Location Estimation Method Research Articles

Recognition of interface and category of roadway roof strata based on drilling parameters

Measurement while drilling is an important part of the intelligent development of coal mines and the development trend of roof formation detection. The main purpose of this paper is to study the best identification method of rock interface and category based on drilling parameters. Firstly, the borehole drilling device was independently developed; Secondly, 4 concrete blocks were poured to simulate the different rock formations; Thirdly, displacement, rotational speed, torque, and sound pressure level data were collected during drilling the concrete blocks, and the original data was processed by the local linear kernel estimation method; Finally, K-means clustering and change points detection method, was used to identify the rock formation interface respectively, and BP neural network and support vector machine method was used to identify the rock category respectively. Experimental results show that the local linear kernel estimation method based on exponentially weighted loss function can better reflect the response characteristics of drilling parameters. The change point detection method based on the penetration rate is better for the rock interface prediction (the sum of the two interface errors is 10.25 mm). Besides, with the penetration rate and sound pressure level as input parameters, BP neural network and support vector machine can achieve the operational classification of rock formations. This research is of great significance for understanding the prediction of the interface and category of rock formation in coal mines, which can not only promote safe mining but also reflect efficient coal production.

An improved version of local activities estimation to enhance motor imagery classification

ObjectiveThe common spatial pattern (CSP) and its variants are popular in the EEG-based motor imagery BCIs. However, this method has some drawbacks, especially when a few labeled samples are available. The local activities estimation (LAE) method works well with small training sets, but it is more sensitive to the position of the electrodes. Here, we suggest a combination of the LAE and CSP, namely LAE-CSP, which performs better than both methods. MethodsIn LAE-CSP, the EEG signal passes through regularized CSP and LAE spatial filters after band-pass filtering and then the data dimension are reduced based on the physiological data. Afterwards, the features are extracted using fast Fourier transform (FFT). In this work, CSP, FBRCSP, EA-CSP, LAE and LAE-CSP, methods were evaluated and compared. Three sets of motor imagery data from BCI competition III and IV along with Cho et al. dataset, including EEG signals from 28 subjects were used in this study. For each dataset, the training set were selected in 21 different sizes. ResultsLAE-CSP performs better than all tested methods. Particularly it has good performance using only ten labeled samples per class, with an average accuracy of almost 80 %. ConclusionsLAE-CSP reliably enhances the performance of the motor imagery-based brain-computer interfaces. SignificanceThe LAE-CSP takes advantage of the LAE and CSP and compensates for the drawbacks of both methods.

Non-line-of-sight object location estimation from scattered light using plenoptic data.

We investigate the use of plenoptic data for locating non-line-of-sight (NLOS) objects from a scattered light signature. Using Fourier analysis, the resolution limits of the depth and transversal location estimates are derived from fundamental considerations on scattering physics and measurement noise. Based on the refocusing algorithm developed in the computer vision field, we derive an alternative formulation of the projection slice theorem in a form directly connecting the light field and a full spatial frequency spectrum including both depth and transversal dimensions. Using this alternative formulation, we propose an efficient spatial frequency filtering method for location estimation that is defined on a newly introduced mixed space frequency plane and achieves the theoretically limited depth resolution. A comparison with experimental results is reported.

Penerapan Model Mixed Geographically Weighted Regression dengan Fungsi Pembobot Adaptive Tricube pada IPM 30 Kabupaten/Kota di Provinsi Kalimantan Timur, Kalimantan Tengah dan Kalimantan Selatan Tahun 2016

Mixed Geographically Weighted Regression (MGWR) model is a Geographically Weighted Regression (GWR) model which has global (equal value) and local (inequal value) parameters at every different observation location. The goal of this study is to obtain MGWR model of the Human Development Index (HDI) data and find out significant factors influencing the HDI in each district (city) East Kalimantan, Central Kalimantan and South Kalimantan province in 2016. Parameter estimation method is conducted in two stages namely local parameter estimation and global parameter estimation. Local parameter estimation method is Maximum Likelihood Estimation (MLE), with spatial weighting is calculated by adaptive tricube weighting function and optimum bandwidth determination uses the Akaike Information Criteria (AIC). Global parameter estimation method is Ordinary Least Square (OLS). Based on the result of MGWR parameter testing, it was concluded that the school enrollment rates (SMP) and poor people percentage affected the HDI of 30 districts (cities) in East Kalimantan, Central Kalimantan and South Kalimantan. Meanwhile the population density affected the HDI of two districts namely HDI of Samarinda and Bontang.

