Abstract: A new hybrid method to realise high-precision
distortion determination for optical ultra-precision 3D measurement
systems based on stereo cameras using active light projection is
introduced. It consists of two phases: the basic distortion
determination and the refinement. The refinement phase of the
procedure uses a plane surface and projected fringe patterns as
calibration tools to determine simultaneously the distortion of both
cameras within an iterative procedure. The new technique may be
performed in the state of the device “ready for measurement" which
avoids errors by a later adjustment. A considerable reduction of
distortion errors is achieved and leads to considerable improvements
of the accuracy of 3D measurements, especially in the precise
measurement of smooth surfaces.
Abstract: The use of 3D computer-aided design (CAD) models
to support construction project planning has been increasing in the
previous year. 3D CAD models reveal more planning ideas by
visually showing the construction site environment in different stages
of the construction process. Using 3D CAD models together with
scheduling software to prepare construction plan can identify errors
in process sequence and spatial arrangement, which is vital to the
success of a construction project. A number of 4D (3D plus time)
CAD tools has been developed and utilized in different construction
projects due to the awareness of their importance. Virtual prototyping
extends the idea of 4D CAD by integrating more features for
simulating real construction process. Virtual prototyping originates
from the manufacturing industry where production of products such
as cars and airplanes are virtually simulated in computer before they
are built in the factory. Virtual prototyping integrates 3D CAD,
simulation engine, analysis tools (like structural analysis and
collision detection), and knowledgebase to streamline the whole
product design and production process. In this paper, we present the
application of a virtual prototyping software which has been used in
a few construction projects in Hong Kong to support construction
project planning. Specifically, the paper presents an implementation
of virtual prototyping in a residential building project in Hong Kong.
The applicability, difficulties and benefits of construction virtual
prototyping are examined based on this project.
Abstract: Since the presentation of the backpropagation algorithm, a vast variety of improvements of the technique for training a feed forward neural networks have been proposed. This article focuses on two classes of acceleration techniques, one is known as Local Adaptive Techniques that are based on weightspecific only, such as the temporal behavior of the partial derivative of the current weight. The other, known as Dynamic Adaptation Methods, which dynamically adapts the momentum factors, α, and learning rate, η, with respect to the iteration number or gradient. Some of most popular learning algorithms are described. These techniques have been implemented and tested on several problems and measured in terms of gradient and error function evaluation, and percentage of success. Numerical evidence shows that these techniques improve the convergence of the Backpropagation algorithm.
Abstract: The increasing recognition of the need for education to be closely aligned with team playing, project based learning and problem solving approaches has increase the interest in collaborative learning among university and college instructors. Using online collaboration learning in learning can enhance the outcome and achievement of students as well as improve their communication, critical thinking and personnel skills. The current research aims at examining the effect of OCL on the student's achievement at Kingdom of Bahrain. Numbers of objectives were set to achieve the aim of the research include: investigating the current situation regarding the collaborative learning and OCL at the Kingdom of Bahrain by identifying the advantages and effectiveness of OCL as a learning tool over traditional learning, examining the factors that affect OCL as well as examining the impact of OCL on the student's achievement. To achieve these objectives, quantitative method was adopted. Two hundred and thirty one questionnaires were distributed to students in different local and private universities at Kingdom of Bahrain. The findings of the research show that most of the students prefer to use FTFCL in learning and that OCL is already adopted in some universities especially in University of Bahrain. Moreover, the most factors affecting the adopted OCL are perceived readiness, and guidance and support.
Abstract: This communication is intended to provide some issues for thought on the importance of implementation of Blended Learning in traditional universities, particularly in the Spanish university system. In this respect, we believe that virtual environments are likely to meet some of the needs raised by the Bologna agreement, trying to maintain the quality of teaching and at the same time taking advantage of the functionalities that virtual learning platforms offer. We are aware that an approach of learning from an open and constructivist nature in universities is a complex process that faces significant technological, administrative and human barriers. Therefore, in order to put plans in our universities, it is necessary to analyze the state of the art of some indicators relating to the use of ICT, with special attention to virtual teaching and learning, so that we can identify the main obstacles and design adaptive strategies for their full integration in the education system. Finally, we present major initiatives launched in the European and state framework for the effective implementation of new virtual environments in the area of higher education.
