Abstract: Although lighting systems powered by Photovoltaic
(PV) cells have existed for many years, they are not widely used,
especially in lighting for buildings, due to their high initial cost and
low conversion efficiency. One of the technical challenges facing PV
powered lighting systems has been how to use dc power generated by
the PV module to energize common light sources that are designed to
operate efficiently under ac power. Usually, the efficiency of the dc
light sources is very poor compared to ac light sources. Rapid
developments in LED lighting systems have made this technology a
potential candidate for PV powered lighting systems. This study
analyzed the efficiency of each component of PV powered lighting
systems to identify optimum system configurations for different
applications.
Abstract: In these days, multimedia data is transmitted and
processed in compressed format. Due to the decoding procedure and
filtering for edge detection, the feature extraction process of MPEG-7
Edge Histogram Descriptor is time-consuming as well as
computationally expensive. To improve efficiency of compressed
image retrieval, we propose a new edge histogram generation
algorithm in DCT domain in this paper. Using the edge information
provided by only two AC coefficients of DCT coefficients, we can get
edge directions and strengths directly in DCT domain. The
experimental results demonstrate that our system has good
performance in terms of retrieval efficiency and effectiveness.
Abstract: Fossil fuel-firing power plants dominate electric
power generation in Taiwan, which are also the major contributor to
Green House gases (GHG). CO2 is the most important greenhouse
gas that cause global warming. This paper penetrates the relationship
between carbon trading for GHG reduction and power generation
expansion planning (GEP) problem for the electrical utility. The
Particle Swarm Optimization (PSO) Algorithm is presented to deal
with the generation expansion planning strategy of the utility with
independent power providers (IPPs). The utility has to take both the
IPPs- participation and environment impact into account when a new
generation unit is considering expanded from view of supply side.
Abstract: This paper describes about dynamic reconfiguration to
miniaturize arithmetic circuits in general-purpose processor. Dynamic
reconfiguration is a technique to realize required functions by
changing hardware construction during operation. The proposed
arithmetic circuit performs floating-point arithmetic which is
frequently used in science and technology. The data format is
floating-point based on IEEE754. The proposed circuit is designed
using VHDL, and verified the correct operation by simulations and
experiments.
Abstract: State-of-the-art methods for secondary structure (Porter, Psi-PRED, SAM-T99sec, Sable) and solvent accessibility (Sable, ACCpro) predictions use evolutionary profiles represented by the position specific scoring matrix (PSSM). It has been demonstrated that evolutionary profiles are the most important features in the feature space for these predictions. Unfortunately applying PSSM matrix leads to high dimensional feature spaces that may create problems with parameter optimization and generalization. Several recently published suggested that applying feature extraction for the PSSM matrix may result in improvements in secondary structure predictions. However, none of the top performing methods considered here utilizes dimensionality reduction to improve generalization. In the present study, we used simple and fast methods for features selection (t-statistics, information gain) that allow us to decrease the dimensionality of PSSM matrix by 75% and improve generalization in the case of secondary structure prediction compared to the Sable server.
Abstract: This paper discusses the approach of real-time
controlling of the energy management system using the data
acquisition tool of LabVIEW. The main idea of this inspiration was
to interface the Station (PC) with the system and publish the data on
internet using LabVIEW. In this venture, controlling and switching of
3 phase AC loads are effectively and efficiently done. The phases are
also sensed through devices. In case of any failure the attached
generator starts functioning automatically. The computer sends
command to the system and system respond to the request. The
modern feature is to access and control the system world-wide using
world wide web (internet). This controlling can be done at any time
from anywhere to effectively use the energy especially in developing
countries where energy management is a big problem. In this system
totally integrated devices are used to operate via remote location.
Abstract: In this paper we present a Feed-Foward Neural
Networks Autoregressive (FFNN-AR) model with genetic algorithms
training optimization in order to predict the gross domestic product
growth of six countries. Specifically we propose a kind of weighted
regression, which can be used for econometric purposes, where the
initial inputs are multiplied by the neural networks final optimum
weights from input-hidden layer of the training process. The
forecasts are compared with those of the ordinary autoregressive
model and we conclude that the proposed regression-s forecasting
results outperform significant those of autoregressive model.
Moreover this technique can be used in Autoregressive-Moving
Average models, with and without exogenous inputs, as also the
training process with genetics algorithms optimization can be
replaced by the error back-propagation algorithm.
Abstract: loss of feedwater accident is one of the frequently sever accidents in steam boiler facilities. It threatens the system structural integrity and generates serious hazards and economic loses. The safety analysis of the thermal installations, based extensively on the numeric simulation. The simulation analysis using realistic computer codes like Relap5/Mod3.2 will help understand steam boiler thermal-hydraulic behavior during normal and abnormal conditions. In this study, we are interested on the evaluation of the radiant steam boiler assessment and response to loss-of-feedwater accident. Pressure, temperature and flow rate profiles are presented in various steam boiler system components. The obtained results demonstrate the importance and capability of the Relap5/Mod3.2 code in the thermal-hydraulic analysis of the steam boiler facilities.
