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An Early Stage Researcher's Primer on systems Medicine Terminology

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Network and Systems Medicine

Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

Monitoring of Ash Deposition on the Heated Surface of the Economizer of Boiler in Coal-Fired Power Station

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Monitoring of Ash Deposition on the Heated Surface of the Ec...

第30届中国过程控制会议（CPCC 2019）

作者： CAO-Chao CHEN-Xin WANG-Jingcheng SHI-Yuanhao Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University School of Electrical and Control Engineering North University of China

The problem of ash and slagging on the heating surface of coal-fired power station boilers has always existed in the production and operation process of modern coal-fired power *** smog and slagging on the heated surface will not only cause the heat exchange efficiency to deteriorate, but also cause safety accidents such as corrosion of the heated surface and tube *** order to solve this problem, it is necessary to monitor the ash deposition of the boiler heating surface and develop a pollution degree standard for the soot blowing *** paper presents a monitoring model based on the ash deposition of the economizer heating surface of a coal-fired power station *** the boiler of 600 MW generator set of a power plant in Jiaxing, Zhejiang as the research object, the measured data obtained by the on-site DCS, then the wavelet denoising process, and then the thermodynamic formula is used to calculate the ash pollution factor which can characterize the degree of ash deposition on the heated surface of the *** experimental results prove the validity of the above model and have important significance for the later research on intelligent soot blowing operation

关键词： Coal-fired power plant boiler Economizer Ash deposition Monitoring model,Cleanliness factor

Multi-robot distributed cooperative monitoring of mobile targets 3rd

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Multi-robot distributed cooperative monitoring of mobile tar...

3rd International Conference on Intelligence Science, ICIS 2018

作者： Jiang, J.Q. Xin, B. Dou, L.H. Ding, Y.L. School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9783030013127

Cooperative monitoring targets of mobile robots is of great importance in military, civil, and medical applications. In order to achieve multi-robot coordinated monitoring, this paper proposes a new distributed path planning algorithm. In this algorithm, a method of dynamic sequential decision making is used firstly to determine which robots should be moved to the neighbourhoods of the target path. A distributed cooperative path planning algorithm is then proposed in which the robots can only utilize their local information to make the joint monitoring areas of robots cover the target path. To reduce the chance of detection and ensure enough time to implement monitoring handover, our distributed algorithm can guarantee enough overlapped monitoring areas between adjacent robots. The effectiveness of the algorithm was verified by simulation experiments and a comparison experiment in typical scenarios. © IFIP International Federation for Information processing 2018.

关键词： Monitoring

Gait Phase Classification Based on sEMG Signals Using Long Short-Term Memory for Lower Limb Exoskeleton Robot

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IOP Conference Series: Materials Science and Engineering 2020年第1期853卷

作者： Ye Yuan Ziming Guo Can Wang Shengcai Duan Lufeng Zhang Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergic Systems Shenzhen Institutes of Advanced Technology Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University.

In this work, we present a Long Short-Term Memory Model (LSTMM) for gait phase classification based on sEMG signals to control the lower limb exoskeleton robot which can recognize 2 phases (Stand and Swing) of leg phases between the foot and ground. This model only needs four sEMG signals to control the lower limb exoskeleton robot helping the hemiplegia patient walking. Compared with the existing methods, the proposed model not only avoids the complex sensor systems but also enhances the accuracy of gait phase classification. The experimental results first verify the availability of sEMG data acquisition system on the lower limb exoskeleton robot made by the Shenzhen Institutes of advanced Technologies (SIAT) by quantify the system with gold standard optoelectronic system Vicon, then show that the proposed LSTMM is significantly higher on prediction accuracy and has better robustness for gait phase classification to control the lower limb exoskeleton robot with different speeds. Finally, the maximum accuracy of LSTMM on the gait phase classification is 97.89%.

