Abstract: Soil organic carbon (SOC) plays a key role in soil
fertility, hydrology, contaminants control and acts as a sink or source
of terrestrial carbon content that can affect the concentration of
atmospheric CO2. SOC supports the sustainability and quality of
ecosystems, especially in semi-arid region. This study was
conducted to determine relative importance of 13 different
exploratory climatic, soil and geometric factors on the SOC contents
in one of the semiarid watershed zones in Iran. Two methods
canonical discriminate analysis (CDA) and feed-forward back
propagation neural networks were used to predict SOC. Stepwise
regression and sensitivity analysis were performed to identify
relative importance of exploratory variables. Results from sensitivity
analysis showed that 7-2-1 neural networks and 5 inputs in CDA
models output have highest predictive ability that explains %70 and
%65 of SOC variability. Since neural network models outperformed
CDA model, it should be preferred for estimating SOC.
Abstract: Search for a tertiary substructure that geometrically
matches the 3D pattern of the binding site of a well-studied protein provides a solution to predict protein functions. In our previous work,
a web server has been built to predict protein-ligand binding sites
based on automatically extracted templates. However, a drawback of such templates is that the web server was prone to resulting in many
false positive matches. In this study, we present a sequence-order constraint to reduce the false positive matches of using automatically
extracted templates to predict protein-ligand binding sites. The binding site predictor comprises i) an automatically constructed template library and ii) a local structure alignment algorithm for
querying the library. The sequence-order constraint is employed to
identify the inconsistency between the local regions of the query protein and the templates. Experimental results reveal that the sequence-order constraint can largely reduce the false positive matches and is effective for template-based binding site prediction.
Abstract: This study investigated students- perception of self
efficacy and anxiety in acquiring English language, and consequently
examined the relationship existing among the independent variables,
confounding variables and students- performances in the English
language. The researcher tested the research hypotheses using a
sample group of 318 respondents out of the population size of 400
students. The results obtained revealed that there was a significant
moderate negative relationship between English language anxiety
and performance in English language, but no significant relationship
between self-efficacy and English language performance, among the
middle-school students. There was a significant moderate negative
relationship between English language anxiety and self-efficacy. It
was discovered that general self-efficacy and English language
anxiety represented a significantly more powerful set of predictors
than the set of confounding variables. Thus, the study concluded that
English language anxiety and general self-efficacy were significant
predictors of English language performance among middle-school
students in Satri Si Suriyothai School.
Abstract: This paper investigates the control of a bouncing
ball using Model Predictive Control. Bouncing ball is a benchmark
problem for various rhythmic tasks such as juggling, walking,
hopping and running. Humans develop intentions which may be
perceived as our reference trajectory and tries to track it. The
human brain optimizes the control effort needed to track its
reference; this forms the central theme for control of bouncing ball
in our investigations.
Abstract: In the present study, the surface temperature history of the adaptor part in a two-stage supersonic launch vehicle is accurately predicted. The full Navier-Stokes equations are used to estimate the aerodynamic heat flux and the one-dimensional heat conduction in solid phase is used to compute the temperature history. The instantaneous surface temperature is used to improve the applied heat flux, to improve the accuracy of the results.
Abstract: In many data mining applications, it is a priori known
that the target function should satisfy certain constraints imposed
by, for example, economic theory or a human-decision maker. In this
paper we consider partially monotone prediction problems, where the
target variable depends monotonically on some of the input variables
but not on all. We propose a novel method to construct prediction
models, where monotone dependences with respect to some of
the input variables are preserved by virtue of construction. Our
method belongs to the class of mixture models. The basic idea is to
convolute monotone neural networks with weight (kernel) functions
to make predictions. By using simulation and real case studies,
we demonstrate the application of our method. To obtain sound
assessment for the performance of our approach, we use standard
neural networks with weight decay and partially monotone linear
models as benchmark methods for comparison. The results show that
our approach outperforms partially monotone linear models in terms
of accuracy. Furthermore, the incorporation of partial monotonicity
constraints not only leads to models that are in accordance with the
decision maker's expertise, but also reduces considerably the model
variance in comparison to standard neural networks with weight
decay.
