374 research outputs found
Developmental Flight Test of a Powered Approach Stability Augmentation System on the U.S. Navy\u27s E- 2C Hawkeye 2000 Aircraft
The E-2C aircraft is a Navy carrier based high-wing, twin engine turboprop powered aircraft used for the Airborne Early Warning (AEW) mission. In the power approach configuration, the aircraft displays strong adverse yaw, weak directional stability, and excessive rudder control power. These antagonistic characteristics, when coupled together, result in an extremely high workload for the pilot during both carrier and field landings. Although the aircraft has a yaw axis stability augmentation system, it is currently only applicable to cruise conditions. Engaging the stability augmentation in the power approach configuration results in a 1 Hz directional oscillation due to the system’s high gain schedule. Additionally, another attribute of the existing system design results in extremely high rudder pedal forces while maintaining sideslip in crosswind conditions.
Northrop Grumman developed Flight Control Computer (FCC) software patches designed to improve the handling qualities on landing approaches. These patches are designed to change the rudder control gain schedule to allow the use of stability augmentation in the power approach configuration and suppress the divergent Phugoid characteristic throughout the flight envelope. The system is a directional axis controller only and termed the Powered Approach Stability Augmentation System (PASAS). Initial flight tests on a developmental system provided the design parameters for the production system, which was eventually installed in the Navy’s newest E-2C variant, termed Hawkeye 2000. The ensuing flight test program consisted of land based test flights during the summer of 2001, and culminated in a ship trial consisting of multiple landings on the USS Truman in March of 2002
Machine Learning Techniques to Evaluate the Approximation of Utilization Power in Circuits
The need for products that are more streamlined, more useful, and have longer battery lives is rising in today's culture. More components are being integrated onto smaller, more complex chips in order to do this. The outcome is higher total power consumption as a result of increased power dissipation brought on by dynamic and static currents in integrated circuits (ICs). For effective power planning and the precise application of power pads and strips by floor plan engineers, estimating power dissipation at an early stage is essential. With more information about the design attributes, power estimation accuracy increases. For a variety of applications, including function approximation, regularization, noisy interpolation, classification, and density estimation, they offer a coherent framework. RBFNN training is also quicker than training multi-layer perceptron networks. RBFNN learning typically comprises of a linear supervised phase for computing weights, followed by an unsupervised phase for determining the centers and widths of the Gaussian basis functions. This study investigates several learning techniques for estimating the synaptic weights, widths, and centers of RBFNNs. In this study, RBF networks—a traditional family of supervised learning algorithms—are examined. Using centers found using k-means clustering and the square norm of the network coefficients, respectively, two popular regularization techniques are examined. It is demonstrated that each of these RBF techniques are capable of being rewritten as data-dependent kernels. Due to their adaptability and quicker training time when compared to multi-layer perceptron networks, RBFNNs present a compelling option to conventional neural network models. Along with experimental data, the research offers a theoretical analysis of these techniques, indicating competitive performance and a few advantages over traditional kernel techniques in terms of adaptability (ability to take into account unlabeled data) and computing complexity. The research also discusses current achievements in using soft k-means features for image identification and other tasks
Learning to Interpret Fluid Type Phenomena via Images
Learning to interpret fluid-type phenomena via images is a long-standing challenging problem in computer vision. The problem becomes even more challenging when the fluid medium is highly dynamic and refractive due to its transparent nature. Here, we consider imaging through such refractive fluid media like water and air. For water, we design novel supervised learning-based algorithms to recover its 3D surface as well as the highly distorted underground patterns. For air, we design a state-of-the-art unsupervised learning algorithm to predict the distortion-free image given a short sequence of turbulent images. Specifically, we design a deep neural network that estimates the depth and normal maps of a fluid surface by analyzing the refractive distortion of a reference background pattern. Regarding the recovery of severely downgraded underwater images due to the refractive distortions caused by water surface fluctuations, we present the distortion-guided network (DG-Net) for restoring distortion-free underwater images. The key idea is to use a distortion map to guide network training. The distortion map models the pixel displacement caused by water refraction. Furthermore, we present a novel unsupervised network to recover the latent distortion-free image. The key idea is to model non-rigid distortions as deformable grids. Our network consists of a grid deformer that estimates the distortion field and an image generator that outputs the distortion-free image. By leveraging the positional encoding operator, we can simplify the network structure while maintaining fine spatial details in the recovered images. We also develop a combinational deep neural network that can simultaneously perform recovery of the latent distortion-free image as well as 3D reconstruction of the transparent and dynamic fluid surface. Through extensive experiments on simulated and real captured fluid images, we demonstrate that our proposed deep neural networks outperform the current state-of-the-art on solving specific tasks
Mapping and FPGA global routing using Mean Field Annealing
Ankara : Department of Computer Engineering and Information Science and Institute of Engineering and Science, Bilkent University, 1994.Thesis (Master's) -- -Bilkent University, 1994.Includes bibliographical references leaves 71-73Haritaoğlu, İsmailM.S
Relative-fuzzy: a novel approach for handling complex ambiguity for software engineering of data mining models
There are two main defined classes of uncertainty namely: fuzziness and ambiguity, where ambiguity is ‘one-to-many’ relationship between syntax and semantic of a proposition. This definition seems that it ignores ‘many-to-many’ relationship ambiguity type of uncertainty. In this thesis, we shall use complex-uncertainty to term many-to-many relationship ambiguity type of uncertainty.
