With the development of space exploration and space environment measurements,the numerous observations of solar,solar wind,and near Earth space environment have been obtained in last 20 years.The accumulation of multi...With the development of space exploration and space environment measurements,the numerous observations of solar,solar wind,and near Earth space environment have been obtained in last 20 years.The accumulation of multiple data makes it possible to better use machine learning technique,which has achieved unforeseen results in industrial applications in last decades,for developing new approaches and models in space weather investigation and prediction.In this paper,the efforts on the forecasting methods for space weather indices,events,and parameters using machine learning are briefly introduced based on the study works in recent years.These investigations indicate that machine learning,especially deep learning technique can be used in automatic characteristic identification,solar eruption prediction,space weather forecasting for solar and geomagnetic indices,and modeling of space environment parameters.展开更多
The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with...The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.展开更多
Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast products.With the use of natural languag...Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast products.With the use of natural language generation methods based on the sequence-to-sequence model,space weather forecast texts can be automatically generated.To conduct our generation tasks at a fine-grained level,a taxonomy of space weather phenomena based on descriptions is presented.Then,our MDH(Multi-Domain Hybrid)model is proposed for generating space weather summaries in two stages.This model is composed of three sequence-to-sequence-based deep neural network sub-models(one Bidirectional Auto-Regressive Transformers pre-trained model and two Transformer models).Then,to evaluate how well MDH performs,quality evaluation metrics based on two prevalent automatic metrics and our innovative human metric are presented.The comprehensive scores of the three summaries generating tasks on testing datasets are 70.87,93.50,and 92.69,respectively.The results suggest that MDH can generate space weather summaries with high accuracy and coherence,as well as suitable length,which can assist forecasters in generating high-quality space weather forecast products,despite the data being starved.展开更多
L2 teaching and learning is a way of using language,but it happens in a particular space—the classroom space,which,to some extent,has a restriction to language using.This paper provides a valuable sight into L2 teach...L2 teaching and learning is a way of using language,but it happens in a particular space—the classroom space,which,to some extent,has a restriction to language using.This paper provides a valuable sight into L2 teaching and learning in the classroom space,and discusses the viewpoint of how to make an actual learning of L2 under the way of teaching.展开更多
The libration control problem of space tether system(STS)for post-capture of payload is studied.The process of payload capture will cause tether swing and deviation from the nominal position,resulting in the failure o...The libration control problem of space tether system(STS)for post-capture of payload is studied.The process of payload capture will cause tether swing and deviation from the nominal position,resulting in the failure of capture mission.Due to unknown inertial parameters after capturing the payload,an adaptive optimal control based on policy iteration is developed to stabilize the uncertain dynamic system in the post-capture phase.By introducing integral reinforcement learning(IRL)scheme,the algebraic Riccati equation(ARE)can be online solved without known dynamics.To avoid computational burden from iteration equations,the online implementation of policy iteration algorithm is provided by the least-squares solution method.Finally,the effectiveness of the algorithm is validated by numerical simulations.展开更多
Complicated electromagnetic environments of the space situational awareness facilities(i.e.,satellite navigation systems,radar)would significantly impact normal operations.Effective monitoring and the corresponding di...Complicated electromagnetic environments of the space situational awareness facilities(i.e.,satellite navigation systems,radar)would significantly impact normal operations.Effective monitoring and the corresponding diagnosis of the jamming signals are essential to normal opera-tions and the innovations in anti-jamming equipment.This paper demonstrates a comprehensive survey on jamming monitoring algorithms and applications.The methods in dealing with jamming signals are summarized primarily.Subsequently,the jamming detection,identification,and direc-tion finding techniques are addressed separately.Based on the established studies,we also provide some potential trends of the demonstrated jamming monitoring issues.展开更多
图像压缩感知能从低采样观测中重建出高质量图像。将深度学习应用于图像压缩感知,可显著提高图像重建质量。然而,基于深度学习的图像压缩感知方法存在模型可解释性差、结构盲目设计而影响重建性能的问题。针对这些问题,提出了一种基于...图像压缩感知能从低采样观测中重建出高质量图像。将深度学习应用于图像压缩感知,可显著提高图像重建质量。然而,基于深度学习的图像压缩感知方法存在模型可解释性差、结构盲目设计而影响重建性能的问题。针对这些问题,提出了一种基于零值域分解的深度图像压缩感知方法(range-null space decomposition based deep image compressive sensing network,RND-Net)。该方法通过全局卷积采样的方式稀疏感知图像的特征信息,通过学习信号相关的采样矩阵,使采样值包含更丰富的图像特征,且相较一般的逐块采样方式,在全局层面上的采样可明显减少块状伪影;基于零值域分解的数学表示,将采样与重建过程转化为端到端深度学习模型,借助深度神经网络拟合所涉及的线性或非线性运算,相比传统方法缩短了模型推理时间,提升了图像重建能力。上述将数学先验知识有效融入数据驱动的方法称为协同驱动,既充分利用了数学先验知识,强化了模型的可解释性,使模型结构更易于设计,又发挥了以深度学习为代表的数据驱动方法的自主寻优能力,相比其他深度压缩感知方法更易于获得全局最优解。在多个测试集上的实验证明,RND-Net与目前图像重建能力较好的算法相比显著提升了图像重建质量,减少了单幅图像重建时间。当采样率为0.1、测试集为BSDS68时,RND-Net比AutoBCS在峰值信噪比(PSNR)上平均高1.02 dB。在测试集Set14上,RND-Net对于混合驱动的GPX-ADMM-Net的平均PSNR和结构相似性指数(SSIM)增益分别为1.15dB和0.0518;重建单幅图像时,RND-Net比GPX-ADMM-Net快约0.1049 s。展开更多
基金Supported by National Natural Science Foundation of China(41574181)。
文摘With the development of space exploration and space environment measurements,the numerous observations of solar,solar wind,and near Earth space environment have been obtained in last 20 years.The accumulation of multiple data makes it possible to better use machine learning technique,which has achieved unforeseen results in industrial applications in last decades,for developing new approaches and models in space weather investigation and prediction.In this paper,the efforts on the forecasting methods for space weather indices,events,and parameters using machine learning are briefly introduced based on the study works in recent years.These investigations indicate that machine learning,especially deep learning technique can be used in automatic characteristic identification,solar eruption prediction,space weather forecasting for solar and geomagnetic indices,and modeling of space environment parameters.
