scholarly journals Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine

Energies ◽  
2015 ◽  
Vol 8 (8) ◽  
pp. 8814-8834 ◽  
Author(s):  
Zhijian Liu ◽  
Hao Li ◽  
Xinyu Zhang ◽  
Guangya Jin ◽  
Kewei Cheng
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Heat Loss ◽  
Support Vector ◽  
Solar Water ◽  
Water Heaters ◽  
Novel Method ◽  
Solar Water Heaters ◽  
Evacuated Tube

scholarly journals SVM-DS fusion based soft fault detection and diagnosis in solar water heaters

2018 ◽  
Vol 37 (3) ◽  
pp. 1125-1146 ◽  
Author(s):  
Song Jiang ◽  
Minjie Lian ◽  
Caiwu Lu ◽  
Shunling Ruan ◽  
Zhe Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
High Reliability ◽  
Evidence Theory ◽  
Fault Detection And Diagnosis ◽  
Support Vector ◽  
Water Heater ◽  
Solar Water ◽  
Water Heaters ◽  
Highly Correlated ◽  
Solar Water Heaters

As faults in the solar water heaters are structurally complicated and highly correlated, an approach of fault diagnosis on the basis of support vector machine and D-S evidence theory has been proposed in this study, attempting to enhance the system’s thermal efficiency and ensure its safety. In the approach presented, information of audio conditions, temperature at the outlet of solar thermal collectors, hourly flow and hourly heat transfer rate are accessible, which facilitate the feature evidence and are diagnosed by using “one-against-one” multi-class support vector machine. Experiments are conducted to diagnose fault information fusion and the results show that the diagnosis approach proposed in this study is of high reliability with fewer uncertainties, indicating that the approach is capable to recognize and diagnose solar water heater faults accurately.

IMPROVEMENT OF DOSE ESTIMATION PROCESS USING ARTIFICIAL NEURAL NETWORKS

2018 ◽  
Vol 184 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Gal Amit ◽  
Hanan Datz
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
Physical Parameters ◽  
Machine Method ◽  
Algorithm Performance ◽  
Artificial Neural ◽  
First Time ◽  
Automatic Algorithm

Abstract We present here for the first time a fast and reliable automatic algorithm based on artificial neural networks for the anomaly detection of a thermoluminescence dosemeter (TLD) glow curves (GCs), and compare its performance with formerly developed support vector machine method. The GC shape of TLD depends on numerous physical parameters, which may significantly affect it. When integrated into a dosimetry laboratory, this automatic algorithm can classify ‘anomalous’ (having any kind of anomaly) GCs for manual review, and ‘regular’ (acceptable) GCs for automatic analysis. The new algorithm performance is then compared with two kinds of formerly developed support vector machine classifiers—regular and weighted ones—using three different metrics. Results show an impressive accuracy rate of 97% for TLD GCs that are correctly classified to either of the classes.

Predicting Students Grades Using Artificial Neural Networks and Support Vector Machine

2019 ◽  
pp. 751-766
Author(s):  
Sajid Umair ◽  
Muhammad Majid Sharif
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Student Performance ◽  
Semantic Analysis ◽  
Vital Role ◽  
Support Vector ◽  
Data Set ◽  
Important Research Topic ◽  
Artificial Neural

Prediction of student performance on the basis of habits has been a very important research topic in academics. Studies show that selection of the correct data set also plays a vital role in these predictions. In this chapter, the authors took data from different schools that contains student habits and their comments, analyzed it using latent semantic analysis to get semantics, and then used support vector machine to classify the data into two classes, important for prediction and not important. Finally, they used artificial neural networks to predict the grades of students. Regression was also used to predict data coming from support vector machine, while giving only the important data for prediction.

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