Abstract: Salient points are frequently used to represent local
properties of the image in content-based image retrieval. In this paper,
we present a reduction algorithm that extracts the local most salient
points such that they not only give a satisfying representation of an
image, but also make the image retrieval process efficiently. This
algorithm recursively reduces the continuous point set by their
corresponding saliency values under a top-down approach. The
resulting salient points are evaluated with an image retrieval system
using Hausdoff distance. In this experiment, it shows that our method
is robust and the extracted salient points provide better retrieval
performance comparing with other point detectors.
Abstract: With high speed vessels getting ever more sophisti-cated, travelling at higher and higher speeds and operating in With high speed vessels getting ever more sophisticated,
travelling at higher and higher speeds and operating in areas of
high maritime traffic density, training becomes of the highest priority
to ensure that safety levels are maintained, and risks are adequately
mitigated. Training onboard the actual craft on the actual route still
remains the most effective way for crews to gain experience. However,
operational experience and incidents during the last 10 years
demonstrate the need for supplementary training whether in the area
of simulation or man to man, man/ machine interaction. Training and
familiarisation of the crew is the most important aspect in preventing
incidents. The use of simulator, computer and web based training
systems in conjunction with onboard training focusing on critical
situations will improve the man machine interaction and thereby
reduce the risk of accidents. Today, both ship simulator and bridge
teamwork courses are now becoming the norm in order to improve
further emergency response and crisis management skills. One of the
main causes of accidents is the human factor. An efficient way to
reduce human errors is to provide high-quality training to the personnel
and to select the navigators carefully.areas of high maritime traffic density, training becomes of the highest priority to ensure that safety levels are maintained, and risks are adequately mitigated. Training onboard the actual craft on the actual route still remains the most effective way for crews to gain experience. How-ever, operational experience and incidents during the last 10 years demonstrate the need for supplementary training whether in the area of simulation or man to man, man/ machine interaction. Training and familiarisation of the crew is the most important aspect in preventing incidents. The use of simulator, computer and web based training systems in conjunction with onboard training focusing on critical situations will improve the man machine interaction and thereby reduce the risk of accidents. Today, both ship simulator and bridge teamwork courses are now becoming the norm in order to improve further emergency response and crisis management skills. One of the main causes of accidents is the human factor. An efficient way to reduce human errors is to provide high-quality training to the person-nel and to select the navigators carefully. KeywordsCBT - WBT systems, Human factors.
Abstract: This paper is concerned with studying the forgetting factor of the recursive least square (RLS). A new dynamic forgetting factor (DFF) for RLS algorithm is presented. The proposed DFF-RLS is compared to other methods. Better performance at convergence and tracking of noisy chirp sinusoid is achieved. The control of the forgetting factor at DFF-RLS is based on the gradient of inverse correlation matrix. Compared with the gradient of mean square error algorithm, the proposed approach provides faster tracking and smaller mean square error. In low signal-to-noise ratios, the performance of the proposed method is superior to other approaches.
Abstract: In most wheat growing moderate regions and
especially in the north of Iran climate, is affected grain filling by
several physical and abiotic stresses. In this region, grain filling often
occurs when temperatures are increasing and moisture supply is
decreasing. The experiment was designed in RCBD with split plot
arrangements with four replications. Four irrigation treatments
included (I0) no irrigation (check); (I1) one irrigation (50 mm) at
heading stage; (I2) two irrigation (100 mm) at heading and anthesis
stage; and (I3) three irrigation (150 mm) at heading, anthesis and
early grain filling growth stage, two wheat cultivars (Milan and
Shanghai) were cultured in the experiment. Totally raining was 453
mm during the growth season. The result indicated that biological
yield, grain yield and harvest index were significantly affected by
irrigation levels. I3 treatment produced more tillers number in m2,
fertile tillers number in m2, harvest index and biological yield. Milan
produced more tillers number in m2, fertile tillers in m2, while
Shanghai produced heavier tillers and grain 1000 weight. Plant height
was significant in wheat varieties while were not statistically
significant in irrigation levels. Milan produced more grain yield,
harvest index and biological yield. Grain yield shown that I1, I2, and
I3 produced increasing of 5228 (21%), 5460 (27%) and 5670 (29%)
kg ha-1, respectively. There was an interaction of irrigation and
cultivar on grain yields. In the absence of the irrigation reduced grain
1000 weight from 45 to 40 g. No irrigation reduced soil moisture
extraction during the grain filling stage. Current assimilation as a
source of carbon for grain filling depends on the light intercepting
viable green surfaces of the plant after anthesis that due to natural
senescence and the effect of various stresses. At the same time the
demand by the growing grain is increasing. It is concluded from
research work that wheat crop irrigated Milan cultivar could increase
the grain yield in comparison with Shanghai cultivar. Although, the
grain yield of Shanghai under irrigation was slightly lower than
Milan. This grain yield also was related to weather condition, sowing
date, plant density and location conditions and management of
fertilizers, because there was not significant difference in biological
and straw yield. The best result was produced by I1 treatment. I2 and
I3 treatments were not significantly difference with I1 treatment.
