Abstract: The aim of the study is to improve the understanding
of latent and sensible thermal energy storage within a paraffin wax
media by an array of cylindrical tubes arranged both in in-line and
staggered layouts. An analytical and experimental study is carried out
in a horizontal shell-and-tube type system during melting process.
Pertamina paraffin-wax was used as a phase change material (PCM),
while the tubes are embedded in the PCM. From analytical study we
can obtain the useful information in designing a thermal energy
storage such as: the motion of interface, amount of material melted at
any time in the process, and the heat storage characteristic during
melting. The use of staggered tubes is proposed compared to in-line
layout in a heat exchanger as thermal storage. The experimental study
is used to verify the validity of the analytical predictions. From the
comparisons, the analytical and experimental data are in a good
agreement.
Abstract: Prediction of maximum local scour is necessary for
the safety and economical design of the bridges. A number of
equations have been developed over the years to predict local scour
depth using laboratory data and a few pier equations have also been
proposed using field data. Most of these equations are empirical in
nature as indicated by the past publications. In this paper attempts
have been made to compute local depth of scour around bridge pier in
dimensional and non-dimensional form by using linear regression,
simple regression and SVM (Poly & Rbf) techniques along with few
conventional empirical equations. The outcome of this study suggests
that the SVM (Poly & Rbf) based modeling can be employed as an
alternate to linear regression, simple regression and the conventional
empirical equations in predicting scour depth of bridge piers. The
results of present study on the basis of non-dimensional form of
bridge pier scour indicate the improvement in the performance of
SVM (Poly & Rbf) in comparison to dimensional form of scour.
Abstract: The critical concern of satellite operations is to ensure
the health and safety of satellites. The worst case in this perspective
is probably the loss of a mission, but the more common interruption
of satellite functionality can result in compromised mission
objectives. All the data acquiring from the spacecraft are known as
Telemetry (TM), which contains the wealth information related to the
health of all its subsystems. Each single item of information is
contained in a telemetry parameter, which represents a time-variant
property (i.e. a status or a measurement) to be checked. As a
consequence, there is a continuous improvement of TM monitoring
systems to reduce the time required to respond to changes in a
satellite's state of health. A fast conception of the current state of the
satellite is thus very important to respond to occurring failures.
Statistical multivariate latent techniques are one of the vital learning
tools that are used to tackle the problem above coherently.
Information extraction from such rich data sources using advanced
statistical methodologies is a challenging task due to the massive
volume of data. To solve this problem, in this paper, we present a
proposed unsupervised learning algorithm based on Principle
Component Analysis (PCA) technique. The algorithm is particularly
applied on an actual remote sensing spacecraft. Data from the
Attitude Determination and Control System (ADCS) was acquired
under two operation conditions: normal and faulty states. The models
were built and tested under these conditions, and the results show that
the algorithm could successfully differentiate between these
operations conditions. Furthermore, the algorithm provides
competent information in prediction as well as adding more insight
and physical interpretation to the ADCS operation.
Abstract: This paper integrates Octagon and Square Search
pattern (OCTSS) motion estimation algorithm into H.264/AVC
(Advanced Video Coding) video codec in Adaptive Group of Pictures
(AGOP) mode. AGOP structure is computed based on scene change
in the video sequence. Octagon and square search pattern block-based
motion estimation method is implemented in inter-prediction process
of H.264/AVC. Both these methods reduce bit rate and computational
complexity while maintaining the quality of the video sequence
respectively. Experiments are conducted for different types of video
sequence. The results substantially proved that the bit rate,
computation time and PSNR gain achieved by the proposed method
is better than the existing H.264/AVC with fixed GOP and AGOP.
With a marginal gain in quality of 0.28dB and average gain in bitrate
of 132.87kbps, the proposed method reduces the average computation
time by 27.31 minutes when compared to the existing state-of-art
H.264/AVC video codec.
Abstract: In the present study, analysis of heat transfer is carried
out in the slip flow region for the fluid flowing between two parallel
plates by employing the asymmetric heat fluxes at surface of the
plates. The flow is assumed to be hydrodynamically and thermally
fully developed for the analysis. The second order velocity slip and
viscous dissipation effects are considered for the analysis. Closed
form expressions are obtained for the Nusselt number as a function of
Knudsen number and modified Brinkman number. The limiting
condition of the present prediction for Kn = 0, Kn2 = 0, and Brq1 = 0
is considered and found to agree well with other analytical results.
