Abstract: A method has been developed for preparing load
models for power flow and stability. The load modeling
(LOADMOD) computer software transforms data on load class mix,
composition, and characteristics into the from required for
commonly–used power flow and transient stability simulation
programs. Typical default data have been developed for load
composition and characteristics. This paper defines LOADMOD
software and describes the dynamic and static load modeling
techniques used in this software and results of initial testing for
BAKHTAR power system.
Abstract: The full length mitochondrial small subunit ribosomal
(mt-rns) gene has been characterized for Ophiostoma novo-ulmi
subspecies americana. The gene was also characterized for
Ophiostoma ulmi and a group II intron was noted in the mt-rns gene
of O. ulmi. The insertion in the mt-rns gene is at position S952 and it
is a group IIB1 intron that encodes a double motif LAGLIDADG
homing endonuclease from an open reading frame located within a
loop of domain III. Secondary structure models for the mt-rns RNA
of O. novo-ulmi subsp. americana and O. ulmi were generated to
place the intron within the context of the ribosomal RNA. The in vivo
splicing of the O.ul-mS952 group II intron was confirmed with
reverse transcription-PCR. A survey of 182 strains of Dutch Elm
Diseases causing agents showed that the mS952 intron was absent in
what is considered to be the more aggressive species O. novo-ulmi
but present in strains of the less aggressive O. ulmi. This observation
suggests that the O.ul-mS952 intron can be used as a PCR-based
molecular marker to discriminate between O. ulmi and O. novo-ulmi
subsp. americana.
Abstract: Road rage is an increasingly prevalent expression of
aggression in our society. Its dangers are apparent and understanding
its causes may shed light on preventative measures. This study
involved a fifteen-minute survey administered to 147 undergraduate
students at a North Eastern suburban university. The survey
consisted of a demographics section, questions regarding financial
investment in respondents- vehicles, experience driving, habits of
driving, experiences witnessing role models driving, and an
evaluation of road rage behavior using the Driving Vengeance
Questionnaire. The study found no significant differences in driving
aggression between respondents who were financially invested in
their vehicle compared to those who were not, or between
respondents who drove in heavy traffic hours compared to those who
did not, suggesting internal factors correlate with aggressive driving
habits. The study also found significant differences in driving
aggression between males versus females, those with more points on
their license versus fewer points, and those who witnessed parents
driving aggressively very often versus rarely or never. Additional
studies can investigate how witnessing parents driving aggressively
is related to future driving behaviors.
Abstract: This manuscript presents, palmprint recognition by
combining different texture extraction approaches with high accuracy.
The Region of Interest (ROI) is decomposed into different frequencytime
sub-bands by wavelet transform up-to two levels and only the
approximate image of two levels is selected, which is known as
Approximate Image ROI (AIROI). This AIROI has information of
principal lines of the palm. The Competitive Index is used as the
features of the palmprint, in which six Gabor filters of different
orientations convolve with the palmprint image to extract the orientation
information from the image. The winner-take-all strategy
is used to select dominant orientation for each pixel, which is
known as Competitive Index. Further, PCA is applied to select highly
uncorrelated Competitive Index features, to reduce the dimensions of
the feature vector, and to project the features on Eigen space. The
similarity of two palmprints is measured by the Euclidean distance
metrics. The algorithm is tested on Hong Kong PolyU palmprint
database. Different AIROI of different wavelet filter families are also
tested with the Competitive Index and PCA. AIROI of db7 wavelet
filter achievs Equal Error Rate (EER) of 0.0152% and Genuine
Acceptance Rate (GAR) of 99.67% on the palm database of Hong
Kong PolyU.
Abstract: This paper presents probabilistic horizontal seismic
hazard assessment of Naghan, Iran. It displays the probabilistic
estimate of Peak Ground Horizontal Acceleration (PGHA) for the
return period of 475, 950 and 2475 years. The output of the
probabilistic seismic hazard analysis is based on peak ground
acceleration (PGA), which is the most common criterion in designing
of buildings. A catalogue of seismic events that includes both
historical and instrumental events was developed and covers the
period from 840 to 2009. The seismic sources that affect the hazard
in Naghan were identified within the radius of 200 km and the
recurrence relationships of these sources were generated by Kijko
and Sellevoll. Finally Peak Ground Horizontal Acceleration (PGHA)
has been prepared to indicate the earthquake hazard of Naghan for
different hazard levels by using SEISRISK III software.
