Abstract: The building sector is responsible, in many
industrialized countries, for about 40% of the total energy
requirements, so it seems necessary to devote some efforts in this
area in order to achieve a significant reduction of energy
consumption and of greenhouse gases emissions.
The paper presents a study aiming at providing a design
methodology able to identify the best configuration of the system
building/plant, from a technical, economic and environmentally point
of view.
Normally, the classical approach involves a building's energy
loads analysis under steady state conditions, and subsequent selection
of measures aimed at improving the energy performance, based on
previous experience made by architects and engineers in the design
team. Instead, the proposed approach uses a sequence of two wellknown
scientifically validated calculation methods (TRNSYS and
RETScreen), that allow quite a detailed feasibility analysis.
To assess the validity of the calculation model, an existing,
historical building in Central Italy, that will be the object of
restoration and preservative redevelopment, was selected as a casestudy.
The building is made of a basement and three floors, with a
total floor area of about 3,000 square meters.
The first step has been the determination of the heating and
cooling energy loads of the building in a dynamic regime by means,
which allows simulating the real energy needs of the building in
function of its use. Traditional methodologies, based as they are on
steady-state conditions, cannot faithfully reproduce the effects of
varying climatic conditions and of inertial properties of the structure.
With this model is possible to obtain quite accurate and reliable
results that allow identifying effective combinations building-HVAC
system.
The second step has consisted of using output data obtained as
input to the calculation model, which enables to compare different
system configurations from the energy, environmental and financial
point of view, with an analysis of investment, and operation and
maintenance costs, so allowing determining the economic benefit of
possible interventions.
The classical methodology often leads to the choice of
conventional plant systems, while our calculation model provides a
financial-economic assessment for innovative energy systems and
low environmental impact.
Computational analysis can help in the design phase, particularly
in the case of complex structures with centralized plant systems, by
comparing the data returned by the calculation model for different
design options.
Abstract: Attributes and methods are the basic contents of an
object-oriented class. The connectivity among these class members
and the relationship between the class and other classes play an
important role in determining the quality of an object-oriented
system. Class cohesion evaluates the degree of relatedness of class
attributes and methods, whereas class coupling refers to the degree to
which a class is related to other classes. Researchers have proposed
several class cohesion and class coupling measures. However, the
correlation between class coupling and class cohesion measures has
not been thoroughly studied. In this paper, using classes of three
open-source Java systems, we empirically investigate the correlation
between several measures of connectivity-based class cohesion and
coupling. Four connectivity-based cohesion measures and eight
coupling measures are considered in the empirical study. The
empirical study results show that class connectivity-based cohesion
and coupling internal quality attributes are inversely correlated. The
strength of the correlation depends highly on the cohesion and
coupling measurement approaches.
Abstract: Thin-walled elements with a matrix set on a base of
high-valuable Portland cement with dispersed reinforcement from
alkali-resistant glass fibres are used in a range of applications as
claddings of buildings and infrastructure constructions as well as
various architectural elements of residential buildings.
Even though their elementary thickness and therefore total weight
is quite low, architects and building companies demand on even
further decreasing of the bulk density of these fibre-cement elements
for the reason of loading elimination of connected superstructures
and easier assembling in demand conditions.
By the means of various kinds of light-weight aggregates it is
possible to achieve light-weighing of these composite elements.
From the range of possible fillers with different material properties
granulated expanded glass worked the best.
By the means of laboratory testing an effect of two fillers based on
expanded glass on the fibre reinforced cement composite was
verified.
Practical applicability was tested in the production of commonly
manufactured glass fibre reinforced concrete elements, such as
channels for electrical cable deposition, products for urban equipment
and especially various cladding elements.
Even though these are not structural elements, it is necessary to
evaluate also strength characteristics and resistance to environment
for their durability in certain applications.
Abstract: This work deals with parameter identification of
permanent magnet motors, a class of ac motor which is particularly
important in industrial automation due to characteristics like
applications high performance, are very attractive for applications
with limited space and reducing the need to eliminate because they
have reduced size and volume and can operate in a wide speed range,
without independent ventilation. By using experimental data and
genetic algorithm we have been able to extract values for both the
motor inductance and the electromechanical coupling constant, which
are then compared to measured and/or expected values.
Abstract: The detection of moving objects from a video image
sequences is very important for object tracking, activity recognition,
and behavior understanding in video surveillance.
The most used approach for moving objects detection / tracking is
background subtraction algorithms. Many approaches have been
suggested for background subtraction. But, these are illumination
change sensitive and the solutions proposed to bypass this problem
are time consuming.
