Acoustic Emission (AE) Based Leak Detection of Water Distribution Pipeline Subject to Failure of Socket Joint Using Logistic Regression Algorithm

2017 ◽  
Author(s):  
G. Q. Zhou ◽  
S. Z. Li
Keyword(s):  
Acoustic Emission ◽  
Logistic Regression ◽  
Water Distribution ◽  
Leak Detection ◽  
Logistic Regression Algorithm

scholarly journals Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews

2020 ◽  
Vol 30 (1) ◽  
pp. 192-208 ◽  
Author(s):  
Hamza Aldabbas ◽  
Abdullah Bajahzar ◽  
Meshrif Alruily ◽  
Ali Adil Qureshi ◽  
Rana M. Amir Latif ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Language Processing ◽  
Mobile Application ◽  
Knowledge Engineering ◽  
Machine Learning Algorithms ◽  
Application Development ◽  
User Reviews ◽  
N Gram ◽  
Logistic Regression Algorithm ◽  
Google Play

Abstract To maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and express their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral.

Theorical Analysis of Leakage in High Pressure Pipe Using Acoustic Emission Method

2012 ◽  
Vol 445 ◽  
pp. 917-922 ◽  
Author(s):  
Saman Davoodi ◽  
Amir Mostafapour
Keyword(s):  
High Pressure ◽  
Acoustic Emission ◽  
Degrees Of Freedom ◽  
Oil And Gas ◽  
Pipe Wall ◽  
Leak Detection ◽  
Stress Waves ◽  
Motion Equation ◽  
Non Linear ◽  
Acoustic Emission Test

Leak detection is one of the most important problems in the oil and gas pipelines. Where it can lead to financial losses, severe human and environmental impacts. Acoustic emission test is a new technique for leak detection. Leakage in high pressure pipes creates stress waves resulting from localized loss of energy. Stress waves are transmitted through the pipe wall which will be recorded by using acoustic sensor or accelerometer installed on the pipe wall. Knowledge of how the pipe wall vibrates by acoustic emission resulting from leakage is a key parameter for leak detection and location. In this paper, modeling of pipe vibration caused by acoustic emission generated by escaping of fluid has been done. Donnells non linear theory for cylindrical shell is used to deriving of motion equation and simply supported boundary condition is considered. By using Galerkin method, the motion equation has been solved and a system of non linear equations with 6 degrees of freedom is obtained. To solve these equations, ODE tool of MATLAB software and Rung-Kuta numerical method is used and pipe wall radial displacement is obtained. For verification of this theory, acoustic emission test with continues leak source has been done. Vibration of wall pipe was recorded by using acoustic emission sensors. For better analysis, Fast Fourier Transform (FFT) was taken from theoretical and experimental results. By comparing the results, it is found that the range of frequencies which carried the most amount of energy is same which expresses the affectivity of the model.

scholarly journals Study of a Privacy Preserving Logistic Regression Algorithm (PPLRA) For Data Privacy in the Context of Big Data

2021 ◽  
Vol 2083 (3) ◽  
pp. 032059
Author(s):  
Qiang Chen ◽  
Meiling Deng
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Privacy Protection ◽  
Data Privacy ◽  
Absolute Error ◽  
Average Absolute Error ◽  
Regression Algorithms ◽  
Hadoop Platform ◽  
Logistic Regression Algorithm ◽  
Computing Speed

Abstract Regression algorithms are commonly used in machine learning. Based on encryption and privacy protection methods, the current key hot technology regression algorithm and the same encryption technology are studied. This paper proposes a PPLAR based algorithm. The correlation between data items is obtained by logistic regression formula. The algorithm is distributed and parallelized on Hadoop platform to improve the computing speed of the cluster while ensuring the average absolute error of the algorithm.

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