An Effective Fuel-Level Data Cleaning and Repairing Method for Vehicle Monitor Platform

Zhu, Yukai; Duan, Xuting; Hu, Junjie; Sheng, Zhengguo; Chen, Min; Wang, Jian; Wang, Yunpeng

doi:10.1109/tii.2018.2878396

Cited by 12 publications

(5 citation statements)

References 22 publications

(24 reference statements)

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“…Also, the author provides a method of automatic tuning. In order to clean and repair fuel level data, Tian et al [85] propose a modified Gaussian mixture model (GMM) based on the synchronous iteration method, which uses the particle swarm optimization algorithm and the steepest descent algorithm to optimize the parameters of GMM and uses linear interpolation-based algorithm to correct data errors. Shumway and Stoffer [79] use the EM [28] algorithm combined with the spatial state model [56], [70] to predict and smooth the time series.…”

Section: E Othersmentioning

confidence: 99%

Time Series Data Cleaning: A Survey

Wang

2020

IEEE Access

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Errors are prevalent in time series data, which is particularly common in the industrial field. Data with errors could not be stored in the database, which results in the loss of data assets. At present, to deal with these time series containing errors, besides keeping original erroneous data, discarding erroneous data and manually checking erroneous data, we can also use the cleaning algorithm widely used in the database to automatically clean the time series data. This survey provides a classification of time series data cleaning techniques and comprehensively reviews the state-of-the-art methods of each type. Besides we summarize data cleaning tools, systems and evaluation criteria from research and industry. Finally, we highlight possible directions time series data cleaning. INDEX TERMS Data cleaning, data quality, time series.

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Section: E Othersmentioning

confidence: 99%

Time Series Data Cleaning: A Survey

Wang

2020

IEEE Access

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“…Therefore, recent trends in transportation sustainability tend toward energy cost reduction in creative ways [20]. Recently, Tian et al [21] argued that the fuel monitor platform does not show accurate data due to interference, noise, and collision errors, though a high-precious fuel level sensor detects the level of fuel in a vehicle. Therefore, they proposed an effective and efficient method of repairing and cleaning the data to allow for a more precise fuel consumption.…”

Section: Theoretical Framework Of Related Researchmentioning

confidence: 99%

Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study

et al. 2020

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Public transportation is a vital service provided to enable a community to carry out daily activities. One of the mass transportations used in an area is a bus. Moreover, the smart transportation concept is an integrated application of technology and strategy in the transportation system. Using smart idea is the key to the application of the Internet of Things. The ways to improve the management transportation system become a bottleneck for the traditional data analytics solution, one of the answers used in machine learning. This paper uses the Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithm for the best prediction of travel time with a lower error rate on a case study of a university shuttle bus. Apart from predicting the travel time, this study also considers the fuel cost and gas emission from transportation. The analysis of the experiment shows that the ANN outperformed the SVM. Furthermore, a recommender system is used to recommend suitable routes for the chosen scenario. The experiments extend the discussion with a range of future directions on the stipulated field of study.

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“…In general fuel level is measured by using sensors but there are many errors associated with it. So, a model [1] has been developed to measure fuel level accurately in which it adopts the threshold, time quantum to get fuel data and errors are corrected by linear interpolation. Another system [2] has been developed which deals with the reliability issues of a diesel-powered fuel cell.…”

Section: Related Workmentioning

confidence: 99%

An Effective IoT based Fuel and Cost Monitoring System

Rosaline*¹,

Sheela²,

Hasmitha³

et al. 2019

IJRTE

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As vehicles count increases day by day, the fuel consumption is also increasing. With this tremendous increase in fuel consumption, fuel price is also increasing rapidly. By taking this as an advantage, fuel bunkers are stealing fuel and this cannot be noticed by users because we have no control over the machine. A monitoring system is required to track the fuel level. So, we are proposing a system that helps to indicate the quantity of fuel for the amount we have paid. A petrol level sensor that receives real time information about tank’s fuel level is used which is directly connected to the Arduino mega. A webpage is created to get the petrol cost. The page provides the user with an Admin username and password. The petrol cost is updated only through that webpage. Once the petrol cost is updated, then the IOT module used to receive the petrol cost and transmit it to the Arduino mega. For enabling the cost displaying feature, we have to provide a signal to the controller indicating the need for displaying the petrol cost that is to be added. Now once the switch is in pressed state, the 2.4 TFT display is used to display the petrol cost and when the key is denounced, the cost is reset to zero.

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An Effective Fuel-Level Data Cleaning and Repairing Method for Vehicle Monitor Platform

Abstract: Yunpeng (2019) An effective fuel level data cleaning and repairing method for vehicle monitor platform. IEEE Transactions on Industrial Informatics, 15 (1). pp. 410-416.

Cited by 12 publications

References 22 publications

Time Series Data Cleaning: A Survey

Time Series Data Cleaning: A Survey

Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study

An Effective IoT based Fuel and Cost Monitoring System

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