Modeling Estimation Research Articles

Review of machine learning-based Mineral Resource estimation

Mineral Resources estimation plays a crucial role in the profitability of the future of mining operations. The conventional geostatistical methods used for grade estimation require expertise, understanding and knowledge of the spatial statistics, resource modelling, geology, mining engineering as well as clean validated data to build accurate block models. However, the geostatistical models are sensitive to changes in data and would have to be rebuilt on newly acquired data with different characteristics, which has proved to be a time-consuming process. Machine learning methods have in recent years been proposed as an alternative to the geostatistical methods to alleviate the problems these might suffer from in Mineral Resource estimation. In this paper, a systematic literature review of machine learning methods used in Mineral Resource estimation is presented. This has been conducted on such studies published during the period 1990 to 2019. The types, performances, and capabilities, of several machine learning methods have been evaluated and compared against each other, and against the conventional geostatistical methods. The results, based on 31 research studies, show that the machine learning-based methods have outperformed the conventional grade estimation modelling methods. The review also shows there is active research on applying machine learning to grade estimation from exploration through to exploitation. Further improvements can be expected if advanced machine learning techniques are to be used.

Grey fuzzy prediction model of soil organic matter content using hyper-spectral data

PurposeIn order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.Design/methodology/approachBased on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.FindingsThe results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R2 = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.Practical implicationsThe research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.Originality/valueThe paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

DiFiR-CT: Distance field representation to resolve motion artifacts in computed tomography.

Motion during data acquisition leads to artifacts in computed tomography (CT) reconstructions. In cases such as cardiac imaging, not only is motion unavoidable, but evaluating the motion of the object is of clinical interest. Reducing motion artifacts has typically been achieved by developing systems with faster gantry rotation or via algorithms which measure and/or estimate the displacement. However, these approaches have had limited success due to both physical constraints as well as the challenge of estimating non-rigid, temporally varying, and patient-specific motionfields. To develop a novel reconstruction method which generates time-resolved, artifact-free images without estimation or explicit modeling of themotion. We describe an analysis-by-synthesis approach which progressively regresses a solution consistent with the acquired sinogram. In our method, we focus on the movement of object boundaries. Not only are the boundaries the source of image artifacts, but object boundaries can simultaneously be used to represent both the object as well as its motion over time without the need for an explicit motion model. We represent the object boundaries via a signed distance function (SDF) which can be efficiently modeled using neural networks. As a result, optimization can be performed under spatial and temporal smoothness constraints without the need for explicit motion estimation. We illustrate the utility of DiFiR-CT in three imaging scenarios with increasing motion complexity: translation of a small circle, heart-like change in an ellipse's diameter, and a complex topological deformation. Compared to filtered backprojection, DiFiR-CT provides high quality image reconstruction for all three motions without hyperparameter tuning or change to the architecture. We also evaluate DiFiR-CT's robustness to noise in the acquired sinogram and found its reconstruction to be accurate across a wide range of noise levels. Lastly, we demonstrate how the approach could be used for multi-intensity scenes and illustrate the importance of the initial segmentation providing a realistic initialization. Code and supplemental movies are available at https://kunalmgupta.github.io/projects/DiFiR-CT.html. Projection data can be used to accurately estimate a temporally-evolving scene without the need for explicit motion estimation using a neural implicit representation and analysis-by-synthesisapproach.

Optimal Parameter Determination of Membrane Bioreactor to Boost Biohydrogen Production-Based Integration of ANFIS Modeling and Honey Badger Algorithm

Hydrogen is a new promising energy source. Three operating parameters, including inlet gas flow rate, pH and impeller speed, mainly determine the biohydrogen production from membrane bioreactor. The work aims to boost biohydrogen production by determining the optimal values of the control parameters. The proposed methodology contains two parts: modeling and parameter estimation. A robust ANIFS model to simulate a membrane bioreactor has been constructed for the modeling stage. Compared with RMS, thanks to ANFIS, the RMSE decreased from 2.89 using ANOVA to 0.0183 using ANFIS. Capturing the proper correlation between the inputs and output of the membrane bioreactor process system encourages the constructed ANFIS model to predict the output performance exactly. Then, the optimal operating parameters were identified using the honey badger algorithm. During the optimization process, inlet gas flow rate, pH and impeller speed are used as decision variables, whereas the biohydrogen production is the objective function required to be maximum. The integration between ANFIS and HBA boosted the hydrogen production yield from 23.8 L to 25.52 L, increasing by 7.22%.

