scholarly journals THE PREDICTION OF ENTERPRISES ACTIVITY INDICATORS TAKING INTO ACCOUNT THE INITIAL DATA UNCERTAINTY

2019 ◽  
pp. 94-99
Author(s):  
Yu. Gagarin ◽  
S. Gagarina
Keyword(s):  
Initial Data ◽  
Linear Models ◽  
Data Uncertainty ◽  
Time Intervals ◽  
Initial Information ◽  
Interval Estimates ◽  
Point Estimates ◽  
Predicted Values ◽  
Parameter Values ◽  
Confluent Analysis

The mathematical method of making forecasts when the initial data uncertainty have been considered. To obtain point and interval estimates of the parameters of mathematical models that take into account errors in the values of the function and argument, the use methods of confluent analysis has been proposed. For linear models, point estimates and confidence intervals of predictions have been found. The developed method is used to solve the problem of forecasting the volume of services provided by organizations of the housing and utilities complex. The sales volume simulating for different values of time intervals has been conducted and the influence of the initial information error on the model parameter values and predicted values has been analyzed.

scholarly journals Application of Mixed Linear Models in the Analysis of Road Surface Features

2015 ◽  
Vol 54 (1) ◽  
pp. 101-109 ◽  
Author(s):  
Jurgita Židanavičiūtė ◽  
Audrius Vaitkus
Keyword(s):  
Linear Models ◽  
Road Surface ◽  
Linear Regression Models ◽  
Mixed Linear Models ◽  
Time Intervals ◽  
Surface Strength ◽  
The Road ◽  
Unequally Spaced ◽  
Units Of Analysis ◽  
The Impact

The data were collected by researchers at the Road Research Institute, in a study investigating the impact of differentfactors on road surface strength. In this statistical analysis, we apply linear mixed models (LMMs) to clustered longitudinal data, inwhich the units of analysis (points in the road) are nested within clusters (sample of four different road segments), and repeatedmeasures of road strength in these different points are collected over time with unequally spaced time intervals. The data arebalanced – each cluster has the same number of units, which are measured at the same number of time points. Because of correlateddata and different clusters in which data could be correlated, linear regression models are not appropriate here, and therefore linearmixed models are applied.

scholarly journals Probability distributions of COVID-19 tweet posted trends use a nonhomogeneous Poisson process

2020 ◽  
Vol 1 (4) ◽  
pp. 229-238
Author(s):  
Devi Munandar ◽  
Sudradjat Supian ◽  
Subiyanto Subiyanto
Keyword(s):  
Social Media ◽  
Poisson Process ◽  
Exponential Distribution ◽  
Probability Distributions ◽  
Nonhomogeneous Poisson Process ◽  
Time Interval ◽  
Initial State ◽  
Time Intervals ◽  
Time Parameters ◽  
Parameter Values

The influence of social media in disseminating information, especially during the COVID-19 pandemic, can be observed with time interval, so that the probability of number of tweets discussed by netizens on social media can be observed. The nonhomogeneous Poisson process (NHPP) is a Poisson process dependent on time parameters and the exponential distribution having unequal parameter values and, independently of each other. The probability of no occurrence an event in the initial state is one and the probability of an event in initial state is zero. Using of non-homogeneous Poisson in this paper aims to predict and count the number of tweet posts with the keyword coronavirus, COVID-19 with set time intervals every day. Posting of tweets from one time each day to the next do not affect each other and the number of tweets is not the same. The dataset used in this study is crawling of COVID-19 tweets three times a day with duration of 20 minutes each crawled for 13 days or 39 time intervals. The result of this study obtained predictions and calculated for the probability of the number of tweets for the tendency of netizens to post on the situation of the COVID-19 pandemic.

scholarly journals ALGORITHMS IN TAKING TACTICAL DECISIONS

2017 ◽  
Vol 17 ◽  
pp. 76-86
Author(s):  
O. Yu. Bululukov
Keyword(s):  
Initial Data ◽  
Mathematical Knowledge ◽  
Automated Systems ◽  
Mathematical Methods ◽  
Initial Information ◽  
Logical Sequence ◽  
Wide Range ◽  
Mathematical Algorithms ◽  
Tactical Decisions ◽  
Optimization Efficiency

