Abstract: Predicting earthquakes is an important issue in the
study of geography. Accurate prediction of earthquakes can help
people to take effective measures to minimize the loss of personal
and economic damage, such as large casualties, destruction of
buildings and broken of traffic, occurred within a few seconds.
United States Geological Survey (USGS) science organization
provides reliable scientific information about Earthquake Existed
throughout history & the Preliminary database from the National
Center Earthquake Information (NEIC) show some useful factors to
predict an earthquake in a seismic area like Aleutian Arc in the U.S.
state of Alaska. The main advantage of this prediction method that it
does not require any assumption, it makes prediction according to the
future evolution of the object's time series. The article compares
between simulation data result from trained BP and RBF neural
network versus actual output result from the system calculations.
Therefore, this article focuses on analysis of data relating to real
earthquakes. Evaluation results show better accuracy and higher
speed by using radial basis functions (RBF) neural network.
Abstract: The knowledge of biodiesel density over large ranges
of temperature and pressure is important for predicting the behavior
of fuel injection and combustion systems in diesel engines, and for
the optimization of such systems. In this study, cottonseed oil was
transesterified into biodiesel and its density was measured at
temperatures between 288 K and 358 K and pressures between 0.1
MPa and 30 MPa, with expanded uncertainty estimated as ±1.6 kg⋅m-
3. Experimental pressure-volume-temperature (pVT) cottonseed data
was used along with literature data relative to other 18 biodiesels, in
order to build a database used to test the correlation of density with
temperarure and pressure using the Goharshadi–Morsali–Abbaspour
equation of state (GMA EoS). To our knowledge, this is the first that
density measurements are presented for cottonseed biodiesel under
such high pressures, and the GMA EoS used to model biodiesel
density. The new tested EoS allowed correlations within 0.2 kg·m-3
corresponding to average relative deviations within 0.02%. The built
database was used to develop and test a new full predictive model
derived from the observed linear relation between density and degree
of unsaturation (DU), which depended from biodiesel FAMEs
profile. The average density deviation of this method was only about
3 kg.m-3 within the temperature and pressure limits of application.
These results represent appreciable improvements in the context of
density prediction at high pressure when compared with other
equations of state.
Abstract: This work presents a Computational Fluid Dynamics
(CFD) simulation of a butterfly valve used to control the flow of
combustible gas mixture in an industrial process setting.The work
uses CFD simulation to analyze the flow characteristics in the
vicinity of the valve, including the pressure distributions and
Frequency spectrum of the pressure pulsations downstream the valves
and the vortex shedding allow predicting the torque fluctuations
acting on the valve shaft and the possibility of generating mechanical
vibration and resonance.These fluctuations are due to aerodynamic
torque resulting from fluid turbulence and vortex shedding in the
valve vicinity.
The valve analyzed is located in a pipeline between two opposing
90o elbows, which exposes the valve and the surrounding structure to
the turbulence generated upstream and downstream the elbows at
either end of the pipe.CFD simulations show that the best location for
the valve from a vibration point of view is in the middle of the pipe
joining the elbows.
Abstract: This study discovers a novel framework of individual
level technology adoption known as I-P (Individual- Privacy) towards
health information application in Smart National Identity Card. Many
countries introduced smart national identity card (SNIC) with various
applications such as health information application embedded inside
it. However, the degree to which citizens accept and use some of the
embedded applications in smart national identity remains unknown to
many governments and application providers as well. Moreover, the
factors of trust, perceived risk, Privacy concern and perceived
credibility need to be incorporated into more comprehensive models
such as extended Unified Theory of Acceptance and Use of
Technology known as UTAUT2. UTAUT2 is a mainly widespread
and leading theory up to now. This research identifies factors
affecting the citizens’ behavioural intention to use health information
application embedded in SNIC and extends better understanding on
the relevant factors that the government and the application providers
would need to consider in predicting citizens’ new technology
acceptance in the future. We propose a conceptual framework by
combining the UTAUT2 and Privacy Calculus Model constructs and
also adding perceived credibility as a new variable. The proposed
framework may provide assistance to any government planning,
decision, and policy makers involving e-government projects.
Empirical study may be conducted in the future to provide proof and
empirically validate this I-P framework.
Abstract: A relationship between face and signature biometrics
is established in this paper. A new approach is developed to predict
faces from signatures by using artificial intelligence. A multilayer
perceptron (MLP) neural network is used to generate face details
from features extracted from signatures, here face is the physical
biometric and signatures is the behavioural biometric. The new
method establishes a relationship between the two biometrics and
regenerates a visible face image from the signature features.
Furthermore, the performance efficiencies of our new technique are
demonstrated in terms of minimum error rates compared to published
work.