Grain yield of I1 indicated that wheat is under soil moisture
deficiency. Therefore, I1 irrigation was better than I0.
Abstract: Voltage collapse is instability of heavily loaded electric
power systems that cause to declining voltages and blackout. Power
systems are predicated to become more heavily loaded in the future
decade as the demand for electric power rises while economic and
environmental concerns limit the construction of new transmission
and generation capacity. Heavily loaded power systems are closer to
their stability limits and voltage collapse blackouts will occur if
suitable monitoring and control measures are not taken. To control
transmission lines, it can be used from FACTS devices.
In this paper Harmony search algorithm (HSA) and Genetic
Algorithm (GA) have applied to determine optimal location of
FACTS devices in a power system to improve power system stability.
Three types of FACTS devices (TCPAT, UPFS, and SVC) have been
introduced. Bus under voltage has been solved by controlling reactive
power of shunt compensator. Also a combined series-shunt
compensators has been also used to control transmission power flow
and bus voltage simultaneously.
Different scenarios have been considered. First TCPAT, UPFS, and
SVC are placed solely in transmission lines and indices have been
calculated. Then two types of above controller try to improve
parameters randomly. The last scenario tries to make better voltage
stability index and losses by implementation of three types controller
simultaneously. These scenarios are executed on typical 34-bus test
system and yields efficiency in improvement of voltage profile and
reduction of power losses; it also may permit an increase in power
transfer capacity, maximum loading, and voltage stability margin.
Abstract: New generation mobile communication networks have
the ability of supporting triple play. In order that, Orthogonal
Frequency Division Multiplexing (OFDM) access techniques have
been chosen to enlarge the system ability for high data rates
networks. Many of cross-layer modeling and optimization schemes
for Quality of Service (QoS) and capacity of downlink multiuser
OFDM system were proposed. In this paper, the Maximum Weighted
Capacity (MWC) based resource allocation at the Physical (PHY)
layer is used. This resource allocation scheme provides a much better
QoS than the previous resource allocation schemes, while
maintaining the highest or nearly highest capacity and costing similar
complexity. In addition, the Delay Satisfaction (DS) scheduling at the
Medium Access Control (MAC) layer, which allows more than one
connection to be served in each slot is used. This scheduling
technique is more efficient than conventional scheduling to
investigate both of the number of users as well as the number of
subcarriers against system capacity. The system will be optimized for
different operational environments: the outdoor deployment scenarios
as well as the indoor deployment scenarios are investigated and also
for different channel models. In addition, effective capacity approach
[1] is used not only for providing QoS for different mobile users, but
also to increase the total wireless network's throughput.
Abstract: In this work, a Modified Functional Link Artificial
Neural Network (M-FLANN) is proposed which is simpler than a
Multilayer Perceptron (MLP) and improves upon the universal
approximation capability of Functional Link Artificial Neural
Network (FLANN). MLP and its variants: Direct Linear Feedthrough
Artificial Neural Network (DLFANN), FLANN and
M-FLANN have been implemented to model a simulated Water Bath
System and a Continually Stirred Tank Heater (CSTH). Their
convergence speed and generalization ability have been compared.
The networks have been tested for their interpolation and
extrapolation capability using noise-free and noisy data. The results
show that M-FLANN which is computationally cheap, performs
better and has greater generalization ability than other networks
considered in the work.