Abstract: This paper presents a new methodology to select test
cases from regression test suites. The selection strategy is based on
analyzing the dynamic behavior of the applications that written in
any programming language. Methods based on dynamic analysis are
more safe and efficient. We design a technique that combine the code
based technique and model based technique, to allow comparing the
object oriented of an application that written in any programming
language. We have developed a prototype tool that detect changes
and select test cases from test suite.
Abstract: Gas flaring is one of the most GHG emitting sources in the oil and gas industries. It is also a major way for wasting such an energy that could be better utilized and even generates revenue. Minimize flaring is an effective approach for reducing GHG emissions and also conserving energy in flaring systems. Integrating waste and flared gases into the fuel gas networks (FGN) of refineries is an efficient tool. A fuel gas network collects fuel gases from various source streams and mixes them in an optimal manner, and supplies them to different fuel sinks such as furnaces, boilers, turbines, etc. In this article we use fuel gas network model proposed by Hasan et al. as a base model and modify some of its features and add constraints on emission pollution by gas flaring to reduce GHG emissions as possible. Results for a refinery case study showed that integration of flare gas stream with waste and natural gas streams to construct an optimal FGN can significantly reduce total annualized cost and flaring emissions.
Abstract: Embedding Sustainability in technological curricula has become a crucial factor for educating engineers with competences in sustainability. The Technical University of Catalonia UPC, in 2008, designed the Sustainable Technology Excellence Program STEP 2015 in order to assure a successful Sustainability Embedding. This Program takes advantage of the opportunity that the redesign of all Bachelor and Master Degrees in Spain by 2010 under the European Higher Education Area framework offered. The STEP program goals are: to design compulsory courses in each degree; to develop the conceptual base and identify reference models in sustainability for all specialties at UPC; to create an internal interdisciplinary network of faculty from all the schools; to initiate new transdisciplinary research activities in technology-sustainability-education; to spread the know/how attained; to achieve international scientific excellence in technology-sustainability-education and to graduate the first engineers/architects of the new EHEA bachelors with sustainability as a generic competence. Specifically, in this paper authors explain their experience in leading the STEP program, and two examples are presented: Industrial Robotics subject and the curriculum for the School of Architecture.
Abstract: There are several approaches in trying to solve the
Quantitative 1Structure-Activity Relationship (QSAR) problem.
These approaches are based either on statistical methods or on
predictive data mining. Among the statistical methods, one should
consider regression analysis, pattern recognition (such as cluster
analysis, factor analysis and principal components analysis) or partial
least squares. Predictive data mining techniques use either neural
networks, or genetic programming, or neuro-fuzzy knowledge. These
approaches have a low explanatory capability or non at all. This
paper attempts to establish a new approach in solving QSAR
problems using descriptive data mining. This way, the relationship
between the chemical properties and the activity of a substance
would be comprehensibly modeled.
Abstract: Mining frequent tree patterns have many useful
applications in XML mining, bioinformatics, network routing, etc.
Most of the frequent subtree mining algorithms (i.e. FREQT,
TreeMiner and CMTreeMiner) use anti-monotone property in the
phase of candidate subtree generation. However, none of these
algorithms have verified the correctness of this property in tree
structured data. In this research it is shown that anti-monotonicity
does not generally hold, when using weighed support in tree pattern
discovery. As a result, tree mining algorithms that are based on this
property would probably miss some of the valid frequent subtree
patterns in a collection of trees. In this paper, we investigate the
correctness of anti-monotone property for the problem of weighted
frequent subtree mining. In addition we propose W3-Miner, a new
algorithm for full extraction of frequent subtrees. The experimental
results confirm that W3-Miner finds some frequent subtrees that the
previously proposed algorithms are not able to discover.
Abstract: There is strong evidence that water channel proteins
'aquaporins (AQPs)' are central components in plant-water relations
as well as a number of other physiological parameters. We had
previously reported the isolation of 24 plasma membrane intrinsic
protein (PIP) type AQPs. However, the gene numbers in rice and the
polyploid nature of bread wheat indicated a high probability of
further genes in the latter. The present work focused on identification
of further AQP isoforms in bread wheat. With the use of altered
primer design, we identified five genes homologous, designated
PIP1;5b, PIP2;9b, TaPIP2;2, TaPIP2;2a, TaPIP2;2b. Sequence
alignments indicate PIP1;5b, PIP2;9b are likely to be homeologues of
two previously reported genes while the other three are new genes
and could be homeologs of each other. The results indicate further
AQP diversity in wheat and the sequence data will enable physical
mapping of these genes to identify their genomes as well as genetic to
determine their association with any quantitative trait loci (QTLs)
associated with plant-water relation such as salinity or drought
tolerance.