Abstract: Characterization of the engineering behavior of
unsaturated soil is dependent on the soil-water characteristic curve
(SWCC), a graphical representation of the relationship between water
content or degree of saturation and soil suction. A reasonable
description of the SWCC is thus important for the accurate prediction
of unsaturated soil parameters. The measurement procedures for
determining the SWCC, however, are difficult, expensive, and timeconsuming.
During the past few decades, researchers have laid a
major focus on developing empirical equations for predicting the
SWCC, with a large number of empirical models suggested. One of
the most crucial questions is how precisely existing equations can
represent the SWCC. As different models have different ranges of
capability, it is essential to evaluate the precision of the SWCC
models used for each particular soil type for better SWCC estimation.
It is expected that better estimation of SWCC would be achieved via
a thorough statistical analysis of its distribution within a particular
soil class. With this in view, a statistical analysis was conducted in
order to evaluate the reliability of the SWCC prediction models
against laboratory measurement. Optimization techniques were used
to obtain the best-fit of the model parameters in four forms of SWCC
equation, using laboratory data for relatively coarse-textured (i.e.,
sandy) soil. The four most prominent SWCCs were evaluated and
computed for each sample. The result shows that the Brooks and
Corey model is the most consistent in describing the SWCC for sand
soil type. The Brooks and Corey model prediction also exhibit
compatibility with samples ranging from low to high soil water
content in which subjected to the samples that evaluated in this study.
Abstract: A generalized vortex lattice method for complex
lifting surfaces with flap and aileron deflection is formulated. The
method is not restricted by the linearized theory assumption and
accounts for all standard geometric lifting surface parameters:
camber, taper, sweep, washout, dihedral, in addition to flap and
aileron deflection. Thickness is not accounted for since the physical
lifting body is replaced by a lattice of panels located on the mean
camber surface. This panel lattice setup and the treatment of different
wake geometries is what distinguish the present work form the
overwhelming majority of previous solutions based on the vortex
lattice method. A MATLAB code implementing the proposed
formulation is developed and validated by comparing our results to
existing experimental and numerical ones and good agreement is
demonstrated. It is then used to study the accuracy of the widely used
classical vortex-lattice method. It is shown that the classical approach
gives good agreement in the clean configuration but is off by as much
as 30% when a flap or aileron deflection of 30° is imposed. This
discrepancy is mainly due the linearized theory assumption
associated with the conventional method. A comparison of the effect
of four different wake geometries on the values of aerodynamic
coefficients was also carried out and it is found that the choice of the
wake shape had very little effect on the results.
Abstract: An analysis of the Australian Diabetes Screening
Study estimated undiagnosed diabetes mellitus [DM] prevalence in a
high risk general practice based cohort. DM prevalence varied from
9.4% to 18.1% depending upon the diagnostic criteria utilised with
age being a highly significant risk factor. Utilising the gold standard
oral glucose tolerance test, the prevalence of DM was 22-23% in
those aged >= 70 years and
Abstract: This paper presents a comparative study between two
neural network models namely General Regression Neural Network
(GRNN) and Back Propagation Neural Network (BPNN) are used
to estimate radial overcut produced during Electrical Discharge
Machining (EDM). Four input parameters have been employed:
discharge current (Ip), pulse on time (Ton), Duty fraction (Tau) and
discharge voltage (V). Recently, artificial intelligence techniques, as
it is emerged as an effective tool that could be used to replace
time consuming procedures in various scientific or engineering
applications, explicitly in prediction and estimation of the complex
and nonlinear process. The both networks are trained, and the
prediction results are tested with the unseen validation set of the
experiment and analysed. It is found that the performance of both the
networks are found to be in good agreement with average percentage
error less than 11% and the correlation coefficient obtained for the
validation data set for GRNN and BPNN is more than 91%. However,
it is much faster to train GRNN network than a BPNN and GRNN is
often more accurate than BPNN. GRNN requires more memory space
to store the model, GRNN features fast learning that does not require
an iterative procedure, and highly parallel structure. GRNN networks
are slower than multilayer perceptron networks at classifying new
cases.
Abstract: Technical analysis comprised by various technical indicators is a holistic way of representing price movement of stocks in the market. Various forms of indicators have evolved from the primitive ones in the past decades. There have been many attempts to introduce volume as a major determinant to determine strong patterns in market forecasting. The law of demand defines the relationship between the volume and price. Most of the traders are familiar with the volume game. Including the time dimension to the law of demand provides a different visualization to the theory. While attempting the same, it was found that there are different thresholds in the market for different companies. These thresholds have a significant influence on the price. This article is an attempt in determining the thresholds for companies using the three dimensional graphs for optimizing the portfolios. It also emphasizes on the magnitude of importance of volumes as a key factor for determining of predicting strong price movements, bullish and bearish markets. It uses a comprehensive data set of major companies which form a major chunk of the Indian automotive sector and are thus used as an illustration.
