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Correction to: 2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales

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Archives of virology 2021年第12期166卷 3567-3579页

作者： Jens H Kuhn Scott Adkins Bernard R Agwanda Rim Al Kubrusli Sergey V Alkhovsky Gaya K Amarasinghe Tatjana Avšič-Županc María A Ayllón Justin Bahl Anne Balkema-Buschmann Matthew J Ballinger Christopher F Basler Sina Bavari Martin Beer Nicolas Bejerman Andrew J Bennett Dennis A Bente Éric Bergeron Brian H Bird Carol D Blair Kim R Blasdell Dag-Ragnar Blystad Jamie Bojko Wayne B Borth Steven Bradfute Rachel Breyta Thomas Briese Paul A Brown Judith K Brown Ursula J Buchholz Michael J Buchmeier Alexander Bukreyev Felicity Burt Carmen Büttner Charles H Calisher Mengji Cao Inmaculada Casas Kartik Chandran Rémi N Charrel Qi Cheng Yuya Chiaki Marco Chiapello Il-Ryong Choi Marina Ciuffo J Christopher S Clegg Ian Crozier Elena Dal Bó Juan Carlos de la Torre Xavier de Lamballerie Rik L de Swart Humberto Debat Nolwenn M Dheilly Emiliano Di Cicco Nicholas Di Paola Francesco Di Serio Ralf G Dietzgen Michele Digiaro Olga Dolnik Michael A Drebot J Felix Drexler William G Dundon W Paul Duprex Ralf Dürrwald John M Dye Andrew J Easton Hideki Ebihara Toufic Elbeaino Koray Ergünay Hugh W Ferguson Anthony R Fooks Marco Forgia Pierre B H Formenty Jana Fránová Juliana Freitas-Astúa Jingjing Fu Stephanie Fürl Selma Gago-Zachert George Fú Gāo María Laura García Adolfo García-Sastre Aura R Garrison Thomas Gaskin Jean-Paul J Gonzalez Anthony Griffiths Tony L Goldberg Martin H Groschup Stephan Günther Roy A Hall John Hammond Tong Han Jussi Hepojoki Roger Hewson Jiang Hong Ni Hong Seiji Hongo Masayuki Horie John S Hu Tao Hu Holly R Hughes Florian Hüttner Timothy H Hyndman M Ilyas Risto Jalkanen Dàohóng Jiāng Gilda B Jonson Sandra Junglen Fujio Kadono Karia H Kaukinen Michael Kawate Boris Klempa Jonas Klingström Gary Kobinger Igor Koloniuk Hideki Kondō Eugene V Koonin Mart Krupovic Kenji Kubota Gael Kurath Lies Laenen Amy J Lambert Stanley L Langevin Benhur Lee Elliot J Lefkowitz Eric M Leroy Shaorong Li Longhui Li Jiànróng Lǐ Huazhen Liu Igor S Lukashevich Piet Maes William Marciel de Souza Marco Marklewitz Sergio H Marshall Shin-Yi L Marzano Se Integrated Research Facility at Fort Detrick National Institute of Allergy and Infectious Diseases National Institutes of Health Frederick MD USA. kuhnjens@mail.nih.gov. United States Department of Agriculture Agricultural Research Service US Horticultural Research Laboratory Fort Pierce FL USA. Zoology Department National Museums of Kenya Nairobi Kenya. Jomo Kenyatta University of Agriculture & Technology Nairobi Kenya. Division Phytomedicine Faculty of Life Sciences Humboldt-Universität zu Berlin Berlin Germany. D.I. Ivanovsky Institute of Virology of N.F. Gamaleya National Center on Epidemiology and Microbiology of Ministry of Health of Russian Federation Moscow Russia. Department of Pathology and Immunology Washington University School of Medicine St. Louis MO USA. Faculty of Medicine University of Ljubljana Ljubljana Slovenia. Centro de Biotecnología y Genómica de Plantas Universidad Politécnica de Madrid-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria Campus de Montegancedo Pozuelo de Alarcón Madrid Spain. Departamento de Biotecnología-Biología Vegetal Escuela Técnica Superior de Ingeniería Agronómica Alimentaria y de Biosistemas Universidad Politécnica de Madrid Madrid Spain. Center for Ecology of Infectious Diseases Department of Infectious Diseases Department of Epidemiology and Biostatistics Insitute of Bioinformatics University of Georgia Athens GA USA. Institute of Novel and Emerging Infectious Diseases Friedrich-Loeffler-Institut Federal Research Institute for Animal Health Greifswald-Insel Riems Germany. Department of Biological Sciences Mississippi State University Mississippi State MS USA. Center for Microbial Pathogenesis Institute for Biomedical Sciences Georgia State University Atlanta GA USA. Edge BioInnovation Consulting and Mgt Frederick MD USA. Institute of Diagnostic Virology Friedrich-Loeffler-Institut Greifswald-Insel Riems Germany. UFyMA INTA-CONICET Cordoba Argentina. Genomics and Bioinformatics D

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Adaptive partitioning PCA model for improving fault detection and isolation

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Chinese Journal of Chemical engineering 2015年第6期23卷 981-991页

作者：刘康玲金鑫费正顺梁军 State Key Lab of Industrial Control Technology Institute of Industrial Control Technology Zhejiang University School of Automation and Electrical Engineering Zhejiang University of Science and Technology

In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing *** method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.

