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Zhengyang Zhang - IEEE Xplore Author Profile
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Zhengyang Zhang

Publications
11
Citations
10
Publications by Year
20202025
Co-Authors:
Show All Co-Authors (40)
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Zhengyang Zhang

Affiliation

Key Laboratory of Computing Power Network and Information Security
Ministry of Education
Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing
Shandong Fundamental Research Center for Computer Science
Jinan, China

Publication Topics

Biography

Zhengyang Zhang received the B.S. degree in geographic information science from the Chang'an University, Xi'an, China, in 2024.,His research interest includes deep learning and the application research of Google Earth Engine.(Based on document published on 11 September 2024).
Publications
11
Citations
10
Publications by Year
20202025
Co-Authors:
Show All Co-Authors (40)
Author's Published Works

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Comprehensive Feature Processing Based on Attention Mechanism for Co-Salient Object Detection

Guohua Lv;Mao Yuan;Zengbin Zhang;Zhengyang Zhang;Zhenhui Ding;Guangxiao Ma

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Co-salient object detection (CoSOD) aims to detect common salient objects across multiple related images. However, existing methods often struggle with limited attention coverage, missing some co-salient objects. To address this, we propose a two-stage feature processing module (FPM) comprising comprehensive feature extraction module (CFE) and feature enhancement module (FEM). CFE extracts compreh...Show More

Comprehensive Feature Processing Based on Attention Mechanism for Co-Salient Object Detection

Guohua Lv;Mao Yuan;Zengbin Zhang;Zhengyang Zhang;Zhenhui Ding;Guangxiao Ma

Year: 2025 | Conference Paper |

Deep Reinforcement Learning With Fuzzy Feature Fusion for Cooperative Control in Traffic Light and Connected Autonomous Vehicles

Liang Xu;Zhengyang Zhang;Han Jiang;Bin Zhou;Haiyang Yu;Yilong Ren

IEEE Transactions on Fuzzy Systems
Year: 2025 | Volume: 33, Issue: 1 | Journal Article |
A mixed traffic environment of manual driving and automatic driving will become the norm in future intelligent transportation systems. The deep reinforcement learning (DRL) method has shown significant promise in cooperative control for traffic lights and connected autonomous vehicles (CAV) in a mixed-traffic environment. However, the uncertainty and noise in integrating agents' observations can l...Show More

Deep Reinforcement Learning With Fuzzy Feature Fusion for Cooperative Control in Traffic Light and Connected Autonomous Vehicles

Liang Xu;Zhengyang Zhang;Han Jiang;Bin Zhou;Haiyang Yu;Yilong Ren

Year: 2025 | Volume: 33, Issue: 1 | Journal Article |

Lane Detection in Complex Scenes Based on Deep Snake

Zhengyang Zhang;Erdun Zhao

2024 2nd International Conference on Machine Vision, Image Processing & Imaging Technology (MVIPIT)
Year: 2024 | Conference Paper |
Aiming at the challenge of lane detection in complex scenes, a lane detection algorithm based on Anchor, LaneSnakeNet, is proposed. This algorithm generates initial lane anchors through clustering and uses ResNet as the backbone to refine lane prediction across different layers on the output feature maps. A detection module LaneSnake based on self-attention mechanism and global feature fusion has ...Show More

Lane Detection in Complex Scenes Based on Deep Snake

Zhengyang Zhang;Erdun Zhao

Year: 2024 | Conference Paper |

LGG-NeXt: A Next Generation CNN and Transformer Hybrid Model for the Diagnosis of Alzheimer's Disease Using 2D Structural MRI

Jing Bai;Zhengyang Zhang;Yue Yin;Weikang Jin;Talal Ahmed Ali Ali;Yong Xiong;Zhu Xiao

IEEE Journal of Biomedical and Health Informatics
Year: 2024 | Early Access Article |
Incurable Alzheimer's disease (AD) plagues many elderly people and families. It is important to accurately diagnose and predict it at an early stage. However, the existing methods have shortcomings, such as inability to learn local and global information and the inability to extract effective features. In this paper, we propose a lightweight classification network Local and Global Graph ConvNeXt. ...Show More

LGG-NeXt: A Next Generation CNN and Transformer Hybrid Model for the Diagnosis of Alzheimer's Disease Using 2D Structural MRI

Jing Bai;Zhengyang Zhang;Yue Yin;Weikang Jin;Talal Ahmed Ali Ali;Yong Xiong;Zhu Xiao

Year: 2024 | Early Access Article |

Prescribed Performance Attitude Control for Hypersonic Flight Vehicle based on Predetermined-Time Extended State Observer

Xinyue Du;Yetong Lin;Liang Xu;Yuehui Ji;Zhengyang Zhang;Junjie Liu

2024 IEEE 25th China Conference on System Simulation Technology and its Application (CCSSTA)
Year: 2024 | Conference Paper |
In this paper, in order to solve the problems of high nonlinearity, strong coupling and external interference in hypersonic flight vehicle (HFV) control, an active disturbance rejection controller based on predetermined-time extended state observer (PTESO) and prescribed performance control (PPC) is proposed to realize the tracking of attitude angle. We design three independent controllers for the...Show More

Prescribed Performance Attitude Control for Hypersonic Flight Vehicle based on Predetermined-Time Extended State Observer

