scholarly journals Problems of Estimating the Resources of Accompanying Elements: A Case Study from the Cu-Ag Rudna Deposit (Legnica-Głogów Copper District, Poland)

Minerals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1431
Author(s):  
Justyna Auguścik-Górajek ◽  
Jacek Mucha ◽  
Monika Wasilewska-Błaszczyk ◽  
Wojciech Kaczmarek
Keyword(s):  
Estimation Method ◽  
Recovery Process ◽  
Ore Deposits ◽  
Economic Consequences ◽  
Main Element ◽  
Accurate Estimation ◽  
Estimation Errors ◽  
Reliable Assessment ◽  
One Year ◽  
Sampling Pattern

As a result of the exploitation of ore deposits, in addition to the main elements, the accompanying elements are also partially recovered. Some of them increase the profitability of exploitation, while others reduce it because they hinder the recovery of the main elements and thus increase the costs of the recovery process. A comprehensive economic calculation to assess the profitability of ore mining depends on an appropriately accurate estimation of the resources of both the main and associated elements. This issue was analyzed with the example of the Cu-Ag Rudna ore deposit (LGCD, Poland). The subject of the assessment was the resources prediction accuracy of the main element (Cu) and four (4) accompanying elements (Co, Ni, Pb, and V) using geostatistical estimation method, in particular the ordinary kriging after the estimation of the relative variograms for describing the spatial variability structures of elements abundance. It was found that the standard kriging errors (deviations) in accompanying elements resources that are scheduled for exploitation within a one-year period in some parts of deposits are drastically greater (2 to 5 times) than the estimation errors of the main element resources. This is due to the sparse sampling pattern for their determinations and/or the high variability (among others nugget effect) of their abundance. In this situation, without additional sampling and a denser sampling pattern, the possibilities of a reliable assessment of the influence of accompanying elements on the economic consequences of exploitation are very limited.

scholarly journals Optical Enhancement of Exoskeleton-Based Estimation of Glenohumeral Angles

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Camilo Cortés ◽  
Luis Unzueta ◽  
Ana de los Reyes-Guzmán ◽  
Oscar E. Ruiz ◽  
Julián Flórez
Keyword(s):  
Estimation Method ◽  
Position Estimation ◽  
Accurate Estimation ◽  
Joint Angles ◽  
Estimation Errors ◽  
Marker Position ◽  
Human Movements ◽  
Posture Estimation ◽  
Kinematic Models ◽  
Method Accuracy

In Robot-Assisted Rehabilitation (RAR) the accurate estimation of the patient limb joint angles is critical for assessing therapy efficacy. In RAR, the use of classic motion capture systems (MOCAPs) (e.g., optical and electromagnetic) to estimate the Glenohumeral (GH) joint angles is hindered by the exoskeleton body, which causes occlusions and magnetic disturbances. Moreover, the exoskeleton posture does not accurately reflect limb posture, as their kinematic models differ. To address the said limitations in posture estimation, we propose installing the cameras of an optical marker-based MOCAP in the rehabilitation exoskeleton. Then, the GH joint angles are estimated by combining the estimated marker poses and exoskeleton Forward Kinematics. Such hybrid system prevents problems related to marker occlusions, reduced camera detection volume, and imprecise joint angle estimation due to the kinematic mismatch of the patient and exoskeleton models. This paper presents the formulation, simulation, and accuracy quantification of the proposed method with simulated human movements. In addition, a sensitivity analysis of the method accuracy to marker position estimation errors, due to system calibration errors and marker drifts, has been carried out. The results show that, even with significant errors in the marker position estimation, method accuracy is adequate for RAR.