Abstract: Myocardial infarction is one of the leading causes of
death in the world. Some of these deaths occur even before the
patient reaches the hospital. Myocardial infarction occurs as a result
of impaired blood supply. Because the most of these deaths are due to
coronary artery disease, hence the awareness of the warning signs of
a heart attack is essential. Some heart attacks are sudden and intense,
but most of them start slowly, with mild pain or discomfort, then
early detection and successful treatment of these symptoms is vital to
save them. Therefore, importance and usefulness of a system
designing to assist physicians in early diagnosis of the acute heart
attacks is obvious. The main purpose of this study would be to enable patients to
become better informed about their condition and to encourage them
to seek professional care at an earlier stage in the appropriate
situations. For this purpose, the data were collected on 711 heart
patients in Iran hospitals. 28 attributes of clinical factors can be
reported by patients; were studied. Three logistic regression models
were made on the basis of the 28 features to predict the risk of heart
attacks. The best logistic regression model in terms of performance
had a C-index of 0.955 and with an accuracy of 94.9%. The variables,
severe chest pain, back pain, cold sweats, shortness of breath, nausea
and vomiting, were selected as the main features.
Abstract: The reheating furnace is used to reheat the steel slabs
before the hot-rolling process. The supported system includes the
stationary/moving beams, and the skid buttons which block some
thermal radiation transmitted to the bottom of the slabs. Therefore, it is
important to analyze the steel slab temperature distribution during the
heating period. A three-dimensional mathematical transient heat
transfer model for the prediction of temperature distribution within the
slab has been developed. The effects of different skid button height
(H=60mm, 90mm, and 120mm) and different gap distance between
two slabs (S=50mm, 75mm, and 100mm) on the slab skid mark
formation and temperature profiles are investigated. Comparison with
the in-situ experimental data from Steel Company in Taiwan shows
that the present heat transfer model works well for the prediction of
thermal behavior of the slab in the reheating furnace. It is found that
the skid mark severity decreases with an increase in the skid button
height. The effect of gap distance is important only for the slab edge
planes, while it is insignificant for the slab central planes.
Abstract: An industrial system for the production of white
liquor of a paper industry, Klabin Paraná Papéis, formed by ten
reactors was modeled, simulated, and analyzed. The developed model
considered possible water losses by evaporation and reaction, in
addition to variations in volumetric flow of lime mud across the
reactors due to composition variations. The model predictions agreed
well with the process measurements at the plant and the results
showed that the slaking reaction is nearly complete at the third
causticizing reactor, while causticizing ends by the seventh reactor.
Water loss due to slaking reaction and evaporation occurs more
pronouncedly in the slaking reaction than in the final causticizing
reactors; nevertheless, the lime mud flow remains nearly constant
across the reactors.
Abstract: This article presents an alternative collapse capacity
intensity measure in the three elements form which is influenced by
the spectral ordinates at periods longer than that of the first mode
period at near and far source sites. A parameter, denoted by β, is
defined by which the spectral ordinate effects, up to the effective
period (2T1), on the intensity measure are taken into account. The
methodology permits to meet the hazard-levelled target extreme
event in the probabilistic and deterministic forms. A MATLAB code
is developed involving OpenSees to calculate the collapse capacities
of the 8 archetype RC structures having 2 to 20 stories for regression
process. The incremental dynamic analysis (IDA) method is used to
calculate the structure’s collapse values accounting for the element
stiffness and strength deterioration. The general near field set
presented by FEMA is used in a series of performing nonlinear
analyses. 8 linear relationships are developed for the 8structutres
leading to the correlation coefficient up to 0.93. A collapse capacity
near field prediction equation is developed taking into account the
results of regression processes obtained from the 8 structures. The
proposed prediction equation is validated against a set of actual near
field records leading to a good agreement. Implementation of the
proposed equation to the four archetype RC structures demonstrated
different collapse capacities at near field site compared to those of
FEMA. The reasons of differences are believed to be due to
accounting for the spectral shape effects.
Abstract: The knitted fabric suffers a deformation in its
dimensions due to stretching and tension factors, transverse and
longitudinal respectively, during the process in rectilinear knitting
machines so it performs a dry relaxation shrinkage procedure and
thermal action of prefixed to obtain stable conditions in the knitting.
This paper presents a dry relaxation shrinkage prediction of Bordeaux
fiber using a feed forward neural network and linear regression
models. Six operational alternatives of shrinkage were predicted. A
comparison of the results was performed finding neural network
models with higher levels of explanation of the variability and
prediction. The presence of different reposes is included. The models
were obtained through a neural toolbox of Matlab and Minitab
software with real data in a knitting company of Southern
Guanajuato. The results allow predicting dry relaxation shrinkage of
each alternative operation.
