ∼ 0.2 M☉ for one lens and ∼ 0.3 − 0.5 M☉ for two others. DarkLensCode analysis supports these findings, estimating lens masses in the range of∼ 0.42 − 1.66 M☉ and dark lens probabilities exceeding 60%. These results strongly indicate that the lenses are stellar remnants, such as white dwarfs or neutron stars. Conclusions. While further investigations are required to confirm the nature of these lenses, we demonstrate a straightforward yet effective approach to identifying stellar remnant candidates. © 2025, CC BY.

关键词： White dwarfs

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The learning of fuzzy cognitive maps with noisy data: A rapid and robust learning method with maximum entropy

arXiv

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arXiv 2019年

作者： Feng, Guoliang Lu, Wei Pedrycz, Witold Yang, Jianhua Liu, Xiaodong School of Control Science and Engineering Dalian University of Technology Dalian116023 China School of Automation Engineering Northeast Electric Power University Jilin132012 China School of Control Science and Engineering Dalian University of Technology Dalian116023 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

Numerous learning methods for fuzzy cognitive maps (FCMs), such as the Hebbian-based and the population-based learning methods, have been developed for modeling and simulating dynamic systems. However, these methods are faced with several obvious limitations. Most of these models are extremely time consuming when learning the large-scale FCMs with hundreds of nodes. Furthermore, the FCMs learned by those algorithms lack robustness when the experimental data contain noise. In addition, reasonable distribution of the weights is rarely considered in these algorithms, which could result in the reduction of the performance of the resulting FCM. In this article, a straightforward, rapid, and robust learning method is proposed to learn FCMs from noisy data, especially, to learn large-scale FCMs. The crux of the proposed algorithm is to equivalently transform the learning problem of FCMs to a classic-constrained convex optimization problem in which the least-squares term ensures the robustness of the well-learned FCM and the maximum entropy term regularizes the distribution of the weights of the well-learned FCM. A series of experiments covering two frequently used activation functions (the sigmoid and hyperbolic tangent functions) are performed on both synthetic datasets with noise and real-world datasets. The experimental results show that the proposed method is rapid and robust against data containing noise and that the well-learned weights have better distribution. In addition, the FCMs learned by the proposed method also exhibit superior performance in comparison with the existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Fuzzy rules

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Universal Spectra of Random Lindblad Operators

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Physical Review Letters 2019年第14期123卷 140403-140403页

作者： Sergey Denisov Tetyana Laptyeva Wojciech Tarnowski Dariusz Chruściński Karol Życzkowski Department of Computer Science OsloMet–Oslo Metropolitan University NO-0130 Oslo Norway Department of Control Theory and Systems Dynamics Lobachevsky University Gagarina Avenue 23 Nizhny Novgorod 603950 Russia Marian Smoluchowski Institute of Physics Uniwersytet Jagielloński Krakow Poland Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Grudziadzka 5/7 87–100 Torun Poland Center for Theoretical Physics Polish Academy of Sciences 02-668 Warszawa Poland

To understand the typical dynamics of an open quantum system in continuous time, we introduce an ensemble of random Lindblad operators, which generate completely positive Markovian evolution in the space of the density matrices. The spectral properties of these operators, including the shape of the eigenvalue distribution in the complex plane, are evaluated by using methods of free probabilities and explained with non-Hermitian random matrix models. We also demonstrate the universality of the spectral features. The notion of an ensemble of random generators of Markovian quantum evolution constitutes a step towards categorization of dissipative quantum chaos.

关键词： Open quantum systems Quantum chaos Quantum master equation Random matrix theory

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Robust medical instrument segmentation challenge 2019

