个人信息
参与实验室科研项目
人机智能协同关键技术及其在智能制造中的应用
非可信智能驱动的可靠智造
研究课题
基于质量预测的主板功能测试策略优化研究
学术成果
共撰写/参与撰写专利 2 项，录用/发表论文 2 篇，投出待录用论文0篇。
patent
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锡膏印刷质量异常预测方法、系统、设备及存储介质
赵云波,
刘斌琨,
康宇,
曹洋,
陈龙鑫,
and 李佳玉
2025
[pdf]
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基于图注意力神经网络的印刷电路板质量预测方法
赵云波,
李佳玉,
康宇,
柏鹏,
谈正军,
徐久存,
刘斌琨,
and 许镇义
Conference Articles
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Functional Evaluation for Printed Circuit Board Based on Surface Mount Technology Process Data
Jiayu Li,
Yunbo Zhao,
and Binkun Liu
In 2024 11th International Forum on Electrical Engineering and Automation (IFEEA)
2024
[doi]
[pdf]
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A Novel Haptic Takeover Method Based on Human-Machine Collaboration States
Yunbo Zhao,
Zuhao Xie,
Chang Xu,
Xiuhua Liang,
Ruiyu Xia,
and Jiayu Li
In 2024 14th Asian Control Conference (ASCC)
2024
[Abs]
[pdf]
The current stage of autonomous driving calls for drivers to remain actively engaged within the control loop in anticipation of the need for takeover operations. However, regaining control of the vehicle from a state of low situation awareness (SA) poses a challenge. To address this issue, this research introduces a novel takeover method based on haptic shared control, ensuring a smooth and safe takeover process. A symmetric softmax function is formulated to evaluate muscle state, taking into account the varying torque thresholds associated with different vehicle speeds, as well as utilizing the driver’s cognitive state to characterize their SA. Within the takeover process, a coordinator leverages the driver’s SA and human-machine intention similarity to determine the current state of human-machine collaboration. Subsequently, different control allocation strategies are then adopted for different states, and the driver is guided through force feedback. Experiment results demonstrate the effectiveness of the proposed method, showcasing its ability to facilitate a smooth and safe transfer of control, regardless of the presence of conflicts or the harmonious state existing between the human driver and the automated system.
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