Abstract: Decision tree algorithms have very important place at
classification model of data mining. In literature, algorithms use
entropy concept or gini index to form the tree. The shape of the
classes and their closeness to each other some of the factors that
affect the performance of the algorithm. In this paper we introduce a
new decision tree algorithm which employs data (attribute) folding
method and variation of the class variables over the branches to be
created. A comparative performance analysis has been held between
the proposed algorithm and C4.5.
Abstract: AAM has been successfully applied to face alignment,
but its performance is very sensitive to initial values. In case the initial
values are a little far distant from the global optimum values, there
exists a pretty good possibility that AAM-based face alignment may
converge to a local minimum. In this paper, we propose a progressive
AAM-based face alignment algorithm which first finds the feature
parameter vector fitting the inner facial feature points of the face and
later localize the feature points of the whole face using the first
information. The proposed progressive AAM-based face alignment
algorithm utilizes the fact that the feature points of the inner part of the
face are less variant and less affected by the background surrounding
the face than those of the outer part (like the chin contour). The
proposed algorithm consists of two stages: modeling and relation
derivation stage and fitting stage. Modeling and relation derivation
stage first needs to construct two AAM models: the inner face AAM
model and the whole face AAM model and then derive relation matrix
between the inner face AAM parameter vector and the whole face
AAM model parameter vector. In the fitting stage, the proposed
algorithm aligns face progressively through two phases. In the first
phase, the proposed algorithm will find the feature parameter vector
fitting the inner facial AAM model into a new input face image, and
then in the second phase it localizes the whole facial feature points of
the new input face image based on the whole face AAM model using
the initial parameter vector estimated from using the inner feature
parameter vector obtained in the first phase and the relation matrix
obtained in the first stage. Through experiments, it is verified that the
proposed progressive AAM-based face alignment algorithm is more
robust with respect to pose, illumination, and face background than the
conventional basic AAM-based face alignment algorithm.
Abstract: In this work we present an efficient approach for face
recognition in the infrared spectrum. In the proposed approach
physiological features are extracted from thermal images in order to
build a unique thermal faceprint. Then, a distance transform is used
to get an invariant representation for face recognition. The obtained
physiological features are related to the distribution of blood vessels
under the face skin. This blood network is unique to each individual
and can be used in infrared face recognition. The obtained results are
promising and show the effectiveness of the proposed scheme.
Abstract: In this study, a fuzzy integrated logical forecasting method (FILF) is extended for multi-variate systems by using a vector autoregressive model. Fuzzy time series forecasting (FTSF) method was recently introduced by Song and Chissom [1]-[2] after that Chen improved the FTSF method. Rather than the existing literature, the proposed model is not only compared with the previous FTS models, but also with the conventional time series methods such as the classical vector autoregressive model. The cluster optimization is based on the C-means clustering method. An empirical study is performed for the prediction of the chartering rates of a group of dry bulk cargo ships. The root mean squared error (RMSE) metric is used for the comparing of results of methods and the proposed method has superiority than both traditional FTS methods and also the classical time series methods.
Abstract: A triangular fin with variable fin base thickness is analyzed and optimized using a two-dimensional analytical method. The influence of fin base height and fin base thickness on the temperature in the fin is listed. For the fixed fin volumes, the maximum heat loss, the corresponding optimum fin effectiveness, fin base height and fin tip length as a function of the fin base thickness, convection characteristic number and dimensionless fin volume are represented. One of the results shows that the optimum heat loss increases whereas the corresponding optimum fin effectiveness decreases with the increase of fin volume.
Abstract: The model of neural networks on the small-world
topology, with metric (local and random connectivity) is investigated.
The synaptic weights are random, driving the network towards a
chaotic state for the neural activity. An ordered macroscopic neuron
state is induced by a bias in the network connections. When the
connections are mainly local, the network emulates a block-like
structure. It is found that the topology and the bias compete to
influence the network to evolve into a global or a block activity
ordering, according to the initial conditions.
Abstract: One of the most important power quality issues is voltage flicker. Nowadays this issue also impacts the power system all over the world. The fact of the matter is that the more and the larger capacity of wind generator has been installed. Under unstable wind power situation, the variation of output current and voltage have caused trouble to voltage flicker. Hence, the major purpose of this study is to analyze the impact of wind generator on voltage flicker of power system. First of all, digital simulation and analysis are carried out based on wind generator operating under various system short circuit capacity, impedance angle, loading, and power factor of load. The simulation results have been confirmed by field measurements.
