SSME Post Test Diagnostic System: Systems Section

An assessment of engine and component health is routinely made after each test firing or flight firing of a Space Shuttle Main Engine (SSME). Currently, this health assessment is done by teams of engineers who manually review sensor data, performance data, and engine and component operating histories. Based on review of information from these various sources, an evaluation is made as to the health of each component of the SSME and the preparedness of the engine for another test or flight. The objective of this project - the SSME Post Test Diagnostic System (PTDS) - is to develop a computer program which automates the analysis of test data from the SSME in order to detect and diagnose anomalies. This report primarily covers work on the Systems Section of the PTDS, which automates the analyses performed by the systems/performance group at the Propulsion Branch of NASA Marshall Space Flight Center (MSFC). This group is responsible for assessing the overall health and performance of the engine, and detecting and diagnosing anomalies which involve multiple components (other groups are responsible for analyzing the behavior of specific components). The PTDS utilizes several advanced software technologies to perform its analyses. Raw test data is analyzed using signal processing routines which detect features in the data, such as spikes, shifts, peaks, and drifts. Component analyses are performed by expert systems, which use 'rules-of-thumb' obtained from interviews with the MSFC data analysts to detect and diagnose anomalies. The systems analysis is performed using case-based reasoning. Results of all analyses are stored in a relational database and displayed via an X-window-based graphical user interface which provides ranked lists of anomalies and observations by engine component, along with supporting data plots for each

Real-time sensor data validation

This report describes the status of an on-going effort to develop software capable of detecting sensor failures on rocket engines in real time. This software could be used in a rocket engine controller to prevent the erroneous shutdown of an engine due to sensor failures which would otherwise be interpreted as engine failures by the control software. The approach taken combines analytical redundancy with Bayesian belief networks to provide a solution which has well defined real-time characteristics and well-defined error rates. Analytical redundancy is a technique in which a sensor's value is predicted by using values from other sensors and known or empirically derived mathematical relations. A set of sensors and a set of relations among them form a network of cross-checks which can be used to periodically validate all of the sensors in the network. Bayesian belief networks provide a method of determining if each of the sensors in the network is valid, given the results of the cross-checks. This approach has been successfully demonstrated on the Technology Test Bed Engine at the NASA Marshall Space Flight Center. Current efforts are focused on extending the system to provide a validation capability for 100 sensors on the Space Shuttle Main Engine

SSME HPOTP post-test diagnostic system enhancement project

An assessment of engine and component health is routinely made after each test or flight firing of a space shuttle main engine (SSME). Currently, this health assessment is done by teams of engineers who manually review sensor data, performance data, and engine and component operating histories. Based on review of information from these various sources, an evaluation is made as to the health of each component of the SSME and the preparedness of the engine for another test or flight. The objective of this project is to further develop a computer program which automates the analysis of test data from the SSME high-pressure oxidizer turbopump (HPOTP) in order to detect and diagnose anomalies. This program fits into a larger system, the SSME Post-Test Diagnostic System (PTDS), which will eventually be extended to assess the health and status of most SSME components on the basis of test data analysis. The HPOTP module is an expert system, which uses 'rules-of-thumb' obtained from interviews with experts from NASA Marshall Space Flight Center (MSFC) to detect and diagnose anomalies. Analyses of the raw test data are first performed using pattern recognition techniques which result in features such as spikes, shifts, peaks, and drifts being detected and written to a database. The HPOTP module then looks for combination of these features which are indicative of known anomalies, using the rules gathered from the turbomachinery experts. Results of this analysis are then displayed via a graphical user interface which provides ranked lists of anomalies and observations by engine component, along with supporting data plots for each

Relational agents : effecting change through human-computer relationships

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2003.Includes bibliographical references (p. 205-219).What kinds of social relationships can people have with computers? Are there activities that computers can engage in that actively draw people into relationships with them? What are the potential benefits to the people who participate in these human-computer relationships? To address these questions this work introduces a theory of Relational Agents, which are computational artifacts designed to build and maintain long-term, social-emotional relationships with their users. These can be purely software humanoid animated agents--as developed in this work--but they can also be non-humanoid or embodied in various physical forms, from robots, to pets, to jewelry, clothing, hand-helds, and other interactive devices. Central to the notion of relationship is that it is a persistent construct, spanning multiple interactions; thus, Relational Agents are explicitly designed to remember past history and manage future expectations in their interactions with users. Finally, relationships are fundamentally social and emotional, and detailed knowledge of human social psychology--with a particular emphasis on the role of affect--must be incorporated into these agents if they are to effectively leverage the mechanisms of human social cognition in order to build relationships in the most natural manner possible. People build relationships primarily through the use of language, and primarily within the context of face-to-face conversation. Embodied Conversational Agents--anthropomorphic computer characters that emulate the experience of face-to-face conversation--thus provide the substrate for this work, and so the relational activities provided by the theory will primarily be specific types of verbal and nonverbal conversational behaviors used by people to negotiate and maintain relationships.(cont.) This work also provides an analysis of the types of applications in which having a human-computer relationship is advantageous to the human participant. In addition to applications in which the relationship is an end in itself (e.g., in entertainment systems), human-computer relationships are important in tasks in which the human is attempting to undergo some change in behavior or cognitive or emotional state. One such application is explored here: a system for assisting the user through a month-long health behavior change program in the area of exercise adoption. This application involves the research, design and implementation of relational agents as well as empirical evaluation of their ability to build relationships and effect change over a series of interactions with users.by Timothy Wallace Bickmore.Ph.D

Adapting a Geriatrics Health Counseling Virtual Agent for the Chinese Culture

Abstract. The design of a virtual conversational agent that provides cardiovascular health counseling to hospitalized geriatrics patients in China is described, along with the linguistic and cultural adaptations performed to tailor the agent for China. Results of a preliminary study comparing conversations with the agent to conversations with a geriatrician in a hospital in Shanghai demonstrated high levels of patient acceptance and satisfaction with the agent, although not as high as for the human doctor.

