L
Liang Zhao
Researcher at Central China Normal University
Publications - 54
Citations - 1629
Liang Zhao is an academic researcher from Central China Normal University. The author has contributed to research in topics: Computer science & Fourier transform. The author has an hindex of 10, co-authored 40 publications receiving 768 citations. Previous affiliations of Liang Zhao include Nanjing University of Science and Technology & Centre College.
Papers
More filters
Journal ArticleDOI
Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey
TL;DR: In this article, the authors investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling.
Posted Content
Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey
TL;DR: In this article, the authors investigated the research development, current trends and intellectual structure of topic modeling based on Latent Dirichlet Allocation (LDA), and summarized challenges and introduced famous tools and datasets in topic modelling based on LDA.
Journal ArticleDOI
Cross-layer congestion control of wireless sensor networks based on fuzzy sliding mode control
TL;DR: A fuzzy sliding mode congestion control algorithm (FSMC) is presented, which adaptively regulates the queue length of buffer in congested nodes and significantly reduces the impact of external uncertain disturbance and has good performance, such as rapid convergence, lower average delay, less packet loss ratio and higher throughput.
Journal ArticleDOI
Unitary discrete linear canonical transform: analysis and application
TL;DR: A sufficient condition on the sampling rates chosen in the discretization to ensure unitarity is presented, and a proof of the existence of all of the unitary matrices is offered.
Journal ArticleDOI
Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums
TL;DR: An unsupervised model, namely temporal emotion-aspect model (TEAM), modeling time jointly with emotions and aspects to capture emotion- aspect evolutions over time is presented, indicating that content-related aspects were the main focus with higher probabilities to negative and confused emotions.