Abstract: In recent years, the number of the cases of information
leaks is increasing. Companies and Research Institutions make various
actions against information thefts and security accidents. One of the
actions is adoption of the crime prevention system, including the
monitoring system by surveillance cameras. In order to solve
difficulties of multiple cameras monitoring, we develop the automatic
human tracking system using mobile agents through multiple
surveillance cameras to track target persons. In this paper, we develop
the monitor which confirms mobile agents tracing target persons, and
the simulator of video picture analysis to construct the tracking
algorithm.
Abstract: In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods.
Abstract: To reveal the temperature field distribution of disc
brake in downward belt conveyor, mathematical models of heat
transfer for disc brake were established combined with heat transfer
theory. Then, the simulation process was stated in detail and the
temperature field of disc brake under conditions of dynamic speed and
dynamic braking torque was numerically simulated by using ANSYS
software. Finally the distribution and variation laws of temperature
field in the braking process were analyzed. Results indicate that the
maximum surface temperature occurs at a time before the brake end
and there exist large temperature gradients in both radial and axial
directions, while it is relatively small in the circumferential direction.
Abstract: Scale Invariant Feature Transform (SIFT) has been
widely applied, but extracting SIFT feature is complicated and
time-consuming. In this paper, to meet the demand of the real-time
applications, SIFT is parallelized and optimized on cluster system,
which is named pSIFT. Redundancy storage and communication are
used for boundary data to improve the performance, and before
representation of feature descriptor, data reallocation is adopted to
keep load balance in pSIFT. Experimental results show that pSIFT
achieves good speedup and scalability.
Abstract: The objective of this research was to study the
influence of marketing mix on customers purchasing behavior. A
total of 397 respondents were collected from customers who were the
patronages of the Chatuchak Plaza market. A questionnaire was
utilized as a tool to collect data. Statistics utilized in this research
included frequency, percentage, mean, standard deviation, and
multiple regression analysis. Data were analyzed by using Statistical
Package for the Social Sciences. The findings revealed that the
majority of respondents were male with the age between 25-34 years
old, hold undergraduate degree, married and stay together. The
average income of respondents was between 10,001-20,000 baht. In
terms of occupation, the majority worked for private companies. The
research analysis disclosed that there were three variables of
marketing mix which included price (X2), place (X3), and product
(X1) which had an influence on the frequency of customer
purchasing. These three variables can predict a purchase about 30
percent of the time by using the equation; Y1 = 6.851 + .921(X2) +
.949(X3) + .591(X1). It also found that in terms of marketing mixed,
there were two variables had an influence on the amount of customer
purchasing which were physical characteristic (X6), and the process
(X7). These two variables are 17 percent predictive of a purchasing
by using the equation: Y2 = 2276.88 + 2980.97(X6) + 2188.09(X7).
Abstract: This paper presents a novel method for inferring the
odor based on neural activities observed from rats- main olfactory
bulbs. Multi-channel extra-cellular single unit recordings were done
by micro-wire electrodes (tungsten, 50μm, 32 channels) implanted in
the mitral/tufted cell layers of the main olfactory bulb of anesthetized
rats to obtain neural responses to various odors. Neural response
as a key feature was measured by substraction of neural firing rate
before stimulus from after. For odor inference, we have developed a
decoding method based on the maximum likelihood (ML) estimation.
The results have shown that the average decoding accuracy is about
100.0%, 96.0%, 84.0%, and 100.0% with four rats, respectively. This
work has profound implications for a novel brain-machine interface
system for odor inference.
Abstract: The genus Fumaria L. (Papaveraceae) in Iran
comprises 8 species with a vast medicinal use in Asian folk
medicine. These herbs are considered to be useful in the
treatment of gastrointestinal disease and skin disorders.
Antioxidant activities of alkaloids and phenolic extracts of
these species had been studied previously. These species are:
F. officinalis, F. parviflora, F. asepala, F. densiflora, F.
schleicheri, F. vaillantii and F. indica. More than 50
populations of Fumaria species were sampled from nature. In
this study different fatty acids are extracted. Their picks were
recorded by GC technique. This species contain some kind of
fatty acids with antioxidant effects. A part of these lipids are
phospholipids. As these are unsaturated fatty acids they may
have industrial use as natural additive to cosmetics, dermal
and oral medicines. The presences of different materials are
discussed. Our studies for antioxidant effects of these
substances are continued.
