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Institution

Beihang University

EducationBeijing, China
About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.


Papers
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Journal ArticleDOI
TL;DR: A comprehensive survey of algorithms proposed for binary neural networks, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error are presented.

346 citations

Journal ArticleDOI
TL;DR: A novel overall distribution MPPT algorithm to rapidly search the area near the global maximum power points, which is further integrated with the particle swarm optimization (PSO) MPPT algorithms to improve the accuracy of MPPT.
Abstract: Solar photovoltaic (PV) systems under partial shading conditions (PSCs) have a nonmonotonic P – V characteristic with multiple local maximum power points, which makes the existing maximum power point tracking (MPPT) algorithms unsatisfactory performance for global MPPT, if not invalid. This paper proposes a novel overall distribution (OD) MPPT algorithm to rapidly search the area near the global maximum power points, which is further integrated with the particle swarm optimization (PSO) MPPT algorithm to improve the accuracy of MPPT. Through simulations and experimentations, the higher effectiveness and accuracy of the proposed OD-PSO MPPT algorithm in solar PV systems is demonstrated in comparison to two existing artificial intelligence MPPT algorithms.

345 citations

Journal ArticleDOI
TL;DR: This work offers a simple and viable option of HTL modification to realize highly efficient OSCs by mixing WOx nanoparticles with a poly(3,4-ethylenedioxythiophene):polystyrene sulfonate emulsion, the surface free energy of the HTL is improved and the morphology of the active layer is optimized.
Abstract: With rapid development for tens of years, organic solar cells (OSCs) have attracted much attention for their potential in practical applications. As an important photovoltaic parameter, the fill factor (FF) of OSCs stands for the effectiveness of charge generation and collection, which significantly depends on the properties of the interlayer and active layer. Here, a facile and effective strategy to improve the FF through hole-transporting layer (HTL) modification is demonstrated. By mixing WOx nanoparticles with a poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) emulsion, the surface free energy of the HTL is improved and the morphology of the active layer is optimized. Benefiting from increased carrier lifetime, a device based on WOx :PEDOT:PSS HTL exhibits a boosted performance with an FF of 80.79% and power conversion efficiency of 14.57% PCE. The results are certified by the National Institute of Metrology (NIM), which, to date, are the highest values in this field with certification. This work offers a simple and viable option of HTL modification to realize highly efficient OSCs.

345 citations

Journal ArticleDOI
TL;DR: This technical note investigates consensus problems of a class of second-order continuous-time multi-agent systems with time-delay and jointly-connected topologies and derives a sufficient condition in terms of linear matrix inequalities (LMIs) for average consensus of the system.
Abstract: This technical note investigates consensus problems of a class of second-order continuous-time multi-agent systems with time-delay and jointly-connected topologies. We first introduce a neighbor-based linear protocol with time-delay. Then we derive a sufficient condition in terms of linear matrix inequalities (LMIs) for average consensus of the system. Furthermore, we discuss the case where the time-delay affects only the information that is being transmitted and show that consensus can be reached with arbitrary bounded time-delay. Finally, simulation results are provided to demonstrate the effectiveness of our theoretical results.

344 citations

Journal ArticleDOI
TL;DR: In this paper, a combination of Gaussian process (GP) combined with a numerical weather prediction (NWP) model was applied to wind power forecasting up to one day ahead, and the proposed model provided around 9% to 14% improvement in forecasting accuracy compared to an artificial neural network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data.
Abstract: Since wind at the earth's surface has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safe and economic use of wind energy. In this paper, we investigated a combination of numeric and probabilistic models: a Gaussian process (GP) combined with a numerical weather prediction (NWP) model was applied to wind-power forecasting up to one day ahead. First, the wind-speed data from NWP was corrected by a GP, then, as there is always a defined limit on power generated in a wind turbine due to the turbine controlling strategy, wind power forecasts were realized by modeling the relationship between the corrected wind speed and power output using a censored GP. To validate the proposed approach, three real-world datasets were used for model training and testing. The empirical results were compared with several classical wind forecast models, and based on the mean absolute error (MAE), the proposed model provides around 9% to 14% improvement in forecasting accuracy compared to an artificial neural network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data.

343 citations


Authors

Showing all 67500 results

NameH-indexPapersCitations
Yi Chen2174342293080
H. S. Chen1792401178529
Alan J. Heeger171913147492
Lei Jiang1702244135205
Wei Li1581855124748
Shu-Hong Yu14479970853
Jian Zhou128300791402
Chao Zhang127311984711
Igor Katkov12597271845
Tao Zhang123277283866
Nicholas A. Kotov12357455210
Shi Xue Dou122202874031
Li Yuan12194867074
Robert O. Ritchie12065954692
Haiyan Wang119167486091
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
2023205
20221,178
20216,767
20206,916
20197,080