Abstract: In this paper we describe our efforts to design and
implement an agent development framework that has the potential to
scale to the size of any underlying network suitable for various ECommerce
activities. The main novelty in our framework is it-s
capability to allow the development of sophisticated, secured agents
which are simple enough to be practical.
We have adopted FIPA agent platform reference Model as
backbone for implementation along with XML for agent
Communication and Java Cryptographic Extension and architecture
to realize the security of communication information between agents.
The advantage of our architecture is its support of agents
development in different languages and Communicating with each
other using a more open standard i.e. XML
Abstract: Surface roughness (Ra) is one of the most important requirements in machining process. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process take place. This research presents the development of mathematical model for surface roughness prediction before milling process in order to evaluate the fitness of machining parameters; spindle speed, feed rate and depth of cut. 84 samples were run in this study by using FANUC CNC Milling α-Τ14ιE. Those samples were randomly divided into two data sets- the training sets (m=60) and testing sets(m=24). ANOVA analysis showed that at least one of the population regression coefficients was not zero. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the surface roughness is most influenced by the feed rate. By using Multiple Regression Method equation, the average percentage deviation of the testing set was 9.8% and 9.7% for training data set. This showed that the statistical model could predict the surface roughness with about 90.2% accuracy of the testing data set and 90.3% accuracy of the training data set.
Abstract: Artificial Neural Network (ANN)s are best suited for
prediction and optimization problems. Trained ANNs have found
wide spread acceptance in several antenna design systems. Four
parameters namely antenna radiation resistance, loss resistance, efficiency,
and inductance can be used to design an antenna layout though
there are several other parameters available. An ANN can be trained
to provide the best and worst case precisions of an antenna design
problem defined by these four parameters. This work describes the
use of an ANN to generate the four mentioned parameters for a loop
antenna for the specified frequency range. It also provides insights
to the prediction of best and worst-case design problems observed
in applications and thereby formulate a model for physical layout
design of a loop antenna.
Abstract: Scheduling algorithms are used in operating systems
to optimize the usage of processors. One of the most efficient
algorithms for scheduling is Multi-Layer Feedback Queue (MLFQ)
algorithm which uses several queues with different quanta. The most
important weakness of this method is the inability to define the
optimized the number of the queues and quantum of each queue. This
weakness has been improved in IMLFQ scheduling algorithm.
Number of the queues and quantum of each queue affect the response
time directly. In this paper, we review the IMLFQ algorithm for
solving these problems and minimizing the response time. In this
algorithm Recurrent Neural Network has been utilized to find both
the number of queues and the optimized quantum of each queue.
Also in order to prevent any probable faults in processes' response
time computation, a new fault tolerant approach has been presented.
In this approach we use combinational software redundancy to
prevent the any probable faults. The experimental results show that
using the IMLFQ algorithm results in better response time in
comparison with other scheduling algorithms also by using fault
tolerant mechanism we improve IMLFQ performance.
Abstract: Emerging adulthood, between the ages of 18 and 25, as a new developmental stage extending from adolescence to young adulthood. According to Arnett [2004], there are experiments related to identity in three basic fields which are love, work and view of the world in emerging adulthood. When the literature related to identity is examined, it is seen that identity has been studied more with adolescent, and studies were concentrated on the relationship of identity with many demographic variables neglecting important variables such as marital status, parental status and SES. Thus, the main aim of this study is to determine whether identity statuses differenciate with marital status, parental status and SES. A total of 700 emerging adults participated in this study, and the mean age was 22,45 years [SD = 3.76]. The sample was made up of 347 female and 353 male. All participants in the study were students from colleges. Student responses to the Extended Version of the Objective Measure of Ego Identity Status [EOM-EIS-2] used to classify students into one of the four identity statuses. SPSS 15.00 program wasa used to analyse data. Percentage, frequency and X2 analysis were used in the analysis of data. When the findings of the study is viewed as a whole, the most frequently observed identity status in the group is found to be moratorium. Also, identity statuses differenciate with marital status, parental status and SES. Findings were discussed in the context of emerging adulthood.
