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Xiaofei Zhang - IEEE Xplore Author Profile
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Xiaofei Zhang

Also published under: Xiao-fei Zhang

Publications
14
Citations
28
Publications by Year
20092023
Co-Authors:
Show All Co-Authors (25)
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Xiaofei Zhang

Also published under: Xiao-fei Zhang

Affiliation

School of Vehicle and Mobility
Tsinghua University
Beijing, China

Publication Topics

Biography

Xiaofei Zhang received the B.S. degree in automation from Hebei United University, in 2014, received the M.S. degree in control engineering from Inner Mongolia University of Science and Technology, in 2016, and received the Ph.D. degree from Beijing Institute of Technology, in 2021. He is currently pursuing the post-doctorate at the School of Vehicle and Mobility, Tsinghua University. His current research interests include adaptive control, machine learning, and safety of the intended functionality.(Based on document published on 3 December 2019).
Publications
14
Citations
28
Publications by Year
20092023
Co-Authors:
Show All Co-Authors (25)
Author's Published Works

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EEG Signal Analysis for Early Detection of Critical Road Events and Emergency Response in Autonomous Driving

Xiaofei Zhang;Xin Xiao;Yingkai Yang;Zhenmao Hao;Jun Li;Hong Wang

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Autonomous driving technology has the potential to revolutionize the transportation industry. However, ensuring the safety of passengers and other traffic participants is a crucial challenge that must be addressed. One major concern is how the vehicle should react in emergency situations, where split-second decisions must be made to avoid collisions and minimize damage. This study presents a novel...Show More

EEG Signal Analysis for Early Detection of Critical Road Events and Emergency Response in Autonomous Driving

Xiaofei Zhang;Xin Xiao;Yingkai Yang;Zhenmao Hao;Jun Li;Hong Wang

Year: 2023 | Conference Paper |

Adaptive Control of Discrete-time Nonlinear Systems Using ITF-ORVFL

Xiaofei Zhang;Hongbin Ma;Wenchao Zuo;Man Luo

IEEE/CAA Journal of Automatica Sinica
Year: 2022 | Volume: 9, Issue: 3 | Journal Article |
Cited by: Papers (5)
Random vector functional ink (RVFL) networks belong to a class of single hidden layer neural networks in which some parameters are randomly selected. Their network structure in which contains the direct links between inputs and outputs is unique, and stability analysis and real-time performance are two difficulties of the control systems based on neural networks. In this paper, combining the advan...Show More

Adaptive Control of Discrete-time Nonlinear Systems Using ITF-ORVFL

Xiaofei Zhang;Hongbin Ma;Wenchao Zuo;Man Luo

Year: 2022 | Volume: 9, Issue: 3 | Journal Article |

An Adaptive Dynamic Programming Algorithm Based on ITF-OELM for Discrete-Time Systems

Xiaofei Zhang;Hongbin Ma;Junyong Chen;Weixue Li

2021 33rd Chinese Control and Decision Conference (CCDC)
Year: 2021 | Conference Paper |
Adaptive dynamic programming (ADP) is a kind of intelligent control method, and it is a non-model-based method that can directly approximate the optimal control policy via online learning. The gradient algorithm is usually used to update weights of action networks and critic networks, however it is clear that gradient descent-based learning methods are generally very slow due to improper learning ...Show More

An Adaptive Dynamic Programming Algorithm Based on ITF-OELM for Discrete-Time Systems

Xiaofei Zhang;Hongbin Ma;Junyong Chen;Weixue Li

Year: 2021 | Conference Paper |

Model Free Adaptive Control Algorithm based on ReOSELM for Autonomous Driving Vehicles

Xiaofei Zhang;Hongbin Ma;Zhichao Wang;Mingyu Fan;Bolin Zhao

2021 40th Chinese Control Conference (CCC)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Different road conditions and dynamic environment bring significant challenges to the control system of autonomous driving vehicle (ADV). As is known, historical data collected from ADV contains valuable information about control systems, therefore, it is a promising thing to study adaptive control algorithms that have certain learning ability. In order to improve the control performance of ADV an...Show More