Abstract: The composition, vapour pressure, and heat capacity
of nine biodiesel fuels from different sources were measured. The
vapour pressure of the biodiesel fuels is modeled assuming an ideal
liquid phase of the fatty acid methyl esters constituting the fuel. New
methodologies to calculate the vapour pressure and ideal gas and
liquid heat capacities of the biodiesel fuel constituents are proposed.
Two alternative optimization scenarios are evaluated: 1) vapour
pressure only; 2) vapour pressure constrained with liquid heat
capacity. Without physical constraints, significant errors in liquid
heat capacity predictions were found whereas the constrained
correlation accurately fit both vapour pressure and liquid heat
capacity.
Abstract: In this paper parametric analytical studies have been carried out to examine the intrinsic flow physics pertaining to the liftoff time of solid propellant rockets. Idealized inert simulators of solid rockets are selected for numerical studies to examining the preignition chamber dynamics. Detailed diagnostic investigations have been carried out using an unsteady two-dimensional k-omega turbulence model. We conjectured from the numerical results that the altered variations of the igniter jet impingement angle, turbulence level, time and location of the first ignition, flame spread characteristics, the overall chamber dynamics including the boundary layer growth history are having bearing on the time for nozzle flow chocking for establishing the required thrust for the rocket liftoff. We concluded that the altered flow choking time of strap-on motors with the pre-determined identical ignition time at the lift off phase will lead to the malfunctioning of the rocket. We also concluded that, in the light of the space debris, an error in predicting the liftoff time can lead to an unfavorable launch window amounts the satellite injection errors and/or the mission failures.
Abstract: In a previously developed fast vortex method, the
diffusion of the vortex sheet induced at the solid wall by the no-slip
boundary conditions was modeled according to the approximation
solution of Koumoutsakos and converted into discrete blobs in the
vicinity of the wall. This scheme had been successfully applied to a
simulation of the flow induced with an impulsively initiated circular
cylinder. In this work, further modifications on this vortex method are
attempted, including replacing the approximation solution by the
boundary-element-method solution, incorporating a new algorithm for
handling the over-weak vortex blobs, and diffusing the vortex sheet
circulation in a new way suitable for high-curvature solid bodies. The
accuracy is thus largely improved. The predictions of lift and drag
coefficients for a uniform flow past a NASA airfoil agree well with the
existing literature.
Abstract: This paper presents the study of induced currents and
temperature distribution in gear heated by induction process using 2D
finite element (FE) model. The model is developed by coupling
Maxwell and heat transfer equations into a multi-physics model. The
obtained results allow comparing the medium frequency (MF) and
high frequency (HF) cases and the effect of machine parameters on
the evolution of induced currents and temperature during heating.
The sensitivity study of the temperature profile is conducted and the
case hardness is predicted using the final temperature profile. These
results are validated using tests and give a good understanding of
phenomena during heating process.
Abstract: Eco-driving allows the driver to optimize his/her behaviour in order to achieve several types of benefits: reducing pollution emissions, increasing road safety, and fuel saving. One of the main rules for adopting eco-driving is to anticipate the traffic events by avoiding strong acceleration or braking and maintaining a steady speed when possible. Therefore, drivers have to comply with speed limits and time headway. The present study explored the role of three types of motivation and social norms in predicting French drivers- intentions to comply with speed limits and time headway as eco-driving practices as well as examine the variations according to gender and age. 1234 drivers with ages between 18 and 75 years old filled in a questionnaire which was presented as part of an online survey aiming to better understand the drivers- road habits. It included items assessing: a) behavioural intentions to comply with speed limits and time headway according to three types of motivation: reducing pollution emissions, increasing road safety, and fuel saving, b) subjective and descriptive social norms regarding the intention to comply with speed limits and time headway, and c) sociodemographical variables. Drivers expressed their intention to frequently comply with speed limits and time headway in the following 6 months; however, they showed more intention to comply with speed limits as compared to time headway regardless of the type of motivation. The subjective injunctive norms were significantly more important in predicting drivers- intentions to comply with speed limits and time headway as compared to the descriptive norms. In addition, the most frequently reported type of motivation for complying with speed limits and time headway was increasing road safety followed by fuel saving and reducing pollution emissions, hence underlining a low motivation to practice eco-driving. Practical implications of the results are discussed.