Xinyue Du;Yetong Lin;Liang Xu;Yuehui Ji;Zhengyang Zhang;Junjie Liu

Year: 2024 | Conference Paper |

A Generalized Deep Learning-Based Method for Rapid Co-Seismic Landslide Mapping

Jing Yang;Mingtao Ding;Wubiao Huang;Zhenhong Li;Zhengyang Zhang;Jing Wu;Jianbing Peng

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2024 | Volume: 17 | Journal Article |
The rapid mapping of co-seismic landslides is essential for emergency management and loss assessment. Deep learning algorithms generally follow a supervised learning workflow, where the trained model is used to predict landslides in surrounding areas, achieving landslide mapping with high accuracy. For a new study area landslide extraction task, the performance of the model trained on a specific d...Show More

A Generalized Deep Learning-Based Method for Rapid Co-Seismic Landslide Mapping

Jing Yang;Mingtao Ding;Wubiao Huang;Zhenhong Li;Zhengyang Zhang;Jing Wu;Jianbing Peng

Year: 2024 | Volume: 17 | Journal Article |

AUV 3D Trajectory Tracking based on Linear Active Disturbance Rejection Control

Junjie Liu;Xin Chen;Pengfei Li;Yetong Lin;Yuehui Ji;Zhengyang Zhang

2024 36th Chinese Control and Decision Conference (CCDC)
Year: 2024 | Conference Paper |
Autonomous underwater vehicle (AUV) has the characteristics of nonlinear, time-varying, strong coupling and easy to be disturbed. In order to further improve the AUV trajectory tracking control effect, a five-degree-of-freedom spatial motion model was established and the linear active disturbance rejection control method was introduced into AUV trajectory tracking control. Considering the characte...Show More

AUV 3D Trajectory Tracking based on Linear Active Disturbance Rejection Control

Junjie Liu;Xin Chen;Pengfei Li;Yetong Lin;Yuehui Ji;Zhengyang Zhang

Year: 2024 | Conference Paper |

Optimizing Traffic Signal Control in Mixed Traffic Scenarios: A Predictive Traffic Information-Based Deep Reinforcement Learning Approach

Zhengyang Zhang;Bin Zhou;Bugao Zhang;Ping Cheng;Der-Horng Lee;Simon Hu

2024 Forum for Innovative Sustainable Transportation Systems (FISTS)
Year: 2024 | Conference Paper |
The rapid advancement of Connected Autonomous Vehicles (CAVs) is a driving force in the evolution of smart cities and Intelligent Transportation Systems (ITS). This has spurred extensive research in both fields, with a significant focus on vehicle-to-infrastructure (V2I) communication. Deep reinforcement learning is emerging as a popular method in this realm. However, current literature shows a si...Show More

Optimizing Traffic Signal Control in Mixed Traffic Scenarios: A Predictive Traffic Information-Based Deep Reinforcement Learning Approach

Zhengyang Zhang;Bin Zhou;Bugao Zhang;Ping Cheng;Der-Horng Lee;Simon Hu

Year: 2024 | Conference Paper |

A Situation Deduction Method for Rail Transit Network Based on Dynamic Bayesian Network with Gaussian Mixture Model

Zhengyang Zhang;Wei Dong;Xinya Sun;Yindong Ji

2023 4th International Conference on Computer Engineering and Intelligent Control (ICCEIC)
Year: 2023 | Conference Paper |
In the past work, we proposed a transport capacity risk prediction method based on linear Gaussian Bayesian network. Aiming at the problem that the global capacity risk index is single and the existing methods are difficult to deal with dynamic and nonlinear data, this paper proposes a method of transportation safety situation deduction for rail transit network based on dynamic Bayesian network an...Show More

A Situation Deduction Method for Rail Transit Network Based on Dynamic Bayesian Network with Gaussian Mixture Model

Zhengyang Zhang;Wei Dong;Xinya Sun;Yindong Ji

Year: 2023 | Conference Paper |

Application of Ray Casting Algorithm in Medical Image Enhancement and Edge Detection

Zhengyang Zhang

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT)
Year: 2023 | Conference Paper |
The Ray Casting algorithm plays an important role in improving medical images and edge detection. Medical imaging typically includes a large amount of image data, such as computed tomography, magnetic resonance imaging, etc. These data need to be effectively and accurately processed and analyzed in order for doctors to make correct diagnoses. The Ray Casting algorithm is a method that allows for e...Show More

Application of Ray Casting Algorithm in Medical Image Enhancement and Edge Detection

Zhengyang Zhang

Year: 2023 | Conference Paper |

Study on High-Speed and Smooth Transfer of Robot Motion Trajectory Based on Modified S-Shaped Acceleration/Deceleration Algorithm

Shanxiang Fang;Jianguo Cao;Zhengyang Zhang;Qinjian Zhang;Weidong Cheng

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (10)
To meet the requirements on industrial robots concerning their fast-moving pick-and-place or obstacle avoidance, this article puts forward a novel high-speed and smooth transfer control algorithm for spatial elliptic trajectory - an modified S-shaped acceleration/deceleration (ACC/DEC) control algorithm based on a piecewise continuous jerk curve in order to achieve the smooth motion at a high spee...Show More

Study on High-Speed and Smooth Transfer of Robot Motion Trajectory Based on Modified S-Shaped Acceleration/Deceleration Algorithm

Shanxiang Fang;Jianguo Cao;Zhengyang Zhang;Qinjian Zhang;Weidong Cheng

Year: 2020 | Volume: 8 | Journal Article |

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