6 research outputs found

    Herramientas de evolución colectiva para el tratamiento de problemas distribuidos

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    Resumen: En este trabajo se lleva a cabo el desarrollo y aplicación de un algoritmo distribuido Embodied Evolution (dEE) en un entorno real. Para ello, se hace un estudio de las técnicas de aprendizaje más utilizadas en el campo del machine learning y se aplican en diferentes problemas. En primer lugar, se utiliza un algoritmo genético simple sin sistema de decisión en un problema discreto. Posteriormente se resuelve un problema continuo mediante un algoritmo neuroevolutivo (NEAT). Por último, se desarrolla el algoritmo dEE para un problema continuo multiagente y se comprueba que funcione adecuadamente antes de que ser utilizado en el problema realResumo: Neste traballo lévase a cabo o desenvolvemento e a aplicación dun algoritmo distribuído Embodied Evolution (dEE) nun entorno real. Para iso, faise un estudo das técnicas de aprendizaxe máis utilizadas no campo do machine learning e aplícanse a diferentes problemas. En primeiro lugar, utilízase un algoritmo xenético simple sen sistema de decisión nun problema discreto. De seguido, resólvese un problema continuo coa axuda dun algoritmo neuroevolutivo (NEAT). Por último, o algoritmo dEE é desenvolvido para un problema continuo multiaxente e compróbase que funcione de forma axeitada antes de ser usado no problema real.Abstract: Within the following Bachelor thesis, the development and implementation of a distributed Embodied Evolution (dEE) algorithm are carried out in a real environment. For this purpose, a study is conducted on the most used learning techniques in the field of machine learning and it is applied in different problems. First of all, it is adopted a simple genetic algorithm without a decision system in a discrete problem. Then, a continuous problem is solved by a neuroevolutive algorithm (NEAT). Finally, a dEE algorithm is developed for a multiagent continuous problem and it is checked that it works properly before its use in a real problem.Traballo fin de grao (UDC.EPS). Enxeñaría en tecnoloxías industriais. Curso 2016/201

    Developing an agent-based evacuation simulation model based on the study of human behaviour in fire investigation reports

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    Fire disasters happen every day all over the world. These hazardous events threaten people's lives and force an immediate movement of people wanting to escape from a dangerous area. Evacuation drills are held to encourage people to practise evacuation skills and to ensure they are familiar with the environment. However, these drills cannot accurately represent real emergency situations and, in some cases, people may be injured during practice. Therefore, modelling pedestrian motion and crowd dynamics in evacuation situations has important implications for human safety, building design, and evacuation processes. This thesis focuses on indoor pedestrian evacuation in fire disasters. To understand how humans behave in emergency situations, and to simulate more realistic human behaviour, this thesis studies human behaviour from fire investigation reports, which provide a variety details about the building, fire circumstance, and human behaviour from professional fire investigation teams. A generic agent-based evacuation model is developed based on common human behaviour that indentified in the fire investigation reports studied. A number of human evacuation behaviours are selected and then used to design different types of agents, assigning with various characteristics. In addition, the interactions between various agents and an evacuation timeline are modelled to simulate human behaviour and evacuation phenomena during evacuation. The application developed is validated using three specific real fire cases to evaluate how closely the simulation results reflected reality. The model provides information on the number of casualties, high-risk areas, egress selections, and evacuation time. In addition, changes to the building configuration, number of occupants, and location of fire origin are tested in order to predict potential risk areas, building capacity and evacuation time for different situations. Consequently, the application can be used to inform building designs, evacuation plans, and priority rescue processes

    A Dynamic Data Driven Application System for Vehicle Tracking

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    AbstractTracking the movement of vehicles in urban environments using fixed position sensors, mobile sensors, and crowd-sourced data is a challenging but important problem in applications such as law enforcement and defense. A dynamic data driven application system (DDDAS) is described to track a vehicle's movements by repeatedly identifying the vehicle under investigation from live image and video data, predicting probable future locations, and repositioning sensors or retargeting requests for information in order to reacquire the vehicle. An overview of the envisioned system is described that includes image processing algorithms to detect and recapture the vehicle from live image data, a computational framework to predict probable vehicle locations at future points in time, and a power aware data distribution management system to disseminate data and requests for information over ad hoc wireless communication networks. A testbed under development in the midtown area of Atlanta, Georgia in the United States is briefly described

    A perennial simulation framework for integrated crisis management studies

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    Ph.DDOCTOR OF PHILOSOPH

    DDDAS for Fire and Agent Evacuation Modeling of the Rhode Island Nightclub Fire

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    Abstract. A Dynamic Data Driven Application System (DDDAS) was created to study interaction between fire and agent models during a fire evacuation. The analysis from that research can be used to validate proposed ideas in evacuation and building designs to ensure safety of buildings given various agent behav-iors. Two separate models were used to simulate the components of the emer-gency situation: fire and agent. The independent models were able to run using data computed by the other interacting models, allowing careful examination of real-time interactions in a situation. Through study of the interactions, a better understanding is gained of how individual variables such as exit position and width affect the evacuation process and escape rate in the given scenario. Exits can be relocated and changed to quickly assess the effect on the model. The re-sults can be used for improving building design and regulations as well as train-ing first responders.
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