scholarly journals Comparison of Three Location Estimation Methods of an Autonomous Driving Robot for Underground Mines

2020 ◽  
Vol 10 (14) ◽  
pp. 4831
Author(s):  
Heonmoo Kim ◽  
Yosoon Choi
Keyword(s):  
Autonomous Robots ◽  
Estimation Error ◽  
Autonomous Driving ◽  
Underground Mine ◽  
Location Estimation ◽  
Underground Mines ◽  
Estimation Methods ◽  
Measurement Unit ◽  
Average Error ◽  
Average Estimation Error

In this study, we compared the accuracy of three location estimation methods of an autonomous driving robot for underground mines: an inertial measurement unit with encoder (IMU + encoder) sensors, Light Detecting and Ranging with encoder (LiDAR + encoder) sensors, and IMU with LiDAR and encoder (IMU + LiDAR + encoder) sensors. An accuracy comparison experiment was conducted in an indoor laboratory composed of four sections (X-change, X-Y change, X-Z change, and Y-change sections) that simulated an underground mine. The robot’s location was estimated using each of the three location estimation methods as the autonomous driving robot moved, and the results accuracy was analyzed by comparing the estimated location with the robot’s actual location. From the results of the indoor experiments, the average estimation error of the IMU + LiDAR + encoder sensors was approximately 0.09 m, that of the IMU + encoder was 0.19 m, and that of the LiDAR + encoder was 0.81 m. In a field experiment, the average error of the IMU + LiDAR + encoder was approximately 0.11 m, that of the IMU + encoder was 0.17 m, and that of the LiDAR + encoder was 0.70 m. In conclusion, the IMU + LiDAR + encoder method, which uses three types of sensors, showed the highest accuracy in estimating the location of autonomous robots in an underground mine.

scholarly journals Infrastructure-Free Indoor Pedestrian Tracking with Smartphone Acoustic-Based Enhancement

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2458 ◽  
Author(s):  
Chao Liu ◽  
Sining Jiang ◽  
Shuo Zhao ◽  
Zhongwen Guo
Keyword(s):  
Doppler Effect ◽  
Location Estimation ◽  
Location Based Services ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Average Error ◽  
Pedestrian Tracking ◽  
Tracking Accuracy ◽  
Acoustic Frequency ◽  
Indoor Pedestrian Tracking

Indoor pedestrian tracking has been identified as a key technology for indoor location-based services such as emergency locating, advertising, and gaming. However, existing smartphone-based approaches to pedestrian tracking in indoor environments have various limitations including a high cost of infrastructure constructing, labor-intensive fingerprint collection, and a vulnerability to moving obstacles. Moreover, our empirical study reveals that the accuracy of indoor locations estimated by a smartphone Inertial Measurement Unit (IMU) decreases severely when the pedestrian is arbitrarily wandering with an unstable speed. To improve the indoor tracking performance by enhancing the location estimation accuracy, we exploit smartphone-based acoustic techniques and propose an infrastructure-free indoor pedestrian tracking approach, called iIPT. The novelty of iIPT lies in the pedestrian speed reliability metric, which characterizes the reliability of the pedestrian speed provided by the smartphone IMU, and in a speed enhancing method, where we adjust a relatively less reliable pedestrian speed to the more reliable speed of a passing by “enhancer” based on the acoustic Doppler effect. iIPT thus changes the encountered pedestrians from an“obstacle” into an “enhancer.” Extensive real-world experiments in indoor scenarios have been conducted to verify the feasibility of realizing the acoustic Doppler effect between smartphones and to identify the applicable acoustic frequency range and transmission distance while reducing battery consumption. The experiment results demonstrate that iIPT can largely improve the tracking accuracy and decrease the average error compared with a conventional IMU-based method.

scholarly journals The Perfect Storm: Gene Tree Estimation Error, Incomplete Lineage Sorting, and Ancient Gene Flow Explain the Most Recalcitrant Ancient Angiosperm Clade, Malpighiales

2020 ◽  
Author(s):  
Liming Cai ◽  
Zhenxiang Xi ◽  
Emily Moriarty Lemmon ◽  
Alan R Lemmon ◽  
Austin Mast ◽  
...  
Keyword(s):  
Gene Flow ◽  
Incomplete Lineage Sorting ◽  
Estimation Error ◽  
Gene Tree ◽  
Species Tree ◽  
Flowering Plant ◽  
Estimation Methods ◽  
Lineage Sorting ◽  
Tree Estimation ◽  
Perfect Storm

