scholarly journals Using simulation experiments to test historical explanations: the development of the German dye industry 1857-1913

2015 ◽  
Vol 26 (4) ◽  
pp. 907-932 ◽  
Author(s):  
Thomas Brenner ◽  
Johann Peter Murmann
Keyword(s):  
Simulation Experiments ◽  
Dye Industry

The Coal-Tar Dye Industry

1909 ◽  
Vol 67 (1745supp) ◽  
pp. 380-380
Keyword(s):  
Coal Tar ◽  
Dye Industry

COGNITIVE MODELING AS A METHOD TO CONTROL MIGRATION TEMPERATURE

2020 ◽  
pp. 69-87
Author(s):  
Oleg Patlasov ◽  
Oleg Luchko ◽  
Svetlana Mukhametdinova
Keyword(s):  
Temperature Control ◽  
Cognitive Modeling ◽  
Statistical Data ◽  
Controlling Factors ◽  
Simulation Experiments ◽  
Approaches To Studying ◽  
Economic Problems ◽  
Qualitative And Quantitative ◽  
The Social ◽  
Expert Assessments

The research describes one of the approaches to designing a productive mechanism for migration temperature control considering it as an integral qualitative and quantitative indicator of the social and economic problems level associated with migration processes. The analysis of various approaches to studying migration processes impact on socioeconomic situation in recipient countries has been carried out. Some cognitive models have been developed basing on the questionnaire results’ analysis, expert assessments, statistical data. A series of simulation experiments have been carried out using software specially developed to automate the cognitive modeling processes.In the course of our experiments, some changes in the target factor. i.e., in migration temperature, have been detected as a result from different intensity impulses impacting on individual controlling factors. Within the developed models framework, several proposals have been put forward concerning the productive mechanism for migration temperature control.

State Estimation Using Randomly Delayed Measurements

1993 ◽  
Vol 115 (1) ◽  
pp. 19-26 ◽  
Author(s):  
A. Ray ◽  
L. W. Liou ◽  
J. H. Shen
Keyword(s):  
State Estimation ◽  
Minimum Variance ◽  
The State ◽  
Sensor Data ◽  
State Estimator ◽  
Uniform Asymptotic Stability ◽  
Simulation Experiments ◽  
State Estimate ◽  
Sampling Instant ◽  
Recursive Relations

This paper presents a modification of the conventional minimum variance state estimator to accommodate the effects of randomly varying delays in arrival of sensor data at the controller terminal. In this approach, the currently available sensor data is used at each sampling instant to obtain the state estimate which, in turn, can be used to generate the control signal. Recursive relations for the filter dynamics have been derived, and the conditions for uniform asymptotic stability of the filter have been conjectured. Results of simulation experiments using a flight dynamic model of advanced aircraft are presented for performance evaluation of the state estimation filter.

scholarly journals A simulated annealing-based algorithm for selecting balanced samples

2021 ◽  
Author(s):  
Roberto Benedetti ◽  
Maria Michela Dickson ◽  
Giuseppe Espa ◽  
Francesco Pantalone ◽  
Federica Piersimoni
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Sample Selection ◽  
Auxiliary Information ◽  
Real Data ◽  
Simulation Experiments ◽  
Balanced Sampling ◽  
Inclusion Probabilities ◽  
Random Method ◽  
Annealing Algorithms

AbstractBalanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.

scholarly journals A regularized hidden Markov model for analyzing the ‘hot shoe’ in football

2021 ◽  
pp. 1471082X2110080
Author(s):  
Marius Ötting ◽  
Groll Andreas
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Simulation Experiments ◽  
Hot Hand ◽  
Special Cases ◽  
Psychology And Economics ◽  
Likelihood Approach ◽  
Penalized Likelihood Approach ◽  
Hot Hand Effect

We propose a penalized likelihood approach in hidden Markov models (HMMs) to perform automated variable selection. To account for a potential large number of covariates, which also may be substantially correlated, we consider the elastic net penalty containing LASSO and ridge as special cases. By quadratically approximating the non-differentiable penalty, we ensure that the likelihood can be maximized numerically. The feasibility of our approach is assessed in simulation experiments. As a case study, we examine the ‘hot hand’ effect, whose existence is highly debated in different fields, such as psychology and economics. In the present work, we investigate a potential ‘hot shoe’ effect for the performance of penalty takers in (association) football, where the (latent) states of the HMM serve for the underlying form of a player.

scholarly journals Time Granularity Impact on Propagation of Disruptions in a System-of-Systems Simulation of Infrastructure and Business Networks

2021 ◽  
Vol 18 (8) ◽  
pp. 3922
Author(s):  
Mateusz Iwo Dubaniowski ◽  
Hans Rudolf Heinimann
Keyword(s):  
Recovery Time ◽  
System Of Systems ◽  
Future Research ◽  
Business Networks ◽  
Simulation Experiments ◽  
Huge Impact ◽  
High Level ◽  
Systems Simulation ◽  
Different Characteristics ◽  
The Impact

A system-of-systems (SoS) approach is often used for simulating disruptions to business and infrastructure system networks allowing for integration of several models into one simulation. However, the integration is frequently challenging as each system is designed individually with different characteristics, such as time granularity. Understanding the impact of time granularity on propagation of disruptions between businesses and infrastructure systems and finding the appropriate granularity for the SoS simulation remain as major challenges. To tackle these, we explore how time granularity, recovery time, and disruption size affect the propagation of disruptions between constituent systems of an SoS simulation. To address this issue, we developed a high level architecture (HLA) simulation of three networks and performed a series of simulation experiments. Our results revealed that time granularity and especially recovery time have huge impact on propagation of disruptions. Consequently, we developed a model for selecting an appropriate time granularity for an SoS simulation based on expected recovery time. Our simulation experiments show that time granularity should be less than 1.13 of expected recovery time. We identified some areas for future research centered around extending the experimental factors space.

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