Role of artificial intelligence in moderating the innovative financial process of the banking sector: a research based on structural equation modeling

This study seeks to find the moderating role of AI in the association between a bank’s innovative financial process and the bank’s market share. The data were analyzed using SPSS and SmartPLS software. The estimations were performed using structural equation modeling estimation techniques such as the measurement model, outer loading, convergent validity, discriminant validity, and SEM estimations. The initial estimations indicated factor as well as construct reliability and validity. The study concluded that an innovative financial process plays a vital role in enhancing the bank’s market share. However, artificial intelligence could not significantly moderate the relationship. The policymakers in the banking industry of Pakistan need to consider the up-gradation in the system of their financial process by innovation and artificial intelligence usage awareness in their existing staff as well their banking customers. Future research may include a similar model for Islamic as well as commercial banks in a comparative model. Additionally, future research may also include more banks as innovative financial institutions to get a greater sample size for a possible influence of artificial intelligence.

Special invited paper: The SCORE normalization, especially for heterogeneous network and text data

SCORE was introduced as a spectral approach to network community detection. Since many networks have severe degree heterogeneity, the ordinary spectral clustering (OSC) approach to community detection may perform unsatisfactorily. SCORE alleviates the effect of degree heterogeneity by introducing a new normalization idea in the spectral domain and makes OSC more effective. SCORE is easy to use and computationally fast. It adapts easily to new directions and sees an increasing interest in practice. In this paper, we review the basics of SCORE, the adaption of SCORE to network mixed membership estimation and topic modeling, and the application of SCORE in real data, including two datasets on the publications of statisticians. We also review the theoretical “ideology” underlying SCORE. We show that in the spectral domain, SCORE converts a simplicial cone to a simplex and provides a simple and direct link between the simplex and network memberships. SCORE attains an exponential rate and a sharp phase transition in community detection, and achieves optimal rates in mixed membership estimation and topic modeling.

Analysis of Empirical Bayesian Kriging Methods for Optimization of Measured Resources Estimation of Laterite Nickel

Laterite nickel deposits are formed from the intense weathering of ultramafic igneous rocks. The distribution of Ni content in an area does not always have a stationary data. One method of resource estimation and precise geological modeling is the Empirical Bayesian Kriging (EBK) method which is known to be able to perform precise estimates for stationary and non-stationary data. The results of this study were found that thickness of the limonite layer will increase as the slope decreases, while in the saprolite zone the thickness will increase with increasing the degree of slope up to the limit of 16°-25° then the thickness will decrease when crossing that limit. In the limonite zone, the concentration of Ni is maximally concentrated at a high degree of slope (>16°), while in the saprolite zone, Ni content is maximally concentrated on a slope of 16°-25° and then decreases in Ni content when passing through a slope of 25°. Validation of the estimation results and geological modeling using RMSE, MAE, MAPE, and linear regression in the limonite and saprolite zones indicate that the EBK method is a precise method for resource estimation and geological modeling.

Online SOC Estimation of Supercapacitor Energy Storage System Based on Fractional-Order Model

Fast and accurate estimation of state of charge (SOC) of supercapacitors is essential for the safe and reliable operation of energy storage systems. However, existing SOC estimation methods are based on the integer-order model of supercapacitors and do not take into account the fractional-order characteristics of supercapacitors. Hence, the accuracy of SOC estimation results usually ignores the impact of modeling and relies on the correction effect of the algorithm. In this paper, a supercapacitor energy storage system is firstly established for fractional-order modeling and fractional-order SOC estimation of supercapacitors. Next, a Kalman filter is devised to track the SOC of supercapacitors in real time, and it can correct the estimation results online. Finally, experiments under different working conditions are conducted to demonstrate the effectiveness of the proposed fractional-order modeling and SOC estimation scheme. Compared with the data obtained by the traditional integer-order method, the accuracy of the proposed fractional-order method is higher, and the mean relative errors are less than 5%. With the online correction of the Kalman filter, the maximum errors under different working conditions are limited to 1.3%.

