1,121 research outputs found
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences
We propose a neural sequence-to-sequence model for direction following, a
task that is essential to realizing effective autonomous agents. Our
alignment-based encoder-decoder model with long short-term memory recurrent
neural networks (LSTM-RNN) translates natural language instructions to action
sequences based upon a representation of the observable world state. We
introduce a multi-level aligner that empowers our model to focus on sentence
"regions" salient to the current world state by using multiple abstractions of
the input sentence. In contrast to existing methods, our model uses no
specialized linguistic resources (e.g., parsers) or task-specific annotations
(e.g., seed lexicons). It is therefore generalizable, yet still achieves the
best results reported to-date on a benchmark single-sentence dataset and
competitive results for the limited-training multi-sentence setting. We analyze
our model through a series of ablations that elucidate the contributions of the
primary components of our model.Comment: To appear at AAAI 2016 (and an extended version of a NIPS 2015
Multimodal Machine Learning workshop paper
Predictive maintenance in hydropower plants : a case study of valves and servomotors
Digitalization has opened the opportunity for a fourth industrial revolution and the hydropower industry is taking charge of enabling digitalization in their operation. There are a lot of studies on predictive maintenance, however, there are, to our knowledge no studies on system-specific predictive maintenance for hydropower. To bridge this gap, the idea of system-specific, Machine Learning driven Predictive Maintenance is explored. Two systems are chosen as a use-case for this thesis: valves and servomotors. With the increasing amount of intermittent renewable energy resources entering the power system, the need for flexibility in the power grid is unequivocal. Valves and servomotors are key components of hydropower control and thus will play a pivotal role in securing flexibility to the grid.
The first system assessed is the main valve. In order to make this analysis easily applicable, the data that is already being collected at Nore 1 hydropower plant is analyzed in order to assess the possibility of maintenance prediction from limited data. Unfortunately, this did not achieve the desired results for the data collected from the valve sensors. This is due to the fact that only one variable was measured, in this case, the opening and closing time-lag of the valve. However, this thesis presents a framework for data collection that allows the use of Machine Learning for predictive maintenance. Various sensors are suggested based on several published works on predictive maintenance.
The second system assessed is the servomotor that controls the guiding vanes in a Francis turbine. Servomotors are key components of hydropower control. Due to the data not being collected by Statkraft at the time of the study, this data was provided by one of Statkrafts suppliers. By making use of the historical data of pressure as a function of the piston position, a boundary for where new values should be expected is computed by making use of One Class Support Vector Machine. Another embodiment of this case is presented where force is given as a function of piston position, which yielded better results. When new values are being measured, the data is presented as a bullet chart that visualizes the distance of new values compared to the boundary computed by the One Class Support Vector Machine. This tool could easily be applied to other servomotors which perform other tasks such as controlling water injection to a Pelton turbine or opening and closing of the valve, whether they are butterfly or ball valves.
Suggestions for further data collection are presented in order to make use of more data for the use of Machine Learning in Predictive Maintenance.Digitalisering har ledet frem til en fjerde industriell revolusjon og vannkraft bransjen er i ferd med å digitalisere sin operasjon. Under literaturstudien er det ikke funnet noen publiseringer innen systemspesifikk maskinlæringsdrevet predikativ vedlikehold. I denne masteroppgaven blir muligheten for bruk av systemspesifikk, maskinlæringdrevet predikativ vedlikehold innen vannkraftverk utforsket for å vekke interesse innen dette feltet. To av vannkraftverkenes maskiner er brukt som eksempler og utforsket: ventiler og servomotor. Økende mengder uregulerbar strøm er introdusert i kraftnettet og behovet for fleksibilitet øker. Ventiler og servomotor er nøkkeldeler av vannkraftverk regulering og spiller en stor rolle i å sikre flexibilitet til strømnettet.
Det første systemet som ble analysert er ventiler. For å gjøre analysen og resultatene enkelt anvendbare, blir data som allerede er innsamlet analysert for å utforske muligheten for predikativ vedlikehold med begrenset data. Analysene basert på data samlet inn fra sensorne montert på ventilene ble dessverre ikke konklusive. Det er utfordrende å forutsi fremtiden når man bare har en variabel å ta utgangspunkt i. Likevel presenteres det et prinsipielt rammeverk for innsamling av data som gjør det mulig å ta i bruk maskinlæring for predikativ vedlikehold. Ulike sensorer er foreslått, basert på relevant litteratur innen ventiler og maskinlæring drevet predikativ vedlikehold.
Det andre systemet analysert under studien er servomotorer som styrer vannet i en Francis turbin ved å regulere vinklingen til skovlene. Dataen innsamlet om servomotoren er en god indikator på tilstanden til servomotoren. Ettersom dataen var ikke samlet inn av Statkraft da studien ble utført, ble dataen hentet fra en av Statkraft sine leverandører. En One Class Support Vector Machine ble brukt for å beregne foventet verdi av differansetrykk over stempelkamrene, som funksjon av stempel posisjon. En kulegraf som viser avstanden mellom grensen og nye verdier er visualisert. En annen metode er også presentert hvor man regner ut kraft på begge sider av stempelkamrene gjennom trykk for å vise kraft som funksjon av stempel posisjon. Dette ga bedre valideringsresultater i forventet differansekraft over tempelkamrene. Verktøyet kan enkelt bli anvendt til andre servomotorer som styrer vannmengden i en Pelton turbin eller åpning og lukking av ventilene, uavhengig av om det er spjeld- eller kuleventiler.
