Shulin Liu

EAAI Journal 2023 Journal Article

Continual learning classification method with human-in-the-loop based on the artificial immune system

• Jia Liu
• Dong Li
• Wangweiyi Shan
• Shulin Liu

EAAI Journal 2022 Journal Article

An ensemble and shared selective adversarial network for partial domain fault diagnosis of machinery

• Xiaoyang Liu
• Shulin Liu
• Jiawei Xiang
• Ruixue Sun
• Yuan Wei

EAAI Journal 2020 Journal Article

A scale-adaptive positive selection algorithm based on B-cell immune mechanisms for anomaly detection

• Hongli Zhang
• Zhongyuan Ren
• Shaojie Xin
• Shulin Liu
• Chao Lan
• Xin Sun

ICRA Conference 2017 Conference Paper

Kinematic design of a novel 4-DOF parallel manipulator

• Cuncun Wu
• Guilin Yang
• Chin-Yin Chen
• Shulin Liu
• Tianjiang Zheng

A new four degrees-of-freedom (DOF) parallel manipulator that can produce 3-DOF translations and 1-DOF rotation (3T1R), has been proposed in this paper. It has two identical limbs connected to the moving platform through passive revolute joints, and each limb has two identical branches driven by a pair of base mounted collinear prismatic joints. Due to such a unique “4-2-1” kinematic structure, the 4-DOF parallel manipulator has the advantages of simple kinematics, large workspace, high speed, and high positioning accuracy. These advantages make it an appropriate candidate for high-speed and high-precision pick-and-place operations. To validate the proposed parallel manipulator design, mobility analysis is conducted based on the screw theory. Other critical design analysis issues, such as displacement, singularity, and workspace analyses, have been addressed in details.

EAAI Journal 2016 Journal Article

A boundary-fixed negative selection algorithm with online adaptive learning under small samples for anomaly detection

• Dong Li
• Shulin Liu
• Hongli Zhang

AAAI Conference 2016 Conference Paper

A Probabilistic Soft Logic Based Approach to Exploiting Latent and Global Information in Event Classification

• Shulin Liu
• Kang Liu
• Shizhu He
• Jun Zhao

Global information such as event-event association, and latent local information such as fine-grained entity types1, are crucial to event classification. However, existing methods typically focus on sophisticated local features such as part-ofspeech tags, either fully or partially ignoring the aforementioned information. By contrast, this paper focuses on fully employing them for event classification. We notice that it is difficult to encode some global information such as eventevent association for previous methods. To resolve this problem, we propose a feasible approach which encodes global information in the form of logic using Probabilistic Soft Logic model. Experimental results show that, our proposed approach advances state-of-the-art methods, and achieves the best F1 score to date on the ACE data set.