Abstract: The main objective of Automatic Generation Control (AGC) is to balance the total system generation against system load losses so that the desired frequency and power interchange with neighboring systems is maintained. Any mismatch between generation and demand causes the system frequency to deviate from its nominal value. Thus high frequency deviation may lead to system collapse. This necessitates a very fast and accurate controller to maintain the nominal system frequency. This paper deals with a novel approach of artificial intelligence (AI) technique called Hybrid Neuro-Fuzzy (HNF) approach for an (AGC). The advantage of this controller is that it can handle the non-linearities at the same time it is faster than other conventional controllers. The effectiveness of the proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in a two area interconnected power system. The result shows that intelligent controller is having improved dynamic response and at the same time faster than conventional controller.
Abstract: The paper proposes a new concept in developing
collaborative design system. The concept framework involves
applying simulation of supply chain management to collaborative
design called – 'SCM–Based Design Tool'. The system is developed
particularly to support design activities and to integrate all facilities
together. The system is aimed to increase design productivity and
creativity. Therefore, designers and customers can collaborate by the
system since conceptual design. JAG: Jewelry Art Generator based
on artificial intelligence techniques is integrated into the system.
Moreover, the proposed system can support users as decision tool
and data propagation. The system covers since raw material supply
until product delivery. Data management and sharing information are
visually supported to designers and customers via user interface. The
system is developed on Web–assisted product development
environment. The prototype system is presented for Thai jewelry
industry as a system prototype demonstration, but applicable for
other industry.
Abstract: Conventionally the selection of parameters depends
intensely on the operator-s experience or conservative technological
data provided by the EDM equipment manufacturers that assign
inconsistent machining performance. The parameter settings given by
the manufacturers are only relevant with common steel grades. A
single parameter change influences the process in a complex way.
Hence, the present research proposes artificial neural network (ANN)
models for the prediction of surface roughness on first commenced
Ti-15-3 alloy in electrical discharge machining (EDM) process. The
proposed models use peak current, pulse on time, pulse off time and
servo voltage as input parameters. Multilayer perceptron (MLP) with
three hidden layer feedforward networks are applied. An assessment
is carried out with the models of distinct hidden layer. Training of the
models is performed with data from an extensive series of
experiments utilizing copper electrode as positive polarity. The
predictions based on the above developed models have been verified
with another set of experiments and are found to be in good
agreement with the experimental results. Beside this they can be
exercised as precious tools for the process planning for EDM.
Abstract: This paper presents the averaging model of a buck
converter derived from the generalized state-space averaging method.
The sliding mode control is used to regulate the output voltage of the
converter and taken into account in the model. The proposed model
requires the fast computational time compared with those of the full
topology model. The intensive time-domain simulations via the exact
topology model are used as the comparable model. The results show
that a good agreement between the proposed model and the switching
model is achieved in both transient and steady-state responses. The
reported model is suitable for the optimal controller design by using
the artificial intelligence techniques.
Abstract: In this work Artificial Intelligence (AI) techniques like Fuzzy logic, Genetic Algorithms and Particle Swarm Optimization have been used to improve the performance of the Automatic Generation Control (AGC) system. Instead of applying Genetic Algorithms and Particle swarm optimization independently for optimizing the parameters of the conventional AGC with PI controller, an intelligent tuned Fuzzy logic controller (acting as the secondary controller in the AGC system) has been designed. The controller gives an improved dynamic performance for both hydrothermal and thermal-thermal power systems under a variety of operating conditions.
Abstract: At present, intelligent planning in the Graphplan framework is a focus of artificial intelligence. While the Creating or Destroying Objects Planning (CDOP) is one unsolved problem of this field, one of the difficulties, too. In this paper, we study this planning problem and bring forward the idea of transforming objects to propositions, based on which we offer an algorithm, Creating or Destroying Objects in the Graphplan framework (CDOGP). Compared to Graphplan, the new algorithm can solve not only the entire problems that Graphplan do, but also a part of CDOP. It is for the first time that we introduce the idea of object-proposition, and we emphasize the discussion on the representations of creating or destroying objects operator and an algorithm in the Graphplan framework. In addition, we analyze the complexity of this algorithm.
Abstract: Protein 3D structure prediction has always been an
important research area in bioinformatics. In particular, the
prediction of secondary structure has been a well-studied research
topic. Despite the recent breakthrough of combining multiple
sequence alignment information and artificial intelligence algorithms
to predict protein secondary structure, the Q3 accuracy of various
computational prediction algorithms rarely has exceeded 75%. In a
previous paper [1], this research team presented a rule-based method
called RT-RICO (Relaxed Threshold Rule Induction from Coverings)
to predict protein secondary structure. The average Q3 accuracy on
the sample datasets using RT-RICO was 80.3%, an improvement
over comparable computational methods. Although this demonstrated
that RT-RICO might be a promising approach for predicting
secondary structure, the algorithm-s computational complexity and
program running time limited its use. Herein a parallelized
implementation of a slightly modified RT-RICO approach is
presented. This new version of the algorithm facilitated the testing of
a much larger dataset of 396 protein domains [2]. Parallelized RTRICO
achieved a Q3 score of 74.6%, which is higher than the
consensus prediction accuracy of 72.9% that was achieved for the
same test dataset by a combination of four secondary structure
prediction methods [2].
