Investigations on the Influence of Variations in Hidden Neurons and Training Data Percentage on the Efficiency of Concrete Carbonation Depth Prediction with ANN

2019 ◽  
pp. 958-965
Author(s):  
Ikenna D. Uwanuakwa ◽  
Pinar Akpinar
Keyword(s):  
Training Data ◽  
Carbonation Depth ◽  
Concrete Carbonation ◽  
Depth Prediction ◽  
Hidden Neurons ◽  
And Training

A Study on Carbonization Performance of Concrete by Freeze-Thaw Action in Ningxia

2014 ◽  
Vol 488-489 ◽  
pp. 407-410
Author(s):  
Guo Ping Xia
Keyword(s):  
Carbon Dioxide Concentration ◽  
Influential Factor ◽  
Carbonation Depth ◽  
Autonomous Region ◽  
Cement Ratio ◽  
Freeze Thaw ◽  
Concrete Carbonation ◽  
Depth Prediction ◽  
Ningxia Hui Autonomous Region ◽  
Yanchi County

In this paper, the effect of carbonization performance of concrete by freeze-thaw action in Ningxia Hui Autonomous Region have been studied. Based on the concrete carbonation depth prediction model modified, the effects of the carbonation depth of concrete by freeze-thaw action with dry weather in Yinchuan and Yanchi County are studied. These influential factor include water-cement ratio, cement dosage, the air relative humidity, carbon dioxide concentration.

scholarly journals Research on Concrete Carbonation Depth Prediction Algorithm Based on BP-AR

2019 ◽  
Vol 242 ◽  
pp. 062019
Author(s):  
Xuehua Yang ◽  
Junqi Yu ◽  
Zhenping Dong ◽  
Anjun Zhao ◽  
Tingjian Wei
Keyword(s):  
Prediction Algorithm ◽  
Carbonation Depth ◽  
Concrete Carbonation ◽  
Depth Prediction

scholarly journals Neural Network Method Based on Concrete Carbonation Depth Prediction

2021 ◽  
Vol 825 (1) ◽  
pp. 012020
Author(s):  
Duo Wu ◽  
Yuanrong Liu ◽  
Yuxue Yin ◽  
Zhiyong Deng ◽  
Zhifu Liu
Keyword(s):  
Neural Network ◽  
Carbonation Depth ◽  
Neural Network Method ◽  
Concrete Carbonation ◽  
Depth Prediction ◽  
Network Method

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Gradient Descent ◽  
Autonomous Driving ◽  
Driving Behavior ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Safe Driving ◽  
Processing Pipeline ◽  
Self Driving Cars ◽  
And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

scholarly journals Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics

2021 ◽  
Vol 7 (3) ◽  
pp. 59
Author(s):  
Yohanna Rodriguez-Ortega ◽  
Dora M. Ballesteros ◽  
Diego Renza
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Training Data ◽  
Video Editing ◽  
Forgery Detection ◽  
Copy Move Forgery Detection ◽  
The Impact ◽  
And Training ◽  
Selection Of ◽  
Traditional Image

With the exponential growth of high-quality fake images in social networks and media, it is necessary to develop recognition algorithms for this type of content. One of the most common types of image and video editing consists of duplicating areas of the image, known as the copy-move technique. Traditional image processing approaches manually look for patterns related to the duplicated content, limiting their use in mass data classification. In contrast, approaches based on deep learning have shown better performance and promising results, but they present generalization problems with a high dependence on training data and the need for appropriate selection of hyperparameters. To overcome this, we propose two approaches that use deep learning, a model by a custom architecture and a model by transfer learning. In each case, the impact of the depth of the network is analyzed in terms of precision (P), recall (R) and F1 score. Additionally, the problem of generalization is addressed with images from eight different open access datasets. Finally, the models are compared in terms of evaluation metrics, and training and inference times. The model by transfer learning of VGG-16 achieves metrics about 10% higher than the model by a custom architecture, however, it requires approximately twice as much inference time as the latter.

scholarly journals Exploiting heterogeneity in operational neural networks by synaptic plasticity

2021 ◽  
Author(s):  
Serkan Kiranyaz ◽  
Junaid Malik ◽  
Habib Ben Abdallah ◽  
Turker Ince ◽  
Alexandros Iosifidis ◽  
...  
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Network Model ◽  
Neuron Model ◽  
Linear Operators ◽  
Training Data ◽  
Learning Performance ◽  
Minimal Network ◽  
Hidden Layer ◽  
Hidden Neurons

