scholarly journals Intelligent Agents in Extreme Conditions – Modeling and Simulation of Suicide Bombing for Risk Assessment

10.5772/8383 ◽  
2010 ◽  
Author(s):  
Zeeshan-ul-hassan Usmani ◽  
Fawzi Alghamdi ◽  
Daniel Kirk
Keyword(s):  
Risk Assessment ◽  
Modeling And Simulation ◽  
Intelligent Agents ◽  
Extreme Conditions ◽  
Suicide Bombing

scholarly journals Fluid-inspired field representation for risk assessment in road scenes

2020 ◽  
Vol 6 (4) ◽  
pp. 401-415
Author(s):  
Xuanpeng Li ◽  
Lifeng Zhu ◽  
Qifan Xue ◽  
Dong Wang ◽  
Yongjie Jessica Zhang
Keyword(s):  
Risk Assessment ◽  
Intelligent Agents ◽  
Autonomous Vehicles ◽  
Diffusion Processes ◽  
Fluid Model ◽  
Tracking Error ◽  
Dynamic Environment ◽  
Collision Risk ◽  
Field Representation ◽  
Sensing Technology

AbstractPrediction of the likely evolution of traffic scenes is a challenging task because of high uncertainties from sensing technology and the dynamic environment. It leads to failure of motion planning for intelligent agents like autonomous vehicles. In this paper, we propose a fluid-inspired model to estimate collision risk in road scenes. Multi-object states are detected and tracked, and then a stable fluid model is adopted to construct the risk field. Objects’ state spaces are used as the boundary conditions in the simulation of advection and diffusion processes. We have evaluated our approach on the public KITTI dataset; our model can provide predictions in the cases of misdetection and tracking error caused by occlusion. It proves a promising approach for collision risk assessment in road scenes.

scholarly journals Specifics of modeling and simulation of the risk on the communication and ensure systems

2021 ◽  
Vol 7 ◽  
pp. 17-28
Author(s):  
Vasil Kadrev ◽  
Rosen Pasarelski
Keyword(s):  
Risk Assessment ◽  
Modeling And Simulation ◽  
Simulation Modeling ◽  
Communication Systems ◽  
Initial Conditions ◽  
Analytical Models ◽  
Environmental Damage ◽  
Loss Of Life ◽  
Advantages And Disadvantages ◽  
Risk Assessment Models

The aim of the proposed work is to study the characteristics of modeling and simulation of risk on the ensure systems, as specific communication systems. On the ensure systems case of interest is Probabilistic Risk Assessment (PRA). The assessment focuses on predicting the probability of failures that can lead to injury and / or loss of life and / or severe damage to the system and / or environmental damage. The result of PRA modeling is to determine the probability for a particular result, but with severe consequences, and to identify those events or components, which will most likely lead to this result. Risk assessment models are typically use to assess system safety and to decide on resource management to prevent accidents. Results of analyzes performed using analytical models, as well as simulation modeling of risk on ensure systems, under various specific initial conditions, are presented. Based on these results, the peculiarities (advantages and disadvantages), as well as the perspectives of the analytical and simulation modeling, can be seen. Based on the examined examples are illustrated the actual results, related to the principles and peculiarities of the analytical and simulation modeling in the field of the risk assessment in the ensure systems according to the sampling survey.

scholarly journals Seasonal climate predictions for marine risk assessment in the Barents Sea

2021 ◽  
Author(s):  
Iuliia Polkova ◽  
Laura Schaffer ◽  
Øivin Aarnes ◽  
Johanna Baehr
Keyword(s):  
Risk Assessment ◽  
Wind Speed ◽  
Barents Sea ◽  
Prediction Skill ◽  
Boreal Summer ◽  
Extreme Conditions ◽  
Climate Services ◽  
Seasonal Climate ◽  
The Barents Sea ◽  
Climate Indicators