V̇o2, V̇co2, and RQ in a respiratory chamber: accurate estimation based on a new mathematical model using the Kalman-Bucy method

2004 ◽  
Vol 96 (3) ◽  
pp. 1045-1054 ◽  
Author(s):  
L. Granato ◽  
A. Brandes ◽  
C. Bruni ◽  
A. V. Greco ◽  
G. Mingrone
Keyword(s):  
Mathematical Model ◽  
Measurement Errors ◽  
Estimation Method ◽  
Accurate Estimation ◽  
Production Rates ◽  
Estimation Errors ◽  
Respiratory Chamber ◽  
Residual Noise ◽  
Proposed Model

A respiratory chamber is used for monitoring O2 consumption (V̇o2), CO2 production (V̇co2), and respiratory quotient (RQ) in humans, enabling long term (24-h) observation under free-living conditions. Computation of V̇o2 and V̇co2 is currently done by inversion of a mass balance equation, with no consideration of measurement errors and other uncertainties. To improve the accuracy of the results, a new mathematical model is suggested in the present study explicitly accounting for the presence of such uncertainties and error sources and enabling the use of optimal filtering methods. Experiments have been realized, injecting known gas quantities and estimating them using the proposed mathematical model and the Kalman-Bucy (KB) estimation method. The estimates obtained reproduce the known production rates much better than standard methods; in particular, the mean error when fitting the known production rates is 15.6 ± 0.9 vs. 186 ± 36 ml/min obtained using a conventional method. Experiments with 11 humans were carried out as well, where V̇o2 and V̇co2 were estimated. The variance of the estimation errors, produced by the KB method, appears relatively small and rapidly convergent. Spectral analysis is performed to assess the residual noise content in the estimates, revealing large improvement: 2.9 ± 0.8 vs. 3,440 ± 824 (ml/min)2 and 1.8 ± 0.5 vs. 2,057 ± 532 (ml/min)2, respectively, for V̇o2 and V̇co2 estimates. Consequently, the accuracy of the computed RQ is also highly improved (0.3 × 10-4 vs. 800 × 10-4). The presented study demonstrates the validity of the proposed model and the improvement in the results when using a KB estimation method to resolve it.

scholarly journals Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles

2021 ◽  
Vol 13 (7) ◽  
pp. 168781402110277
Author(s):  
Yankai Hou ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Chunbao Song ◽  
Zhenpo Wang
Keyword(s):  
Electric Vehicles ◽  
Real World ◽  
Regression Models ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Accurate Estimation ◽  
Support Vector ◽  
Battery Degradation ◽  
Operational Data

Accurate estimation of the degree of battery aging is essential to ensure safe operation of electric vehicles. In this paper, using real-world vehicles and their operational data, a battery aging estimation method is proposed based on a dual-polarization equivalent circuit (DPEC) model and multiple data-driven models. The DPEC model and the forgetting factor recursive least-squares method are used to determine the battery system’s ohmic internal resistance, with outliers being filtered using boxplots. Furthermore, eight common data-driven models are used to describe the relationship between battery degradation and the factors influencing this degradation, and these models are analyzed and compared in terms of both estimation accuracy and computational requirements. The results show that the gradient descent tree regression, XGBoost regression, and light GBM regression models are more accurate than the other methods, with root mean square errors of less than 6.9 mΩ. The AdaBoost and random forest regression models are regarded as alternative groups because of their relative instability. The linear regression, support vector machine regression, and k-nearest neighbor regression models are not recommended because of poor accuracy or excessively high computational requirements. This work can serve as a reference for subsequent battery degradation studies based on real-time operational data.

scholarly journals Robust Directional Angle Estimation of Underwater Acoustic Sources Using a Marine Vehicle

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3062 ◽  
Author(s):  
Jinwoo Choi ◽  
Jeonghong Park ◽  
Yoongeon Lee ◽  
Jongdae Jung ◽  
Hyun-Taek Choi
Keyword(s):  
Source Localization ◽  
Angular Displacement ◽  
Estimation Method ◽  
Time Delay Estimation ◽  
Accurate Estimation ◽  
Acoustic Source ◽  
Angle Estimation ◽  
Underwater Acoustic ◽  
Acoustic Sources ◽  
Marine Vehicle

Acoustic source localization is used in many underwater applications. Acquiring an accurate directional angle for an acoustic source is crucial for source localization. To achieve this purpose, this paper presents a method for directional angle estimation of underwater acoustic sources using a marine vehicle. It is assumed that the vehicle is equipped with two hydrophones and that the acoustic source transmits a specific signal repeatedly. The proposed method provides a probabilistic model for time delay estimation. The probability is recursively updated by prediction and update steps. The prediction step performs a probability transition using the angular displacement of the marine vehicle. The predicted probability is updated using a generalized cross correlation function with a verification process using entropy measurement. The proposed method can provide a reliable and accurate estimation of the directional angles of underwater acoustic sources. Experimental results demonstrate good performance of the proposed probabilistic directional angle estimation method in both an inland water environment and a harbor environment.