The issues of using mathematical knowledge in the investigation of crimes are considered. It’s indicated that the application of mathematical methods in the investigation is limited by the wide range of ways of establishing evidentiary information, which are not always subjected to the mathematical calculations. We have drawn attention to the fact that the modern use of mathematical methods is basically reduced to the use of algorithms in various aspects of the process of investigating crimes. It’s defined that the purpose of algorithmic investigation activity is its optimization, efficiency, objectivity of the results, reduction of the terms of investigation and creation of real conditions for the development of automated systems for processing and evaluating of the received information, taking decisions in certain typical situations. The importance of criminalistic algorithms in choosing optimal tactical solutions is substantiated in the situations, where there are several variants of solutions and the ambiguity of the expected results when making a decision. Two groups of investigation tasks are presented which are divided among themselves, depending on the clarity of the initial information, which was used in their formation. There is shown a relationship that exists between the analysis of the initial information in the formulation of a tactical task and the choice of an algorithm for its solution by making tactical decisions. It’s pointed out that the uncertainty and incompleteness of the initial data of the tactical task considerably complicates the use of mathematical algorithms. There is analyzed the use of algorithms in the construction: of a private technique for investigating crimes; of a tactical operation; when forming a system of tactical methods. Critical remarks are presented about the available modern algorithms (programs) of the investigator’s actions as non-concrete, multivariate and not reflecting the logical sequence of actions aimed at accomplishing the tasks of investigation and achieving the desired result.

Mathematical model identification of gas turbine engine with account for initial data uncertainty

2021 ◽  
Vol 28 (3) ◽  
pp. 171-185
Author(s):  
Oleg Baturin ◽  
Paul Nikolalde ◽  
Grigorii Popov ◽  
Anastssia Korneeva ◽  
Ivan Kudryashov
Keyword(s):  
Mathematical Model ◽  
Initial Data ◽  
Gas Turbine ◽  
Model Identification ◽  
Gas Turbine Engine ◽  
Data Uncertainty ◽  
Turbine Engine

ON THE REGULARITY OF THREE-DIMENSIONAL ROTATING EULER–BOUSSINESQ EQUATIONS

1999 ◽  
Vol 09 (07) ◽  
pp. 1089-1121 ◽  
Author(s):  
A. BABIN ◽  
A. MAHALOV ◽  
B. NICOLAENKO
Keyword(s):  
Initial Data ◽  
Three Dimensional ◽  
Fluid Flows ◽  
Boussinesq Equations ◽  
Stable Stratification ◽  
Primitive Equations ◽  
Time Interval ◽  
Regular Solutions ◽  
Time Intervals ◽  
Long Time

The 3-D rotating Boussinesq equations (the "primitive" equations of geophysical fluid flows) are analyzed in the asymptotic limit of strong stable stratification. The resolution of resonances and a nonstandard small divisor problem are the basis for error estimates for such fast singular oscillating limits. Existence on infinite time intervals of regular solutions to the viscous 3-D "primitive" equations is proven for initial data in Hα, α≥ 3/4. Existence on a long-time interval T*of regular solutions to the 3-D inviscid equations is proven for initial data in Hα, α > 5/2 (T*→∞ as the frequency of gravity waves →∞).

Patterns of Center of Pressure Migration during Prolonged Unconstrained Standing

Motor Control ◽  
1999 ◽  
Vol 3 (1) ◽  
pp. 12-27 ◽  
Author(s):  
Marcos Duarte ◽  
Valdimir M. Zatsiorsky
Keyword(s):  
Healthy Subjects ◽  
Center Of Pressure ◽  
Large Displacement ◽  
Time Intervals ◽  
Migration Patterns ◽  
Software Code ◽  
Continuous Displacement ◽  
Parameter Values ◽  
Anterior Posterior ◽  
Average Position