Dynamic Cell Modeling for Accurate SOC Estimation in Autonomous Electric Vehicles

Dynamic Cell Modeling for Accurate SOC Estimation in Autonomous Electric Vehicles

Dispersion of PM and VOC pollutants from open burning of municipal solid wastes on host communities: emission inventory estimation and dispersion modelling study

This study focused on the dispersion of PM and VOC pollutants from the open burning of solid waste on the Sokoto-Aiyekale dump site in Ilorin, Nigeria, owing to the scarcity of information on pollutants' impact on onsite workers and the population surrounding the dump site.

Relative Entropy (RE)-Based LTI System Modeling Equipped With Simultaneous Time Delay Estimation and Online Modeling

Relative Entropy (RE)-Based LTI System Modeling Equipped With Simultaneous Time Delay Estimation and Online Modeling

BayesTICS: Local temporal image correlation spectroscopy and Bayesian simulation technique for sparse estimation of diffusion in fluorescence imaging.

The dynamics and fusion of vesicles during the last steps of exocytosis are not well established yet in cell biology. An open issue is the characterization of the diffusion process at the plasma membrane. Total internal reflection fluorescence microscopy (TIRFM) has been successfully used to analyze the coordination of proteins involved in this mechanism. It enables to capture dynamics of proteins with high frame rate and reasonable signal-to-noise values. Nevertheless, methodological approaches that can analyze and estimate diffusion in local small areas at the scale of a single diffusing spot within cells, are still lacking. To address this issue, we propose a novel correlation-based method for local diffusion estimation. As a starting point, we consider Fick's second law of diffusion that relates the diffusive flux to the gradient of the concentration. Then, we derive an explicit parametric model which is further fitted to time-correlation signals computed from regions of interest (ROI) containing individual spots. Our modeling and Bayesian estimation framework are well appropriate to represent isolated diffusion events and are robust to noise, ROI sizes, and localization of spots in ROIs. The performance of BayesTICS is shown on both synthetic and real TIRFM images depicting Transferrin Receptor proteins.

EXTENSIONAL TECTONIC INVESTIGATION BASED ON GRAVITY AND MAGNETIC DATA ANALYSIS IN THE GULF OF TOMINI, INDONESIA

The enigmatic genesis of Sulawesi Island remains a debate among earth scientists up to the present day. The Northern Arm of Sulawesi is designated as a volcanic province, while the Eastern Arm is identified as an ophiolite zone along with several micro continental fragments. However, the tectonic relationship in the marine area between these two arms is not clearly defined. The existence of isolated volcanic islands, namely the Una-Una and Togian islands increases the complexity of the area. A regional study utilizing gravity and magnetic methods is needed in the Gulf of Tomini, North Sulawesi, Indonesia. Therefore, a comprehensive analysis was conducted, involving 3D inverse modelling and Moho depth estimation based on the Parker-Oldenburg algorithm using 3D Euler deconvolution to obtain the average depth of causative bodies. As a result, the presence of extensional tectonics were revealed and identified as multiple parallel dextral strike-slip faults across the study area. All of these faults show northwest-southeast lineaments consistent with the existing seismic sections. The extensional tectonic mechanism triggers crustal thinning and rifting, leading to the build-up of graben structures such as the Gorontalo, Tomini, and Poso basins. Additionally, it influences volcanic activity around the Togian and Una-Una islands. The major axes of the inferred opening area are oriented in the northeast-southwest direction, perpendicular to the lineaments of these faults. The opening area coincides with the orientation of Lalanga and Togian ridges. Finally, a regional geological structure is proposed across the Gulf of Tomini linking both the Northern and Eastern arms of Sulawesi.

