Using Pressure Sensors towards Pipeline Leakage Detection

Exaudi, Kemahyanto; Passarella, Rossi; Rendyansyah,; Zulfahmi, Rido

doi:10.1109/icecos.2018.8605251

Cited by 9 publications

(2 citation statements)

References 4 publications

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“…Project-Based Learning (PBL) [22], [6], [23], [24], [19]a, [20]a, [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46] 83% Mission-Based Learning (MBL) [47] 3%…”

Section: Estrategias De Enseñanzamentioning

confidence: 99%

Enseñanza de programación mediante MIT App Inventor: una revisión de literatura

Vinueza-Morales¹,

Chacón-Luna²,

Rodas-Silva³

et al. 2020

Proceedings of the 18th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering, Integrat

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MIT App Inventor is a visual block programming environment created for mobile application development by novice programmers, and is widely used around the world for learning programming. This paper describes the literature review conducted by applying search and selection criteria, using specialized search engines, followed by the analysis of the 35 selected studies (experiments and experiences) on the teaching-learning process of programming conducted by teachers from various countries, using App Inventor. The findings indicate a high acceptance in the academic community of App Inventor as an effective tool for motivation and performance of students who are initiated in programming, without distinction in the educational level.

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Section: Estrategias De Enseñanzamentioning

confidence: 99%

Enseñanza de programación mediante MIT App Inventor: una revisión de literatura

Vinueza-Morales¹,

Chacón-Luna²,

Rodas-Silva³

et al. 2020

Proceedings of the 18th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering, Integrat

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“…Corroded pipes lead to small cracks that eventually form leaks. 4. In the transportation of fluids for longer distances through a pipeline, leaks can also occur due to harsh environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

Studies on Leak Detection in Process Pipelines Using Artificial Neural Networks/Machine Learning

Meda¹,

Harshitha²,

`Hulake³

et al. 2022

ECS Trans.

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In process industries, leakage in pipelines is common and an environmental, health and economic issue to be addressed without fail. To detect the presence of leaks in pipelines, there are several conventional leak detection techniques that are available. From the literature, conventional leak detection techniques can be mainly classified into: hardware-based, visual and software-based methods. Researchers have focused more on software-based methods due to simple and reliable operation. Under software-based methods, using machine learning algorithms that is a data driven approach for leak detection and localization is becoming popular because of learning capabilities. Therefore, in this review, several recent conventional leak detection methods and artificial neural networks/machine learning based leak detection methods are described. It is an attempt made to identify leak detection techniques along with the common methodology followed for building a leak detection system using artificial neural networks/machine learning.

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