scholarly journals Stochastic Predictions of Ore Production in an Underground Limestone Mine Using Different Probability Density Functions: A Comparative Study Using Big Data from ICT System

2021 ◽  
Vol 11 (9) ◽  
pp. 4301
Author(s):  
Dahee Jung ◽  
Jieun Baek ◽  
Yosoon Choi
Keyword(s):  
Big Data ◽  
Probability Distribution ◽  
Probability Density ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Real Data ◽  
Probability Density Functions ◽  
Density Functions ◽  
Travel Times ◽  
Event Simulation

This study stochastically predicted ore production through discrete event simulation using four different probability density functions for truck travel times. An underground limestone mine was selected as the study area. The truck travel time was measured by analyzing the big data acquired from information and communications technology (ICT) systems in October 2018, and probability density functions (uniform, triangular, normal, and observed probability distribution of real data) were determined using statistical values. A discrete event simulation model for a truck haulage system was designed, and truck travel times were randomly generated using a Monte Carlo simulation. The ore production that stochastically predicted fifty times for each probability density function was analyzed and represented as a value of lower 10% (P10), 50% (P50), and 90% (P90). Ore production was underestimated when a uniform and triangular distribution was used, as the actual ore production was similar to that of P90. Conversely, the predicted ore production of P50 was relatively consistent with the actual ore production when using the normal and observed probability distribution of real data. The root mean squared error (RMSE) for predicting ore production for ten days in October 2018 was the lowest (24.9 ton/day) when using the observed probability distribution.

Discrete Event Simulation Of The COVID-19 Sample Collection Point Operation

2021 ◽  
Author(s):  
Martina Kuncova ◽  
Katerina Svitkova ◽  
Alena Vackova ◽  
Milena Vankova
Keyword(s):  
Discrete Event Simulation ◽  
Waiting Times ◽  
Discrete Event ◽  
Real Data ◽  
Simulation Software ◽  
Sample Collection ◽  
The Real ◽  
Event Simulation ◽  
Collection Process ◽  
Collection Point

The year 2020 was very challenging for everyone due to the COVID-19 pandemic. Many people turn their lives upside down from day to day. Politicians had to impose completely unprecedented measures, and doctors immediately had to adapt to the huge influx of patients and the massive demand for testing. Of course, not all processes could be planned completely efficiently, given that the situation literally changes from minute to minute, but sometimes better planning could improve the real processes. This contribution deals with the application of simulation software SIMUL8 to the analysis of the COVID-19 sample collection process in a drive-in point in a hospital. The main aim is to create a model based on the real data and then to find out the suitable number of other staff (medics) helping a doctor during the process to decrease the number of unattended patients and their waiting times.

Analysis, Simulation and Optimization of the Milking Process in a Cowshed for the Production of Parmigiano Reggiano

2016 ◽  
Vol 12 (9) ◽  
pp. 851-865 ◽  
Author(s):  
Mattia Armenzoni ◽  
Eleonora Bottani ◽  
Marta Rinaldi ◽  
Sergio Amedeo Gallo ◽  
Roberto Montanari
Keyword(s):  
Discrete Event Simulation ◽  
Discrete Event ◽  
Real Data ◽  
Simulation And Optimization ◽  
Event Simulation ◽  
Discrete Event Simulation Model ◽  
Before And After ◽  
Real Performance ◽  
Time Required ◽  
Parmigiano Reggiano

Abstract The aim of this study is to optimize the milking process of a cowshed, located near Parma (Italy), which provides milk to some dairy companies, for the production of Parmigiano Reggiano cheese. The ultimate goal of the analysis is to reduce the time required for milking operations, thus optimizing the whole management of the farm processes. A discrete-event simulation model is designed under Simul8™ to reproduce the main processes of the cowshed and the movements of the animals inside the cowshed, before and after milking. The model exploits real data collected from the direct observation of the farm and is validated by comparing the results provided with the real performance of the milking activities. Then, it is used to test two new configurations of the cowshed layout and to assess their performance, in terms of the total time required for milking. Interesting savings in the total milking time are found.

scholarly journals Compositional Data Modeling through Dirichlet Innovations

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2477
Author(s):  
Seitebaleng Makgai ◽  
Andriette Bekker ◽  
Mohammad Arashi
Keyword(s):  
Maximum Likelihood ◽  
Probability Density ◽  
Gamma Distribution ◽  
Compositional Data ◽  
Dirichlet Distribution ◽  
Real Data ◽  
Probability Density Functions ◽  
Density Functions ◽  
Data Sets ◽  
Method Of Maximum Likelihood

The Dirichlet distribution is a well-known candidate in modeling compositional data sets. However, in the presence of outliers, the Dirichlet distribution fails to model such data sets, making other model extensions necessary. In this paper, the Kummer–Dirichlet distribution and the gamma distribution are coupled, using the beta-generating technique. This development results in the proposal of the Kummer–Dirichlet gamma distribution, which presents greater flexibility in modeling compositional data sets. Some general properties, such as the probability density functions and the moments are presented for this new candidate. The method of maximum likelihood is applied in the estimation of the parameters. The usefulness of this model is demonstrated through the application of synthetic and real data sets, where outliers are present.

