Abstract: This paper presents a new problem solving approach
that is able to generate optimal policy solution for finite-state
stochastic sequential decision-making problems with high data
efficiency. The proposed algorithm iteratively builds and improves
an approximate Markov Decision Process (MDP) model along with
cost-to-go value approximates by generating finite length trajectories
through the state-space. The approach creates a synergy between an
approximate evolving model and approximate cost-to-go values to
produce a sequence of improving policies finally converging to the
optimal policy through an intelligent and structured search of the
policy space. The approach modifies the policy update step of the
policy iteration so as to result in a speedy and stable convergence to
the optimal policy. We apply the algorithm to a non-holonomic
mobile robot control problem and compare its performance with
other Reinforcement Learning (RL) approaches, e.g., a) Q-learning,
b) Watkins Q(λ), c) SARSA(λ).
Abstract: Researches show that probability-statistical 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 the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods is considered. According to the purpose of this problem 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. For GTE technical condition more adequate model making dynamics of skewness and kurtosis coefficients- changes are analysed. 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 drawing 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. 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 stageby- stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine technical condition was made.
Abstract: Isobaric vapor-liquid equilibrium measurements are reported for the binary mixtures of n-Butylamine and Triethylamine with Cumene at 97.3 kPa. The measurements have been performed using a vapor recirculating type (modified Othmer's) equilibrium still. The binary mixture of n-Butylamine + Cumene shows positive deviation from ideality. Triethylamine + Cumene mixture shows negligible deviation from ideality. None of the systems form an azeotrope. The activity coefficients have been calculated taking into consideration the vapor phase nonideality. The data satisfy the thermodynamic consistency test of Herington. The activity coefficients have been satisfactorily correlated by means of the Margules, NRTL, and Black equations. The activity coefficient values obtained by the UNIFAC model are also reported.
Abstract: Oxidative stress and overwhelming free radicals
associated with diabetes mellitus are likely to be linked with
development of certain complication such as retinopathy,
nephropathy and neuropathy. Treatment of diabetic subjects with
antioxidant may be of advantage in attenuating these complications.
Olive leaf (Oleaeuropaea), has been endowed with many beneficial
and health promoting properties mostly linked to its antioxidant
activity. This study aimed to evaluate the significance of
supplementation of Olive leaves extract (OLE) in reducing oxidative
stress, hyperglycemia and hyperlipidemia in Sterptozotocin (STZ)-
induced diabetic rats. After induction of diabetes, a significant rise in
plasma glucose, lipid profiles except High density lipoproteincholestrol
(HDLc), malondialdehyde (MDA) and significant decrease
of plasma insulin, HDLc and Plasma reduced glutathione GSH as
well as alteration in enzymatic antioxidants was observed in all
diabetic animals. During treatment of diabetic rats with 0.5g/kg body
weight of Olive leaves extract (OLE) the levels of plasma (MDA)
,(GSH), insulin, lipid profiles along with blood glucose and
erythrocyte enzymatic antioxidant enzymes were significantly
restored to establish values that were not different from normal
control rats. Untreated diabetic rats on the other hand demonstrated
persistent alterations in the oxidative stress marker (MDA), blood
glucose, insulin, lipid profiles and the antioxidant parameters. These
results demonstrate that OLE may be of advantage in inhibiting
hyperglycemia, hyperlipidemia and oxidative stress induced by
diabetes and suggest that administration of OLE may be helpful in
the prevention or at least reduced of diabetic complications
associated with oxidative stress.
Abstract: A continuum model is presented to study vdW
interaction on buckling analysis of multi-walled walled carbon
nanotube. In previous studies, only the vdW interaction between
adjacent two layers was considered and the vdW interaction between
the other two layers was neglected. The results show that the vdW
interaction cofficients are dependent on the change of interlayer
spacing and the radii of tubes. With increase of radii the vdW
coefficients approach a constant value. The numerical results show
that the effect of vdW interaction on the critical strain for a doublewalled
CNT is negligible when the radius is large enough for the
both the cases of before and after buckling.
