Abstract: The concept of the new government should focus on
forming a new relationship between public servants and citizens of
the state, formed on the principles of transparency, accountability,
protection of citizens' rights. These principles are laid down in the
problem of administrative reform in the Republic of Kazakhstan.
Also, this wish arises, contributing to the improvement of the system
of political management in our country. For the full realization of the
goals is necessary to develop a special state program designed to
improve the regulatory framework for public service, improving
training, retraining and advanced training of civil servants, forming a
system of incentives in public service and other activities aimed at
achieving the efficiency of the entire system government.
Abstract: In this paper back-propagation artificial neural
network (BPANN) is employed to predict the limiting drawing ratio
(LDR) of the deep drawing process. To prepare a training set for
BPANN, some finite element simulations were carried out. die and
punch radius, die arc radius, friction coefficient, thickness, yield
strength of sheet and strain hardening exponent were used as the
input data and the LDR as the specified output used in the training of
neural network. As a result of the specified parameters, the program
will be able to estimate the LDR for any new given condition.
Comparing FEM and BPANN results, an acceptable correlation was
found.
Abstract: In this paper we present a Feed-Foward Neural
Networks Autoregressive (FFNN-AR) model with genetic algorithms
training optimization in order to predict the gross domestic product
growth of six countries. Specifically we propose a kind of weighted
regression, which can be used for econometric purposes, where the
initial inputs are multiplied by the neural networks final optimum
weights from input-hidden layer of the training process. The
forecasts are compared with those of the ordinary autoregressive
model and we conclude that the proposed regression-s forecasting
results outperform significant those of autoregressive model.
Moreover this technique can be used in Autoregressive-Moving
Average models, with and without exogenous inputs, as also the
training process with genetics algorithms optimization can be
replaced by the error back-propagation algorithm.
Abstract: Haptics has been used extensively in many applications especially in human machine interaction and virtual reality systems. Haptic technology allows user to perceive virtual reality as in real world. However, commercially available haptic devices are expensive and may not be suitable for educational purpose. This paper describes the design and development of a low cost haptic knob, with only one degree of freedom, for use in rehabilitation or training hand pronation and supination. End-effectors can be changed to suit different applications or variation in hand sizes and hand orientation.
Abstract: This paper presents performance analysis of the
Evolutionary Programming-Artificial Neural Network (EPANN)
based technique to optimize the architecture and training parameters
of a one-hidden layer feedforward ANN model for the prediction of
energy output from a grid connected photovoltaic system. The ANN
utilizes solar radiation and ambient temperature as its inputs while the
output is the total watt-hour energy produced from the grid-connected
PV system. EP is used to optimize the regression performance of the
ANN model by determining the optimum values for the number of
nodes in the hidden layer as well as the optimal momentum rate and
learning rate for the training. The EPANN model is tested using two
types of transfer function for the hidden layer, namely the tangent
sigmoid and logarithmic sigmoid. The best transfer function, neural
topology and learning parameters were selected based on the highest
regression performance obtained during the ANN training and testing
process. It is observed that the best transfer function configuration for
the prediction model is [logarithmic sigmoid, purely linear].
Abstract: Temperature is one of the most principle factors affects aquaculture system. It can cause stress and mortality or superior environment for growth and reproduction. This paper presents the control of pond water temperature using artificial intelligence technique. The water temperature is very important parameter for shrimp growth. The required temperature for optimal growth is 34oC, if temperature increase up to 38oC it cause death of the shrimp, so it is important to control water temperature. Solar thermal water heating system is designed to supply an aquaculture pond with the required hot water in Mersa Matruh in Egypt. Neural networks are massively parallel processors that have the ability to learn patterns through a training experience. Because of this feature, they are often well suited for modeling complex and non-linear processes such as those commonly found in the heating system. Artificial neural network is proposed to control water temperature due to Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques. They have been used to solve complicated practical problems. Moreover this paper introduces a complete mathematical modeling and MATLAB SIMULINK model for the aquaculture system. The simulation results indicate that, the control unit success in keeping water temperature constant at the desired temperature by controlling the hot water flow rate.
Abstract: The performances of small and medium enterprises
have stagnated in the last two decades. This has mainly been due to
the emergence of HIV / Aids. The disease has had a detrimental
effect on the general economy of the country leading to morbidity
and mortality of the Kenyan workforce in their primary age. The
present study sought to establish the economic impact of HIV / Aids
on the micro-enterprise development in Obunga slum – Kisumu, in
terms of production loss, increasing labor related cost and to establish
possible strategies to address the impact of HIV / Aids on microenterprises.