Abstract: There are a lot of extensions made to the classic model of multi-layer perceptron (MLP). A notable amount of them has been designed to hasten the learning process without considering the quality of generalization. The paper proposes a new MLP extension based on exploiting topology of the input layer of the network. Experimental results show the extended model to improve upon generalization capability in certain cases. The new model requires additional computational resources to compare to the classic model, nevertheless the loss in efficiency isn-t regarded to be significant.
Abstract: This paper presents an analytical model to estimate
the cost of an optimized design of reinforced concrete isolated
footing base on structural safety. Flexural and optimized formulas for
square and rectangular footingare derived base on ACI building code
of design, material cost and optimization. The optimization
constraints consist of upper and lower limits of depth and area of
steel. Footing depth and area of reinforcing steel are to be minimized
to yield the optimal footing dimensions. Optimized footing materials
cost of concrete, reinforcing steel and formwork of the designed
sections are computed. Total cost factor TCF and other cost factors
are developed to generalize and simplify the calculations of footing
material cost. Numerical examples are presented to illustrate the
model capability of estimating the material cost of the footing for a
desired axial load.
Abstract: Doubly fed induction machines DFIM are used
mainly for wind energy conversion in MW power plants. This paper
presents a new strategy of field oriented control ,it is based on the
principle of a double flux orientation of stator and rotor at the same
time. Therefore, the orthogonality created between the two oriented
fluxes, which must be strictly observed, leads to generate a linear and
decoupled control with an optimal torque. The obtained simulation
results show the feasibility and the effectiveness of the suggested
method.
Abstract: Restoration of endodontically treated teeth is a
common problem in dentistry, related to the fractures occurring in
such teeth and to concentration of forces little information regarding
variation of basic preparation guidelines in stress distribution has
been available. To date, there is still no agreement in the literature
about which material or technique can optimally restore
endodontically treated teeth. The aim of the present study was to
evaluate the influence of the core height and restoration materials on
corono-radicular restored upper first premolar. The first step of the
study was to achieve 3D models in order to analyze teeth, dowel and
core restorations and overlying full ceramic crowns. The FEM model
was obtained by importing the solid model into ANSYS finite
element analysis software. An occlusal load of 100 N was conducted,
and stresses occurring in the restorations, and teeth structures were
calculated. Numerical simulations provide a biomechanical
explanation for stress distribution in prosthetic restored teeth. Within
the limitations of the present study, it was found that the core height
has no important influence on the stress generated in coronoradicular
restored premolars. It can be drawn that the cervical regions
of the teeth and restorations were subjected to the highest stress
concentrations.
Abstract: Diagnostic and detection of the arterial stiffness is
very important; which gives indication of the associated increased risk of cardiovascular diseases. To make a cheap and easy method for general screening technique to avoid the future cardiovascular
complexes , due to the rising of the arterial stiffness ; a proposed algorithm depending on photoplethysmogram to be used. The
photoplethysmograph signals would be processed in MATLAB. The
signal will be filtered, baseline wandering removed, peaks and
valleys detected and normalization of the signals should be achieved
.The area under the catacrotic phase of the photoplethysmogram
pulse curve is calculated using trapezoidal algorithm ; then will used
in cooperation with other parameters such as age, height, blood
pressure in neural network for arterial stiffness detection. The Neural
network were implemented with sensitivity of 80%, accuracy 85%
and specificity of 90% were got from the patients data. It is
concluded that neural network can detect the arterial STIFFNESS
depending on risk factor parameters.
Abstract: The dynamic speckle or biospeckle is an interference
phenomenon generated at the reflection of a coherent light by an
active surface or even by a particulate or living body surface. The
above mentioned phenomenon gave scientific support to a method
named biospeckle which has been employed to study seed viability,
biological activity, tissue senescence, tissue water content, fruit
bruising, etc. Since the above mentioned method is not invasive and
yields numerical values, it can be considered for possible automation
associated to several processes, including selection and sorting.
Based on these preliminary considerations, this research work
proposed to study the interaction of a laser beam with vegetative
samples by measuring the incident light intensity and the transmitted
light beam intensity at several vegetative slabs of varying thickness.
Tests were carried on fifteen slices of apple tissue divided into three
thickness groups, i.e., 4 mm, 5 mm, 18 mm and 22 mm. A diode laser
beam of 10mW and 632 nm wavelength and a Samsung digital
camera were employed to carry the tests. Outgoing images were
analyzed by comparing the gray gradient of a fixed image column of
each image to obtain a laser penetration scale into the tissue,
according to the slice thickness.