Abstract: This paper presents the applications of computational intelligence techniques to economic load dispatch problems. The fuel cost equation of a thermal plant is generally expressed as continuous quadratic equation. In real situations the fuel cost equations can be discontinuous. In view of the above, both continuous and discontinuous fuel cost equations are considered in the present paper. First, genetic algorithm optimization technique is applied to a 6- generator 26-bus test system having continuous fuel cost equations. Results are compared to conventional quadratic programming method to show the superiority of the proposed computational intelligence technique. Further, a 10-generator system each with three fuel options distributed in three areas is considered and particle swarm optimization algorithm is employed to minimize the cost of generation. To show the superiority of the proposed approach, the results are compared with other published methods.
Abstract: The necessity of solving multi dimensional
complicated scientific problems beside the necessity of several
objective functions optimization are the most motive reason of born
of artificial intelligence and heuristic methods.
In this paper, we introduce a new method for multiobjective
optimization based on learning automata. In the proposed method,
search space divides into separate hyper-cubes and each cube is
considered as an action. After gathering of all objective functions
with separate weights, the cumulative function is considered as the
fitness function. By the application of all the cubes to the cumulative
function, we calculate the amount of amplification of each action and
the algorithm continues its way to find the best solutions. In this
Method, a lateral memory is used to gather the significant points of
each iteration of the algorithm. Finally, by considering the
domination factor, pareto front is estimated. Results of several
experiments show the effectiveness of this method in comparison
with genetic algorithm based method.
Abstract: Advancement in Artificial Intelligence has lead to the
developments of various “smart" devices. Character recognition
device is one of such smart devices that acquire partial human
intelligence with the ability to capture and recognize various
characters in different languages. Firstly multiscale neural training
with modifications in the input training vectors is adopted in this
paper to acquire its advantage in training higher resolution character
images. Secondly selective thresholding using minimum distance
technique is proposed to be used to increase the level of accuracy of
character recognition. A simulator program (a GUI) is designed in
such a way that the characters can be located on any spot on the
blank paper in which the characters are written. The results show that
such methods with moderate level of training epochs can produce
accuracies of at least 85% and more for handwritten upper case
English characters and numerals.
Abstract: As the enormous amount of on-line text grows on the
World-Wide Web, the development of methods for automatically
summarizing this text becomes more important. The primary goal of
this research is to create an efficient tool that is able to summarize
large documents automatically. We propose an Evolving
connectionist System that is adaptive, incremental learning and
knowledge representation system that evolves its structure and
functionality. In this paper, we propose a novel approach for Part of
Speech disambiguation using a recurrent neural network, a paradigm
capable of dealing with sequential data. We observed that
connectionist approach to text summarization has a natural way of
learning grammatical structures through experience. Experimental
results show that our approach achieves acceptable performance.
Abstract: Soccer simulation is an effort to motivate researchers and practitioners to do artificial and robotic intelligence research; and at the same time put into practice and test the results. Many researchers and practitioners throughout the world are continuously working to polish their ideas and improve their implemented systems. At the same time, new groups are forming and they bring bright new thoughts to the field. The research includes designing and executing robotic soccer simulation algorithms. In our research, a soccer simulation player is considered to be an intelligent agent that is capable of receiving information from the environment, analyze it and to choose the best action from a set of possible ones, for its next move. We concentrate on developing a two-phase method for the soccer player agent to choose its best next move. The method is then implemented into our software system called Nexus simulation team of Ferdowsi University. This system is based on TsinghuAeolus[1] team that was the champion of the world RoboCup soccer simulation contest in 2001 and 2002.
Abstract: Subjective loneliness describes people who feel a
disagreeable or unacceptable lack of meaningful social relationships,
both at the quantitative and qualitative level. The studies to be
presented tested an Italian 18-items self-report loneliness measure,
that included items adapted from scales previously developed,
namely a short version of the UCLA (Russell, Peplau and Cutrona,
1980), and the 11-items Loneliness scale by De Jong-Gierveld &
Kamphuis (JGLS; 1985). The studies aimed at testing the developed
scale and at verifying whether loneliness is better conceptualized as a
unidimensional (so-called 'general loneliness') or a bidimensional
construct, namely comprising the distinct facets of social and
emotional loneliness. The loneliness questionnaire included 2 singleitem
criterion measures of sad mood, and social contact, and asked
participants to supply information on a number of socio-demographic
variables. Factorial analyses of responses obtained in two
preliminary studies, with 59 and 143 Italian participants respectively,
showed good factor loadings and subscale reliability and confirmed
that perceived loneliness has clearly two components, a social and an
emotional one, the latter measured by two subscales, a 7-item
'general' loneliness subscale derived from UCLA, and a 6–item
'emotional' scale included in the JGLS. Results further showed that
type and amount of loneliness are related, negatively, to frequency of
social contacts, and, positively, to sad mood. In a third study data
were obtained from a nation-wide sample of 9.097 Italian subjects,
12 to about 70 year-olds, who filled the test on-line, on the Italian
web site of a large-audience magazine, Focus. The results again
confirmed the reliability of the component subscales, namely social,
emotional, and 'general' loneliness, and showed that they were
highly correlated with each other, especially the latter two.