Abstract The genomic revolution offers renewed hope of resolving rapid radiations in the Tree of Life. The development of the multispecies coalescent (MSC) model and improved gene tree estimation methods can better accommodate gene tree heterogeneity caused by incomplete lineage sorting (ILS) and gene tree estimation error stemming from the short internal branches. However, the relative influence of these factors in species tree inference is not well understood. Using anchored hybrid enrichment, we generated a data set including 423 single-copy loci from 64 taxa representing 39 families to infer the species tree of the flowering plant order Malpighiales. This order includes nine of the top ten most unstable nodes in angiosperms, which have been hypothesized to arise from the rapid radiation during the Cretaceous. Here, we show that coalescent-based methods do not resolve the backbone of Malpighiales and concatenation methods yield inconsistent estimations, providing evidence that gene tree heterogeneity is high in this clade. Despite high levels of ILS and gene tree estimation error, our simulations demonstrate that these two factors alone are insufficient to explain the lack of resolution in this order. To explore this further, we examined triplet frequencies among empirical gene trees and discovered some of them deviated significantly from those attributed to ILS and estimation error, suggesting gene flow as an additional and previously unappreciated phenomenon promoting gene tree variation in Malpighiales. Finally, we applied a novel method to quantify the relative contribution of these three primary sources of gene tree heterogeneity and demonstrated that ILS, gene tree estimation error, and gene flow contributed to 10.0%, 34.8%, and 21.4% of the variation, respectively. Together, our results suggest that a perfect storm of factors likely influence this lack of resolution, and further indicate that recalcitrant phylogenetic relationships like the backbone of Malpighiales may be better represented as phylogenetic networks. Thus, reducing such groups solely to existing models that adhere strictly to bifurcating trees greatly oversimplifies reality, and obscures our ability to more clearly discern the process of evolution.

scholarly journals The Function Design for the Communication-Based Train Control (CBTC) System: How to Solve the Problems in the Underground Mine Rail Transportation?

2021 ◽  
Vol 4 (2) ◽  
pp. 31
Author(s):  
Haoxuan Yu ◽  
Shuai Li
Keyword(s):  
Mining Industry ◽  
Underground Mine ◽  
Underground Mines ◽  
Rail Transportation ◽  
Monitoring And Control ◽  
Transportation Equipment ◽  
Train Control ◽  
Function Design ◽  
The Future ◽  
Underground Transportation

With the continuous development of the mining industry, the world’s major mines have gradually entered the intelligent stage. In intelligent underground mines, the operation roads of the underground transportation equipment are very complicated, and the monitoring and control of the underground traffic have become the problems to be solved in the intelligent underground mines. Therefore, on the basis of solving the practical problems of underground mines, the concept paper discusses the possibility of the communication-based train control (CBTC) system being applied to underground mines through the summary and induction of the related literature. As mining engineers, we have proposed the function design for the CBTC system to solve the problems in underground mine rail transportation, but we still need to continue to work hard for the future development of the underground mines. The concept paper serves as a guide to the Tossing out a brick to get a jade gem, and it has implications for the development and the future of underground mine transportation.

scholarly journals Real-Time Single Image Depth Perception in the Wild with Handheld Devices

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 15
Author(s):  
Filippo Aleotti ◽  
Giulio Zaccaroni ◽  
Luca Bartolomei ◽  
Matteo Poggi ◽  
Fabio Tosi ◽  
...  
Keyword(s):  
Real Time ◽  
Depth Perception ◽  
Depth Estimation ◽  
Autonomous Driving ◽  
Estimation Methods ◽  
Handheld Devices ◽  
Single Image ◽  
Handheld Device ◽  
Time Performance ◽  
In The Wild

Depth perception is paramount for tackling real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image would represent the most versatile solution since a standard camera is available on almost any handheld device. Nonetheless, two main issues limit the practical deployment of monocular depth estimation methods on such devices: (i) the low reliability when deployed in the wild and (ii) the resources needed to achieve real-time performance, often not compatible with low-power embedded systems. Therefore, in this paper, we deeply investigate all these issues, showing how they are both addressable by adopting appropriate network design and training strategies. Moreover, we also outline how to map the resulting networks on handheld devices to achieve real-time performance. Our thorough evaluation highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications. Indeed, to further support this evidence, we report experimental results concerning real-time, depth-aware augmented reality and image blurring with smartphones in the wild.

