动态时间序列是许多现代推荐系统的关键特征,主要是为了寻求基于用户最近执行的动作来捕获用户活动的“上下文”,然而大部分基于长短期记忆网络的序列模型只考虑了用户的短期兴趣,忽略了长期兴趣。为提升序列推荐的性能,提出一种基于LST...动态时间序列是许多现代推荐系统的关键特征,主要是为了寻求基于用户最近执行的动作来捕获用户活动的“上下文”,然而大部分基于长短期记忆网络的序列模型只考虑了用户的短期兴趣,忽略了长期兴趣。为提升序列推荐的性能,提出一种基于LSTM的长短期偏好序列推荐方法LLSPRec(Long Short-term Preference Recommendation Based on LSTM)。该方法使用LSTM对用户的时间序列进行建模,聚合了序列之间的相关特征信息,得到用户的近期偏好,通过距离度量学习对用户本身和候选项目距离进行建模,得到用户的长期偏好,并根据用户的意图动态地整合用户的长期和近期偏好,从而准确地描述用户兴趣,提高推荐结果的多样性。展开更多
Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical bus...Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical busy area control airspace,an complexity measurement indicator system is established.We find that operation in area sector is characterized by aggregation and continuity,and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components.Using principle components,discrete features and time series features are constructed.Based on Gaussian kernel function,Euclidean distance and dynamic time warping(DTW)are used to measure the similarity of the features.Then the matrices of similarity are input in Spectral Clustering.The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system.Finally,actual vertical operation decisions for area sector and results of identification are compared,which are visualized by metric multidimensional scaling(MDS)plots.We find that identification results can well reflect the operation at peak hours,but controllers make different decisions under the similar conditions before dawn.The compliance rate of busy operation mode and division decisions at peak hours is 96.7%.The results also show subjectivity of actual operation and objectivity of identification.In most scenes,we observe that similar air traffic activities provide regularity for initiatives,validating the potential of this approach for initiatives and other artificial intelligence support.展开更多
This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is intro...This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.展开更多
The modeling of dynamical systems from a time series implemented by our DSA program introduces binary trees of height D with all leaves on the same level, and the related subtrees of height L 〈 D. These are called e-...The modeling of dynamical systems from a time series implemented by our DSA program introduces binary trees of height D with all leaves on the same level, and the related subtrees of height L 〈 D. These are called e-trees and e-subtrees. The recursive and nonrecursive versions of the traversal algorithms for the trees with dynamically created nodes are discussed. The original nonrecursive algorithms that return the pointer to the next node in preorder, inorder and postorder traversals are presented. The space-time complexity analysis shows and the execution time measurements confirm that for these O（2D） algorithms, the recursive versions have approximately 10-25% better time constants. Still, the use of nonrecursive algorithms may be more appropriate in several occasions.展开更多
文摘动态时间序列是许多现代推荐系统的关键特征,主要是为了寻求基于用户最近执行的动作来捕获用户活动的“上下文”,然而大部分基于长短期记忆网络的序列模型只考虑了用户的短期兴趣,忽略了长期兴趣。为提升序列推荐的性能,提出一种基于LSTM的长短期偏好序列推荐方法LLSPRec(Long Short-term Preference Recommendation Based on LSTM)。该方法使用LSTM对用户的时间序列进行建模,聚合了序列之间的相关特征信息,得到用户的近期偏好,通过距离度量学习对用户本身和候选项目距离进行建模,得到用户的长期偏好,并根据用户的意图动态地整合用户的长期和近期偏好,从而准确地描述用户兴趣,提高推荐结果的多样性。
基金the National Natural Science Foundation of China(Nos.71731001,61573181,71971114)the Fundamental Research Funds for the Central Universities(No.NS2020045)。
文摘Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical busy area control airspace,an complexity measurement indicator system is established.We find that operation in area sector is characterized by aggregation and continuity,and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components.Using principle components,discrete features and time series features are constructed.Based on Gaussian kernel function,Euclidean distance and dynamic time warping(DTW)are used to measure the similarity of the features.Then the matrices of similarity are input in Spectral Clustering.The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system.Finally,actual vertical operation decisions for area sector and results of identification are compared,which are visualized by metric multidimensional scaling(MDS)plots.We find that identification results can well reflect the operation at peak hours,but controllers make different decisions under the similar conditions before dawn.The compliance rate of busy operation mode and division decisions at peak hours is 96.7%.The results also show subjectivity of actual operation and objectivity of identification.In most scenes,we observe that similar air traffic activities provide regularity for initiatives,validating the potential of this approach for initiatives and other artificial intelligence support.
基金Supported by the Shandong Natural Science Foundation(ZR2013BL008)
文摘This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.
文摘The modeling of dynamical systems from a time series implemented by our DSA program introduces binary trees of height D with all leaves on the same level, and the related subtrees of height L 〈 D. These are called e-trees and e-subtrees. The recursive and nonrecursive versions of the traversal algorithms for the trees with dynamically created nodes are discussed. The original nonrecursive algorithms that return the pointer to the next node in preorder, inorder and postorder traversals are presented. The space-time complexity analysis shows and the execution time measurements confirm that for these O（2D） algorithms, the recursive versions have approximately 10-25% better time constants. Still, the use of nonrecursive algorithms may be more appropriate in several occasions.
