Abstract: With major technological advances and to reduce the
cost of training apprentices for real-time critical systems, it was
necessary the development of Intelligent Tutoring Systems for
training apprentices in these systems. These systems, in general, have
interactive features so that the learning is actually more efficient,
making the learner more familiar with the mechanism in question. In
the home stage of learning, tests are performed to obtain the student's
income, a measure on their use. The aim of this paper is to present a
framework to model an Intelligent Tutoring Systems using the UML
language. The various steps of the analysis are considered the
diagrams required to build a general model, whose purpose is to
present the different perspectives of its development.
Abstract: Clustering large populations is an important problem
when the data contain noise and different shapes. A good clustering
algorithm or approach should be efficient enough to detect clusters
sensitively. Besides space complexity, time complexity also gains
importance as the size grows. Using hierarchies we developed a new
algorithm to split attributes according to the values they have and
choosing the dimension for splitting so as to divide the database
roughly into equal parts as much as possible. At each node we
calculate some certain descriptive statistical features of the data
which reside and by pruning we generate the natural clusters with a
complexity of O(n).
Abstract: Information regarding early onset neonatal sepsis
(EONS) pathogens may vary between regions. Global perspectives
showed Group B Streptococcal (GBS) as the most common causative
pathogens, but the widespread use of intrapartum antibiotics has
changed the pathogens pattern towards gram negative
microorganisms, especially E. coli. Objective of this study is to
describe the pathogens isolated, to assess current treatment and risk
of EONS. Records of 899 neonates born in three General Hospitals
between 2009 until 2012 were retrospectively reviewed. Proven was
found in 22 (3%) neonates. The majority was isolated with gram
positive organisms, 17 (2.3%). All grams positive and most gram
negative organisms showed sensitivity to the tested antibiotics. Only
two rare gram negative organisms showed total resistant. Male was
possible risk of proven EONS. Although proven EONS remains
uncommon in Malaysia, nonetheless, the effect of intrapartum
antibiotics still required continuous surveillance.
Abstract: This paper illustrates the use of a combined neural
network model for classification of electrocardiogram (ECG) beats.
We present a trainable neural network ensemble approach to develop
customized electrocardiogram beat classifier in an effort to further
improve the performance of ECG processing and to offer
individualized health care.
We process a three stage technique for detection of premature
ventricular contraction (PVC) from normal beats and other heart
diseases. This method includes a denoising, a feature extraction and a
classification. At first we investigate the application of stationary
wavelet transform (SWT) for noise reduction of the
electrocardiogram (ECG) signals. Then feature extraction module
extracts 10 ECG morphological features and one timing interval
feature. Then a number of multilayer perceptrons (MLPs) neural
networks with different topologies are designed.
The performance of the different combination methods as well as
the efficiency of the whole system is presented. Among them,
Stacked Generalization as a proposed trainable combined neural
network model possesses the highest recognition rate of around 95%.
Therefore, this network proves to be a suitable candidate in ECG
signal diagnosis systems. ECG samples attributing to the different
ECG beat types were extracted from the MIT-BIH arrhythmia
database for the study.
Abstract: The clustering ensembles combine multiple partitions
generated by different clustering algorithms into a single clustering
solution. Clustering ensembles have emerged as a prominent method
for improving robustness, stability and accuracy of unsupervised
classification solutions. So far, many contributions have been done to
find consensus clustering. One of the major problems in clustering
ensembles is the consensus function. In this paper, firstly, we
introduce clustering ensembles, representation of multiple partitions,
its challenges and present taxonomy of combination algorithms.
Secondly, we describe consensus functions in clustering ensembles
including Hypergraph partitioning, Voting approach, Mutual
information, Co-association based functions and Finite mixture
model, and next explain their advantages, disadvantages and
computational complexity. Finally, we compare the characteristics of
clustering ensembles algorithms such as computational complexity,
robustness, simplicity and accuracy on different datasets in previous
techniques.
