272 research outputs found
Nonlinear Analysis and Dynamic Structure in the Energy Market
This research assesses the dynamic structure of the energy sector of the aggregate economy in the context of nonlinear mechanisms. Earlier studies have focused mainly on the price of the energy products when detecting nonlinearities in time series data of the energy market, and there is little mention of the production side of the market. Moreover, there is a lack of exploration about the implication of high dimensionality and time aggregation when analyzing the market's fundamentals. This research will address these gaps by including the quantity side of the market in addition to the price and by systematically incorporating various frequencies for sample sizes in three essays. The goal of this research is to provide an inclusive and exhaustive examination of the dynamics in the energy markets. The first essay begins with the application of statistical techniques, and it incorporates the most well-known univariate tests for nonlinearity with distinct power functions over alternatives and tests different null hypotheses. It utilizes the daily spot price observations on five major products in the energy market. The results suggest that the time series daily spot prices of the energy products are highly nonlinear in their nature. They demonstrate apparent evidence of general nonlinear serial dependence in each individual series, as well as nonlinearity in the first, second, and third moments of the series. The second essay examines the underlying mechanism of crude oil production and identifies the nonlinear structure of the production market by utilizing various monthly time series observations of crude oil production: the U.S. field, Organization of the Petroleum Exporting Countries (OPEC), non-OPEC, and the world production of crude oil. The finding implies that the time series data of the U.S. field, OPEC, and the world production of crude oil exhibit deep nonlinearity in their structure and are generated by nonlinear mechanisms. However, the dynamics of the non-OPEC production time series data does not reveal signs of nonlinearity. The third essay explores nonlinear structure in the case of high dimensionality of the observations, different frequencies of sample sizes, and division of the samples into sub-samples. It systematically examines the robustness of the inference methods at various levels of time aggregation by employing daily spot prices on crude oil for 26 years as well as monthly spot price index on crude oil for 41 years. The daily and monthly samples are divided into sub-samples as well. All the tests detect strong evidence of nonlinear structure in the daily spot price of crude oil; whereas in monthly observations the evidence of nonlinear dependence is less dramatic, indicating that the nonlinear serial dependence will not be as intense when the time aggregation increase in time series observations
Improving Energy Efficiency in MANETs by Multi-Path Routing
Some multi-path routing algorithm in MANET, simultaneously send information
to the destination through several directions to reduce end-to-end delay. In
all these algorithms, the sent traffic through a path affects the adjacent path
and unintentionally increases the delay due to the use of adjacent paths.
Because, there are repetitive competitions among neighboring nodes, in order to
obtain the joint channel in adjacent paths. The represented algorithm in this
study tries to discover the distinct paths between source and destination nodes
with using Omni directional antennas, to send information through these
simultaneously. For this purpose, the number of active neighbors is counted in
each direction with using a strategy. These criterions are effectively used to
select routes. Proposed algorithm is based on AODV routing algorithm, and in
the end it is compared with AOMDV, AODVM, and IZM-DSR algorithms which are
multi-path routing algorithms based on AODV and DSR. Simulation results show
that using the proposed algorithm creates a significant improvement in energy
efficiency and reducing end-to-end delay
For Kernel Range Spaces a Constant Number of Queries Are Sufficient
We introduce the notion of an -cover for a kernel range space. A
kernel range space concerns a set of points and the
space of all queries by a fixed kernel (e.g., a Gaussian kernel ). For a point set of size , a query returns a
vector of values , where the th coordinate for . An -cover is a subset of points so for any that for some . This is a smooth analog of
Haussler's notion of -covers for combinatorial range spaces (e.g.,
defined by subsets of points within a ball query) where the resulting vectors
are in instead of . The kernel versions of these
range spaces show up in data analysis tasks where the coordinates may be
uncertain or imprecise, and hence one wishes to add some flexibility in the
notion of inside and outside of a query range.
Our main result is that, unlike combinatorial range spaces, the size of
kernel -covers is independent of the input size and dimension
. We obtain a bound of , where
hides log factors in that can
depend on the kernel. This implies that by relaxing the notion of boundaries in
range queries, eventually the curse of dimensionality disappears, and may help
explain the success of machine learning in very high-dimensions. We also
complement this result with a lower bound of almost
, showing the exponential dependence
on is necessary.Comment: 27 page
Observer-based tracking control for single machine infinite bus system via flatness theory
In this research, we aim to use the flatness control theory to develop a useful control scheme for a single machine connected to an infinite bus (SMIB) system taking into account input magnitude and rate saturation constraints. We adopt a fourth-order nonlinear SMIB model along an exciter and a turbine governor as actuators. According to the flatness-based control strategy, first we show that the adopted nominal SMIB model is a flat system. Then, we develop a full linearizing state feedback as well as an outer integral-type loop to ensure suitable tracking performances for the power and voltage as well as the angular velocity outputs. We assume that only the angular velocity of the generator is available to be measured. So, we provide a linear Luenberger observer to estimate the remaining states of the system. Also, the saturation nonlinearities are transferred to the linear part of the system and they are canceled out using their estimations. The efficiency and usefulness of the proposed observer-controller against faults are illustrated using simulation tests in Eurostag and Matlab. The results show that the clearing critical time of the introduced methodology is larger than the classical control approaches and the proposed observer-based flatness controller exhibits over much less control energy compared to the classic IEEE controllers
Using 1-Factorization from Graph Theory for Quantum Speedups on Clique Problems
The clique problems, including -CLIQUE and Triangle Finding, form an
important class of computational problems; the former is an NP-complete
problem, while the latter directly gives lower bounds for Matrix
Multiplication. A number of previous efforts have approached these problems
with Quantum Computing methods, such as Amplitude Amplification. In this paper,
we provide new Quantum oracle designs based on the 1-factorization of complete
graphs, all of which have depth instead of the presented in
previous studies. Also, we discuss the usage of one of these oracles in
bringing the Triangle Finding time complexity down to , compared to the classical record. Finally, we benchmark the
number of required Amplitude Amplification iterations for another presented
oracle, for solving -CLIQUE.Comment: 14 pages, 8 figure
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