|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | A robust approach to identify roof bolts in 3D point cloud data captured from a mobile laser scanner显示文摘Roof bolts such as rock bolts and cable bolts provide structural support in underground mines.Frequent assessment of these support structures is critical to maintain roof stability and minimise safety risks in underground environments.This study proposes a robust workflow to classify roof bolts in 3 D point cloud data and to generate maps of roof bolt density and spacing.The workflow was evaluated for identifying roof bolts in an underground coal mine with suboptimal lighting and global navigation satellite system(GNSS)signals not available.The approach is based on supervised classification using the multi-scale Canupo classifier coupled with a random sample consensus(RANSAC)shape detection algorithm to provide robust roof bolt identification.The issue of sparseness in point cloud data has been addressed through upsampling by using a moving least squares method.The accuracy of roof bolt identification was measured by correct identification of roof bolts(true positives),unidentified roof bolts(false negatives),and falsely identified roof bolts(false positives)using correctness,completeness,and quality metrics.The proposed workflow achieved correct identification of 89.27%of the roof bolts present in the test area.However,considering the false positives and false negatives,the overall quality metric was reduced to 78.54%. | Sarvesh Kumar Singh Simit Raval Bikram Banerjee | 2021 | International Journal of Mining Science and Technology2021，31，2： | 3 |
| 2 | A review of laser scanning for geological and geotechnical applications in underground mining显示文摘Laser scanning can provide timely assessments of mine sites despite adverse challenges in the operational environment.Although there are several published articles on laser scanning,there is a need to review them in the context of underground mining applications.To this end,a holistic review of laser scanning is presented including progress in 3D scanning systems,data capture/processing techniques and primary applications in underground mines.Laser scanning technology has advanced significantly in terms of mobility and mapping,but there are constraints in coherent and consistent data collection at certain mines due to feature deficiency,dynamics,and environmental influences such as dust and water.Studies suggest that laser scanning has matured over the years for change detection,clearance measurements and structure mapping applications.However,there is scope for improvements in lithology identification,surface parameter measurements,logistic tracking and autonomous navigation.Laser scanning has the potential to provide real-time solutions but the lack of infrastructure in underground mines for data transfer,geodetic networking and processing capacity remain limiting factors.Nevertheless,laser scanners are becoming an integral part of mine automation thanks to their affordability,accuracy and mobility,which should support their widespread usage in years to come. | Sarvesh Kumar Singh Bikram Pratap Banerjee Simit Raval | 2023 | International Journal of Mining Science and Technology2023，33，2： | 2 |
| 3 | Mapping Sensitive Vegetation Communities in Mining Eco-space using UAV-LiDAR显示文摘Near earth sensing from uncrewed aerial vehicles or UAVs has emerged as a potential approach for fne-scale environmental monitoring.These systems provide a cost-efective and repeatable means to acquire remotely sensed images in unprecedented spatial detail and a high signal-to-noise ratio.It is increasingly possible to obtain both physiochemical and structural insights into the environment using state-of-art light detection and ranging(LiDAR)sensors integrated onto UAVs.Monitoring sensitive environments,such as swamp vegetation in longwall mining areas,is essential yet challenging due to their inherent complexities.Current practices for monitoring these remote and challenging environments are primarily ground-based.This is partly due to an absent framework and challenges of using UAV-based sensor systems in monitoring such sensitive environments.This research addresses the related challenges in developing a LiDAR system,including a workfow for mapping and potentially monitoring highly heterogeneous and complex environments.This involves amalgamating several design components,including hardware integration,calibration of sensors,mission planning,and developing a processing chain to generate usable datasets.It also includes the creation of new methodologies and processing routines to establish a pipeline for efcient data retrieval and generation of usable products.The designed systems and methods were applied to a peat swamp environment to obtain an accurate geo-spatialised LiDAR point cloud.Performance of the LiDAR data was tested against ground-based measurements on various aspects,including visual assessment for generation LiDAR metrices maps,canopy height model,and fne-scale mapping. | Bikram Pratap Banerjee Simit Raval | 2022 | International Journal of Coal Science & Technology2022，9，3： | 2 |