In this paper, we propose a robust yet computationally efficient
background subtraction approach and, mainly, focus on the ability to
detect moving objects on dynamic scenes, for possible applications in
complex and restricted access areas monitoring, where moving and
motionless persons must be reliably detected. It consists of three
main phases, establishing illumination changes invariance,
background/foreground modeling and morphological analysis for
noise removing.
We handle illumination changes using Contrast Limited Histogram
Equalization (CLAHE), which limits the intensity of each pixel to
user determined maximum. Thus, it mitigates the degradation due to
scene illumination changes and improves the visibility of the video
signal. Initially, the background and foreground images are extracted
from the video sequence. Then, the background and foreground
images are separately enhanced by applying CLAHE.
In order to form multi-modal backgrounds we model each channel
of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture
Model (GMM). Finally, we post process the resulting binary
foreground mask using morphological erosion and dilation
transformations to remove possible noise.
For experimental test, we used a standard dataset to challenge the
efficiency and accuracy of the proposed method on a diverse set of
dynamic scenes.
Abstract: Meeting the growth in demand for digital services
such as social media, telecommunications, and business and cloud
services requires large scale data centres, which has led to an increase
in their end use energy demand. Generally, over 30% of data centre
power is consumed by the necessary cooling overhead. Thus energy
can be reduced by improving the cooling efficiency. Air and liquid
can both be used as cooling media for the data centre. Traditional
data centre cooling systems use air, however liquid is recognised as a
promising method that can handle the more densely packed data
centres. Liquid cooling can be classified into three methods; rack heat
exchanger, on-chip heat exchanger and full immersion of the
microelectronics. This study quantifies the improvements of heat
transfer specifically for the case of immersed microelectronics by
varying the CPU and heat sink location. Immersion of the server is
achieved by filling the gap between the microelectronics and a water
jacket with a dielectric liquid which convects the heat from the CPU
to the water jacket on the opposite side. Heat transfer is governed by
two physical mechanisms, which is natural convection for the fixed
enclosure filled with dielectric liquid and forced convection for the
water that is pumped through the water jacket. The model in this
study is validated with published numerical and experimental work
and shows good agreement with previous work. The results show that
the heat transfer performance and Nusselt number (Nu) is improved
by 89% by placing the CPU and heat sink on the bottom of the
microelectronics enclosure.
Abstract: Previous studies on financial distress prediction choose
the conventional failing and non-failing dichotomy; however, the
distressed extent differs substantially among different financial
distress events. To solve the problem, “non-distressed”, “slightlydistressed”
and “reorganization and bankruptcy” are used in our article
to approximate the continuum of corporate financial health. This paper
explains different financial distress events using the two-stage method.
First, this investigation adopts firm-specific financial ratios, corporate
governance and market factors to measure the probability of various
financial distress events based on multinomial logit models.
Specifically, the bootstrapping simulation is performed to examine the
difference of estimated misclassifying cost (EMC). Second, this work
further applies macroeconomic factors to establish the credit cycle
index and determines the distressed cut-off indicator of the two-stage
models using such index. Two different models, one-stage and
two-stage prediction models are developed to forecast financial
distress, and the results acquired from different models are compared
with each other, and with the collected data. The findings show that the
one-stage model has the lower misclassification error rate than the
two-stage model. The one-stage model is more accurate than the
two-stage model.
Abstract: Africa enjoys some of the best solar radiation levels in
the world averaging between 4-6 kWh/m2/day for most of the year
and the global economic and political conditions that tend to make
African countries more dependent on their own energy resources
have caused growing interest in renewable energy based
technologies. However to-date, implementation of modern Energy
Technologies in Africa is still very low especially the use of solar
conversion technologies. This paper presents literature review and
analysis relating to the techno-economic feasibility of solar
photovoltaic power generation in Africa. The literature is basically
classified into the following four main categories. Techno-economic
feasibility of solar photovoltaic power generation, design methods,
performance evaluations of various systems and policy of potential
future of technological development of photovoltaic (PV) in Africa
by exploring the impact of alternative policy instruments and
technology cost reductions on the financial viability of investing solar
photovoltaic in Africa.
Abstract: In this paper, we used data mining to extract
biomedical knowledge. In general, complex biomedical data
collected in studies of populations are treated by statistical methods,
although they are robust, they are not sufficient in themselves to
harness the potential wealth of data. For that you used in step two
learning algorithms: the Decision Trees and Support Vector Machine
(SVM). These supervised classification methods are used to make the
diagnosis of thyroid disease. In this context, we propose to promote
the study and use of symbolic data mining techniques.