Abstract: The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to resort to the trial and error approach. This paper describes the process of developing three layer feed-forward large neural networks for short-term load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. Particle Swarm Optimization (PSO) is used to develop the optimum large neural network structure and connecting weights for one-day ahead electric load forecasting problem. PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization. Employing PSO algorithms on the design and training of ANNs allows the ANN architecture and parameters to be easily optimized. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. The experimental results show that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional Back Propagation (BP) method. Moreover, it is not only simple to calculate, but also practical and effective. Also, it provides a greater degree of accuracy in many cases and gives lower percent errors all the time for STLF problem compared to BP method. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.
Abstract: Gastric ulceration is a discontinuity in gastric mucosa, usually occurs due to imbalance between the gastric mucosal protective factors, that is called gastric mucosal barrier, and the aggressive factors, to which the mucosa is exposed. This study was carried out on sixty male Sprague-Dowely rats (12- 16 weeks old) allocated into two groups. The first control group and the second Gastric lesion group which induced by oral administration of a single daily dose of aspirin at a dose of 300 mg/kg body weight for 7 consecutive-days (6% aspirin solution will be prepared and each rat will be given 5 ml of that solution/kg body weight). Blood is collected 1, 2 and 3 weeks after induction of gastric ulceration. Significant increase in serum copper, nitric oxide, and prostaglandin E2 all over the period of experiment. Significant decrease in erythrocyte superoxide dismutase (t-SOD) activities, serum (calcium, phosphorus, glucose and insulin) levels. Non-significant changes in serum sodium and potassium levels are obtained.
Abstract: Since the advent of the information era, the Internet has
brought various positive effects in everyday life. Nevertheless,
recently, problems and side-effects have been noted. Internet
witch-trials and spread of pornography are only a few of these
problems.In this study, problems and causes of malicious replies on
internet boards were analyzed, using the key ideas of game theory. The
study provides a mathematical model for the internet reply game to
devise three possible plans that could efficiently counteract malicious
replies. Furthermore, seven specific measures that comply with one of
the three plans were proposed and evaluated according to the
importance and utility of each measure using the orthogonal array
survey and SPSS conjoint analysis.The conclusion was that the most
effective measure would be forbidding unsigned user access to
malicious replies. Also notable was that some analytically proposed
measures, when implemented, could backfire and encourage malicious
replies.
Abstract: This article summarizes ways to verify neutron
fluence for neutron transmutation doping of silicon with phosphorus
on the LVR-15 reactor. Neutron fluence is determined using
activation detectors placed along the crystal in a strip or encapsulated
in a rod holder. Holders are placed at the centre of a water-filled
capsule or in an aluminum or silicon ingot that simulates a real single
crystal. If the diameter of the crystal is significantly less than the
capsule diameter and water from the primary circuit enters the free
space in the capsule, neutron interaction in the water changes neutron
fluence, affecting axial irradiation homogeneity. The effect of
moving the capsule vertically in the channel relative to maximum
neutron fluence in the reactor core was also measured. Even a small
shift of the capsule-s centre causes great irradiation inhomogeneity.
This effect was measured using activation detectors, and was also
confirmed by MCNP calculation.
Abstract: With a surge of stream processing applications novel
techniques are required for generation and analysis of association
rules in streams. The traditional rule mining solutions cannot handle
streams because they generally require multiple passes over the data
and do not guarantee the results in a predictable, small time. Though
researchers have been proposing algorithms for generation of rules
from streams, there has not been much focus on their analysis.
We propose Association rule profiling, a user centric process for
analyzing association rules and attaching suitable profiles to them
depending on their changing frequency behavior over a previous
snapshot of time in a data stream.
Association rule profiles provide insights into the changing nature
of associations and can be used to characterize the associations. We
discuss importance of characteristics such as predictability of
linkages present in the data and propose metric to quantify it. We
also show how association rule profiles can aid in generation of user
specific, more understandable and actionable rules.