Abstract: Fractional-order controller was proven to perform better than the integer-order controller. However, the absence of a pole at origin produced marginal error in fractional-order control system. This study demonstrated the enhancement of the fractionalorder PI over the integer-order PI in a steam temperature control. The fractional-order controller was cascaded with an error compensator comprised of a very small zero and a pole at origin to produce a zero steady-state error for the closed-loop system. Some modification on the error compensator was suggested for different order fractional integrator that can improve the overall phase margin.
Abstract: Land use change, if not based on proper scientific
investigation affects other physical, chemical, and biological
properties of soil and leading to increased destruction and erosion. It
was imperative to study the effects of changing rangelands to
farmlands on some Soil quality indexes. Undisturbed soil samples
were collected from the depths of 0-10 and 10-30 centimeter in
pasture with good vegetation cover(GP), pasture with medium
vegetation cover(MP), abandoned dry land farming(ADF) and
degraded dry land farming(DDF) land uses in Ghareh Aghaj
watershed of Isfahan province. The results revealed that organic
matter(OM), cation exchange capacity(CEC) and available
potassium(AK) decreasing in the depth of 0-10 centimeter were 66.6,
38.8 and 70 percent and in the depth of 10-30 centimeter were 58,
61.4 and 83.5 percent respectively in DDF comparison with GP.
Concerning to the results, it seems that land use change can decrease
soil quality and increase soil degradation and lead in undesirable
consequences.
Abstract: We investigated the effects of modified
preprogrammed training mode Chase Trainer from Balance Trainer
(BT3, HurLab, Tampere, Finland) on athlete who experienced
unilateral Patellofemoral Pain Syndrome (PFPS). Twenty-seven
athletes with mean age= 14.23 ±1.31 years, height = 164.89 ± 7.85
cm, weight = 56.94 ± 9.28 kg were randomly assigned to two groups:
experiment (EG; n = 14) and injured (IG; n = 13). EG performed a
series of Chase Trainer program which required them to shift their
body weight at different directions, speeds and angle of leaning twice
a week for duration of 8 weeks. The static postural control and
perceived pain level measures were taken at baseline, after 6 weeks
and 8 weeks of training. There was no significant difference in any of
tested variables between EG and IG before and after 6-week the
intervention period. However, after 8-week of training, the postural
control (eyes open) and perceived pain level of EG improved
compared to IG (p
Abstract: Semiconductor detector arrays are widely used in
high-temperature plasma diagnostics. They have a fast response,
which allows observation of many processes and instabilities in
tokamaks. In this paper, there are reviewed several diagnostics based
on semiconductor arrays as cameras, AXUV photodiodes (referred
often as fast “bolometers") and detectors of both soft X-rays and
visible light installed on the COMPASS tokamak recently. Fresh
results from both spring and summer campaigns in 2012 are
introduced. Examples of the utilization of the detectors are shown on
the plasma shape determination, fast calculation of the radiation
center, two-dimensional plasma radiation tomography in different
spectral ranges, observation of impurity inflow, and also on
investigation of MHD activity in the COMPASS tokamak discharges.
Abstract: Purpose: Planning and dosimetry of different VMAT algorithms (SmartArc, Ergo++, Autobeam) is compared with IMRT for Head and Neck Cancer patients. Modelling was performed to rule out the causes of discrepancies between planned and delivered dose. Methods: Five HNC patients previously treated with IMRT were re-planned with SmartArc (SA), Ergo++ and Autobeam. Plans were compared with each other and against IMRT and evaluated using DVHs for PTVs and OARs, delivery time, monitor units (MU) and dosimetric accuracy. Modelling of control point (CP) spacing, Leaf-end Separation and MLC/Aperture shape was performed to rule out causes of discrepancies between planned and delivered doses. Additionally estimated arc delivery times, overall plan generation times and effect of CP spacing and number of arcs on plan generation times were recorded. Results: Single arc SmartArc plans (SA4d) were generally better than IMRT and double arc plans (SA2Arcs) in terms of homogeneity and target coverage. Double arc plans seemed to have a positive role in achieving improved Conformity Index (CI) and better sparing of some Organs at Risk (OARs) compared to Step and Shoot IMRT (ss-IMRT) and SA4d. Overall Ergo++ plans achieved best CI for both PTVs. Dosimetric validation of all VMAT plans without modelling was found to be lower than ss-IMRT. Total MUs required for delivery were on average 19%, 30%, 10.6% and 6.5% lower than ss-IMRT for SA4d, SA2d (Single arc with 20 Gantry Spacing), SA2Arcs and Autobeam plans respectively. Autobeam was most efficient in terms of actual treatment delivery times whereas Ergo++ plans took longest to deliver. Conclusion: Overall SA single arc plans on average achieved best target coverage and homogeneity for both PTVs. SA2Arc plans showed improved CI and some OARs sparing. Very good dosimetric results were achieved with modelling. Ergo++ plans achieved best CI. Autobeam resulted in fastest treatment delivery times.