arXiv

引用

arXiv 2020年

作者： Roβ, Tobias Reinke, Annika Full, Peter M. Wagner, Martin Kenngott, Hannes Apitz, Martin Hempe, Hellena Filimon, Diana Mindroc Scholz, Patrick Tran, Thuy Nuong Bruno, Pierangela Arbelaéz, Pablo Bian, Gui-Bin Bodenstedt, Sebastian Bolmgren, Jon Lindström Bravo-Sánchez, Laura Chen, Hua-Bin González, Cristina Guo, Dong Halvorsen, Pål Heng, Pheng-Ann Hosgor, Enes Hou, Zeng-Guang Isensee, Fabian Jha, Debesh Jiang, Tingting Kirtac, Kadir Jin, Yueming Kletz, Sabrina Leger, Stefan Li, Zhixuan Maier-Hein, Klaus H. Ni, Zhen-Liang Riegler, Michael A. Schoeffmann, Klaus Shi, Ruohua Speidel, Stefanie Stenze, Michael Twick, Isabell Wang, Gutai Wang, Jiacheng Wang, Liansheng Wang, Lu Zhang, Yujie Zhou, Yan-Jie Zhu, Lei Wiesenfarth, Manuel Kopp-Schneider, Annette Müller-Stich, Beat P. Maier-Hein, Lena Heidelberg Germany University of Heidelberg Germany Heidelberg Germany Department of General Heidelberg University Hospital Visceral and Transplant Surgery Heidelberg Germany caresyntax Berlin Germany University of Chinese Academy Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China SimulaMet Oslo Norway Tromsø Norway Oslo Norway School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Partner Site Dresden Heidelberg Germany Faculty of Medicine and University Hospital Carl Gustav Carus Technische Universität Dresden Dresden Germany Dresden Germany Universidad de Los Andes Bogotá Colombia Peking University Peking China Department of Computer Science School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong German Cancer Research Center Division of Biostatistics Heidelberg Germany Institute of Information Technology Klagenfurt University Austria

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and roboticassisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization;algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts). Copyright © 2020, The Authors. All rights reserved.

关键词： Endoscopy

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Crash Detection Using IMU Sensors 21

Crash Detection Using IMU Sensors

引用

21st International Conference on control systems and computer science, CSCS 2017

作者： Cismas, Alexandru Matei, Ioana Ciobanu, Vlad Casu, George Faculty of Automatic Control and Computer Science University Politehnica Bucharest Bucharest Romania Faculty of Military Electronic and Information Systems Military Technical Academy Bucharest Romania

ISBN: (纸本)9781538618394

More than 50% of the motorcycle accidents are fatal for the rider. 70% of those are due to the time needed for the emergency service to arrive at the accident. This leads us to a necessity in finding a device able to detect accidents. This document explains how to use the MPU6050 IMU sensor in an embedded device for measuring the Euler angles (roll, pitch, yaw) and also the G Forces on different axes. © 2017 IEEE.

关键词： Accidents

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Cost-optimal model predictive scheduling of home appliances

引用

IFAC-PapersOnLine 2017年第1期50卷 3344-3349页

作者： Bálint R. Magyar A. Hangos K.M. Department of Electrical Engineering and Information Systems Faculty of Information Technology University of Pannonia Veszprém Hungary Process Control Research Group HAS Research Institute for Computer Science and Control Budapest Hungary

A novel heuristic model-based optimal scheduling algorithm is proposed in this paper to operate heating and cooling type home appliances connected to smart grids where the price of the electrical energy is known in advance and temperature constraints are present. The properties and the use of the proposed algorithm are shown using a simple refrigerator model. The accuracy and the computational properties of the proposed method are compared to the schedule generated by the MPT toolbox. The algorithm works well with a relatively short prediction horizon using a fraction of the computing time needed for the MPT-based method. The robustness of the algorithm is also investigated with respect to the load of the refrigerator. It is shown that the designed schedule with an empty refrigerator is always suitable, yet not optimal, for any loaded case. © 2017

关键词： Demand side management Heuristics Model predictive control Scheduling algorithms Smart grids

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Input optics systems of the KAGRA detector during O3GK (vol 2023, 023F01, 2023)

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PROGRESS OF THEORETICAL AND EXPERIMENTAL PHYSICS 2023年第5期2023卷

