title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¸Ð½Ð´Ð¸ÐºÐ°ÑÐ¸Ð¸ ÑÑÐ¾Ð²Ð½Ñ ÑÐ¾Ð¿Ð»Ð¸Ð²Ð° Ð½Ð° Ð¿Ð¾Ð¿Ð»Ð°Ð²ÐºÐ¾Ð²ÑÑ Ð´Ð°ÑÑÐ¸ÐºÐ°Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of software for evaluating the effectiveness of fuel level indication system that using float sensors
Creators: name: Ð¡Ð°Ð¹ÑÑÐ»Ð¸Ð½, ÐÐ¼Ð¸ÑÑÐ¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÑÐ¸ÑÑÐµÐ¼Ð° Ð¼Ð¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³Ð° ÑÑÐ¾Ð²Ð½Ñ ÑÐ¾Ð¿Ð»Ð¸Ð²Ð°
Ð´Ð°ÑÑÐ¸Ðº ÑÑÐ¾Ð²Ð½Ñ ÑÐ¾Ð¿Ð»Ð¸Ð²Ð°
Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð¹ ÑÑÐ´
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¿Ð¾Ð¸ÑÐºÐ° Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¹
ÑÐµÐºÑÑÑÐµÐ½ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
fuel level monitoring system
fuel level sensor
time series
anomaly detection algorithms
recurrent neural networks

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description: The relevance of this work is due to the need for timely detection of fuel level sensor malfunctions in automated fuel level monitoring systems, particularly in vehicles, as a malfunctioning sensor in a vehicles tank can have serious consequences for vehicle operation. Given this problem, this work focuses on the development of software that performs timely detection of float fuel level sensor malfunctions. The following tasks were accomplished during the work: studied the principles of operation and the structure of the fuel level monitoring system, explored the main algorithms for anomaly detection in time series, investigated the architecture of the LSTM recurrent neural network, developed program implementation of anomaly detection algorithms and the LSTM neural network specifically for the considered problem, conducted an analysis of the implemented algorithms, designed a user interface for the application. The software implementation of all methods was done using the Python programming language. The outcome of this work is an implemented desktop application for analyzing the operation of the monitoring system sensors.