Abstract: Pyrite (FeS2) is a promising candidate for cathode
materials in batteries because of it`s high theoretical capacity, low
cost and non-toxicity. In this study, nano size iron disulfide thin film
was prepared on graphite substrate through a new method as battery
cathode. In this way, acetylene black and poly vinylidene fluoride
were used as electron conductor and binder, respectively. Fabricated
thin films were analyzed by XRD and SEM. These results and
electrochemical data confirm improvement of battery discharge
capacity in comparison with commercial type of pyrite.
Abstract: This paper presents a method of reducing the feedback
delay time of DWA(Data Weighted Averaging) used in sigma-delta
modulators. The delay time reduction results from the elimination of
the latch at the quantizer output and also from the falling edge
operation. The designed sigma-delta modulator improves the timing
margin about 16%. The sub-circuits of sigma-delta modulator such as
SC(Switched Capacitor) integrator, 9-level quantizer, comparator, and
DWA are designed with the non-ideal characteristics taken into
account. The sigma-delta modulator has a maximum SNR (Signal to
Noise Ratio) of 84 dB or 13 bit resolution.
Abstract: The trend in the world of Information Technology
(IT) is getting increasingly large and difficult projects rather than
smaller and easier. However, the data on large-scale IT project
success rates provide cause for concern. This paper seeks to answer
why large-scale IT projects are different from and more difficult than
other typical engineering projects. Drawing on the industrial
experience, a compilation of the conditions that influence failure is
presented. With a view to improve success rates solutions are
suggested.
Abstract: The effects of irrigation with dairy factory wastewater
on soil properties were investigated at two sites that had received
irrigation for > 60 years. Two adjoining paired sites that had never
received DFE were also sampled as well as another seven fields from
a wider area around the factory. In comparison with paired sites that
had not received effluent, long-term wastewater irrigation resulted in
an increase in pH, EC, extractable P, exchangeable Na and K and
ESP. These changes were related to the use of phosphoric acid,
NaOH and KOH as cleaning agents in the factory. Soil organic C
content was unaffected by DFE irrigation but the size (microbial
biomass C and N) and activity (basal respiration) of the soil
microbial community were increased. These increases were
attributed to regular inputs of soluble C (e.g. lactose) present as milk
residues in the wastewater. Principal component analysis (PCA) of
the soils data from all 11sites confirmed that the main effects of DFE
irrigation were an increase in exchangeable Na, extractable P and
microbial biomass C, an accumulation of soluble salts and a liming
effect. PCA analysis of soil bacterial community structure, using
PCR-DGGE of 16S rDNA fragments, generally separated individual
sites from one another but did not group them according to irrigation
history. Thus, whilst the size and activity of the soil microbial
community were increased, the structure and diversity of the
bacterial community remained unaffected.
Abstract: We investigate nonfactorizable contributions to
D → ¤Ç¤Ç decay modes. We perform isospin analysis of the
nonfactorizable contributions to these decays. Obtaining the
factorizable contributions from spectator-quark diagrams using
= 3 C N , we determine nonfactorizable amplitudes for these decays
and predict their branching ratios.
