A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data

Abstract:With the rise and development of information technology (IT) services, the amount of data generated is rapidly increasing. Data from many different places are inconsistent. Data capture, storage and analysis have major challenges. Most data analysis methods are unable to handle such large amounts of data. Many studies employ neural networks, mostly specifying the number of hidden layers and neurons according to experience or formula. Different sets of network topologies have different results, and the best network model is selected. This investigation proposes a system based on the ensemble neural network (ENN). It creates multiple network models, each with different numbers of hidden layers and neurons. A model that does not achieve the accuracy rate is discarded. The proposed system derives the weighted average of all remaining network models to improve the accuracy of the prediction. This study applies the proposed method to generate agricultural yield predictions. The agricultural production process in Taiwan is more complex than those of manufacturing or other industries. The Council of Agriculture provides agricultural forecasting primarily based on the planted area and experience to predict the yield, but without consideration of the overall planting environment. This work applies the proposed data analysis method to agriculture. The method based on ENN has a much lower error rate than traditional back-propagation neural networks, while multiple regression analysis has an error rate of 12.4%. Experimental results reveal that the ENN method is better than traditional back-propagation neural networks and multiple regression analysis.

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“…This is based on mathematical principles, and can be categorized as descriptive statistics and inferential statistics [5][6][7].…”

Section: Discussionmentioning

confidence: 99%

“…(2) Data cleaning Planting area x 4 Air temperature x 5 Cost of production x 6 Market trading price Y Total harvest (…”

Section: ) Data Integrationmentioning

confidence: 99%

Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method

et al. 2016

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“…However, these techniques may no longer be applicable if the problems of multi-path propagation of wireless signals exist [13]. The study indicated that the availability of GPS may be lower in urban roads, and the GPS modules may be invalid [9]. Furthermore, higher power consumption is required by these techniques [1].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, higher power consumption is required by these techniques [1]. Therefore, some studies proposed cellular-based positioning methods to analyze the signals of cellular networks for reliably estimating the locations of mobile stations [1,5,9,10].…”

Section: Introductionmentioning

confidence: 99%

A Mobile Positioning Method Based on Deep Learning Techniques

Chen

Qishan

2019

Electronics

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This study proposes a mobile positioning method that 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 4525 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., two timestamps and three timestamps); from the experimental results, it can be observed that the average error of location estimation was 9.19 m by the proposed mobile positioning method with two timestamps.

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“…Furthermore, higher power consumptions are required by these techniques [1]. Therefore, some studies proposed cellular-based positioning methods to analyze the signals of cellular networks for location estimation [1, 6,8,11,13,14]. Although cellular-based positioning methods can estimate the locations of mobile stations without GPS modules, big errors of estimated locations may be obtained.…”

Section: Introductionmentioning

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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A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data

Cited by 11 publications

References 31 publications

Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method

Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method

A Mobile Positioning Method Based on Deep Learning Techniques

A Mobile Positioning Method Based on Deep Learning Techniques

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