Loneliness scores were significantly predicted by sex, age, education
level, sad mood and social contact, and, less so, by other variables –
e.g., geographical area and profession. The scale validity was
confirmed by the results of a fourth study, with elderly men and
women (N 105) living at home or in residential care units. The three
subscales were significantly related, among others, to depression, and
to various measures of the extension of, and satisfaction with, social
contacts with relatives and friends. Finally, a fifth study with 315
career-starters showed that social and emotional loneliness correlate
with life satisfaction, and with measures of emotional intelligence.
Altogether the results showed a good validity and reliability in the
tested samples of the entire scale, and of its components.
Abstract: Mobile Ad hoc network consists of a set of mobile
nodes. It is a dynamic network which does not have fixed topology.
This network does not have any infrastructure or central
administration, hence it is called infrastructure-less network. The
change in topology makes the route from source to destination as
dynamic fixed and changes with respect to time. The nature of
network requires the algorithm to perform route discovery, maintain
route and detect failure along the path between two nodes [1]. This
paper presents the enhancements of ARA [2] to improve the
performance of routing algorithm. ARA [2] finds route between
nodes in mobile ad-hoc network. The algorithm is on-demand source
initiated routing algorithm. This is based on the principles of swarm
intelligence. The algorithm is adaptive, scalable and favors load
balancing. The improvements suggested in this paper are handling of
loss ants and resource reservation.
Abstract: School physical education, through its objectives and
contents, efficiently valorizes the pupils- abilities, developing them,
especially the coordinative skill component, which is the basis of
movement learning, of the development of the daily motility and also
of the special, refined motility required by the practice of certain
sports. Medium school age offers the nervous and motor substratum
needed for the acquisition of complex motor habits, a substratum that
is essential for the coordinative skill. Individuals differ as to the level
at which this function is performed, the extent to which this function
turns an individual into a person that is adapted and adaptable to
complex and various situations. Spatio-temporal orientation, together
with movement combination and coupling, and with kinesthetic,
balance, motor reaction, movement transformation and rhythm
differentiation form the coordinative skills. From our viewpoint,
these are characteristic features with high levels of manifestation in a
complex psychomotor act - valorizing the quality of one-s talent - as
well as indices pertaining to one-s psychomotor intelligence and
creativity.
Abstract: During the past decade, pond aeration systems have
been developed which will sustain large quantities of fish and
invertebrate biomass. Dissolved Oxygen (DO) is considered to be
among the most important water quality parameters in fish culture.
Fishponds in aquaculture farms are usually located in remote areas
where grid lines are at far distance. Aeration of ponds is required to
prevent mortality and to intensify production, especially when
feeding is practical, and in warm regions. To increase pond
production it is necessary to control dissolved oxygen. Artificial
intelligence (AI) techniques are becoming useful as alternate
approaches to conventional techniques or as components of
integrated systems. They have been used to solve complicated
practical problems in various areas and are becoming more and more
popular nowadays. This paper presents a new design of diffused
aeration system using fuel cell as a power source. Also fuzzy logic
control Technique (FLC) is used for controlling the speed of air flow
rate from the blower to air piping connected to the pond by adjusting
blower speed. MATLAB SIMULINK results show high performance
of fuzzy logic control (FLC).
Abstract: The paper presents the applications of artificial
intelligence technique called adaptive tabu search to design the
controller of a buck converter. The averaging model derived from the
DQ and generalized state-space averaging methods is applied to
simulate the system during a searching process. The simulations
using such averaging model require the faster computational time
compared with that of the full topology model from the software
packages. The reported model is suitable for the work in the paper in
which the repeating calculation is needed for searching the best
solution. The results will show that the proposed design technique
can provide the better output waveforms compared with those
designed from the classical method.
Abstract: Tablet computers and Multifunctional Hardcopy Devices (MHDs) are common devices in daily life. Though, many scientific studies have not been published. The tablet computers are straightforward to use whereas the MHDs are comparatively difficult to use. Thus, to assist different levels of users, we propose combining these two devices to achieve straightforward intelligent user interface (UI) and versatile What You See Is What You Get (WYSIWYG) document management and production. Our approach to this issue is to design an intelligent user dependent UI for a MHD applying a tablet computer. Furthermore, we propose hardware interconnection and versatile intelligent software between these two devices. In this study, we first provide a state-of-the-art survey on MHDs and tablet computers, and their interconnections. Secondly we provide a comparative UI survey on two state-of-the-art MHDs with a proposal of a novel UI for the MHDs using Jakob Nielsen-s Ten Usability Heuristics Evaluation.