scholarly journals Noise masking method based on an effective ratio mask estimation in Gammatone channels

2018 ◽  
Vol 7 ◽  
Author(s):  
Feng Bao ◽  
Waleed H. Abdulla
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Wiener Filter ◽  
Power Spectra ◽  
Auditory Scene Analysis ◽  
Accurate Estimation ◽  
Noise Power ◽  
Noise Masking ◽  
Time Frequency ◽  
Mask Estimation

In computational auditory scene analysis, the accurate estimation of binary mask or ratio mask plays a key role in noise masking. An inaccurate estimation often leads to some artifacts and temporal discontinuity in the synthesized speech. To overcome this problem, we propose a new ratio mask estimation method in terms of Wiener filtering in each Gammatone channel. In the reconstruction of Wiener filter, we utilize the relationship of the speech and noise power spectra in each Gammatone channel to build the objective function for the convex optimization of speech power. To improve the accuracy of estimation, the estimated ratio mask is further modified based on its adjacent time–frequency units, and then smoothed by interpolating with the estimated binary masks. The objective tests including the signal-to-noise ratio improvement, spectral distortion and intelligibility, and subjective listening test demonstrate the superiority of the proposed method compared with the reference methods.

scholarly journals When, How, and to What Extent Are Individuals with Unresponsive Wakefulness Syndrome Able to Progress? Functional Independence

2020 ◽  
Vol 10 (12) ◽  
pp. 990
Author(s):  
José Olaya ◽  
Enrique Noé ◽  
María Dolores Navarro ◽  
Myrtha O’Valle ◽  
Carolina Colomer ◽  
...  
Keyword(s):  
Minimally Conscious State ◽  
Functional Independence ◽  
Conscious State ◽  
Functional Improvement ◽  
Accurate Estimation ◽  
Unresponsive Wakefulness Syndrome ◽  
Functional Changes ◽  
Domain Specific ◽  
One Year ◽  
Minimally Conscious

Accurate estimation of the functional independence of patients with unresponsive wakefulness syndrome (UWS) is essential to adjust family and clinical expectations and plan long-term necessary resources. Although different studies have described the clinical course of these patients, they have methodological limitations that could restrict generalization of the results. This study investigates the neurobehavioral progress of 100 patients with UWS consecutively admitted to a neurorehabilitation center using systematic weekly assessments based on standardized measures, and the functional independence staging of those patients who emerged from a minimally conscious state (MCS) during the first year post-emergence. Our results showed that one year after emergence, most patients were severely dependent, although some of them showed extreme or moderate severity. Clinically meaningful functional improvement was less likely to occur in cognitively-demanding activities, such as activities of daily living and executive function. Consequently, the use of specific and staging functional independence measures, with domain-specific evaluations, are recommended to detect the functional changes that might be expected in these patients. The information provided by these instruments, together with that obtained from repeated assessments of the preserved consciousness with standardized instruments, could help clinicians to adjust expectations and plan necessary resources for this population.

scholarly journals A Study on Sustainable Consumption of Fuel—An Estimation Method of Aircraft

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7559
Author(s):  
Lisha Li ◽  
Shuming Yuan ◽  
Yue Teng ◽  
Jing Shao
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Influencing Factors ◽  
Carbon Emission ◽  
Estimation Method ◽  
Civil Aviation ◽  
Accurate Estimation ◽  
Low Carbon ◽  
Fuel Price ◽  
Aircraft Fuel

