Abstract: As the use of geothermal energy grows internationally
more effort is required to monitor and protect areas with rare and
important geothermal surface features. A number of approaches are
presented for developing and calibrating numerical geothermal
reservoir models that are capable of accurately representing
geothermal surface features. The approaches are discussed in the
context of cases studies of the Rotorua geothermal system and the
Orakei-korako geothermal system, both of which contain important
surface features. The results show that models are able to match the
available field data accurately and hence can be used as valuable
tools for predicting the future response of the systems to changes in
use.
Abstract: Speech Segmentation is the measure of the change
point detection for partitioning an input speech signal into regions
each of which accords to only one speaker. In this paper, we apply
two features based on multi-scale product (MP) of the clean speech,
namely the spectral centroid of MP, and the zero crossings rate of
MP. We focus on multi-scale product analysis as an important tool
for segmentation extraction. The MP is based on making the product
of the speech wavelet transform coefficients (WTC). We have
estimated our method on the Keele database. The results show the
effectiveness of our method. It indicates that the two features can find
word boundaries, and extracted the segments of the clean speech.
Abstract: Neurons in the nervous system communicate with
each other by producing electrical signals called spikes. To
investigate the physiological function of nervous system it is essential
to study the activity of neurons by detecting and sorting spikes in the
recorded signal. In this paper a method is proposed for considering
the spike sorting problem which is based on the nonlinear modeling
of spikes using exponential autoregressive model. The genetic
algorithm is utilized for model parameter estimation. In this regard
some selected model coefficients are used as features for sorting
purposes. For optimal selection of model coefficients, self-organizing
feature map is used. The results show that modeling of spikes with
nonlinear autoregressive model outperforms its linear counterpart.
Also the extracted features based on the coefficients of exponential
autoregressive model are better than wavelet based extracted features
and get more compact and well-separated clusters. In the case of
spikes different in small-scale structures where principal component
analysis fails to get separated clouds in the feature space, the
proposed method can obtain well-separated cluster which removes
the necessity of applying complex classifiers.
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: These days, the field of tissue engineering is getting
serious attention due to its usefulness. Bone tissue engineering helps
to address and sort-out the critical sized and non-healing orthopedic
problems by the creation of manmade bone tissue. We will design
and validate an efficient numerical model, which will simulate the
effective diffusivity in bone tissue engineering. Our numerical model
will be based on the finite element analysis of the diffusion-reaction
equations. It will have the ability to optimize the diffusivity, even
at multi-scale, with the variation of time. It will also have a special
feature “parametric sweep”, with which we will be able to predict
the oxygen, glucose and cell density dynamics, more accurately. We
will fix these problems by modifying the governing equations, by
selecting appropriate spatio-temporal finite element schemes and by
transient analysis.
Abstract: Currently, there is excessively growing information
about places on Facebook, which is the largest social network but
such information is not explicitly organized and ranked. Therefore
users cannot exploit such data to recommend places conveniently and
quickly. This paper proposes a Facebook application and an Android
application that recommend places based on the number of check-ins
of those places, the distance of those places from the current location,
the number of people who like Facebook page of those places, and
the number of talking about of those places. Related Facebook data is
gathered via Facebook API requests. The experimental results of the
developed applications show that the applications can recommend
places and rank interesting places from the most to the least. We have
found that the average satisfied score of the proposed Facebook
application is 4.8 out of 5. The users’ satisfaction can increase by
adding the app features that support personalization in terms of
interests and preferences.
Abstract: One image is worth more than thousand words.
Images if analyzed can reveal useful information. Low level image
processing deals with the extraction of specific feature from a single
image. Now the question arises: What technique should be used to
extract patterns of very large and detailed image database? The
answer of the question is: “Image Mining”. Image Mining deals with
the extraction of image data relationship, implicit knowledge, and
another pattern from the collection of images or image database. It is
nothing but the extension of Data Mining. In the following paper, not
only we are going to scrutinize the current techniques of image
mining but also present a new technique for mining images using
Genetic Algorithm.