基金Project supported by the National Key Research and Development Program of China(Grant No.2023YFF1204402)the National Natural Science Foundation of China(Grant Nos.12074079 and 12374208)+1 种基金the Natural Science Foundation of Shanghai(Grant No.22ZR1406800)the China Postdoctoral Science Foundation(Grant No.2022M720815).
文摘The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.
基金Supported by the Key Research Program of the Chinese Academy of Sciences(ZDRE-KT-2021-3)。
文摘Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast products.With the use of natural language generation methods based on the sequence-to-sequence model,space weather forecast texts can be automatically generated.To conduct our generation tasks at a fine-grained level,a taxonomy of space weather phenomena based on descriptions is presented.Then,our MDH(Multi-Domain Hybrid)model is proposed for generating space weather summaries in two stages.This model is composed of three sequence-to-sequence-based deep neural network sub-models(one Bidirectional Auto-Regressive Transformers pre-trained model and two Transformer models).Then,to evaluate how well MDH performs,quality evaluation metrics based on two prevalent automatic metrics and our innovative human metric are presented.The comprehensive scores of the three summaries generating tasks on testing datasets are 70.87,93.50,and 92.69,respectively.The results suggest that MDH can generate space weather summaries with high accuracy and coherence,as well as suitable length,which can assist forecasters in generating high-quality space weather forecast products,despite the data being starved.
基金a part of the project,"The Research of the New Type of College English Teaching Group"(No.Y-B/2011/04),supported by 2011"12.5"Program of Jiansu Education Science Research~~
文摘L2 teaching and learning is a way of using language,but it happens in a particular space—the classroom space,which,to some extent,has a restriction to language using.This paper provides a valuable sight into L2 teaching and learning in the classroom space,and discusses the viewpoint of how to make an actual learning of L2 under the way of teaching.
基金supported by the National Natural Science Foundation of China(No.62111530051)the Fundamental Research Funds for the Central Universities(No.3102017JC06002)the Shaanxi Science and Technology Program,China(No.2017KW-ZD-04).
文摘The libration control problem of space tether system(STS)for post-capture of payload is studied.The process of payload capture will cause tether swing and deviation from the nominal position,resulting in the failure of capture mission.Due to unknown inertial parameters after capturing the payload,an adaptive optimal control based on policy iteration is developed to stabilize the uncertain dynamic system in the post-capture phase.By introducing integral reinforcement learning(IRL)scheme,the algebraic Riccati equation(ARE)can be online solved without known dynamics.To avoid computational burden from iteration equations,the online implementation of policy iteration algorithm is provided by the least-squares solution method.Finally,the effectiveness of the algorithm is validated by numerical simulations.
基金supported by the National Key Research and De-velopment Program of China(2020YFB0505601)。
文摘Complicated electromagnetic environments of the space situational awareness facilities(i.e.,satellite navigation systems,radar)would significantly impact normal operations.Effective monitoring and the corresponding diagnosis of the jamming signals are essential to normal opera-tions and the innovations in anti-jamming equipment.This paper demonstrates a comprehensive survey on jamming monitoring algorithms and applications.The methods in dealing with jamming signals are summarized primarily.Subsequently,the jamming detection,identification,and direc-tion finding techniques are addressed separately.Based on the established studies,we also provide some potential trends of the demonstrated jamming monitoring issues.
文摘图像压缩感知能从低采样观测中重建出高质量图像。将深度学习应用于图像压缩感知,可显著提高图像重建质量。然而,基于深度学习的图像压缩感知方法存在模型可解释性差、结构盲目设计而影响重建性能的问题。针对这些问题,提出了一种基于零值域分解的深度图像压缩感知方法(range-null space decomposition based deep image compressive sensing network,RND-Net)。该方法通过全局卷积采样的方式稀疏感知图像的特征信息,通过学习信号相关的采样矩阵,使采样值包含更丰富的图像特征,且相较一般的逐块采样方式,在全局层面上的采样可明显减少块状伪影;基于零值域分解的数学表示,将采样与重建过程转化为端到端深度学习模型,借助深度神经网络拟合所涉及的线性或非线性运算,相比传统方法缩短了模型推理时间,提升了图像重建能力。上述将数学先验知识有效融入数据驱动的方法称为协同驱动,既充分利用了数学先验知识,强化了模型的可解释性,使模型结构更易于设计,又发挥了以深度学习为代表的数据驱动方法的自主寻优能力,相比其他深度压缩感知方法更易于获得全局最优解。在多个测试集上的实验证明,RND-Net与目前图像重建能力较好的算法相比显著提升了图像重建质量,减少了单幅图像重建时间。当采样率为0.1、测试集为BSDS68时,RND-Net比AutoBCS在峰值信噪比(PSNR)上平均高1.02 dB。在测试集Set14上,RND-Net对于混合驱动的GPX-ADMM-Net的平均PSNR和结构相似性指数(SSIM)增益分别为1.15dB和0.0518;重建单幅图像时,RND-Net比GPX-ADMM-Net快约0.1049 s。
