Publications

Abstract not provided.

In this work, we developed a self-organizing map (SOM) technique for using web-based text analysis to forecast when a group is undergoing a phase change. By 'phase change', we mean that an organization has fundamentally shifted attitudes or behaviors. For instance, when ice melts into water, the characteristics of the substance change. A formerly peaceful group may suddenly adopt violence, or a violent organization may unexpectedly agree to a ceasefire. SOM techniques were used to analyze text obtained from organization postings on the world-wide web. Results suggest it may be possible to forecast phase changes, and determine if an example of writing can be attributed to a group of interest.

The purpose of this work was to help develop a research roadmap and small proof ofconcept for addressing key problems and gaps from the perspective of using text analysis methods as a primary tool for detecting when a group is undergoing a phase change. Self- rganizing map (SOM) techniques were used to analyze text data obtained from the tworld-wide web. Statistical studies indicate that it may be possible to predict phase changes, as well as detect whether or not an example of writing can be attributed to a group of interest.

Abstract not provided.

A particle filter based algorithm was developed to track vehicles in a network of roads under the assumption of sporadic and non-persistent sensor data. It is assumed we have a number of sensors that provide position and velocity information only, which are scattered at possibly uneven intervals throughout the road system of interest. Further, the sensor ranges do not overlap, meaning we do not have constant eyes on target. The algorithm was based on the particle filter, but differed from the classical particle filter in two fundamental ways. First, particle weights are not used. Instead, a correspondence function is calculated only when a sensor is tripped, giving weight to the validity of the sensor report. Potentially this results in a computational savings. Second, we do not periodically resample particles. Results demonstrate the approach can effectively track multiple targets in simulations with sparse surveillance. © 2006 IEEE.

A Self Organizing Map (SOM) approach was used to analyze physiological data taken from a group of subjects participating in a cooperative video shooting game. The ultimate aim was to discover signatures of group cooperation, conflict, leadership, and performance. Such information could be fed back to participants in a meaningful way, and ultimately increase group performance in national security applications, where the consequences of a poor group decision can be devastating. Results demonstrated that a SOM can be a useful tool in revealing individual and group signatures from physiological data, and could ultimately be used to heighten group performance.

This report summarizes the results of a five-month LDRD late start project which explored the potential of enabling technology to improve the performance of small groups. The purpose was to investigate and develop new methods to assist groups working in high consequence, high stress, ambiguous and time critical situations, especially those for which it is impractical to adequately train or prepare. A testbed was constructed for exploratory analysis of a small group engaged in tasks with high cognitive and communication performance requirements. The system consisted of five computer stations, four with special devices equipped to collect physiologic, somatic, audio and video data. Test subjects were recruited and engaged in a cooperative video game. Each team member was provided with a sensor array for physiologic and somatic data collection while playing the video game. We explored the potential for real-time signal analysis to provide information that enables emergent and desirable group behavior and improved task performance. The data collected in this study included audio, video, game scores, physiological, somatic, keystroke, and mouse movement data. The use of self-organizing maps (SOMs) was explored to search for emergent trends in the physiological data as it correlated with the video, audio and game scores. This exploration resulted in the development of two approaches for analysis, to be used concurrently, an individual SOM and a group SOM. The individual SOM was trained using the unique data of each person, and was used to monitor the effectiveness and stress level of each member of the group. The group SOM was trained using the data of the entire group, and was used to monitor the group effectiveness and dynamics. Results suggested that both types of SOMs were required to adequately track evolutions and shifts in group effectiveness. Four subjects were used in the data collection and development of these tools. This report documents a proof of concept study, and its observations are preliminary. Its main purpose is to demonstrate the potential for the tools developed here to improve the effectiveness of groups, and to suggest possible hypotheses for future exploration.

This report summarizes the results of a three-year LDRD project on prognostics and health management. System failure over some future time interval (an alternative definition is the capability to predict the remaining useful life of a system). Prognostics are integrated with health monitoring (through inspections, sensors, etc.) to provide an overall PHM capability that optimizes maintenance actions and results in higher availability at a lower cost. Our goal in this research was to develop PHM tools that could be applied to a wide variety of equipment (repairable, non-repairable, manufacturing, weapons, battlefield equipment, etc.) and require minimal customization to move from one system to the next. Thus, our approach was to develop a toolkit of reusable software objects/components and architecture for their use. We have developed two software tools: an Evidence Engine and a Consequence Engine. The Evidence Engine integrates information from a variety of sources in order to take into account all the evidence that impacts a prognosis for system health. The Evidence Engine has the capability for feature extraction, trend detection, information fusion through Bayesian Belief Networks (BBN), and estimation of remaining useful life. The Consequence Engine involves algorithms to analyze the consequences of various maintenance actions. The Consequence Engine takes as input a maintenance and use schedule, spares information, and time-to-failure data on components, then generates maintenance and failure events, and evaluates performance measures such as equipment availability, mission capable rate, time to failure, and cost. This report summarizes the capabilities we have developed, describes the approach and architecture of the two engines, and provides examples of their use. 'Prognostics' refers to the capability to predict the probability of

Abstract not provided.