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Jingjie Luo - IEEE Xplore Author Profile

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Author details

Jingjie Luo

Publications

1

Citations

6

Publications by Year

20232023

Co-Authors:

Xuewen DongSheng GaoWeisong ShiJianming Zhu

Jingjie Luo

Affiliation

School of Information

Central University of Finance and Economics

Beijing, China

Publication Topics

Aggregate Results,
Blockchain,
Communication Overhead,
Computational Overhead,
Convolutional Neural Network,
Data Privacy,
Design Goals,
Dimensional Vector,
Encryption Time,
Federated Learning,
Group Elements,
Inference Attacks

Biography

Jingjie Luo received the B.S. degree from Nanjing Normal University, Nanjing, China, in 2020. He is currently pursuing the M.S. degree with the School of Information, Central University of Finance and Economics. His current research interests include federated learning and privacy protection.(Based on document published on 27 April 2023).

Publications

1

Citations

6

Publications by Year

20232023

Co-Authors:

Xuewen Dong
Sheng Gao
Weisong Shi
Jianming Zhu

Author's Published Works

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Conferences (3)

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Manafaddin Namazov(2)
Gancho Vachkov(1)
I. Emre Celikkale(1)
Burak Tekgun(1)
Abbas Alili(1)
Head of Process Automation Engineering Department, Baku Higher Oil School, Baku, Azerbaijan(1)
Department of Electrical and Electronics Engineering, Cumhuriyet University, Sivas, Turkey(1)
Department Baku Higher Oil School (BHOS), Distance Learning Center, Sofia, Bulgaria(1)
Department Baku Higher Oil School (BHOS), Process Automation Engineering, Baku, Azerbaijan(1)
2012 International Symposium on Innovations in Intelligent Systems and Applications(1)
2017 IEEE International Conference on Mechatronics and Automation (ICMA)(1)
2018 Global Smart Industry Conference (GloSIC)(1)
IEEE(3)
Chelyabinsk, Russia(1)
Takamatsu, Japan(1)
Trabzon, Turkey(1)
Disturbance Signal(2)
Fuzzy Control(2)
Fuzzy Logic(2)
Fuzzy Model(2)
Fuzzy Rules(2)

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Fuzzy Logic Control Design for 2-Link Robot Manipulator in MATLAB/Simulink via Robotics Toolbox

Manafaddin Namazov

2018 Global Smart Industry Conference (GloSIC)

Year: 2018 | Conference Paper |

Cited by: Papers (2)

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In this paper, it is aimed to achieve fuzzy logic control of a simple 2-link robot manipulator via Robotics and Fuzzy Logic Toolboxes of MATLAB©. The blocks from these toolboxes were blended in the Simulink© environment in order to obtain the simulations. The robot block available in the Robotics Toolbox was used to create the model by modifying the block parameters. Next, a PD-controller was desi...Show More

Fuzzy Logic Control Design for 2-Link Robot Manipulator in MATLAB/Simulink via Robotics Toolbox

Manafaddin Namazov

2018 Global Smart Industry Conference (GloSIC)

Year: 2018 | Conference Paper |

Grid fuzzy models with variable boundaries and their application to monitoring of plant operations

Gancho Vachkov;Abbas Alili;Manafaddin Namazov

2017 IEEE International Conference on Mechatronics and Automation (ICMA)

Year: 2017 | Conference Paper |

Cited by: Papers (4)

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This paper describes a special type of fuzzy models with incomplete grid-type fuzzy rule base in the multidimensional input space. The number and locations of the fuzzy rules are automatically defined by the concrete distribution of the training data and by the assumptions for the size and the structure of the grid. A simplified iterative learning algorithm for calculating the singletons of such G...Show More

Grid fuzzy models with variable boundaries and their application to monitoring of plant operations

Gancho Vachkov;Abbas Alili;Manafaddin Namazov

2017 IEEE International Conference on Mechatronics and Automation (ICMA)

Year: 2017 | Conference Paper |

Design of a stable Takagi-Sugeno Fuzzy Control system via LMIs with disturbance rejection

Manafeddin Namazov;Burak Tekgun;I. Emre Celikkale

2012 International Symposium on Innovations in Intelligent Systems and Applications

Year: 2012 | Conference Paper |

Cited by: Papers (2)

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In this paper, we have presented the control of an inverted pendulum system controlled by a dc motor via a gear train using the T-S fuzzy approach. Stability conditions of this system with disturbance rejection can be formulized in terms of linear matrix inequalities (LMIs), which can be solved using the LMI Toolbox in MATLAB. To simplify the procedure of T-S fuzzy modeling and control, a software...Show More

Design of a stable Takagi-Sugeno Fuzzy Control system via LMIs with disturbance rejection

Manafeddin Namazov;Burak Tekgun;I. Emre Celikkale

2012 International Symposium on Innovations in Intelligent Systems and Applications

Year: 2012 | Conference Paper |

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