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: The industrial process adds to engineering wood
products features absent in solid wood, with homogeneous structure
and reduced defects, improved physical and mechanical properties,
bio-deterioration, resistance and better dimensional stability,
improving quality and increasing the reliability of structures wood.
These features combined with using fast-growing trees, make them
environmentally ecological products, ensuring a strong consumer
market. The wood I-joists are manufactured by the industrial profiles
bonding flange and web, an important aspect of the production of
wooden I-beams is the adhesive joint that bonds the web to the
flange. Adhesives can effectively transfer and distribute stresses,
thereby increasing the strength and stiffness of the composite. The
objective of this study is to evaluate different resins in a shear strain
specimens with the aim of analyzing the most efficient resin and
possibility of using national products, reducing the manufacturing
cost. First was conducted a literature review, where established the
geometry and materials generally used, then established and analyzed
8 national resins and produced six specimens for each.
Abstract: Iran has several potential for using renewable
energies, so use them could significantly contribute to energy supply.
The purpose of this paper is to identify the potential of the country
and select the appropriate DG technologies with consideration the
potential and primary energy resources in the regions. In this context,
hybrid energy systems proportionate with the potential of different
regions will be determined based on technical, economic, and
environmental aspect. In the following the proposed structure will be
optimized in terms of size and cost. DG technologies used in this
project include photovoltaic system, wind turbine, diesel generator
and battery bank. The HOMER software is applied for choosing the
appropriate structure and the optimization of system sizing. The
results have been analyzed in terms of technical and economic. The
performance and the cost of each project demonstrate the appropriate
structure of hybrid energy system in that region.
Abstract: In this paper, a nonlinear Finite Element Analysis
(FEA) was carried out using ANSYS software to build a model able
of predicting the behavior of Reinforced Concrete (RC) beams with
unbonded reinforcement. The FEA model was compared to existing
experimental data by other researchers. The existing experimental
data consisted of 16 beams that varied from structurally sound beams
to beams with unbonded reinforcement with different unbonded
lengths and reinforcement ratios. The model was able to predict the
ultimate flexural strength, load-deflection curve, and crack pattern of
concrete beams with unbonded reinforcement. It was concluded that
when the when the unbonded length is less than 45% of the span,
there will be no decrease in the ultimate flexural strength due to the
loss of bond between the steel reinforcement and the surrounding
concrete regardless of the reinforcement ratio. Moreover, when the
reinforcement ratio is relatively low, there will be no decrease in
ultimate flexural strength regardless of the length of unbond.
Abstract: Image search engines rely on the surrounding textual
keywords for the retrieval of images. It is a tedious work for the
search engines like Google and Bing to interpret the user’s search
intention and to provide the desired results. The recent researches
also state that the Google image search engines do not work well on
all the images. Consequently, this leads to the emergence of efficient
image retrieval technique, which interprets the user’s search intention
and shows the desired results. In order to accomplish this task, an
efficient image re-ranking framework is required. Sequentially, to
provide best image retrieval, the new image re-ranking framework is
experimented in this paper. The implemented new image re-ranking
framework provides best image retrieval from the image dataset by
making use of re-ranking of retrieved images that is based on the
user’s desired images. This is experimented in two sections. One is
offline section and other is online section. In offline section, the reranking
framework studies differently (reference classes or Semantic
Spaces) for diverse user query keywords. The semantic signatures get
generated by combining the textual and visual features of the images.
In the online section, images are re-ranked by comparing the
semantic signatures that are obtained from the reference classes with
the user specified image query keywords. This re-ranking
methodology will increases the retrieval image efficiency and the
result will be effective to the user.
Abstract: A parametric study on circular thin-walled pipes
subjected to pure bending is performed. Both straight and curved
pipes are considered. Ratio D/t, initial pipe curvature and internal
pressure are the parameters varying in the analyses. The study is
mainly FEA-based.