Though the development of China’s civil aviation and the improvement of control ability have strengthened the safety operation and support ability effectively, the airlines are under the pressure of operation costs due to the increase of aircraft fuel price. With the development of optimization controlling methods in flight management systems, it becomes increasingly challenging to cut down flight fuel consumption by control the flight status of the aircraft. Therefore, the airlines both at home and abroad mainly rely on the accurate estimation of aircraft fuel to reduce fuel consumption, and further reduce its carbon emission. The airlines have to take various potential factors into consideration and load more fuel to cope with possible negative situation during the flight. Therefore, the fuel for emergency use is called PBCF (Performance-Based Contingency Fuel). The existing PBCF forecasting method used by China Airlines is not accurate, which fails to take into account various influencing factors. This paper aims to find a method that could predict PBCF more accurately than the existing methods for China Airlines.This paper takes China Eastern Airlines as an example. The experimental data of flight fuel of China Eastern Airlines Co, Ltd. were collected to find out the relevant parameters affecting the fuel consumption, which is followed by the establishment of the LSTM neural network through the parameters and collected data. Finally, through the established neural network model, the PBCF addition required by the airline with different influencing factors is output. It can be seen from the results that the all the four models are available for the accurate prediction of fuel consumption. The amount of data of A319 is much larger than that of A320 and A330, which leads to higher accuracy of the model trained by A319. The study contributes to the calculation methods in the fuel-saving project, and helps the practitioners to learn about a particular fuel calculation method. The study brought insights for practitioners to achieve the goal of low carbon emission and further contributed to their progress towards circular economy.

scholarly journals Spatio-Temporal Crime Analysis Using KDE and ARIMA Models in the Indian Context

2020 ◽  
Vol 12 (4) ◽  
pp. 1-19
Author(s):  
Prathap Rudra Boppuru ◽  
Ramesha K.
Keyword(s):  
Data Mining ◽  
Predictive Analytics ◽  
Time Series Prediction ◽  
Arima Model ◽  
Estimation Method ◽  
Time Of Day ◽  
Crime Patterns ◽  
Model Time Series ◽  
Spatio Temporal ◽  
One Year

In developing countries like India, crime plays a detrimental role in economic growth and prosperity. With the increase in delinquencies, law enforcement needs to deploy limited resources optimally to protect citizens. Data mining and predictive analytics provide the best options for the same. This paper examines the news feed data collected from various sources regarding crime in India and Bangalore city. The crimes are then classified on the geographic density and the crime patterns such as time of day to identify and visualize the distribution of national and regional crime such as theft, murder, alcoholism, assault, etc. In total, 68 types of crime-related dictionary keywords are classified into six classes based on the news feed data collected for one year. Kernel density estimation method is used to identify the hotspots of crime. With the help of the ARIMA model, time series prediction is performed on the data. The diversity of crime patterns is visualized in a customizable way with the help of a data mining platform.

Importance of oscillations in alveolar gas concentrations in the analysis of rebreathing data

1986 ◽  
Vol 61 (3) ◽  
pp. 1104-1113 ◽  
Author(s):  
K. H. Weisiger ◽  
G. D. Swanson
Keyword(s):  
Time Course ◽  
Continuous Process ◽  
Inert Gases ◽  
Accurate Estimation ◽  
Gas Solubility ◽  
Estimation Errors ◽  
Gas Uptake ◽  
Cyclic Model ◽  
Low Solubility ◽  
Cyclic Data

Cyclic rebreathing of a soluble inert gas can be used to estimate lung tissue volume (Vt) and pulmonary blood flow (Qc). A recently proposed method for analyzing such cyclic data (Respir. Physiol. 48: 255–279, 1982) mathematically assumes that ventilation is a continuous process. However, neglecting the cyclic nature of ventilation may prevent the accurate estimation of Vt and Qc. We evaluated this possibility by simulating the uptake of soluble inert gases during rebreathing using a cyclic model of gas exchange. Under cyclic uptake conditions alveolar gases follow an oscillating time course, because gas concentrations tend to increase during inspiration and to decrease during expiration. We found that neglecting these alveolar gas oscillations leads to the underestimation of soluble gas uptake by blood, particularly during the early rebreathing breaths. When continuous ventilation is assumed Vt and Qc are overestimated unless rapid rebreathing rates, large tidal volumes, and gases of moderately low solubility are used. Under these conditions the amplitude of the cyclic oscillations is minimized, the alveolar time course more closely resembles that expected from continuous ventilation, and the resulting errors are minimized. Alternatively, when the effect of oscillating alveolar gas concentrations on mass transfer are considered, these estimation errors can be eliminated without restricting rebreathing rate or gas solubility. We conclude that failure to consider the effect of cyclic rebreathing on the time course of alveolar gas concentrations may result in significant errors when evaluating rebreathing data for Vt and Qc.

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