The study was necessitated by the observation that most
micro-enterprises in the slum are facing severe economic and social
crisis due to the impact of HIV / Aids, they get depleted and close
down within a short time due to death of skilled and experience
workforce. The study was carried out between June 2008 and June
2009 in Obunga slum. Data was subjected to computer aided
statistical analysis that included descriptive statistic, chi-squared and
ANOVA techniques. Chi-squared analysis on the micro-enterprise
owners opinion on the impact of HIV / Aids on depletion of microenterprise
compared to other diseases indicated high levels of the
negative effects of the disease at significance levels of P
Abstract: In this article, we propose a methodology for the
characterization of the suspended matter along Algiers-s bay. An
approach by multi layers perceptron (MLP) with training by back
propagation of the gradient optimized by the algorithm of Levenberg
Marquardt (LM) is used. The accent was put on the choice of the
components of the base of training where a comparative study made
for four methods: Random and three alternatives of classification by
K-Means. The samples are taken from suspended matter image,
obtained by analytical model based on polynomial regression by
taking account of in situ measurements. The mask which selects the
zone of interest (water in our case) was carried out by using a multi
spectral classification by ISODATA algorithm. To improve the
result of classification, a cleaning of this mask was carried out using
the tools of mathematical morphology. The results of this study
presented in the forms of curves, tables and of images show the
founded good of our methodology.
Abstract: We present a hybrid architecture of recurrent neural
networks (RNNs) inspired by hidden Markov models (HMMs). We
train the hybrid architecture using genetic algorithms to learn and
represent dynamical systems. We train the hybrid architecture on a
set of deterministic finite-state automata strings and observe the
generalization performance of the hybrid architecture when presented
with a new set of strings which were not present in the training data
set. In this way, we show that the hybrid system of HMM and RNN
can learn and represent deterministic finite-state automata. We ran
experiments with different sets of population sizes in the genetic
algorithm; we also ran experiments to find out which weight
initializations were best for training the hybrid architecture. The
results show that the hybrid architecture of recurrent neural networks
inspired by hidden Markov models can train and represent dynamical
systems. The best training and generalization performance is
achieved when the hybrid architecture is initialized with random real
weight values of range -15 to 15.
Abstract: The paper deals with results of a project “Interoperability Workplaces to Support Teaching of Security Management in a Computer Network". This project is focused on the perspectives and possibilities of "new approaches" to education, training and crisis communication of rescue teams in the Czech Republic. It means that common technologies considering new perspectives are used to educate selected members of crisis management. The main part concentrates on possibilities of application of new technology and computer-aided tools to education and training of Integrated Rescue System teams.This project uses the COST principle for the creation of specialized centers and for all communication between these workplaces.
Abstract: We have applied new accelerated algorithm for linear
discriminate analysis (LDA) in face recognition with support vector
machine. The new algorithm has the advantage of optimal selection
of the step size. The gradient descent method and new algorithm has
been implemented in software and evaluated on the Yale face
database B. The eigenfaces of these approaches have been used to
training a KNN. Recognition rate with new algorithm is compared
with gradient.
Abstract: A novel low-cost flight simulator with the development
goals cost effectiveness and high performance has been realized for
meeting the huge pilot training needs of airlines. The simulator
consists of an aircraft dynamics model, a sophisticated designed
low-profile electrical driven motion system with a subsided cabin, a
mixed reality based semi-virtual cockpit system, a control loading
system and some other subsystems. It shows its advantages over
traditional flight simulator by its features achieved with open
architecture, software solutions and low-cost hardware.
Abstract: This paper presents an online method that learns the
corresponding points of an object from un-annotated grayscale images
containing instances of the object. In the first image being
processed, an ensemble of node points is automatically selected
which is matched in the subsequent images. A Bayesian posterior
distribution for the locations of the nodes in the images is formed.
The likelihood is formed from Gabor responses and the prior assumes
the mean shape of the node ensemble to be similar in a translation
and scale free space. An association model is applied for separating
the object nodes and background nodes. The posterior distribution is
sampled with Sequential Monte Carlo method. The matched object
nodes are inferred to be the corresponding points of the object
instances. The results show that our system matches the object nodes
as accurately as other methods that train the model with annotated
training images.
Abstract: Numerical analysis of flow characteristics and
separation efficiency in a high-efficiency cyclone has been performed.
