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- 저자 : Zhiqiang Pu (검색결과 3 건)
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Embryoid bodies formation from chicken primordial germ cells
Hui Xiong,Yabin Pu,Qingyun Hu,Zhiqiang Shan,Pengfei Hu,Weijun Guan,Yuehui Ma 한국통합생물학회 2015 Animal cells and systems Vol.19 No.3
Primordial germ cells (PGCs) were demonstrated to be multipotential because of their differentiation ability in all embryonic lineages and because the pluripotential nature of PGCs caters for recent researches on stem cells. PGCs were cultured in suspension to form embryoid bodies (EBs). The characterization of PGCs and EBs was assessed by immunofluorescence technique, reverse transcription-polymerase chain reaction (RT-PCR) and paraffin section for Haematoxylin and Eosin (HE) stain. We established a stable chicken PGC line in vitro and prepared masses of EBs from PGCs. We also demonstrated that PGCs expressed stage-specific and stem cell-specific surface makers: SSEA-1, SSEA-3, Oct4, and Sox2. EBs expressed specific genes from three germ layers: gene AFP from entoderm, gene GATA6 from mesoderm, and gene Sox3 from ectoderm. The HE staining illustrated that EBs developed different cell types; relatively larger EBs (440 μm in diameter) were obtained, which contract rhythmically. We concluded that chicken PGCs were suitable for the formation of EBs. Abundant larger size EBs could be prepared in an effective way with our protocol, which could construct a good animal model and provide a useful clinical platform in studying human embryonic development and cell transplantation field.
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Tianle Zhang,Zhen Liu,Zhiqiang Pu,Jianqiang Yi,Yanyan Liang,Du Zhang 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.7
Although deep reinforcement learning has recently achieved some successes in robot navigation, there are still unsolved problems. Particularly, a robot guided by a distant ultimate goal is easy to get stuck in danger or encounter collisions in dynamic crowded environments due to the lack of long-term perspectives. In this paper, a novel subgoal-guided approach based on two-level hierarchical deep reinforcement learning with spatial-temporal graph attention networks (ST-GANets), called SG-HDRL, is proposed for a robot navigating in a dynamic crowded environment with autonomous obstacles, e.g., crowd. Specifically, the high-level policy, that models the spatialtemporal relation between the robot and the obstacles using the obstacles’ trajectories by the designed high-level ST-GANet, generates intermediate subgoals from a longer-term perspective over higher temporal scales. The subgoals give a favorable and collision-free direction to avoid encountering danger or collisions while approaching the ultimate goal. The low-level policy, that similarly implements the designed low-level ST-GANet to implicitly predict the obstacles’ motions, takes the subgoals as short-term guidance through an intrinsic reward incentive to generate primitive actions for the robot. Simulation results demonstrate that SG-HDRL using ST-GANets has better performances compared with state-of-the-art baselines. Furthermore, the proposed SG-HDRL is deployed to an experimental platform based on omnidirectional cars, and experiment results validate the effectiveness and practicability of the proposed SG-HDRL.
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Hedonic Coalition Formation for Distributed Task Allocation in Heterogeneous Multi-agent System
Lexing Wang,Tenghai Qiu,Zhiqiang Pu,Jianqiang Yi,Jinying Zhu,Wanmai Yuan 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.4
Due to the complexity of tasks in the real world, multiple agents with different capabilities tend to cooperate to handle diverse requirements of these tasks by forming coalitions. To solve the problem of finding optimal heterogeneous coalition compositions, this paper proposes a novel distributed hedonic coalition formation game method to solve the task allocation problem for multiple heterogeneous agents. Firstly, to quantify the intention of an agent joining each coalition, a utility function for each agent is designed based on the cost and the reward with regard to the given tasks, where the heterogeneous requirements of tasks are also considered. Then, a preference relation related to the utility function is designed for the self-interested agents autonomously choose to join or leave a coalition. Subsequently, a theorem is presented, and analyses have been conducted to show that the proposed method achieves a Nash-stable solution in the heterogeneous system. Further, to develop a Nash stable partition result, a distributed hedonic coalition formation algorithm containing prioritization and consensus stages is designed for each agent to make decisions. The algorithm is implemented based on local interactions with neighbor agents under a connected communication network. Finally, simulations are conducted to verify the performance of the proposed method. Results show that the proposed method has the feasibility in solving heterogeneous composition and the broader scalability in different scenarios.
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