Wyniki wyszukiwania - Biblioteka Nauki

1

3D LoD3 Modeling of High Building Using Terrestrial Laser Scanning and Unmanned Aerial Vehicle: A Case Study in Halong City, Vietnam

100%

Le T. T. H. , Nguyen Q. L.

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tom no. 2, vol. 1

303--310

EN

3D urban building models play an important role in the association, convergence and integration of economic and social urban data. 3D building reconstruction can be done from both the lidar and image-based point clouds, however, the lidar point clouds has dominated the research giving the 3D buildings reconstruction from aerial images point clouds less attention. The UAV images can be acquired at low cost, the workflow can be automated with minimal technical knowhow limitation. This promotes the necessity to understand and question to what extent the 3D buildings from UAV point clouds are complete and correct from data processing to parameter settings. This study focuses on proposing a process for building 3D geospatial data for a smart city using geospatial data collected by UAV and Terrestrial Laser Scanner. The experimental results have produced 3D geospatial data of high building in LoD3, with the root mean square error of the received test points mΔx=3.8 cm, mΔy=3.1 cm, and mΔH=7.5 cm.

2

General Geometric Model of GNSS Position Time Series for Crustal Deformation Studies – A Case Study of CORS Stations in Vietnam

80%

Tran D. T. , Nguyen Q. L. , Nguyen D. H.

Inżynieria Mineralna

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2021

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tom no. 2

183--197

EN

In processing of position time series of crustal deformation monitoring stations by continuous GNSS station, it is very important to determine the motion model to accurately determine the displacement velocity and other movements in the time series. This paper proposes (1) the general geometric model for analyzing GNSS position time series, including common phenomena such as linear trend, seasonal term, jumps, and post-seismic deformation; and (2) the approach for directly estimating time decay of postseismic deformations from GNSS position time series, which normally is determined based on seismic models or the physical process seismicity, etc. This model and approach are tested by synthetic position time series, of which the calculation results show that the estimated parameters are equal to the given parameters. In addition they were also used to process the real data which is GNSS position time series of 4 CORS stations in Vietnam, then the estimated velocity of these stations: DANA (n, e, u = -9.5, 31.5, 1.5 mm/year), HCMC (n, e, u = -9.5, 26.2, 1.9 mm/year), NADI (n, e, u = -10.6, 31.5, -13.4 mm/year), and NAVI (n, e, u = -13.9, 32.8, -1.1 mm/year) is similar to previous studies.

3

Developing an Advanced Soft Computational Model for Estimating Blast-Induced Ground Vibration in Nui Beo Open-pit Coal Mine (Vietnam) Using Artificial Neural Network

51%

Nguyen H. , Bui X. N. , Tran Q. H. , Nguyen Q. L. , Vu D. H. , Pham V. H. , Le Q. T. , Nguyen P. V.

Inżynieria Mineralna

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2019

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tom R. 21, nr 2/2

58--73

EN

The principal object of this study is blast-induced ground vibration (PPV), which is one of the dangerous side effects of blasting operations in an open-pit mine. In this study, nine artificial neural networks (ANN) models were developed to predict blast-induced PPV in Nui Beo open-pit coal mine, Vietnam. Multiple linear regression and the United States Bureau of Mines (USBM) empirical techniques are also conducted to compare with nine developed ANN models. 136 blasting operations were recorded in many years used for this study with 85% of the whole datasets (116 blasting events) was used for training and the rest 15% of the datasets (20 blasting events) for testing. Root Mean Square Error (RMSE), Determination Coefficient (R2), and Mean Absolute Error (MAE) are used to compare and evaluate the performance of the models. The results revealed that ANN technique is more superior to other techniques for estimating blast-induced PPV. Of the nine developed ANN models, the ANN 7-10-8-5-1 model with three hidden layers (ten neurons in the first hidden layer, eight neurons in the second layers, and five neurons in the third hidden layer) provides the most outstanding performance with an RMSE of 1.061, R2 of 0.980, and MAE of 0.717 on testing datasets. Based on the obtained results, ANN technique should be applied in preliminary engineering for estimating blast-induced PPV in open-pit mine.

4

A Lasso and Elastic-Net Regularized Generalized Linear Model for Predicting Blast-Induced Air Over-pressure in Open-Pit Mines

51%

Bui X. N. , Nguyen H. , Tran Q. H. , Bui H. B. , Nguyen Q. L. , Nguyen D. A. , Le T. T. H. , Pham V. V.

Inżynieria Mineralna

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2019

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tom R. 21, nr 2/2

8--20

EN

Air overpressure (AOp) is one of the products of blasting operations in open-pit mines which have a great impact on the environment and public health. It can be dangerous for the lungs, brain, hearing and the other human senses. In addition, the impact on the surrounding environment such as the vibration of buildings, break the glass door systems are also dangerous agents caused by AOp. Therefore, it should be properly controlled and forecasted to minimize the impacts on the environment and public health. In this paper, a Lasso and Elastic-Net Regularized Generalized Linear Model (GLMNET) was developed for predicting blast-induced AOp. The United States Bureau of Mines (USBM) empirical technique was also applied to estimate blast-induced AOp and compare with the developed GLMNET model. Nui Beo open-pit coal mine, Vietnam was selected as a case study. The performance indices are used to evaluate the performance of the models, including Root Mean Square Error (RMSE), Determination Coefficient (R2), and Mean Absolute Error (MAE). For this aim, 108 blasting events were investigated with the Maximum of explosive charge capacity, monitoring distance, powder factor, burden, and the length of stemming were considered as input variables for predicting AOp. As a result, a robust GLMNET model was found for predicting blast-induced AOp with an RMSE of 1.663, R2 of 0.975, and MAE of 1.413 on testing datasets. Whereas, the USBM empirical method only reached an RMSE of 2.982, R2 of 0.838, and MAE of 2.162 on testing datasets.

5

Use of Unmanned Aerial Vehicles for 3D topographic Mapping and Monitoring the Air Quality of Open-pit Mines

41%

Bui X. N. , Lee C. , Nguyen Q. L. , Adeel A. , Cao X. C. , Nguyen V. N. , Le V. C. , Nguyen H. , Le Q. T. , Duong T. H. , Nguyen V. D.

Inżynieria Mineralna

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2019

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tom R. 21, nr 2/2

223--239

EN

Recently remarkable advancement development of unmanned aerial vehicles (UAVs) has been observed and their applications have been shown in many fields such as agriculture, industry, and environmental management. However, in the mining industry, the application of UAV technology remains potential. This paper presents a low-cost unmanned aerial vehicle technology-based system for 3D mapping and air quality monitoring at open-pit mine sites in Vietnam. The system includes several dust sensors that are mounted on a low-cost rotary-wing type UAV. The system collects a variety of data, mainly images and airborne pollutant concentrations. To evaluate the performance of the proposed system, field tests were carried out at the Coc Sau coal mine. Based on the images transmitted to the ground monitoring station, large scale 3D topographic maps were successfully modeled. In addition, sensors mounted on the UAV system were able to monitor the levels of environmental variables associated with the air quality within the pit such as temperature, dust, CO, CO2, and NOx. The field test results in this study illustrate the applicability of the low-cost UAV for the 3D mapping and the air quality monitoring at large and deep coal pits with relatively high accuracy.