Several models based on the experimental observation for a design
purpose were proposed. However, the model is only estimated the
cyclone's performance under the limited environments; it is difficult to
obtain a general model for all types of cyclones. The purpose of this
study is to find out the flow characteristics and separation efficiency
numerically. The Reynolds stress model (RSM) was employed instead
of a standard k-ε or a k-ω model which was suitable for isotropic
turbulence and it could predict the pressure drop and the Rankine
vortex very well. For small particles, there were three significant
components (entrance of vortex finder, cone, and dust collector) for
the particle separation. In the present work, the particle re-entraining
phenomenon from the dust collector to the cyclone body was observed
after considerable time. This re-entrainment degraded the separation
efficiency and was one of the significant factors for the separation
efficiency of the cyclone.
Abstract: recurrent neural network (RNN) is an efficient tool for
modeling production control process as well as modeling services. In
this paper one RNN was combined with regression model and were
employed in order to be checked whether the obtained data by the
model in comparison with actual data, are valid for variable process
control chart. Therefore, one maintenance process in workshop of
Esfahan Oil Refining Co. (EORC) was taken for illustration of
models. First, the regression was made for predicting the response
time of process based upon determined factors, and then the error
between actual and predicted response time as output and also the
same factors as input were used in RNN. Finally, according to
predicted data from combined model, it is scrutinized for test values
in statistical process control whether forecasting efficiency is
acceptable. Meanwhile, in training process of RNN, design of
experiments was set so as to optimize the RNN.
Abstract: Recently many research has been conducted to
retrieve pertinent parameters and adequate models for automatic
music genre classification. In this paper, two measures based upon
information theory concepts are investigated for mapping the features
space to decision space. A Gaussian Mixture Model (GMM) is used
as a baseline and reference system. Various strategies are proposed
for training and testing sessions with matched or mismatched
conditions, long training and long testing, long training and short
testing. For all experiments, the file sections used for testing are
never been used during training. With matched conditions all
examined measures yield the best and similar scores (almost 100%).
With mismatched conditions, the proposed measures yield better
scores than the GMM baseline system, especially for the short testing
case. It is also observed that the average discrimination information
measure is most appropriate for music category classifications and on
the other hand the divergence measure is more suitable for music
subcategory classifications.
Abstract: Artificial Neural Networks (ANNs) have been used successfully in many scientific, industrial and business domains as a method for extracting knowledge from vast amounts of data. However the use of ANN techniques in the sporting domain has been limited. In professional sport, data is stored on many aspects of teams, games, training and players. Sporting organisations have begun to realise that there is a wealth of untapped knowledge contained in the data and there is great interest in techniques to utilise this data. This study will use player data from the elite Australian Football League (AFL) competition to train and test ANNs with the aim to predict the onset of injuries. The results demonstrate that an accuracy of 82.9% was achieved by the ANNs’ predictions across all examples with 94.5% of all injuries correctly predicted. These initial findings suggest that ANNs may have the potential to assist sporting clubs in the prediction of injuries.
Abstract: In this study, a fuzzy similarity approach for Arabic web pages classification is presented. The approach uses a fuzzy term-category relation by manipulating membership degree for the training data and the degree value for a test web page. Six measures are used and compared in this study. These measures include: Einstein, Algebraic, Hamacher, MinMax, Special case fuzzy and Bounded Difference approaches. These measures are applied and compared using 50 different Arabic web-pages. Einstein measure was gave best performance among the other measures. An analysis of these measures and concluding remarks are drawn in this study.
Abstract: In this paper a one-dimension Self Organizing Map
algorithm (SOM) to perform feature selection is presented. The
algorithm is based on a first classification of the input dataset on a
similarity space. From this classification for each class a set of
positive and negative features is computed. This set of features is
selected as result of the procedure. The procedure is evaluated on an
in-house dataset from a Knowledge Discovery from Text (KDT)
application and on a set of publicly available datasets used in
international feature selection competitions. These datasets come
from KDT applications, drug discovery as well as other applications.
The knowledge of the correct classification available for the training
and validation datasets is used to optimize the parameters for positive
and negative feature extractions. The process becomes feasible for
large and sparse datasets, as the ones obtained in KDT applications,
by using both compression techniques to store the similarity matrix
and speed up techniques of the Kohonen algorithm that take
advantage of the sparsity of the input matrix. These improvements
make it feasible, by using the grid, the application of the
methodology to massive datasets.
Abstract: This paper presents a robust proportionalderivative
(PD) based cerebellar model articulation
controller (CMAC) for vertical take-off and landing flight
control systems. Successful on-line training and recalling
process of CMAC accompanying the PD controller is
developed. The advantage of the proposed method is mainly
the robust tracking performance against aerodynamic
parametric variation and external wind gust. The
effectiveness of the proposed algorithm is validated through
the application of a vertical takeoff and landing aircraft
control system.