Equivalent structural parameters based non-destructive prediction of sustainable concrete strength using machine learning models via piezo sensor

Measurement ◽  
2022 ◽  
Vol 187 ◽  
pp. 110202
Author(s):  
Tushar Bansal ◽  
Visalakshi Talakokula ◽  
Kaliyan Mathiyazhagan
Keyword(s):  
Machine Learning ◽  
Structural Parameters ◽  
Concrete Strength ◽  
Learning Models ◽  
Sustainable Concrete ◽  
Non Destructive ◽  
Machine Learning Models

scholarly journals TasselGAN: An Application of the Generative Adversarial Model for Creating Field-Based Maize Tassel Data

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Snehal Shete ◽  
Srikant Srinivasan ◽  
Timothy A. Gonsalves
Keyword(s):  
Machine Learning ◽  
Plant Traits ◽  
Plant Phenotyping ◽  
Learning Models ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Maize Tassel ◽  
Adversarial Model ◽  
Non Destructive ◽  
Machine Learning Models

Machine learning-based plant phenotyping systems have enabled high-throughput, non-destructive measurements of plant traits. Tasks such as object detection, segmentation, and localization of plant traits in images taken in field conditions need the machine learning models to be developed on training datasets that contain plant traits amidst varying backgrounds and environmental conditions. However, the datasets available for phenotyping are typically limited in variety and mostly consist of lab-based images in controlled conditions. Here, we present a new method called TasselGAN, using a variant of a deep convolutional generative adversarial network, to synthetically generate images of maize tassels against sky backgrounds. Both foreground tassel images and background sky images are generated separately and merged together to form artificial field-based maize tassel data to aid the training of machine learning models, where there is a paucity of field-based data. The effectiveness of the proposed method is demonstrated using quantitative and perceptual qualitative experiments.

scholarly journals Highly Sensitive Terahertz Non-destructive Testing Technology for Stone Relics Deterioration Prediction Using SVM-Based Machine Learning Models

2020 ◽  
Author(s):  
Tianhua Meng ◽  
Rong Huang ◽  
Yuhe Lu ◽  
Hongmei Liu ◽  
Jianguang Ren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Non Destructive Testing ◽  
Learning Models ◽  
Destructive Testing ◽  
Practical Applications ◽  
Highly Sensitive ◽  
Testing Technology ◽  
Non Destructive ◽  
Machine Learning Models

Abstract The hollowing deterioration of stone relics required effective non-destructive testing (NDT) methods for their timely restoration and maintenance. To this end, a new NDT method based on terahertz (THz) technology by using support vector machine (SVM)-based machine learning models was developed to assess and diagnose the hollowing deterioration of the Yungang Grottoes. According to experiment design, a series of hollowing deterioration samples with various thicknesses of hollowing deterioration were prepared and then measured by using THz time-domain spectroscopy (THz-TDS). Based on the THz-TDS results of 30 randomly selected samples, a SVM-based hollowing deterioration prediction model (SVM-HDPM) was established by analyzing the relationship between the hollowing samples and the THz spectral information. The reliability and accuracy of the model was further proved by verified and compared with using the THz spectral data of the remaining 10 samples. The experimental results with the linear kernel function greatly demonstrated that the SVM-HDPM can have superior prediction accuracy with a mean square error (MSE) of 3.303E-4, implying that the model is feasible for the prediction the hollowing deterioration of the stone relics. Moreover, one data preprocess was introduced into SVM-HDPM to meet the needs of field-based test. The predicted results of five different hollowing deterioration with different flaked stone thickness revealed good performance with MSE value as low as 4.46E-4. Therefore, it is believed that the proposed method can be regarded as an effective NDT technique with practical applications in analyzing cultural relics and have promising future prospects in inspection stone relics-like ancient heritage for hidden flaws.

scholarly journals Highly sensitive Terahertz non-destructive testing technology for stone relics deterioration prediction using SVM-based machine learning models

2021 ◽  
Author(s):  
Tianhua Meng ◽  
Rong Huang ◽  
Yuhe Lu ◽  
Hongmei Liu ◽  
Jianguang Ren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Non Destructive Testing ◽  
Learning Models ◽  
Destructive Testing ◽  
Practical Applications ◽  
Highly Sensitive ◽  
Testing Technology ◽  
Non Destructive ◽  
Machine Learning Models

Abstract The hollowing deterioration of stone relics required effective non-destructive testing (NDT) methods for their timely restoration and maintenance. To this end, a new NDT method based on terahertz (THz) technology by using support vector machine (SVM)-based machine learning models was developed to assess and diagnose the hollowing deterioration of the Yungang Grottoes. According to experiment design, a series of hollowing deterioration samples with various thicknesses of hollowing deterioration were prepared and then measured by using THz time-domain spectroscopy (THz-TDS). Based on the THz-TDS results of 30 randomly selected samples, a SVM-based hollowing deterioration prediction model (SVM-HDPM) was established by analyzing the relationship between the hollowing samples and the THz spectral information. The reliability and accuracy of the model was further proved by verified and compared with using the THz spectral data of the remaining 10 samples. The experimental results with the linear kernel function greatly demonstrated that the SVM-HDPM can have superior prediction accuracy, implying that the model is feasible for the prediction the hollowing deterioration of the stone relics. Moreover, one data preprocess was introduced into SVM-HDPM to meet the needs of field-based test. The predicted results of five different hollowing deterioration with different flaked stone thickness revealed good performance with very low mean square error (MSE) value. Therefore, it is believed that the proposed method can be regarded as an effective NDT technique with practical applications in analyzing cultural relics and have promising future prospects in inspection stone relics-like ancient heritage for hidden flaws.

scholarly journals Highly sensitive terahertz non‐destructive testing technology for stone relics deterioration prediction using SVM-based machine learning models

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Tianhua Meng ◽  
Rong Huang ◽  
Yuhe Lu ◽  
Hongmei Liu ◽  
Jianguang Ren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Non Destructive Testing ◽  
Learning Models ◽  
Destructive Testing ◽  
Practical Applications ◽  
Highly Sensitive ◽  
Testing Technology ◽  
Non Destructive ◽  
Machine Learning Models

AbstractThe hollowing deterioration of stone relics required effective non-destructive testing (NDT) methods for their timely restoration and maintenance. To this end, a new NDT method based on terahertz (THz) technology by using support vector machine (SVM)-based machine learning models was developed to assess and diagnose the hollowing deterioration of the Yungang Grottoes. According to experiment design, a series of hollowing deterioration samples with various thicknesses of hollowing deterioration were prepared and then measured by using THz time-domain spectroscopy (THz-TDS). Based on the THz-TDS results of 30 randomly selected samples, a SVM-based hollowing deterioration prediction model (SVM-HDPM) was established by analyzing the relationship between the hollowing samples and the THz spectral information. The reliability and accuracy of the model was further proved by verified and compared with using the THz spectral data of the remaining 10 samples. The experimental results with the linear kernel function greatly demonstrated that the SVM-HDPM can have superior prediction accuracy, implying that the model is feasible for the prediction the hollowing deterioration of the stone relics. Moreover, one data preprocess was introduced into SVM-HDPM to meet the needs of field-based test. The predicted results of five different hollowing deterioration with different flaked stone thickness revealed good performance with very low mean square error (MSE) value. Therefore, it is believed that the proposed method can be regarded as an effective NDT technique with practical applications in analyzing cultural relics and have promising future prospects in inspection stone relics-like ancient heritage for hidden flaws.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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