The framework is implemented as SUPAR: System for Usercentric
Profiling of Association Rules in streaming data. The
proposed system offers following capabilities:
i) Continuous monitoring of frequency of streaming item-sets
and detection of significant changes therein for association rule
profiling.
ii) Computation of metrics for quantifying predictability of
associations present in the data.
iii) User-centric control of the characterization process: user
can control the framework through a) constraint specification and b)
non-interesting rule elimination.
Abstract: Discretization of spatial derivatives is an important
issue in meshfree methods especially when the derivative terms
contain non-linear coefficients. In this paper, various methods used
for discretization of second-order spatial derivatives are investigated
in the context of Smoothed Particle Hydrodynamics. Three popular
forms (i.e. "double summation", "second-order kernel derivation",
and "difference scheme") are studied using one-dimensional unsteady
heat conduction equation. To assess these schemes, transient response
to a step function initial condition is considered. Due to parabolic
nature of the heat equation, one can expect smooth and monotone
solutions. It is shown, however in this paper, that regardless of
the type of kernel function used and the size of smoothing radius,
the double summation discretization form leads to non-physical
oscillations which persist in the solution. Also, results show that when
a second-order kernel derivative is used, a high-order kernel function
shall be employed in such a way that the distance of inflection
point from origin in the kernel function be less than the nearest
particle distance. Otherwise, solutions may exhibit oscillations near
discontinuities unlike the "difference scheme" which unconditionally
produces monotone results.
Abstract: An attempt has been made to investigate the
machinability of zirconia toughened alumina (ZTA) inserts while
turning AISI 4340 steel. The insert was prepared by powder
metallurgy process route and the machining experiments were
performed based on Response Surface Methodology (RSM) design
called Central Composite Design (CCD). The mathematical model of
flank wear, cutting force and surface roughness have been developed
using second order regression analysis. The adequacy of model has
been carried out based on Analysis of variance (ANOVA) techniques.
It can be concluded that cutting speed and feed rate are the two most
influential factor for flank wear and cutting force prediction. For
surface roughness determination, the cutting speed & depth of cut
both have significant contribution. Key parameters effect on each
response has also been presented in graphical contours for choosing
the operating parameter preciously. 83% desirability level has been
achieved using this optimized condition.
Abstract: In this paper, we propose an improvement of pattern
growth-based PrefixSpan algorithm, called I-PrefixSpan. The general idea of I-PrefixSpan is to use sufficient data structure for Seq-Tree
framework and separator database to reduce the execution time and
memory usage. Thus, with I-PrefixSpan there is no in-memory database stored after index set is constructed. The experimental result
shows that using Java 2, this method improves the speed of PrefixSpan up to almost two orders of magnitude as well as the memory usage to more than one order of magnitude.
Abstract: This paper presents the effectiveness of artificial
intelligent technique to apply for pattern recognition and
classification of Partial Discharge (PD). Characteristics of PD signal
for pattern recognition and classification are computed from the
relation of the voltage phase angle, the discharge magnitude and the
repeated existing of partial discharges by using statistical and fractal
methods. The simplified fuzzy ARTMAP (SFAM) is used for pattern
recognition and classification as artificial intelligent technique. PDs
quantities, 13 parameters from statistical method and fractal method
results, are inputted to Simplified Fuzzy ARTMAP to train system
for pattern recognition and classification. The results confirm the
effectiveness of purpose technique.
Abstract: Recently in the field of bridges that are newly built or
repaired, fast construction is required more than ever. For these
reasons, precast prefabricated bridge that enables rapid construction is
actively discussed and studied today. In South Korea, it is called
modular bridge. Cross beam is an integral component of modular
bridge. It functions for load distribution, reduction of bending
moment, resistance of horizontal strength on lateral upper structure. In
this study, the structural characteristics of domestic and foreign cross
beam types were compared. Based on this, alternative cross beam
connection types suitable for modular bridge were selected. And
bulb-T girder specimens were fabricated with each type of connection.
The behavior of each specimen was analyzed under static loading, and
cross beam connection type which is expected to be best suited to
modular bridge proposed.