<p>Marine risk embraces an assessment of likelihoods and consequences of impacts from climate fluctuations in order to identify time and regions vulnerable to climate hazards. This information can support sustainable and safe marine activities. The marine risk assessment is a part of the marine service provided by the DNV GL (short for Det Norske Veritas and Germanischer Lloyd). In their current risk application, likelihoods of extreme conditions on the sea are based on historical observations and atmospheric reanalyses. We assess predicted likelihoods of extreme conditions over 1990-2017 in the boreal summer (prediction months 2-4) from the seasonal forecast system provided by the German Meteorological Service (DWD). We chose summer as it represents the time of the open-water season, when the highest marine activity in the Barents Sea takes place. We selected three indicators from the marine risk assessment. Two of them represent meteorological properties such as wind speed and 2-meter temperature (T2m). The third indicator – the wind chill index (WCI) is a combination of the previous two and represents heat loss from the human body to its surroundings during cold and windy weather. As expected, the prediction skill assessment suggests different levels of predictability for the three indicators, with T2m having the highest skill followed by WCI and wind speed. The prediction skill represents the "trust layer" superimposed on the predicted likelihoods and used as input fields for marine risk assessment. From the likelihood maps for the test period of summer 2020 follows that large areas of the Barents Sea represent favorable conditions for marine operations considering high prediction skill and low likelihood for extreme WCI (>1000 W/m<sup>2</sup>) and T2m (<0 °C) conditions in July and August. The wind speed (>13.9 m/s) is poorly predictable beyond the first lead month. Thus, if risk assessment is based on a suite of climate indicators with the heterogeneous prediction skill, the total risk assessment might be limited by the skill of the indicator with the lowest prediction skill. However, not all climate indicators are equally contributing to the risk assessment. The study describes a workflow for application of seasonal climate predictions and points to a few lessons learned, which can be useful to future climate services.</p>

scholarly journals Crowd Modeling and Simulation for Safer Building Design

2020 ◽  
Vol 11 (2) ◽  
pp. 77-88
Author(s):  
Hazlina Selamat ◽  
Nurulaqilla Khamis ◽  
Nuritaasma Mohd Ghani
Keyword(s):  
Risk Assessment ◽  
Modeling And Simulation ◽  
Assessment Tool ◽  
Building Design ◽  
Crowd Simulation ◽  
Force Model ◽  
Emergency Evacuations ◽  
Crowd Modeling ◽  
Planning And Design ◽  
Crowd Movement

Crowd modeling and simulation are very important in the investigation and study of the dynamics of a crowd. They can be used not only to understand the behavior of a crowd in different environments, but also in risk assessment of spaces and in designing spaces that are safer for crowds, especially during emergency evacuations. This paper provides an overview of the use of the crowd simulation model for three main purposes; (1) as a modeling tool to simulate behavior of a crowd in different environments, (2) as a risk assessment tool to assess the risk posed in the environment, and (3) as an optimization tool to optimize the design of a building or space so as to ensure safer crowd movement and evacuation. Result shows that a simulation using the magnetic force model with a pathfinding feature provides a realistic crowd simulation and the use of ABC optimization can reduce evacuation time and improve evacuation comfort. This paper is expected to provide readers with a clearer idea on how crowd models are used in ensuring safer building planning and design.

scholarly journals Fluid-based field representation for collision risk assessment on the road scenes

2019 ◽  
Author(s):  
Xuanpeng Li ◽  
Lifeng Zhu
Keyword(s):  
Risk Assessment ◽  
State Space ◽  
Intelligent Agents ◽  
Autonomous Vehicles ◽  
Dynamic Environment ◽  
Driving Safety ◽  
Collision Risk ◽  
Field Representation ◽  
On The Road ◽  
Short Term Prediction

Prediction of the likely evolution in the traffic scenes is a challenging task because of high uncertainty of sensing technology and dynamic environment. It leads to failure of planning for intelligent agents like autonomous vehicles. In this paper, we propose a fluid-based physical model to present the influence of surrounding object's motion on driving safety. In our pipeline, the input sensor could be LiDAR, camera, or multi-modal data. We use a Kalman filter to estimate the state space of each detected object, and adopt the properties of stable fluid to build a riskmap based on the density field. The noisy state space are then modeled as the boundary conditions in the simulation of advection and diffusion process. We test our approach on the public KITTI dataset and find this model could handle the short-term prediction in case of misdetection and tracking failure caused by object occlusion, which shows promising in collision risk assessment on road scenes.

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