7 research outputs found
The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies
This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed
Reservoir Computing: computation with dynamical systems
In het onderzoeksgebied Machine Learning worden systemen onderzocht die kunnen leren op basis van voorbeelden. Binnen dit onderzoeksgebied zijn de recurrente neurale netwerken een belangrijke deelgroep. Deze netwerken zijn abstracte modellen van de werking van delen van de hersenen. Zij zijn in staat om zeer complexe temporele problemen op te lossen maar zijn over het algemeen zeer moeilijk om te trainen. Recentelijk zijn een aantal gelijkaardige methodes voorgesteld die dit trainingsprobleem elimineren. Deze methodes worden aangeduid met de naam Reservoir Computing. Reservoir Computing combineert de indrukwekkende rekenkracht van recurrente neurale netwerken met een eenvoudige trainingsmethode. Bovendien blijkt dat deze trainingsmethoden niet beperkt zijn tot neurale netwerken, maar kunnen toegepast worden op generieke dynamische systemen. Waarom deze systemen goed werken en welke eigenschappen bepalend zijn voor de prestatie is evenwel nog niet duidelijk.
Voor dit proefschrift is onderzoek gedaan naar de dynamische eigenschappen van generieke Reservoir Computing systemen. Zo is experimenteel aangetoond dat de idee van Reservoir Computing ook toepasbaar is op niet-neurale netwerken van dynamische knopen. Verder is een maat voorgesteld die gebruikt kan worden om het dynamisch regime van een reservoir te meten. Tenslotte is een adaptatieregel geïntroduceerd die voor een breed scala reservoirtypes de dynamica van het reservoir kan afregelen tot het gewenste dynamisch regime. De technieken beschreven in dit proefschrift zijn gedemonstreerd op verschillende academische en ingenieurstoepassingen
Bibliography of Lewis Research Center technical publications announced in 1993
This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1993. All the publications were announced in the 1993 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses
Bibliography of Lewis Research Center technical publications announced in 1992
This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1992. All the publications were announced in the 1992 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses
Bibliography of Lewis Research Center Technical Publications announced in 1991
This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific engineering work performed and managed by the Lewis Research Center in 1991. All the publications were announced in the 1991 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses
3D-in-2D Displays for ATC.
This paper reports on the efforts and accomplishments
of the 3D-in-2D Displays for ATC project at the end of Year 1.
We describe the invention of 10 novel 3D/2D visualisations that
were mostly implemented in the Augmented Reality ARToolkit.
These prototype implementations of visualisation and interaction
elements can be viewed on the accompanying video. We have
identified six candidate design concepts which we will further
research and develop. These designs correspond with the early
feasibility studies stage of maturity as defined by the NASA
Technology Readiness Level framework. We developed the
Combination Display Framework from a review of the literature,
and used it for analysing display designs in terms of display
technique used and how they are combined. The insights we
gained from this framework then guided our inventions and the
human-centered innovation process we use to iteratively invent.
Our designs are based on an understanding of user work
practices. We also developed a simple ATC simulator that we
used for rapid experimentation and evaluation of design ideas.
We expect that if this project continues, the effort in Year 2 and 3
will be focus on maturing the concepts and employment in a
operational laboratory settings
DATA-DRIVEN ANALYTICAL MODELS FOR IDENTIFICATION AND PREDICTION OF OPPORTUNITIES AND THREATS
During the lifecycle of mega engineering projects such as: energy facilities,
infrastructure projects, or data centers, executives in charge should take into account
the potential opportunities and threats that could affect the execution of such projects.
These opportunities and threats can arise from different domains; including for
example: geopolitical, economic or financial, and can have an impact on different
entities, such as, countries, cities or companies. The goal of this research is to provide
a new approach to identify and predict opportunities and threats using large and diverse
data sets, and ensemble Long-Short Term Memory (LSTM) neural network models to
inform domain specific foresights. In addition to predicting the opportunities and
threats, this research proposes new techniques to help decision-makers for deduction
and reasoning purposes. The proposed models and results provide structured output to
inform the executive decision-making process concerning large engineering projects
(LEPs). This research proposes new techniques that not only provide reliable timeseries
predictions but uncertainty quantification to help make more informed decisions.
The proposed ensemble framework consists of the following components: first,
processed domain knowledge is used to extract a set of entity-domain features; second,
structured learning based on Dynamic Time Warping (DTW), to learn similarity
between sequences and Hierarchical Clustering Analysis (HCA), is used to determine
which features are relevant for a given prediction problem; and finally, an automated
decision based on the input and structured learning from the DTW-HCA is used to
build a training data-set which is fed into a deep LSTM neural network for time-series
predictions. A set of deeper ensemble programs are proposed such as Monte Carlo
Simulations and Time Label Assignment to offer a controlled setting for assessing the
impact of external shocks and a temporal alert system, respectively. The developed
model can be used to inform decision makers about the set of opportunities and threats
that their entities and assets face as a result of being engaged in an LEP accounting for
epistemic uncertainty