Abstract: Seismic design criteria based on performance of
structures have recently been adopted by practicing engineers in
response to destructive earthquakes. A simple but efficient
structural-analysis tool capable of predicting both the strength and
ductility is needed to analyze reinforced concrete (RC) structures
under such event. A three-dimensional lattice model is developed in
this study to analyze torsions in high-strength RC members.
Optimization techniques for determining optimal variables in each
lattice model are introduced. Pure torsion tests of RC members are
performed to validate the proposed model. Correlation studies
between the numerical and experimental results confirm that the
proposed model is well capable of representing salient features of the
experimental results.
Abstract: This paper aims at experimental and numerical investigation of springback behavior of sheet metals during L-bending process with emphasis on Stribeck-type friction modeling. The coefficient of friction in Stribeck curve depends on sliding velocity and contact pressure. The springback behavior of mild steel and aluminum alloy 6022-T4 sheets was studied experimentally and using numerical simulations with ABAQUS software with two types of friction model: Coulomb friction and Stribeck friction. The influence of forming speed on springback behavior was studied experimentally and numerically. The results showed that Stribeck-type friction model has better results in predicting springback in sheet metal forming. The FE prediction error for mild steel and 6022-T4 AA is 23.8%, 25.5% respectively, using Coulomb friction model and 11%, 13% respectively, using Stribeck friction model. These results show that Stribeck model is suitable for simulation of sheet metal forming especially at higher forming speed.
Abstract: Diesel vehicle should be equipped with emission after-treatment devices as NOx reduction catalyst and particulate filtersin order to meet more stringer diesel emission standard. Urea-SCR is being developed as the most efficient method of reducing NOx emissions in the after-treatment devices of diesel engines, and recent studies have begun to mount the Urea-SCR device for diesel passenger cars and light duty vehicles. In the present study, the effects of the mixer on the efficiency of urea-SCR System (i.e., NH3uni- formityindex (NH3 UI) is investigated by predicting the transport phenomena in the urea-SCR system. The three dimensional Eulerian-Lagrangian CFD simulationfor internal flow and spray characteristics in front of SCR is carried out by using STAR-CCM+ 7.06 code. In addition, the paper proposes a method to minimize the wall-wetting around the urea injector in order to prevent injector blocks caused by solid urea loading.
Abstract: Mobile robotics is gaining an increasingly important
role in modern society. Several potentially dangerous or laborious
tasks for human are assigned to mobile robots, which are increasingly
capable. Many of these tasks need to be performed within a specified
period, i.e, meet a deadline. Missing the deadline can result in
financial and/or material losses. Mechanisms for predicting the
missing of deadlines are fundamental because corrective actions can
be taken to avoid or minimize the losses resulting from missing the
deadline. In this work we propose a simple but reliable deadline
missing prediction mechanism for mobile robots through the use of
historical data and we use the Pioneer 3-DX robot for experiments
and simulations, one of the most popular robots in academia.
Abstract: Shoreline erosion problems caused by global warming and sea level rising may result in losing of land areas, so it should be examined regularly to reduce possible negative impacts. Initially in this study, three sets of survey images obtained from the years of 1990, 2001, and 2010, respectively, are digitalized by using graphical software to establish the spatial coordinates of six major beaches around the island of Taiwan. Then, by overlaying the known multi-period images, the change of shoreline can be observed from their distribution of coordinates. In addition, the neural network approximation is used to develop a model for predicting shoreline variation in the years of 2015 and 2020. The comparison results show that there is no significant change of total sandy area for all beaches in the three different periods. However, the prediction results show that two beaches may exhibit an increasing of total sandy areas under a statistical 95% confidence interval. The proposed method adopted in this study may be applicable to other shorelines of interest around the world.
Abstract: Wind energy is one of the clean renewable energy. However, the low frequency (20-200HZ) noise generated from the wind turbine blades, which bothers the residents, becomes the major problem to be developed. It is useful for predicting the aerodynamic noise by flow field and pressure distribution analysis on the wind turbine blades. Therefore, the main objective of this study is to use different turbulence models to analyze the flow field and pressure distributions of the wing blades.
Three-dimensional Computation Fluid Dynamics (CFD) simulation of the flow field was used to calculate the flow phenomena for the National Renewable Energy Laboratory (NREL) Phase VI horizontal axis wind turbine rotor. Two different flow cases with different wind speeds were investigated: 7m/s with 72rpm and 15m/s with 72rpm.