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The 2024 Motile Active Matter Roadmap

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arXiv 2024年

作者： Gompper, Gerhard Stone, Howard A. Kurzthaler, Christina Saintillan, David Peruani, Fernado Fedosov, Dmitry A. Auth, Thorsten Cottin-Bizonne, Cecile Ybert, Christophe Clément, Eric Darnige, Thierry Lindner, Anke Goldstein, Raymond E. Liebchen, Benno Binysh, Jack Souslov, Anton Isa, Lucio di Leonardo, Roberto Frangipane, Giacomo Gu, Hongri Nelson, Bradley J. Brauns, Fridtjof Marchetti, M. Cristina Cichos, Frank Heuthe, Veit-Lorenz Bechinger, Clemens Korman, Amos Feinerman, Ofer Cavagna, Andrea Giardina, Irene Jeckel, Hannah Drescher, Knut Theoretical Physics of Living Matter Institute for Advanced Simulation Forschungszentrum Jülich Jülich Germany Princeton University Department of Mechanical and Aerospace Engineering PrincetonNJ United States Max Planck Institute for the Physics of Complex Systems Center for Systems Biology Dresden Cluster of Excellence Physics of Life TU Dresden Dresden Germany University of California San Diego United States CY Cergy Paris University France Université de Lyon Université Claude Bernard Lyon 1 CNRS Institut Lumière Matière Villeurbanne France Laboratoire PMMH-ESPCI UMR 7636 CNRS-PSL-Research University Sorbonne Université Université Paris Cité Paris France Institut Universitaire de France Paris France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Technische Universität Darmstadt Darmstadt64289 Germany Institute of Physics Universiteit van Amsterdam Science Park 904 Amsterdam1098 XH Netherlands T.C.M. Group Cavendish Laboratory University of Cambridge CambridgeCB3 0HE United Kingdom Laboratory for Soft Materials and Interfaces Department of Materials ETH Zurich Zurich8093 Switzerland Dipartimento di Fisica Sapienza Università di Roma Italy Department of Physics University of Konstanz Konstanz Germany Institute of Robotics and Intelligent Systems ETH Zürich Zurich Switzerland Kavli Institute for Theoretical Physics University of California Santa BarbaraCA93106 United States Department of Physics University of California Santa BarbaraCA93106 United States Molecular Nanophotonics Leipzig University Leipzig04013 Germany UMI FILOFOCS Israel Department of Physics of Complex Systems Weizmann Institute of Science Israel Rome Italy Dipartimento di Fisica Sapienza Università di Roma INFN Unità di Roma 1 Rome Italy Department of Biology and Biological Engineering California Institute of Technology Pasadena91125 United States Biozentrum University of Basel Basel4056 Switzerl

Activity and autonomous motion are fundamental aspects of many living and engineering systems. Here, the scale of biological agents covers a wide range, from nanomotors, cytoskeleton, and cells, to insects, fish, birds, and people. Inspired by biological active systems, various types of autonomous synthetic nano- and micromachines have been designed, which provide the basis for multifunctional, highly responsive, intelligent active materials. A major challenge for understanding and designing active matter is their inherent non-equilibrium nature due to persistent energy consumption, which invalidates equilibrium concepts such as free energy, detailed balance, and time-reversal symmetry. Furthermore, interactions in ensembles of active agents are often non-additive and non-reciprocal. An important aspect of biological agents is their ability to sense the environment, process this information, and adjust their motion accordingly. It is an important goal for the engineering of microrobotic systems to achieve similar functionality. With many fundamental properties of motile active matter now reasonably well understood and under control, the ground is prepared for the study of physical aspects and mechanisms of motion in complex environments, of the behavior of systems with new physical features like chirality, of the development of novel micromachines and microbots, of the emergent collective behavior and swarming of intelligent self-propelled particles, and of particular features of microbial systems. The vast complexity of phenomena and mechanisms involved in the self-organization and dynamics of motile active matter poses major challenges, which can only be addressed by a truly interdisciplinary effort involving scientists from biology, chemistry, ecology, engineering, mathematics, and physics. The 2024 motile active matter roadmap of Journal of Physics: Condensed Matter reviews the current state of the art of the field and provides guidance for further progress in t

关键词： Microrobots

Water supply prediction based on initialized attention residual network

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arXiv 2019年

作者： Long, Yuhao Wang, Jingcheng Wang, Jingyi Automation Department Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University Xi'an Shaanxi710021 China

