Abstract: The present work analyses different parameters of end
milling to minimize the surface roughness for AISI D2 steel. D2 Steel
is generally used for stamping or forming dies, punches, forming
rolls, knives, slitters, shear blades, tools, scrap choppers, tyre
shredders etc. Surface roughness is one of the main indices that
determines the quality of machined products and is influenced by
various cutting parameters. In machining operations, achieving
desired surface quality by optimization of machining parameters, is a
challenging job. In case of mating components the surface roughness
become more essential and is influenced by the cutting parameters,
because, these quality structures are highly correlated and are
expected to be influenced directly or indirectly by the direct effect of
process parameters or their interactive effects (i.e. on process
environment). In this work, the effects of selected process parameters
on surface roughness and subsequent setting of parameters with the
levels have been accomplished by Taguchi’s parameter design
approach. The experiments have been performed as per the
combination of levels of different process parameters suggested by
L9 orthogonal array. Experimental investigation of the end milling of
AISI D2 steel with carbide tool by varying feed, speed and depth of
cut and the surface roughness has been measured using surface
roughness tester. Analyses of variance have been performed for mean
and signal-to-noise ratio to estimate the contribution of the different
process parameters on the process.
Abstract: Due to the rapid increase of Internet, web opinion
sources dynamically emerge which is useful for both potential
customers and product manufacturers for prediction and decision
purposes. These are the user generated contents written in natural
languages and are unstructured-free-texts scheme. Therefore, opinion
mining techniques become popular to automatically process customer
reviews for extracting product features and user opinions expressed
over them. Since customer reviews may contain both opinionated and
factual sentences, a supervised machine learning technique applies
for subjectivity classification to improve the mining performance. In
this paper, we dedicate our work is the task of opinion
summarization. Therefore, product feature and opinion extraction is
critical to opinion summarization, because its effectiveness
significantly affects the identification of semantic relationships. The
polarity and numeric score of all the features are determined by
Senti-WordNet Lexicon. The problem of opinion summarization
refers how to relate the opinion words with respect to a certain
feature. Probabilistic based model of supervised learning will
improve the result that is more flexible and effective.
Abstract: The turbocharger and turbocharging have been the
inherent component of diesel engines, so that critical parameters of
such engines, as BSFC (Brake Specific Fuel Consumption) or
thermal efficiency, fuel consumption, BMEP (Brake Mean Effective
Pressure), the power density output and emission level have been
improved extensively. In general, the turbocharger can be considered
as the most complex component of diesel engines, because it has
closely interrelated turbomachinery concepts of the turbines and the
compressors to thermodynamic fundamentals of internal combustion
engines and stress analysis of all components.
In this paper, a waste gate for a conventional single stage radial
turbine is investigated by consideration of turbochargers operation
constrains and engine operation conditions, without any detail
designs in the turbine and the compressor. Amount of opening waste
gate which extended between the ranges of full opened and closed
valve, is demonstrated by limiting compressor boost pressure ratio.
Obtaining of an optimum point by regard above mentioned items is
surveyed by three linked meanline modeling programs together
which consist of Turbomatch®, Compal®, Rital® madules in concepts
NREC® respectively.
Abstract: Development of a method to estimate gene functions is
an important task in bioinformatics. One of the approaches for the
annotation is the identification of the metabolic pathway that genes are
involved in. Since gene expression data reflect various intracellular
phenomena, those data are considered to be related with genes’
functions. However, it has been difficult to estimate the gene function
with high accuracy. It is considered that the low accuracy of the
estimation is caused by the difficulty of accurately measuring a gene
expression. Even though they are measured under the same condition,
the gene expressions will vary usually. In this study, we proposed a
feature extraction method focusing on the variability of gene
expressions to estimate the genes' metabolic pathway accurately. First,
we estimated the distribution of each gene expression from replicate
data. Next, we calculated the similarity between all gene pairs by KL
divergence, which is a method for calculating the similarity between
distributions. Finally, we utilized the similarity vectors as feature
vectors and trained the multiclass SVM for identifying the genes'
metabolic pathway. To evaluate our developed method, we applied the
method to budding yeast and trained the multiclass SVM for
identifying the seven metabolic pathways. As a result, the accuracy
that calculated by our developed method was higher than the one that
calculated from the raw gene expression data. Thus, our developed
method combined with KL divergence is useful for identifying the
genes' metabolic pathway.