Abstract: The purpose of this study is to investigate the kinematic
characteristics and differences of the snatch barbell trajectory of 53 kg
class female weight lifters. We take the 2014 Taiwan College Cup
players as examples, and tend to make kinematic applications through
the proven weightlifting barbell track system. The competition videos
are taken by consumer camcorder with a tripod which set up at the side
of the lifter. The results will be discussed in three parts, the first part is
various lifting phase, the second part is the compare lifting between
success and unsuccessful, and the third part is to compare the
outstanding player with the general. Conclusion through the barbell
can be used to observe the trajectories of our players lifting the usual
process cannot be observed in the presence of malfunction or habits, so
that the coach can find the problem and guide the players more
accurately. Our system can be applied in practice and competition to
increase the resilience of the lifter on the field.
Abstract: Natural hydrocarbon seepage has helped petroleum
exploration as a direct indicator of gas and/or oil subsurface
accumulations. Surface macro-seeps are generally an indication of a
fault in an active Petroleum Seepage System belonging to a Total
Petroleum System. This paper describes a case study in which
multiple analytical techniques were used to identify and characterize
trace petroleum-related hydrocarbons and other volatile organic
compounds in groundwater samples collected from Sousse aquifer
(Central Tunisia). The analytical techniques used for analyses of
water samples included gas chromatography-mass spectrometry (GCMS),
capillary GC with flame-ionization detection, Compound
Specific Isotope Analysis, Rock Eval Pyrolysis. The objective of the
study was to confirm the presence of gasoline and other petroleum
products or other volatile organic pollutants in those samples in order
to assess the respective implication of each of the potentially
responsible parties to the contamination of the aquifer. In addition,
the degree of contamination at different depths in the aquifer was also
of interest. The oil and gas seeps have been investigated using
biomarker and stable carbon isotope analyses to perform oil-oil and
oil-source rock correlations. The seepage gases are characterized by
high CH4 content, very low δ13CCH4 values (-71,9 ‰) and high
C1/C1–5 ratios (0.95–1.0), light deuterium–hydrogen isotope ratios (-
198 ‰) and light δ13CC2 and δ13CCO2 values (-23,8‰ and-23,8‰
respectively) indicating a thermogenic origin with the contribution of
the biogenic gas. An organic geochemistry study was carried out on
the more ten oil seep samples. This study includes light hydrocarbon
and biomarkers analyses (hopanes, steranes, n-alkanes, acyclic
isoprenoids, and aromatic steroids) using GC and GC-MS. The
studied samples show at least two distinct families, suggesting two
different types of crude oil origins: the first oil seeps appears to be
highly mature, showing evidence of chemical and/or biological
degradation and was derived from a clay-rich source rock deposited
in suboxic conditions. It has been sourced mainly by the lower
Fahdene (Albian) source rocks. The second oil seeps was derived
from a carbonate-rich source rock deposited in anoxic conditions,
well correlated with the Bahloul (Cenomanian-Turonian) source rock.
Abstract: Multi-Level Inverter technology has been developed in the area of high-power medium-voltage energy scheme, because of their advantages such as devices of lower rating can be used thereby enabling the schemes to be used for high voltage applications. Reduced Total Harmonic Distortion (THD).Since the dv/dt is low; the Electromagnetic Interference from the scheme is low. To avoid the switching losses Lower switching frequencies can be used. In this paper present a survey of various topologies, control strategy and modulation techniques used by these inverters. Here the regenerative and superior topologies are also discussed.
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: In this study, the root of the name Lykaonia and the geographical area defined as Lykaonia Region are mentioned. In this context, information concerning the settlements of Paleolithic Age, Neolithic Age and Chalcolithic Age are given place. Particularly the settlements belonging to Classical Age are localized and brief information about the history of these settlements is provided. In the light of this information, roads of Antique period in the region are evaluated.
Abstract: Learning Management System (LMS) is the system
which uses to manage the learning in order to grouping the content
and learning activity between the lecturer and learner including
online examination and evaluation. Nowadays, it is the borderless
learning era so the learning activities can be accessed from
everywhere in the world and also anytime via the information
technology and media. The learner can easily access to the
knowledge so the different in time and distance is not a constraint for
learning anymore.
The learning pattern which was used in this research is the
integration of the in-class learning and online learning via internet
and will be able to monitor the progress by the Learning management
system which will create the fast response and accessible learning
process via the social media. In order to increase the capability and
freedom of the learner, the system can show the current and history
of the learning document, video conference and also has the chat
room for the learner and lecturer to interact to each other.
So the objectives of the “The Design and Applied of Learning
Management System via Social Media on Internet: Case Study of
Operating System for Business Subject” are to expand the
opportunity of learning and to increase the efficiency of learning as
well as increase the communication channel between lecturer and
student. The data of this research was collect from 30 users of the
system which are students who enroll in the subject. And the result of
the research is in the “Very Good” which is conformed to the
hypothesis.
Abstract: This work introduces a simple device designed to
perform in-situ direct shear and sinkage tests on granular materials
as sand, clays, or regolith. It consists of a box nested within a larger
box. Both have open bottoms, allowing them to be lowered into the
material. Afterwards, two rotating plates on opposite sides of the
outer box will rotate outwards in order to clear regolith on either
side, providing room for the inner box to move relative to the plates
and perform a shear test without the resistance of the surrounding
soil. From this test, Coulomb parameters, including cohesion and
internal friction angle, as well as, Bekker parameters can be inferred.
This device has been designed for a laboratory setting, but with few
modifications, could be put on the underside of a rover for use in
a remote location. The goal behind this work is to ultimately create
a compact, but accurate measuring tool to put onto a rover or any
kind of exploratory vehicle to test for regolith properties of celestial
bodies.
Abstract: This paper proposes a rotational invariant texture
feature based on the roughness property of the image for psoriasis
image analysis. In this work, we have applied this feature for image
classification and segmentation. The fuzzy concept is employed to
overcome the imprecision of roughness. Since the psoriasis lesion is
modeled by a rough surface, the feature is extended for calculating
the Psoriasis Area Severity Index value. For classification and
segmentation, the Nearest Neighbor algorithm is applied. We have
obtained promising results for identifying affected lesions by using
the roughness index and severity level estimation.
Abstract: In this paper, groundwater seepage into Amirkabir
tunnel has been estimated using analytical and numerical methods for
14 different sections of the tunnel. Site Groundwater Rating (SGR)
method also has been performed for qualitative and quantitative
classification of the tunnel sections. The obtained results of above
mentioned methods were compared together. The study shows
reasonable accordance with results of the all methods unless for two
sections of tunnel. In these two sections there are some significant
discrepancies between numerical and analytical results mainly
originated from model geometry and high overburden. SGR and the
analytical and numerical calculations, confirm high concentration of
seepage inflow in fault zones. Maximum seepage flow into tunnel has
been estimated 0.425 lit/sec/m using analytical method and 0.628
lit/sec/m using numerical method occured in crashed zone. Based on
SGR method, six sections of 14 sections in Amirkabir tunnel axis are
found to be in "No Risk" class that is supported by the analytical and
numerical seepage value of less than 0.04 lit/sec/m.
Abstract: Maize constitutes a major agrarian production for use
by the vast population but despite its economic importance; it has not
been produced to meet the economic needs of the country. Achieving
optimum yield in maize can meaningfully be supported by land
suitability analysis in order to guarantee self-sufficiency for future
production optimization. This study examines land suitability for
maize production through the analysis of the physicochemical
variations in soil properties and other land attributes over space using
a Geographic Information System (GIS) framework.
Physicochemical parameters of importance selected include slope,
landuse, physical and chemical properties of the soil, and climatic
variables. Landsat imagery was used to categorize the landuse,
Shuttle Radar Topographic Mapping (SRTM) generated the slope and
soil samples were analyzed for its physical and chemical components.
Suitability was categorized into highly, moderately and marginally
suitable based on Food and Agricultural Organisation (FAO)
classification, using the Analytical Hierarchy Process (AHP)
technique of GIS. This result can be used by small scale farmers for
efficient decision making in the allocation of land for maize
production.
Abstract: The aim of this work is to build a model based on
tissue characterization that is able to discriminate pathological and
non-pathological regions from three-phasic CT images. With our
research and based on a feature selection in different phases, we are
trying to design a neural network system with an optimal neuron
number in a hidden layer. Our approach consists of three steps:
feature selection, feature reduction, and classification. For each
region of interest (ROI), 6 distinct sets of texture features are
extracted such as: first order histogram parameters, absolute gradient,
run-length matrix, co-occurrence matrix, autoregressive model, and
wavelet, for a total of 270 texture features. When analyzing more
phases, we show that the injection of liquid cause changes to the high
relevant features in each region. Our results demonstrate that for
detecting HCC tumor phase 3 is the best one in most of the features
that we apply to the classification algorithm. The percentage of
detection between pathology and healthy classes, according to our
method, relates to first order histogram parameters with accuracy of
85% in phase 1, 95% in phase 2, and 95% in phase 3.