Forslag til videre data innsamling er presentert for å ta i bruk maskinlæring for predikativ vedlikehold.StatkraftsubmittedVersionM-M
Semantic memory
The Encyclopedia of Human Behavior, Second Edition is a comprehensive three-volume reference source on human action and reaction, and the thoughts, feelings, and physiological functions behind those actions
NASA JSC neural network survey results
A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc
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ForChaos: Real Time Application DDoS detection using Forecasting and Chaos Theory in Smart Home IoT Network
Recently, D/DoS attacks have been launched by zombie IoT devices in smart home networks. They pose a great threat to to network systems with Application Layer DDoS attacks being especially hard to detect due to their stealth and seemingly legitimacy. In this paper, we propose we propose ForChaos, a lightweight detection algorithm for IoT devices, that is based on forecasting and chaos theory to identify flooding and DDoS attacks. For every time-series behaviour collected, a forecasting-technique prediction is generated, based on a number of features, and the error between the two values is calcualted. In order to assess the error of the forecasting from the actual value, the lyapunov exponent is used to detect potential malicious behaviour. In NS-3 we evaluate our detection algorithm through a series of experiments in Flooding and Slow-Rate DDoS attacks. The results are presented and discussed in detail and compared with related studies, demonstrating its effectiveness and robustness
Information Flow in Computational Systems
We develop a theoretical framework for defining and identifying flows of
information in computational systems. Here, a computational system is assumed
to be a directed graph, with "clocked" nodes that send transmissions to each
other along the edges of the graph at discrete points in time. We are
interested in a definition that captures the dynamic flow of information about
a specific message, and which guarantees an unbroken "information path" between
appropriately defined inputs and outputs in the directed graph. Prior measures,
including those based on Granger Causality and Directed Information, fail to
provide clear assumptions and guarantees about when they correctly reflect
information flow about a message. We take a systematic approach---iterating
through candidate definitions and counterexamples---to arrive at a definition
for information flow that is based on conditional mutual information, and which
satisfies desirable properties, including the existence of information paths.
Finally, we describe how information flow might be detected in a noiseless
setting, and provide an algorithm to identify information paths on the
time-unrolled graph of a computational system.Comment: Significantly revised version which was accepted for publication at
the IEEE Transactions on Information Theor
Interconnect architectures for dynamically partially reconfigurable systems
Dynamically partially reconfigurable FPGAs (Field-Programmable Gate Arrays) allow
hardware modules to be placed and removed at runtime while other parts of the system
keep working. With their potential benefits, they have been the topic of a great
deal of research over the last decade. To exploit the partial reconfiguration capability of
FPGAs, there is a need for efficient, dynamically adaptive communication infrastructure
that automatically adapts as modules are added to and removed from the system.
Many bus and network-on-chip (NoC) architectures have been proposed to exploit this
capability on FPGA technology. However, few realizations have been reported in the
public literature to demonstrate or compare their performance in real world applications.
While partial reconfiguration can offer many benefits, it is still rarely exploited in practical
applications. Few full realizations of partially reconfigurable systems in current
FPGA technologies have been published. More application experiments are required to
understand the benefits and limitations of implementing partially reconfigurable systems
and to guide their further development. The motivation of this thesis is to fill this
research gap by providing empirical evidence of the cost and benefits of different interconnect
architectures. The results will provide a baseline for future research and will
be directly useful for circuit designers who must make a well-reasoned choice between
the alternatives.
This thesis contains the results of experiments to compare different NoC and bus interconnect
architectures for FPGA-based designs in general and dynamically partially
reconfigurable systems. These two interconnect schemes are implemented and evaluated
in terms of performance, area and power consumption using FFT (Fast Fourier
Transform) andANN(Artificial Neural Network) systems as benchmarks. Conclusions
drawn from these results include recommendations concerning the interconnect approach
for different kinds of applications. It is found that a NoC provides much better
performance than a single channel bus and similar performance to a multi-channel bus
in both parallel and parallel-pipelined FFT systems. This suggests that a NoC is a better choice for systems with multiple simultaneous communications like the FFT. Bus-based
interconnect achieves better performance and consume less area and power than NoCbased
scheme for the fully-connected feed-forward NN system. This suggests buses
are a better choice for systems that do not require many simultaneous communications
or systems with broadcast communications like a fully-connected feed-forward NN.
Results from the experiments with dynamic partial reconfiguration demonstrate that
buses have the advantages of better resource utilization and smaller reconfiguration
time and memory than NoCs. However, NoCs are more flexible and expansible. They
have the advantage of placing almost all of the communication infrastructure in the
dynamic reconfiguration region. This means that different applications running on the
FPGA can use different interconnection strategies without the overhead of fixed bus
resources in the static region.
Another objective of the research is to examine the partial reconfiguration process and
reconfiguration overhead with current FPGA technologies. Partial reconfiguration allows
users to efficiently change the number of running PEs to choose an optimal powerperformance
operating point at the minimum cost of reconfiguration. However, this
brings drawbacks including resource utilization inefficiency, power consumption overhead
and decrease in system operating frequency. The experimental results report a
50% of resource utilization inefficiency with a power consumption overhead of less
than 5% and a decrease in frequency of up to 32% compared to a static implementation.
The results also show that most of the drawbacks of partial reconfiguration implementation
come from the restrictions and limitations of partial reconfiguration design flow.
If these limitations can be addressed, partial reconfiguration should still be considered
with its potential benefits.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 201
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