Abstract: Artificial Intelligence (AI) methods are increasingly being used for problem solving. This paper concerns using AI-type learning machines for power quality problem, which is a problem of general interest to power system to provide quality power to all appliances. Electrical power of good quality is essential for proper operation of electronic equipments such as computers and PLCs. Malfunction of such equipment may lead to loss of production or disruption of critical services resulting in huge financial and other losses. It is therefore necessary that critical loads be supplied with electricity of acceptable quality. Recognition of the presence of any disturbance and classifying any existing disturbance into a particular type is the first step in combating the problem. In this work two classes of AI methods for Power quality data mining are studied: Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). We show that SVMs are superior to ANNs in two critical respects: SVMs train and run an order of magnitude faster; and SVMs give higher classification accuracy.
Abstract: This paper presents the optimal controller design of
the generator control unit in the aircraft power system. The adaptive
tabu search technique is applied to tune the controller parameters
until the best terminal output voltage of generator is achieved. The
output response from the system with the controllers designed by the
proposed technique is compared with those from the conventional
method. The transient simulations using the commercial software
package show that the controllers designed from the adaptive tabu
search algorithm can provide the better output performance compared
with the result from the classical method. The proposed design
technique is very flexible and useful for electrical aircraft engineers.
Abstract: Nowadays predicting political risk level of country
has become a critical issue for investors who intend to achieve
accurate information concerning stability of the business
environments. Since, most of the times investors are layman and
nonprofessional IT personnel; this paper aims to propose a
framework named GECR in order to help nonexpert persons to
discover political risk stability across time based on the political
news and events.
To achieve this goal, the Bayesian Networks approach was
utilized for 186 political news of Pakistan as sample dataset.
Bayesian Networks as an artificial intelligence approach has been
employed in presented framework, since this is a powerful technique
that can be applied to model uncertain domains. The results showed
that our framework along with Bayesian Networks as decision
support tool, predicted the political risk level with a high degree of
accuracy.
Abstract: Needs of an efficient information retrieval in recent
years in increased more then ever because of the frequent use of
digital information in our life. We see a lot of work in the area of
textual information but in multimedia information, we cannot find
much progress. In text based information, new technology of data
mining and data marts are now in working that were started from the
basic concept of database some where in 1960.
In image search and especially in image identification,
computerized system at very initial stages. Even in the area of image
search we cannot see much progress as in the case of text based
search techniques. One main reason for this is the wide spread roots
of image search where many area like artificial intelligence,
statistics, image processing, pattern recognition play their role. Even
human psychology and perception and cultural diversity also have
their share for the design of a good and efficient image recognition
and retrieval system.
A new object based search technique is presented in this paper
where object in the image are identified on the basis of their
geometrical shapes and other features like color and texture where
object-co-relation augments this search process.
To be more focused on objects identification, simple images are
selected for the work to reduce the role of segmentation in overall
process however same technique can also be applied for other
images.
Abstract: This paper discusses a qualitative simulator QRiOM
that uses Qualitative Reasoning (QR) technique, and a process-based
ontology to model, simulate and explain the behaviour of selected
organic reactions. Learning organic reactions requires the application
of domain knowledge at intuitive level, which is difficult to be
programmed using traditional approach. The main objective of
QRiOM is to help learners gain a better understanding of the
fundamental organic reaction concepts, and to improve their
conceptual comprehension on the subject by analyzing the multiple
forms of explanation generated by the software. This paper focuses
on the generation of explanation based on causal theories to explicate
various phenomena in the chemistry subject. QRiOM has been tested
with three classes problems related to organic chemistry, with
encouraging results. This paper also presents the results of
preliminary evaluation of QRiOM that reveal its explanation
capability and usefulness.
Abstract: Cognitive Science appeared about 40 years ago,
subsequent to the challenge of the Artificial Intelligence, as common
territory for several scientific disciplines such as: IT, mathematics,
psychology, neurology, philosophy, sociology, and linguistics. The
new born science was justified by the complexity of the problems
related to the human knowledge on one hand, and on the other by the
fact that none of the above mentioned sciences could explain alone
the mental phenomena. Based on the data supplied by the
experimental sciences such as psychology or neurology, models of
the human mind operation are built in the cognition science. These
models are implemented in computer programs and/or electronic
circuits (specific to the artificial intelligence) – cognitive systems –
whose competences and performances are compared to the human
ones, leading to the psychology and neurology data reinterpretation,
respectively to the construction of new models. During these
processes if psychology provides the experimental basis, philosophy
and mathematics provides the abstraction level utterly necessary for
the intermission of the mentioned sciences.
The ongoing general problematic of the cognitive approach
provides two important types of approach: the computational one,
starting from the idea that the mental phenomenon can be reduced to
1 and 0 type calculus operations, and the connection one that
considers the thinking products as being a result of the interaction
between all the composing (included) systems. In the field of
psychology measurements in the computational register use classical
inquiries and psychometrical tests, generally based on calculus
methods. Deeming things from both sides that are representing the
cognitive science, we can notice a gap in psychological product
measurement possibilities, regarded from the connectionist
perspective, that requires the unitary understanding of the quality –
quantity whole. In such approach measurement by calculus proves to
be inefficient. Our researches, deployed for longer than 20 years,
lead to the conclusion that measuring by forms properly fits to the
connectionism laws and principles.
Abstract: This paper is motivated by the aspect of uncertainty in
financial decision making, and how artificial intelligence and soft
computing, with its uncertainty reducing aspects can be used for
algorithmic trading applications that trade in high frequency.
This paper presents an optimized high frequency trading system that
has been combined with various moving averages to produce a hybrid
system that outperforms trading systems that rely solely on moving
averages. The paper optimizes an adaptive neuro-fuzzy inference
system that takes both the price and its moving average as input,
learns to predict price movements from training data consisting of
intraday data, dynamically switches between the best performing
moving averages, and performs decision making of when to buy or
sell a certain currency in high frequency.
Abstract: This paper presents application artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), to estimate the real power transfer between generators and loads. Since these AI techniques adopt supervised learning, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of both AI methods compared to that of the MNE method. The mean squared error of the estimate of ANN and ANFIS power transfer allocation methods are 1.19E-05 and 2.97E-05, respectively. Furthermore, when compared to MNE method, ANN and ANFIS methods computes generator contribution to loads within 20.99 and 39.37msec respectively whereas the MNE method took 360msec for the calculation of same real power transfer allocation.
Abstract: The school / university orientation interests a broad and
often badly informed public. Technically, it is an important
multicriterion decision problem, which supposes the combination of
much academic professional and/or lawful knowledge, which in turn
justifies software resorting to the techniques of Artificial Intelligence.
CORUS is an expert system of the "Conseil et ORientation
Universitaire et Scolaire", based on a knowledge representation
language (KRL) with rules and objects, called/ known as Ibn Rochd.
CORUS was developed thanks to DéGSE, a workshop of cognitive
engineering which supports this LRC. CORUS works out many
acceptable solutions for the case considered, and retains the most
satisfactory among them. Several versions of CORUS have extended
its services gradually.
Abstract: Interest in Human Consciousness has been revived in the late 20th century from different scientific disciplines. Consciousness studies involve both its understanding and its application. In this paper, a computational model of the minimum consciousness functions necessary in my point of view for Artificial Intelligence applications is presented with the aim of improving the way computations will be made in the future. In section I, human consciousness is briefly described according to the scope of this paper. In section II, a minimum set of consciousness functions is defined - based on the literature reviewed - to be modelled, and then a computational model of these functions is presented in section III. In section IV, an analysis of the model is carried out to describe its functioning in detail.