Making It Personal: End-User Authoring of Health Narratives Delivered by Virtual Agents

Abstract. We describe a design study in which five different tools are compared for end-user authoring of personal stories to be told by an embodied conversational agent. The tools provide varying degrees of control over the agent's verbal and nonverbal behavior. Results indicate that users are more satisfied when their stories are delivered by a virtual agent compared to plain text, are more satisfied when provided with tools to control the agent's prosody compared to facial display of emotion, and are most satisfied when they have the most control over all aspects of the agent's delivery

Is the media equation a flash in the pan? The durability and longevity of social responses to computers

ABSTRACT Research on social responses to computers often assesses only first-impression reactions during a single experimental session, providing limited knowledge about the lasting effect of the results. In this work, we assess the lasting strength of social desirability bias effects on an interface designed to track exercise, manipulated to have high or low personalization (text vs. anthropomorphic conversational character). After 40 days of daily interactions by 25 participants, we found that self-reported exercise was more accurate when reported to the character vs. text. We also find that, for both conditions, participants' decision to initiate a session is greater when they have done more exercise. Moreover, we show that this effect significantly increases over time for participants in the character condition, and decreases for participants in the text condition. This study demonstrates that Media Equation effects can grow stronger or weaker over time, depending upon the presentation of the interface

An End-to-End Conversational Style Matching Agent

We present an end-to-end voice-based conversational agent that is able to engage in naturalistic multi-turn dialogue and align with the interlocutor's conversational style. The system uses a series of deep neural network components for speech recognition, dialogue generation, prosodic analysis and speech synthesis to generate language and prosodic expression with qualities that match those of the user. We conducted a user study (N=30) in which participants talked with the agent for 15 to 20 minutes, resulting in over 8 hours of natural interaction data. Users with high consideration conversational styles reported the agent to be more trustworthy when it matched their conversational style. Whereas, users with high involvement conversational styles were indifferent. Finally, we provide design guidelines for multi-turn dialogue interactions using conversational style adaptation

First impressions: users’ judgments of virtual agents’ personality and interpersonal attitude in first encounters

In first encounters people quickly form impressions of each other’s personality and interpersonal attitude. We conducted a study to investigate how this transfers to first encounters between humans and virtual agents. In the study, subjects’ avatars approached greeting agents in a virtual museum rendered in both first and third person perspective. Each agent exclusively exhibited nonverbal immediacy cues (smile, gaze and proximity) during the approach. Afterwards subjects judged its personality (extraversion) and interpersonal attitude (hostility/friendliness). We found that within only 12.5 seconds of interaction subjects formed impressions of the agents based on observed behavior. In particular, proximity had impact on judgments of extraversion whereas smile and gaze on friendliness. These results held for the different camera perspectives. Insights on how the interpretations might change according to the user’s own personality are also provided

Adaptation of the Gabby conversational agent system to improve the sexual and reproductive health of young women in Lesotho

IntroductionYoung women from the low-middle-income country of Lesotho in southern Africa frequently report limited knowledge regarding sexual and reproductive health issues and engage in risky sexual behaviors. The purpose of this study is to describe the adaptation of an evidence-based conversational agent system for implementation in Lesotho and provide qualitative data pertaining to the success of the said adaptation.MethodsAn embodied conversational agent system used to provide preconception health advice in the United States was clinically and culturally adapted for use in the rural country of Lesotho in southern Africa. Inputs from potential end users, health leaders, and district nurses guided the adaptations. Focus group discussions with young women aged 18–28 years who had used the newly adapted system renamed “Nthabi” for 3–4 weeks and key informant interviews with Ministry of Health leadership were conducted to explore their views of the acceptability of the said adaptation. Data were analyzed using NVivo software, and a thematic content analysis approach was employed in the study.ResultsA total of 33 women aged 18–28 years used Nthabi for 3–4 weeks; eight (24.2%) of them were able to download and use the app on their mobile phones and 25 (75.8%) of them used the app on a tablet provided to them. Focus group participants (n = 33) reported that adaptations were culturally appropriate and provided relevant clinical information. The participants emphasized that the physical characteristics, personal and non-verbal behaviors, utilization of Sesotho words and idioms, and sensitively delivered clinical content were culturally appropriate for Lesotho. The key informants from the Ministry leadership (n = 10) agreed that the adaptation was successful, and that the system holds great potential to improve the delivery of health education in Lesotho. Both groups suggested modifications, such as using the local language and adapting Nthabi for use by boys and young men.ConclusionsClinically tailored, culturally sensitive, and trustworthy content provided by Nthabi has the potential to improve accessibility of sexual and reproductive health information to young women in the low-middle-income country of Lesotho