This research proposes a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. The proposed approach is based on Relative-Fuzzy Logic (RFL), a novel type of fuzzy logic. RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic.
To achieve the goal of proposing RFL, a question is needed to be answered, which is: how these two approaches; i.e. fuzzy logic and possible-world, can be mixed to produce a new membership value set (and later logic) that able to handle fuzziness and multiple viewpoints at the same time? Achieving such goal comes via providing possible world logic the ability to quantifying multiple viewpoints and also model fuzziness in each of these multiple viewpoints and expressing that in a new set of membership value.
Furthermore, a new architecture of Hierarchical Neural Network (HNN) called ML/RFL-Based Net has been developed in this research, along with a new learning algorithm and new recalling algorithm. The architecture, learning algorithm and recalling algorithm of ML/RFL-Based Net follow the principles of RFL. This new type of HNN is considered to be a RFL computation machine.
The ability of the Relative Fuzzy-based DM prediction model to tackle the problem of complex ambiguity type of uncertainty has been tested. Special-purpose Integrated Development Environment (IDE) software, which generates a DM prediction model for speech recognition, has been developed in this research too, which is called RFL4ASR. This special purpose IDE is an extension of the definition of the traditional IDE.
Using multiple sets of TIMIT speech data, the prediction model of type ML/RFL-Based Net has classification accuracy of 69.2308%. This accuracy is higher than the best achievements of WEKA data mining machines given the same speech data
NFV orchestration in edge and fog scenarios
Mención Internacional en el título de doctorLas infraestructuras de red actuales soportan una
variedad diversa de servicios como video bajo demanda,
video conferencias, redes sociales, sistemas
de educación, o servicios de almacenamiento de
fotografías. Gran parte de la población mundial ha
comenzado a utilizar estos servicios, y los utilizan
diariamente. Proveedores de Cloud y operadores
de infraestructuras de red albergan el tráfico de
red generado por estos servicios, y sus tareas de
gestión no solo implican realizar el enrutamiento
del tráfico, sino también el procesado del tráfico de
servicios de red. Tradicionalmente, el procesado
del tráfico ha sido realizado mediante aplicaciones/
programas desplegados en servidores que estaban
dedicados en exclusiva a tareas concretas
como la inspección de paquetes. Sin embargo, en
los últimos anos los servicios de red se han virtualizado
y esto ha dado lugar al paradigma de
virtualización de funciones de red (Network Function
Virtualization (NFV) siguiendo las siglas en
ingles), en el que las funciones de red de un servicio
se ejecutan en contenedores o máquinas virtuales
desacopladas de la infraestructura hardware. Como
resultado, el procesado de tráfico se ha ido
haciendo más flexible gracias al laxo acople del
software y hardware, y a la posibilidad de compartir
funciones de red típicas, como firewalls, entre
los distintos servicios de red.
NFV facilita la automatización de operaciones
de red, ya que tareas como el escalado, o la migración
son típicamente llevadas a cabo mediante
un conjunto de comandos previamente definidos
por la tecnología de virtualización pertinente, bien
mediante contenedores o máquinas virtuales. De
todos modos, sigue siendo necesario decidir el en rutamiento y procesado del tráfico de cada servicio
de red. En otras palabras, que servidores tienen
que encargarse del procesado del tráfico, y que
enlaces de la red tienen que utilizarse para que las
peticiones de los usuarios lleguen a los servidores
finales, es decir, el conocido como embedding problem.
