14,650 research outputs found
Perceived Audiovisual Quality Modelling based on Decison Trees, Genetic Programming and Neural Networks
Our objective is to build machine learning based models that predict
audiovisual quality directly from a set of correlated parameters that are
extracted from a target quality dataset. We have used the bitstream version of
the INRS audiovisual quality dataset that reflects contemporary real-time
configurations for video frame rate, video quantization, noise reduction
parameters and network packet loss rate. We have utilized this dataset to build
bitstream perceived quality estimation models based on the Random Forests,
Bagging, Deep Learning and Genetic Programming methods.
We have taken an empirical approach and have generated models varying from
very simple to the most complex depending on the number of features used from
the quality dataset. Random Forests and Bagging models have overall generated
the most accurate results in terms of RMSE and Pearson correlation coefficient
values. Deep Learning and Genetic Programming based bitstream models have also
achieved good results but that high performance was observed only with a
limited range of features. We have also obtained the epsilon-insensitive RMSE
values for each model and have computed the significance of the difference
between the correlation coefficients.
Overall we conclude that computing the bitstream information is worth the
effort it takes to generate and helps to build more accurate models for
real-time communications. However, it is useful only for the deployment of the
right algorithms with the carefully selected subset of the features. The
dataset and tools that have been developed during this research are publicly
available for research and development purposes
A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems
This paper presents a review of the literature on State Estimation (SE) in
power systems. While covering some works related to SE in transmission systems,
the main focus of this paper is Distribution System State Estimation (DSSE).
The paper discusses a few critical topics of DSSE, including mathematical
problem formulation, application of pseudo-measurements, metering instrument
placement, network topology issues, impacts of renewable penetration, and
cyber-security. Both conventional and modern data-driven and probabilistic
techniques have been reviewed. This paper can provide researchers and utility
engineers with insights into the technical achievements, barriers, and future
research directions of DSSE
Self-Organization in Traffic Lights: Evolution of Signal Control with Advances in Sensors and Communications
Traffic signals are ubiquitous devices that first appeared in 1868. Recent
advances in information and communications technology (ICT) have led to
unprecedented improvements in such areas as mobile handheld devices (i.e.,
smartphones), the electric power industry (i.e., smart grids), transportation
infrastructure, and vehicle area networks. Given the trend towards
interconnectivity, it is only a matter of time before vehicles communicate with
one another and with infrastructure. In fact, several pilots of such
vehicle-to-vehicle and vehicle-to-infrastructure (e.g. traffic lights and
parking spaces) communication systems are already operational. This survey of
autonomous and self-organized traffic signaling control has been undertaken
with these potential developments in mind. Our research results indicate that,
while many sophisticated techniques have attempted to improve the scheduling of
traffic signal control, either real-time sensing of traffic patterns or a
priori knowledge of traffic flow is required to optimize traffic. Once this is
achieved, communication between traffic signals will serve to vastly improve
overall traffic efficiency
Multi-objective evolutionary algorithms for quantum circuit discovery
Quantum hardware continues to advance, yet finding new quantum algorithms -
quantum software - remains a challenge, with classically trained computer
programmers having little intuition of how computational tasks may be performed
in the quantum realm. As such, the idea of developing automated tools for
algorithm development is even more appealing for quantum computing than for
classical. Here we develop a robust, multi-objective evolutionary search
strategy to design quantum circuits 'from scratch', by combining and
parameterizing a task-generic library of quantum circuit elements. When applied
to 'ab initio' design of quantum circuits for the input/output mapping
requirements of the quantum Fourier transform and Grover's search algorithm, it
finds textbook circuit designs, along with alternative structures that achieve
the same functionality. Exploiting its multi-objective nature, the discovery
algorithm can trade off performance measures such as accuracy, circuit width or
depth, gate count, or implementability - a crucial requirement for
first-generation quantum processors and applications.Comment: 9 pages, 5 figure
Optimal Allocation of Series FACTS Devices Under High Penetration of Wind Power Within a Market Environment
Series FACTS devices are one of the key enablers for very high penetration of
renewables due to their capabilities in continuously controlling power flows on
transmission lines. This paper proposes a bilevel optimization model to
optimally locate variable series reactor (VSR) and phase shifting transformer
(PST) in the transmission network considering high penetration of wind power.