Abstract: The goal of image segmentation is to cluster pixels
into salient image regions. Segmentation could be used for object
recognition, occlusion boundary estimation within motion or stereo
systems, image compression, image editing, or image database lookup.
In this paper, we present a color image segmentation using
support vector machine (SVM) pixel classification. Firstly, the pixel
level color and texture features of the image are extracted and they
are used as input to the SVM classifier. These features are extracted
using the homogeneity model and Gabor Filter. With the extracted
pixel level features, the SVM Classifier is trained by using FCM
(Fuzzy C-Means).The image segmentation takes the advantage of
both the pixel level information of the image and also the ability of
the SVM Classifier. The Experiments show that the proposed method
has a very good segmentation result and a better efficiency, increases
the quality of the image segmentation compared with the other
segmentation methods proposed in the literature.
Abstract: In this research (using induction furnace process)
nodular iron with three different percentages of copper (residual,
0.5% and 1,2%) was obtained. Chemical analysis was performed by
mass spectrometry and microstructures were characterized by Optical
Microscopy (ASTM E3) and Scanning Electron Microscopy (SEM).
The study of mechanical behavior was carried out in a mechanical
test machine (ASTM E8) and a Pin on disk tribometer (ASTM G99)
was used to assess wear resistance. It is observed that the dissolution
of copper in crystal lattice increases the pearlite structure improving
the wear and hardness behavior, but producing a contrary effect on
the energy absorption.
Abstract: ESPRIT-TLS method appears a good choice for high
resolution fault detection in induction machines. It has a very high
effectiveness in the frequency and amplitude identification.
Contrariwise, it presents a high computation complexity which
affects its implementation in real time fault diagnosis. To avoid this
problem, a Fast-ESPRIT algorithm that combined the IIR band-pass
filtering technique, the decimation technique and the original
ESPRIT-TLS method was employed to enhance extracting accurately
frequencies and their magnitudes from the wind stator current with
less computation cost. The proposed algorithm has been applied to
verify the wind turbine machine need in the implementation of an online,
fast, and proactive condition monitoring. This type of remote
and periodic maintenance provides an acceptable machine lifetime,
minimize its downtimes and maximize its productivity. The
developed technique has evaluated by computer simulations under
many fault scenarios. Study results prove the performance of Fast-
ESPRIT offering rapid and high resolution harmonics recognizing
with minimum computation time and less memory cost.
Abstract: The characteristic requirement for producing
rectangular shape bottles was a uniform thickness of the plastic bottle
wall. Die shaping was a good technique which controlled the wall
thickness of bottles. An advance technology which was the finite
element method (FEM) for blowing parison to be a rectangular shape
bottle was conducted to reduce waste plastic from a trial and error
method of a die shaping and parison control method. The artificial
intelligent (AI) comprised of artificial neural network and genetic
algorithm was selected to optimize the die gap shape from the FEM
results. The application of AI technique could optimize the suitable
die gap shape for the parison blow molding which did not depend on
the parison control method to produce rectangular bottles with the
uniform wall. Particularly, this application can be used with cheap
blow molding machines without a parison controller therefore it will
reduce cost of production in the bottle blow molding process.
Abstract: In this paper, we propose an automatic verification
technology of software patches for user virtual environments on IaaS
Cloud to decrease verification costs of patches. In these days, IaaS
services have been spread and many users can customize virtual
machines on IaaS Cloud like their own private servers. Regarding to
software patches of OS or middleware installed on virtual machines,
users need to adopt and verify these patches by themselves. This task
increases operation costs of users. Our proposed method replicates
user virtual environments, extracts verification test cases for user
virtual environments from test case DB, distributes patches to virtual
machines on replicated environments and conducts those test cases
automatically on replicated environments. We have implemented the
proposed method on OpenStack using Jenkins and confirmed the
feasibility. Using the implementation, we confirmed the effectiveness
of test case creation efforts by our proposed idea of 2-tier abstraction
of software functions and test cases. We also evaluated the automatic
verification performance of environment replications, test cases
extractions and test cases conductions.