Redshift requirements for cosmic shear with intrinsic alignment

Intrinsic alignment (IA) modelling and photometric redshift estimation are two of the main sources of systematic uncertainty in weak lensing surveys. We investigate the impact of redshift errors and their interplay with different IA models. Generally, errors on the mean δz and on the width σz of the redshift bins can both lead to biases in cosmological constraints. We find that such biases can, however, only be partially resolved by marginalizing over δz and σz . For Stage-III surveys, δz and σz cannot be well constrained due to limited statistics. The resulting biases are thus sensitive to prior volume effects. For Stage-IV surveys, we observe that marginalizing over the redshift parameters has an impact and reduces the bias. We derive requirements on the uncertainty of σz and δz for both Stage-III and Stage-IV surveys. We assume that the redshift systematic errors on S 8 should be less than half of the statistical errors, and the median bias should be smaller than 0.25σ. We find that the uncertainty on δz has to be ≲ 0.025 for the NLA IA model with a Stage-III survey. We find no requirement threshold for σz since the requirements are met even for our maximum prior width of 0.3. For the TATT IA model, the uncertainty on δz has to be ≲ 0.02 and the uncertainty on σz has to be ≲ 0.2. Current redshift precision of Stage-III surveys is therefore high enough to meet these requirements. For Stage-IV surveys, systematic effects will be more important due to the higher statistical precision. In this case, the uncertainty on δz has to be ≲ 0.005 and the uncertainty on σz should be ≲ 0.1, with no significant dependence on the IA model. This required high precision will be a challenge for the redshift calibration of these future surveys. Finally, we investigate whether the interplay between redshift systematics and IA modelling can explain the S 8-tension between cosmic shear results and CMB measurements. We find that this is unlikely to explain the current S 8-tension. The code that was used to conduct this analysis is publicly available.[refrigerator: https://cosmo-gitlab.phys.ethz.ch/cosmo_public/refrigerator.]

The impact of biostatistics on hazard characterization using in vitro developmental neurotoxicity assays.

In chemical safety assessment, benchmark concentrations (BMC) and their associated uncertainty are needed for the toxicological evaluation of in vitro data sets. A BMC estimation is derived from concentration-response modelling and results from various statistical decisions, which depend on factors such as experimental design and assay endpoint features. In current data practice, the experimenter is often responsible for the data analysis and therefore relies on statistical software, often without being aware of the software default settings and how they can impact the outputs of data analysis. To provide more insight into how statistical decision-making can influence the outcomes of data analysis and interpretation, we have developed an automated platform that includes statistical methods for BMC estimation, a novel endpoint-specific hazard classification system, and routines that flag data sets that are outside the applicability domain for an automatic data evaluation. We used case studies on a large dataset produced by a developmental neurotoxicity (DNT) in vitro battery (DNT IVB). Here we focused on the BMC and its confidence interval (CI) estimation as well as on final hazard classification. We identified five crucial statistical decisions the experimenter must make during data analysis: choice of replicate averaging, response data normalization, regression modelling, BMC and CI estimation, and choice of benchmark response levels. The insights gained are intended to raise more awareness among experimenters on the importance of statistical decisions and methods but also to demonstrate how important fit-for-purpose, internationally harmonized and accepted data evaluation and analysis procedures are for objective hazard classification.

Introduction to the Special Issue on Advances on Modeling and State Estimation for Industrial Processes

Introduction to the Special Issue on Advances on Modeling and State Estimation for Industrial Processes

Статистический анализ и моделирование данных о ликвидации пожаров на топливно-энергетических предприятиях

PURPOSE. Using the example of statistical data on fires at fuel and energy enterprises for the period from 2000 to 2020, stored in the federal database “Fires”, an algorithm for these data statistical analysis is proposed for constructing a statistical digital twin (SDT) of the type of fires under consideration. The SDT under development is aimed at creating any necessary and adequate to the initial data sample set of records (dataset) for the simulated class of fires for subsequent modeling and other studies. The objectives of this work are the construction of an algorithm and the study of the main key points in constructing the SDT. The object of the study is a set of fires, whose characteristics are enclosed contained in the form of separate records in the database, while the subject of the study is a set of statistical static and dynamic models that should be built on the basis of these data analysis. METHODS. Methods of probability theory, mathematical statistics, simulation modeling and statistical estimation are used. An approach based on the joint use of the Monte Carlo method and bootstrap principles to form adequate training samples is proposed. FINDINGS. MCB-modeling algorithm (Monte Carlo bootstrap) is proposed for generating a statistical sample of data on fires in the amount necessary for machine learning of a wide class of decision support models. The relevance of the proposed approach is due to the fact that in some cases the available statistics are insufficient in volume for building decision support models and other studies. RESEARCH APPLICATION FIELD. SDT, built on the basis of MCB-modeling, will allow forming data samples of the required volume, which are similar to the original databases. CONCLUSIONS. The application of the proposed algorithm for constructing SDT for MCB-modeling will allow building machine-learning models for their use as part of decision support systems in safety systems.

