Modeling the escalation/de-escalation of response operation levels in disaster response networks using hierarchical Colored Petri Nets (CPN) approach

In emergency situations, the first responders need to act promptly to situations, while the disaster management authorities' response might not be at the same speed. Delays by disaster management authorities had a direct impact on response operations led response teams to the shift away from command and control structures to net-centric ones. This paper is part of a study to examine patterns of emerging organizational relations and coordination structures in disaster response operations. Understanding coordination patterns in disaster chaos can help to integrate those patterns in the planning and execution in the modern disaster management operations. We use dynamic modeling methods to anal…

Data Driven Seal Wear Classifications using Acoustic Emissions and Artificial Neural Networks

The work presented in this paper is built on a series of experiments aiming to develop a data-driven and automated method for seal diagnostics using Acoustic Emission (AE) features. Seals in machineries operate in harsh conditions, and seal wear in hydraulic cylinders results in fluid leakage, and instability of the piston rod movement. Therefore, regular inspection of seals is required using automated approaches to improve productivity and to reduce unscheduled maintenance. In this study, we implemented a data-driven diagnostics approach which utilizes AE measurements along with light weight Artificial Neural Networks (ANN) as a classifier to investigate the performance and resources (hard…

Behind the Scenes of Scenario-Based Training: Understanding Scenario Design and Requirements in High-Risk and Uncertain Environments

Simulation exercises as a training tool for enhancing preparedness for emergency response are widely adopted in disaster management. This paper addresses current scenario design processes, proposes an alternative approach for simulation exercises and introduces a conceptual design of an adaptive scenario generator. Our work is based on a systematic literature review and observations made during TRIPLEX-2016 exercise in Farsund, Norway. The planning process and scenario selection of simulation exercises impact directly the effectiveness of intra- and interorganizational cooperation. However, collective learning goals are rarely addressed and most simulations are focused on institution-specif…

Enhancing Disaster Response for Hazardous Materials Using Emerging Technologies: The Role of AI and a Research Agenda

Despite all efforts like the introduction of new training methods and personal protective equipment, the need to reduce the number of First Responders (FRs) fatalities and injuries remains. Reports show that advances in technology have not yet resulted in protecting FRs from injuries, health impacts, and odorless toxic gases effectively. Currently, there are emerging technologies that can be exploited and applied in emergency management settings to improve FRs protection. The aim of this paper is threefold: First, to conduct scenario analysis and situations that currently threat the first responders. Second, to conduct gap analysis concerning the new technology needs in relations to the pro…

A Deep Learning Approach for Automated Fault Detection on Solar Modules Using Image Composites

Aerial inspection of solar modules is becoming increasingly popular in automatizing operations and maintenance in large-scale photovoltaic power plants. Current practices are typically time-consuming as they make use of manual acquisitions and analysis of thousands of images to scan for faults and anomalies in the modules. In this paper, we explore and evaluate the use of computer vision and deep learning methods for automating the analysis of fault detection and classification in large scale photovoltaic module installations. We use convolutional neural networks to analyze thermal and visible color images acquired by cameras mounted on unmanned aerial vehicles. We generate composite images…

Automated Scenario Generation for Training of Humanitarian Responders in High-Risk Settings

Not a Target. A Deep Learning Approach for a Warning and Decision Support System to Improve Safety and Security of Humanitarian Aid Workers

Humanitarian aid workers who try to provide aid to the most vulnerable populations in the Middle East or Africa are risking their own lives and safety to help others. The current lack of a collaborative real-time information system to predict threats prevents responders and local partners from developing a shared understanding of potentially threatening situations, causing increased response times and leading to inadequate protection. To solve this problem, this paper presents a threat detection and decision support system that combines knowledge and information from a network of responders with automated and modular threat detection. The system consists of three parts. It first collects te…