Abstract: Aspect Oriented Programming promises many
advantages at programming level by incorporating the cross cutting
concerns into separate units, called aspects. Join Points are
distinguishing features of Aspect Oriented Programming as they
define the points where core requirements and crosscutting concerns
are (inter)connected. Currently, there is a problem of multiple
aspects- composition at the same join point, which introduces the
issues like ordering and controlling of these superimposed aspects.
Dynamic strategies are required to handle these issues as early as
possible. State chart is an effective modeling tool to capture dynamic
behavior at high level design. This paper provides methodology to
formulate the strategies for multiple aspect composition at high level,
which helps to better implement these strategies at coding level. It
also highlights the need of designing shared join point at high level,
by providing the solutions of these issues using state chart diagrams
in UML 2.0. High level design representation of shared join points
also helps to implement the designed strategy in systematic way.
Abstract: The Random Coefficient Dynamic Regression (RCDR)
model is to developed from Random Coefficient Autoregressive
(RCA) model and Autoregressive (AR) model. The RCDR model
is considered by adding exogenous variables to RCA model. In this
paper, the concept of the Maximum Likelihood (ML) method is used
to estimate the parameter of RCDR(1,1) model. Simulation results
have shown the AIC and BIC criterion to compare the performance of
the the RCDR(1,1) model. The variables as the stationary and weakly
stationary data are good estimates where the exogenous variables
are weakly stationary. However, the model selection indicated that
variables are nonstationarity data based on the stationary data of the
exogenous variables.
Abstract: One of the main environmental problems which affect extensive areas in the world is soil salinity. Traditional data collection methods are neither enough for considering this important environmental problem nor accurate for soil studies. Remote sensing data could overcome most of these problems. Although satellite images are commonly used for these studies, however there are still needs to find the best calibration between the data and real situations in each specified area. Neyshaboor area, North East of Iran was selected as a field study of this research. Landsat satellite images for this area were used in order to prepare suitable learning samples for processing and classifying the images. 300 locations were selected randomly in the area to collect soil samples and finally 273 locations were reselected for further laboratory works and image processing analysis. Electrical conductivity of all samples was measured. Six reflective bands of ETM+ satellite images taken from the study area in 2002 were used for soil salinity classification. The classification was carried out using common algorithms based on the best composition bands. The results showed that the reflective bands 7, 3, 4 and 1 are the best band composition for preparing the color composite images. We also found out, that hybrid classification is a suitable method for identifying and delineation of different salinity classes in the area.
Abstract: Society has grown to rely on Internet services, and the
number of Internet users increases every day. As more and more
users become connected to the network, the window of opportunity
for malicious users to do their damage becomes very great and
lucrative. The objective of this paper is to incorporate different
techniques into classier system to detect and classify intrusion from
normal network packet. Among several techniques, Steady State
Genetic-based Machine Leaning Algorithm (SSGBML) will be used
to detect intrusions. Where Steady State Genetic Algorithm (SSGA),
Simple Genetic Algorithm (SGA), Modified Genetic Algorithm and
Zeroth Level Classifier system are investigated in this research.
