Some aspect of human features of the use of Unmanned Aerial Systems in a disaster-specific division

Restás, Ágoston; Dudás, Zoltán

doi:10.1109/icuas.2013.6564791

Cited by 4 publications

(1 citation statement)

References 1 publication

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“…Neural network systems are using GIS (Geographical Information Systems) to mitigation disaster lost and recover survivor [47]. The other implementation area of the ANN is unmanned car driving to conduct the vehicle out of drivers [48]. Early warning system includes a sensor to get information concern disaster is very useful to protect urban live in city, town or metropolitan area.…”

Section: Neural Network Modelsmentioning

confidence: 99%

Estimating Risk Pressure Factor (RPF) with Artificial Neural Network (ANN) to Locate Search and Rescue (SAR) Team Station.

Macit¹

2019

Turkish Journal of Forecasting

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A B S T R A C TEarthquake is one of the natural disaster types that suddenly breaks regular human life. Rescue activities in disasters are one of the most critical stages of modern disaster management. This management stage, as mentioned earlier, includes all the activities that need to be done after the disaster. Search and Rescue (SAR) teams perform one of these most critical activities after the earthquake post-disaster period. Search and rescue teams that will rescue and relief after a disaster are selected according to the criteria selected. Location layout selection problems are NP-Hard, and obtaining hard results is in the class of these problems. One of these criteria is the Risk Pressure Factor (RPF) used in determining the priorities of the risk areas. Determining the level of risk level is very difficult and also these are difficult to predict. In this study, it is aimed to estimate this parametric value by using an artificial neural network (ANN) method which is applied in many fields. And then in this study, a prediction model was constructed by using the backpropagation method which is a suitable propagation method in the ANN method and results are obtained from the MATLAB program. The resulting risk-pressure factor (RPF) value can be used as a parameter in the proposed mathematical model. As a result of the study, the missing parameter of the mathematical model will be found in the estimation of a parameter belonging to the proposed mathematical model. 27In this study, we propose a model including risk pressure factor and forecast the Search and Rescue (SAR) team location and layout mathematical model with the artificial neural network (ANN) method. The aim of this study predicts the risk pressure factor by means of ANN methods. The proposal mathematical model locates the SAR station in exact time window with planning long term period. Literature ReviewANN method can be classified into four main groups such as time-series, simulation, qualitative and cause-effect methods. ANN has many advantages consider to other forecasting methods. Developing better forecasting approaches to reduce or eliminate computation time and its total cost. The Prediction models are based on historical data acquired by a user, and these data are computed by statistical methods [5]. For instance, some of the research concerned with the prediction of using water budget [6], air pollution data prediction [7], emergency event prediction [8,9], and rainfall trends [10], wind speed behaviour [11][12][13][14], financial models and prediction [15]. The other approach of this survey is classified as strategic location and layout problems as static or deterministic location problems, dynamic location layout problems or location problems under uncertainty [16]. Emergency service, medical plant, and ambulance service point location and layout problem are surveyed by many authors [17][18][19][20][21][22][23][24]. While some surveys cover only location layout, the other relatives from these research area such as disaster [25][26][27]. S...

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Section: Neural Network Modelsmentioning

confidence: 99%

Estimating Risk Pressure Factor (RPF) with Artificial Neural Network (ANN) to Locate Search and Rescue (SAR) Team Station.

Macit¹

2019

Turkish Journal of Forecasting

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Unmanned aerial system for post disaster identification

Mukherjee¹,

Chakraborty²,

Azar³

et al. 2014

International Conference on Circuits, Communication, Control and Computing

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The Future of Drones and their Public Acceptance

Macias

Barrado

Pastor

et al. 2019

2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)

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Any emergent technology in history has raised an initial rejection by part of the society. Added to the several problems that the non-mature technology may have, the lack of any previous experience about side effects and the humans psychological fear to the unknown play an important influence in its acceptance. As drones bring up high social and economic expectations due to their capabilities and bussiness applications, the social acceptance is key to the complete development of drone technology's potential. Experts believe that social acceptance is ruled by a balance between beneficial usages and inconvenient issues regarding the technology. This balance in the aeronautical sector is also conditioned by the strict safety policies and regulations of the airspace and the current airspace users. To analyse this balance situation in actual and future environments, regarding drone technology, different use cases will be presented. These use cases have been proposed and analysed by different stakeholders from the U-space community network (UCN), a network of airspace and drone stakeholders who participated in the context of the SESAR CORUS project.The purpose of this paper is to analyse some of these use cases by obtaining responses from different stakeholders point of view using a survey in order to see how economical, safety and political aspects are balanced in each one of the cases. From the survey responses we will perform an analysis by means of three different acceptance indicators, one for each aspect commented.Main results and conclusions point out that the economical indicator is, in general, positive, especially for the low cost payload use cases. In contrast the economic indicator is close to neutral for city transport and airports use cases, which leads to propose some economic promotion action may be needed to make them a reality. For the safety indicator we observe that they are close to negative values as use case complexity increases. Thus we can conclude that some of the proposed missions start affecting the current levels of safety. Finally, the political indicator is mostly neutral, with some positive trends for scenarios related with inspection tasks or done in non-populated areas.

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Some aspect of human features of the use of Unmanned Aerial Systems in a disaster-specific division

Cited by 4 publications

References 1 publication

Estimating Risk Pressure Factor (RPF) with Artificial Neural Network (ANN) to Locate Search and Rescue (SAR) Team Station.

Estimating Risk Pressure Factor (RPF) with Artificial Neural Network (ANN) to Locate Search and Rescue (SAR) Team Station.

Unmanned aerial system for post disaster identification

The Future of Drones and their Public Acceptance

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