The upper level problem seeks to minimize the \textcolor{black}{investment
cost} on series FACTS, the cost of wind power curtailment and possible load
shedding. The lower level problems capture the market clearing under different
operating scenarios. Due to the poor scalability of formulation, the
\textsl{shift factor} structure of FACTS allocation is derived. A customized
reformulation and decomposition algorithm is designed and implemented to solve
the proposed bilevel model with binary variables in both upper and lower
levels. Detailed numerical results based on 118-bus system demonstrate the
efficient performance of the proposed planning model and the important role of
series FACTS for facilitating the integration of wind power.Comment: Accepted by IEEE Transactions on Power Systems;
doi:10.1109/TPWRS.2018.283450
Symbol-level and Multicast Precoding for Multiuser Multiantenna Downlink: A Survey, Classification and Challenges
Precoding has been conventionally considered as an effective means of
mitigating the interference and efficiently exploiting the available in the
multiantenna downlink channel, where multiple users are simultaneously served
with independent information over the same channel resources. The early works
in this area were focused on transmitting an individual information stream to
each user by constructing weighted linear combinations of symbol blocks
(codewords). However, more recent works have moved beyond this traditional view
by: i) transmitting distinct data streams to groups of users and ii) applying
precoding on a symbol-per-symbol basis. In this context, the current survey
presents a unified view and classification of precoding techniques with respect
to two main axes: i) the switching rate of the precoding weights, leading to
the classes of block- and symbol-level precoding, ii) the number of users that
each stream is addressed to, hence unicast-/multicast-/broadcast- precoding.
Furthermore, the classified techniques are compared through representative
numerical results to demonstrate their relative performance and uncover
fundamental insights. Finally, a list of open theoretical problems and
practical challenges are presented to inspire further research in this area.Comment: Submitted to IEEE Communications Surveys & Tutorial
An Information-Theoretic Approach to PMU Placement in Electric Power Systems
This paper presents an information-theoretic approach to address the phasor
measurement unit (PMU) placement problem in electric power systems. Different
from the conventional 'topological observability' based approaches, this paper
advocates a much more refined, information-theoretic criterion, namely the
mutual information (MI) between the PMU measurements and the power system
states. The proposed MI criterion can not only include the full system
observability as a special case, but also can rigorously model the remaining
uncertainties in the power system states with PMU measurements, so as to
generate highly informative PMU configurations. Further, the MI criterion can
facilitate robust PMU placement by explicitly modeling probabilistic PMU
outages. We propose a greedy PMU placement algorithm, and show that it achieves
an approximation ratio of (1-1/e) for any PMU placement budget. We further show
that the performance is the best that one can achieve in practice, in the sense
that it is NP-hard to achieve any approximation ratio beyond (1-1/e). Such
performance guarantee makes the greedy algorithm very attractive in the
practical scenario of multi-stage installations for utilities with limited
budgets. Finally, simulation results demonstrate near-optimal performance of
the proposed PMU placement algorithm.Comment: 8 pages, 7 figure
A Semidefinite Programming Based Search Strategy for Feature Selection with Mutual Information Measure
Feature subset selection, as a special case of the general subset selection
problem, has been the topic of a considerable number of studies due to the
growing importance of data-mining applications. In the feature subset selection
problem there are two main issues that need to be addressed: (i) Finding an
appropriate measure function than can be fairly fast and robustly computed for
high-dimensional data. (ii) A search strategy to optimize the measure over the
subset space in a reasonable amount of time. In this article mutual information
between features and class labels is considered to be the measure function. Two
series expansions for mutual information are proposed, and it is shown that
most heuristic criteria suggested in the literature are truncated
approximations of these expansions. It is well-known that searching the whole
subset space is an NP-hard problem. Here, instead of the conventional
sequential search algorithms, we suggest a parallel search strategy based on
semidefinite programming (SDP) that can search through the subset space in
polynomial time. By exploiting the similarities between the proposed algorithm
and an instance of the maximum-cut problem in graph theory, the approximation
ratio of this algorithm is derived and is compared with the approximation ratio
of the backward elimination method. The experiments show that it can be
misleading to judge the quality of a measure solely based on the classification
accuracy, without taking the effect of the non-optimum search strategy into
account.Comment: IEEETrans On Pattern Analysis and Machine Intelligenc
A hybrid GA–PS–SQP method to solve power system valve-point economic dispatch problems
This study presents a new approach based on a hybrid algorithm consisting of Genetic Algorithm (GA), Pattern Search (PS) and Sequential Quadratic Programming (SQP) techniques to solve the well-known power system Economic dispatch problem (ED). GA is the main optimizer of the algorithm, whereas PS and SQP are used to fine tune the results of GA to increase confidence in the solution. For illustrative purposes, the algorithm has been applied to various test systems to assess its effectiveness. Furthermore, convergence characteristics and robustness of the proposed method have been explored through comparison with results reported in literature. The outcome is very encouraging and suggests that the hybrid GA–PS–SQP algorithm is very efficient in solving power system economic dispatch problem
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