Communication and Localization With Extremely Large Lens Antenna Array

Achieving high-rate communication with accurate localization and wireless environment sensing has emerged as an important trend of beyond-fifth and sixth generation cellular systems. Extension of the antenna array to an extremely large scale is a potential technology for achieving such goals. However, the super massive operating antennas significantly increases the computational complexity of the system. Motivated by the inherent advantages of lens antenna arrays in reducing system complexity, we consider communication and localization problems with an ex tremely large lens antenna array, which we call “ExLens”. Since radiative near-field property emerges in the setting, we derive the closed-form array response of the lens antenna array with spherical wave, which includes the array response obtained on the basis of uniform plane wave as a special case. Our derivation result reveals a window effect for energy focusing property of ExLens, which indicates that ExLens has great potential in position sensing and multi-user communication. We also propose an effective method for location and channel parameters estimation, which is able to achieve the localization performance close to the Cramer-Rao lower bound. Finally, we examine the multi-user communication performance of ExLens that serves coexisting near-field and far-field users. Numerical results demonstrate the effectiveness of the proposed channel estimation method and show that ExLens with a minimum mean square error receiver achieves significant spectral efficiency gains and complexity-and-cost reductions compared with a uniform linear array.

О полях напряжений в гетерогенных средах, определяемых методом акустической эмиссии

Local internal stress estimation method based on the S.N. Zhurkov kinetic concept of solids fracture is introduced. The stress field is computed from the time series of acoustic emission intervals between the successive signals. For the case of two structurally quite different materials the time evolution of this stress is examined.

Manifold Optimization for High-Accuracy Spatial Location Estimation Using Ultrasound Waves

This paper designs a high accuracy spatial location estimation method using ultrasound waves by exploiting the fixed geometry of the transmitters. Assuming an equilateral triangle antenna configuration, where three antennas are placed as the vertices of an equilateral triangle, the spatial location problem can be formulated as a non-convex optimization problem whose interior is shown to admit a Riemannian manifold structure. The investigation of the geometry of the newly introduced manifold, i.e. the manifold of all equilateral triangles in R^3, allows the design of highly efficient optimization algorithms. Simulation results are presented to compare the performance of the proposed approach against popular methods from the literature. The results suggest that the proposed Riemannian-based methods outperform the state-of-the-art methods. Furthermore, the proposed Riemannian methods require much smaller computation time as compared with popular generic non-convex approaches.

Self-Driving Algorithm and Location Estimation Method for Small Environmental Monitoring Robot in Underground Mines

In underground mine environments where various hazards exist, such as tunnel collapse, toxic gases, the application of autonomous robots can improve the stability of exploration and efficiently perform repetitive exploratory operations. In this study, we developed a small autonomous driving robot for unmanned environmental monitoring in underground mines. The developed autonomous driving robot controls the steering according to the distance to the tunnel wall measured using the light detection and ranging sensor mounted on the robot to estimate its location by simultaneously considering the measured values of the inertial measurement unit and encoder sensors. In addition, the robot autonomously drives through the underground mine and performs environmental monitoring using the temperature/humidity, gas, and particle sensors mounted on the robot. As a result of testing the performance of the developed robot at an amethyst mine in Korea, the robot was found to be able to autonomously drive through tunnel sections with ∼28 m length, ∼2.5 m height, and∼3 m width successfully. The average error of location estimation was approximately 0.16 m. Using environmental monitoring sensors, temperature of 15–17◦C, humidity of 42%–43%, oxygen concentration of 15.6%–15.7%, and particle concentration of 0.008–0.38 mg/m3 were measured in the experimental area, and no harmful gases were detected. In addition, an environmental monitoring map could be created using the measured values of the robot’s location coordinates and environmental factors recorded during autonomous driving.

Wide-Area Identification of the Size and Location of Loss of Generation Events by Sparse PMUs

Timely identification of the size and location of loss of generation (LoG) events improves the effectiveness of remedial actions taken against this type of disturbances. This paper sets forth a novel method for LoG localization and size estimation by phasor measurements received in the control center within a reasonable wait time following the event inception. The bus impedance matrix is utilized to obtain a transfer function from the LoG location to variations of voltage and current phasors following the event. This results in an overdetermined system of linear equations whose closed-form solution provides the LoG location and size based upon the sum of squared residuals concept. The proposed method removes the need for the reception of specific measurements in the control center, and/or knowing the system inertia. This is in contrast with previous methods resorting to the swing equation of the center of inertia. The method lends itself to real-time applications for its robustness against partial communication network failures and losses of the time synchronization signal. Extensive simulations conducted on the IEEE 39-bus and 118-bus test systems verify the effectiveness and superiority of the proposed method over existing ones.

