Crowd Evacuation Simulation Based on WebGIS

2014 ◽  
Vol 519-520 ◽  
pp. 1509-1512
Author(s):  
Wen Jun Xu ◽  
Zhu Dong Song ◽  
Yi Da Huang ◽  
Zheng Li Xu ◽  
Yi Yuan Li ◽  
...  
Keyword(s):  
Emergency Management ◽  
Time Management ◽  
Large Scale ◽  
Management Research ◽  
Evacuation Simulation ◽  
Evacuation Plans ◽  
Novel Approach ◽  
Simulation Based ◽  
Crowd Evacuation ◽  
Distributed Decision

In order to improve the efficiency of people evacuation from hazardous regions and reduce the ill effects of disasters, a distributed decision support system for large scale crowd evacuation is proposed. The system is supported by the technology of Web Geographic Information System (WebGIS). Basically, by analyzing the system framework we may acquire a novel approach to establish a distributed large-scale crowd evacuation platform for emergency management. Research results show that the proposed system is useful for devising evacuation plans in advance as well as real-time management under disaster.

scholarly journals A meta-modeling framework in public health emergency management

SIMULATION ◽  
2017 ◽  
Vol 94 (5) ◽  
pp. 401-419
Author(s):  
Bin Chen ◽  
Peng Zhang
Keyword(s):  
Public Health ◽  
Emergency Management ◽  
Code Generation ◽  
Large Scale ◽  
Public Health Emergency ◽  
Modeling Framework ◽  
Artificial Society ◽  
Novel Approach ◽  
Meta Modeling ◽  
Emergency Scenarios

Epidemic transmission is a common type of public health emergency that is difficult to forecast and often causes substantial harm. Artificial societal models provide a novel approach to the study of public health problems. However, public health emergency management (PHEM) always involves multi-disciplinary and multi-hierarchical models that complicate the work of modeling. Models are also made more complex by the consideration of new requirements and interactions. Therefore, we propose a domain-specific methodology to guide the modeling process in PHEM. By analyzing domain characteristics and modeling requirements, a meta-modeling framework can be constructed, containing the basic elements with which to construct an artificial society to study epidemic transmission. In this paper, the designs of meta-models are discussed in detail, and domain models are implemented by code generation, which enables the support of large-scale, agent-based computational experiments on the KD-ACP platform. Case studies of Ebola are outlined, emergency scenarios are reconstructed based on pre-designed meta-models, and “scenario-response” experiments are presented. This study provides a valuable framework and methodology with which to study complex social problems in PHEM. The proposed method has been verified effectively and efficiently.

scholarly journals Simulation-Based Optimisation for Autonomous Transportation Systems Using a Parallel Real-Coded Genetic Algorithm with Scalable Nonuniform Mutation

2021 ◽  
Vol 21 (3) ◽  
pp. 127-144
Author(s):  
Andranik S. Akopov ◽  
Levon A. Beklaryan ◽  
Armen L. Beklaryan
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Traffic Accidents ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Mutation Operators ◽  
Novel Approach ◽  
Real Coded Genetic Algorithm ◽  
Simulation Based ◽  
Power Mutation

Abstract This work presents a novel approach to the simulation-based optimisation for Autonomous Transportation Systems (ATS) with the use of the proposed parallel genetic algorithm. The system being developed uses GPUs for the implementation of a massive agent-based model of Autonomous Vehicle (AV) behaviour in an Artificial Multi-Connected Road Network (AMСRN) consisting of the “Manhattan Grid” and the “Circular Motion Area” that are crossed. A new parallel Real-Coded Genetic Algorithm with a Scalable Nonuniform Mutation (RCGA-SNUM) is developed. The proposed algorithm (RCGA-SNUM) has been examined with the use of known test instances and compared with parallel RCGAs used with other mutation operators (e.g., standard mutation, Power Mutation (PM), mutation with Dynamic Rates (DMR), Scalable Uniform Mutation (SUM), etc.). As a result, RCGA-SNUM demonstrates superiority in solving large-scale optimisation problems when decision variables have wide feasible ranges and multiple local extrema are observed. Following this, RCGA-SNUM is applied to minimising the number of potential traffic accidents in the AMСRN.

Green synthesis, characterization of silver sulfide nanoparticles and antibacterial activity evaluation

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Antibacterial Activity ◽  
Large Scale ◽  
Research Work ◽  
Silver Sulfide ◽  
Morphological Properties ◽  
Effective Diameter ◽  
Novel Approach ◽  
Transmission Electron ◽  
Green Route ◽  
Silver Sulfide Nanoparticles

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

scholarly journals PRISMS-Fatigue computational framework for fatigue analysis in polycrystalline metals and alloys

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Mohammadreza Yaghoobi ◽  
Krzysztof S. Stopka ◽  
Aaditya Lakshmanan ◽  
Veera Sundararaghavan ◽  
John E. Allison ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Large Scale ◽  
Metals And Alloys ◽  
Analysis Tool ◽  
Computational Framework ◽  
Crystal Plasticity Finite Element ◽  
Polycrystalline Metals ◽  
Simulation Based ◽  
Open Source Framework

AbstractThe PRISMS-Fatigue open-source framework for simulation-based analysis of microstructural influences on fatigue resistance for polycrystalline metals and alloys is presented here. The framework uses the crystal plasticity finite element method as its microstructure analysis tool and provides a highly efficient, scalable, flexible, and easy-to-use ICME community platform. The PRISMS-Fatigue framework is linked to different open-source software to instantiate microstructures, compute the material response, and assess fatigue indicator parameters. The performance of PRISMS-Fatigue is benchmarked against a similar framework implemented using ABAQUS. Results indicate that the multilevel parallelism scheme of PRISMS-Fatigue is more efficient and scalable than ABAQUS for large-scale fatigue simulations. The performance and flexibility of this framework is demonstrated with various examples that assess the driving force for fatigue crack formation of microstructures with different crystallographic textures, grain morphologies, and grain numbers, and under different multiaxial strain states, strain magnitudes, and boundary conditions.

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