Real-time and accurate water supply forecast is crucial for water plant. However, most existing methods are likely affected by factors such as weather and holidays, which lead to a decline in the reliability of water supply prediction. In this paper, we address a generic artificial neural network, called Initialized Attention Residual Network (IARN), which is combined with an attention module and residual modules. Specifically, instead of continuing to use the recurrent neural network (RNN) in time-series tasks, we try to build a convolution neural network (CNN) to recede the disturb from other factors, relieve the limitation of memory size and get a more credible results. Our method achieves state-of-the-art performance on several datasets, in terms of accuracy, robustness and generalization ability. Copyright © 2019, The Authors. All rights reserved.

关键词： Water supply

Convolutional ordinal regression forest for image ordinal estimation

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arXiv 2020年

作者： Zhu, Haiping Shan, Hongming Zhang, Yuheng Che, Lingfu Xu, Xiaoyang Zhang, Junping Shi, Jianbo Wang, Fei-Yue The Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China The Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-inspired Technology Shanghai201210 China The GRASP Laboratory University of Pennsylvania PhiladelphiaPA United States The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Institute of Systems Engineering Macau University of Science and Technology 999078 China The University of Chinese Academy of Sciences Beijing100049 China

Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression problem. Recent methods formulate an ordinal regression problem as a series of binary classification problems. Such methods cannot ensure that the global ordinal relationship is preserved since the relationships among different binary classifiers are neglected. We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation, which can integrate ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships. The advantages of the proposed CORF are twofold. First, instead of learning a series of binary classifiers independently, the proposed method aims at learning an ordinal distribution for ordinal regression by optimizing those binary classifiers simultaneously. Second, the differentiable decision trees in the proposed CORF can be trained together with the ordinal distribution in an end-to-end manner. The effectiveness of the proposed CORF is verified on two image ordinal estimation tasks, i.e. facial age estimation and image aesthetic assessment, showing significant improvements and better stability over the state-of-the-art ordinal regression methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision trees

Printability and properties of PSNZT piezoelectric ceramics fabricated by DLP additive manufacturing

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Ceramics International 2025年

作者： Yu, Mengyuan Fan, Guifen Li, Yingkang Ling, Xile Gong, Feng Liu, Gang Bai, Jiaming Li, Mantian Zhou, Peng Zhang, Dongyang Sun, Lining Hubei Key Laboratory of Plasma Chemistry and Advanced Materials School of Materials Science and Engineering Wuhan Institute of Technology Wuhan430205 China Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic University Shenzhen518055 China MOE Huazhong University of Science and Technology Wuhan430074 China Department of Mechanical and Electronic Engineering College of Mechatronics and Control Engineering Shenzhen University Shenzhen518055 China Shenzhen Blueswin Industrial Co. Ltd Shenzhen518117 China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China Institute of Critical Materials for Integrated Circuits Shenzhen Polytechnic University Shenzhen518055 China Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Suzhou215021 China

Achieving the synergy of low viscosity, high printing precision and enhanced curing ability for lead zirconate titanate (PZT) components is a critical issue in the field of digital light processing (DLP). Herein, the effect of calcination temperature on the morphology and grain size of PSNZT [(Pb0.92Sr0.08)(Zr0.533Ti0.443Nb0.024)O3]ceramic powder has been investigated, and a lowest viscosity, higher printing precision and enhanced curing ability of PSNZT ceramic slurry has been achieved, resulting in a high piezoelectric constant (d33 = 379pC/N) and a high dielectric constant (Εr = 1308@100 KHz) via DLP process combined with following sintering densification (1220 °C/3 h). © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Piezoelectric ceramics

EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks

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arXiv 2020年

作者： Meng, Lubin Huang, Jian Zeng, Zhigang Jiang, Xue Yu, Shan Jung, Tzyy-Ping Lin, Chin-Teng Chavarriaga, Ricardo Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Brainnetome Center National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China La Jolla CA United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine Ucsd La Jolla CA United States Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Australia Zhaw DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is implementable in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography