Abstract: Organization of video databases is becoming difficult
task as the amount of video content increases. Video classification
based on the content of videos can significantly increase the speed of
tasks such as browsing and searching for a particular video in a
database. In this paper, a content-based videos classification system
for the classes indoor and outdoor is presented. The system is
intended to be used on a mobile platform with modest resources. The
algorithm makes use of the temporal redundancy in videos, which
allows using an uncomplicated classification model while still
achieving reasonable accuracy. The training and evaluation was done
on a video database of 443 videos downloaded from a video sharing
service. A total accuracy of 87.36% was achieved.
Abstract: In this paper, we proposed a method to design a
model-following adaptive controller for linear/nonlinear plants.
Radial basis function neural networks (RBF-NNs), which are known
for their stable learning capability and fast training, are used to
identify linear/nonlinear plants. Simulation results show that the
proposed method is effective in controlling both linear and nonlinear
plants with disturbance in the plant input.
Abstract: This research paper deals with the implementation of face recognition using neural network (recognition classifier) on low-resolution images. The proposed system contains two parts, preprocessing and face classification. The preprocessing part converts original images into blurry image using average filter and equalizes the histogram of those image (lighting normalization). The bi-cubic interpolation function is applied onto equalized image to get resized image. The resized image is actually low-resolution image providing faster processing for training and testing. The preprocessed image becomes the input to neural network classifier, which uses back-propagation algorithm to recognize the familiar faces. The crux of proposed algorithm is its beauty to use single neural network as classifier, which produces straightforward approach towards face recognition. The single neural network consists of three layers with Log sigmoid, Hyperbolic tangent sigmoid and Linear transfer function respectively. The training function, which is incorporated in our work, is Gradient descent with momentum (adaptive learning rate) back propagation. The proposed algorithm was trained on ORL (Olivetti Research Laboratory) database with 5 training images. The empirical results provide the accuracy of 94.50%, 93.00% and 90.25% for 20, 30 and 40 subjects respectively, with time delay of 0.0934 sec per image.
Abstract: This paper presents a web based remote access
microcontroller laboratory. Because of accelerated development in
electronics and computer technologies, microcontroller-based devices
and appliances are found in all aspects of our daily life. Before the
implementation of remote access microcontroller laboratory an
experiment set is developed by teaching staff for training
microcontrollers. Requirement of technical teaching and industrial
applications are considered when experiment set is designed.
Students can make the experiments by connecting to the experiment
set which is connected to the computer that set as the web server. The
students can program the microcontroller, can control digital and
analog inputs and can observe experiment. Laboratory experiment
web page can be accessed via www.elab.aku.edu.tr address.
Abstract: An additive fuzzy system comprising m rules with
n inputs and p outputs in each rule has at least t m(2n + 2 p + 1)
parameters needing to be tuned. The system consists of a large
number of if-then fuzzy rules and takes a long time to tune its
parameters especially in the case of a large amount of training data
samples. In this paper, a new learning strategy is investigated to cope
with this obstacle. Parameters that tend toward constant values at the
learning process are initially fixed and they are not tuned till the end
of the learning time. Experiments based on applications of the
additive fuzzy system in function approximation demonstrate that the
proposed approach reduces the learning time and hence improves
convergence speed considerably.
Abstract: In this paper, a new learning approach for network
intrusion detection using naïve Bayesian classifier and ID3 algorithm
is presented, which identifies effective attributes from the training
dataset, calculates the conditional probabilities for the best attribute
values, and then correctly classifies all the examples of training and
testing dataset. Most of the current intrusion detection datasets are
dynamic, complex and contain large number of attributes. Some of
the attributes may be redundant or contribute little for detection
making. It has been successfully tested that significant attribute
selection is important to design a real world intrusion detection
systems (IDS). The purpose of this study is to identify effective
attributes from the training dataset to build a classifier for network
intrusion detection using data mining algorithms. The experimental
results on KDD99 benchmark intrusion detection dataset demonstrate
that this new approach achieves high classification rates and reduce
false positives using limited computational resources.
Abstract: This paper presents the results of the authors in designing, experimenting, assessing and transferring an innovative approach to energy education in secondary schools, aimed to enhance the quality of learning in terms of didactic curricula and pedagogic methods. The training is online delivered to youngsters via e-Books and portals specially designed for this purpose or by learning by doing via interactive games. An online educational methodology is available teachers.
Abstract: Nowadays the control of stator voltage at a constant frequency is one of the traditional and low expense methods in order to control the speed of induction motors near its nominal speed. The torque of induction motor is a nonlinear function of the firing angle, phase angle and speed. In this paper the speed control of induction motor regarding various load torque and under different conditions will be investigated based on a fuzzy controller with inverse training.
Abstract: The design of a pattern classifier includes an attempt
to select, among a set of possible features, a minimum subset of
weakly correlated features that better discriminate the pattern classes.
This is usually a difficult task in practice, normally requiring the
application of heuristic knowledge about the specific problem
domain. The selection and quality of the features representing each
pattern have a considerable bearing on the success of subsequent
pattern classification. Feature extraction is the process of deriving
new features from the original features in order to reduce the cost of
feature measurement, increase classifier efficiency, and allow higher
classification accuracy. Many current feature extraction techniques
involve linear transformations of the original pattern vectors to new
vectors of lower dimensionality. While this is useful for data
visualization and increasing classification efficiency, it does not
necessarily reduce the number of features that must be measured
since each new feature may be a linear combination of all of the
features in the original pattern vector. In this paper a new approach is
presented to feature extraction in which feature selection, feature
extraction, and classifier training are performed simultaneously using
a genetic algorithm. In this approach each feature value is first
normalized by a linear equation, then scaled by the associated weight
prior to training, testing, and classification. A knn classifier is used to
evaluate each set of feature weights. The genetic algorithm optimizes
a vector of feature weights, which are used to scale the individual
features in the original pattern vectors in either a linear or a nonlinear
fashion. By this approach, the number of features used in classifying
can be finely reduced.
Abstract: Programmable logic controllers are the main controllers in the today's industries; they are used for several applications in industrial control systems and there are lots of examples exist from the PLC applications in industries especially in big companies and plants such as refineries, power plants, petrochemical companies, steel companies, and food and production companies. In the PLCs there are some functions in the function library in software that can be used in PLC programs as basic program elements. The aim of this project are introducing and implementing a new function block of a neural network to the function library of PLC. This block can be applied for some control applications or nonlinear functions calculations after it has been trained for these applications. The implemented neural network is a Perceptron neural network with three layers, three input nodes and one output node. The block can be used in manual or automatic mode. In this paper the structure of the implemented function block, the parameters and the training method of the network are presented by considering the especial method of PLC programming and its complexities. Finally the application of the new block is compared with a classic simulated block and the results are presented.
Abstract: The Norwegian Military Academy (Army) has
initiated a project with the main ambition to explore possible avenues
to enhancing operational effectiveness through an increased use of
simulation-based training and exercises. Within a cost/benefit
framework, we discuss opportunities and limitations of vertical and
horizontal integration of the existing tactical training system. Vertical
integration implies expanding the existing training system to span the
full range of training from tactical level (platoon, company) to
command and staff level (battalion, brigade). Horizontal integration
means including other domains than army tactics and staff
procedures in the training, such as military ethics, foreign languages,
leadership and decision making. We discuss each of the integration
options with respect to purpose and content of training, "best
practice" for organising and conducting simulation-based training,
and suggest how to evaluate training procedures and measure
learning outcomes. We conclude by giving guidelines towards further
explorative work and possible implementation.
Abstract: In this paper, we propose a robust face relighting
technique by using spherical space properties. The proposed method
is done for reducing the illumination effects on face recognition.
Given a single 2D face image, we relight the face object by
extracting the nine spherical harmonic bases and the face spherical
illumination coefficients. First, an internal training illumination
database is generated by computing face albedo and face normal
from 2D images under different lighting conditions. Based on the
generated database, we analyze the target face pixels and compare
them with the training bootstrap by using pre-generated tiles. In this
work, practical real time processing speed and small image size were
considered when designing the framework. In contrast to other works,
our technique requires no 3D face models for the training process
and takes a single 2D image as an input. Experimental results on
publicly available databases show that the proposed technique works
well under severe lighting conditions with significant improvements
on the face recognition rates.
Abstract: We investigated the response of testosterone (T),
growth hormone (GH), cortisol (C), steroid hormone binding
globulin (SHBG), insulin-like growth factor (IGF-1), insulin-like
growth factor binding protein-3 (IGFBP-3), and some anaboliccatabolic
indexes, i.e.: T/C, T/SHBG, and IGF-1/IGFBP-3 to
maximal exercise in endurance-trained athletes (TREN) and
untrained subjects (CG). The baseline concentration of IGF-1 was
higher in athletes (TREN) when compared to the CG (p
Abstract: In determining the electromagnetic properties of
magnetic materials, hysteresis modeling is of high importance. Many
models are available to investigate those characteristics but they tend
to be complex and difficult to implement. In this paper a new
qualitative hysteresis model for ferromagnetic core is presented,
based on the function approximation capabilities of adaptive neuro
fuzzy inference system (ANFIS). The proposed ANFIS model
combined the neural network adaptive capabilities and the fuzzy
logic qualitative approach can restored the hysteresis curve with a
little RMS error. The model accuracy is good and can be easily
adapted to the requirements of the application by extending or
reducing the network training set and thus the required amount of
measurement data.
Abstract: In this paper we propose an enhanced equalization technique for multi-carrier code division multiple access (MC-CDMA). This method is based on the control of Equal Gain Combining (EGC) technique. Indeed, we introduce a new level changer to the EGC equalizer in order to adapt the equalization parameters to the channel coefficients. The optimal equalization level is, first, determined by channel training. The new approach reduces drastically the mutliuser interferences caused by interferes, without increasing the noise power. To compare the performances of the proposed equalizer, the theoretical analysis and numerical performances are given.
Abstract: Object: Review recent publications of patient safety
culture to investigate the relationship between leadership behavior,
safety culture, and safety performance in the healthcare industry.
Method: This study is a cross-sectional study, 350 questionnaires were
mailed to hospital workers with 195 valid responses obtained, and a
55.7% valid response rate. Confirmatory factor analysis (CFA) was
carried out to test the factor structure and determine if the composite
reliability was significant with a factor loading of >0.5, resulting in an
acceptable model fit. Results: Through the analysis of One-way
ANOVA, the results showed that physicians significantly have more
negative patient safety culture perceptions and safety performance
perceptions than non- physicians. Conclusions: The path analysis
results show that leadership behavior affects safety culture and safety
performance in the health care industry. Safety performance was
affected and improved with contingency leadership and a positive
patient safety organization culture. The study suggests improving
safety performance by providing a well-managed system that
includes: consideration of leadership, hospital worker training
courses, and a solid safety reporting system.
Abstract: This paper presents an approach which is based on the
use of supervised feed forward neural network, namely multilayer
perceptron (MLP) neural network and finite element method (FEM)
to solve the inverse problem of parameters identification. The
approach is used to identify unknown parameters of ferromagnetic
materials. The methodology used in this study consists in the
simulation of a large number of parameters in a material under test,
using the finite element method (FEM). Both variations in relative
magnetic permeability and electrical conductivity of the material
under test are considered. Then, the obtained results are used to
generate a set of vectors for the training of MLP neural network.
Finally, the obtained neural network is used to evaluate a group of
new materials, simulated by the FEM, but not belonging to the
original dataset. Noisy data, added to the probe measurements is used
to enhance the robustness of the method. The reached results
demonstrate the efficiency of the proposed approach, and encourage
future works on this subject.
Abstract: In this paper is shown that the probability-statistic methods application, especially at the early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence is considered the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods. Training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. Thus for GTE technical condition more adequate model making are analysed dynamics of skewness and kurtosis coefficients' changes. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes- dynamics of GTE work parameters allows to draw conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values- changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. For checking of models adequacy is considered the Fuzzy Multiple Correlation Coefficient of Fuzzy Multiple Regression. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stage-bystage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine temperature condition was made.
Abstract: Support vector machines (SVMs) are considered to be
the best machine learning algorithms for minimizing the predictive
probability of misclassification. However, their drawback is that for
large data sets the computation of the optimal decision boundary is a
time consuming function of the size of the training set. Hence several
methods have been proposed to speed up the SVM algorithm. Here
three methods used to speed up the computation of the SVM
classifiers are compared experimentally using a musical genre
classification problem. The simplest method pre-selects a random
sample of the data before the application of the SVM algorithm. Two
additional methods use proximity graphs to pre-select data that are
near the decision boundary. One uses k-Nearest Neighbor graphs and
the other Relative Neighborhood Graphs to accomplish the task.
Abstract: The quick training algorithms and accurate solution
procedure for incremental learning aim at improving the efficiency of
training of SVR, whereas there are some disadvantages for them, i.e.
the nonconvergence of the formers for changeable training set and
the inefficiency of the latter for a massive dataset. In order to handle
the problems, a new training algorithm for a changeable training
set, named Approximation Incremental Training Algorithm (AITA),
was proposed. This paper explored the reason of nonconvergence
theoretically and discussed the realization of AITA, and finally
demonstrated the benefits of AITA both on precision and efficiency.