Abstract: This paper describes an experimental investigation of
the drying behavior and conditions of rosehip in a convective
cyclone-type dryer. Drying experiments were conducted at air inlet
temperatures of 50, 60 and 70 o C and air velocities of 0.5, 1 and 1.5
ms–1. The parametric values obtained from the experiments were
fitted to the Newton mathematical models. Consequently, the drying
model developed by Newton model showed good agreement with the
data obtained from the experiments. Concluding, it was obtained that;
(i) the temperature is the major effect on the drying process, (ii) air
velocity has low effect on the drying of rosehip, (iii) the C-vitamin is
observed to change according to the temperature, moisture, drying
time and flow types. The changing ratio is found to be in the range of
0.70-0.74.
Abstract: A new distance-adjusted approach is proposed in
which static square contours are defined around an estimated
symbol in a QAM constellation, which create regions that
correspond to fixed step sizes and weighting factors. As a
result, the equalizer tap adjustment consists of a linearly
weighted sum of adaptation criteria that is scaled by a variable
step size. This approach is the basis of two new algorithms: the
Variable step size Square Contour Algorithm (VSCA) and the
Variable step size Square Contour Decision-Directed
Algorithm (VSDA). The proposed schemes are compared with
existing blind equalization algorithms in the SCA family in
terms of convergence speed, constellation eye opening and
residual ISI suppression. Simulation results for 64-QAM
signaling over empirically derived microwave radio channels
confirm the efficacy of the proposed algorithms. An RTL
implementation of the blind adaptive equalizer based on the
proposed schemes is presented and the system is configured to
operate in VSCA error signal mode, for square QAM signals
up to 64-QAM.
Abstract: Many supervised induction algorithms require discrete
data, even while real data often comes in a discrete
and continuous formats. Quality discretization of continuous
attributes is an important problem that has effects on speed,
accuracy and understandability of the induction models. Usually,
discretization and other types of statistical processes are applied
to subsets of the population as the entire population is practically
inaccessible. For this reason we argue that the discretization
performed on a sample of the population is only an estimate of
the entire population. Most of the existing discretization methods,
partition the attribute range into two or several intervals using
a single or a set of cut points. In this paper, we introduce a
technique by using resampling (such as bootstrap) to generate
a set of candidate discretization points and thus, improving the
discretization quality by providing a better estimation towards
the entire population. Thus, the goal of this paper is to observe
whether the resampling technique can lead to better discretization
points, which opens up a new paradigm to construction of
soft decision trees.
Abstract: Availability and mobilization of revenue is the main
essential with which an economy is managed and run. While
planning or while making the budgets nations set revenue targets to
be achieved. But later when the accounts are closed the actual
collections of revenue through taxes or even the non-tax revenue
collection would invariably be different as compared to the initial
estimates and targets set to be achieved. This revenue-gap distorts the
whole system and the economy disturbing all the major macroeconomic
indicators. This study is aimed to find out short and long
term impact of revenue gap on budget deficit, debt burden and
economic growth on the economy of Pakistan. For this purpose the
study uses autoregressive distributed lag approach to cointegration
and error correction mechanism on three different models for the
period 1980 to 2009. The empirical results show that revenue gap has
a short and long run relationship with economic growth and budget
deficit. However, revenue gap has no impact on debt burden.
Abstract: The special constraints of sensor networks impose a
number of technical challenges for employing them. In this review,
we study the issues and existing protocols in three areas: coverage
and routing. We present two types of coverage problems: to
determine the minimum number of sensor nodes that need to perform
active sensing in order to monitor a certain area; and to decide the
quality of service that can be provided by a given sensor network.
While most routing protocols in sensor networks are data-centric,
there are other types of routing protocols as well, such as
hierarchical, location-based, and QoS-aware. We describe and
compare several protocols in each group. We present several multipath
routing protocols and single-path with local repair routing
protocols, which are proposed for recovering from sensor node
crashes. We also discuss some transport layer schemes for reliable
data transmission in lossy wireless channels.