Abstract: To create a solution for a specific problem in machine
learning, the solution is constructed from the data or by use a search
method. Genetic algorithms are a model of machine learning that can
be used to find nearest optimal solution. While the great advantage of
genetic algorithms is the fact that they find a solution through
evolution, this is also the biggest disadvantage. Evolution is inductive,
in nature life does not evolve towards a good solution but it evolves
away from bad circumstances. This can cause a species to evolve into
an evolutionary dead end. In order to reduce the effect of this
disadvantage we propose a new a learning tool (criteria) which can be
included into the genetic algorithms generations to compare the
previous population and the current population and then decide
whether is effective to continue with the previous population or the
current population, the proposed learning tool is called as Keeping
Efficient Population (KEP). We applied a GA based on KEP to the
production line layout problem, as a result KEP keep the evaluation
direction increases and stops any deviation in the evaluation.
Abstract: An attempt has been made to develop a
seminumerical model to study temperature variations in dermal
layers of human limbs. The model has been developed for two
dimensional steady state case. The human limb has been assumed to
have elliptical cross section. The dermal region has been divided
into three natural layers namely epidermis, dermis and subdermal
tissues. The model incorporates the effect of important physiological
parameters like blood mass flow rate, metabolic heat generation, and
thermal conductivity of the tissues. The outer surface of the limb is
exposed to the environment and it is assumed that heat loss takes
place at the outer surface by conduction, convection, radiation, and
evaporation. The temperature of inner core of the limb also varies at
the lower atmospheric temperature. Appropriate boundary conditions
have been framed based on the physical conditions of the problem.
Cubic splines approach has been employed along radial direction and
Fourier series along angular direction to obtain the solution. The
numerical results have been computed for different values of
eccentricity resembling with the elliptic cross section of the human
limbs. The numerical results have been used to obtain the
temperature profile and to study the relationships among the various
physiological parameters.
Abstract: Water samples were collected from river Pandu at six
stations where human and animal activities were high. Composite
samples were analyzed for dissolved oxygen (DO), biochemical
oxygen demand (BOD), chemical oxygen demand (COD) , pH values
during dry and wet seasons as well as the harmattan period. The total
data points were used to establish relationships between the
parameters and data were also subjected to statistical analysis and
expressed as mean ± standard error of mean (SEM) at a level of
significance of p
Abstract: Accurate demand forecasting is one of the most key
issues in inventory management of spare parts. The problem of
modeling future consumption becomes especially difficult for lumpy
patterns, which characterized by intervals in which there is no
demand and, periods with actual demand occurrences with large
variation in demand levels. However, many of the forecasting
methods may perform poorly when demand for an item is lumpy.
In this study based on the characteristic of lumpy demand patterns
of spare parts a hybrid forecasting approach has been developed,
which use a multi-layered perceptron neural network and a
traditional recursive method for forecasting future demands. In the
described approach the multi-layered perceptron are adapted to
forecast occurrences of non-zero demands, and then a conventional
recursive method is used to estimate the quantity of non-zero
demands. In order to evaluate the performance of the proposed
approach, their forecasts were compared to those obtained by using
Syntetos & Boylan approximation, recently employed multi-layered
perceptron neural network, generalized regression neural network
and elman recurrent neural network in this area. The models were
applied to forecast future demand of spare parts of Arak
Petrochemical Company in Iran, using 30 types of real data sets. The
results indicate that the forecasts obtained by using our proposed
mode are superior to those obtained by using other methods.
Abstract: This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, depth of cut and engagement) leading to minimum surface roughness in face milling of high silicon stainless steel by coupling neural network (NN) and Electromagnetism-like Algorithm (EM). In this regard, the advantages of statistical experimental design technique, experimental measurements, artificial neural network, and Electromagnetism-like optimization method are exploited in an integrated manner. To this end, numerous experiments on this stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness is created by using a back propogation neural network, then the optimization problem was solved by using EM optimization. Additional experiments were performed to validate optimum surface roughness value predicted by EM algorithm. It is clearly seen that a good agreement is observed between the predicted values by EM coupled with feed forward neural network and experimental measurements. The obtained results show that the EM algorithm coupled with back propogation neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.
