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Yuqin Guo

Publications and source records attributed to Yuqin Guo.

3 recordsLinked to original sources

Streets-Tian Conjecture on several special types of Hermitian manifolds

A Hermitian-symplectic metric is a Hermitian metric whose K\"ahler form is given by the $(1,1)$-part of a closed $2$-form. Streets-Tian Conjecture states that a compact complex manifold admitting a Hermitian-symplectic metric must be K\"ahlerian (i.e., admitting a K\"ahler metric). The conjecture is known to be true in complex dimension $2$ but is still open in complex dimensions $3$ or higher. In this article, we confirm the conjecture for some special types of compact Hermitian manifolds, including Chern K\"ahler-like manifolds, non-balanced Bismut torsion parallel (BTP) manifolds, and compact quotients of Lie groups whose Lie algebra contains a $J$-invariant abelian ideal of codimension $2$. The last type is a natural generalization to (compact quotients of) almost abelian Lie groups. The non-balanced BTP case contains all Vaisman manifolds and all Bismut K\"ahler-like manifolds as subsets. These results extend some of the earlier works on the topic by Fino, Kasuya, Vezzoni, Angella, Otiman, Paradiso, and others. Our approach is elementary in nature, by giving explicit descriptions of Hermitian-symplectic metrics on such spaces as well as the pathways of deforming them into K\"ahler ones, aimed at illustrating the algebraic complexity and subtlety of Streets-Tian Conjecture.

math.DG

Underwater Intention Recognition using Head Motion and Throat Vibration for Supernumerary Robotic Assistance

This study presents a multi-modal mechanism for recognizing human intentions while diving underwater, aiming to achieve natural human-robot interactions through an underwater superlimb for diving assistance. The underwater environment severely limits the divers' capabilities in intention expression, which becomes more challenging when they intend to operate tools while keeping control of body postures in 3D with the various diving suits and gears. The current literature is limited in underwater intention recognition, impeding the development of intelligent wearable systems for human-robot interactions underwater. Here, we present a novel solution to simultaneously detect head motion and throat vibrations under the water in a compact, wearable design. Experiment results show that using machine learning algorithms, we achieved high performance in integrating these two modalities to translate human intentions to robot control commands for an underwater superlimb system. This study's results paved the way for future development in underwater intention recognition and underwater human-robot interactions with supernumerary support.

cs.RO