Input Data Preparation for Fire Behavior Fuel Modeling of Bulgarian Test Cases

2015 ◽  
pp. 335-342 ◽  
Author(s):  
Georgi Dobrinkov ◽  
Nina Dobrinkova
Keyword(s):  
Input Data ◽  
Fire Behavior ◽  
Test Cases ◽  
Data Preparation

scholarly journals From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling

2019 ◽  
Vol 8 (9) ◽  
pp. 376 ◽  
Author(s):  
Zhi-Wei Hou ◽  
Cheng-Zhi Qin ◽  
A-Xing Zhu ◽  
Peng Liang ◽  
Yi-Jie Wang ◽  
...  
Keyword(s):  
Service Composition ◽  
Input Data ◽  
Knowledge Bases ◽  
Future Research ◽  
Knowledge Representation And Reasoning ◽  
Data Preparation ◽  
Preparation Methods ◽  
Construction Of Knowledge ◽  
Task Oriented ◽  
Geographic Modeling

One of the key concerns in geographic modeling is the preparation of input data that are sufficient and appropriate for models. This requires considerable time, effort, and expertise since geographic models and their application contexts are complex and diverse. Moreover, both data and data pre-processing tools are multi-source, heterogeneous, and sometimes unavailable for a specific application context. The traditional method of manually preparing input data cannot effectively support geographic modeling, especially for complex integrated models and non-expert users. Therefore, effective methods are urgently needed that are not only able to prepare appropriate input data for models but are also easy to use. In this review paper, we first analyze the factors that influence data preparation and discuss the three corresponding key tasks that should be accomplished when developing input data preparation methods for geographic models. Then, existing input data preparation methods for geographic models are discussed through classifying into three categories: manual, (semi-)automatic, and intelligent (i.e., not only (semi-)automatic but also adaptive to application context) methods. Supported by the adoption of knowledge representation and reasoning techniques, the state-of-the-art methods in this field point to intelligent input data preparation for geographic models, which includes knowledge-supported discovery and chaining of data pre-processing functionalities, knowledge-driven (semi-)automatic workflow building (or service composition in the context of geographic web services) of data preprocessing, and artificial intelligent planning-based service composition as well as their parameter-settings. Lastly, we discuss the challenges and future research directions from the following aspects: Sharing and reusing of model data and workflows, integration of data discovery and processing functionalities, task-oriented input data preparation methods, and construction of knowledge bases for geographic modeling, all assisting with the development of an easy-to-use geographic modeling environment with intelligent input data preparation.

Fire Protection of High-Performance Concrete Using Protective Lining

2011 ◽  
Vol 82 ◽  
pp. 758-763
Author(s):  
Eike Wolfram Klingsch ◽  
Andrea Frangi ◽  
Mario Fontana
Keyword(s):  
High Performance ◽  
Input Data ◽  
High Performance Concrete ◽  
Fire Behavior ◽  
Explosive Spalling ◽  
Protective Lining ◽  
In Fire ◽  
Concrete Elements ◽  
Basic Input ◽  
Fire Design

The paper presents results of experimental and numerical analyses on the fire behavior of concrete elements protected by sprayed protective linings. Particular attention is given to high- (HPC) and ultrahigh performance concrete (UHPC), as HPC and UHPC tend to exhibit explosive spalling in fire due to low porosity. The results provide basic input data for the development of simplified rules for the fire design of concrete structures protected by sprayed protective linings.

scholarly journals SOME STATISTICAL TECHNIQUES APPLIED TO ENGINEERING MECHANICS PROBLEMS

2020 ◽  
Vol 57 (6A) ◽  
pp. 10
Author(s):  
Tham Hong Duong
Keyword(s):  
Taguchi Method ◽  
Input Data ◽  
Technical Specification ◽  
Engineering Problem ◽  
Statistical Techniques ◽  
Test Cases ◽  
Statistical Tools ◽  
Technology Process ◽  
Engineering Problems ◽  
Engineering Mechanics

This article deals with statistical techniques normally used in Engineering. Variables or parameters in models of Engineering Mechanics always face data:  a) of materials (with technical specification); b) of analysing model using specific software; c) of measurement using variety of devices and approaches; and d) of the technology process of manufacture (outcome). An engineering object to be studied has k variables and each variable has m values or level of status, it will need mk cases to be solved. This has to conduct a very large number of test cases to be solved for target objective(s). A Taguchi Method will be applied for finding solution in which much less effort of computation is paid and other different conditions of noise could be taken into account. Besides, other statistical tools, ANOVA have also proved to be useful in quantifying uncertainties in engineering problems, both in aleatory (nature) and epistemic (knowledge and measurement) categories. A typical example of engineering problem is chosen to study using above-mentioned Taguchi method and statistical tools. This method is very useful for design of experiments, both in traditional laboratory and computer numerical modeling and it can used to optimize the set of input data for obtaining the best results of outcome product.

Molecular replacement withMOLREP

2009 ◽  
Vol 66 (1) ◽  
pp. 22-25 ◽  
Author(s):  
Alexei Vagin ◽  
Alexei Teplyakov
Keyword(s):  
Rigid Body ◽  
Electron Density ◽  
Input Data ◽  
Molecular Replacement ◽  
Data Preparation ◽  
X Ray ◽  
Search Models ◽  
Automatic Mode ◽  
Structural Superposition ◽  
Fully Automatic

MOLREPis an automated program for molecular replacement that utilizes a number of original approaches to rotational and translational search and data preparation. Since the first publication describing the program,MOLREPhas acquired a variety of features that include weighting of the X-ray data and search models, multi-copy search, fitting the model into electron density, structural superposition of two models and rigid-body refinement. The program can run in a fully automatic mode using optimized parameters calculated from the input data.

scholarly journals Modeling Ground Firefighting Resource Activities to Manage Risk Given Uncertain Weather

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1077 ◽  
Author(s):  
Erin J. Belval ◽  
Yu Wei ◽  
Michael Bevers
Keyword(s):  
Fire Behavior ◽  
Mixed Integer ◽  
Test Cases ◽  
Control Line ◽  
Fire Growth ◽  
Complex Decision ◽  
Line Construction ◽  
Wildland Firefighting ◽  
And Behavior ◽  
Decision Environments

Wildland firefighting requires managers to make decisions in complex decision environments that hold many uncertainties; these decisions need to be adapted dynamically over time as fire behavior evolves. Models used in firefighting decisions should also have the capability to adapt to changing conditions. In this paper, detailed line construction constraints are presented for use with a stochastic mixed integer fire growth and behavior program. These constraints allow suppression actions to interact dynamically with stochastic predicted fire behavior and account for many of the detailed line construction considerations. Such considerations include spatial restrictions for fire crew travel and operations. Crew safety is also addressed; crews must keep a variable safety buffer between themselves and the fire. Fireline quality issues are accounted for by comparing control line capacity with fireline intensity to determine when a fireline will hold. The model assumes crews may work at varying production rates throughout their shifts, providing flexibility to fit work assignments with the predicted fire behavior. Nonanticipativity is enforced to ensure solutions are feasible for all modeled weather scenarios. Test cases demonstrate the model’s utility and capability on a raster landscape.

scholarly journals SAMICS: input data preparation

1979 ◽  
Author(s):  
R.G. Chamberlain ◽  
R.W. Aster
Keyword(s):  
Input Data ◽  
Data Preparation
Sign in / Sign up

Export Citation Format

Share Document