Haihang Wang

Zhishuo Zhang, Chengxiang Tan, Haihang Wang et al.

Entity alignment(EA) is a crucial task for integrating cross-lingual and cross-domain knowledge graphs(KGs), which aims to discover entities referring to the same real-world object from different KGs. Most existing methods generate aligning entity representation by mining the relevance of triple elements via embedding-based methods, paying little attention to triple indivisibility and entity role diversity. In this paper, a novel framework named TTEA -- Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment is proposed to overcome the above issues considering ensemble triple specificity and entity role features. Specifically, the ensemble triple representation is derived by regarding relation as information carrier between semantic space and type space, and hence the noise influence during spatial transformation and information propagation can be smoothly controlled via specificity-aware triple attention. Moreover, our framework uses triple-ware entity enhancement to model the role diversity of triple elements. Extensive experiments on three real-world cross-lingual datasets demonstrate that our framework outperforms state-of-the-art methods.

Haihang Wang, He Xu, Chen Yang et al.

A new fluid-driven soft robot hand in this study uses the idea of the bionics and has the anthropomorphic form, which is oriented to the flexible grasp function. The soft robot hand is composed of a new kind of multi-freedom soft finger and soft palm, which realizes the characteristic grasping function of forehand and backhand. Combined with the fine fluid control system, the soft hand can realize flexible grasping under high pressure, so as to realize flexible grasping operation for different types of target objects in the underwater environment. The soft robot hand was controlled based on water hydraulic platform, Finally, the soft robot hand and the fine fluid control system were connected to form the underwater soft robot hand experiment platform.

Haihang Wang, Fares J. Abu-Dakka, Tran Nguyen Le et al.

Soft robotic hands and grippers are increasingly attracting attention as a robotic end-effector. Compared with rigid counterparts, they are safer for human-robot and environment-robot interactions, easier to control, lower cost and weight, and more compliant. Current soft robotic hands have mostly focused on the soft fingers and bending actuators. However, the palm is also essential part for grasping. In this work, we propose a novel design of soft humanoid hand with pneumatic soft fingers and soft palm. The hand is inexpensive to fabricate. The configuration of the soft palm is based on modular design which can be easily applied into actuating all kinds of soft fingers before. The splaying of the fingers, bending of the whole palm, abduction and adduction of the thumb are implemented by the soft palm. Moreover, we present a new design of soft finger, called hybrid bending soft finger (HBSF). It can both bend in the grasping axis and deflect in the side-to-side axis as human-like motion. The functions of the HBSF and soft palm were simulated by SOFA framework. And their performance was tested in experiments. The 6 fingers with 1 to 11 segments were tested and analyzed. The versatility of the soft hand is evaluated and testified by the grasping experiments in real scenario according to Feix taxonomy. And the results present the diversity of grasps and show promise for grasping a variety of objects with different shapes and weights.