Prolonged (>30 min) unconstrained standing (PUS) was studied in 10 young healthy subjects. The usual methods of stabilographic analysis assume a random center of pressure (COP) migration. This study was based on the opposite idea and showed that during PUS, specific and consistent patterns of the COP migration can be recognized by a computer algorithm. Three COP migration patterns were found: (a) shifting, a fast displacement of the average position of COP from one region to another; (b) fidgeting, a fast and large displacement and returning of COP to approximately the same position; and (c) drifting, a slow continuous displacement of the average position of COP. A software code was written and default parameter values were chosen for recognizing COP migration patterns. For 30-min PUS the following patterns were identified: Shifting was generally observed every 316 ± 292 sec in the anterior-posterior (a-p) direction with an average shift amplitude of 17 ± 15 mm, and every 199 ± 148 sec in the medial-lateral (m-1) direction with an average shift amplitude of 22 ± 38 mm. Corresponding time intervals for fidgeting were 59 ± 15 sec in the a-p direction and 49±16 sec in the m-1 direction. The average drift-to-drift interval was 319 ± 173 sec in the a-p direction and 529 ± 333 sec in the m-1 direction.

Simulation of Freeway Weaving Areas

2002 ◽  
Vol 1802 (1) ◽  
pp. 115-124 ◽  
Author(s):  
Alexander Skabardonis
Keyword(s):  
Operating Conditions ◽  
Model Parameters ◽  
Sensitivity Factor ◽  
Microscopic Simulation ◽  
Lane Changing ◽  
Wide Range ◽  
Design Characteristics ◽  
Predicted Values ◽  
Demand Patterns ◽  
Parameter Values

The operation of freeway weaving sections is characterized by intense lane-changing maneuvers and complex vehicle interactions that often create bottlenecks along freeway facilities. The CORSIM microscopic simulation model was applied to simulate the operation of eight realworld weaving sites in California under a wide range of operating conditions. The results indicate that CORSIM with default parameter values underpredicts the speeds in the weaving section by about 19% on average. Numerous simulation runs were made with different values of the model parameters. The following parameters were found to significantly affect the CORSIM results: ( a) car-following sensitivity factor, ( b) lane-changing aggressiveness factor, and ( c) percentage of freeway through vehicles that yield to merging traffic. The calibrated CORSIM model reasonably replicated observed traffic operations at all test sites. The predicted average speeds were within ±5 mph for most test sites. Good agreement between measured and predicted values was obtained for all the combinations of design characteristics and demand patterns.

scholarly journals SYNTHESIZING THE LÜ ATTRACTOR BY PARAMETER-SWITCHING

2011 ◽  
Vol 21 (01) ◽  
pp. 323-331 ◽  
Author(s):  
MARIUS-F. DANCA
Keyword(s):  
Finite Time ◽  
Present Author ◽  
Control Parameter ◽  
Initial Value ◽  
Step Size ◽  
Time Intervals ◽  
Synthesis Algorithm ◽  
Finite Set ◽  
Parameter Values ◽  
Parameter Switching

In this letter we synthesize numerically the Lü attractor starting from the generalized Lorenz and Chen systems, by switching the control parameter inside a chosen finite set of values on every successive adjacent finite time intervals. A numerical method with fixed step size for ODEs is used to integrate the underlying initial value problem. As numerically and computationally proved in this work, the utilized attractors synthesis algorithm introduced by the present author before, allows to synthesize the Lü attractor starting from any finite set of parameter values.

scholarly journals M-dwarf exoplanet surface density distribution

2018 ◽  
Vol 612 ◽  
pp. L3 ◽  
Author(s):  
Michael R. Meyer ◽  
Adam Amara ◽  
Maddalena Reggiani ◽  
Sascha P. Quanz
Keyword(s):  
Density Distribution ◽  
Surface Density ◽  
Probability Distributions ◽  
Mass Range ◽  
Normal Function ◽  
Point Estimates ◽  
Points Of View ◽  
Log Normal ◽  
Parameter Values ◽  
M Dwarf

Aims. We fit a log-normal function to the M-dwarf orbital surface density distribution of gas giant planets, over the mass range 1–10 times that of Jupiter, from 0.07 to 400 AU. Methods. We used a Markov chain Monte Carlo approach to explore the likelihoods of various parameter values consistent with point estimates of the data given our assumed functional form. Results. This fit is consistent with radial velocity, microlensing, and direct-imaging observations, is well-motivated from theoretical and phenomenological points of view, and predicts results of future surveys. We present probability distributions for each parameter and a maximum likelihood estimate solution. Conclusions. We suggest that this function makes more physical sense than other widely used functions, and we explore the implications of our results on the design of future exoplanet surveys.

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