Abstract: The work reported through this paper is an
experimental work conducted on High Performance Concrete (HPC)
with super plasticizer with the aim to develop some models suitable
for prediction of compressive strength of HPC mixes. In this study,
the effect of varying proportions of fly ash (0% to 50% @ 10%
increment) on compressive strength of high performance concrete has
been evaluated. The mix designs studied were M30, M40 and M50 to
compare the effect of fly ash addition on the properties of these
concrete mixes. In all eighteen concrete mixes that have been
designed, three were conventional concretes for three grades under
discussion and fifteen were HPC with fly ash with varying
percentages of fly ash. The concrete mix designing has been done in
accordance with Indian standard recommended guidelines. All the
concrete mixes have been studied in terms of compressive strength at
7 days, 28 days, 90 days, and 365 days. All the materials used have
been kept same throughout the study to get a perfect comparison of
values of results. The models for compressive strength prediction
have been developed using Linear Regression method (LR), Artificial
Neural Network (ANN) and Leave-One-Out Validation (LOOV)
methods.
Abstract: Different countries have introduced different schemes
and policies to counter global warming. The rationale behind the
proposed policies and the potential barriers to successful
implementation of the policies adopted by the countries were
analyzed and estimated based on different models. It is argued that
these models enhance the transparency and provide a better
understanding to the policy makers. However, these models are
underpinned with several structural and baseline assumptions. These
assumptions, modeling features and future prediction of emission
reductions and other implication such as cost and benefits of a
transition to a low-carbon economy and its economy wide impacts
were discussed. On the other hand, there are potential barriers in the
form political, financial, and cultural and many others that pose a
threat to the mitigation options.
Abstract: Predicting earnings management is vital for the capital
market participants, financial analysts and managers. The aim of this
research is attempting to respond to this query: Is there a significant
difference between the regression model and neural networks’
models in predicting earnings management, and which one leads to a
superior prediction of it? In approaching this question, a Linear
Regression (LR) model was compared with two neural networks
including Multi-Layer Perceptron (MLP), and Generalized
Regression Neural Network (GRNN). The population of this study
includes 94 listed companies in Tehran Stock Exchange (TSE)
market from 2003 to 2011. After the results of all models were
acquired, ANOVA was exerted to test the hypotheses. In general, the
summary of statistical results showed that the precision of GRNN did
not exhibit a significant difference in comparison with MLP. In
addition, the mean square error of the MLP and GRNN showed a
significant difference with the multi variable LR model. These
findings support the notion of nonlinear behavior of the earnings
management. Therefore, it is more appropriate for capital market
participants to analyze earnings management based upon neural
networks techniques, and not to adopt linear regression models.
Abstract: This work explores the inter-region investment
behaviors of Integrated Circuit (IC) design industry from Taiwan to
China using the amount of foreign direct investment (FDI). According
to the mutual dependence among different IC design industrial
locations, Lotka-Volterra model is utilized to explore the FDI
interactions between South and East China. Effects of inter-regional
collaborations on FDI flows into China are considered. The analysis
results show that FDIs into South China for IC design industry
significantly inspire the subsequent FDIs into East China, while FDIs
into East China for Taiwan’s IC design industry significantly hinder
the subsequent FDIs into South China. Because the supply chain along
IC industry includes upstream IC design, midstream manufacturing, as
well as downstream packing and testing enterprises, IC design industry
has to cooperate with IC manufacturing, packaging and testing
industries in the same area to form a strong IC industrial cluster.
Taiwan’s IC design industry implement the largest FDI amount into
East China and the second largest FDI amount into South China
among the four regions: North, East, Mid-West and South China. If IC
design houses undertake more FDIs in South China, those in East
China are urged to incrementally implement more FDIs into East
China to maintain the competitive advantages of the IC supply chain in
East China. On the other hand, as the FDIs in East China rise, the FDIs
in South China will successively decline since capitals have
concentrated in East China. In addition, this investigation proves that
the prediction of Lotka-Volterra model in FDI trends is accurate
because the industrial interactions between the two regions are
included. Finally, this work confirms that the FDI flows cannot reach a
stable equilibrium point, so the FDI inflows into East and South China
will expand in the future.