Bajo el paraguas del paradigma NFV, a este
problema se le conoce en inglés como Virtual Network
Embedding (VNE), y esta tesis utiliza el termino
“NFV orchestration algorithm” para referirse
a los algoritmos que resuelven este problema. El
problema del VNE es NP-hard, lo cual significa
que que es imposible encontrar una solución optima
en un tiempo polinómico, independientemente
del tamaño de la red. Como consecuencia, la comunidad
investigadora y de telecomunicaciones
utilizan heurísticos que encuentran soluciones de
manera más rápida que productos para la resolución
de problemas de optimización.
Tradicionalmente, los “NFV orchestration algorithms”
han intentado minimizar los costes de
despliegue derivados de las soluciones asociadas.
Por ejemplo, estos algoritmos intentan no consumir
el ancho de banda de la red, y usar rutas cortas
para no utilizar tantos recursos. Además, una tendencia
reciente ha llevado a la comunidad investigadora
a utilizar algoritmos que minimizan el
consumo energético de los servicios desplegados,
bien mediante la elección de dispositivos con un
consumo energético más eficiente, o mediante el
apagado de dispositivos de red en desuso. Típicamente,
las restricciones de los problemas de VNE se
han resumido en un conjunto de restricciones asociadas
al uso de recursos y consumo energético, y las
soluciones se diferenciaban por la función objetivo
utilizada. Pero eso era antes de la 5a generación de
redes móviles (5G) se considerase en el problema
de VNE. Con la aparición del 5G, nuevos servicios
de red y casos de uso entraron en escena. Los estándares
hablaban de comunicaciones ultra rápidas
y fiables (Ultra-Reliable and Low Latency Communications
(URLLC) usando las siglas en inglés) con
latencias por debajo de unos pocos milisegundos y
fiabilidades del 99.999%, una banda ancha mejorada
(enhanced Mobile Broadband (eMBB) usando
las siglas en inglés) con notorios incrementos en
el flujo de datos, e incluso la consideración de comunicaciones
masivas entre maquinas (Massive
Machine-Type Communications (mMTC) usando
las siglas en inglés) entre dispositivos IoT. Es más,
paradigmas como edge y fog computing se incorporaron a la tecnología 5G, e introducían la idea
de tener dispositivos de computo más cercanos al
usuario final. Como resultado, el problema del VNE
tenía que incorporar los nuevos requisitos como
restricciones a tener en cuenta, y toda solución
debía satisfacer bajas latencias, alta fiabilidad, y
mayores tasas de transmisión.
Esta tesis estudia el problema des VNE, y propone
algunos heurísticos que lidian con las restricciones
asociadas a servicios 5G en escenarios
edge y fog, es decir, las soluciones propuestas se
encargan de asignar funciones virtuales de red a
servidores, y deciden el enrutamiento del trafico
en las infraestructuras 5G con dispositivos edge y
fog. Para evaluar el rendimiento de las soluciones
propuestas, esta tesis estudia en primer lugar la
generación de grafos que representan redes 5G.
Los mecanismos propuestos para la generación de
grafos sirven para representar distintos escenarios
5G. En particular, escenarios de federación en
los que varios dominios comparten recursos entre
ellos. Los grafos generados también representan
servidores en el edge, así como dispositivos fog con
una batería limitada. Además, estos grafos tienen
en cuenta los requisitos de estándares, y la demanda
que se espera en las redes 5G. La generación de
grafos propuesta sirve para representar escenarios
federación en los que varios dominios comparten
recursos entre ellos, y redes 5G con servidores edge,
así como dispositivos fog estáticos o móviles con
una batería limitada. Los grafos generados para
infraestructuras 5G tienen en cuenta los requisitos
de estándares, y la demanda de red que se espera
en las redes 5G. Además, los grafos son diferentes
en función de la densidad de población, y el área
de estudio, es decir, si es una zona industrial, una
autopista, o una zona urbana.