Four kinds of RANS-based turbulence models, Standard k-ε, Realizable k-ε, SST k-ω, and v2f, were used to predict and analyze the results in the present work. The results show that the predictions on pressure distributions with SST k-ω and v2f turbulence models have good agreements with experimental data.
Abstract: Australia is a country of some 7,700 million square kilometers with a population of about 22.6 million. At present water security is a major challenge for Australia. In some areas the use of water resources is approaching and in some parts it is exceeding the limits of sustainability. A focal point of proposed national water conservation programs is the recycling of both urban stormwater and treated wastewater. But till now it is not widely practiced in Australia, and particularly stormwater is neglected. In Australia, only 4% of stormwater and rainwater is recycled, whereas less than 1% of reclaimed wastewater is reused within urban areas. Therefore, accurately monitoring, assessing and predicting the availability, quality and use of this precious resource are required for better management. As stormwater is usually of better quality than untreated sewage or industrial discharge, it has better public acceptance for recycling and reuse, particularly for non-potable use such as irrigation, watering lawns, gardens, etc. Existing stormwater recycling practice is far behind of research and no robust technologies developed for this purpose. Therefore, there is a clear need for using modern technologies for assessing feasibility of stormwater harvesting and reuse. Numerical modeling has, in recent times, become a popular tool for doing this job. It includes complex hydrological and hydraulic processes of the study area. The hydrologic model computes stormwater quantity to design the system components, and the hydraulic model helps to route the flow through stormwater infrastructures. Nowadays water quality module is incorporated with these models. Integration of Geographic Information System (GIS) with these models provides extra advantage of managing spatial information. However for the overall management of a stormwater harvesting project, Decision Support System (DSS) plays an important role incorporating database with model and GIS for the proper management of temporal information. Additionally DSS includes evaluation tools and Graphical user interface. This research aims to critically review and discuss all the aspects of stormwater harvesting and reuse such as available guidelines of stormwater harvesting and reuse, public acceptance of water reuse, the scopes and recommendation for future studies. In addition to these, this paper identifies, understand and address the importance of modern technologies capable of proper management of stormwater harvesting and reuse.
Abstract: Several meteorological parameters were used for the
prediction of monthly average daily global solar radiation on
horizontal using recurrent neural networks (RNNs). Climatological
data and measures, mainly air temperature, humidity, sunshine
duration, and wind speed between 1995 and 2007 were used to design
and validate a feed forward and recurrent neural network based
prediction systems. In this paper we present our reference system
based on a feed-forward multilayer perceptron (MLP) as well as the
proposed approach based on an RNN model. The obtained results
were promising and comparable to those obtained by other existing
empirical and neural models. The experimental results showed the
advantage of RNNs over simple MLPs when we deal with time series
solar radiation predictions based on daily climatological data.
Abstract: Finned-tube heat exchangers are predominantly used in space conditioning systems, as well as other applications requiring heat exchange between two fluids. The design of finned-tube heat exchangers requires the selection of over a dozen design parameters by the designer such as tube pitch, tube diameter, tube thickness, etc… Finned-tube heat exchangers are common devices; however, their performance characteristics are complicated. In this paper numerical studies have been carried out to analyze the performances of finned tube heat exchanger (without fins considered for experimental purpose) by predicting the characteristics of temperature difference and pressure drop. In this study, a design considering 5 design variables and also maximizing the temperature difference and pressure drop was suggested by applying DOE. During this process, L18 orthogonal array was adopted. Parametric analytical studies have been carried out using ANOVA to determine the relative importance of each variable with respect to the temperature difference and the pressure drop. Following the results, the final design was suggested by predicting the optimum design therefore confirming the optimized condition.
Abstract: The practical application of the Computational Fluid Dynamics (CFD), for predicting the flow pattern around Multipurpose Amphibious Vehicle (MAV) hull has made much progress over the last decade. Today, several of the CFD tools play an important role in the land and water going vehicle hull form design. CFD has been used for analysis of MAV hull resistance, sea-keeping, maneuvering and investigating its variation when changing the hull form due to varying its parameters, which represents a very important task in the principal and final design stages. Resistance analysis based on CFD (Computational Fluid Dynamics) simulation has become a decisive factor in the development of new, economically efficient and environmentally friendly hull forms. Three-dimensional finite volume method (FVM) based on Reynolds Averaged Navier-Stokes equations (RANS) has been used to simulate incompressible flow around three types of MAV hull bow models in steady-state condition. Finally, the flow structure and streamlines, friction and pressure resistance and velocity contours of each type of hull bow will be compared and discussed.