Abstract: In this paper an open agent-based modular framework
for personalized and adaptive curriculum generation in e-learning
environment is proposed. Agent-based approaches offer several
potential advantages over alternative approaches. Agent-based
systems exhibit high levels of flexibility and robustness in dynamic
or unpredictable environments by virtue of their intrinsic autonomy.
The presented framework enables integration of different types of
expert agents, various kinds of learning objects and user modeling
techniques. It creates possibilities for adaptive e-learning process.
The KM e-learning system is in a process of implementation in
Varna Free University and will be used for supporting the
educational process at the University.
Abstract: Successful intelligence (SI) is the integrated set of the
ability needed to attain success in life, within individual-s sociocultural
context. People are successfully intelligent by recognizing
their strengths and weaknesses. They will find ways to strengthen
their weakness and maintain their strength or even improve it. SI
people can shape, select, and adapt to the environments by using
balance of higher-ordered thinking abilities including; critical,
creative, and applicative. Aims: The purposes of this study were to;
1) develop curriculum that promotes SI for nursing students, and 2)
study the effectiveness of the curriculum development. Method:
Research and Development was a method used for this study. The
design was divided into two phases; 1) the curriculum development
which composed of three steps (needs assessment, curriculum
development and curriculum field trail), and 2) the curriculum
implementation. In this phase, a pre-experimental research design
(one group pretest-posttest design) was conducted. The sample
composed of 49 sophomore nursing students of Boromarajonani
College of Nursing, Surin, Thailand who enrolled in Nursing care of
Health problem course I in 2011 academic year. Data were carefully
collected using 4 instruments; 1) Modified essay questions test
(MEQ) 2) Nursing Care Plan evaluation form 3) Group processing
observation form (α = 0.74) and 4) Satisfied evaluation form of
learning (α = 0.82). Data were analyzed using descriptive statistics
and content analysis. Results: The results revealed that the sample
had post-test average score of SI higher than pre-test average score
(mean difference was 5.03, S.D. = 2.84). Fifty seven percentages of
the sample passed the MEQ posttest at the criteria of 60 percentages.
Students demonstrated the strategies of how to develop nursing care
plan. Overall, students- satisfaction on teaching performance was at
high level (mean = 4.35, S.D. = 0.46). Conclusion: This curriculum
can promote the attribute of characteristic of SI person and was
highly required to be continued.
Abstract: Bioinformatics and computational biology involve
the use of techniques including applied mathematics,
informatics, statistics, computer science, artificial intelligence,
chemistry, and biochemistry to solve biological problems
usually on the molecular level. Research in computational
biology often overlaps with systems biology. Major research
efforts in the field include sequence alignment, gene finding,
genome assembly, protein structure alignment, protein structure
prediction, prediction of gene expression and proteinprotein
interactions, and the modeling of evolution. Various
global rearrangements of permutations, such as reversals and
transpositions,have recently become of interest because of their
applications in computational molecular biology. A reversal is
an operation that reverses the order of a substring of a permutation.
A transposition is an operation that swaps two adjacent
substrings of a permutation. The problem of determining the
smallest number of reversals required to transform a given
permutation into the identity permutation is called sorting by
reversals. Similar problems can be defined for transpositions
and other global rearrangements. In this work we perform a
study about some genome rearrangement primitives. We show
how a genome is modelled by a permutation, introduce some
of the existing primitives and the lower and upper bounds
on them. We then provide a comparison of the introduced
primitives.
Abstract: This paper discusses a method for improving accuracy
of fuzzy-rule-based classifiers using particle swarm optimization
(PSO). Two different fuzzy classifiers are considered and optimized.
The first classifier is based on Mamdani fuzzy inference system
(M_PSO fuzzy classifier). The second classifier is based on Takagi-
Sugeno fuzzy inference system (TS_PSO fuzzy classifier). The
parameters of the proposed fuzzy classifiers including premise
(antecedent) parameters, consequent parameters and structure of
fuzzy rules are optimized using PSO. Experimental results show that
higher classification accuracy can be obtained with a lower number
of fuzzy rules by using the proposed PSO fuzzy classifiers. The
performances of M_PSO and TS_PSO fuzzy classifiers are compared
to other fuzzy based classifiers
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: In the current age, retrieval of relevant information
from massive amount of data is a challenging job. Over the years,
precise and relevant retrieval of information has attained high
significance. There is a growing need in the market to build systems,
which can retrieve multimedia information that precisely meets the
user's current needs. In this paper, we have introduced a framework
for refining query results before showing it to the user, using ambient
intelligence, user profile, group profile, user location, time, day, user
device type and extracted features. A prototypic tool was also
developed to demonstrate the efficiency of the proposed approach.
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.