Reformative translation model for probability density functions of amplitude processes of crosswind-excited super-tall buildings

2019 ◽  
Vol 23 (5) ◽  
pp. 847-856
Author(s):  
Wei Hao ◽  
Qingshan Yang
Keyword(s):  
Probability Distribution ◽  
Probability Density ◽  
Tall Buildings ◽  
Extreme Value ◽  
Probability Density Functions ◽  
Cumulative Distribution ◽  
Extreme Value Analysis ◽  
Density Functions ◽  
Translation Model ◽  
Non Gaussian

At the vicinity of vortex lock-in wind speed, the nonlinear aerodynamic damping effect of super-tall buildings is significant, which can greatly promote the surge of vortex-induced vibration in the crosswind direction, where the crosswind response characterized by harmonic amplitude shows narrow-band hardening non-Gaussian characteristic with the kurtosis well below 3, and the corresponding probability distribution of amplitude process distinctly differs from that of typical random buffeting response. Although the moment-based Hermite translation model has been widely used for estimating the extreme value distribution of non-Gaussian process, it fails to represent the probability distribution of hardening non-Gaussian amplitude process, notably for the response with a kurtosis close to 1.5. In this study, a new translation model based on orthogonal expansion of random processes is developed for obtaining the non-Gaussian amplitude process from an underlying Gaussian amplitude process, and the probability density function of the non-Gaussian amplitude process is derived by mapping the cumulative distribution function. The coefficients of translation model are determined by minimizing the errors between the estimated probability density functions and target values through nonlinear optimization, and the closed-form semi-empirical formulations, which connect the model coefficients with response kurtosis, are also proposed using least-square curve fitting. Moreover, the effectiveness and monotonicity of the proposed translation model are examined. This model can be readily incorporated into the extreme value analysis of crosswind response and facilitate the evaluation of wind-induced fatigue of super-tall buildings.

Probability Density Functions

2019 ◽  
pp. 108-130
Author(s):  
Therese M. Donovan ◽  
Ruth M. Mickey
Keyword(s):  
Bayesian Inference ◽  
Probability Distribution ◽  
Probability Density ◽  
Probability Distributions ◽  
Random Variable ◽  
Probability Density Functions ◽  
Density Functions ◽  
Normal Distributions ◽  
Likelihood Profile ◽  
Variable Probability

This chapter builds on probability distributions. Its focus is on general concepts associated with probability density functions (pdf’s), which are distributions associated with continuous random variables. The continuous uniform and normal distributions are highlighted as examples of pdf’s. These and other pdf’s can be used to specify prior distributions, likelihoods, and/or posterior distributions in Bayesian inference. Although this chapter specifically focuses on the continuous uniform and normal distributions, the concepts discussed in this chapter will apply to other continuous probability distributions. By the end of the chapter, the reader should be able to define and use the following terms for a continuous random variable: random variable, probability distribution, parameter, probability density, likelihood, and likelihood profile.

scholarly journals Aggregation of GPS, WLAN, and BLE Localization Measurements for Mobile Devices in Simulated Environments

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1694
Author(s):  
Kamil Książek ◽  
Krzysztof Grochla
Keyword(s):  
Probability Density ◽  
Wireless Lan ◽  
Discrete Event ◽  
Measurement Data ◽  
Mobility Model ◽  
Probability Density Functions ◽  
Location Estimation ◽  
Average Error ◽  
Density Functions ◽  
Positioning Systems

There are multiple available technologies to find the location of a mobile device, such as the Global Positioning System (GPS), Bluetooth Low-Energy beacons (BLE), and Wireless LAN (WLAN) localization. We propose a novel method to estimate the location of a moving device by aggregating information from multiple positioning systems into a single, more precise location estimation. The aggregated location is calculated as the place in which the product of the probability density functions (PDF) of individual methods has the maximum value. The experimental probability density functions of the three analyzed technologies are fitted by gamma distributions based on error histograms found in the literature and measurement data. The location measurements of the individual technologies are provided at different time instants, so the weighted product of the PDFs is used to improve aggregation accuracy. The discrete event-simulation model was used to evaluate the aggregation method with the Gauss–Markov mobility model. Simulations demonstrated that the calculated aggregated location was more accurate than any of the methods taken as the input, and average error was decreased by almost 13% compared to an arithmetic mean of the three considered localization methods, and by more than 36% compared to the single method with the highest accuracy.

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