Tras detallar la generación de grafos que representan
redes 5G, esta tesis propone algoritmos de
orquestación NFV para resolver con el problema
del VNE. Primero, se centra en escenarios federados
en los que los servicios de red se tienen que
asignar no solo a la infraestructura de un dominio,
sino a los recursos compartidos en la federación
de dominios. Dos problemas diferentes han sido estudiados,
uno es el problema del VNE propiamente
dicho sobre una infraestructura federada, y el otro
es la delegación de servicios de red. Es decir, si
un servicio de red se debe desplegar localmente
en un dominio, o en los recursos compartidos por
la federación de dominios; a sabiendas de que el último caso supone el pago de cuotas por parte del
dominio local a cambio del despliegue del servicio
de red. En segundo lugar, esta tesis propone
OKpi, un algoritmo de orquestación NFV para conseguir
la calidad de servicio de las distintas slices
de las redes 5G. Conceptualmente, el slicing consiste
en partir la red de modo que cada servicio
de red sea tratado de modo diferente dependiendo
del trozo al que pertenezca. Por ejemplo, una
slice de eHealth reservara los recursos de red necesarios
para conseguir bajas latencias en servicios
como operaciones quirúrgicas realizadas de manera
remota. Cada trozo (slice) está destinado a
unos servicios específicos con unos requisitos muy
concretos, como alta fiabilidad, restricciones de
localización, o latencias de un milisegundo. OKpi
es un algoritmo de orquestación NFV que consigue
satisfacer los requisitos de servicios de red en los
distintos trozos, o slices de la red. Tras presentar
OKpi, la tesis resuelve el problema del VNE en redes
5G con dispositivos fog estáticos y móviles. El
algoritmo de orquestación NFV presentado tiene
en cuenta las limitaciones de recursos de computo
de los dispositivos fog, además de los problemas
de falta de cobertura derivados de la movilidad de
los dispositivos.
Para concluir, esta tesis estudia el escalado
de servicios vehiculares Vehicle-to-Network (V2N),
que requieren de bajas latencias para servicios como
la prevención de choques, avisos de posibles
riesgos, y conducción remota. Para estos servicios,
los atascos y congestiones en la carretera pueden
causar el incumplimiento de los requisitos de latencia.
Por tanto, es necesario anticiparse a esas
circunstancias usando técnicas de series temporales
que permiten saber el tráfico inminente en los
siguientes minutos u horas, para así poder escalar
el servicio V2N adecuadamente.Current network infrastructures handle a diverse
range of network services such as video
on demand services, video-conferences, social
networks, educational systems, or photo
storage services. These services have been
embraced by a significant amount of the
world population, and are used on a daily basis.
Cloud providers and Network operators’
infrastructures accommodate the traffic rates
that the aforementioned services generate, and
their management tasks do not only involve
the traffic steering, but also the processing of
the network services’ traffic. Traditionally,
the traffic processing has been assessed via
applications/programs deployed on servers
that were exclusively dedicated to a specific
task as packet inspection. However, in recent
years network services have stated to be
virtualized and this has led to the Network
Function Virtualization (Network Function
Virtualization (NFV)) paradigm, in which the
network functions of a service run on containers
or virtual machines that are decoupled
from the hardware infrastructure. As a result,
the traffic processing has become more flexible
because of the loose coupling between
software and hardware, and the possibility
of sharing common network functions, as
firewalls, across multiple network services.
NFV eases the automation of network operations,
since scaling and migrations tasks
are typically performed by a set of commands
predefined by the virtualization technology,
either containers or virtual machines. However,
it is still necessary to decide the traffic steering and processing of every network
service. In other words, which servers will
hold the traffic processing, and which are the
network links to be traversed so the users’ requests
reach the final servers, i.e., the network
embedding problem. Under the umbrella of
NFV, this problem is known as Virtual Network
Embedding (VNE), and this thesis refers
as “NFV orchestration algorithms” to those
algorithms solving such a problem. The VNE
problem is a NP-hard, meaning that it is impossible
to find optimal solutions in polynomial
time, no matter the network size. As a
consequence, the research and telecommunications
community rely on heuristics that find
solutions quicker than a commodity optimization
solver.
