Neural Networks in Petrol Station Objects Calibration

Gorawski, Marcin; Skrzewski, Mirosław; Gorawski, Michał; Gorawska, Anna

doi:10.1007/978-3-319-27161-3_65

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…These objects are the days when the fuel station is open, which are divided into two categories: "day without leak" and "day with leak". For the leak detection to work correctly, the leak detection system must distinguish between changes in the inventory record caused by leakage and those produced by other causes such as evaporation, volume change due to temperature, and other reasons mentioned by Gorawski, Skrzewski [30]. The paper study used a pattern recognition method to distinguish between these two scenarios.…”

Section: Leak Detection Techniquesmentioning

confidence: 99%

Current practice of early leak detection methods for underground storage tanks

murad

Husain

Zaki

et al. 2022

J. Phys.: Conf. Ser.

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This article aims to provide general review on current practice of leak detection methods of underground storage tanks (UST). Fuel (i.e. gasoline and diesel oil) leakage from UST can contaminate groundwater and drinking water with various hydrocarbon contaminants. These leaks create ponds of fuel that spill into the land and aquifers, polluting and seriously destroying habitats. Numerous efforts have been focused on the development of leak detection to the tanks. However, without the opportunity to conduct fault intensity calibration and estimate a product’s lifetime, there is a lack of information provided to consider the condition of previous underlying leakage. As a result, it is too late whether the harm has already been done. There are methods of detection that have been studied for the past ten years. Many approaches have been practised to detect leakage. Specific sensing devices will combine with additional applications that analyse and interpret the data to detect storage tank leaks. Various methods will provide different results depending on the feature chosen. Some approaches will use machine learning to analyse the provided data and provide the best leak detection result. This paper will explore the best leak detection techniques to improve underground tanks’ structural integrity. At the end, this paper will give some overview on current practice early detection methods on underground storage tanks for future research.

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Section: Leak Detection Techniquesmentioning

confidence: 99%

Current practice of early leak detection methods for underground storage tanks

murad

Husain

Zaki

et al. 2022

J. Phys.: Conf. Ser.

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show abstract

“…A good representation of such infrastructure is very helpful in locating, e.g., leakage in these systems, especially leaks in gas and fuel lines are very dangerous and need to be quickly localized. As the inconsistencies can exist from nay different sources such as malfunctioning sensor or miscalculation of equipment [3], it is important to supply effective algorithms that identify real leaks without false positive alerts [4]. As all the above-mentioned infrastructures are defined as an LI, their characteristics can be easy translated into graphs.…”

Section: Introductionmentioning

confidence: 99%

Performance Tests of Smart City IoT Data Repositories for Universal Linear Infrastructure Data and Graph Databases

Gorawski

Grochla

2019

SN COMPUT. SCI.

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The infrastructure managed by Smart City systems includes mainly linear structures, such as pipelines, electric power lines, railway lines, etc.. In this paper, we propose the transcription of linear infrastructure to graph representation and storing needed meta-data in a graph database, merged with the traditional relational database used for device management. Such an approach enables fast acquisition of data about infrastructure's properties and allows merging information about the structure of the managed infrastructure with information devices' properties and statistics. We develop a graph generator that generates virtual network basing on representative examples of linear infrastructure, incorporation of a data from existing metering systems with the generated structure, which automates the deployment and allows to evaluate the performance of Smart City monitoring and management system. We present results of performance tests that show and the creation time of graph database structures in Neo4j and the difference between the performance of the Microsoft SQL Server database and the Neo4j database in a few Smart City use cases. The results show that the use of the graph database to execute queries related to linear infrastructure allows decreasing the response time up to 9 times.

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Anomaly Detection in Data Streams: The Petrol Station Simulator

Gorawska

Pasterak

2016

Communications in Computer and Information Science

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Neural Networks in Petrol Station Objects Calibration

Cited by 7 publications

References 8 publications

Current practice of early leak detection methods for underground storage tanks

Current practice of early leak detection methods for underground storage tanks

Performance Tests of Smart City IoT Data Repositories for Universal Linear Infrastructure Data and Graph Databases

Anomaly Detection in Data Streams: The Petrol Station Simulator

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