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| 1 | The FGOALS climate system model as a modeling tool for supporting climate sciences:An overview显示文摘Climate system models are useful tools for understanding the interactions among the components of the climate system and predicting/projecting future climate change. The development of climate models has been a central focus of the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences(LASG/IAP) since the establishment of the laboratory in 1985. In China, many pioneering component models and fully coupled models of the climate system have been developed by LASG/IAP. The fully coupled climate system developed in the recent decade is named FGOALS(Flexible Global Ocean-Atmosphere-Land System Model). In this paper, an application-oriented review of the LASG/IAP FGOALS model is presented. The improved model performances are demonstrated in the context of cloud-radiation processes, Asian monsoon, ENSO phenomena, Atlantic Meridional Overturning Circulation(AMOC) and sea ice. The FGOALS model has contributed to both CMIP5(Coupled Model Intercomparison Project-phase 5) and IPCC(Intergovernmental Panel on Climate Change) AR5(the Fifth Assessment Report). The release of FGOALS data has supported the publication of nearly 500 papers around the world. The results of FGOALS are cited ~106 times in the IPCC WG1(Working Group 1) AR5. In addition to the traditional long-term simulations and projections, near-term decadal climate prediction is a new set of CMIP experiment, progress of LAGS/IAP in the development of nearterm decadal prediction system is reviewed. The FGOALS model has supported many Chinese national-level research projects and contributed to the national climate change assessment report. The crucial role of FGOALS as a modeling tool for supporting climate sciences is highlighted by demonstrating the model's performances in the simulation of the evolution of Earth's climate from the past to the future. | TianJun Zhou Bin Wang YongQiang Yu YiMin Liu WeiPeng Zheng LiJuan Li Bo Wu PengFei Lin Zhun Guo WenMin Man Qing Bao AnMin Duan HaiLong Liu XiaoLong Chen Bian He JianDong Li LiWei Zou XiaoCong Wang LiXia Zhang Yong Sun WenXia Zhang | 2018 | Earth and Planetary Physics2018，2，4： | 6 |
| 2 | Multi-Residual Module Stacked Hourglass Networks for Human Pose Estimation显示文摘A multi-residual module stacked hourglass network(MRSH)was proposed to improve the accuracy and robustness of human body pose estimation.The network uses multiple hourglass sub-networks and three new residual modules.In the hourglass sub-network,the large receptive field residual module(LRFRM)and the multi-scale residual module(MSRM)are first used to learn the spatial relationship between features and body parts at various scales.Only the improved residual module(IRM)is used when the resolution is minimized.The final network uses four stacked hourglass sub-networks,with intermediate supervision at the end of each hourglass,repeating high-low(from high resolution to low resolution)and low-high(from low resolution to high resolution)learning.The network was tested on the public datasets of Leeds sports poses(LSP)and MPII human pose.The experimental results show that the proposed network has better performance in human pose estimation. | Wenxia Bao Yaping Yang Dong Liang Ming Zhu | 2020 | Journal of Beijing Institute of Technology2020，29，1： | 6 |
| 3 | Recognition of Group Activities Using Complex Wavelet Domain Based Cayley-Klein Metric Learning显示文摘A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning.Non-sampled dual-tree complex wavelet packet transform(NS-DTCWPT)is used to decompose the human images in videos into multi-scale and multi-resolution.An improved local binary pattern(ILBP)and an inner-distance shape context(IDSC)combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient features.The extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization problem.The group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric learning.Experimental results on behave video set,group activity video set,and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms. | Gensheng Hu Min Li Dong Liang Mingzhu Wan Wenxia Bao | 2018 | Journal of Beijing Institute of Technology2018，27，4： | 0 |
| 4 | Method for wheat ear counting based on frequency domain decomposition of MSVF-ISCT显示文摘Wheat ear counting is a prerequisite for the evaluation of wheat yield.A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield estimation.The frequency domain decomposition of wheat ear image is completed by multiscale support value filter(MSVF)combined with improved sampled contourlet transform(ISCT).Support Vector Machine(SVM)is the classic classification and regression algorithm of machine learning.MSVF based on this has strong frequency domain filtering and generalization ability,which can effectively remove the complex background,while the multi-direction characteristics of ISCT enable it to represent the contour and texture information of wheat ears.In order to improve the level of wheat yield prediction,MSVF-ISCT method is used to decompose the ear image in multiscale and multi direction in frequency domain,reduce the interference of irrelevant information,and generate the sub-band image with more abundant information components of ear feature information.Then,the ear feature is extracted by morphological operation and maximum entropy threshold segmentation,and the skeleton thinning and corner detection algorithms are used to count the results.The number of wheat ears in the image can be accurately counted.Experiments show that compared with the traditional algorithms based on spatial domain,this method significantly improves the accuracy of wheat ear counting,which can provide guidance and application for the field of agricultural precision yield estimation. | Wenxia Bao Ze Lin Gensheng Hu Dong Liang Linsheng Huang Xin Zhang | 2023 | Information Processing in Agriculture2023，10，2： | 0 |
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