E_Swish Beta: Modifying Swish Activation Function for Deep Learning Improvement

2021 ◽  
pp. 267-279
Author(s):  
Abdulwahed Salam ◽  
Abdelaaziz El Hibaoui
Keyword(s):  
Deep Learning ◽  
Activation Function ◽  
Learning Improvement

Inverse design of layered periodic wave barriers based on deep learning

2021 ◽  
pp. 146442072110168
Author(s):  
Chen-Xu Liu ◽  
Gui-Lan Yu
Keyword(s):  
Deep Learning ◽  
Activation Function ◽  
Periodic Wave ◽  
P Wave ◽  
Soil Parameters ◽  
S Wave ◽  
Output Frequency ◽  
Great Generality ◽  
Typical Range ◽  
Deep Learning Model

This study presents an approach based on deep learning to design layered periodic wave barriers with consideration of typical range of soil parameters. Three cases are considered where P wave and S wave exist separately or simultaneously. The deep learning model is composed of an autoencoder with a pretrained decoder which has three branches to output frequency attenuation domains for three different cases. A periodic activation function is used to improve the design accuracy, and condition variables are applied in the code layer of the autoencoder to meet the requirements of practical multi working conditions. Forty thousand sets of data are generated to train, validate, and test the model, and the designed results are highly consistent with the targets. The presented approach has great generality, feasibility, rapidity, and accuracy on designing layered periodic wave barriers which exhibit good performance in wave suppression in targeted frequency range.

Application of Deep Learning Technique to Predict Downhole Pressure Differential in Eccentric Annulus of Ultra-Deep Well

2021 ◽  
Author(s):  
Shwetank Krishna ◽  
Syahrir Ridha ◽  
Suhaib Umer Ilyas ◽  
Scott Campbell ◽  
Uday Bhan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pressure Gradient ◽  
Field Data ◽  
Activation Function ◽  
Pressure Differential ◽  
Optimal Arrangement ◽  
Eccentric Annulus ◽  
Learning Framework ◽  
Deep Well ◽  
Deep Wells

Abstract Accurate prediction of downhole pressure differential (surge/swab pressure gradient) in the eccentric annulus of ultra-deep wells during tripping operation is a necessity to optimize well geometry, reduction of drilling anomalies, and prevention of hazardous drilling accidents. Therefore, a new predictive model is developed to forecast surge/swab pressure gradient by using feed-forward and backpropagation deep neural networks (FFBP-DNN). A theoretical-based model is developed that follows the physical and mechanical aspects of surge/swab pressure generation in eccentric annulus during tripping operation. The data generated from this model, field data, and experimental data are used to train and test the FFBP-DNN networks. The network is developed used Keras’s deep learning framework. After testing the models, the most optimal arrangement of FFBP-DNN is the ReLU algorithm as an activation function, 4-hidden layers, the learning rate of 0.003, and 2300 of training numbers. The optimum FFBP-DNN model is validated by comparing it with field data (Wells K 470 and K 480, North Sea). It shows an excellent argument between predicted data and field data with an error range of ±7.68 %.

scholarly journals Deep Learning-Based Beamforming for Millimeter-Wave Systems Using Parametric ReLU Activation Function

2021 ◽  
Author(s):  
Alshimaa Hamdy ◽  
Tarek Abed Soliman ◽  
Mohamed Rihan ◽  
Moawad I. Dessouky
Keyword(s):  
Deep Learning ◽  
Performance Improvement ◽  
Millimeter Wave ◽  
Antenna Arrays ◽  
Activation Function ◽  
Model Accuracy ◽  
Learning Network ◽  
Parametric Rectified Linear Unit ◽  
Complexity Cost ◽  
Deep Learning Network

Abstract Beamforming design is a crucial stage in millimeter-wave systems with massive antenna arrays. We propose a deep learning network for the design of the precoder and combiner in hybrid architectures. The proposed network employs a parametric rectified linear unit (PReLU) activation function which improves model accuracy with almost no complexity cost compared to other functions. The proposed network accepts practical channel estimation input and can be trained to enhance spectral efficiency considering the hardware limitation of the hybrid design. Simulation shows that the proposed network achieves small performance improvement when compared to the same network with the ReLU activation function.

scholarly journals Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
J. M. Torres ◽  
R. M. Aguilar
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Electricity Market ◽  
Power Plants ◽  
Wind Farm ◽  
Polynomial Regression ◽  
Supply And Demand ◽  
Feedforward Neural Networks ◽  
Activation Function ◽  
Renewable Energies