关键词： Adaptive partitioning Fault detection Fault isolation Principal component analysis

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An Event-Triggered Approach to State Estimation for Neural Networks with Individual Triggering Thresholds

An Event-Triggered Approach to State Estimation for Neural N...

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第35届中国控制会议

作者： Licheng Wang Zidong Wang Lei Zou Guoliang Wei Shanghai Key Lab of Modern Optical System Department of Control Science and EngineeringUniversity of Shanghai for Science and Technology Department of Computer Science Brunel University LondonUxbridge Research Institute of Intelligent Control and Systems Harbin Institute of Technology

ISBN: (纸本)9781509009107

In this paper, the state estimation problem is investigated for a class of discrete-time stochastic neural networks with event-triggered transmission(ETT) mechanism. Different from the traditional periodic communication mechanism, the ETT mechanism employed in this paper possesses the advantage of mitigating the network traf c resulting from the unnecessary sending and receiving data between the sensors and the estimators. Moreover, the proposed ETT mechanism is implemented individually for each neuron. That is, the measurement output of each neuron is transmitted to the estimator only when its corresponding triggering condition with the speci ed triggering threshold is satis ed, thereby re ecting the engineering practice in a more realistic way. The aim of the addressed problem is to design an estimator for the neural networks subject to the ETT mechanism such that the estimation error is asymptotically bounded in the mean square. Furthermore, the estimator gain matrix is explicitly parameterized in the sense of minimizing such an asymptotic error upper bound. Finally, a numerical example is presented to demonstrate the effectiveness of the results.

关键词： Event-triggered scheme Neural networks Individual triggering threshold State estimation Asymptotic boundedness

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An efficient latent variable optimization approach with stochastic constraints for complex industrial process

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Chinese Journal of Chemical engineering 2015年第10期23卷 1670-1678页

作者：费正顺刘康玲胡斌梁军 School of Automation and Electrical Engineering Zhejiang University of Science and Technology State Key Lab of Industrial Control Technology Institute of Industrial Control Technology Department of Control Science and Engineering Zhejiang University Department of Electrical Engineering University of Notre Dame

For complex chemical processes,process optimization is usually performed on causal models from first principle *** the mechanism models cannot be obtained easily,restricted model built by process data is used for dynamic process optimization.A new strategy is proposed for complex process optimization,in which latent variables are used as decision variables and statistics is used to describe *** the constraint condition will be more complex by projecting the original variable to latent space,Hotelling T^2 statistics is introduced for constraint formulation in latent *** this way,the constraint is simplified when the optimization is solved in low-dimensional space of latent *** validity of the methodology is illustrated in pH-level optimal control process and practical polypropylene grade transition process.

关键词： Data-driven model Optimization Partial least square Polymerization

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Mining miRNAs2target genes interactions from biomedical literature

Mining miRNAs2target genes interactions from biomedical lite...

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2016 IEEE International Conference of Online Analysis and Computing Science(ICOACS 2016)

作者： Lejun Gong Kaiyu Jiang Ronggen Yang Geng Yang Jiangsu High Technology Research Key Lab for Wireless Sensor Networks College of Computer Science & TechnologyNanjing University of Posts and Telecommunications College of Intelligent Science and Control Engineering Jinling Institute of Technology

This work describes an approach to mine miRNAs2 target genes interactions related to breast cancer from ***,the work obtain 53 miRNAs and 2130protein-coding genes regarding to the breast cancer,then using prior knowledge to extract the relationship between miRNAs and protein-coding genes,we achieved 2231 miRNAs2 target interactions *** also constructed a miRNAs2 target network,which could be used to clearly show the extracted miRNAs2 target interactions.

关键词： miRNA2target interaction breast cancer enrichment analysis relationship extraction

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Direct Fuzzy Adaptive Inverse control Based on Particle Swarm Optimization Algorithm

Direct Fuzzy Adaptive Inverse Control Based on Particle Swar...

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第26届中国过程控制会议（CPCC2015）

作者： LIU Fu-cai LIU Nan DOU Jin-mei Key Lab of Industrial Computer Control Engineering of Hebei Yanshan University

T-S fuzzy inverse model identification based on particle swarm optimization(PSO) algorithm is proposed for a class of nonlinear process, and it is applied to direct adaptive inverse control of nonlinear system. In the first, the fuzzy space is divided by fuzzy grid diagonal method and the Gaussian function with uncertain center and width is used as the membership function, the fuzzy inverse model is obtained by using PSO algorithm to optimize both of the antecedent and consequent parameters in the process of inverse modeling in off-line manner. Afterwards the initial inverse model is connected to the plant in series and the consequent parameters of the inverse model are tuned by the variable-step least mean square(VSLMS) algorithm in on-line manner while its copy is connected to the plant as system controller. This approach realizes the adaptive inverse control of nonlinear system based on PSO algorithm. Simulation results on a hybrid nonlinear system and level control of a spherical tank show the validity of this method.

