Research Groups

High-end Equipment System Integration and Simulation Strategic Innovation Group

Team Introduction:

Taking national development strategy as the guidance, "high-end equipment system integration and simulation" strategic innovation team put its focus on the core scientific problems acquiring most social value and application prospect, and influence of the national development and future. Based on common interests, goals and research achievements, relying on the research features and advantages of two key disciplines, that are peak discipline "system science" and defense characteristic discipline "system and simulation", focusing on intelligent manufacturing and defense, applying the principles and methods of system theory, information theory and control theory to conduct modeling, analysis, diagnosis, synthesis, integration and simulation of the structure, performance and function of high-end equipment complex system, and from the perspective of system science and system engineering to provide strong theoretical support for the design and manufacture of high-end equipment system.

Through talent cultivation and scientific and technological services, the innovative development of intelligent manufacturing, robotics and sophisticated weaponry in China have been promoted, and then the national strategy of "Made in China 2025" and the construction plan of Shanghai science and technology innovation center will be served.

Research Direction:

·Performance analysis, synthesis and simulation of high-end equipment system with incomplete information: Information incompleteness refers to data loss, saturation, quantification, sampling, time delay and other phenomena that often occur in the equipment system. In most literature, it is always indirectly assumed that the system measurement signal includes the continuous real signal under noise interference. It is well known that the accuracy of measurement data plays a decisive role in the performance of the system. The stability, robustness and non-vulnerability of high-end equipment systems are studied under the condition of incomplete information. On this basis, the system state acquisition, system synthesis and simulation provide important theoretical support for the design and manufacturing of high-end equipment systems.

·Integrated cooperative control, fault diagnosis and safety monitoring of high-end equipment system: Focusing on the structure and optimization design of the high-end equipment system concerning needs of industrial production process and modern military, and network attack, data loss and other problems of multi-aiming complex systems, the collaborative planning, synchronization, consistency, and integrated control, as well as fault diagnosis and safety monitoring during operation have been under research, which ensures the safety, sound operation of the high-end equipment system.

·Optimization and integration of big data, artificial intelligence and high-end equipment system: The design, manufacturing and operation of high-end equipment will generate massive data, which contains a large amount of available information. Through big data technologies such as data mining, important information of equipment system can be obtained, and some more problems can be revealed, providing important basis for the system performance improvement and safe operation. The team will apply big data technology and artificial intelligence algorithm to carry out research on the structure and performance of high-end equipment system, providing theoretical basis for further optimization, analysis and integration of the system.

Basic Condition:

A new round of scientific and technological revolution and industrial reform is poised for development. Major developed countries in the world have taken high-end equipment as their focus, expanded their strategic layout, seized the commanding heights of global technological and industrial competition and rebuilt their national competitive advantages. America's National Strategic Plan for Advanced Manufacturing, Germany's Industry 4.0, Britain's High-value Manufacturing, France's New Industrial France, and Japan's Robot Strategy are all committed to promoting the integration of advanced manufacturing technology and information technology, focusing on the development of high-end equipment.

University of Shanghai for Science and Technology is originated from University of Hujiang, which was founded in 1906. It used to be the Shanghai Mechanical Engineering Institute under the Ministry of Machine-Building Industry, which enjoys the reputation of "the Huangpu Military Academy for Manufacturing" in China. It is a key university under the jurisdiction of Shanghai municipality, and a university jointly built by State Administration of Science Technology and Industry for National Defense and Shanghai Municipal People's Government. System Science and System Engineering Discipline conduct research on complex system, including natural system and human society system, and complex artificial system. High-end equipment is an advanced complex artificial system with many parts, which require many advanced technologies and powerful functions. High-end equipment generally has high research and development cost, long production cycle, complex system structure and involves mass high-tech.

With a long-term cooperation, the team has carried out many research work on integration and simulation of complex system in high-end equipment, forming several distinctive and internationally influential research directions. The team members have published nearly 500 papers (including accepted papers) on SCI journals in the field of systems and control, including more than 100 papers on top journals in the field. A total of more than 70 papers were listed as ESI high-citation papers (nearly 10 of which were listed in the top 0.1% of ESI hot papers). The team published 7 monographs, and conducted over 30 national and provincial scientific research projects, and won one Second Class National Prize of Natural Science, and many provincial and ministerial scientific and technological awards.

