Abstract: In this paper, an efficient structural approach for
recognizing on-line handwritten digits is proposed. After reading
the digit from the user, the slope is estimated and normalized for
adjacent nodes. Based on the changing of signs of the slope values,
the primitives are identified and extracted. The names of these
primitives are represented by strings, and then a finite state
machine, which contains the grammars of the digits, is traced to
identify the digit. Finally, if there is any ambiguity, it will be
resolved. Experiments showed that this technique is flexible and
can achieve high recognition accuracy for the shapes of the digits
represented in this work.
Abstract: The paper presents dynamic programming based model as a planning tool for the maintenance of electric power systems. Every distribution component has an exponential age depending reliability function to model the fault risk. In the moment of time when the fault costs exceed the investment costs of the new component the reinvestment of the component should be made. However, in some cases the overhauling of the old component may be more economical than the reinvestment. The comparison between overhauling and reinvestment is made by optimisation process. The goal of the optimisation process is to find the cost minimising maintenance program for electric power distribution system.
Abstract: In order to provide existing SOAP (Simple Object
Access Protocol)-based Web services with users who are familiar with
REST (REpresentational State Transfer)-style Web services, this
paper proposes Web service providing method using Web service
transformation. This enables SOAP-based service providers to define
rules for mapping from RESTful Web services to SOAP-based ones.
Using these mapping rules, HTTP request messages for RESTful
services are converted automatically into SOAP-based service
invocations. Web service providers need not develop duplicate
RESTful services and they can avoid programming mediation
modules per service. Furthermore, they need not equip mediation
middleware like ESB (Enterprise Service Bus) only for the purpose of
transformation of two different Web service styles.
Abstract: Face Recognition has always been a fascinating research area. It has drawn the attention of many researchers because of its various potential applications such as security systems, entertainment, criminal identification etc. Many supervised and unsupervised learning techniques have been reported so far. Principal Component Analysis (PCA), Self Organizing Maps (SOM) and Independent Component Analysis (ICA) are the three techniques among many others as proposed by different researchers for Face Recognition, known as the unsupervised techniques. This paper proposes integration of the two techniques, SOM and PCA, for dimensionality reduction and feature selection. Simulation results show that, though, the individual techniques SOM and PCA itself give excellent performance but the combination of these two can also be utilized for face recognition. Experimental results also indicate that for the given face database and the classifier used, SOM performs better as compared to other unsupervised learning techniques. A comparison of two proposed methodologies of SOM, Local and Global processing, shows the superiority of the later but at the cost of more computational time.
Abstract: Nowadays, OCR systems have got several
applications and are increasingly employed in daily life. Much
research has been done regarding the identification of Latin,
Japanese, and Chinese characters. However, very little investigation
has been performed regarding Farsi/Arabic characters recognition.
Probably the reason is difficulty and complexity of those characters
identification compared to the others and limitation of IT activities in
Farsi and Arabic speaking countries. In this paper, a technique has
been employed to identify isolated Farsi/Arabic characters. A chain
code based algorithm along with other significant peculiarities such
as number and location of dots and auxiliary parts, and the number of
holes existing in the isolated character has been used in this study to
identify Farsi/Arabic characters. Experimental results show the
relatively high accuracy of the method developed when it is tested on
several standard Farsi fonts.
Abstract: This paper presents a time control liquids mixing
system in the tanks as an application of fuzzy time control discrete
model. The system is designed for a wide range of industrial
applications. The simulation design of control system has three
inputs: volume, viscosity, and selection of product, along with the
three external control adjustments for the system calibration or to
take over the control of the system autonomously in local or
distributed environment. There are four controlling elements: rotatory
motor, grinding motor, heating and cooling units, and valves
selection, each with time frame limit. The system consists of three
controlled variables measurement through its sensing mechanism for
feed back control. This design also facilitates the liquids mixing
system to grind certain materials in tanks and mix with fluids under
required temperature controlled environment to achieve certain
viscous level. Design of: fuzzifier, inference engine, rule base,
deffuzifiers, and discrete event control system, is discussed. Time
control fuzzy rules are formulated, applied and tested using
MATLAB simulation for the system.
Abstract: The prediction of Software quality during development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. “Whether a module is faulty or not" approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.
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: Grid environments include aggregation of
geographical distributed resources. Grid is put forward in three types
of computational, data and storage. This paper presents a research on
data grid. Data grid is used for covering and securing accessibility to
data from among many heterogeneous sources. Users are not worry
on the place where data is located in it, provided that, they should get
access to the data. Metadata is used for getting access to data in data
grid. Presently, application metadata catalogue and SRB middle-ware
package are used in data grids for management of metadata. At this
paper, possibility of updating, streamlining and searching is provided
simultaneously and rapidly through classified table of preserving
metadata and conversion of each table to numerous tables.
