83 Papers
114 Citations
Hongchun Qu is an academic researcher from Chongqing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 10, co-authored 46 publications. Previous affiliations of Hongchun Qu include University of Maine & Iowa State University.
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Papers
Hesperidin ameliorates behavioral impairments and neuropathology of transgenic APP/PS1 mice
TL;DR: Hesperidin, a flavanone glycoside found abundantly in citrus fruits, was orally given to 5-month-old transgenic APP/PS1 mice, a mouse model of cerebral amyloidosis for Alzheimer's disease, and significantly restored deficits in non-cognitive nesting ability and social interaction.
86
Output feedback predictive control of interval type-2 T-S fuzzy systems with Markovian packet loss
Xiaoming Tang,Li Deng,Jimin Yu,Hongchun Qu +3 more
- 09 Nov 2017
TL;DR: A new technique for refreshing the estimation error bound, which plays the key role of guaranteeing the recursive feasibility of optimization problem, is provided in this paper.
50
Simulation-based modeling of wild blueberry pollination
Hongchun Qu,Frank Drummond +1 more
TL;DR: A spatially-explicit agent-based simulation model is presented that enables exploration of how various factors, including plant spatial arrangements, outcrossing and self-pollination, bee species compositions and weather conditions, in isolation and combination, affect pollination efficiency throughout a blueberry bloom season.
49
Supervised discriminant Isomap with maximum margin graph regularization for dimensionality reduction
TL;DR: A novel dimensionality reduction method called supervised discriminant Isomap is proposed to solve the first two problems mentioned above and can capture more discriminative information from raw data than other isomap based methods.
37
A Lightweight Intrusion Detection Method Based on Fuzzy Clustering Algorithm for Wireless Sensor Networks
TL;DR: The results showed that the intrusion detection method in this paper could effectively identify whether the abnormal data came from a network attack or just a noise and extra energy consumption can be avoided in all sensor monitoring nodes of the sensor network where the method has been deployed.