Artificial intelligence in edge devices

Singh, Anubhav; Saini, Kavita; Nagar, Varad; Aseri, Vinay; Sankhla, Mahipal Singh; Pandit, Pritam P.; Chopade, Rushikesh L.

doi:10.1016/bs.adcom.2022.02.013

Cited by 11 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Within the domain of image and signal analyses, DL has garnered remarkable success, employing diverse architectures like DFFNNs and Deep Convolutional Neural Networks (DCNNs) to extract salient features from input data. DNNs encompass two pivotal learning stages: the forward pass and backpropagation [45][46][47]. Counteracting overfitting during training necessitates the integration of dropout layers within DNNs, facilitating the removal of different neurons at various training stages based on probabilistic criteria [48,49].…”

Section: Artificial Neural Network and Deep Feed-forward Neural Networkmentioning

confidence: 99%

Prediction of Normalized Shear Modulus and Damping Ratio for Granular Soils Over a Wide Strain Range Using Deep Neural Network Modelling

Bayat,

Mousavi,

et al. 2023

Preprint

View full text Add to dashboard Cite

Dynamic properties (i.e., shear modulus and damping ratio) of geomaterials play a vital role in civil engineering applications and are essential for reliable dynamic response analysis. This paper presents a novel approach for predicting the normalized shear modulus (G/Gmax) and damping ratio (D) of granular soils across a wide strain range using a Deep Neural Network (DNN) modeling strategy. Traditional methods for predicting these properties often rely on empirically derived relationships that may not capture the full complexity of granular soil behavior under varying strain conditions. A comprehensive dataset of shear modulus and damping ratio measurements from laboratory cyclic triaxial (CT) and resonant column (RC) tests conducted under various conditions is utilized. The dataset covers a wide range of strain levels, allowing for a more robust and versatile modeling approach. For predicting the G/Gmax and D of granular soils, a Deep Feed-Forward Neural Network (DFFNN) model was developed to learn the features from input data. The proposed model considers the influence of grading characteristics (Gravel Content, GC, median particle size, D50, Uniformity Coefficient, Cu, and Coefficient of Curvature, Cc), shear strain (\(\gamma\)), void ratio (e), mean effective confining pressure (\({\sigma ^{\prime}_m}\)), consolidation stress ratio (KC) and specimens’ preparation method (S-P) as input data. The empirical models (EMs) and three other intelligent techniques, namely Shallow Neural Network (SNN), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR) were used for comparison. The testing accuracy of the proposed DFFNN for predicting the G/Gmax and D was 0.9830 and 0.9396, respectively. The results demonstrate that the proposed DFFNN modeling strategy provides a highly accurate means of predicting G/Gmax and D for granular soils across a broad shear strain range. This method offers advantages over EMs by incorporating a data-driven approach that can adapt to the specific behavior of different granular soil types and loading conditions.

show abstract

Section: Artificial Neural Network and Deep Feed-forward Neural Networkmentioning

confidence: 99%

Prediction of Normalized Shear Modulus and Damping Ratio for Granular Soils Over a Wide Strain Range Using Deep Neural Network Modelling

Bayat,

Mousavi,

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…With the development of computer technology, intelligence has become an indispensable and important part of the future social development. In the field of construction machinery, product quality is one of the most important and critical topics [1][2]. For a new thing, there will be many uncertain factors.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Testing the Perfection of Construction Machinery Parts Based on Artificial Intelligence

2023

KME

View full text Add to dashboard Cite

With the continuous development of artificial intelligence, more and more researchers begin to attach importance to the realization of perfection testing in products, especially the intelligent and simulation technology. In this paper, based on the combination of computer and robot, the simulation experiment on mechanical structure is carried out. This paper first introduces the important role of machine vision system in the process of defect detection of automobile parts, then expounds the method of feature recognition of automobile parts, related principles and key technical basic knowledge points, etc. Finally, a three-dimensional entity model simulating real products is constructed using MATLAB software, and its results are calculated and analyzed. The concepts of intelligence and simulation are described in detail. The test results show that, the accuracy of the model in detecting the degree of perfection of product parts is more than 90%, and the error rate is low. This shows that the testing performance of the model is excellent.

show abstract

Comprehensive review and future prospects of multi-level fan control strategies in data centers for joint optimization of thermal management systems

Cao,

Li,

Luo

et al. 2024

Journal of Building Engineering

View full text Add to dashboard Cite

Artificial intelligence in edge devices

Cited by 11 publications

References 31 publications

Prediction of Normalized Shear Modulus and Damping Ratio for Granular Soils Over a Wide Strain Range Using Deep Neural Network Modelling

Prediction of Normalized Shear Modulus and Damping Ratio for Granular Soils Over a Wide Strain Range Using Deep Neural Network Modelling

A New Method for Testing the Perfection of Construction Machinery Parts Based on Artificial Intelligence

Comprehensive review and future prospects of multi-level fan control strategies in data centers for joint optimization of thermal management systems

Contact Info

Product

Resources

About