Principle Investigator: Wang Zidong, Professor, Doctoral Supervisor

Professor Wang Zidong has conducted research on complex systems, intelligent data analysis and signal processing, (bioinformatics, medical image processing, data mining, computational intelligence, robust control) for many years. He has rich experience and academic attainments in the analysis and synthesis of complex system, multi-source information analysis, computational intelligence and robust control research. He achieved many leading international research outcomes, with 7 monograph published, nearly 300 papers published in SCI journal, more than 5000 citation in SCI journal, more than 150 international conference papers. In 2012, Thompson Reuters chosed him as the only Hottest Researcher in the field of computing and data processing.

His research work has been supported by the Alexander von Humboldt Foundation (in 1996), JSPS Research Fellowship of Japan (in 1998), William Mong Visiting Research Fellowship of Hong Kong (in 2002), Engineering And Physical Sciences Research Council, the Lefield Foundation (United Kingdom), Biotechology and Biological Sciences Research Council (United Kingdom), The Royal Society and the University of Brunel (British). Professor Wang Zidong also won one Second Class National Prize of Natural Science, two National Science and Technology Progress Second Award and one National Science and Technology Progress Third Award.

Team Composition:

“High-end equipment system integration and simulation” strategic innovation team is under the leadership of Professor Wang Zidong, who is Internationally renowned scholar in the field of systems and data computing, IEEE Fellow. The scientific team composes more than 20 scholars, Chair Professor of “Changjiang Scholar”, fellow of New Century Excellent Talents Supporting Program of Ministry of Education , Shanghai Orientalists, Shanghai “Shuguang Scholars”, Shanghai “Hujiang Talents Program”, Shanghai Science and Technology Development Funds.

Selected papers:

1.Sunjie Zhang, Zidong Wang, Derui Ding, Guoliang Wei, Fuad E. Alsaadi, and TasawarHayat, A Gain-Scheduling Approach to Non-fragile H-infinity Fuzzy Control Subjectto Fading Channels, IEEE Transactions on Fuzzy Systems, DOI: 10.1109/TFUZZ.2016.2641023.

2.Derui Ding, Zidong Wang, Daniel W. C. Ho, and Guoliang Wei, Distributed recursive filtering for stochastic systems under uniform quantizations and deception attacks through sensor networks, Automatica, vol. 78, pp. 231-240, Apr. 2017.

3.Derui Ding, Zidong Wang, Bo Shen, and Guoliang Wei, Event-triggered consensus control for discrete-time stochastic multi-agent systems:

4.The input-to-state stability in probability, Automatica, vol. 62, pp. 284-291, Dec. 2015.

5.Fei Han, Guoliang Wei, Derui Ding, and Yan Song, Local condition-based consensus filtering with stochastic nonlinearities and multiple missing measurements, IEEE Transactions on Automatic Control, vol. 62, no. 9, pp. 4784-4790, Sep. 2017.

6.Guoliang Wei, Shuai Liu, Yan Song, Yurong Liu, Probability-guaranteed set-membership filtering for systems with incomplete measurements, Automatica, vol. 60, pp. 12-16, Oct. 2015.

7.Yongxiong Wang, Yubo Shi and Guoliang Wei, A Novel Local Feature Descriptor Based on Energy Information for Human Activity Recognition, Neurocomputing, vol. 228, no. 3, pp. 19-28, Mar. 2017.

8.Dongkai Zhang, Chaoli Wang, Guoliang Wei, Hengjun Zhang and Hua Chen, State-feedback stabilisation for stochastic non-holonomic mobile robots with uncertain visual servoing parameters. International Journal of Systems Science, vol. 45, no. 7, pp. 1451-1460, Apr. 2014.

9.Quan Jiang, Chao Bi and Ruoyu Huang, A new phase-delay-free method to detect back EMF zero-crossing points for sensorless control of spindle motors, IEEE Transactions on Magnetics, vol. 41, no. 7, pp. 2287-2294, Jul 2005.

10.Kun Xia, Jing Lu, Chao Bi, Yuan Tan and Bin Dong. Dynamic commutation torque-ripple reduction for brushless DC motor based on quasi-Z-source net, IET Electric Power Applications, vol.10, no. 9, pp. 819-826, Nov. 2016.

11.Qing-qing Yuan and Kun Xia, Current Decoupling Control for the Three-level PWM Rectifier with a Low Switching Frequency, Journal of Electrical Engineering and Technology, vol. 10, no. 1, pp. 280-287, Jan. 2015.


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