Abstract: Chatter vibrations and process instabilities are the
most important factors limiting the productivity of the milling
process. Chatter can leads to damage of the tool, the part or the
machine tool. Therefore, the estimation and prediction of the process
stability is very important. The process stability depends on the
spindle speed, the depth of cut and the width of cut. In milling, the
process conditions are defined in the NC-program. While the spindle
speed is directly coded in the NC-program, the depth and width of cut
are unknown. This paper presents a new simulation based approach
for the prediction of the depth and width of cut of a milling process.
The prediction is based on a material removal simulation with an
analytically represented tool shape and a multi-dexel approach for the
workpiece. The new calculation method allows the direct estimation
of the depth and width of cut, which are the influencing parameters of
the process stability, instead of the removed volume as existing
approaches do. The knowledge can be used to predict the stability of
new, unknown parts. Moreover with an additional vibration sensor,
the stability lobe diagram of a milling process can be estimated and
improved based on the estimated depth and width of cut.
Abstract: During welding or flame cutting of metals, the
prediction of heat affected zone (HAZ) is critical. There is need to
develop a simple mathematical model to calculate the temperature
variation in HAZ and derivative analysis can be used for this purpose.
This study presents analytical solution for heat transfer through
conduction in mild steel plate. The homogeneous and nonhomogeneous
boundary conditions are single variables. The full field
analytical solutions of temperature measurement, subjected to local
heating source, are derived first by method of separation of variables
followed with the experimental visualization using infrared imaging.
Based on the present work, it is suggested that appropriate heat input
characteristics controls the temperature distribution in and around
HAZ.
Abstract: Healthcare safety has been perceived important. It is
essential to prevent troubles in healthcare processes for healthcare
safety. Trouble prevention is based on trouble prediction using
accumulated knowledge on processes, troubles, and countermeasures.
However, information on troubles has not been accumulated in
hospitals in the appropriate structure, and it has not been utilized
effectively to prevent troubles. In the previous study, however a
detailed knowledge acquisition process for trouble prediction was
proposed, the knowledgebase for countermeasures was not involved.
In this paper, we aim to propose the structure of the knowledgebase for
countermeasures, in the knowledge acquisition process for trouble
prediction in healthcare process. We first design the structure of
countermeasures and propose the knowledge representation form on
countermeasures. Then, we evaluate the validity of the proposal, by
applying it into an actual hospital.
Abstract: Tamil handwritten document is taken as a key source
of data to identify the writer. Tamil is a classical language which has
247 characters include compound characters, consonants, vowels and
special character. Most characters of Tamil are multifaceted in
nature. Handwriting is a unique feature of an individual. Writer may
change their handwritings according to their frame of mind and this
place a risky challenge in identifying the writer. A new
discriminative model with pooled features of handwriting is proposed
and implemented using support vector machine. It has been reported
on 100% of prediction accuracy by RBF and polynomial kernel based
classification model.
Abstract: When high strength reinforced concrete is exposed to
high temperature due to a fire, deteriorations occur such as loss in
strength and elastic modulus, cracking and spalling of the concrete.
Therefore, it is important to understand risk of structural safety in
building structures by studying structural behaviors and rehabilitation
of fire damaged high strength concrete structures. This paper aims at
investigating rehabilitation effect on fire damaged high strength
concrete beams using experimental and analytical methods. In the
experiments, flexural specimens with high strength concrete are
exposed to high temperatures according to ISO 834 standard time
temperature curve. From four-point loading test, results show that
maximum loads of the rehabilitated beams are similar to or higher than
those of the non-fire damaged RC beam. In addition, structural
analyses are performed using ABAQUS 6.10-3 with same conditions
as experiments to provide accurate predictions on structural and
mechanical behaviors of rehabilitated RC beams. The parameters are
the fire cover thickness and strengths of repairing mortar. Analytical
results show good rehabilitation effects, when the results predicted
from the rehabilitated models are compared to structural behaviors of
the non-damaged RC beams. In this study, fire damaged high strength concrete beams are
rehabilitated using polymeric cement mortar. The predictions from the
finite element (FE) models show good agreements with the
experimental results and the modeling approaches can be used to
investigate applicability of various rehabilitation methods for further
study.
Abstract: Tamil handwritten document is taken as a key source of data to identify the writer. Tamil is a classical language which has 247 characters include compound characters, consonants, vowels and special character. Most characters of Tamil are multifaceted in nature. Handwriting is a unique feature of an individual. Writer may change their handwritings according to their frame of mind and this place a risky challenge in identifying the writer. A new discriminative model with pooled features of handwriting is proposed and implemented using support vector machine. It has been reported on 100% of prediction accuracy by RBF and polynomial kernel based classification model.