Abstract: The aim of this study is to test the “work values"
inventory developed by Tevruz and Turgut and to utilize the concept
in a model, which aims to create a greater understanding of the work
experience. In the study multiple effects of work values, work-value
congruence and work centrality on organizational citizenship
behavior are examined. In this respect, it is hypothesized that work
values and work-value congruence predict organizational citizenship
behavior through work centrality. Work-goal congruence test, Tevruz
and Turgut-s work values inventory are administered along with
Kanungo-s work centrality and Podsakoff et al.-s [47] organizational
citizenship behavior test to employees working in Turkish SME-s.
The study validated that Tevruz and Turgut-s work values inventory
and the work-value congruence test were reliable and could be used
for future research. The study revealed the mediating role of work
centrality only for the relationship of work values and the
responsibility dimension of citizenship behavior. Most important, this
study brought in an important concept, work-value congruence,
which enables a better understanding of work values and their
relation to various attitudinal variables.
Abstract: This research explores on the development of the structure of Carbon Credit Registry System those accords to the need of future events in Thailand. This research also explores the big picture of every connected system by referring to the design of each system, the Data Flow Diagram, and the design in term of the system-s data using DES standard. The purpose of this paper is to show how to design the model of each system. Furthermore, this paper can serve as guideline for designing an appropriate Carbon Credit Registry System.
Abstract: The method of gait identification based on the nearest neighbor classification technique with motion similarity assessment by the dynamic time warping is proposed. The model based kinematic motion data, represented by the joints rotations coded by Euler angles and unit quaternions is used. The different pose distance functions in Euler angles and quaternion spaces are considered. To evaluate individual features of the subsequent joints movements during gait cycle, joint selection is carried out. To examine proposed approach database containing 353 gaits of 25 humans collected in motion capture laboratory is used. The obtained results are promising. The classifications, which takes into consideration all joints has accuracy over 91%. Only analysis of movements of hip joints allows to correctly identify gaits with almost 80% precision.
Abstract: The performance of high-resolution schemes is investigated for unsteady, inviscid and compressible multiphase flows. An Eulerian diffuse interface approach has been chosen for the simulation of multicomponent flow problems. The reduced fiveequation and seven equation models are used with HLL and HLLC approximation. The authors demonstrated the advantages and disadvantages of both seven equations and five equations models studying their performance with HLL and HLLC algorithms on simple test case. The seven equation model is based on two pressure, two velocity concept of Baer–Nunziato [10], while five equation model is based on the mixture velocity and pressure. The numerical evaluations of two variants of Riemann solvers have been conducted for the classical one-dimensional air-water shock tube and compared with analytical solution for error analysis.
Abstract: Determining depth of anesthesia is a challenging problem
in the context of biomedical signal processing. Various methods
have been suggested to determine a quantitative index as depth of
anesthesia, but most of these methods suffer from high sensitivity
during the surgery. A novel method based on energy scattering of
samples in the wavelet domain is suggested to represent the basic
content of electroencephalogram (EEG) signal. In this method, first
EEG signal is decomposed into different sub-bands, then samples
are squared and energy of samples sequence is constructed through
each scale and time, which is normalized and finally entropy of the
resulted sequences is suggested as a reliable index. Empirical Results
showed that applying the proposed method to the EEG signals can
classify the awake, moderate and deep anesthesia states similar to
BIS.
Abstract: This paper presents an adaptive motion estimator
that can be dynamically reconfigured by the best algorithm
depending on the variation of the video nature during the lifetime
of an application under running. The 4 Step Search (4SS) and the
Gradient Search (GS) algorithms are integrated in the estimator in
order to be used in the case of rapid and slow video sequences
respectively. The Full Search Block Matching (FSBM) algorithm
has been also integrated in order to be used in the case of the
video sequences which are not real time oriented.
In order to efficiently reduce the computational cost while
achieving better visual quality with low cost power, the proposed
motion estimator is based on a Variable Block Size (VBS) scheme
that uses only the 16x16, 16x8, 8x16 and 8x8 modes.
Experimental results show that the adaptive motion estimator
allows better results in term of Peak Signal to Noise Ratio
(PSNR), computational cost, FPGA occupied area, and dissipated
power relatively to the most popular variable block size schemes
presented in the literature.