Fractal Parameter Space of Lorenz-like Attractors: A Hierarchical Approach

2014 ◽  
pp. 87-104
Author(s):  
Tingli Xing ◽  
Jeremy Wojcik ◽  
Michael A. Zaks ◽  
Andrey Shilnikov
Keyword(s):  
Parameter Space ◽  
Hierarchical Approach ◽  
Fractal Parameter

Investigation of the applicability domains of the bubble cluster mathematical model

2011 ◽  
Vol 8 (1) ◽  
pp. 65-73
Author(s):  
E.Sh. Nasibullaeva ◽  
I.Sh. Akhatov
Keyword(s):  
Mathematical Model ◽  
Parameter Space ◽  
Acoustic Field ◽  
Initial Radius ◽  
Large Drop ◽  
Bubble Cluster ◽  
The Mathematical Model

The mathematical model of a bubble cluster subjected to an acoustic field is investigated. In this model the cluster is considered as a large drop containing a liquid and a set of microbubbles. Areas of applicability of the mathematical model of the bubble cluster in the parameter space (α, R_0) are constructed, where α is the bubble concentration in the cluster; R_0 is the initial radius of the cluster.

Injuries and Occupational Safety

2017 ◽  
Author(s):  
Dawn N. Castillo ◽  
Timothy J. Pizatella ◽  
Nancy A. Stout
Keyword(s):  
United States ◽  
Ionizing Radiation ◽  
Occupational Safety ◽  
Occupational Injury ◽  
Occupational Injuries ◽  
Mechanical Energy ◽  
The United States ◽  
Hierarchical Approach ◽  
Safety Hazards ◽  
Injury Control

This chapter describes occupational injuries and their prevention. It describes in detail the causes of injuries and epidemiology of injuries. Occupational injuries are caused by acute exposure in the workplace to safety hazards, such as mechanical energy, electricity, chemicals, and ionizing radiation, or from the sudden lack of essential agents, such as oxygen or heat. This chapter describes the nature and the magnitude of occupational injuries in the United States. It provides data on risk of injuries in different occupations and industries. Finally, it discusses prevention of injuries, using a hierarchical approach to occupational injury control.

Rapid Characterization and Parameter Space Exploration of Perovskites Using an Automated Routine

2019 ◽  
Vol 22 (1) ◽  
pp. 6-17 ◽  
Author(s):  
Elisabeth Reinhardt ◽  
Ahmed M. Salaheldin ◽  
Monica Distaso ◽  
Doris Segets ◽  
Wolfgang Peukert
Keyword(s):  
Parameter Space ◽  
Space Exploration ◽  
Rapid Characterization

scholarly journals Search Acceleration of Evolutionary Multi-Objective Optimization Using an Estimated Convergence Point

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 129 ◽  
Author(s):  
Yan Pei ◽  
Jun Yu ◽  
Hideyuki Takagi
Keyword(s):  
Parameter Space ◽  
Statistical Tests ◽  
Distribution Characteristic ◽  
Multi Objective Optimization ◽  
Pareto Improvement ◽  
Pareto Solutions ◽  
Pareto Solution ◽  
Multi Objective ◽  
Objective Space ◽  
Convergence Point

We propose a method to accelerate evolutionary multi-objective optimization (EMO) search using an estimated convergence point. Pareto improvement from the last generation to the current generation supports information of promising Pareto solution areas in both an objective space and a parameter space. We use this information to construct a set of moving vectors and estimate a non-dominated Pareto point from these moving vectors. In this work, we attempt to use different methods for constructing moving vectors, and use the convergence point estimated by using the moving vectors to accelerate EMO search. From our evaluation results, we found that the landscape of Pareto improvement has a uni-modal distribution characteristic in an objective space, and has a multi-modal distribution characteristic in a parameter space. Our proposed method can enhance EMO search when the landscape of Pareto improvement has a uni-modal distribution characteristic in a parameter space, and by chance also does that when landscape of Pareto improvement has a multi-modal distribution characteristic in a parameter space. The proposed methods can not only obtain more Pareto solutions compared with the conventional non-dominant sorting genetic algorithm (NSGA)-II algorithm, but can also increase the diversity of Pareto solutions. This indicates that our proposed method can enhance the search capability of EMO in both Pareto dominance and solution diversity. We also found that the method of constructing moving vectors is a primary issue for the success of our proposed method. We analyze and discuss this method with several evaluation metrics and statistical tests. The proposed method has potential to enhance EMO embedding deterministic learning methods in stochastic optimization algorithms.

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