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Rong Qu

Researcher at University of Nottingham

Publications -  294
Citations -  8834

Rong Qu is an academic researcher from University of Nottingham. The author has contributed to research in topics: Contextual image classification & Heuristics. The author has an hindex of 43, co-authored 282 publications receiving 7277 citations. Previous affiliations of Rong Qu include Queen's University Belfast & Information Technology University.

Papers
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Book ChapterDOI

Investigating a Hybrid Metaheuristic For Job Shop Rescheduling

TL;DR: In this article, a genetic algorithm was used to develop building blocks (antibodies) of partial schedules that can be used to construct backup solutions (antigens) when disturbances occur during production.
Journal ArticleDOI

An ACO for Energy-Efficient and Traffic-Aware Virtual Machine Placement in Cloud Computing

TL;DR: In this paper, an energy and traffic-aware ant colony optimization (ETA-ACO) algorithm is proposed to address the virtual machine placement problem, where the total power consumption of physical machines (PMs) and switches and the total network bandwidth resource consumption among VMs are jointly minimized.
Patent

Polarizing synthetic aperture radar (SAR) image change detection method based on deep curvelet differential deep stack network (DSN)

TL;DR: Zhang et al. as mentioned in this paper proposed a polarizing SAR image change detection method based on a deep curvelet differential DSN, which mainly solves the problems that the polarising SAR image image detection in a conventional method does not consider the proper multi-scale characteristics of the polarifying SAR images, and the detection precision is not high.
Patent

Polarimetric SAR image target detection method based on NSCT stepped net model

TL;DR: In this article, a polarimetric SAR image image target detection method based on an NSCT (Non Sub-sampled Contourlet Transform) stepped net model is proposed.
Journal ArticleDOI

Community evolution prediction based on a self-adaptive timeframe in social networks

TL;DR: In this paper , a new community evolution model is developed from the perspective of the universality of the timeframe, and a new optimized timeframe partitioning algorithm is proposed, which adaptively adjusts the size and number of time windows according to the information fluctuations of the specific network at an acceptable extra computational cost.