AbstractThe recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) that are homogenous only with a linear neuron model. As a heterogenous network model, ONNs are based on a generalized neuron model that can encapsulate any set of non-linear operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data. However, the default search method to find optimal operators in ONNs, the so-called Greedy Iterative Search (GIS) method, usually takes several training sessions to find a single operator set per layer. This is not only computationally demanding, also the network heterogeneity is limited since the same set of operators will then be used for all neurons in each layer. To address this deficiency and exploit a superior level of heterogeneity, in this study the focus is drawn on searching the best-possible operator set(s) for the hidden neurons of the network based on the “Synaptic Plasticity” paradigm that poses the essential learning theory in biological neurons. During training, each operator set in the library can be evaluated by their synaptic plasticity level, ranked from the worst to the best, and an “elite” ONN can then be configured using the top-ranked operator sets found at each hidden layer. Experimental results over highly challenging problems demonstrate that the elite ONNs even with few neurons and layers can achieve a superior learning performance than GIS-based ONNs and as a result, the performance gap over the CNNs further widens.

scholarly journals Effects of Environmental Factors on Concrete Carbonation Depth and Compressive Strength

Materials ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2167 ◽  
Author(s):  
Ying Chen ◽  
Peng Liu ◽  
Zhiwu Yu
Keyword(s):  
Compressive Strength ◽  
Phase Composition ◽  
Relative Humidity ◽  
Co2 Concentration ◽  
Crystal Structure Analysis ◽  
Hydration Products ◽  
Carbonation Depth ◽  
Concrete Carbonation ◽  
Before And After ◽  
Compressive Strength Of Concrete

The influence of temperature, CO2 concentration and relative humidity on the carbonation depth and compressive strength of concrete was investigated. Meanwhile, phase composition, types of hydration products and microstructure characteristics of samples before and after the carbonation were analyzed by XRD and ESEM. Research results demonstrate that temperature, CO2 concentration and relative humidity influence the carbonation depth and compressive strength of concrete significantly. There is a linear relationship between temperature and carbonation depth, as well as the compressive strength of concrete. CO2 concentration and relative humidity present a power function and a polynomial function with carbonation depth of concrete, respectively. The concrete carbonation depth increases with the increase of relative humidity and reaches the maximum value when the relative humidity is 70%. Significant differences of phase composition, hydration products and microstructure are observed before and after the carbonation. Carbonization products of samples are different with changes of temperatures (10 °C, 20 °C and 30 °C). The result of crystal structure analysis indicates that the carbonation products are mainly polyhedral spherical vaterite and aragonite.

scholarly journals Covariate-Profile Similarity Weighting and Bagging Studies with the Study Strap: Multi-Study Learning for Human Neurochemical Sensing

2019 ◽  
Author(s):  
Gabriel Loewinger ◽  
Prasad Patil ◽  
Kenneth Kishida ◽  
Giovanni Parmigiani
Keyword(s):  
New Technology ◽  
R Package ◽  
Training Data ◽  
Tuning Parameter ◽  
Ensemble Classifiers ◽  
Profile Similarity ◽  
Special Cases ◽  
Study Heterogeneity ◽  
Brain Measurements ◽  
And Training

Prediction settings with multiple studies have become increasingly common. Ensembling models trained on individual studies has been shown to improve replicability in new studies. Motivated by a groundbreaking new technology in human neuroscience, we introduce two generalizations of multi-study ensemble predictions. First, while existing methods weight ensemble elements by cross-study prediction performance, we extend weighting schemes to also incorporate covariate similarity between training data and target validation studies. Second, we introduce a hierarchical resampling scheme to generate pseudo-study replicates (“study straps”) and ensemble classifiers trained on these rather than the original studies themselves. We demonstrate analytically that existing methods are special cases. Through a tuning parameter, our approach forms a continuum between merging all training data and training with existing multi-study ensembles. Leveraging this continuum helps accommodate different levels of between-study heterogeneity.Our methods are motivated by the application of Voltammetry in humans. This technique records electrical brain measurements and converts signals into neurotransmitter concentration estimates using a prediction model. Using this model in practice presents a cross-study challenge, for which we show marked improvements after application of our methods. We verify our methods in simulations and provide the studyStrap R package.

Study on the Influence of Freeze-Thaw on the Carbonation Property of Fly Ash Concrete

2013 ◽  
Vol 357-360 ◽  
pp. 939-943 ◽  
Author(s):  
Jian Gang Niu ◽  
Liang Yan ◽  
Hai Tao Zhai
Keyword(s):  
Fly Ash ◽  
Laboratory Simulation ◽  
Influence Coefficient ◽  
Carbonation Depth ◽  
Freeze Thaw ◽  
Fly Ash Concrete ◽  
Concrete Carbonation ◽  
Thaw Cycle ◽  
Freeze Thaw Cycle

Based on the coupling testing program of freeze-thaw and carbonation, the laboratory simulation test is carried out. The laws of carbonation depth of the fly ash concrete suffered the freeze-thaw cycle in different test modes and the influence of fly ash dosage on concrete carbonation depth after the freeze-thaw cycle are studied. Defining the influence coefficient of the freeze-thaw cycles on carbonation depth of concrete, the mechanism of coupling of freeze-thaw and carbonation is analyzed,and the role of freeze-thaw and carbonation in the coupling process are obtained.

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