Novel secure positioning method in ultra‐wide band framework based on overshadowing attack probabilistic model

The extensive use of Internet of things (IoT) devices demonstrates an increasing demand for secure frameworks. Position altering attacks are amongst the most severe vulnerabilities in two-way time of arrival distance measurement technique, which is the most common in distance measurements by IoT devices. The overshadowing attack as a distance enlargement attack replays the authentic signal with a certain amount of delay and sensible power amplification. Therefore, the attack detection is difficult and requires a precise model to estimate its behaviour. The contribution of this study is a location estimation method based on the overshadowing attack probabilistic model that estimates the attack outcome delay and corrects the erroneous measurements. The overshadow delay, which is the delay between the authentic signal and the attacked one, plays a key role in this process. The method has been evaluated by three attack scenarios and compared to the conventional maximum likelihood (CML) and mixture secure location of things (M-SLOT) methods. The results of the proposed method demonstrate root mean squared error (RMSE) of 0.48 in position estimation. With the same dataset, RMSEs of M-SLOT and CML methods are 4.69 and 9.84, respectively, which confirm the better performance of the proposed method.

A fast human action recognition network based on spatio-temporal features

Artificial intelligence models are widely used in the field of human activity recognition, and human action recognition is an important aspect of human activity recognition. The core of human action recognition is to understand the temporal relationship between video frames. Almost all state-of-the-art methods of human action recognition in videos use optical flow. However, traditional local optical flow estimation methods areexpensive and not trained end-to-end. In this paper, we propose a fast network for human action recognition. Our purpose is to improve the efficiency of optical flow feature extraction and explore the fusion method of spatio-temporal features. For spatio-temporal features, our method combines spatial features and temporal features into fusion features. In addition, we propose CNN with OFF instead of the VGG16 network, which is used to process optical flow features to obtain abundant features. Our model only needs RGB inputs to get the state-of-the-art accuracy of 91.5% on UCF-101, 67.9% on HMDB51, 83.3% on MSR Daily Activity3D, and 91.25% on Florence 3D action, respectively. Compared with most state-of-the-art video action recognition models, our proposed model can effectively improve the accuracy of human action recognition.

GEOSTATISTICAL APPROACH FOR THE ESTIMATION OF SHEAR-HOSTED GOLD DEPOSIT: A CASE STUDY OF THE OBUASI GOLD DEPOSIT, GHANA

Underground mining at Obuasi in Ghana has been in operation since 1947. This paper uses geostatistical methods to evaluate gold ore blocks to ensure reliable grades for mining large tonnage and low-grade resources. Historically, the principal ores were low tonnage, high grade and relatively homogeneous quartz stockwork with simple geometry and average bulk grades in the range of 20-30 g/t that were evaluated using conventional polygonal methods and mined by semi-mechanized means. Currently, the ore is a shear-hosted mixed quartz vein and disseminated sulphide type deposit of low grade that is mined using highly mechanized means. The need therefore arises for a re-assessment of the estimation procedures to ensure prolonged and more profitable mining. Both diamond drill (DD) core and stope/cross-cut channel samples were taken from Block 1 at the mine for analyses and re-assessment. A wireframe model was used to constrain the three dimensional (3D) block model of the deposit. Ordinary kriging (OK) and multiple indicator kriging (MIK) geostatistical methods were used to estimate gold grades. Grade distribution is positively skewed with high spatial variability and extreme values while background values are established as <0.6 g/t. The Spatial variability is characterized by fitting models on experimental variograms. The MIK approach mitigates the effects of outliers and establish grades that are consistently lower than the OK and the weighted average method that are widely used at the mine. The MIK method, a non-linear, non-parametric method of local grade estimation are applicable to the deposit architecture. Profoundly, the MIK method is a more reliable approach considering the fact that the MCF based on the estimates at the mine are high despite operational deficiencies on the mine. The results from this study demonstrates usefulness of geostatistics to determine the architecture of Au mineralization at the deposit scale.

