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2,914 results on '"Wang, Yanjun"'

101. From Aircraft Tracking Data to Network Delay Model: A Data-Driven Approach Considering En-Route Congestion

Author

Lin, Yu, Li, Lishuai, Ren, Pan, Wang, Yanjun, and Szeto, W. Y.

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

En-route congestion causes delays in air traffic networks and will become more prominent as air traffic demand will continue to increase yet airspace volume cannot grow. However, most existing studies on flight delay modeling do not consider en-route congestion explicitly. In this study, we propose a new flight delay model, Multi-layer Air Traffic Network Delay (MATND) model, to capture the impact of en-route congestion on flight delays over an air traffic network. This model is developed by a data-driven approach, taking aircraft tracking data and flight schedules as inputs to characterize a national air traffic network, as well as a system-level model approach, modeling the delay process based on queueing theory. The two approaches combined make the network delay model a close representation of reality and easy-to-implement for what-if scenario analysis. The proposed MATND model includes 1) a data-driven method to learn a network composed of airports, en-route congestion points, and air corridors from aircraft tracking data, 2) a stochastic and dynamic queuing network model to calculate flight delays and track their propagation at both airports and in en-route congestion areas, in which the delays are computed via a space-time decomposition method. Using one month of historical aircraft tracking data over China's air traffic network, MATND is tested and shows to give an accurate quantification of delays of the national air traffic network. "What-if" scenario analyses are conducted to demonstrate how the proposed model can be used for the evaluation of air traffic network improvement strategies, where the manipulation of reality at such a scale is impossible. Results show that MATND is computationally efficient, well suited for evaluating the impact of policy alternatives on system-wide delay at a macroscopic level., Comment: 57 pages, 40 figures

Published
2020

102. An Incremental Clustering Method for Anomaly Detection in Flight Data

Author

Zhao, Weizun, Li, Lishuai, Alam, Sameer, and Wang, Yanjun

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Safety is a top priority for civil aviation. New anomaly detection methods, primarily clustering methods, have been developed to monitor pilot operations and detect any risks from such flight data. However, all existing anomaly detection methods are offlline learning - the models are trained once using historical data and used for all future predictions. In practice, new flight data are accumulated continuously and analyzed every month at airlines. Clustering such dynamically growing data is challenging for an offlline method because it is memory and time intensive to re-train the model every time new data come in. If the model is not re-trained, false alarms or missed detections may increase since the model cannot reflect changes in data patterns. To address this problem, we propose a novel incremental anomaly detection method based on Gaussian Mixture Model (GMM) to identify common patterns and detect outliers in flight operations from digital flight data. It is a probabilistic clustering model of flight operations that can incrementally update its clusters based on new data rather than to re-cluster all data from scratch. It trains an initial GMM model based on historical offlline data. Then, it continuously adapts to new incoming data points via an expectation-maximization (EM) algorithm. To track changes in flight operation patterns, only model parameters need to be saved. The proposed method was tested on three sets of simulation data and two sets of real-world flight data. Compared with the traditional offline GMM method, the proposed method can generate similar clustering results with significantly reduced processing time (57 % - 99 % time reduction in testing sets) and memory usage (91 % - 95 % memory usage reduction in testing sets). Preliminary results indicate that the incremental learning scheme is effective in dealing with dynamically growing data in flight data analytics.

Published
2020
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108. Fotagliptin monotherapy with alogliptin as an active comparator in patients with uncontrolled type 2 diabetes mellitus: a randomized, multicenter, double-blind, placebo-controlled, phase 3 trial

Author

Xu, Mingtong, Sun, Kan, Xu, Wenjie, Wang, Chuan, Yan, Dewen, Li, Shu, Cong, Li, Pi, Yinzhen, Song, Weihong, Sun, Qingyuan, Xiao, Rijun, Peng, Weixia, Wang, Jianping, Peng, Hui, Zhang, Yawei, Duan, Peng, Zhang, Meiying, Liu, Jianying, Huang, Qingmei, Li, Xuefeng, Bao, Yan, Zeng, Tianshu, Wang, Kun, Qin, Li, Wu, Chaoming, Deng, Chunying, Huang, Chenghu, Yan, Shuang, Zhang, Wei, Li, Meizi, Sun, Li, Wang, Yanjun, Li, HongMei, Wang, Guang, Pang, Shuguang, Zheng, Xianling, Wang, Haifang, Wang, Fujun, Su, Xiuhai, Ma, Yujin, Zhang, Wei, Li, Ziling, Xie, Zuoling, Xu, Ning, Ni, Lin, Zhang, Li, Deng, Xiangqun, Pan, Tianrong, Dong, Qijuan, Wu, Xiaohong, Shen, Xingping, Zhang, Xin, Zou, Qijing, Jiang, Chengxia, Xi, Jue, Ma, Jianhua, Sun, Jingchao, and Yan, Li

Published
2023
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118. Mobile-Assisted Language Learning in Chinese Higher Education Context: A Systematic Review from the Perspective of the Situated Learning Theory

Author

Li, Fan, Fan, Si, and Wang, Yanjun

Abstract

Recent years have witnessed numerous systematic investigations on mobile-assisted language learning (MALL). However, very few research synthesis studies focused on the higher education context in mainland China. This paper provides a systematic review of the findings of 23 studies published between 2015 and 2020 in mainland China. The aim of this review is twofold: 1) to examine prevalent researched elements in extant MALL studies; and 2) to investigate ways in which the two original components of the Situated Learning Theory (SLT), including Legitimate Peripheral Participation and Communities of Practice (Lave & Wenger, 1991), have a bearing on MALL. This systematic review resulted in two major findings. First, five key researched elements were revealed, e.g., target language teaching areas, and adopted applications/software. Second, guided by the two original elements of SLT, four derived elements were identified, namely authenticity, social interactions and collaborations, apprenticeship learning, and beliefs and behaviours. The findings suggest the need to develop sound MALL pedagogies associated with sociocultural aspects of language learning in relevant contexts. This review study also provides insights into how Chinese language professionals and practitioners can improve curriculum design and resource development to adapt to future trends in MALL.

Published
2022
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130. Reasoning About Recursive Tree Traversals

Author

Wang, Yanjun, Liu, Jinwei, Zhang, Dalin, and Qiu, Xiaokang

Subjects
Computer Science - Programming Languages
Abstract

Traversals are commonly seen in tree data structures, and performance-enhancing transformations between tree traversals are critical for many applications. Existing approaches to reasoning about tree traversals and their transformations are ad hoc, with various limitations on the class of traversals they can handle, the granularity of dependence analysis, and the types of possible transformations. We propose Retreet, a framework in which one can describe general recursive tree traversals, precisely represent iterations, schedules and dependences, and automatically check data-race-freeness and transformation correctness. The crux of the framework is a stack-based representation for iterations and an encoding to Monadic Second-Order (MSO) logic over trees. Experiments show that our framework can reason about traversals with sophisticated mutual recursion on real-world data structures such as CSS and cycletrees.

Published
2019

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