Abstract: Corporate credit rating prediction using statistical and
artificial intelligence (AI) techniques has been one of the attractive
research topics in the literature. In recent years, multiclass
classification models such as artificial neural network (ANN) or
multiclass support vector machine (MSVM) have become a very
appealing machine learning approaches due to their good
performance. However, most of them have only focused on classifying
samples into nominal categories, thus the unique characteristic of the
credit rating - ordinality - has been seldom considered in their
approaches. This study proposes new types of ANN and MSVM
classifiers, which are named OMANN and OMSVM respectively.
OMANN and OMSVM are designed to extend binary ANN or SVM
classifiers by applying ordinal pairwise partitioning (OPP) strategy.
These models can handle ordinal multiple classes efficiently and
effectively. To validate the usefulness of these two models, we applied
them to the real-world bond rating case. We compared the results of
our models to those of conventional approaches. The experimental
results showed that our proposed models improve classification
accuracy in comparison to typical multiclass classification techniques
with the reduced computation resource.
Abstract: Load forecasting has become in recent years one of the major areas of research in electrical engineering. Most traditional forecasting models and artificial intelligence neural network techniques have been tried out in this task. Artificial neural networks (ANN) have lately received much attention, and a great number of papers have reported successful experiments and practical tests. This article presents the development of an ANN-based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). The proposed ANN is trained with weather-related data and historical electric load-related data using the data from the calendar years 2001, 2002, 2003, and 2004 for training. The model tested for one week at five different seasons, typically, winter, spring, summer, Ramadan and fall seasons, and the mean absolute average error for one hour-ahead load forecasting found 1.12%.
Abstract: We describe a work with an evolutionary computing
algorithm for non photo–realistic rendering of a target image. The
renderings are produced by genetic programming. We have used two
different types of strokes: “empty triangle" and “filled triangle" in
color level. We compare both empty and filled triangular strokes to
find which one generates more aesthetic pleasing images. We found
the filled triangular strokes have better fitness and generate more
aesthetic images than empty triangular strokes.
Abstract: Ontology is a terminology which is used in artificial
intelligence with different meanings. Ontology researching has an
important role in computer science and practical applications,
especially distributed knowledge systems. In this paper we present an
ontology which is called Computational Object Knowledge Base
Ontology. It has been used in designing some knowledge base
systems for solving problems such as the system that supports
studying knowledge and solving analytic geometry problems, the
program for studying and solving problems in Plane Geometry, the
knowledge system in linear algebra.
Abstract: This paper presents recent work on the improvement
of the robotics vision based control strategy for underwater pipeline
tracking system. The study focuses on developing image processing
algorithms and a fuzzy inference system for the analysis of the
terrain. The main goal is to implement the supervisory fuzzy learning
control technique to reduce the errors on navigation decision due to
the pipeline occlusion problem. The system developed is capable of
interpreting underwater images containing occluded pipeline, seabed
and other unwanted noise. The algorithm proposed in previous work
does not explore the cooperation between fuzzy controllers,
knowledge and learnt data to improve the outputs for underwater
pipeline tracking. Computer simulations and prototype simulations
demonstrate the effectiveness of this approach. The system accuracy
level has also been discussed.
Abstract: Work Breakdown Structure (WBS) is one of the
most vital planning processes of the project management since it
is considered to be the fundamental of other processes like
scheduling, controlling, assigning responsibilities, etc. In fact
WBS or activity list is the heart of a project and omission of a
simple task can lead to an irrecoverable result. There are some
tools in order to generate a project WBS. One of the most
powerful tools is mind mapping which is the basis of this article.
Mind map is a method for thinking together and helps a project
manager to stimulate the mind of project team members to
generate project WBS. Here we try to generate a WBS of a
sample project involving with the building construction using the
aid of mind map and the artificial intelligence (AI) programming
language. Since mind map structure can not represent data in a
computerized way, we convert it to a semantic network which can
be used by the computer and then extract the final WBS from the
semantic network by the prolog programming language. This
method will result a comprehensive WBS and decrease the
probability of omitting project tasks.
Abstract: Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.
Abstract: Advancements in the field of artificial intelligence
(AI) made during this decade have forever changed the way we look
at automating spacecraft subsystems including the electrical power
system. AI have been used to solve complicated practical problems
in various areas and are becoming more and more popular nowadays.
In this paper, a mathematical modeling and MATLAB–SIMULINK
model for the different components of the spacecraft power system is
presented. Also, a control system, which includes either the Neural
Network Controller (NNC) or the Fuzzy Logic Controller (FLC) is
developed for achieving the coordination between the components of
spacecraft power system as well as control the energy flows. The
performance of the spacecraft power system is evaluated by
comparing two control systems using the NNC and the FLC.
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: 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: 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: 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.