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1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Tian, Daxin Kang, Lu Zheng, Kunxian Duan, Xuting Rong, Hui Guo, Peng Wang, Wenyang Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No.37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No.68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No.30 Beijing100083 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University XueYuan Road No.37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

Fatigue driving might affect the traffic safety when the vehicles are on the cruising state in highway. Trying to solve this problem, this paper uses the movement pattern of the fish to the vehicles fleet, and develops a model of vehicle group with realistic restrictions based on the existed fish algorithms, the mobile behavioral model and cluster behavior model, and then demonstrates the feasibility of applying the fish behavior to the cooperative movement of vehicle groups through analyzing the trajectory, velocity and spacing of vehicles. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Vehicles

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REFF: REliable and Fast Forwarding in Vehicular Ad-hoc Network 1st

REFF: REliable and Fast Forwarding in Vehicular Ad-hoc Netwo...

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1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Tian, Daxin Dai, Ziyi Zheng, Kunxian Zhou, Jianshan Duan, Xuting Guo, Peng Rong, Hui Wang, Wenyang Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No. 37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No. 68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No. 30 Beijing100083 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University XueYuan Road No. 37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

Vehicular Ad-Hoc Network (VANET) has emerged as an increasingly dominant technology for future connected vehicle and vehicular networks, where the focus of the development of VANET lies in the standardization of message transmission and dissemination via multi-hop broadcasting. However, the current communication protocols concerning VANET face many challenges, including data flooding and collision, transmission delay and other problems. Most of the challenges are closely related to next-hop selection. Therefore, this paper proposes a new routing protocol named REliable and Fast Forwarding (REFF) to optimize the selection of nodes in VANET. In this protocol, node filtering and node evaluation are two main steps. Distance between previous node and candidate node, relative velocity between previous node and candidate nodes, included angle between direction of target node’s velocity and candidate node’s velocity and transmission power of candidate node are adopted as indexes to help select a specific node as the next hop using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). By using this technique, the number of candidates as next-hop is largely reduced, avoiding the data flooding and resulting transmission relay. In addition, simulations based on experiments are done to verify the feasibility. The results show that message achieves a faster and more reliable transmission using REFF. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Vehicular ad hoc networks

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Study on Food Information Online Monitoring System Utilized Wireless Sensor Network

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Advance Journal of Food Science and Technology 2016年第2期11卷 137-142页

作者： Li Bo Yang Hui Ting-Ting Li Huang Dechang School of Information Engineering East China Jiaotong University Key Laboratory Advanced Control and Optimization of Jiangxi Province Nanchang 330013 China

The farmland information monitoring generally has the characteristics of complex environment, more monitoring point and wide coverage. Wireless sensor networks technical is the main means to realize variables information collection of agricultural environment network. In order to solve the problem of reliable real-time transmission, a farmland information online monitoring based on wireless sensor network has been designed, which the ZigBee wireless communication technology was used. Monitoring data of different farmland regions are transmitted through the GPRS network to the monitoring center securely and reliably. The use of data monitoring center is aimed at collecting data for processing. The results show that the online monitoring system of field information can automatically complete the data acquisition and transmission throw GPRS network, the data can be sent to the data server with high stability, high reliability, punctuality, meeting the basic requirements of on-line monitoring of farmland information.

关键词： GPRS network structure wireless sensor network ZigBee technology

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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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Component content distribution profile control in rare earth countercurrent extraction process

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Chinese Journal of Chemical Engineering 2015年第1期23卷 192-198页

作者：杨辉徐芳萍陆荣秀丁永权 School of Electrical and Electronic Engineering East China Jiaotong University Key Laboratory of Advanced Control & Optimization of Jiangxi Province

Considering that the on-line measurement and automatic control of element component content(ECC) are difficult to perform in rare earth cascade extraction process, the ECC distribution profile is dynamically regulated at all stages to assess the effect of product purity control. Focusing on the theory of countercurrent extraction, the technology parameters and pre-setting flow-rates during the extract process are designed. Under varying process parameters, a novel step by step model is also proposed for each stage to analyze the impact on the distribution profile change. Combining the mass balance model and ECC changing trend at the monitoring stage, the ECC distribution profile can be automatically regulated by dynamically compensating the related extract or scrubbing liquid flow-rate. To this end, the required product purity at the two outlets is achieved. Based on Wincc and Matlab dynamic simulators, a specific Pr/Nd cascade extraction process is used to illustrate and demonstrate the application of the present approach.

关键词： Rare earth cascade extraction Balance model Distribution regulation Pr/Nd

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Soft measurement for component content based on adaptive model of Pr/Nd color features

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Chinese Journal of Chemical Engineering 2015年第12期23卷 1981-1986页

作者：陆荣秀杨辉 School of Mechanical & Electrical Engineering Nanchang University School of Electrical and Electronic Engineering East China Jiaotong University Key Laboratory of Advanced Control & Optimization of Jiangxi Province

For measurement of component content in the extraction and separation process of praseodymium/neodymium(Pr/Nd), a soft measurement method was proposed based on modeling of ion color features, which is suitable for fast estimation of component content in production field. Feature analysis on images of the solution is conducted,which are captured from Pr/Nd extraction/separation field. H/S components in the HSI color space are selected as model inputs, so as to establish the least squares support vector machine(LSSVM) model for Nd(Pr) content,while the model parameters are determined with the GA algorithm. To improve the adaptability of the model,the adaptive iteration algorithm is used to correct parameters of the LSSVM model, on the basis of model correction strategy and new sample data. Using the field data collected from rare earth extraction production, predictive methods for component content and comparisons are given. The results indicate that the proposed method presents good adaptability and high prediction precision, so it is applicable to the fast detection of element content in the rare earth extraction.