Traditionally, NFV orchestration algorithms
have tried to minimize the deployment
costs derived from their solutions. For example,
they try to not exhaust the network
bandwidth, and use short paths to use less
network resources. Additionally, a recent
tendency led the research community towards
algorithms that minimize the energy consumption
of the deployed services, either
by selecting more energy efficient devices
or by turning off those network devices that
remained unused. VNE problem constraints
were typically summarized in a set of resources/energy constraints, and the solutions
differed on which objectives functions were
aimed for. But that was before 5th generation
of mobile networks (5G) were considered
in the VNE problem. With the appearance
of 5G, new network services and use cases
started to emerge. The standards talked about
Ultra Reliable Low Latency Communication
(Ultra-Reliable and Low Latency Communications
(URLLC)) with latencies below few
milliseconds and 99.999% reliability, an enhanced
mobile broadband (enhanced Mobile
Broadband (eMBB)) with significant data
rate increases, and even the consideration
of massive machine-type communications
(Massive Machine-Type Communications
(mMTC)) among Internet of Things (IoT) devices.
Moreover, paradigms such as edge and
fog computing blended with the 5G technology
to introduce the idea of having computing
devices closer to the end users. As a result, the VNE problem had to incorporate the new
requirements as constraints to be taken into
account, and every solution should either
satisfy low latencies, high reliability, or larger
data rates.
This thesis studies the VNE problem, and
proposes some heuristics tackling the constraints
related to 5G services in Edge and
fog scenarios, that is, the proposed solutions
assess the assignment of Virtual Network
Functions to resources, and the traffic steering
across 5G infrastructures that have Edge and
Fog devices. To evaluate the performance
of the proposed solutions, the thesis studies
first the generation of graphs that represent
5G networks. The proposed mechanisms to
generate graphs serve to represent diverse 5G
scenarios. In particular federation scenarios
in which several domains share resources
among themselves. The generated graphs
also represent edge servers, so as fog devices
with limited battery capacity. Additionally,
these graphs take into account the standard
requirements, and the expected demand for
5G networks. Moreover, the graphs differ depending
on the density of population, and the
area of study, i.e., whether it is an industrial
area, a highway, or an urban area.
After detailing the generation of graphs
representing the 5G networks, this thesis proposes
several NFV orchestration algorithms
to tackle the VNE problem. First, it focuses
on federation scenarios in which network services
should be assigned not only to a single
domain infrastructure, but also to the shared
resources of the federation of domains. Two
different problems are studied, one being the
VNE itself over a federated infrastructure, and
the other the delegation of network services.
That is, whether a network service should be
deployed in a local domain, or in the pool
of resources of the federation domain; knowing
that the latter charges the local domain
for hosting the network service. Second, the
thesis proposes OKpi, a NFV orchestration
algorithm to meet 5G network slices quality
of service. Conceptually, network slicing consists
in splitting the network so network services
are treated differently based on the slice
they belong to. For example, an eHealth network
slice will allocate the network resources necessary to meet low latencies for network
services such as remote surgery. Each network
slice is devoted to specific services with
very concrete requirements, as high reliability,
location constraints, or 1ms latencies. OKpi is
a NFV orchestration algorithm that meets the
network service requirements among different
slices. It is based on a multi-constrained
shortest path heuristic, and its solutions satisfy
latency, reliability, and location constraints.
After presenting OKpi, the thesis tackles the
VNE problem in 5G networks with static/moving
fog devices. The presented NFV orchestration
algorithm takes into account the limited
computing resources of fog devices, as well
as the out-of-coverage problems derived from
the devices’ mobility.
To conclude, this thesis studies the scaling
of Vehicle-to-Network (V2N) services, which
require low latencies for network services as
collision avoidance, hazard warning, and remote
driving. For these services, the presence
of traffic jams, or high vehicular traffic congestion
lead to the violation of latency requirements.
Hence, it is necessary to anticipate to
such circumstances by using time-series techniques
that allow to derive the incoming vehicular
traffic flow in the next minutes or hours,
so as to scale the V2N service accordingly.The 5G Exchange (5GEx) project (2015-2018) was an EU-funded project (H2020-ICT-2014-2 grant agreement 671636).
The 5G-TRANSFORMER project (2017-2019) is an EU-funded project (H2020-ICT-2016-2 grant agreement 761536).
The 5G-CORAL project (2017-2019) is an EU-Taiwan project (H2020-ICT-2016-2 grant agreement 761586).Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Ioannis Stavrakakis.- Secretario: Pablo Serrano Yáñez-Mingot.- Vocal: Paul Horatiu Patra
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