Abstract: Binary Decision Diagrams (BDDs) are useful data
structures for symbolic Boolean manipulations. BDDs are used in
many tasks in VLSI/CAD, such as equivalence checking, property
checking, logic synthesis, and false paths. In this paper we describe a
new approach for the realization of a BDD package. To perform
manipulations of Boolean functions, the proposed approach does not
depend on the recursive synthesis operation of the IF-Then-Else
(ITE). Instead of using the ITE operation, the basic synthesis
algorithm is done using Boolean NOR operation.
Abstract: In this paper comparison of Reflector Antenna
analyzing techniques based on wave and ray nature of optics is
presented for an offset reflector antenna using GRASP (General
Reflector antenna Analysis Software Package) software. The results
obtained using PO (Physical Optics), PTD (Physical theory of
Diffraction), and GTD (Geometrical Theory of Diffraction) are
compared. The validity of PO and GTD techniques in regions around
the antenna, caustic behavior of GTD in main beam, and deviation of
GTD in case of near-in sidelobes of radiation pattern are discussed.
The comparison for far-out sidelobes predicted by PO, PO + PTD
and GTD is described. The effect of Direct Radiations from feed
which results in feed selection for the system is addressed.
Abstract: Gas turbine systems with wet compression have a
potential for future power generation, since they can offer a high
efficiency and a high specific power with a relatively low cost. In this
study influence of ambient condition on the performance of the wet
compression process is investigated with a non-equilibrium analytical
modeling based on droplet evaporation. Transient behaviors of droplet
diameter and temperature of mixed air are investigated for various
ambient temperatures. Special attention is paid for the effects of
ambient temperature, pressure ratio, and water injection ratios on the
important wet compression variables including compressor outlet
temperature and compression work. Parametric studies show that
downing of the ambient temperature leads to lower compressor outlet
temperature and consequently lower consumption of compression
work even in wet compression processes.
Abstract: We present an Electronic Nose (ENose), which is
aimed at identifying the presence of one out of two gases, possibly
detecting the presence of a mixture of the two. Estimation of the
concentrations of the components is also performed for a volatile
organic compound (VOC) constituted by methanol and acetone, for
the ranges 40-400 and 22-220 ppm (parts-per-million), respectively.
Our system contains 8 sensors, 5 of them being gas sensors (of the
class TGS from FIGARO USA, INC., whose sensing element is a tin
dioxide (SnO2) semiconductor), the remaining being a temperature
sensor (LM35 from National Semiconductor Corporation), a
humidity sensor (HIH–3610 from Honeywell), and a pressure sensor
(XFAM from Fujikura Ltd.).
Our integrated hardware–software system uses some machine
learning principles and least square regression principle to identify at
first a new gas sample, or a mixture, and then to estimate the
concentrations. In particular we adopt a training model using the
Support Vector Machine (SVM) approach with linear kernel to teach
the system how discriminate among different gases. Then we apply
another training model using the least square regression, to predict
the concentrations.
The experimental results demonstrate that the proposed
multiclassification and regression scheme is effective in the
identification of the tested VOCs of methanol and acetone with
96.61% correctness. The concentration prediction is obtained with
0.979 and 0.964 correlation coefficient for the predicted versus real
concentrations of methanol and acetone, respectively.
Abstract: The demands of taller structures are becoming imperative almost everywhere in the world in addition to the challenges of material and labor cost, project time line etc. This paper conducted a study keeping in view the challenging nature of high-rise construction with no generic rules for deflection minimizations and frequency control. The effects of cyclonic wind and provision of outriggers on 28-storey, 42-storey and 57-storey are examined in this paper and certain conclusions are made which would pave way for researchers to conduct further study in this particular area of civil engineering. The results show that plan dimensions have vital impacts on structural heights. Increase of height while keeping the plan dimensions same, leads to the reduction in the lateral rigidity. To achieve required stiffness increase of bracings sizes as well as introduction of additional lateral resisting system such as belt truss and outriggers is required.