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Bae, Y. Baiotti, L. Bajpai, R. Barton, M. A. Cannon, K. Cao, Z. Capocasa, E. Chan, M. Chen, C. Chen, K. Chen, Y. Chiang, C-I Chu, H. Chu, Y-K Eguchi, S. Enomoto, Y. Flaminio, R. Fujii, Y. Fujikawa, Y. Fukunaga, M. Fukushima, M. Furuhata, T. Gao, D. Ge, G-G Ha, S. Hagiwara, A. Haino, S. Han, W-B Hasegawa, K. Hattori, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hiranuma, Y. Hirata, N. Hirose, E. Hong, Z. Hsieh, B-H Huang, G-Z Huang, H-Y Huang, P. Huang, Y-C Huang, Y-J Hui, D. C. Y. Ide, S. Ikenoue, B. Imam, S. Inayoshi, K. Inoue, Y. Ioka, K. Ito, K. Itoh, Y. Izumi, K. Jeon, C. Jin, H-B Jung, K. Jung, P. Kaihotsu, K. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kang, G. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawasaki, T. Kim, C. Kim, J. Kim, J. C. Kim, Ws Kim, Y-M Kimura, N. Kita, N. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A. K. H. Kotake, K. Kozakai, C. Kozu, R. Kumar, R. Kume, J. Kuo, C. Kuo, H-S Kuromiya, Y. Kuroyanagi, S. Kusayanagi, K. Kwak, K. Lee, H. K. Lee, H. W. Lee, R. Leonardi, M. Li, K. L. Lin, L. C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H. L. Liu, G. C. Luo, L-W Majorana, E. Marchio, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyazaki, Y. Miyo, K. Miyoki, S. Mori, Y. Morisaki, S. Moriwaki, Y. Nagano, K. Nagano, S. Nakamura, K. Nakano, H. Nakano, M. Nakashima, R. Nakayama, Y. Narikawa, T. Naticchioni, L. Negishi, R. Quynh, L. Nguyen Ni, W-T Nishizawa, A. Nozaki, S. Obuchi, Y. Ogaki, W. Oh, J. J. Oh, S. H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohta, H. Okutani, Y. Okutomi, K. Oohara, K. Ooi, C. Oshino, S. Otabe, S. Pan, K-C Pang, H. Parisi, A. Park, J. Arellano, F. E. Pena Pinto, I. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sakuno, Y. Sato, S. Sato, T. Sawada, T. Sekiguchi, T. Sekiguchi, Y. Shao, L. Shibagaki, S. Shimizu, R. Shimoda, T. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Son, E. J. Sotani, H. Sugimoto, R. Suresh, J. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Graduate School of Science and Technology Hirosaki University 3 Bunkyo-cho Hirosaki Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Republic of Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Road Danshui Dist. New Taipei City 25137 Taiwan Department of Physics

KAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25 to March 10, 2020, and its first joint observation with the GEO 600 detector from April 7 to April 21, 2020 (O3GK). This study presents an overview of the input optics systems of the KAGRA detector, which consist of various optical systems, such as a laser source, its intensity and frequency stabilization systems, modulators, a Faraday isolator, mode-matching telescopes, and a high-power beam dump. These optics were successfully delivered to the KAGRA interferometer and operated stably during the observations. The laser frequency noise was observed to limit the detector sensitivity above a few kilohertz, whereas the laser intensity did not significantly limit the detector sensitivity.

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Patient specific model based induction of hypnosis using fractional order control

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IFAC-PapersOnLine 2017年第1期50卷 15097-15102页

作者： Copot, Dana Muresan, Cristina De Keyser, Robin Ionescu, Clara Ghent University Research Group on Dynamical Systems and Control Technologiepark 914 GhentB9052 Belgium Technical University of Cluj-Napoca Faculty of Automation and Computer Science G. Baritiu 26-28 Cluj Napoca400027 Romania

Optimal and safe control of drug delivery systems with continuous infusion protocol is of key importance to avoid over- or under-dosing of the patient. By implementing close-loops one is able to optimize the amount of drug given to the patient. In this paper a robust control methodology is presented. Emerging tools from fractional calculus have been considered and a fractional order PI controller for drug dosing during hypnosis has been designed. In this paper a robust fractional order control of hypnosis is proposed. The controller has been evaluated on an artificial data set of 24 patients and the results indicate that such a control strategy is robust to uncertainty stemming from the inter- and intra-patient variability. © 2017

关键词： controlled drug delivery Calculations control engineering controllers Robust control Robustness (control systems) Targeted drug delivery Uncertainty analysis Continuous infusions control methodology Drug delivery system Fractional order control hypnosis Patient specific model Personalized healthcare uncertainty

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