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Institution

Nanjing Institute of Technology

EducationNanjing, China
About: Nanjing Institute of Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 3989 authors who have published 3722 publications receiving 25436 citations.


Papers
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Journal ArticleDOI
TL;DR: A new tuning method for fractional order proportional and derivative (PD ¿) or FO-PD controller is proposed for a class of typical second-order plants and shows that the closed-loop system can achieve favorable dynamic performance and robustness.
Abstract: In recent years, it is remarkable to see the increasing number of studies related to the theory and application of fractional order controller (FOC), specially PI ? D ? controller, in many areas of science and engineering. Research activities are focused on developing new analysis and design methods for fractional order controllers as an extension of classical control theory. In this paper, a new tuning method for fractional order proportional and derivative (PD ?) or FO-PD controller is proposed for a class of typical second-order plants. The tuned FO-PD controller can ensure that the given gain crossover frequency and phase margin are fulfilled, and furthermore, the phase derivative w. r. t. the frequency is zero, i.e., the phase Bode plot is flat at the given gain crossover frequency. Consequently, the closed-loop system is robust to gain variations. The FOC design method proposed in the paper is practical and simple to apply. Simulation and experimental results show that the closed-loop system can achieve favorable dynamic performance and robustness.

378 citations

Journal ArticleDOI
15 Jun 2013-Energy
TL;DR: In this paper, a feasibility study of an autonomous hybrid wind/photovoltaics (PV)/battery power system for a household in Urumqi, China, has been carried out using Hybrid Optimization Model for Electric Renewables (HOMER) simulation software.

247 citations

Journal ArticleDOI
TL;DR: A new perceptual image quality assessment (IQA) metric based on the human visual system (HVS) is proposed that performs efficiently with convolution operations at multiscales, gradient magnitude, and color information similarity, and a perceptual-based pooling.
Abstract: A fast reliable computational quality predictor is eagerly desired in practical image/video applications, such as serving for the quality monitoring of real-time coding and transcoding. In this paper, we propose a new perceptual image quality assessment (IQA) metric based on the human visual system (HVS). The proposed IQA model performs efficiently with convolution operations at multiscales, gradient magnitude, and color information similarity, and a perceptual-based pooling. Extensive experiments are conducted using four popular large-size image databases and two multiply distorted image databases, and results validate the superiority of our approach over modern IQA measures in efficiency and efficacy. Our metric is built on the theoretical support of the HVS with lately designed IQA methods as special cases.

218 citations

Journal ArticleDOI
TL;DR: A positive relationship between carbon emissions and financial development as well as a gross domestic product is revealed and the promotion of technological innovation and the use of renewable energy consumption will help in achieving the goals set by COP21.

211 citations

Journal ArticleDOI
TL;DR: In this paper, an experimental study on the melting behavior of phase change material (PCM) in metal foams has been carried out at the pore scale, where aluminum foams were embedded to enhance the heat transfer.

208 citations


Authors

Showing all 4027 results

NameH-indexPapersCitations
Li Chen105173255996
Wei Xing Zheng7474618979
Xiaobo Zhang5933212851
Ming Cheng5866513936
Jun Wu5318912007
Mengyin Fu412386284
Su Guo401108010
Xiaoli Wang391844708
Song-Nam Hong302273324
Namyoon Lee28772907
Jian-yu Lu281033265
Feng Xue251792006
Liwen Mu25841737
Michael Mao Wang241752218
Xiang Yu23632403
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202330
202254
2021535
2020559
2019441
2018282