关键词： particle swarm optimization T-S fuzzy inverse model adaptive inverse control variable-step least mean square nonlinear system

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Distributed containment control with dynamic leaders based on binocular vision

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Journal of Information and Computational Science 2015年第4期12卷 1319-1327页

作者： Shao, Nuan Li, Huiguang Liu, Le Li, Guoyou Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

In this paper, we study the distributed containment control problem for multi-robot system with dynamic leaders in the presence of parametric uncertainties. The binocular vision system is used as the sensing device to provide real-time status information for the followers, so firstly, a new binocular visual model for dynamic target is established. Then RBF neural networks are employed to approximate the unknown nonlinear functions in the system model of followers, and the approximated values are introduced into the designed sliding mode controllers to make compensation. Finally, simulation results are presented to show the validity of the proposed control strategy. ©, 2015, Binary Information Press

关键词： Multipurpose robots

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Output stabilization for a class of nonlinear systems via high-gain observer with limited gain power

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IFAC-PapersOnLine 2015年第11 11期48卷 730-735页

作者： Wang, Lei Astolfi, Daniele Su, Hongye Marconi, Lorenzo Isidori, Alberto State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China CASY -DEI University of Bologna Italy MINES ParisTech PSL Research University CAS Paris France Department of Computer Control and Management Engineering University of Rome "La Sapienza" Rome Italy

We address the problem of output stabilization for the class of nonlinear minimumphase systems with a well-defined relative degree whose output is affected by measurement noise. In the proposed control structure the derivatives of the output are estimated by means of a new high-gain observer proposed in Astolfi and Marconi [2015], which, with respect to high-gain observers conventionally used in literature, has limited gain power. The advantage in using the proposed observer is in its implementation for systems with high relative degree and in the sensitivity to the sensor noise. Simulation results are presented to show the effectiveness of the proposed control structure. © 2015

关键词： Nonlinear systems Stabilization control structure High gain observers Measurement Noise Minimum phase systems Normal form Output stabilization Relative degrees Sensor noise

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Distributed coordination in multi-agent systems： a graph Laplacian perspective

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Frontiers of Information Technology & Electronic engineering 2015年第6期16卷 429-448页

作者： Zhi-min HAN Zhi-yun LIN Min-yue FU Zhi-yong CHEN State Key Laboratory of Industrial Control Technology College of Electrical Engineering Zhejiang University Hangzhou 310027 China School of Electrical Engineering and Computer Science University of Newcastle Callaghan NSW 2308 Australia State Key Laboratory of Industrial Control Technology Department of Control Science and Engineering Zhejiang University Hangzhou 310027 China

This paper reviews some main results and progress in distributed multi-agent coordination from a graph Laplacian perspective. Distributed multi-agent coordination has been a very active subject studied extensively by the systems and control community in last decades, including distributed consensus, formation control, sensor localization, distributed optimization, etc. The aim of this paper is to provide both a comprehensive survey of existing literature in distributed multi-agent coordination and a new perspective in terms of graph Laplacian to categorize the fundamental mechanisms for distributed coordination. For different types of graph Laplacians, we summarize their inherent coordination features and specific research issues. This paper also highlights several promising research directions along with some open problems that are deemed important for future study.

关键词： Multi-agent systems Distributed coordination Graph Laplacian

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Fuzzy adaptive robust control for space robot considering the effect of the gravity

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Chinese Journal of Aeronautics 2014年第6期27卷 1562-1570页

作者： Qin Li Liu Fucai Liang Lihuan Gao Jingfang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

Space robot is assembled and tested in gravity environment, and completes on-orbit service（OOS） in microgravity environment. The kinematic and dynamic characteristic of the robot will change with the variations of gravity in different working condition. Fully considering the change of kinematic and dynamic models caused by the change of gravity environment, a fuzzy adaptive robust control（FARC） strategy which is adaptive to these model variations is put forward for trajectory tracking control of space robot. A fuzzy algorithm is employed to approximate the nonlinear uncertainties in the model, adaptive laws of the parameters are constructed, and the approximation error is compensated by using a robust control algorithm. The stability of the control system is guaranteed based on the Lyapunov theory and the trajectory tracking control simulation is performed. The simulation results are compared with the proportional plus derivative（PD） controller, and the effectiveness to achieve better trajectory tracking performance under different gravity environment without changing the control parameters and the advantage of the proposed controller are verified.

关键词： Fuzzy adaptive Microgravity Robustness Space robot Trajectory tracking control

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