Analysis of the wave propagation paths in numerical reinforced concrete models

The paper aims to study the characteristics of the wave propagation paths in reinforced concrete and to improve the existing velocity models. Therefore, coefficients of reflection and transmission with regard to energy were analyzed for each component of the reinforced concrete (cement matrix, aggregate grains, air voids and reinforcement bars) in case of a free- and joint-material boundary, respectively. The findings were employed to investigate the wave propagation paths in cracked concrete and reinforced concrete. Three-dimensional numerical models to approximate the heterogeneous concrete (numerical concrete model, NCM) and reinforced concrete (numerical reinforced concrete model, NRCM) were implemented in a rotated staggered-grid finite-differences scheme. Numerical simulations of the elastic wave propagation were performed to illustrate the interaction of waves with boundaries. Both velocity models, NCM and NRCM, were applied to localize acoustic emissions (AE). An established source localization estimation method (FastWay) served as a tool to validate the heterogeneous velocity models. FastWay was applied to a synthetic cracked reinforced concrete beam to localize six randomly located AE sources. The influence of non-straight wave propagation paths on the performance of the localization method was discussed.

The effect of renewable and non-renewable energy consumption on economic growth: Non-parametric evidence

We employ a non-parametric modelling technique to examine the time-varying impact of renewable and non-renewable energy consumption on economic growth. The specific nonparametric method that we employ is the local linear dummy variable estimation (LLDVE) method, which we apply to OECD and non-OECD panels for the period 1990 to 2015. While previous studies employing parametric models have recognised the existence of non-linearities and instability in the relationship between renewable (and non-renewable) energy consumption and economic growth, the LLDVE method has the advantage that it does not make any assumptions about functional form and, hence, is better able to approximate the non-linear relationship. Our estimates suggest that non-renewable energy consumption exerts a positive and significant impact on economic growth across OECD nations with the coefficient function exhibiting an upward trajectory over time. The impact of renewable energy consumption on economic growth, however, is statistically indistinguishable from zero in these countries for most of the study period. Both renewable and non-renewable energy consumption promote economic growth in non-OECD countries, suggesting that developing countries may play an important role in the transition process to renewables despite constraints in technical progress.

Robust Nonstationary Local Slope Estimation

The plane-wave destruction (PWD) method has been a widely used local slope estimation method in the seismic community. It is based on the discretization of the plane-wave partial differential equation (PDE) and the linearization of the PDE with respect to the local slope. Solving the linearized inverse problem for the slope perturbation is equivalent to solving a smoothness constrained optimization based on a shaping regularization method. The smoothness constraint in the shaping regularization is important in that it not only controls the stability and smoothness of the solution, i.e., slope perturbation, but also affects the accuracy and resolution of the solution. The traditional PWD algorithm is not easy to compromise between the smoothness and resolution of the estimated local slope because it uses a stationary triangle smoothing operator as the shaping operator. Here, we propose to improve the robustness of the PWD algorithm by introducing a nonstationary triangle smoothing operator into the shaping regularization framework in order to adaptively constrain the solution according to the local signal reliability. The smoothing is weak in areas with a higher probability of signals and is strong in areas with a higher likelihood of noise. The smoothing radius can be adaptively estimated based on an optimization model, which is solved by a line-search method. The proposed new slope estimation method is referred to as a nonstationary method compared with the traditional stationary one. The effectiveness and benefits of the new slope estimation method are validated via several synthetic and field data examples.

Comparing local ancestry inference models in populations of two- and three-way admixture.

Local ancestry estimation infers the regional ancestral origin of chromosomal segments in admixed populations using reference populations and a variety of statistical models. Integrating local ancestry into complex trait genetics has the potential to increase detection of genetic associations and improve genetic prediction models in understudied admixed populations, including African Americans and Hispanics. Five methods for local ancestry estimation that have been used in human complex trait genetics are LAMP-LD (2012), RFMix (2013), ELAI (2014), Loter (2018), and MOSAIC (2019). As users rather than developers, we sought to perform direct comparisons of accuracy, runtime, memory usage, and usability of these software tools to determine which is best for incorporation into association study pipelines. We find that in the majority of cases RFMix has the highest median accuracy with the ranking of the remaining software dependent on the ancestral architecture of the population tested. Additionally, we estimate the O(n) of both memory and runtime for each software and find that for both time and memory most software increase linearly with respect to sample size. The only exception is RFMix, which increases quadratically with respect to runtime and linearly with respect to memory. Effective local ancestry estimation tools are necessary to increase diversity and prevent population disparities in human genetics studies. RFMix performs the best across methods, however, depending on application, other methods perform just as well with the benefit of shorter runtimes. Scripts used to format data, run software, and estimate accuracy can be found at https://github.com/WheelerLab/LAI_benchmarking.