Model Free Adaptive Control Algorithm based on ReOSELM for Autonomous Driving Vehicles

Xiaofei Zhang;Hongbin Ma;Zhichao Wang;Mingyu Fan;Bolin Zhao

Year: 2021 | Conference Paper |

Compact Model-Free Adaptive Control Algorithm for Discrete-Time Nonlinear Systems

Xiaofei Zhang;Hongbin Ma;Xinghong Zhang;You Li

IEEE Access
Year: 2019 | Volume: 7 | Journal Article |
Cited by: Papers (3)
In model free adaptive control (MFAC), a virtual equivalent dynamic linearized model is built. The linearization length constants (LLCs) of the virtual equivalent dynamic linearized model are selected by the practitioner based on experience. In this paper, the optimal LLCs are investigated, and compact model free adaptive control (CMFAC) is introduced for a class of unknown discrete-time nonlinear...Show More

Compact Model-Free Adaptive Control Algorithm for Discrete-Time Nonlinear Systems

Xiaofei Zhang;Hongbin Ma;Xinghong Zhang;You Li

Year: 2019 | Volume: 7 | Journal Article |

PICO and OS-ELM-LRF Based Online Learning System for Object Detection

Man Luo;Hongbin Ma;Xin Wang;Xiaofei Zhang

2019 IEEE International Conference on Mechatronics and Automation (ICMA)
Year: 2019 | Conference Paper |
Cited by: Papers (1)
In this paper, we propose a complete online learning framework for object detection system creatively. The framework efficiently combines Pixel Intensity Comparisons Organized in Decision Trees (PICO) and Local Receptive Fields Based Extreme Learning Machine with Online Sequential Learning Mechanism (OS-ELM-LRF). OS-ELM-LRF is the modified ELM-LRF for which we add the online sequential mechanism. ...Show More

PICO and OS-ELM-LRF Based Online Learning System for Object Detection

Man Luo;Hongbin Ma;Xin Wang;Xiaofei Zhang

Year: 2019 | Conference Paper |

An Adaptive Sliding Mode Controller with The Exponential and Power Reaching Law for Discrete Systems

Xiaofei Zhang;Hongbin Ma;Zhuang Li;Wenchao Zao

2018 37th Chinese Control Conference (CCC)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
With the development of aeronautics and astronautics, the response speed of servo system need be faster. First, In order to improve the dynamic quality of servo system, the exponential and power reaching law, which combines the advantages of the exponential reaching law and the power reaching law, is introduced. Second, the chattering of the sliding mode controller (SMC) with the exponential and p...Show More

An Adaptive Sliding Mode Controller with The Exponential and Power Reaching Law for Discrete Systems

Xiaofei Zhang;Hongbin Ma;Zhuang Li;Wenchao Zao

Year: 2018 | Conference Paper |

Application of sliding mode controller with proportional integral switching gain in robot

Xiaofei Zhang;Hongbin Ma;Nannan Li;Xinghong Zhang

2017 IEEE International Conference on Unmanned Systems (ICUS)
Year: 2017 | Conference Paper |
Proportional control plays a role in stabilizing the system, and sliding mode variable structure control algorithm has strong robustness to external disturbances and the change of system parameters. The sliding mode controller (SMC) with proportional integral switching gain is given in order to weaken the chattering problem of sliding mode variable structure control, and the stability analysis is ...Show More

Application of sliding mode controller with proportional integral switching gain in robot

Xiaofei Zhang;Hongbin Ma;Nannan Li;Xinghong Zhang

Year: 2017 | Conference Paper |

ASR Normalization for Machine Translation

Heyan Huang;Chong Feng;Jiande Wang;Xiaofei Zhang

2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics
Year: 2010 | Conference Paper |
Cited by: Patents (1)
In natural spoken language there are many meaningless modal particles and dittographes, furthermore ASR (automatic speech recognition) often has some recognition errors and the ASR results have no punctuations. Therefore, the translation would be rather poor if the ASR results are directly translated by MT (machine translation). In this paper, an ASR normalization approach was introduced for machi...Show More