It is found that negative curvatures (opposite to bending moment)
considerably increase stiffness and buckling limit of the pipe when no
internal pressure is acting and, similarly, positive curvatures decrease
the stiffness and buckling limit. For internal pressurised pipes the
effects of initial pipe curvature are less relevant. Results show that
this phenomenon is in relationship with the cross-section deformation
due to bending moment, which undergoes relevant ovalisation for no
pressurised pipes and little ovalisation for pressurised pipes.
Abstract: This paper introduces novel approaches to partitioning
and mapping in terms of model-based embedded multicore system
engineering and further discusses benefits, industrial relevance and
features in common with existing approaches. In order to assess
and evaluate results, both approaches have been applied to a real
industrial application as well as to various prototypical demonstrative
applications, that have been developed and implemented for
different purposes. Evaluations show, that such applications improve
significantly according to performance, energy efficiency, meeting
timing constraints and covering maintaining issues by using
the AMALTHEA platform and the implemented approaches.
Furthermore, the model-based design provides an open, expandable,
platform independent and scalable exchange format between
OEMs, suppliers and developers on different levels. Our proposed
mechanisms provide meaningful multicore system utilization since
load balancing by means of partitioning and mapping is effectively
performed with regard to the modeled systems including hardware,
software, operating system, scheduling, constraints, configuration and
more data.
Abstract: The development, operation and maintenance of
Integrated Waste Management Systems (IWMS) affects essentially
the sustainable concern of every region. The features of such systems
have great influence on all of the components of sustainability. In
order to reach the optimal way of processes, a comprehensive
mapping of the variables affecting the future efficiency of the system
is needed such as analysis of the interconnections among the
components and modeling of their interactions. The planning of a
IWMS is based fundamentally on technical and economical
opportunities and the legal framework. Modeling the sustainability
and operation effectiveness of a certain IWMS is not in the scope of
the present research. The complexity of the systems and the large
number of the variables require the utilization of a complex approach
to model the outcomes and future risks. This complex method should
be able to evaluate the logical framework of the factors composing
the system and the interconnections between them. The authors of
this paper studied the usability of the Fuzzy Cognitive Map (FCM)
approach modeling the future operation of IWMS’s. The approach
requires two input data set. One is the connection matrix containing
all the factors affecting the system in focus with all the
interconnections. The other input data set is the time series, a
retrospective reconstruction of the weights and roles of the factors.
This paper introduces a novel method to develop time series by
content analysis.
Abstract: Solenoid operated electromagnetic control valve
(ECV) playing an important role for car’s air conditioning control
system. ECV is used in external variable displacement swash plate
type compressor and controls the entire air conditioning system by
means of a pulse width modulation (PWM) input signal supplying
from an external source (controller). Complete form of ECV contains
number of internal features like valve body, core, valve guide,
plunger, guide pin, plunger spring, bellows etc. While designing the
ECV; dimensions of different internal items must meet the standard
requirements as it is quite challenging. In this research paper,
especially the dimensioning of ECV body and its three pressure ports
through which the air/refrigerant passes are considered. Here internal
leakage test analysis of ECV body is being carried out from its
discharge port (Pd) to crankcase port (Pc) when the guide valve is
placed inside it. The experiments have made both in ordinary and
digital system using different assumptions and thereafter compare the
results.
Abstract: In and around Erode District, it is estimated that more
than 1250 chemical and allied textile processing fabric industries are
affected, partially closed and shut off for various reasons such as poor
management, poor supplier performance, lack of planning for
productivity, fluctuation of output, poor investment, waste analysis,
labor problems, capital/labor ratio, accumulation of stocks, poor
maintenance of resources, deficiencies in the quality of fabric, low
capacity utilization, age of plant and equipment, high investment and
input but low throughput, poor research and development, lack of
energy, workers’ fear of loss of jobs, work force mix and work ethic.
The main objective of this work is to analyze the existing conditions
in textile fabric sector, validate the break even of Total Productivity
(TP), analyze, design and implement fuzzy sets and mathematical
programming for improvement of productivity and quality
dimensions in the fabric processing industry. It needs to be
compatible with the reality of textile and fabric processing industries.
