Vehicular Navigation Based on the Fusion of 3D-RISS and Machine Learning Enhanced Visual Data in Challenging Environments

Sun, Yunlong; Guan, Lei; Wu, Menghao; Gao, Yanbin; Chang, Zhanyuan

doi:10.3390/electronics9010193

Cited by 12 publications

(2 citation statements)

References 26 publications

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“…When dividing the whole study area with the above-mentioned unit block size, as long as ore bodies are included, they are assigned as vertical blocks of ore bodies, and there are a total of 14064 known ore bodies. It should be pointed out that it is extremely inaccurate to divide ore bodies at this scale for reserve estimation, but the block model constructed is intended to serve as positive sample markers in a wireless sensor [18,19].…”

Section: Three-dimensional Geological Modelingmentioning

confidence: 99%

[Retracted] Geological Mineral Energy and Classification Based on Machine Learning

2021

Wireless Communications and Mobile Computing

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In order to mine geological mineral energy and study on geological mineral energy classification, a method based on a wireless sensor was proposed. Of logistic regression, artificial neural networks, random forests, and main wireless sensor algorithms of support vector machine (SVM) with the model in the application of the energy mineral resource prediction practice effects are reviewed and discuss the practical application in the process of sample selection, the wrong points existing in the cost, the uncertainty evaluation, and performance evaluation of the model using wireless sensor algorithm, random forest of the probability distribution of mineralization in the study area is calculated, and five prospecting potential areas are delineated. The results show that the ratio of ore-bearing unit and non-ore-bearing unit is 1 : 1, and the best random forest training model is obtained. 70% of the training sample set was randomly selected as the training set, and the remaining 30% was used as the test set to construct the random forest model. The training accuracy of the model is 96.7%, and the testing accuracy is 96.5%. Both model training accuracy and model testing accuracy are very high, which proves the accuracy of RF model construction and achieves satisfactory results. In this study, a wireless sensor is successfully applied to 3D mineral energy prediction, which makes a positive exploration for mineral resource prediction and evaluation in the future. Finally, the prediction of mineral resource energy based on a wireless sensor is an important trend of future development.

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Section: Three-dimensional Geological Modelingmentioning

confidence: 99%

[Retracted] Geological Mineral Energy and Classification Based on Machine Learning

2021

Wireless Communications and Mobile Computing

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“…In addition, with the rapid development and application of the Artificial Intelligent (AI) technology, the AI-related intelligent technology is also a potential research direction for the optimal estimation of the multi-sensor-fused intelligent PIG surveying system [ 96 , 97 , 98 , 99 ].…”

Section: Trends and Challenges For Small-diameter Intelligent Pig mentioning

confidence: 99%

A Comprehensive Review of Micro-Inertial Measurement Unit Based Intelligent PIG Multi-Sensor Fusion Technologies for Small-Diameter Pipeline Surveying

et al. 2020

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It is of great importance for pipeline systems to be is efficient, cost-effective and safe during the transportation of the liquids and gases. However, underground pipelines often experience leaks due to corrosion, human destruction or theft, long-term Earth movement, natural disasters and so on. Leakage or explosion of the operating pipeline usually cause great economical loss, environmental pollution or even a threat to citizens, especially when these accidents occur in human-concentrated urban areas. Therefore, the surveying of the routed pipeline is of vital importance for the Pipeline Integrated Management (PIM). In this paper, a comprehensive review of the Micro-Inertial Measurement Unit (MIMU)-based intelligent Pipeline Inspection Gauge (PIG) multi-sensor fusion technologies for the transport of liquids and gases purposed for small-diameter pipeline (D<30cm) surveying is demonstrated. Firstly, four types of typical small-diameter intelligent PIGs and their corresponding pipeline-defects inspection technologies and defects-positioning technologies are investigated according to the various pipeline defects inspection and localization principles. Secondly, the multi-sensor fused pipeline surveying technologies are classified into two main categories, the non-inertial-based and the MIMU-based intelligent PIG surveying technology. Moreover, five schematic diagrams of the MIMU fused intelligent PIG fusion technology is also surveyed and analyzed with details. Thirdly, the potential research directions and challenges of the popular intelligent PIG surveying techniques by multi-sensor fusion system are further presented with details. Finally, the review is comprehensively concluded and demonstrated.

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Application of LSTM Neural Network in RISS/GNSS Integrated Vehicle Navigation System

Liu

Gao

Sun³

et al. 2022

Lecture Notes in Electrical Engineering

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Vehicular Navigation Based on the Fusion of 3D-RISS and Machine Learning Enhanced Visual Data in Challenging Environments

Cited by 12 publications

References 26 publications

[Retracted] Geological Mineral Energy and Classification Based on Machine Learning

[Retracted] Geological Mineral Energy and Classification Based on Machine Learning

A Comprehensive Review of Micro-Inertial Measurement Unit Based Intelligent PIG Multi-Sensor Fusion Technologies for Small-Diameter Pipeline Surveying

Application of LSTM Neural Network in RISS/GNSS Integrated Vehicle Navigation System

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