70,174 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
Data granulation by the principles of uncertainty
Researches in granular modeling produced a variety of mathematical models,
such as intervals, (higher-order) fuzzy sets, rough sets, and shadowed sets,
which are all suitable to characterize the so-called information granules.
Modeling of the input data uncertainty is recognized as a crucial aspect in
information granulation. Moreover, the uncertainty is a well-studied concept in
many mathematical settings, such as those of probability theory, fuzzy set
theory, and possibility theory. This fact suggests that an appropriate
quantification of the uncertainty expressed by the information granule model
could be used to define an invariant property, to be exploited in practical
situations of information granulation. In this perspective, a procedure of
information granulation is effective if the uncertainty conveyed by the
synthesized information granule is in a monotonically increasing relation with
the uncertainty of the input data. In this paper, we present a data granulation
framework that elaborates over the principles of uncertainty introduced by
Klir. Being the uncertainty a mesoscopic descriptor of systems and data, it is
possible to apply such principles regardless of the input data type and the
specific mathematical setting adopted for the information granules. The
proposed framework is conceived (i) to offer a guideline for the synthesis of
information granules and (ii) to build a groundwork to compare and
quantitatively judge over different data granulation procedures. To provide a
suitable case study, we introduce a new data granulation technique based on the
minimum sum of distances, which is designed to generate type-2 fuzzy sets. We
analyze the procedure by performing different experiments on two distinct data
types: feature vectors and labeled graphs. Results show that the uncertainty of
the input data is suitably conveyed by the generated type-2 fuzzy set models.Comment: 16 pages, 9 figures, 52 reference
Intrusion Detection Systems Using Adaptive Regression Splines
Past few years have witnessed a growing recognition of intelligent techniques
for the construction of efficient and reliable intrusion detection systems. Due
to increasing incidents of cyber attacks, building effective intrusion
detection systems (IDS) are essential for protecting information systems
security, and yet it remains an elusive goal and a great challenge. In this
paper, we report a performance analysis between Multivariate Adaptive
Regression Splines (MARS), neural networks and support vector machines. The
MARS procedure builds flexible regression models by fitting separate splines to
distinct intervals of the predictor variables. A brief comparison of different
neural network learning algorithms is also given
Enhancement of speed and efficiency of an Internet based gear design optimisation
An internet-based gear design optimisation program has been developed for geographically dispersed teams to collaborate over the internet. The optimisation program implements genetic algorithm. A novel methodology is presented that improves the speed of execution of the optimisation program by integrating artificial neural networks into the system. The paper also proposes a method that allows an improvement to the performance of the back propagation-learning algorithm. This is done by rescaling the output data patterns to lie slightly below and above the two extreme values of the full range neural activation function. Experimental tests show the reduction of execution time by approximately 50%, as well as an improvement in the training and generalisation errors and the rate of learning of the network
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