The highly risk events from productivity and quality dimension were
found by fuzzy systems and results are wrapped up among the textile
fabric processing industry.
Abstract: As the current status and growth of Indian automobile
industry is remarkable, transportation sectors are the main concern in
terms of energy security and climate change. Due to rising demand of
fuel and its dependency on foreign countries that affects the GDP of
nation, suggests that penetration of electrical vehicle will increase in
near future. So in this context analysis is done if the 10 percent of
conventional vehicles including cars, three wheelers and two
wheelers becomes electrical vehicles in near future which is also a
part of Nations Electric Mobility Mission Plan then the saving which
improves the nation’s economy is analyzed in detail. Whether the
Indian electricity grid is capable of taking this load with current
generation and demand all over the country is also analyzed in detail.
Current situation of Indian grid is analyzed and how the gap between
generation and demand can be reduced is discussed in terms of
increasing generation capacity and energy conservation measures.
Electrical energy conservation measures in Industry and especially in
rural areas have been analyzed to improve performance of Indian
electricity grid in context of electrical vehicle penetration in near
future. Author was a part of Vishvakarma yojna in which energy
losses were measured in 255 villages of Gujarat and solutions were
suggested to mitigate them and corresponding reports was submitted
to the authorities of Gujarat government.
Abstract: Composite materials, due to their unique properties
such as high strength to weight ratio, corrosion resistance, and impact
resistance have huge potential as structural materials in automotive,
construction and transportation applications. However, these
properties often come at higher cost owing to complex design
methods, difficult manufacturing processes and raw material cost.
Traditionally, tapered laminated composite structures are
manufactured using autoclave manufacturing process by ply drop off
technique. Autoclave manufacturing though very powerful suffers
from high capital investment and higher energy consumption. As per
the current trends in composite manufacturing, Out of Autoclave
(OoA) processes are looked as emerging technologies for
manufacturing the structural composite components for aerospace
and defense applications. However, there is a need for improvement
among these processes to make them reliable and consistent. In this
paper, feasibility of using out of autoclave process to manufacture the
variable thickness cantilever beam is discussed. The minimum weight
design for the composite beam is obtained using constant stress beam
concept by tailoring the thickness of the beam. Ply drop off
techniques was used to fabricate the variable thickness beam from
glass/epoxy prepregs. Experiments were conducted to measure
bending stresses along the span of the cantilever beam at different
intervals by applying the concentrated load at the free end.
Experimental results showed that the stresses in the bean at different
intervals were constant. This proves the ability of OoA process to
manufacture the constant stress beam. Finite element model for the
constant stress beam was developed using commercial finite element
simulation software. It was observed that the simulation results
agreed very well with the experimental results and thus validated
design and manufacturing approach used.
Abstract: Karst term is the determiner of a variety of areas or
landforms and unique perspectives that have been formed in result of
the of the ingredients dissolution of rocks constituter by natural
waters. Shiraz area with an area of 5322km2 is located in the simple
folded belt in the southern part of Zagros Mountain of Fars, and is
surrounded with Limestone Mountains (Asmari formation). Shiraz
area is located in Calcareous areas. The infrastructure of this city is
lime and absorbing wells that the city can influence the Limestone
dissolution and those accelerate its rate and increase the cavitation
below the surface. Dasht-e Arjan is a graben, which has been created
as the result of activity of two normal faults in its east and west sides.
It is a complete sample of Karst plains (Polje) which has been created
with the help of tectonic forces (fault) and dissolution process of
water in Asmari limestone formation. It is located 60km. off south
west of Shiraz (on Kazeroon-Shiraz road). In 1971, UNESCO has
recognized this plain as a reserve of biosphere. It is considered as one
of the world’s most beautiful geological phenomena, so that most of
the world’s geologists are interested in visiting this place. The
purpose of this paper is to identify and introduce landscapes of Karst
features shiraz city and Dasht-e Arjan including Karst dissolution
features (Lapiez, Karst springs, dolines, caves, underground caves,
ponors, and Karst valleys), anticlines and synclines, and Arjan Lake.