Preliminary zoning of negative impact of the projected oil waste polygon at the Samotlor field

This paper provides a comprehensive analysis of the building expediency of a polygon for processing oil wastes in KhMAD-Yugra. Preliminary research has shown the environmentally dangerous impact of its project location relative to Nizhnevartovsk city, village Izluchinsk and village Bolshetarkhovo, as well as other important objects. The method of location estimation based on the main rules of life safety is proposed. Its application will reduce the environmentally dangerous impact and make it possible to choose the most optimal location of the polygon. An environmental pollution assessment scale has been developed, presented in tabular form for convenience. The area of zones of different dangerous influence degrees is calculated. The article describes the increase in the probability of earthquakes, wildland fires and floods in the area of the Samotlor field polygon due to global warming. The article also considers the prospect of using GIS programs in modeling environmental phenomena that affect oil and gas production facilities.

Iterative passive-source location estimation and velocity inversion using geometric-mean reverse-time migration and full-waveform inversion

SUMMARYPassive-seismic provides useful information for reservoir monitoring and structural imaging; for example, the locations of microseismic events are helpful to understand the extension of the hydraulic fracturing. However, passive-seismic imaging still faces some challenges. First, it is not easy to know where the passive-seismic events happened, which is known as passive-source locating. Additionally, the accuracy of the subsurface velocity model will influence the accuracy of the estimated passive-source locations and the quality of the structural imaging obtained from the passive-seismic data. Therefore the velocity inversion using the passive-seismic data is required to provide the velocity with higher accuracy. Focusing on these challenges, we develop an iterative passive-source location estimation and velocity inversion method using geometric-mean reverse-time migration (GmRTM) and full-waveform inversion (FWI). In each iteration, the source location is estimated using a high-resolution GmRTM method, which provides a better focusing of passive-source imaging compared to conventional wavefield scanning method. The passive-source FWI is then followed to optimize the velocity model using the estimated source location provided by GmRTM. The source location estimation and velocity inversion are implemented sequentially. We evaluate this iterative method using the Marmousi model data set. The experiment result and sensitivity analysis indicate that the proposed method is effective to locate the sources and optimize velocity model in the areas with complicated subsurface structures and noisy recordings.

A Pipeline Leak Classification and Location Estimation System With Convolutional Neural Networks

An accurate pipeline leak classification and location estimation method can help to control and reduce the damage to the environment when spills happen. Some of the current research on this topic rely on the direct analysis of target frequencies from the monitoring of sensors. However, this assumes that the frequencies may be known before hand and such analysis can be very cumbersome. In this article, we propose convolutional neural network-based classification and location estimation methods, which use raw data instead of prefiltered (or preconditioned) information. The design approach is fully described and the network structure is discussed. Finally, analysis of experimental results validate the proposed network demonstrating that the classification and location estimation can be done with good accuracy.

Estimating local slope in the time-frequency domain: Velocity-independent seismic imaging in the near surface

Seismic reflection processing for multicomponent data is very time consuming. To automatically streamline and shorten this process, a new approach for estimating the local event slope (local static shift) in the time-frequency domain is proposed and tested. The seismic event slope is determined by comparing the local phase content of Stockwell transformed signals. This calculation allows for noninterfering arrivals to be aligned by iteratively correcting trace by trace. Alternatively, the calculation can be used in a velocity-independent imaging framework with the possibility of exporting the determined time and velocities for each common midpoint gather, which leads to a more robust moveout correction. Synthetic models are used to test the robustness of the calculation and compare it directly to an existing method of local slope estimation. Compared to dynamic time warping, our method is more robust to noise but less robust to large time shifts, which limits our method to shorter geophone spacing. We apply the calculation to near-surface shear-wave data and compare it directly to semblance/normal-moveout processing. Examples demonstrate that the calculation yields an accurate local slope estimate and can produce sections of better or equal quality to sections processed using the conventional approach with much less user time input. It also serves as a first example of velocity-independent processing applied to near-surface reflection data.