Sequence-to-Sequence Recurrent Graph Convolutional Networks for Traffic Estimation and Prediction Using Connected Probe Vehicle Data

Traffic estimation is imperative for conducting fundamental transportation engineering tasks such as transportation planning and traffic safety studies. Additionally, traffic prediction is vital for many data-driven intelligent transportation system applications. Most traffic estimation and prediction methods rely on infrastructure-based sensors to collect traffic parameters. However, infrastructure-based data collection can be costly and time consuming to set up and maintain. Additionally, the spatial distribution of the collected traffic data is limited by the location of the deployed hardware sensors. Probe vehicle data can be used to overcome these limitations. Traffic modeling for estimation and prediction is a complex task due to the stochastic nonlinear spatiotemporal dependencies exhibited by traffic parameters. In this paper, a deep learning-based sequence-to-sequence architecture called Seq2seq GCN-LSTM was proposed to estimate and predict network-wide traffic volume and speed. The proposed method utilizes short-term historical traffic data collected from a low-penetration rate probe vehicle fleet to estimate and predict traffic parameters up to 60-minutes ahead. The method utilizes Graph Convolutional Networks for spatial dependency extraction and Long Short-Term Memory networks to model temporal dependencies. The proposed method generated superior traffic results compared to the baseline models. Additionally, the model demonstrated robustness against perturbations caused by the low rate. Furthermore, the probe vehicle penetration rate was varied to test its effect on the proposed method’s modeling capability. The model was able to maintain traffic volume and speed estimation and prediction performance within a reasonable margin of error using penetration rates as low as 1.5% and 0.5%, respectively.

Application of shaken lattice interferometry based sensors to space navigation

High–sensitivity shaken lattice interferometry (SLI) based sensors have the potential to provide deep space missions with the ability to precisely measure non–gravitational perturbing forces. This work considers the simulation of the OSIRIS-REx mission navigation in the vicinity of Bennu with the addition of measurements from onboard SLI–based accelerometers. The simulation is performed in the Jet Propulsion Laboratory’s (JPL) Mission Analysis, Operations and Navigation Toolkit (MONTE) and incorporates OSIRIS-REx reconstructed trajectory and attitude data from the Navigation and Ancillary Information Facility (NAIF) database. The use of the reconstructed data from NAIF provides realistic true dynamical errors and JPL’s MONTE software allows for a high–fidelity simulation of an integrated trajectory for the filter. The navigation performance and reduction of tracking and complex modelling enabled by the onboard SLI–based sensor are presented for two orbital phases of the OSIRIS–REx mission. Overall, the results show that the addition of SLI–based accelerometer measurements improves navigation performance, when compared to a radiometric tracking only configuration. In addition, results demonstrate that highly–precise accelerometer measurements can effectively replace at least one day of DSN passes over a three–day period, thereby reducing tracking requirements. Furthermore, it is shown that lower–fidelity surface force modeling and parameter estimation is required when using onboard SLI–based accelerometers.

Degradation modeling and reliability estimation for competing risks considering system resistance

Many systems are subjected to random shocks from internal or external factors such as high voltage, over loads, etc., and they often experience a dependent competing-risk process including natural degradation and random shocks. Some systems have the ability to resist the damage of shocks due to its material, structure, and/or special designed affiliated devices. Motivated by the actual engineering problem of reliability modeling for the biodegradable magnesium alloy vascular stent, a new competing risks model is developed considering the time-varying resistance of the system. Hard failure may occur due to a fatal shock, while soft failure happens when the comprehensive effects, including the natural degradation amount and cumulative damage of nonfatal shocks, exceed the predefined threshold. The resistance of the system can influence the damage of nonfatal shocks, and its resistance to shocks will also deteriorate as the system degrades, which is depicted by a time-varying function related to the health state or the service time. In addition, the thresholds to classify shocks into nonfatal shocks (i.e., safety shocks and damage shocks) and fatal shocks, can also be affected by the resistance. A closed-form reliability function is obtained under specific conditions and a simulated algorithm is also provided. Two illustrative examples are conducted to demonstrate the proposed model.