Abstract: Every machine plays roles of client and server
simultaneously in a peer-to-peer (P2P) network. Though a P2P
network has many advantages over traditional client-server models
regarding efficiency and fault-tolerance, it also faces additional
security threats. Users/IT administrators should be aware of risks
from malicious code propagation, downloaded content legality, and
P2P software’s vulnerabilities. Security and preventative measures
are a must to protect networks from potential sensitive information
leakage and security breaches. Bit Torrent is a popular and scalable
P2P file distribution mechanism which successfully distributes large
files quickly and efficiently without problems for origin server. Bit
Torrent achieved excellent upload utilization according to
measurement studies, but it also raised many questions as regards
utilization in settings, than those measuring, fairness, and Bit
Torrent’s mechanisms choice. This work proposed a block selection
technique using Fuzzy ACO with optimal rules selected using ACO.
Abstract: Load modeling is one of the central functions in
power systems operations. Electricity cannot be stored, which means
that for electric utility, the estimate of the future demand is necessary
in managing the production and purchasing in an economically
reasonable way. A majority of the recently reported approaches are
based on neural network. The attraction of the methods lies in the
assumption that neural networks are able to learn properties of the
load. However, the development of the methods is not finished, and
the lack of comparative results on different model variations is a
problem. This paper presents a new approach in order to predict the
Tunisia daily peak load. The proposed method employs a
computational intelligence scheme based on the Fuzzy neural
network (FNN) and support vector regression (SVR). Experimental
results obtained indicate that our proposed FNN-SVR technique gives
significantly good prediction accuracy compared to some classical
techniques.
Abstract: This paper presents small signal stability study carried
over the 140-Bus, 31-Machine, 5-Area MEPE system and validated
on free and open source software: PSAT. Well-established linearalgebra
analysis, eigenvalue analysis, is employed to determine the
small signal dynamic behavior of test system. The aspects of local
and interarea oscillations which may affect the operation and
behavior of power system are analyzed. Eigenvalue analysis is carried
out to investigate the small signal behavior of test system and the
participation factors have been determined to identify the
participation of the states in the variation of different mode shapes.
Also, the variations in oscillatory modes are presented to observe the
damping performance of the test system.
Abstract: The 3D body movement signals captured during
human-human conversation include clues not only to the content of
people’s communication but also to their culture and personality.
This paper is concerned with automatic extraction of this information
from body movement signals. For the purpose of this research, we
collected a novel corpus from 27 subjects, arranged them into groups
according to their culture. We arranged each group into pairs and
each pair communicated with each other about different topics.
A state-of-art recognition system is applied to the problems of
person, culture, and topic recognition. We borrowed modeling,
classification, and normalization techniques from speech recognition.
We used Gaussian Mixture Modeling (GMM) as the main technique
for building our three systems, obtaining 77.78%, 55.47%, and
39.06% from the person, culture, and topic recognition systems
respectively. In addition, we combined the above GMM systems with
Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and
40.63% accuracy for person, culture, and topic recognition
respectively.
Although direct comparison among these three recognition
systems is difficult, it seems that our person recognition system
performs best for both GMM and GMM-SVM, suggesting that intersubject
differences (i.e. subject’s personality traits) are a major
source of variation. When removing these traits from culture and
topic recognition systems using the Nuisance Attribute Projection
(NAP) and the Intersession Variability Compensation (ISVC)
techniques, we obtained 73.44% and 46.09% accuracy from culture
and topic recognition systems respectively.
Abstract: Analyzing DNA microarray data sets is a great
challenge, which faces the bioinformaticians due to the complication
of using statistical and machine learning techniques. The challenge
will be doubled if the microarray data sets contain missing data,
which happens regularly because these techniques cannot deal with
missing data. One of the most important data analysis process on
the microarray data set is feature selection. This process finds the
most important genes that affect certain disease. In this paper, we
introduce a technique for imputing the missing data in microarray
data sets while performing feature selection.
Abstract: Waste silicon carbide (waste SiC) filled high-density
polyethylene (HDPE) with and without surface modifiers were
studied. Two types of surface modifiers namely; high-density
polyethylene-grafted-maleic anhydride (HDPE-g-MA) and 3-aminopropyltriethoxysilane have been used in this study. The
composites were produced using a two roll mill, extruder and shaped
in a hydraulic compression molding machine. The mechanical
properties of polymer composites such as flexural strength and
modulus, impact strength, tensile strength, stiffness and hardness
were investigated over a range of compositions. It was found that,
flexural strength and modulus, tensile modulus and hardness
increased, whereas impact strength and tensile strength decreased
with the increasing in filler contents, compared to the neat HDPE. At
similar filler content, the effect of both surface modifiers increased
flexural modulus, impact strength, tensile strength and stiffness but
reduced the flexural strength. Morphological investigation using
SEM revealed that the improvement in mechanical properties was
due to enhancement of the interfacial adhesion between waste SiC
and HDPE.