关键词： Pr/Nd extraction Color feature Component content Adaptive iterative least squares support vector machine Real-time correction

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Soft measurement for component content based on adaptive model of Pr/Nd color features

Soft measurement for component content based on adaptive mod...

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作者： Lu, Rongxiu Yang, Hui School of Mechanical and Electrical Engineering Nanchang University Nanchang330031 China School of Electrical and Electronic Engineering East China Jiaotong University Nanchang330013 China Key Laboratory of Advanced Control and Optimization of Jiangxi Province Nanchang330013 China

For measurement of component content in the extraction and separation process of praseodymium/neodymium (Pr/Nd), a soft measurement method was proposed based on modeling of ion color features, which is suitable for fast estimation of component content in production field. Feature analysis on images of the solution is conducted, which are captured from Pr/Nd extraction/separation field. H/S components in the HSI color space are selected as model inputs, so as to establish the least squares support vector machine (LSSVM) model for Nd (Pr) content, while the model parameters are determined with the GA algorithm. To improve the adaptability of the model, the adaptive iteration algorithm is used to correct parameters of the LSSVM model, on the basis of model correction strategy and new sample data. Using the field data collected from rare earth extraction production, predictive methods for component content and comparisons are given. The results indicate that the proposed method presents good adaptability and high prediction precision, so it is applicable to the fast detection of element content in the rare earth extraction. © 2015 Elsevier B.V.

关键词： Extraction

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Component Content Multiple Model Predictive control for Ce Pr/Nd Extraction Process

Component Content Multiple Model Predictive Control for Ce P...

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第26届中国过程控制会议（CPCC2015）

作者： Zhang Zhiyong Yang Hui Lu Rongxiu Zhu Fan School of Electrical and Electronic Engineering East China Jiaotong University Key Laboratory of Advanced Control & Optimization of Jiangxi Province

A new multiple-model predictive control scheme for the rare earth extraction process is proposed for control of the com ponent content. Multiple local linear m odels with two-input and two-output of the rare earth cascade extraction process are established, each of which aim s for an operation condition. A com ponent content predictive controller is designed for each local linear model, based on the dynamic compensation of the flow rate of the extraction liquid and scrubbing liquid. A switching algorithm based on the m inimum accumulative error is form ed to select the most appropriate model and controller to satisfy the productive purity requirements. Simulation results of the Ce Pr/Nd cascade extraction process show the effectiveness of the proposed approach.

关键词： Rare earth cascade extraction Multivariable Multiple models Component content predictive control

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Distributed predictive control method based on neighborhood optimization for high-speed electric multiple unit

Distributed predictive control method based on neighborhood ...

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第26届中国过程控制会议（CPCC2015）

作者： Li Zhongqi Yang Hui School of Electrical and Electronic Engineering East China Jiaotong University Key Laboratory of Advanced Control & Optimization of Jiangxi Province

This paper proposes a new predictive control scheme for the high-speed electric multiple unit(EMU), by establishing multiple distributed state-space model for each power unit based on the subspace model identification method, according to the structure characteristics of EMU composed by several coupled power units, and the actual data and the traction/brake characteristic curve. Each power unit is designed by the distributed predictive control method and the neighborhood optimization strategy is used to coordinate such power units. Such a method can take advantage of each power unit output information and status information of the neighborhood in the optimization process, consider the performance index of the neighborhood fully, improve the global control performance of the EMU and reduce the online computation compared to the centralized control mode. The convergence and the stability conditions are discussed in this paper. Simulation results based on the actual CRH380 A operation data show the effectiveness of the proposed method.

关键词： High-speed electric multiple unit Power unit Neighborhood optimization Distributed model predictive control

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Time-series prediction modelling based on an efficient self-organization learning neural network

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IFAC-PapersOnLine 2015年第8期48卷 248-253页

作者： Gang Yang Hui Yang Lizhen Dai School of Electrical and Electronic Engineering East China Jiaotong University Nanchang 330013 China Key Laboratory of Advanced Control & Optimization of Jiangxi Province Nanchang 330013 China

For solving some process control engineering problems which can be treated as a time-series, a fast and accurate self-organization learning strategy is proposed based on the significance evaluation of hidden neurons with respect to the network output. This approach is introduced to optimize the architecture and parameters of span-lateral inhibition neural network (S-LINN) simultaneously. The insignificant neuron(s) will be pruned automated step by step based on the determination of significance index. The proposed self-organizing approach has been tested on one time-series prediction benchmark problem. Simulation results demonstrate that the proposed method has good exploration and exploitation capabilities in terms of searching the optimal structure and parameters for S-LINN.

关键词： time-series self-organization spatial-lateral inhibition neural network significance

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