SSGA is used as a discovery mechanism instead of SGA. SGA
replaces all old rules with new produced rule preventing old good
rules from participating in the next rule generation. Zeroth Level
Classifier System is used to play the role of detector by matching
incoming environment message with classifiers to determine whether
the current message is normal or intrusion and receiving feedback
from environment. Finally, in order to attain the best results,
Modified SSGA will enhance our discovery engine by using Fuzzy
Logic to optimize crossover and mutation probability. The
experiments and evaluations of the proposed method were performed
with the KDD 99 intrusion detection dataset.
Abstract: In composting process, N high-organic wastes loss the
great part of its nitrogen as ammonia; therefore, using compost
amendments can promote the quality of compost due to the decrease
in ammonia volatilization. With regard to the effect of pH on
composting, microorganisms- activity and ammonia volatilization,
sulfuric acid and alkaline wastewater of paper mill (as liming agent
with Ca and Mg ions) were used as compost amendments. Study
results indicated that these amendments are suitable for reclamation
of compost quality properties. These held nitrogen in compost caused
to reduce C/N ratio. Both amendments had a significant effect on
total nitrogen, but it should be used sulfuric acid in fewer amounts
(20 ml/kg fresh organic wastes); and the more amounts of acid is not
proposed.
Abstract: An electrocardiogram (ECG) feature extraction system
based on the calculation of the complex resonance frequency
employing Prony-s method is developed. Prony-s method is applied
on five different classes of ECG signals- arrhythmia as a finite sum
of exponentials depending on the signal-s poles and the resonant
complex frequencies. Those poles and resonance frequencies of the
ECG signals- arrhythmia are evaluated for a large number of each
arrhythmia. The ECG signals of lead II (ML II) were taken from
MIT-BIH database for five different types. These are the ventricular
couplet (VC), ventricular tachycardia (VT), ventricular bigeminy
(VB), and ventricular fibrillation (VF) and the normal (NR). This
novel method can be extended to any number of arrhythmias.
Different classification techniques were tried using neural networks
(NN), K nearest neighbor (KNN), linear discriminant analysis (LDA)
and multi-class support vector machine (MC-SVM).
Abstract: The Multi-Layered Perceptron (MLP) Neural
networks have been very successful in a number of signal processing
applications. In this work we have studied the possibilities and the
met difficulties in the application of the MLP neural networks for the
prediction of daily solar radiation data. We have used the Polack-Ribière algorithm for training the neural networks. A comparison, in
term of the statistical indicators, with a linear model most used in
literature, is also performed, and the obtained results show that the
neural networks are more efficient and gave the best results.
Abstract: Mathematical, graphical and intuitive models are often
constructed in the development process of computational systems.
The Unified Modeling Language (UML) is one of the most popular
modeling languages used by practicing software engineers. This
paper critically examines UML models and suggests an augmented
use case view with the addition of new constructs for modeling
software. It also shows how a use case diagram can be enhanced. The
improved modeling constructs are presented with examples for
clarifying important design and implementation issues.
Abstract: Ren et al. presented an efficient carrier frequency offset
(CFO) estimation method for orthogonal frequency division multiplexing
(OFDM), which has an estimation range as large as the
bandwidth of the OFDM signal and achieves high accuracy without
any constraint on the structure of the training sequence. However,
its detection probability of the integer frequency offset (IFO) rapidly
varies according to the fractional frequency offset (FFO) change. In
this paper, we first analyze the Ren-s method and define two criteria
suitable for detection of IFO. Then, we propose a novel method for
the IFO estimation based on the maximum-likelihood (ML) principle
and the detection criteria defined in this paper. The simulation results
demonstrate that the proposed method outperforms the Ren-s method
in terms of the IFO detection probability irrespective of a value of
the FFO.
Abstract: An ultrasound-assisted activation method for
electroless silver plating is presented in this study. When the
ultrasound was applied during the activation step, the amount of the Pd
species adsorbed on substrate surfaces was higher than that of sample
pretreated with a conventional activation process without ultrasound
irradiation. With this activation method, it was also shown that the
adsorbed Pd species with a size of about 5 nm were uniformly
distributed on the surfaces, thus a smooth and uniform coating on the
surfaces was obtained by subsequent electroless silver plating. The
samples after each step were characterized by AFM, XPS, FIB, and
SEM.