Abstract: In this study, the designed dual stage membrane
bioreactor (MBR) system was conceptualized for the treatment of
cyanide and heavy metals in electroplating wastewater. The design
consisted of a primary treatment stage to reduce the impact of
fluctuations and the secondary treatment stage to remove the residual
cyanide and heavy metal contaminants in the wastewater under
alkaline pH conditions. The primary treatment stage contained
hydrolyzed Citrus sinensis (C. sinensis) pomace and the secondary
treatment stage contained active Aspergillus awamori (A. awamori)
biomass, supplemented solely with C. sinensis pomace extract from
the hydrolysis process. An average of 76.37%, 95.37%, 93.26 and
94.76% and 99.55%, 99.91%, 99.92% and 99.92% degradation
efficiency for total cyanide (T-CN), including the sorption of nickel
(Ni), zinc (Zn) and copper (Cu) were observed after the first and
second treatment stages, respectively. Furthermore, cyanide
conversion by-products degradation was 99.81% and 99.75 for both
formate (CHOO-) and ammonium (NH4
+) after the second treatment
stage. After the first, second and third regeneration cycles of the C.
sinensis pomace in the first treatment stage, Ni, Zn and Cu removal
achieved was 99.13%, 99.12% and 99.04% (first regeneration cycle),
98.94%, 98.92% and 98.41% (second regeneration cycle) and 98.46
%, 98.44% and 97.91% (third regeneration cycle), respectively.
There was relatively insignificant standard deviation detected in all
the measured parameters in the system which indicated
reproducibility of the remediation efficiency in this continuous
system.
Abstract: Orthogonal Frequency Division Multiplexing
(OFDM) is an efficient method of data transmission for high speed
communication systems. However, the main drawback of OFDM
systems is that, it suffers from the problem of high Peak-to-Average
Power Ratio (PAPR) which causes inefficient use of the High Power
Amplifier and could limit transmission efficiency. OFDM consist of
large number of independent subcarriers, as a result of which the
amplitude of such a signal can have high peak values. In this paper,
we propose an effective reduction scheme that combines DCT and
SLM techniques. The scheme is composed of the DCT followed by
the SLM using the Riemann matrix to obtain phase sequences for the
SLM technique. The simulation results show PAPR can be greatly
reduced by applying the proposed scheme. In comparison with
OFDM, while OFDM had high values of PAPR –about 10.4dB our
proposed method achieved about 4.7dB reduction of the PAPR with
low complexities computation. This approach also avoids
randomness in phase sequence selection, which makes it simpler to
decode at the receiver. As an added benefit, the matrices can be
generated at the receiver end to obtain the data signal and hence it is
not required to transmit side information (SI).
Abstract: An appropriate method for fault identification and classification on extra high voltage transmission line using discrete wavelet transform is proposed in this paper. The sharp variations of the generated short circuit transient signals which are recorded at the sending end of the transmission line are adopted to identify the fault. The threshold values involve fault classification and these are done on the basis of the multiresolution analysis. A comparative study of the performance is also presented for Discrete Fourier Transform (DFT) based Artificial Neural Network (ANN) and Discrete Wavelet Transform (DWT). The results prove that the proposed method is an effective and efficient one in obtaining the accurate result within short duration of time by using Daubechies 4 and 9. Simulation of the power system is done using MATLAB.
Abstract: In This Article We establish moment inequality of
dependent random variables,furthermore some theorems of strong law
of large numbers and complete convergence for sequences of dependent
random variables. In particular, independent and identically
distributed Marcinkiewicz Law of large numbers are generalized to
the case of m0-dependent sequences.
