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696 results on '"Su, Jianhua"'

154. Mono‐Heteroatom Substitution for Harnessing Excited‐State Structural Planarization of Dihydrodibenzo[ a,c ]phenazines

Author

Chen, Yi, primary, Chen, Deng‐Gao, additional, Chen, Yi‐An, additional, Wu, Cheng‐Ham, additional, Chang, Kai‐Hsin, additional, Meng, Fan‐Yi, additional, Chen, Meng‐Chi, additional, Lin, Jia‐An, additional, Huang, Chun‐Ying, additional, Su, Jianhua, additional, Tian, He, additional, and Chou, Pi‐Tai, additional

Published
2019
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179. Expression of VEGF and neural repair after alprostadil treatment in a rat model of sciatic nerve crush injury

Author

Tang, Jinrong, Hua, Ye, Su, Jianhua, Zhang, Ping, Zhu, Xuejiang, Wu, Le, Niu, Qi, Xiao, Hang, and Ding, Xinsheng

Subjects
Vascular endothelial growth factor -- Research -- Health aspects, Alprostadil -- Dosage and administration -- Patient outcomes -- Research, Sciatic nerve -- Injuries -- Health aspects -- Research, Peripheral nerve diseases -- Drug therapy -- Patient outcomes -- Research, Health, Drug therapy, Research, Injuries, Patient outcomes, Dosage and administration, Health aspects
Abstract

Byline: Jinrong. Tang, Ye. Hua, Jianhua. Su, Ping. Zhang, Xuejiang. Zhu, Le. Wu, Qi. Niu, Hang. Xiao, Xinsheng. Ding Background: Vasoactive drug alprostadil improves microcirculation and can be effective in [...]

Published
2009

196. Caging a novel object using multi-task learning method.

Author

Su, Jianhua, Chen, Bin, Qiao, Hong, and Liu, Zhi-yong

Subjects
*COMPUTER multitasking, *GEOMETRIC modeling, *KERNEL functions, *KNOWLEDGE transfer
Abstract

Caging grasps provide a way to manipulate an object without full immobilization and enable dealing with the pose uncertainties of the object. Most previous works have constructed caging sets by using the geometric models of the object. This work aims to present a learning-based method for caging a novel object only with its image. A caging set is first defined using the constrained region, and a mapping from the image feature to the caging set is then constructed with kernel regression function. Avoiding the collection of large number of samples, a multi-task learning method is developed to build the regression function, where several different caging tasks are trained with a joint model. In order to transfer the caging experience to a new caging task rapidly, shape similarity for caging knowledge transfer is utilized. Thus, given only the shape context for a novel object, the learner is able to accurately predict the caging set through zero-shot learning. The proposed method can be applied to the caging of a target object in a complex real-world environment, for which the user only needs to know the shape feature of the object, without the need for the geometric model. Several experiments prove the validity of our method. [ABSTRACT FROM AUTHOR]

Published
2019
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199. Design of a robot system for reorienting and assembling irregular parts

Author

Su, Jianhua, Shen, Liancheng, Peng, Zongyu, and Qu, Xiaoyi

Abstract

The design of a robot system for assembling numerous parts, including screws, washers, nuts, and other irregular parts, is important in manufacturing. Although a robot can flexibly manipulate various parts with the aid of vision and force sensors, some challenges remain, for example, precise orientation and precise insertion when uncertainty exists in the positioning and manipulation of irregular objects. In this work, we present a method to design the orienting strategy for positioning the irregular parts based on the constraints formed by the fixture, thus the irregular parts such as the motor can be precisely localized only with the robot squeezing actions. We then develop a peg-in-hole insertion strategy using the force constraints formed by the environment, where fewer robot actions are required to insert the peg, thus enabling efficient and precise peg-in-hole insertions. Finally, we developed a robot assembly system with three robots from Universal Robots to perform all the assembly tasks, including picking, holding, placing, and inserting. Experiments on the assembly of a belt drive unit reveal the efficiency of the proposed method.

Published
2024
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200. Transparent Programmable Luminescent Tags Enabled by Spiro[fluorene-9,9′-xanthene]-Based Hole-Transporting Molecules

Author

Zhang, Daqing, Luo, Xin, Labrador-Páez, Lucía, Li, Jiantong, Fu, Qiliang, Liu, Haichun, Su, Jianhua, Sychugov, Ilya, and Xu, Bo

Abstract

Pure organic ultralong room temperature phosphorescent (URTP) materials have garnered significant attention for applications in luminescent materials, biosensing, and information encryption. These materials offer advantages over heavy metal phosphorescent materials, such as lower cost, reduced biological toxicity, and minimal environmental impact. Herein, for the first time, we demonstrate a series of organic RTP materials based on spiro[fluorene-9,9′-xanthene] (SFX) hole-transporting molecules, specifically X59and X55. Our research presents that incorporating more rigid SFX units significantly extends RTP lifetime and enhances photoluminescence quantum yield (PLQY). The large steric hindrance of the rigid SFX structures suppresses nonradiative molecular motions, thereby prolonging phosphorescence emission. Compared to the baseline molecule X1, experimental results show that molecule X59extends the phosphorescence lifetime by 230 ms, while X55achieves an extension of 260 ms. Furthermore, we highlight the potential of this series of RTP molecules for use in transparent, programmable luminescent tags. Our work not only expands the molecular types of organic RTP materials but also provides innovative strategies for designing long-lived, high-quantum-yield RTP molecules. We envision that this will advance the smart device field of organic phosphorescent materials and their practical applications, such as intelligent labels, tags, and optical sensors.

Published
2024
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