Knowledge-based diagnosis for aerospace systems

The need for automated diagnosis in aerospace systems and the approach of using knowledge-based systems are examined. Research issues in knowledge-based diagnosis which are important for aerospace applications are treated along with a review of recent relevant research developments in Artificial Intelligence. The design and operation of some existing knowledge-based diagnosis systems are described. The systems described and compared include the LES expert system for liquid oxygen loading at NASA Kennedy Space Center, the FAITH diagnosis system developed at the Jet Propulsion Laboratory, the PES procedural expert system developed at SRI International, the CSRL approach developed at Ohio State University, the StarPlan system developed by Ford Aerospace, the IDM integrated diagnostic model, and the DRAPhys diagnostic system developed at NASA Langley Research Center

Examining Linguistic Behavior of a Virtual Museum Guide

As artificial intelligence that uses natural language processing becomes more prevalent, analyzing such software so that it maximally understands us holds all the more significance. Despite the high accuracy at which recurrent neural networks can predict, say, the next word in a sentence, they function differently than a human brain. Given that the sources of difficulty encountered by a natural language processing software may not be intuitive to a human, searching for patterns among its errors provides insight as to where software can be improved. Additionally, given that language is productive, humans interacting with natural language processing technology can produce an unlimited variety of stimuli. When a stimulus is as unpredictable as a human prompted to ask whatever they want, the response it yields reveals how natural language processing software handles variability. At the Center of Science and Industry (COSI), a science museum in Columbus, Ohio, researchers from the Ohio State University have developed an interactive avatar that uses natural language processing. In this context, an avatar can be defined as a human-like bot created to interact with users. Visitors can ask the avatar questions related to linguistics, computer science, and exhibits at the museum. The avatar consists of both an animated visual component and its artificial intelligence software, which processes speech as input and produces a response accordingly. In this case, the artificial intelligence used to process language is a recurrent neural network, pretrained on a large corpus of general English text, then subsequently trained on a smaller corpus of text pertaining to computer science, linguistics, and COSI exhibits. My research focuses on the effectiveness of the avatar's responses.No embargoAcademic Major: Linguistic

Aligning Community Colleges to Their Local Labor Markets

Examines ways to better align community college curricula with employer needs, including analyzing online job ads to gather data on occupation and skill demands; examples of use of labor market information; and the potential and limitations of such data

A bibliometric study of the research area of videogames using Dimensions.ai database

Videogames are a very interesting area of research for fields as diverse as computer science, health, psychology or even social sciences. Every year a growing number of articles are published in different topics inside this field, so it is very convenient to study the different bibliometric data in order to consolidate the research efforts. Thus, the aim of this work is to conduct a study on the distribution of articles related to videogames in the different fields of research, as well as to measure their interest over time, to identify the sources, countries and authors with the highest scientific production. In order to carry out this analysis, the information system Dimensions.ai has been considered, since it covers a large number of documents and allows for easy downloading and analysis of datasets. According to the study, three countries are the most prolific in this area: USA, Canada and UK. The obtained results also indicate that the fields with the highest number of publications are Information and Computer Sciences, Medical and Health Sciences, and Psychology and Cognitive Sciences, in this order. With regard to the impact of the publications, differences between the number of citations, and the number of Altmetric Attention Score, have been found

Measuring Possible Future Selves: Using Natural Language Processing for Automated Analysis of Posts about Life Concerns

Individuals have specific perceptions regarding their lives pertaining to how well they are doing in particular life domains, what their ideas are, and what to pursue in the future. These concepts are called possible future selves (PFS), a schema that contains the ideas of people, who they currently are, and who they wish to be in the future. The goal of this research project is to create a program to capture PFS using natural language processing. This program will allow automated analysis to measure people's perceptions and goals in a particular life domain and assess their view of the importance regarding their thoughts on each part of their PFS. The data used in this study were adopted from Kennard, Willis, Robinson, and Knobloch-Westerwick (2015) in which 214 women, aged between 21-35 years, viewed magazine portrayals of women in gender-congruent and gender-incongruent roles. The participants were prompted to write about their PFS with the questions: "Over the past 7 days, how much have you thought about your current life situation and your future? What were your thoughts? How much have you thought about your goals in life and your relationships? What were your thoughts?" The text PFS responses were then coded for mentions of different life domains and the emotions explicitly expressed from the text-data by human coders. Combinations of machine learning techniques were utilized to show the robustness of machine learning in predicting PFS. Long Short-Term Memory networks (LSTM), Convolutional Neural Networks (CNN), and decision trees were used in the ensemble learning of the machine learning model. Two different training and evaluation methods were used to find the most optimal machine learning approach in analyzing PFS. The machine learning approach was found successful in predicting PFS with high accuracy, labeling a person's concerns over PFS the same as human coders have done in The Allure of Aphrodite. While the models were inaccurate in spotting some measures, for example labeling a person's career concern in the present with around 60% accuracy, it was accurate finding a concern in a person's past romantic life with above 95% accuracy. Overall, the accuracy was found to be around 83% for life-domain concerns.Undergraduate Research Scholarship by the College of EngineeringNo embargoAcademic Major: Computer Science and Engineerin

A layered abduction model of perception: Integrating bottom-up and top-down processing in a multi-sense agent

A layered-abduction model of perception is presented which unifies bottom-up and top-down processing in a single logical and information-processing framework. The process of interpreting the input from each sense is broken down into discrete layers of interpretation, where at each layer a best explanation hypothesis is formed of the data presented by the layer or layers below, with the help of information available laterally and from above. The formation of this hypothesis is treated as a problem of abductive inference, similar to diagnosis and theory formation. Thus this model brings a knowledge-based problem-solving approach to the analysis of perception, treating perception as a kind of compiled cognition. The bottom-up passing of information from layer to layer defines channels of information flow, which separate and converge in a specific way for any specific sense modality. Multi-modal perception occurs where channels converge from more than one sense. This model has not yet been implemented, though it is based on systems which have been successful in medical and mechanical diagnosis and medical test interpretation

Functional reasoning in diagnostic problem solving

This work is one facet of an integrated approach to diagnostic problem solving for aircraft and space systems currently under development. The authors are applying a method of modeling and reasoning about deep knowledge based on a functional viewpoint. The approach recognizes a level of device understanding which is intermediate between a compiled level of typical Expert Systems, and a deep level at which large-scale device behavior is derived from known properties of device structure and component behavior. At this intermediate functional level, a device is modeled in three steps. First, a component decomposition of the device is defined. Second, the functionality of each device/subdevice is abstractly identified. Third, the state sequences which implement each function are specified. Given a functional representation and a set of initial conditions, the functional reasoner acts as a consequence finder. The output of the consequence finder can be utilized in diagnostic problem solving. The paper also discussed ways in which this functional approach may find application in the aerospace field