Abstract: This paper presents parametric probability density
models for call holding times (CHTs) into emergency call center
based on the actual data collected for over a week in the public
Emergency Information Network (EIN) in Mongolia. When the set of
chosen candidates of Gamma distribution family is fitted to the call
holding time data, it is observed that the whole area in the CHT
empirical histogram is underestimated due to spikes of higher
probability and long tails of lower probability in the histogram.
Therefore, we provide the Gaussian parametric model of a mixture of
lognormal distributions with explicit analytical expressions for the
modeling of CHTs of PSNs. Finally, we show that the CHTs for
PSNs are fitted reasonably by a mixture of lognormal distributions
via the simulation of expectation maximization algorithm. This result
is significant as it expresses a useful mathematical tool in an explicit
manner of a mixture of lognormal distributions.
Abstract: Study of soil properties like field capacity (F.C.) and permanent wilting point (P.W.P.) play important roles in study of soil moisture retention curve. Although these parameters can be measured directly, their measurement is difficult and expensive. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. In this investigation, 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. The data set was divided into two subsets for calibration (80%) and testing (20%) of the models and their normality were tested by Kolmogorov-Smirnov method. Both multivariate regression and artificial neural network (ANN) techniques were employed to develop the appropriate PTFs for predicting soil parameters using easily measurable characteristics of clay, silt, O.C, S.P, B.D and CaCO3. The performance of the multivariate regression and ANN models was evaluated using an independent test data set. In order to evaluate the models, root mean square error (RMSE) and R2 were used. The comparison of RSME for two mentioned models showed that the ANN model gives better estimates of F.C and P.W.P than the multivariate regression model. The value of RMSE and R2 derived by ANN model for F.C and P.W.P were (2.35, 0.77) and (2.83, 0.72), respectively. The corresponding values for multivariate regression model were (4.46, 0.68) and (5.21, 0.64), respectively. Results showed that ANN with five neurons in hidden layer had better performance in predicting soil properties than multivariate regression.
Abstract: World has entered in 21st century. The technology of
computer graphics and digital cameras is prevalent. High resolution
display and printer are available. Therefore high resolution images
are needed in order to produce high quality display images and high
quality prints. However, since high resolution images are not usually
provided, there is a need to magnify the original images. One
common difficulty in the previous magnification techniques is that of
preserving details, i.e. edges and at the same time smoothing the data
for not introducing the spurious artefacts. A definitive solution to this
is still an open issue. In this paper an image magnification using
adaptive interpolation by pixel level data-dependent geometrical
shapes is proposed that tries to take into account information about
the edges (sharp luminance variations) and smoothness of the image.
It calculate threshold, classify interpolation region in the form of
geometrical shapes and then assign suitable values inside
interpolation region to the undefined pixels while preserving the
sharp luminance variations and smoothness at the same time.
The results of proposed technique has been compared qualitatively
and quantitatively with five other techniques. In which the qualitative
results show that the proposed method beats completely the Nearest
Neighbouring (NN), bilinear(BL) and bicubic(BC) interpolation. The
quantitative results are competitive and consistent with NN, BL, BC
and others.
Abstract: This paper introduces an automatic voice classification
system for the diagnosis of individual constitution based on Sasang
Constitutional Medicine (SCM) in Traditional Korean Medicine
(TKM). For the developing of this algorithm, we used the voices of
309 female speakers and extracted a total of 134 speech features from
the voice data consisting of 5 sustained vowels and one sentence. The
classification system, based on a rule-based algorithm that is derived
from a non parametric statistical method, presents 3 types of decisions:
reserved, positive and negative decisions. In conclusion, 71.5% of the
voice data were diagnosed by this system, of which 47.7% were
correct positive decisions and 69.7% were correct negative decisions.
Abstract: Recommender systems are usually regarded as an
important marketing tool in the e-commerce. They use important
information about users to facilitate accurate recommendation. The
information includes user context such as location, time and interest
for personalization of mobile users. We can easily collect information
about location and time because mobile devices communicate with the
base station of the service provider. However, information about user
interest can-t be easily collected because user interest can not be
captured automatically without user-s approval process. User interest
usually represented as a need. In this study, we classify needs into two
types according to prior research. This study investigates the
usefulness of data mining techniques for classifying user need type for
recommendation systems. We employ several data mining techniques
including artificial neural networks, decision trees, case-based
reasoning, and multivariate discriminant analysis. Experimental
results show that CHAID algorithm outperforms other models for
classifying user need type. This study performs McNemar test to
examine the statistical significance of the differences of classification
results. The results of McNemar test also show that CHAID performs
better than the other models with statistical significance.
