1,857 research outputs found
Advancing Aircraft Operations in a Net-Centric Environment with the Incorporation of Increasingly Autonomous Systems and Human Teaming
NextGen has begun the modernization of the nations air transportation system, with goals to improve system safety, increase operation efficiency and capacity, provide enhanced predictability, resilience and robustness. With these improvements, NextGen is poised to handle significant increases in air traffic operations, more than twice the number recorded in 2016, by 2025.1 NextGen is evolving toward collaborative decision-making across many agents, including automation, by use of a Net-Centric architecture, which in itself creates a very complex environment in which the navigation and operation of aircraft are to take place. An intricate environment such as this, coupled with the expected upsurge of air traffic operations generates concern respecting the ability of the human-agent to both fly and manage aircraft within. Therefore, it is both necessary and practical to begin the process of increasingly autonomous systems within the cockpit that will act independently to assist the human-agent achieve the overall goal of NextGen. However, the straightforward technological development and implementation of intelligent machines into the cockpit is only part of what is necessary to maintain, at minimum, or improve human-agent functionality, as desired, while operating in NextGen. The full integration of Increasingly Autonomous Systems (IAS) within the cockpit can only be accomplished when the IAS works in concert with the human, formulating trust between the two, thereby establishing a team atmosphere. Imperative to cockpit implementation is ensuring the proper performance of the IAS by the development team and the human-agent with which it will be paired when given a specific piloting, navigation, or observational task. Described in this paper are the steps taken, at NASA Langley Research Center, during the second and third phases of the development of an IAS, the Traffic Data Manager (TDM), its verification and validation by human-agents, and the foundational development of Human Autonomy Teaming (HAT) between the two
Simple but Not Simplistic: Reducing the Complexity of Machine Learning Methods
Programa Oficial de Doutoramento en Computación . 5009V01[Resumo]
A chegada do Big Data e a explosión do Internet das cousas supuxeron un gran
reto para os investigadores en Aprendizaxe Automática, facendo que o proceso de
aprendizaxe sexa mesmo roáis complexo. No mundo real, os problemas da aprendizaxe
automática xeralmente teñen complexidades inherentes, como poden ser as
caracterÃsticas intrÃnsecas dos datos, o gran número de mostras, a alta dimensión dos
datos de entrada, os cambios na distribución entre o conxunto de adestramento e
test, etc. Todos estes aspectos son importantes, e requiren novoS modelos que poi dan
facer fronte a estas situacións. Nesta tese, abordáronse todos estes problemas, tratando
de simplificar o proceso de aprendizaxe automática no escenario actual. En
primeiro lugar, realÃzase unha análise de complexidade para observar como inflúe
esta na tarefa de clasificación, e se é posible que a aplicación dun proceso previo
de selección de caracterÃsticas reduza esta complexidade. Logo, abórdase o proceso
de simplificación da fase de aprendizaxe automática mediante a filosofÃa divide e
vencerás, usando un enfoque distribuÃdo. Seguidamente, aplicamos esa mesma filosofÃa
sobre o proceso de selección de caracterÃsticas. Finalmente, optamos por un
enfoque diferente seguindo a filosofÃa do Edge Computing, a cal permite que os datos
producidos polos dispositivos do Internet das cousas se procesen máis preto de
onde se crearon. Os enfoques propostos demostraron a súa capacidade para reducir
a complexidade dos métodos de aprendizaxe automática tradicionais e, polo tanto,
espérase que a contribución desta tese abra as portas ao desenvolvemento de novos
métodos de aprendizaxe máquina máis simples, máis robustos, e máis eficientes
computacionalmente.[Resumen]
La llegada del Big Data y la explosión del Internet de las cosas han supuesto
un gran reto para los investigadores en Aprendizaje Automático, haciendo que el
proceso de aprendizaje sea incluso más complejo. En el mundo real, los problemas de
aprendizaje automático generalmente tienen complejidades inherentes) como pueden
ser las caracterÃsticas intrÃnsecas de los datos, el gran número de muestras, la alta
dimensión de los datos de entrada, los cambios en la distribución entre el conjunto de
entrenamiento y test, etc. Todos estos aspectos son importantes, y requieren nuevos
modelos que puedan hacer frente a estas situaciones. En esta tesis, se han abordado
todos estos problemas, tratando de simplificar el proceso de aprendizaje automático
en el escenario actual. En primer lugar, se realiza un análisis de complejidad para
observar cómo influye ésta en la tarea de clasificación1 y si es posible que la aplicación
de un proceso previo de selección de caracterÃsticas reduzca esta complejidad.
Luego, se aborda el proceso de simplificación de la fase de aprendizaje automático
mediante la filosofÃa divide y vencerás, usando un enfoque distribuido. A continuación,
aplicamos esa misma filosofÃa sobre el proceso de selección de caracterÃsticas.
Finalmente, optamos por un enfoque diferente siguiendo la filosofÃa del Edge Computing,
la cual permite que los datos producidos por los dispositivos del Internet de
las cosas se procesen más cerca de donde se crearon. Los enfoques propuestos han
demostrado su capacidad para reducir la complejidad de los métodos de aprendizaje
automático tnidicionales y, por lo tanto, se espera que la contribución de esta
tesis abra las puertas al desarrollo de nuevos métodos de aprendizaje máquina más
simples, más robustos, y más eficientes computacionalmente.[Abstract]
The advent of Big Data and the explosion of the Internet of Things, has brought
unprecedented challenges to Machine Learning researchers, making the learning task
more complexo Real-world machine learning problems usually have inherent complexities,
such as the intrinsic characteristics of the data, large number of instauces,
high input dimensionality, dataset shift, etc. AH these aspects matter, and can
fOI new models that can confront these situations. Thus, in this thesis, we have
addressed aH these issues) simplifying the machine learning process in the current
scenario. First, we carry out a complexity analysis to see how it inftuences the
classification models, and if it is possible that feature selection might result in a
deerease of that eomplexity. Then, we address the proeess of simplifying learning
with the divide-and-conquer philosophy of the distributed approaeh. Later, we aim
to reduce the complexity of the feature seleetion preprocessing through the same
philosophy. FinallYl we opt for a different approaeh following the eurrent philosophy
Edge eomputing, whieh allows the data produeed by Internet of Things deviees
to be proeessed closer to where they were ereated. The proposed approaehes have
demonstrated their eapability to reduce the complexity of traditional maehine learning
algorithms, and thus it is expeeted that the eontribution of this thesis will open
the doors to the development of new maehine learning methods that are simpler,
more robust, and more eomputationally efficient
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields
This work presents a first evaluation of using spatio-temporal receptive
fields from a recently proposed time-causal spatio-temporal scale-space
framework as primitives for video analysis. We propose a new family of video
descriptors based on regional statistics of spatio-temporal receptive field
responses and evaluate this approach on the problem of dynamic texture
recognition. Our approach generalises a previously used method, based on joint
histograms of receptive field responses, from the spatial to the
spatio-temporal domain and from object recognition to dynamic texture
recognition. The time-recursive formulation enables computationally efficient
time-causal recognition. The experimental evaluation demonstrates competitive
performance compared to state-of-the-art. Especially, it is shown that binary
versions of our dynamic texture descriptors achieve improved performance
compared to a large range of similar methods using different primitives either
handcrafted or learned from data. Further, our qualitative and quantitative
investigation into parameter choices and the use of different sets of receptive
fields highlights the robustness and flexibility of our approach. Together,
these results support the descriptive power of this family of time-causal
spatio-temporal receptive fields, validate our approach for dynamic texture
recognition and point towards the possibility of designing a range of video
analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure
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