Abstract: Chess is one of the indoor games, which improves the
level of human confidence, concentration, planning skills and
knowledge. The main objective of this paper is to help the chess
players to improve their chess openings using data mining
techniques. Budding Chess Players usually do practices by analyzing
various existing openings. When they analyze and correlate
thousands of openings it becomes tedious and complex for them. The
work done in this paper is to analyze the best lines of Blackmar-
Diemer Gambit(BDG) which opens with White D4... using data
mining analysis. It is carried out on the collection of winning games
by applying association rules. The first step of this analysis is
assigning variables to each different sequence moves. In the second
step, the sequence association rules were generated to calculate
support and confidence factor which help us to find the best
subsequence chess moves that may lead to winning position.
Abstract: In order to optimize annual IT spending and to reduce
the complexity of an entire system architecture, SOA trials have been
started. It is common knowledge that to design an SOA system we
have to adopt the top-down approach, but in reality silo systems are
being made, so these companies cannot reuse newly designed services,
and cannot enjoy SOA-s economic benefits. To prevent this situation,
we designed a generic SOA development process referred to as the
architecture of “mass customization."
To define the generic detail development processes, we did a case
study on an imaginary company. Through the case study, we could
define the practical development processes and found this could vastly
reduce updating development costs.
Abstract: A DEA model can generally evaluate the performance
using multiple inputs and outputs for the same period. However, it is
hard to avoid the production lead time phenomenon some times, such
as long-term project or marketing activity. A couple of models have
been suggested to capture this time lag issue in the context of DEA.
This paper develops a dual-MPO model to deal with time lag effect in
evaluating efficiency. A numerical example is also given to show that
the proposed model can be used to get efficiency and reference set of
inefficient DMUs and to obtain projected target value of input
attributes for inefficient DMUs to be efficient.
Abstract: Data from 1731 Gentile di Puglia lambs, sired by 65 rams over a 5-year period were analyzed by a mixed model to estimate the variance components for heritability. The considered growth traits were: birth weight (BW), weight at 30 days of age (W30) and average daily gain from birth to 30 days of age (DG). Year of birth, sex of lamb, type of birth (single or twin), dam age at lambing and farm were significant sources of variation for all the considered growth traits. The average lamb weights were 3.85±0.16 kg at birth, 9.57±0.91 kg at 30 days of age and the average daily gain was 191±14 g. Estimates of heritability were 0.33±0.05, 0.41±0.06 and 0.16±0.05 respectively for the same traits. These values suggest there is a good opportunity to improve Gentile di Puglia lambs by selecting animals for growth traits.
Abstract: In Supply Chain Management (SCM), strengthening partnerships with suppliers is a significant factor for enhancing competitiveness. Hence, firms increasingly emphasize supplier evaluation processes. Supplier evaluation systems are basically developed in terms of criteria such as quality, cost, delivery, and flexibility. Because there are many variables to be analyzed, this process becomes hard to execute and needs expertise. On this account, this study aims to develop an expert system on supplier evaluation process by designing Artificial Neural Network (ANN) that is supported with Data Envelopment Analysis (DEA). The methods are applied on the data of 24 suppliers, which have longterm relationships with a medium sized company from German Iron and Steel Industry. The data of suppliers consists of variables such as material quality (MQ), discount of amount (DOA), discount of cash (DOC), payment term (PT), delivery time (DT) and annual revenue (AR). Meanwhile, the efficiency that is generated by using DEA is added to the supplier evaluation system in order to use them as system outputs.
Abstract: The problem of exponential stability and periodicity for a class of cellular neural networks (DCNNs) with time-varying delays is investigated. By dividing the network state variables into subgroups according to the characters of the neural networks, some sufficient conditions for exponential stability and periodicity are derived via the methods of variation parameters and inequality techniques. These conditions are represented by some blocks of the interconnection matrices. Compared with some previous methods, the method used in this paper does not resort to any Lyapunov function, and the results derived in this paper improve and generalize some earlier criteria established in the literature cited therein. Two examples are discussed to illustrate the main results.