Abstract: The effect of different tempering temperatures and heat treatment times on the corrosion resistance of austenitic stainless steels in oxalic acid was studied in this work using conventional weight loss and electrochemical measurements. Typical 304 and 316 stainless steel samples were tempered at 150oC, 250oC and 350oC after being austenized at 1050oC for 10 minutes. These samples were then immersed in 1.0M oxalic acid and their weight losses were measured at every five days for 30 days. The results show that corrosion of both types of ASS samples increased with an increase in tempering temperature and time and this was due to the precipitation of chromium carbides at the grain boundaries of these metals. Electrochemical results also confirm that the 304 ASS is more susceptible to corrosion than 316 ASS in this medium. This is attributed to the molybdenum in the composition of the latter. The metallographic images of these samples showed non–uniform distribution of precipitated chromium carbides at the grain boundaries of these metals and unevenly distributed carbides and retained austenite phases which cause galvanic effects in the medium.
Abstract: In Thailand, the practice of pre-hospital Emergency
Medical Service (EMS) in each area reveals the different growth
rates and effectiveness of the practices. Those can be found as the
diverse quality and quantity. To shorten the learning curve prior to
speed-up the practices in other areas, story telling and lessons learnt
from the effective practices are valued as meaningful knowledge. To
this paper, it was to ascertain the factors, lessons learnt and best
practices that have impact as contributing to the success of prehospital
EMS system. Those were formulized as model prior to
speedup the practice in other areas. To develop the model, Malcolm
Baldrige National Quality Award (MBNQA), which is widely
recognized as a framework for organizational quality assessment and
improvement, was chosen as the discussion framework. Remarkably,
this study was based on the consideration of knowledge capture;
however it was not to complete the loop of knowledge activities.
Nevertheless, it was to highlight the recognition of knowledge
capture, which is the initiation of knowledge management.
Abstract: The radio frequency identification (RFID) is a
technology for automatic identification of items, particularly in
supply chain, but it is becoming increasingly important for industrial
applications. Unlike barcode technology that detects the optical
signals reflected from barcode labels, RFID uses radio waves to
transmit the information from an RFID tag affixed to the physical
object. In contrast to today most often use of this technology in
warehouse inventory and supply chain, the focus of this paper is an
overview of the structure of RFID systems used by RFID technology
and it also presents a solution based on the application of RFID for
brand authentication, traceability and tracking, by implementing a
production management system and extending its use to traders.
Abstract: In this paper a nonlinear model is presented to
demonstrate the relation between production and marketing
departments. By introducing some functions such as pricing cost and
market share loss functions it will be tried to show some aspects of
market modelling which has not been regarded before. The proposed
model will be a constrained signomial geometric programming
model. For model solving, after variables- modifications an iterative
technique based on the concept of geometric mean will be introduced
to solve the resulting non-standard posynomial model which can be
applied to a wide variety of models in non-standard posynomial
geometric programming form. At the end a numerical analysis will
be presented to accredit the validity of the mentioned model.
Abstract: A new method for low complexity image coding is presented, that permits different settings and great scalability in the generation of the final bit stream. This coding presents a continuoustone still image compression system that groups loss and lossless compression making use of finite arithmetic reversible transforms. Both transformation in the space of color and wavelet transformation are reversible. The transformed coefficients are coded by means of a coding system in depending on a subdivision into smaller components (CFDS) similar to the bit importance codification. The subcomponents so obtained are reordered by means of a highly configure alignment system depending on the application that makes possible the re-configure of the elements of the image and obtaining different levels of importance from which the bit stream will be generated. The subcomponents of each level of importance are coded using a variable length entropy coding system (VBLm) that permits the generation of an embedded bit stream. This bit stream supposes itself a bit stream that codes a compressed still image. However, the use of a packing system on the bit stream after the VBLm allows the realization of a final highly scalable bit stream from a basic image level and one or several enhance levels.