Meanwhile, with regard to the specific application, the most
appropriate and best division is set and determined. Concurrency of
implementation of some of requests and execution of pipeline is
adaptability as a result of this technique.
Abstract: This paper investigates the problem of designing a robust state-feedback controller for a class of uncertain Markovian jump nonlinear systems that guarantees the L2-gain from an exogenous input to a regulated output is less than or equal to a prescribed value. First, we approximate this class of uncertain Markovian jump nonlinear systems by a class of uncertain Takagi-Sugeno fuzzy models with Markovian jumps. Then, based on an LMI approach, LMI-based sufficient conditions for the uncertain Markovian jump nonlinear systems to have an H performance are derived. An illustrative example is used to illustrate the effectiveness of the proposed design techniques.
Abstract: Speech corpus is one of the major components in a
Speech Processing System where one of the primary requirements
is to recognize an input sample. The quality and details captured
in speech corpus directly affects the precision of recognition. The
current work proposes a platform for speech corpus generation using
an adaptive LMS filter and LPC cepstrum, as a part of an ANN
based Speech Recognition System which is exclusively designed to
recognize isolated numerals of Assamese language- a major language
in the North Eastern part of India. The work focuses on designing an
optimal feature extraction block and a few ANN based cooperative
architectures so that the performance of the Speech Recognition
System can be improved.
Abstract: This paper presents a new heuristic algorithm useful
for long-term planning of survivable WDM networks. A multi-period
model is formulated that combines network topology design and
capacity expansion. The ability to determine network expansion
schedules of this type becomes increasingly important to the
telecommunications industry and to its customers. The solution
technique consists of a Genetic Algorithm that allows generating
several network alternatives for each time period simultaneously and
shortest-path techniques to deduce from these alternatives a least-cost
network expansion plan over all time periods. The multi-period
planning approach is illustrated on a realistic network example.
Extensive simulations on a wide range of problem instances are
carried out to assess the cost savings that can be expected by
choosing a multi-period planning approach instead of an iterative
network expansion design method.
Abstract: The production of a plant can be measured in terms of
seeds. The generation of seeds plays a critical role in our social and
daily life. The fruit production which generates seeds, depends on the
various parameters of the plant, such as shoot length, leaf number,
root length, root number, etc When the plant is growing, some leaves
may be lost and some new leaves may appear. It is very difficult to
use the number of leaves of the tree to calculate the growth of the
plant.. It is also cumbersome to measure the number of roots and
length of growth of root in several time instances continuously after
certain initial period of time, because roots grow deeper and deeper
under ground in course of time. On the contrary, the shoot length of
the tree grows in course of time which can be measured in different
time instances. So the growth of the plant can be measured using the
data of shoot length which are measured at different time instances
after plantation. The environmental parameters like temperature, rain
fall, humidity and pollution are also play some role in production of
yield. The soil, crop and distance management are taken care to
produce maximum amount of yields of plant. The data of the growth
of shoot length of some mustard plant at the initial stage (7,14,21 &
28 days after plantation) is available from the statistical survey by a
group of scientists under the supervision of Prof. Dilip De. In this
paper, initial shoot length of Ken( one type of mustard plant) has
been used as an initial data. The statistical models, the methods of
fuzzy logic and neural network have been tested on this mustard
plant and based on error analysis (calculation of average error) that
model with minimum error has been selected and can be used for the
assessment of shoot length at maturity. Finally, all these methods
have been tested with other type of mustard plants and the particular
soft computing model with the minimum error of all types has been
selected for calculating the predicted data of growth of shoot length.
The shoot length at the stage of maturity of all types of mustard
plants has been calculated using the statistical method on the
predicted data of shoot length.
Abstract: This paper proposes a technique to block adult images displayed in websites. The filter is designed so as to perform even in exceptional cases such as, where face detection is not possible or improper face visibility. This is achieved by using an alternative phase to extract the MFC (Most Frequent Color) from the Human Body regions estimated using a biometric of anthropometric distances between fixed rigidly connected body locations. The logical results generated can be protected from overriding by a firewall or intrusion, by encrypting the result in a SSH data packet.
Abstract: In order to research Internet quantificationally and
better model the performance of network, this paper proposes a novel
AS level network performance model (MNPM), it takes autonomous
system (AS) as basic modeling unit, measures E2E performance
between any two outdegrees of an AS and organizes measurement
results into matrix form which called performance matrix (PM).