| 4 | Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization显示文摘Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizations.The model was calibrated using 80 image samples that are captured for different coal samples in different angles.All the images were captured in RGB color space and converted into five other color spaces(HSI,CMYK,Lab,xyz,Gray)for feature extraction.The intensity component image of HSI color space was further transformed into four frequency components(discrete cosine transform,discrete wavelet transform,discrete Fourier transform,and Gabor filter)for the texture features extraction.A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development.The datasets of the optimized features were used as an input for the model,and their respective coal characteristics(analyzed in the laboratory)were used as outputs of the model.The R-squared values were found to be 0.89,0.92,0.92,and 0.84,respectively,for fixed carbon,ash content,volatile matter,and moisture content.The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression,support vector regression,and radial basis neural network models.The study demonstrates the potential of the machine vision system in automated coal characterization. | Amit Kumar Gorai Simit Raval Ashok Kumar Patel Snehamoy Chatterjee Tarini Gautam | 2021 | International Journal of Coal Science & Technology2021，8，4： | 0 |
| 5 | An investigation of machine learning techniques to estimate minimum horizontal stress magnitude from borehole breakout显示文摘Borehole breakout is a widely utilised phenomenon in horizontal stress orientation determination,and breakout geometrical parameters,such as width and depth,have been used to estimate both horizontal stress magnitudes.However,the accuracy of minimum horizontal stress estimation from borehole breakout remains relatively low in comparison to maximum horizontal stress estimation.This paper aims to compare and improve the minimum horizontal stress estimation via a number of machine learning(ML)regression techniques,including parametric and non-parametric models,which have rarely been explored.ML models were trained based on 79 laboratory data from published literature and validated against 23 field data.A systematic bias was observed in the prediction for the validation dataset whenever the horizontal stress value exceeded the maximum value in the training data.Nevertheless,the pattern was captured,and the removal of systematic bias showed that the artificial neural network is capable of predicting the minimum horizontal stress with an average error rate of 10.16%and a root mean square error of 3.87 MPa when compared to actual values obtained through conventional in-situ measurement techniques.This is a meaningful improvement considering the importance of in-situ stress knowledge for underground operations and the availability of borehole breakout data. | Huasheng Lin Sarvesh Kumar Singh Zizhuo Xiang Won Hee Kang Simit Raval Joung Oh Ismet Canbulat | 2022 | International Journal of Mining Science and Technology2022，32，5： | 0 |
| 6 | Spoil characterisation using UAV-based optical remote sensing in coal mine dumps显示文摘The structural integrity of mine dumps is crucial for mining operations to avoid adverse impacts on the triple bottom-line.Routine temporal assessments of coal mine dumps are a compliant requirement to ensure design reconciliation as spoil off-loading continues over time.Generally,the conventional in-situ coal spoil characterisation is inefficient,laborious,hazardous,and prone to experts'observation biases.To this end,this study explores a novel approach to develop automated coal spoil characterisation using unmanned aerial vehicle(UAV)based optical remote sensing.The textural and spectral properties of the high-resolution UAV images were utilised to derive lithology and geotechnical parameters(i.e.,fabric structure and relative density/consistency)in the proposed workflow.The raw images were converted to an orthomosaic using structure from motion aided processing.Then,structural descriptors were computed per pixel to enhance feature modalities of the spoil materials.Finally,machine learning algorithms were employed with ground truth from experts as training and testing data to characterise spoil rapidly with minimal human intervention.The characterisation accuracies achieved from the proposed approach manifest a digital solution to address the limitations in the conventional characterisation approach. | Sureka Thiruchittampalam Sarvesh Kumar Singh Bikram Pratap Banerjee Nancy F.Glenn Simit Raval | 2023 | International Journal of Coal Science & Technology2023，10，5： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员