Multi-agent simulations for emergency situations in an airport scenario

This paper presents a multi-agent framework using Net- Logo to simulate humanand collective behaviors during emergency evacuations. Emergency situationappears when an unexpected event occurs. In indoor emergency situation, evacuation plans defined by facility manager explain procedure and safety ways tofollow in an emergency situation. A critical and public scenario is an airportwhere there is an everyday transit of thousands of people. In this scenario theimportance is related with incidents statistics regarding overcrowding andcrushing in public buildings. Simulation has the objective of evaluating buildinglayouts considering several possible configurations. Agents could be based onreactive behavior like avoid danger or follow other agent, or in deliberative behaviorbased on BDI model. This tool provides decision support in a real emergencyscenario like an airport, analyzing alternative solutions to the evacuationprocess.Publicad

Multiagent Simulations for Emergency Situations in Buildings

Proceedings of: 13th Ibero-American Conference on Artificial Intelligence (IBERAMIA 2012): Workshop on Intelligent systems for context-based information fusion (ISCIF). Cartagena de Indias, Colombia. 13-16 November 2012.This paper presents a multi-agent framework using NetLogo to simulate human and collective behaviors during emergency evacuations. Emergency situation appears when an unexpected event occurs. In indoor emergency situation, evacuation plans de ned by facility manager explain procedure and safety ways to follow in an emergency situation. Critical and public scenarios are buildings where there is an everyday transit of thousands of people. In this case the importance is related with incidents statistics regarding overcrowding and crushing in public buildings. Simulation has the objective of evaluating building layouts considering several possible con gurations. Agents could be based on reactive behavior like avoid danger or follow other agent, or in deliberative behavior based on BDI model. This tool provides decision support in a real emergency scenario like an public buildings, analyzing alternative solutions to the evacuation process.Publicad

An information fusion framework for context-based accidents prevention

The oil and gas industry is increasingly concerned with achieving and demonstrating good performance with regard occupational health and safety (OHS) issues, through the control of its OHS risks, which is consistent with its core policy and objectives. There are standards to identify and record workplace accidents and incidents to provide guiding means on prevention efforts, indicating specific failures or reference, means of correction of conditions or circumstances that culminated in an accident. Therefore, events recognition is central to OHS, since the system can selectively start proper prediction services according to the user current situation and past knowledge taken from huge databases. In this sense, a fusion framework that combines data from multiples sources to achieve more specific inferences is needed. In this paper we propose a machine learning algorithm to learn from past anomalous events related to accident events in time and space. It also uses additional knowledge, like the contextual knowledge: user profile, event location and time, etc. Our proposed model provides the big picture about risk analysis for that employee at that place in that moment in a real world environment. Our main contribution lies in building a causality model for accident investigation by means of well-defined spatiotemporal constraints in the offshore oil industry domain.This work was partially funded by CNPq BJT Project 407851/2012–7 and CNPq PVE Project 314017/2013–5

Anomaly Detection Based on Sensor Data in Petroleum Industry Applications

Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA), a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.This work was partially funded by the Brazilian National Council for Scientific and Technological Development projects CNPq BJT 407851/2012-7 and CNPq PVE 314017/2013-5 and projects MINECO TEC 2012-37832-C02-01, CICYT TEC 2011-28626-C02-02.Publicad

Crowd-based ambient assisted living to monitor the elderly's health outdoors

The Safeneighborhood Approach Combines Data From Multiple Sources With Collective Intelligence. It Merges Mobile, Ambient, And Ai Technologies With Old-Fashioned Neighborhood Ties To Create Safe Outdoor Spaces For The Elderly. We&#39 Re Exploring Aal Techniques In Outdoor Environments To Increase The Elderly&#39 S Independence Without Them Having To Interact With Technology. Current Research In Outdoor Monitoring Relies Solely On Sensor Data.4 Our Approach, Which We Call Safeneigborhood (Sn), Crowdsources People In The Neighborhood To Revise The Computer&#39 S Inferences From Contextual And Sensor Data. So, Sn Brings The Community Together To Provide A Safer Environment For The Elderly