Abstract: In this paper, a semi-fragile watermarking scheme is proposed for color image authentication. In this particular scheme, the color image is first transformed from RGB to YST color space, suitable for watermarking the color media. Each channel is divided into 4×4 non-overlapping blocks and its each 2×2 sub-block is selected. The embedding space is created by setting the two LSBs of selected sub-block to zero, which will hold the authentication and recovery information. For verification of work authentication and parity bits denoted by 'a' & 'p' are computed for each 2×2 subblock. For recovery, intensity mean of each 2×2 sub-block is computed and encoded upto six to eight bits depending upon the channel selection. The size of sub-block is important for correct localization and fast computation. For watermark distribution 2DTorus Automorphism is implemented using a private key to have a secure mapping of blocks. The perceptibility of watermarked image is quite reasonable both subjectively and objectively. Our scheme is oblivious, correctly localizes the tampering and able to recovery the original work with probability of near one.
Abstract: In the frame of the European Union project entitled EU-Families and Adolescents Quit Tobacco (www.eufaqt.eu) focus group analysis has been carried out in Hungary to acquire qualitative information on attitudes towards smoking in groups of adolescents, parents and educators, respectively. It rendered to identify methods for smoking prevention/ intervention with family approach. The results explored the role of the family in smoking behaviour. Teachers do not feel responsibility in prevention or cessation of smoking. Adolescents are not aware of the addictive effect of the cigarette. Water pipe is popular among adolescent, therefore spreading of more information needed on the harmful effects of water pipe. We outlined the requirement for professionals to provide interventions. Partnership of EU-FAQT project has worked out antismoking interventions for adolescents and their families conducted by psychologists to ensure skill development to prevent and quit tobacco.
Abstract: The Minimal Residual (MR) is modified for adaptive
filtering application. Three forms of MR based algorithm are
presented: i) the low complexity SPCG, ii) MREDSI, and iii)
MREDSII. The low complexity is a reduced complexity version of a
previously proposed SPCG algorithm. Approximations introduced
reduce the algorithm to an LMS type algorithm, but, maintain the
superior convergence of the SPCG algorithm. Both MREDSI and
MREDSII are MR based methods with Euclidean direction of search.
The choice of Euclidean directions is shown via simulation to give
better misadjustment compared to their gradient search counterparts.
Abstract: In this study, multiwall carbon nanotubes (MWNTs)
were modified with nitric acid chemically and by dielectric barrier
discharge (DBD) plasma in an oxygen-based atmosphere. Used
carbon nanotubes (CNTs) were prepared by chemical vapour
deposition (CVD) floating catalyst method. For removing amorphous
carbon and metal catalyst, MWNTs were exposed to dry air and
washed with hydrochloric acid. Heating purified CNTs under helium
atmosphere caused elimination of acidic functional groups. Fourier
transformed infrared spectroscopy (FTIR) shows formation of
oxygen containing groups such as C=O and COOH. Brunauer,
Emmett, Teller (BET) analysis revealed that functionalization causes
generation of defects on the sidewalls and opening of the ends of
CNTs. Results of temperature-programmed desorption (TPD) and gas
chromatography(GC) indicate that nitric acid treatment create more
acidic groups than plasma treatment.
Abstract: According to the governmental data, the cases of oral
cancers doubled in the past 10 years. This had brought heavy burden to
the patients- family, the society, and the country. The literature
generally evidenced the betel nut contained particular chemicals that
can cause oral cancers. Research in Taiwan had also proofed that 90
percent of oral cancer patients had experience of betel nut chewing. It
is thus important to educate the betel-nut hobbyists to cease such a
hazardous behavior. A program was then organized to establish
several training classes across different areas specific to help ceasing
this particular habit. Purpose of this research was to explore the
attitude and intention toward ceasing betel-nut chewing before and
after attending the training classes. 50 samples were taken from a
ceasing class with average age at 45 years old with high school
education (54%). 74% of the respondents were male in service or
agricultural industries. Experiences in betel-nut chewing were 5-20
years with a dose of 1-20 pieces per day. The data had shown that 60%
of the respondents had cigarette smoking habit, and 30% of the
respondents were concurrently alcoholic dependent. Research results
indicated that the attitude, intentions, and the knowledge on oral
cancers were found significant different between before and after
attendance. This provided evidence for the effectiveness of the training
class. However, we do not perform follow-up after the class.
Noteworthy is the test result also shown that participants who were
drivers as occupation, or habitual smokers or alcoholic dependents
would be less willing to quit the betel-nut chewing. The test results
indicated as well that the educational levels and the type of occupation
may have significant impacts on an individual-s decisions in taking
betel-nut or substance abuse.
Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. In this paper, we
investigated three approaches to build a meta-classifier in order to
increase the classification accuracy. The basic idea is to learn a metaclassifier
to optimally select the best component classifier for each
data point. The experimental results show that combining classifiers
can significantly improve the accuracy of classification and that our
meta-classification strategy gives better results than each individual
classifier. For 7083 Reuters text documents we obtained a
classification accuracies up to 92.04%.
Abstract: This paper presents a new steganography approach suitable for Arabic texts. It can be classified under steganography feature coding methods. The approach hides secret information bits within the letters benefiting from their inherited points. To note the specific letters holding secret bits, the scheme considers the two features, the existence of the points in the letters and the redundant Arabic extension character. We use the pointed letters with extension to hold the secret bit 'one' and the un-pointed letters with extension to hold 'zero'. This steganography technique is found attractive to other languages having similar texts to Arabic such as Persian and Urdu.
Abstract: A conventional binding method for low power in a
high-level synthesis mainly focuses on finding an optimal binding for
an assumed input data, and obtains only one binding table. In this
paper, we show that a binding method which uses multiple binding
tables gets better solution compared with the conventional methods
which use a single binding table, and propose a dynamic bus binding
scheme for low power using multiple binding tables. The proposed
method finds multiple binding tables for the proper partitions of an
input data, and switches binding tables dynamically to produce the
minimum total switching activity. Experimental result shows that the
proposed method obtains a binding solution having 12.6-28.9%
smaller total switching activity compared with the conventional
methods.
Abstract: Statistical learning theory was developed by Vapnik. It
is a learning theory based on Vapnik-Chervonenkis dimension. It also
has been used in learning models as good analytical tools. In general, a
learning theory has had several problems. Some of them are local
optima and over-fitting problems. As well, statistical learning theory
has same problems because the kernel type, kernel parameters, and
regularization constant C are determined subjectively by the art of
researchers. So, we propose an evolutionary statistical learning theory
to settle the problems of original statistical learning theory.
Combining evolutionary computing into statistical learning theory,
our theory is constructed. We verify improved performances of an
evolutionary statistical learning theory using data sets from KDD cup.
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: In this paper back-propagation artificial neural network
(BPANN) is employed to predict the deformation of the upsetting
process. To prepare a training set for BPANN, some finite element
simulations were carried out. The input data for the artificial neural
network are a set of parameters generated randomly (aspect ratio d/h,
material properties, temperature and coefficient of friction). The
output data are the coefficient of polynomial that fitted on barreling
curves. Neural network was trained using barreling curves generated
by finite element simulations of the upsetting and the corresponding
material parameters. This technique was tested for three different
specimens and can be successfully employed to predict the
deformation of the upsetting process
Abstract: Foundation of tower crane serves to ensure stability
against vertical and horizontal forces. If foundation stress is not
sufficient, tower crane may be subject to overturning, shearing or
foundation settlement. Therefore, engineering review of stable support
is a highly critical part of foundation design. However, there are not
many professionals who can conduct engineering review of tower
crane foundation and, if any, they have information only on a small
number of cranes in which they have hands-on experience. It is also
customary to rely on empirical knowledge and tower crane renter-s
recommendations rather than designing foundation on the basis of
engineering knowledge. Therefore, a foundation design automation
system considering not only lifting conditions but also overturning
risk, shearing and vertical force may facilitate production of foolproof
foundation design for experts and enable even non-experts to utilize
professional knowledge that only experts can access now. This study
proposes Automatic Design Algorithm for the Tower Crane
Foundations considering load and horizontal force.
Abstract: Through the time, the higher education has changed
the learning system since mother tongue to bilingual, and in this new
century has been coming develop a multilingual education. All as
part of globalization process of the countries and the education.
Nevertheless, this change only has been effectively in countries of the
first world, the rest have been lagging. Therefore, these countries
require strengthen their higher education systems through models that
give way to multilingual and bilingual education. In this way, shows
a new model adapted from a systemic form to allow a higher
bilingual and multilingual education in Latin America. This
systematization aims to increase the skills and competencies
student’s, decrease the time learning of a second tongue, add to
multilingualism in the American Latin Universities, also, contribute
to position the region´s countries in a better global status, and
stimulate the development of new research in this area.