RF Vital Sign Sensing under Free Body Movement

2021 ◽  
Vol 5 (3) ◽  
pp. 1-22
Author(s):  
Jian Gong ◽  
Xinyu Zhang ◽  
Kaixin Lin ◽  
Ju Ren ◽  
Yaoxue Zhang ◽  
...  
Keyword(s):  
Vital Sign ◽  
Estimation Error ◽  
Body Movement ◽  
Vital Signs ◽  
Motion Artifacts ◽  
Body Parts ◽  
Training Scheme ◽  
Pattern Movement ◽  
Free Body ◽  
Average Estimation Error

Radio frequency (RF) sensors such as radar are instrumental for continuous, contactless sensing of vital signs, especially heart rate (HR) and respiration rate (RR). However, decades of related research mainly focused on static subjects, because the motion artifacts from other body parts may easily overwhelm the weak reflections from vital signs. This paper marks a first step in enabling RF vital sign sensing under ambulant daily living conditions. Our solution is inspired by existing physiological research that revealed the correlation between vital signs and body movement. Specifically, we propose to combine direct RF sensing for static instances and indirect vital sign prediction based on movement power estimation. We design customized machine learning models to capture the sophisticated correlation between RF signal pattern, movement power, and vital signs. We further design an instant calibration and adaptive training scheme to enable cross-subjects generalization, without any explicit data labeling from unknown subjects. We prototype and evaluate the framework using a commodity radar sensor. Under a variety of moving conditions, our solution demonstrates an average estimation error of 5.57 bpm for HR and 3.32 bpm for RR across multiple subjects, which largely outperforms state-of-the-art systems.

Utilizing Data-Driven Models to Predict Brittleness in Tuscaloosa Marine Shale: A Machine Learning Approach

2021 ◽  
Author(s):  
Jamal Ahmadov
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Brittleness Index ◽  
Estimation Methods ◽  
Gradient Boosting ◽  
Average Error ◽  
Support Vector ◽  
Marine Shale ◽  
Effective Manner ◽  
Selection Of

Abstract The Tuscaloosa Marine Shale (TMS) formation is a clay- and liquid-rich emerging shale play across central Louisiana and southwest Mississippi with recoverable resources of 1.5 billion barrels of oil and 4.6 trillion cubic feet of gas. The formation poses numerous challenges due to its high average clay content (50 wt%) and rapidly changing mineralogy, making the selection of fracturing candidates a difficult task. While brittleness plays an important role in screening potential intervals for hydraulic fracturing, typical brittleness estimation methods require the use of geomechanical and mineralogical properties from costly laboratory tests. Machine Learning (ML) can be employed to generate synthetic brittleness logs and therefore, may serve as an inexpensive and fast alternative to the current techniques. In this paper, we propose the use of machine learning to predict the brittleness index of Tuscaloosa Marine Shale from conventional well logs. We trained ML models on a dataset containing conventional and brittleness index logs from 8 wells. The latter were estimated either from geomechanical logs or log-derived mineralogy. Moreover, to ensure mechanical data reliability, dynamic-to-static conversion ratios were applied to Young's modulus and Poisson's ratio. The predictor features included neutron porosity, density and compressional slowness logs to account for the petrophysical and mineralogical character of TMS. The brittleness index was predicted using algorithms such as Linear, Ridge and Lasso Regression, K-Nearest Neighbors, Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost and Gradient Boosting. Models were shortlisted based on the Root Mean Square Error (RMSE) value and fine-tuned using the Grid Search method with a specific set of hyperparameters for each model. Overall, Gradient Boosting and Random Forest outperformed other algorithms and showed an average error reduction of 5 %, a normalized RMSE of 0.06 and a R-squared value of 0.89. The Gradient Boosting was chosen to evaluate the test set and successfully predicted the brittleness index with a normalized RMSE of 0.07 and R-squared value of 0.83. This paper presents the practical use of machine learning to evaluate brittleness in a cost and time effective manner and can further provide valuable insights into the optimization of completion in TMS. The proposed ML model can be used as a tool for initial screening of fracturing candidates and selection of fracturing intervals in other clay-rich and heterogeneous shale formations.

scholarly journals End-Effector Pose Estimation in Complex Environments Using Complementary Enhancement and Adaptive Fusion of Multisensor