Abstract: Currently searching through internet is very popular especially in a field of academic. A huge of educational information such as research papers are overload for user. So community-base web sites have been developed to help user search information more easily from process of customizing a web site to need each specifies user or set of user. In this paper propose to use association rule analyze the community group on research paper bookmarking. A set of design goals for community group frameworks is developed and discussed. Additionally Researcher analyzes the initial relation by using association rule discovery between the antecedent and the consequent of a rule in the groups of user for generate the idea to improve ranking search result and development recommender system.
Abstract: Wind turbines with double output induction
generators can operate at variable speed permitting conversion
efficiency maximization over a wide range of wind velocities. This
paper presents the performance analysis of a wind driven double
output induction generator (DOIG) operating at varying shafts speed.
A periodic transient state analysis of DOIG equipped with two
converters is carried out using a hybrid induction machine model.
This paper simulates the harmonic content of waveforms in various
points of drive at different speeds, based on the hybrid model
(dqabc). Then the sinusoidal and trapezoidal pulse-width–modulation
control techniques are used in order to improve the power factor of
the machine and to weaken the injected low order harmonics to the
supply. Based on the frequency spectrum, total harmonics distortion,
distortion factor and power factor. Finally advantages of sinusoidal
and trapezoidal pulse width modulation techniques are compared.
Abstract: The increasing competitiveness in manufacturing
industry is forcing manufacturers to seek effective processing
schedules. The paper presents an optimization manufacture
scheduling approach for dependent details processing with given
processing sequences and times on multiple machines. By defining
decision variables as start and end moments of details processing it is
possible to use straightforward variables restrictions to satisfy
different technological requirements and to formulate easy to
understand and solve optimization tasks for multiple numbers of
details and machines. A case study example is solved for seven base
moldings for CNC metalworking machines processed on five
different machines with given processing order among details and
machines and known processing time-s duration. As a result of linear
optimization task solution the optimal manufacturing schedule
minimizing the overall processing time is obtained. The
manufacturing schedule defines the moments of moldings delivery
thus minimizing storage costs and provides mounting due-time
satisfaction. The proposed optimization approach is based on real
manufacturing plant problem. Different processing schedules variants
for different technological restrictions were defined and implemented
in the practice of Bulgarian company RAIS Ltd. The proposed
approach could be generalized for other job shop scheduling
problems for different applications.
Abstract: Whereas in the third generation nuclear reactors,
dimensions of core and also the kind of coolant and enrichment
percent of fuel have significantly changed than the second
generation, therefore in this article the aim is based on a
comparative investigation between two same power reactors of
second and third generations, that the neutronic parameters of both
reactors such as: K∞, Keff and its details and thermal hydraulic
parameters such as: power density, specific power, volumetric heat
rate, released power per fuel volume unit, volume and mass of clad
and fuel (consisting fissile and fertile fuels), be calculated and
compared together. By this comparing the efficiency and
modification of third generation nuclear reactors than second
generation which have same power can be distinguished.
In order to calculate the cited parameters, some information
such as: core dimensions, the pitch of lattice, the fuel matter, the
percent of enrichment and the kind of coolant are used. For
calculating the neutronic parameters, a neutronic program entitled:
SIXFAC and also related formulas have been used. Meantime for
calculating the thermal hydraulic and other parameters, analytical
method and related formulas have been applied.
Abstract: In this paper, an artificial intelligent technique for
robust digital image watermarking in multiwavelet domain is
proposed. The embedding technique is based on the quantization
index modulation technique and the watermark extraction process
does not require the original image. We have developed an
optimization technique using the genetic algorithms to search for
optimal quantization steps to improve the quality of watermarked
image and robustness of the watermark. In addition, we construct a
prediction model based on image moments and back propagation
neural network to correct an attacked image geometrically before the
watermark extraction process begins. The experimental results show
that the proposed watermarking algorithm yields watermarked image
with good imperceptibility and very robust watermark against various
image processing attacks.