A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate

Guo, Hongwei; Zhuang, Xiaoying; Rabczuk, Timon

doi:10.32604/cmc.2019.06660

Cited by 341 publications

(87 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, authors in [18], proved the importance of choosing a proper activation function for the hidden layers using a mathematical evaluation that helped the model to work on complex mappings required for vast and non-linear data. Hongwei Guo et al in [19] also utilized the advantages of backpropagation algorithms and mathematically induced one for thin plate bending problems. In our proposed work we have considered the factors mentioned in [18,19] for better performance.…”

Section: Related Workmentioning

confidence: 99%

Upsampling Real-Time, Low-Resolution CCTV Videos Using Generative Adversarial Networks

Hazra¹,

Byun²

2020

Electronics

View full text Add to dashboard Cite

Video super-resolution has become an emerging topic in the field of machine learning. The generative adversarial network is a framework that is widely used to develop solutions for low-resolution videos. Video surveillance using closed-circuit television (CCTV) is significant in every field, all over the world. A common problem with CCTV videos is sudden video loss or poor quality. In this paper, we propose a generative adversarial network that implements spatio-temporal generators and discriminators to enhance real-time low-resolution CCTV videos to high-resolution. The proposed model considers both foreground and background motion of a CCTV video and effectively models the spatial and temporal consistency from low-resolution video frames to generate high-resolution videos. Quantitative and qualitative experiments on benchmark datasets, including Kinetics-700, UCF101, HMDB51 and IITH_Helmet2, showed that our model outperforms the existing GAN models for video super-resolution.

show abstract

Section: Related Workmentioning

confidence: 99%

Upsampling Real-Time, Low-Resolution CCTV Videos Using Generative Adversarial Networks

Hazra¹,

Byun²

2020

Electronics

View full text Add to dashboard Cite

show abstract

“…Numerous computational methods have been introduced to solve differential equations, such as FEM [75], Ritz method [76], deep collocation method [77], etc. In this paper, we employed the differential quadrature method (DQM), which was proposed by Bellman et al [78,79] in the 1970s.…”

Section: Solution Proceduresmentioning

confidence: 99%

Frequency Characteristics of Multiscale Hybrid Nanocomposite Annular Plate Based on a Halpin–Tsai Homogenization Model with the Aid of GDQM

et al. 2020

Self Cite

View full text Add to dashboard Cite

In this article, we study the vibration performance of multiscale hybrid nanocomposite (MHC) annular plates (MHCAP) resting on Winkler–Pasternak substrates exposed to nonlinear temperature gradients. The matrix material is reinforced with carbon nanotubes (CNTs) or carbon fibers (CF) at the nano- or macroscale, respectively. The annular plate is modeled based on higher-order shear deformation theory (HSDT). We present a modified Halpin–Tsai model to predict the effective properties of the MHCAP. Hamilton’s principle was employed to establish the governing equations of motion, which is finally solved by the generalized differential quadrature method (GDQM). In order to validate the approach, numerical results were compared with available results from the literature. Subsequently, a comprehensive parameter study was carried out to quantify the influence of different parameters such as stiffness of the substrate, patterns of temperature increase, outer temperature, volume fraction and orientation angle of the CFs, weight fraction and distribution patterns of CNTs, outer radius to inner radius ratio, and inner radius to thickness ratio on the response of the plate. The results show that applying a sinusoidal temperature rise and locating more CNTs in the vicinity of the bottom surface yielded the highest natural frequency.

show abstract

“…The proposed system provides an application of various M.L algorithms in a given text. Second, the different emotion signals are applied to different machine learning classifiers (Guo et al 2019, Anitescu et al 2019, which are simple and effective. This would help computational intelligence experts in developing improved methods for the sentiment classification of text-based emotions.…”

Section: Research Significancementioning

confidence: 99%

Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content

Asghar¹,

Subhan²,

Imran³

et al. 2019

Preprint

View full text Add to dashboard Cite

Emotion detection from the text is an important and challenging problem in text analytics. The opinion-mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification.

show abstract

A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate

Cited by 341 publications

References 51 publications

Upsampling Real-Time, Low-Resolution CCTV Videos Using Generative Adversarial Networks

Upsampling Real-Time, Low-Resolution CCTV Videos Using Generative Adversarial Networks

Frequency Characteristics of Multiscale Hybrid Nanocomposite Annular Plate Based on a Halpin–Tsai Homogenization Model with the Aid of GDQM

Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content

Contact Info

Product

Resources

About