Fuel Consumption Estimation System and Method with Lower Cost

Lo, Chi Lun; Chen, Chi‐Hua; Kuan, Ta Sheng; Lo, Kuen Rong; Cho, Hsun-Jung

doi:10.3390/sym9070105

Cited by 10 publications

(8 citation statements)

References 14 publications

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“…A fuel consumption estimation method based on gene algorithm was proposed in [6]. Each car reports its speed every 30 seconds, and the fueling charge of the car during the month is denoted as Q j for the j-th fuel type.…”

Section: Fuel Consumption Estimation Methodsmentioning

confidence: 99%

Clustering-Based Neural Network for Carbon Dioxide Estimation

Zhong

2023

IEICE Trans. Inf. & Syst.

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In recent years, the applications of deep learning have facilitated the development of green intelligent transportation system (ITS), and carbon dioxide estimation has been one of important issues in green ITS. Furthermore, the carbon dioxide estimation could be modelled as the fuel consumption estimation. Therefore, a clustering-based neural network is proposed to analyze clusters in accordance with fuel consumption behaviors and obtains the estimated fuel consumption and the estimated carbon dioxide. In experiments, the mean absolute percentage error (MAPE) of the proposed method is only 5.61%, and the performance of the proposed method is higher than other methods.

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Section: Fuel Consumption Estimation Methodsmentioning

confidence: 99%

Clustering-Based Neural Network for Carbon Dioxide Estimation

Zhong

2023

IEICE Trans. Inf. & Syst.

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“…This analysis of data is used to determine better ways for fuel consumption, fuel consumption patterns and trends, helping save financial losses along with discovering more eco-friendly ways to consume the limited resource, describes Areeg Abubakr Ibrahim Ahmed [9]. Chi-Lun Lo [10] and partners proposed a fuel utilization estimation framework incorporates OBUs, client gadgets, an information investigation server, and a database server. The OBU incorporates a position module, a middleware module, and a correspondence module as pursues: The position module can bolster a worldwide situating framework (GPS) to get and break down satellite sign for evaluating the area (i.e., longitude and scope) and speed of the vehicle.…”

Section: R Rajashree M Tamilselvimentioning

confidence: 99%

IoT Based Smart Fuel Monitoring System

P¹,

Ganesan²,

Patwari³

et al. 2019

IJRTE

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In today’s fast paced world, monitoring systems are necessary to track the changes in the environment for better understanding of current scenarios and predictions thereof. The same is true for fuel tanks in vehicles as well. By keeping strict track of fuel intake and consumption, vehicles can be made more fuel and cost efficient. This can be done using remote monitoring and data collection systems deployed at the site of the fuel storage tank. This proposed monitoring device is built on Atmega16 computer that takes fuel tank level information from its sensors and analyses this data at the sensor edge to find patterns using edge analytics technology. These patterns and data are streamed to the internet, either an android app or a website. This paper presents the implementation of such a monitoring system based on Internet of Things (IoT) technology to protect the fuel customers from theft at the gas stations and formulate better conservation strategies.

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“…, and 1,1 b are proved and calculated by Equations (14), (15), (16), (17), (18), (19), (20), (21), (22), and (23), respectively.…”

Section: Recurrent Neural Network With One Timestampmentioning

confidence: 99%

A Mobile Positioning Method Based on Deep Learning Techniques

Wu¹,

Chen²,

Qishan³

2018

Preprint

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This study proposes a mobile positioning method which adopts recurrent neural network algorithms to analyze the received signal strength indications from heterogeneous networks (e.g., cellular networks and Wi-Fi networks) for estimating the locations of mobile statioThis study proposes a mobile positioning method which adopts recurrent neural network algorithms to analyze the received signal strength indications from heterogeneous networks (e.g., cellular networks and Wi-Fi networks) for estimating the locations of mobile stations. The recurrent neural networks with multiple consecutive timestamps can be applied to extract the features of time series data for the improvement of location estimation. In practical experimental environments, there are 4,525 records, 59 different base stations, and 582 different Wi-Fi access points detected in Fuzhou University in China. The lower location errors can be obtained by the recurrent neural networks with multiple consecutive timestamps (e.g., 2 timestamps and 3 timestamps); the experimental results can be observed that the average error of location estimation was 9.19 meters by the proposed mobile positioning method with 2 timestamps.

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Fuel Consumption Estimation System and Method with Lower Cost

Cited by 10 publications

References 14 publications

Clustering-Based Neural Network for Carbon Dioxide Estimation

Clustering-Based Neural Network for Carbon Dioxide Estimation

IoT Based Smart Fuel Monitoring System

A Mobile Positioning Method Based on Deep Learning Techniques

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