scispace - formally typeset
Search or ask a question
Institution

Northeastern University (China)

EducationShenyang, China
About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).


Papers
More filters
Journal ArticleDOI
Abstract: Composite materials are used in a wide range of applications such as automotive, aerospace and renewable energy industries. But they have not been properly recycled, due to their inherent nature of heterogeneity, in particular for the thermoset-based polymer composites. The current and future waste management and environmental legislations require all engineering materials to be properly recovered and recycled, from end-of-life (EOL) products such as automobiles, wind turbines and aircrafts. Recycling will ultimately lead to resource and energy saving. Various technologies, mostly focusing on reinforcement fibres and yet to be commercialized, have been developed: mechanical recycling, thermal recycling, and chemical recycling. However, lack of adequate markets, high recycling cost, and lower quality of the recyclates are the major commercialization barriers. To promote composites recycling, extensive R&D efforts are still needed on development of ground-breaking better recyclable composites and much more efficient separation technologies. It is believed that through the joint efforts from design, manufacturing, and end-of-life management, new separation and recycling technologies for the composite materials recycling will be available and more easily recyclable composite materials will be developed in the future.

534 citations

Journal ArticleDOI
TL;DR: A novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method and a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method.
Abstract: In this paper, a novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method. In the design of the controller, only available input-output data is required instead of known system dynamics. A data-driven model is established by a recurrent neural network (NN) to reconstruct the unknown system dynamics using available input-output data. By adding a novel adjustable term related to the modeling error, the resultant modeling error is first guaranteed to converge to zero. Then, based on the obtained data-driven model, the ADP method is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Further, a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method. Based on Lyapunov approach, stability analysis of the closed-loop system is performed to show that the proposed controller guarantees the system state asymptotically tracking the desired trajectory. Additionally, the obtained control input is proven to be close to the optimal control input within a small bound. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed control scheme.

530 citations

Journal ArticleDOI
28 May 2009-ACS Nano
TL;DR: This work suggests that, with proper cell-targeting or tumor-homing peptides or proteins conjugated, the NaYF(4):Yb,Er UCNPs can find potential applications in the in vivo imaging, detection, and diagnosis of cancers.
Abstract: Upconversion fluorescent nanoparticles can convert a longer wavelength radiation (eg, near-infrared light) into a shorter wavelength fluorescence (eg, visible light) and thus have emerged as a new class of fluorescent probes for biomedical imaging Rare-earth doped β-NaYF4:Yb,Er upconversion nanoparticles (UCNPs) with strong UC fluorescence were synthesized in this work by using a solvothermal approach The UCNPs were coated with a thin layer of SiO2 to form core−shell nanoparticles via a typical Stober method, which were further modified with amino groups After surface functionalization, the rabbit anti-CEA8 antibodies were covalently linked to the UCNPs to form the antibody−UCNP conjugates The antibody−UCNP conjugates were used as fluorescent biolabels for the detection of carcinoembryonic antigen (CEA), a cancer biomarker expressed on the surface of HeLa cells The successful conjugation of antibody to the UCNPs was found to lead to the specific attachment of the UCNPs onto the surface of the HeL

523 citations

Journal ArticleDOI
TL;DR: The purpose of this paper is to provide a comprehensive review of the research on stability of continuous-time recurrent Neural networks, including Hopfield neural networks, Cohen-Grossberg neural networks and related models.
Abstract: Stability problems of continuous-time recurrent neural networks have been extensively studied, and many papers have been published in the literature The purpose of this paper is to provide a comprehensive review of the research on stability of continuous-time recurrent neural networks, including Hopfield neural networks, Cohen-Grossberg neural networks, and related models Since time delay is inevitable in practice, stability results of recurrent neural networks with different classes of time delays are reviewed in detail For the case of delay-dependent stability, the results on how to deal with the constant/variable delay in recurrent neural networks are summarized The relationship among stability results in different forms, such as algebraic inequality forms, \(M\) -matrix forms, linear matrix inequality forms, and Lyapunov diagonal stability forms, is discussed and compared Some necessary and sufficient stability conditions for recurrent neural networks without time delays are also discussed Concluding remarks and future directions of stability analysis of recurrent neural networks are given

515 citations

Journal ArticleDOI
TL;DR: This paper proposed a novel defect detection system based on deep learning and focused on a practical industrial application: steel plate defect inspection and employs a baseline convolution neural network to generate feature maps at each stage, and the proposed multilevel feature fusion network (MFN) combines multiple hierarchical features into one feature, which can include more location details of defects.
Abstract: A complete defect detection task aims to achieve the specific class and precise location of each defect in an image, which makes it still challenging for applying this task in practice. The defect detection is a composite task of classification and location, leading to related methods is often hard to take into account the accuracy of both. The implementation of defect detection depends on a special detection data set that contains expensive manual annotations. In this paper, we proposed a novel defect detection system based on deep learning and focused on a practical industrial application: steel plate defect inspection. In order to achieve strong classification ability, this system employs a baseline convolution neural network (CNN) to generate feature maps at each stage, and then the proposed multilevel feature fusion network (MFN) combines multiple hierarchical features into one feature, which can include more location details of defects. Based on these multilevel features, a region proposal network (RPN) is adopted to generate regions of interest (ROIs). For each ROI, a detector, consisting of a classifier and a bounding box regressor, produces the final detection results. Finally, we set up a defect detection data set NEU-DET for training and evaluating our method. On the NEU-DET, our method achieves 74.8/82.3 mAP with baseline networks ResNet34/50 by using 300 proposals. In addition, by using only 50 proposals, our method can detect at 20 ft/s on a single GPU and reach 92% of the above performance, hence the potential for real-time detection.

507 citations


Authors

Showing all 36436 results

NameH-indexPapersCitations
Rui Zhang1512625107917
Hui-Ming Cheng147880111921
Yonggang Huang13679769290
Yang Liu1292506122380
Tao Zhang123277283866
J. R. Dahn12083266025
Terence G. Langdon117115861603
Frank L. Lewis114104560497
Xin Li114277871389
Peng Wang108167254529
David J. Hill107136457746
Jian Zhang107306469715
Xuemin Shen106122144959
Yi Zhang102181753417
Tao Li102248360947
Network Information
Related Institutions (5)
Northeastern University
58.1K papers, 1.7M citations

84% related

Harbin Institute of Technology
109.2K papers, 1.6M citations

83% related

Tsinghua University
200.5K papers, 4.5M citations

81% related

Nanyang Technological University
112.8K papers, 3.2M citations

81% related

Tianjin University
79.9K papers, 1.2M citations

80% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023166
2022906
20214,689
20204,118
20193,653
20182,878