Abstract: The study of the electrical signals produced by neural
activities of human brain is called Electroencephalography. In this
paper, we propose an automatic and efficient EEG signal
classification approach. The proposed approach is used to classify the
EEG signal into two classes: epileptic seizure or not. In the proposed
approach, we start with extracting the features by applying Discrete
Wavelet Transform (DWT) in order to decompose the EEG signals
into sub-bands. These features, extracted from details and
approximation coefficients of DWT sub-bands, are used as input to
Principal Component Analysis (PCA). The classification is based on
reducing the feature dimension using PCA and deriving the supportvectors
using Support Vector Machine (SVM). The experimental are
performed on real and standard dataset. A very high level of
classification accuracy is obtained in the result of classification.
Abstract: Construction cost estimation is one of the most
important aspects of construction project design. For generations, the
process of cost estimating has been manual, time-consuming and
error-prone. This has partly led to most cost estimates to be unclear
and riddled with inaccuracies that at times lead to over- or underestimation
of construction cost. The development of standard set of
measurement rules that are understandable by all those involved in a
construction project, have not totally solved the challenges. Emerging
Building Information Modelling (BIM) technologies can exploit
standard measurement methods to automate cost estimation process
and improve accuracies. This requires standard measurement
methods to be structured in ontological and machine readable format;
so that BIM software packages can easily read them. Most standard
measurement methods are still text-based in textbooks and require
manual editing into tables or Spreadsheet during cost estimation. The
aim of this study is to explore the development of an ontology based
on New Rules of Measurement (NRM) commonly used in the UK for
cost estimation. The methodology adopted is Methontology, one of
the most widely used ontology engineering methodologies. The
challenges in this exploratory study are also reported and
recommendations for future studies proposed.
Abstract: Parameters of flow are calculated in vaneless diffusers
with relative width 0,014–0,10. Inlet angles of flow and similarity
criteria were varied. There is information on flow separation,
boundary layer development, configuration of streamlines.
Polytrophic efficiency, loss coefficient and recovery coefficient are
used to compare effectiveness of diffusers. The sample of
optimization of narrow diffuser with conical walls is presented. Three
wide diffusers with narrowing walls are compared. The work is made
in the R&D laboratory “Gas dynamics of turbo machines” of the TU
SPb.
Abstract: Some regularities of formation of a new structural
state of the thermoplastic polymers - gradually oriented (stretched)
state (GOS) are discussed. Transition into GOS is realized by the
graded oriented stretching - by action of inhomogeneous mechanical
field on the isotropic linear polymers or by zone stretching that is
implemented on a standard tensile-testing machine with using a
specially designed zone stretching device (ZSD). Both technical
approaches (especially zone stretching method) allows to manage the
such quantitative parameters of gradually oriented polymers as a
range of change in relative elongation/orientation degree, length of
this change and profile (linear, hyperbolic, parabolic, logarithmic,
etc.). The possibility of obtaining functionally graded materials
(FGMs) by graded orientation method is briefly discussed. Uniaxial
graded stretching method should be considered as an effective
technological solution to create polymer materials with a
predetermined gradient of physical properties.
Abstract: The aim of this study was to design and simulate a
particular type of Asynchronous State Machine (ASM), namely a
‘traffic light controller’ (TLC), operated at a frequency of 0.5 Hz.
The design task involved two main stages: firstly, designing a 4-bit
binary counter using J-K flip flops as the timing signal and,
subsequently, attaining the digital logic by deploying ASM design
process. The TLC was designed such that it showed a sequence of
three different colours, i.e. red, yellow and green, corresponding to
set thresholds by deploying the least number of AND, OR and NOT
gates possible. The software Multisim was deployed to design such
circuit and simulate it for circuit troubleshooting in order for it to
display the output sequence of the three different colours on the
traffic light in the correct order. A clock signal, an asynchronous 4-
bit binary counter that was designed through the use of J-K flip flops
along with an ASM were used to complete this sequence, which was
programmed to be repeated indefinitely. Eventually, the circuit was
debugged and optimized, thus displaying the correct waveforms of
the three outputs through the logic analyser. However, hazards
occurred when the frequency was increased to 10 MHz. This was
attributed to delays in the feedback being too high.