ASR Normalization for Machine Translation

Heyan Huang;Chong Feng;Jiande Wang;Xiaofei Zhang

Year: 2010 | Volume: 2 | Conference Paper |

Cross-language information retrieval based on weight computation of query keywords translation

Xiao-fei Zhang;He-yan Huang;Ke-liang Zhang

2009 IEEE International Conference on Intelligent Computing and Intelligent Systems
Year: 2009 | Conference Paper |
In cross-language information retrieval (CLIR), the query sentence is often combined with a series of query keywords, rather than a complete natural sentence. Lack of necessary contextual syntactic information in such a query sentence makes it impossible to achieve a unique translation of the query sentence with acceptable precision. In this paper, we convert the translation of query sentence to t...Show More

Cross-language information retrieval based on weight computation of query keywords translation

Xiao-fei Zhang;He-yan Huang;Ke-liang Zhang

Year: 2009 | Volume: 3 | Conference Paper |

The Application of CRFs in Part-of-Speech Tagging

Xiaofei Zhang;Heyan Huang;Zhang Liang

2009 International Conference on Intelligent Human-Machine Systems and Cybernetics
Year: 2009 | Conference Paper |
Cited by: Papers (2)
Conditional random fields (CRFs) for sequence labeling offer advantages over both generative models like hidden Markov model (HMM) and classifiers applied at each sequence position. First, the CRFs don't force to adhere to the independence assumption and thus can depend on arbitrary, non-independent features, without accounting for the distribution of those dependencies. Since CRFs models are able...Show More

The Application of CRFs in Part-of-Speech Tagging

Xiaofei Zhang;Heyan Huang;Zhang Liang

Year: 2009 | Volume: 2 | Conference Paper |

Part-of-speech tagger based on maximum entropy model

Heyan Huang;Xiaofei Zhang

2009 2nd IEEE International Conference on Computer Science and Information Technology
Year: 2009 | Conference Paper |
Cited by: Papers (9)
The maximum entropy (ME) conditional models don't force to adhere to the independence assumption such as in Hidden Markov generative models, and thus the ME-based part-of-speech (POS) tagger can depend on arbitrary, non-independent features, which are benefit to the POS tagging, without accounting for the distribution of those dependencies. Since ME models are able to flexibly utilize a wide varie...Show More

Part-of-speech tagger based on maximum entropy model

Heyan Huang;Xiaofei Zhang

Year: 2009 | Conference Paper |

Chaos anti-control and discrete map model for voltage-programmed buck converter

Xiaofei Zhang;Dao-Yang Zhang

2009 2nd IEEE International Conference on Computer Science and Information Technology
Year: 2009 | Conference Paper |
In order to improve electromagnetic compatibility and deduce ripple of output voltage of the voltage-programmed buck converter, a state-feedback method with saw-tooth wave function to make this circuit system chaotic are proposed. The effectiveness and correction are proved by numerical simulation. Furthermore, in order to analyze physics mechanics from theory about reducing ripple of output volta...Show More

Chaos anti-control and discrete map model for voltage-programmed buck converter

Xiaofei Zhang;Dao-Yang Zhang

Year: 2009 | Conference Paper |

KNN Text Categorization Algorithm Based on Semantic Centre

Xiao-fei Zhang;He-yan Huang;Ke-liang Zhang

2009 International Conference on Information Technology and Computer Science
Year: 2009 | Conference Paper |
Cited by: Papers (5)
As a classical statistical pattern recognition algorithm characterized with high accuracy and stability, KNN has been used widely in text categorization. But since KNNpsilas time complexity is directly proportional to the sample size, its classification speed is very slow. In this paper, we propose a new KNN text categorization algorithm based on semantic centre, which we call SKNN, to speed up th...Show More

KNN Text Categorization Algorithm Based on Semantic Centre

Xiao-fei Zhang;He-yan Huang;Ke-liang Zhang

Year: 2009 | Volume: 1 | Conference Paper |

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