Inter-AS performance calculation is defined according to performance
information stored in PM. Simulation has been implemented to verify
the correctness of MNPM and a practical application of MNPM
(network congestion detection) is given.
Abstract: Parsing is important in Linguistics and Natural
Language Processing to understand the syntax and semantics of a
natural language grammar. Parsing natural language text is
challenging because of the problems like ambiguity and inefficiency.
Also the interpretation of natural language text depends on context
based techniques. A probabilistic component is essential to resolve
ambiguity in both syntax and semantics thereby increasing accuracy
and efficiency of the parser. Tamil language has some inherent
features which are more challenging. In order to obtain the solutions,
lexicalized and statistical approach is to be applied in the parsing
with the aid of a language model. Statistical models mainly focus on
semantics of the language which are suitable for large vocabulary
tasks where as structural methods focus on syntax which models
small vocabulary tasks. A statistical language model based on Trigram
for Tamil language with medium vocabulary of 5000 words has
been built. Though statistical parsing gives better performance
through tri-gram probabilities and large vocabulary size, it has some
disadvantages like focus on semantics rather than syntax, lack of
support in free ordering of words and long term relationship. To
overcome the disadvantages a structural component is to be
incorporated in statistical language models which leads to the
implementation of hybrid language models. This paper has attempted
to build phrase structured hybrid language model which resolves
above mentioned disadvantages. In the development of hybrid
language model, new part of speech tag set for Tamil language has
been developed with more than 500 tags which have the wider
coverage. A phrase structured Treebank has been developed with 326
Tamil sentences which covers more than 5000 words. A hybrid
language model has been trained with the phrase structured Treebank
using immediate head parsing technique. Lexicalized and statistical
parser which employs this hybrid language model and immediate
head parsing technique gives better results than pure grammar and
trigram based model.
Abstract: Unified Modeling Language (UML) is a standard
language for modeling of a system. UML is used to visually specify
the structure and behavior of a system. The system requirements are
captured and then converted into UML specification. UML
specification uses a set of rules and notations, and diagrams to
specify the system requirements. In this paper, we present a tool for
developing the UML specification. The tool will ease the use of the
notations and diagrams for UML specification as well as increase the
understanding and familiarity of the UML specification. The tool will
also be able to check the conformance of the diagrams against each
other for basic compliance of UML specification.
Abstract: KREISIG is a computer simulation program, firstly developed by Munawar (1994) in Germany to optimize signalized roundabout. The traffic movement is based on the car following theory. Turbine method has been implemented for signal setting. The program has then been further developed in Indonesia to meet the traffic characteristics in Indonesia by adjusting the sensitivity of the drivers. Trial and error method has been implemented to adjust the saturation flow. The saturation flow output has also been compared to the calculation method according to 1997 Indonesian Highway Capacity Manual. It has then been implemented to optimize signalized roundabout at Kleringan roundabout in Malioboro area, Yogyakarta, Indonesia. It is found that this method can optimize the signal setting of this roundabout. Therefore, it is recommended to use this program to optimize signalized roundabout.
Abstract: Web usage mining has become a popular research
area, as a huge amount of data is available online. These data can be
used for several purposes, such as web personalization, web structure
enhancement, web navigation prediction etc. However, the raw log
files are not directly usable; they have to be preprocessed in order to
transform them into a suitable format for different data mining tasks.
One of the key issues in the preprocessing phase is to identify web
users. Identifying users based on web log files is not a
straightforward problem, thus various methods have been developed.
There are several difficulties that have to be overcome, such as client
side caching, changing and shared IP addresses and so on. This paper
presents three different methods for identifying web users. Two of
them are the most commonly used methods in web log mining
systems, whereas the third on is our novel approach that uses a
complex cookie-based method to identify web users. Furthermore we
also take steps towards identifying the individuals behind the
impersonal web users. To demonstrate the efficiency of the new
method we developed an implementation called Web Activity
Tracking (WAT) system that aims at a more precise distinction of
web users based on log data. We present some statistical analysis
created by the WAT on real data about the behavior of the Hungarian
web users and a comprehensive analysis and comparison of the three
methods
Abstract: In this paper an effective approach for segmenting
human skin regions in images taken at different environment is
proposed. The proposed method uses a color distance map that is
flexible enough to reliably detect the skin regions even if the
illumination conditions of the image vary. Local image conditions is
also focused, which help the technique to adaptively detect differently
illuminated skin regions of an image. Moreover, usage of local
information also helps the skin detection process to get rid of picking
up much noisy pixels.