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URBAN TRANSIT ASSIGNMENT MODEL BASED ON AUGMENTED NETWORK WITH IN-VEHICLE CONGESTION AND TRANSFER CONGESTION 被引量:8
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作者 Bingfeng SI Ming ZHONG +1 位作者 Xiaobao YANG Ziyou GAO 《Journal of Systems Science and Systems Engineering》 SCIE EI CSCD 2011年第2期155-172,共18页
This paper presents an augmented network model to represent urban transit system.Through such network model,the urban transit assignment problem can be easily modeled like a generalized traffic network.Simultaneously,... This paper presents an augmented network model to represent urban transit system.Through such network model,the urban transit assignment problem can be easily modeled like a generalized traffic network.Simultaneously,the feasible route in such augmented transit network is then defined in accordance with the passengers' behaviors.The passengers' travel costs including walking time,waiting time,in-vehicle time and transfer time are formulated while the congestions at stations and the congestions in transit vehicles are all taken into account.On the base of these,an equilibrium model for urban transit assignment problem is presented and an improved shortest path method based algorithm is also proposed to solve it.Finally,a numerical example is provided to illustrate our approach. 展开更多
关键词 TRANSIT ASSIGNMENT augmented network CONGESTION transfer algorithm
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Knowledge Tracking Model Based on Learning Process 被引量:3
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作者 Yu Zou Xiaodong Yan Wei Li 《Journal of Computer and Communications》 2020年第10期7-17,共11页
<div style="text-align:justify;"> Knowledge tracking model has been a research hotspot in the field of educational data mining for a long time. Knowledge tracking can automatically discover students’ ... <div style="text-align:justify;"> Knowledge tracking model has been a research hotspot in the field of educational data mining for a long time. Knowledge tracking can automatically discover students’ weak knowledge points, which helps to improve students’ self-motivation in learning and realize personalized guidance. The existing KT model has some shortcomings, such as the limitation of the calculation of knowledge growth, and the imperfect forgetting mechanism of the model. To this end, we proposed a new knowledge tracking model based on learning process (LPKT), LPKT applies the idea of Memory Augmented Neural Net-work(MANN).When we model the learning process of students, two additional important factors are considered. One is to consider the current state of knowledge of the students when updating the dynamic matrix of the neural network, and the other is to improve the forgetting mechanism of the model. In this paper we verified the effectiveness and superiority of LPKT through comparative experiments, and proved that the model can improve the effect of knowledge tracking and make the process of deep knowledge tracking easier to understand. </div> 展开更多
关键词 Knowledge Tracking Deep Learning Memory augmented Neural network
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