Making every component of an electrical system work in unison is being made more challenging by the increasing number of renewable energies used, the electrical output of which is difficult to determine beforehand. In Spain, the daily electricity market opens with a 12-hour lead time, where the supply and demand expected for the following 24 hours are presented. When estimating the generation, energy sources like nuclear are highly stable, while peaking power plants can be run as necessary. Renewable energies, however, which should eventually replace peakers insofar as possible, are reliant on meteorological conditions. In this paper we propose using different deep-learning techniques and architectures to solve the problem of predicting wind generation in order to participate in the daily market, by making predictions 12 and 36 hours in advance. We develop and compare various estimators based on feedforward, convolutional, and recurrent neural networks. These estimators were trained and validated with data from a wind farm located on the island of Tenerife. We show that the best candidates for each type are more precise than the reference estimator and the polynomial regression currently used at the wind farm. We also conduct a sensitivity analysis to determine which estimator type is most robust to perturbations. An analysis of our findings shows that the most accurate and robust estimators are those based on feedforward neural networks with a SELU activation function and convolutional neural networks.

Classification of plant diseases using machine and deep learning

2021 ◽  
Vol 11 (1) ◽  
pp. 491-508
Author(s):  
Monika Lamba ◽  
Yogita Gigras ◽  
Anuradha Dhull
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Nearest Neighbor ◽  
Early Recognition ◽  
Activation Function ◽  
Plant Diseases ◽  
Support Vector ◽  
Specificity And Sensitivity ◽  
Proposed Model

Abstract Detection of plant disease has a crucial role in better understanding the economy of India in terms of agricultural productivity. Early recognition and categorization of diseases in plants are very crucial as it can adversely affect the growth and development of species. Numerous machine learning methods like SVM (support vector machine), random forest, KNN (k-nearest neighbor), Naïve Bayes, decision tree, etc., have been exploited for recognition, discovery, and categorization of plant diseases; however, the advancement of machine learning by DL (deep learning) is supposed to possess tremendous potential in enhancing the accuracy. This paper proposed a model comprising of Auto-Color Correlogram as image filter and DL as classifiers with different activation functions for plant disease. This proposed model is implemented on four different datasets to solve binary and multiclass subcategories of plant diseases. Using the proposed model, results achieved are better, obtaining 99.4% accuracy and 99.9% sensitivity for binary class and 99.2% accuracy for multiclass. It is proven that the proposed model outperforms other approaches, namely LibSVM, SMO (sequential minimal optimization), and DL with activation function softmax and softsign in terms of F-measure, recall, MCC (Matthews correlation coefficient), specificity and sensitivity.

scholarly journals Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6218
Author(s):  
Rodrigo Carvalho Barbosa ◽  
Muhammad Shoaib Ayub ◽  
Renata Lopes Rosa ◽  
Demóstenes Zegarra Rodríguez ◽  
Lunchakorn Wuttisittikulkij
Keyword(s):  
Deep Learning ◽  
Traffic Control ◽  
Detection System ◽  
Lightweight Design ◽  
Vehicle Detection ◽  
Activation Function ◽  
Urban Traffic ◽  
Vehicle Classification ◽  
Traffic Light ◽  
Traffic Lights

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.

scholarly journals RMAF: Relu-Memristor-Like Activation Function for Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 72727-72741 ◽  
Author(s):  
Yongbin Yu ◽  
Kwabena Adu ◽  
Nyima Tashi ◽  
Patrick Anokye ◽  
Xiangxiang Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Activation Function

Dental Caries Classification System Using Deep Learning Based Convolutional Neural Network

2020 ◽  
Vol 17 (9) ◽  
pp. 4660-4665
Author(s):  
L. Megalan Leo ◽  
T. Kalpalatha Reddy
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Dental Caries ◽  
Convolutional Neural Network ◽  
Median Filter ◽  
Activation Function ◽  
Radiographic Examination ◽  
Testing Dataset ◽  
The People ◽  
Deep Learning Model

In the modern times, Dental caries is one of the most prevalent diseases of the teeth in the whole world. Almost 90% of the people get affected by cavity. Dental caries is the cavity which occurs due to the remnant food and bacteria. Dental Caries are curable and preventable diseases when it is identified at earlier stage. Dentist uses the radiographic examination in addition with visual tactile inspection to identify the caries. Dentist finds difficult to identify the occlusal, pit and fissure caries. It may lead to sever problem if the cavity left untreated and not identified at the earliest stage. Machine learning can be applied to solve this issue by applying the labelled dataset given by the experienced dentist. In this paper, convolutional based deep learning method is applied to identify the cavity presence in the image. 480 Bite viewing radiography images are collected from the Elsevier standard database. All the input images are resized to 128–128 matrixes. In preprocessing, selective median filter is used to reduce the noise in the image. Pre-processed inputs are given to deep learning model where convolutional neural network with Google Net inception v3 architecture algorithm is implemented. ReLu activation function is used with Google Net to identify the caries that provide the dentists with the precise and optimized results about caries and the area affected. Proposed technique achieves 86.7% accuracy on the testing dataset.

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