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mingrui Luo ◽  
En Li ◽  
Rui Guo ◽  
Jiaxin Liu ◽  
Zize Liang
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Unscented Kalman Filter ◽  
Rbf Neural Network ◽  
Estimation Error ◽  
Estimation Method ◽  
Original Data ◽  
Measurement Unit ◽  
Complex Environments ◽  
End Effector

Redundant manipulators are suitable for working in narrow and complex environments due to their flexibility. However, a large number of joints and long slender links make it hard to obtain the accurate end-effector pose of the redundant manipulator directly through the encoders. In this paper, a pose estimation method is proposed with the fusion of vision sensors, inertial sensors, and encoders. Firstly, according to the complementary characteristics of each measurement unit in the sensors, the original data is corrected and enhanced. Furthermore, an improved Kalman filter (KF) algorithm is adopted for data fusion by establishing the nonlinear motion prediction of the end-effector and the synchronization update model of the multirate sensors. Finally, the radial basis function (RBF) neural network is used to adaptively adjust the fusion parameters. It is verified in experiments that the proposed method achieves better performances on estimation error and update frequency than the original extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithm, especially in complex environments.

scholarly journals Investigation and optimization of main materials consumption when mining iron ores at deep levels of the Underground Mine Group of the PJSC “ArcelorMittal Kryvyi Rih”

2020 ◽  
Vol 201 ◽  
pp. 01026
Author(s):  
Mykola Stupnik ◽  
Vsevolod Kalinichenko ◽  
Olena Kalinichenko ◽  
Sofiia Yakovlieva
Keyword(s):  
Strain State ◽  
Iron Ore ◽  
Deep Levels ◽  
Underground Mine ◽  
Underground Mines ◽  
Iron Ores ◽  
Efficient Technology ◽  
Mining Depth ◽  
Geological Conditions ◽  
Qualitative Characteristics

The work considers conditions of deep levels of the Underground Mine Group for underground ore mining (as underground mines) of the Mining Department of the PJSC “ArcelorMittal Kryvyi Rih” (the PJSC “ArcelorMittal Kryvyi Rih”). The research aims to improve indicators of mined ore mass extraction when mining rich iron ores through studying and optimizing consumption of explosives, enhancing mining technology to provide fulfilment of the underground iron ore mining program. During the research, there are analyzed mining geological and technical conditions of the deposit mining as well as current technologies of iron ore mining at the Underground Mine Group of the PJSC “ArcelorMittal Kryvyi Rih”. The work analyzes the achieved indices and consumption of explosives for drilling and blasting at the Underground Mine Group. The mining geological and technical conditions of the deposit mining as well as current technologies of mining, parameters of preparatory operations, the nomenclature and qualitative characteristics of many types of explosives are determined to have changed. This complicates planning consumption of explosives and making their estimates for work sites. However, this is a reason for selecting highly efficient technology and machinery in deteriorating mining and geological conditions of operating at over 1200 m depths. The work determines dependencies of a stress value on a mining depth and physical properties of rocks, as well as parameters of drilling and blasting operations considering the stress-strain state of the massif under high rock pressure at deep levels of the Mining Group of the PJSC “ArcelorMittal Kryvyi Rih”.

scholarly journals Estimating fusion properties for functionalised acids

2011 ◽  
Vol 11 (16) ◽  
pp. 8385-8394 ◽  
Author(s):  
S. Compernolle ◽  
K. Ceulemans ◽  
J.-F. Müller
Keyword(s):  
Vapour Pressure ◽  
Estimation Error ◽  
Estimation Method ◽  
Organic Aerosol ◽  
Estimation Methods ◽  
Fusion Temperature ◽  
Subcooled Liquid ◽  
Fusion Data ◽  
Simple Estimation ◽  
Data Estimation

Abstract. Multicomponent organic aerosol (OA) is likely to be liquid, or partially liquid. Hence, to describe the partitioning of these components, their liquid vapour pressure is desired. Functionalised acids (e.g. diacids) can be a significant part of OA. But often measurements are available only for solid state vapour pressure, which can differ by orders of magnitude from their liquid counterparts. To convert such a sublimation pressure to a subcooled liquid vapour pressure, fusion properties (two out of these three quantities: fusion enthalpy, fusion entropy, fusion temperature) are required. Unfortunately, experimental knowledge of fusion properties is sometimes missing in part or completely, hence an estimation method is required. Several fusion data estimation methods are tested here against experimental data of functionalised acids, and a simple estimation method is developed, specifically for this family of compounds, with a significantly smaller estimation error than the literature methods.

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