68,120 research outputs found
A comparative study of the AHP and TOPSIS methods for implementing load shedding scheme in a pulp mill system
The advancement of technology had encouraged mankind to design and create useful
equipment and devices. These equipment enable users to fully utilize them in various
applications. Pulp mill is one of the heavy industries that consumes large amount of
electricity in its production. Due to this, any malfunction of the equipment might
cause mass losses to the company. In particular, the breakdown of the generator
would cause other generators to be overloaded. In the meantime, the subsequence
loads will be shed until the generators are sufficient to provide the power to other
loads. Once the fault had been fixed, the load shedding scheme can be deactivated.
Thus, load shedding scheme is the best way in handling such condition. Selected load
will be shed under this scheme in order to protect the generators from being
damaged. Multi Criteria Decision Making (MCDM) can be applied in determination
of the load shedding scheme in the electric power system. In this thesis two methods
which are Analytic Hierarchy Process (AHP) and Technique for Order Preference by
Similarity to Ideal Solution (TOPSIS) were introduced and applied. From this thesis,
a series of analyses are conducted and the results are determined. Among these two
methods which are AHP and TOPSIS, the results shown that TOPSIS is the best
Multi criteria Decision Making (MCDM) for load shedding scheme in the pulp mill
system. TOPSIS is the most effective solution because of the highest percentage
effectiveness of load shedding between these two methods. The results of the AHP
and TOPSIS analysis to the pulp mill system are very promising
Extracting individual contributions from their mixture: a blind source separation approach, with examples from space and laboratory plasmas
Multipoint or multichannel observations in plasmas can frequently be modelled
as an instantaneous mixture of contributions (waves, emissions, ...) of
different origins. Recovering the individual sources from their mixture then
becomes one of the key objectives. However, unless the underlying mixing
processes are well known, these situations lead to heavily underdetermined
problems. Blind source separation aims at disentangling such mixtures with the
least possible prior information on the sources and their mixing processes.
Several powerful approaches have recently been developed, which can often
provide new or deeper insight into the underlying physics. This tutorial paper
briefly discusses some possible applications of blind source separation to the
field of plasma physics, in which this concept is still barely known. Two
examples are given. The first one shows how concurrent processes in the
dynamical response of the electron temperature in a tokamak can be separated.
The second example deals with solar spectral imaging in the Extreme UV and
shows how empirical temperature maps can be built.Comment: expanded version of an article to appear in Contributions to Plasma
Physics (2010
Identification of cellular automata based on incomplete observations with bounded time gaps
In this paper, the problem of identifying the cellular automata (CAs) is considered. We frame and solve this problem in the context of incomplete observations, i.e., prerecorded, incomplete configurations of the system at certain, and unknown time stamps. We consider 1-D, deterministic, two-state CAs only. An identification method based on a genetic algorithm with individuals of variable length is proposed. The experimental results show that the proposed method is highly effective. In addition, connections between the dynamical properties of CAs (Lyapunov exponents and behavioral classes) and the performance of the identification algorithm are established and analyzed
A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification
Convolutional auto-encoders have shown their remarkable performance in
stacking to deep convolutional neural networks for classifying image data
during past several years. However, they are unable to construct the
state-of-the-art convolutional neural networks due to their intrinsic
architectures. In this regard, we propose a flexible convolutional auto-encoder
by eliminating the constraints on the numbers of convolutional layers and
pooling layers from the traditional convolutional auto-encoder. We also design
an architecture discovery method by using particle swarm optimization, which is
capable of automatically searching for the optimal architectures of the
proposed flexible convolutional auto-encoder with much less computational
resource and without any manual intervention. We use the designed architecture
optimization algorithm to test the proposed flexible convolutional auto-encoder
through utilizing one graphic processing unit card on four extensively used
image classification datasets. Experimental results show that our work in this
paper significantly outperform the peer competitors including the
state-of-the-art algorithm.Comment: Accepted by IEEE Transactions on Neural Networks and Learning
Systems, 201
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