Traffic flow forecasting is an important part of elevator group control system (EGCS).This paper applies time series prediction theories based on neural networks(NN) to EGCSs traffic analysis,and establishes a time se...Traffic flow forecasting is an important part of elevator group control system (EGCS).This paper applies time series prediction theories based on neural networks(NN) to EGCSs traffic analysis,and establishes a time series NN traffic flow forecasting model.Simulation results show its validity.展开更多
In this paper, a distributed control strategy is proposed to make a complex dynamical network achieve cluster synchronization, which means that nodes in the same group achieve the same synchronization state, while nod...In this paper, a distributed control strategy is proposed to make a complex dynamical network achieve cluster synchronization, which means that nodes in the same group achieve the same synchronization state, while nodes in different groups achieve different synchronization states. The local and global stability of the cluster synchronization state are analyzed. Moreover, simulation results verify the effectiveness of the new approach.展开更多
A new chaotic particle swarm algorithm is proposed in order to avoid the premature convergence of the particle swarm optimization and the shortcomings of the chaotic optimization, such as slow searching speed and low ...A new chaotic particle swarm algorithm is proposed in order to avoid the premature convergence of the particle swarm optimization and the shortcomings of the chaotic optimization, such as slow searching speed and low accuracy when used in the multivariable systems or in large search space. The new algorithm combines the particle swarm algorithm and the chaotic optimization, using randomness and ergodicity of chaos to overcome the premature convergence of the particle swarm optimization. At the same time, a new neural network feedback linearization control system is built to control the single-machine infinite-bus system. The network parameters are trained by the chaos particle swarm algorithm, which makes the control achieve optimization and the control law of prime mover output torque obtained. Finally, numerical simulation and practical application validate the effectiveness of the method.展开更多
The spatiotemporal relationships in high-resolution during odontogenesis remain poorly understood.We report a cell lineage and atlas of developing mouse teeth.We performed a large-scale(92,688 cells)single cell RNA se...The spatiotemporal relationships in high-resolution during odontogenesis remain poorly understood.We report a cell lineage and atlas of developing mouse teeth.We performed a large-scale(92,688 cells)single cell RNA sequencing,tracing the cell trajectories during odontogenesis from embryonic days 10.5 to 16.5.Combined with an assay for transposase-accessible chromatin with high-throughput sequencing,our results suggest that mesenchymal cells show the specific transcriptome profiles to distinguish the tooth types.Subsequently,we identified key gene regulatory networks in teeth and bone formation and uncovered spatiotemporal patterns of odontogenic mesenchymal cells.CD24^(+)and Plac8^(+)cells from the mesenchyme at the bell stage were distributed in the upper half and preodontoblast layer of the dental papilla,respectively,which could individually induce nonodontogenic epithelia to form tooth-like structures.Specifically,the Plac8^(+)tissue we discovered is the smallest piece with the most homogenous cells that could induce tooth regeneration to date.Our work reveals previously unknown heterogeneity and spatiotemporal patterns of tooth germs that may lead to tooth regeneration for regenerative dentistry.展开更多
文摘Traffic flow forecasting is an important part of elevator group control system (EGCS).This paper applies time series prediction theories based on neural networks(NN) to EGCSs traffic analysis,and establishes a time series NN traffic flow forecasting model.Simulation results show its validity.
基金supported by the Natural Science Foundation of Hohai University under Grant No.2008429211
文摘In this paper, a distributed control strategy is proposed to make a complex dynamical network achieve cluster synchronization, which means that nodes in the same group achieve the same synchronization state, while nodes in different groups achieve different synchronization states. The local and global stability of the cluster synchronization state are analyzed. Moreover, simulation results verify the effectiveness of the new approach.
基金This work is supported by National Natural Science Foundation of China （50776005）.
文摘A new chaotic particle swarm algorithm is proposed in order to avoid the premature convergence of the particle swarm optimization and the shortcomings of the chaotic optimization, such as slow searching speed and low accuracy when used in the multivariable systems or in large search space. The new algorithm combines the particle swarm algorithm and the chaotic optimization, using randomness and ergodicity of chaos to overcome the premature convergence of the particle swarm optimization. At the same time, a new neural network feedback linearization control system is built to control the single-machine infinite-bus system. The network parameters are trained by the chaos particle swarm algorithm, which makes the control achieve optimization and the control law of prime mover output torque obtained. Finally, numerical simulation and practical application validate the effectiveness of the method.
基金supported by the National Key Research and Development Program of China Stem Cell and Translational Research,China(2017YFA0104800)the Research Funds from Health@InnoHK Program launched by Innovation Technology Commission of the Hong Kong SAR,China+4 种基金National Natural Science Foundation of China(81570944 and 92068201)Science and Technology Planning Project of Guangdong Province,China(2020B1212060052)High-level Hospital Construction Project(DFJHBF202110)Youth Innovation Promotion of the Chinese Academy of Sciences(2019348)Guangzhou Key Medical Disciplines(2021–2023)。
文摘The spatiotemporal relationships in high-resolution during odontogenesis remain poorly understood.We report a cell lineage and atlas of developing mouse teeth.We performed a large-scale(92,688 cells)single cell RNA sequencing,tracing the cell trajectories during odontogenesis from embryonic days 10.5 to 16.5.Combined with an assay for transposase-accessible chromatin with high-throughput sequencing,our results suggest that mesenchymal cells show the specific transcriptome profiles to distinguish the tooth types.Subsequently,we identified key gene regulatory networks in teeth and bone formation and uncovered spatiotemporal patterns of odontogenic mesenchymal cells.CD24^(+)and Plac8^(+)cells from the mesenchyme at the bell stage were distributed in the upper half and preodontoblast layer of the dental papilla,respectively,which could individually induce nonodontogenic epithelia to form tooth-like structures.Specifically,the Plac8^(+)tissue we discovered is the smallest piece with the most homogenous cells that could induce tooth regeneration to date.Our work reveals previously unknown heterogeneity and spatiotemporal patterns of tooth germs that may lead to tooth regeneration for regenerative dentistry.