Abstract: In this paper, SFQ (Start Time Fair Queuing)
algorithm is analyzed when this is applied in computer networks to
know what kind of behavior the traffic in the net has when different
data sources are managed by the scheduler. Using the NS2 software
the computer networks were simulated to be able to get the graphs
showing the performance of the scheduler. Different traffic sources
were introduced in the scripts, trying to establish the real scenario.
Finally the results were that depending on the data source, the traffic
can be affected in different levels, when Constant Bite Rate is
applied, the scheduler ensures a constant level of data sent and
received, but the truth is that in the real life it is impossible to ensure
a level that resists the changes in work load.
Abstract: This paper deals with the effect of a power transformer’s vector group on the basic voltage sag characteristics during unbalanced faults at a meshed or radial power network. Specifically, the propagation of voltage sags through a power transformer is studied with advanced short-circuit analysis. A smart method to incorporate this effect on analytical mathematical expressions is proposed. Based on this methodology, the positive effect of transformers of certain vector groups on the mitigation of the expected number of voltage sags per year (sag frequency) at the terminals of critical industrial customers can be estimated.
Abstract: The objective of current study is to investigate the
differences of winning and losing teams in terms of goal scoring and
passing sequences. Total of 31 matches from UEFA-EURO 2012
were analyzed and 5 matches were excluded from analysis due to
matches end up drawn. There are two groups of variable used in the
study which is; i. the goal scoring variable and: ii. passing sequences
variable. Data were analyzed using Wilcoxon matched pair rank test
with significant value set at p < 0.05. Current study found the timing
of goal scored was significantly higher for winning team at 1st half
(Z=-3.416, p=.001) and 2nd half (Z=-3.252, p=.001). The scoring
frequency was also found to be increase as time progressed and the
last 15 minutes of the game was the time interval the most goals
scored. The indicators that were significantly differences between
winning and losing team were the goal scored (Z=-4.578, p=.000),
the head (Z=-2.500, p=.012), the right foot (Z=-3.788,p=.000),
corner (Z=-.2.126,p=.033), open play (Z=-3.744,p=.000), inside the
penalty box (Z=-4.174, p=.000) , attackers (Z=-2.976, p=.003) and
also the midfielders (Z=-3.400, p=.001). Regarding the passing
sequences, there are significance difference between both teams in
short passing sequences (Z=-.4.141, p=.000). While for the long
passing, there were no significance difference (Z=-.1.795, p=.073).
The data gathered in present study can be used by the coaches to
construct detailed training program based on their objectives.
Abstract: Mobile ad hoc network is a collection of mobile
nodes communicating through wireless channels without any existing
network infrastructure or centralized administration. Because of the
limited transmission range of wireless network interfaces, multiple
"hops" may be needed to exchange data across the network. In order
to facilitate communication within the network, a routing protocol is
used to discover routes between nodes. The primary goal of such an
ad hoc network routing protocol is correct and efficient route
establishment between a pair of nodes so that messages may be
delivered in a timely manner. Route construction should be done
with a minimum of overhead and bandwidth consumption. This paper
examines two routing protocols for mobile ad hoc networks– the
Destination Sequenced Distance Vector (DSDV), the table- driven
protocol and the Ad hoc On- Demand Distance Vector routing
(AODV), an On –Demand protocol and evaluates both protocols
based on packet delivery fraction, normalized routing load, average
delay and throughput while varying number of nodes, speed and
pause time.
Abstract: Feature selection plays an important role in applications with high dimensional data. The assessment of the stability of feature selection/ranking algorithms becomes an important issue when the dataset is small and the aim is to gain insight into the underlying process by analyzing the most relevant features. In this work, we propose a graphical approach that enables to analyze the similarity between feature ranking techniques as well as their individual stability. Moreover, it works with whatever stability metric (Canberra distance, Spearman's rank correlation coefficient, Kuncheva's stability index,...). We illustrate this visualization technique evaluating the stability of several feature selection techniques on a spectral binary dataset. Experimental results with a neural-based classifier show that stability and ranking quality may not be linked together and both issues have to be studied jointly in order to offer answers to the domain experts.