Abstract: The paper presents an on-line recognition machine
(RM) for continuous/isolated, dynamic and static gestures that arise
in Flight Deck Officer (FDO) training. RM is based on generic pattern
recognition framework. Gestures are represented as templates using
summary statistics. The proposed recognition algorithm exploits temporal
and spatial characteristics of gestures via dynamic programming
and Markovian process. The algorithm predicts corresponding index
of incremental input data in the templates in an on-line mode.
Accumulated consistency in the sequence of prediction provides a
similarity measurement (Score) between input data and the templates.
The algorithm provides an intuitive mechanism for automatic detection
of start/end frames of continuous gestures. In the present paper,
we consider isolated gestures. The performance of RM is evaluated
using four datasets - artificial (W TTest), hand motion (Yang) and
FDO (tracker, vision-based ). RM achieves comparable results which
are in agreement with other on-line and off-line algorithms such as
hidden Markov model (HMM) and dynamic time warping (DTW).
The proposed algorithm has the additional advantage of providing
timely feedback for training purposes.
Abstract: Based on an analysis of the mechanism of degradation of optical characteristics of the ZnO-pigmented white paint by electron irradiation, a model of single molecular color centers is built. An equation that explains the relationship between the changes of variation of the ZnO-pigmented white paint-s spectrum absorptance and electron fluence is derived. The uncertain parameters in the equation can be calculated using the curve fitting by experimental data. The result indicates that the model can be applied to predict the degradation of optical characteristics of ZnO-pigmented white paint by electron radiation.
Abstract: In DMVC, we have more than one options of sources available for construction of side information. The newer techniques make use of both the techniques simultaneously by constructing a bitmask that determines the source of every block or pixel of the side information. A lot of computation is done to determine each bit in the bitmask. In this paper, we have tried to define areas that can only be well predicted by temporal interpolation and not by multiview interpolation or synthesis. We predict that all such areas that are not covered by two cameras cannot be appropriately predicted by multiview synthesis and if we can identify such areas in the first place, we don-t need to go through the script of computations for all the pixels that lie in those areas. Moreover, this paper also defines a technique based on KLT to mark the above mentioned areas before any other processing is done on the side view.
Abstract: Thirty three re-wetting tests were conducted at
different combinations of temperatures (5.7- 46.30C) and relative
humidites (48.2-88.6%) with barley. Two most commonly used thinlayer
drying and rewetting models i.e. Page and Diffusion were
compared for their ability to the fit the experimental re-wetting data
based on the standard error of estimate (SEE) of the measured and
simulated moisture contents. The comparison shows both the Page
and Diffusion models fit the re-wetting experimental data of barley
well. The average SEE values for the Page and Diffusion models
were 0.176 % d.b. and 0.199 % d.b., respectively. The Page and
Diffusion models were found to be most suitable equations, to
describe the thin-layer re-wetting characteristics of barley over a
typically five day re-wetting. These two models can be used for the
simulation of deep-bed re-wetting of barley occurring during
ventilated storage and deep bed drying.
Abstract: The H.264/AVC standard uses an intra prediction, 9
directional modes for 4x4 luma blocks and 8x8 luma blocks, 4
directional modes for 16x16 macroblock and 8x8 chroma blocks,
respectively. It means that, for a macroblock, it has to perform 736
different RDO calculation before a best RDO modes is determined.
With this Multiple intra-mode prediction, intra coding of H.264/AVC
offers a considerably higher improvement in coding efficiency
compared to other compression standards, but computational
complexity is increased significantly. This paper presents a fast intra
prediction algorithm for H.264/AVC intra prediction based a
characteristic of homogeneity information. In this study, the gradient
prediction method used to predict the homogeneous area and the
quadratic prediction function used to predict the nonhomogeneous
area. Based on the correlation between the homogeneity and block
size, the smaller block is predicted by gradient prediction and
quadratic prediction, so the bigger block is predicted by gradient
prediction. Experimental results are presented to show that the
proposed method reduce the complexity by up to 76.07%
maintaining the similar PSNR quality with about 1.94%bit rate
increase in average.
Abstract: Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.
Abstract: Because of architectural condition and structure application, sometimes mass source and stiffness source are not coincidence, and the structure is irregular. The structure is also might be asymmetric as an asymmetric bracing in plan which leads to unbalance distribution of stiffness or because of unbalance distribution of the mass. Both condition lead to eccentricity and torsion in the structure. The deficiency of ordinary code to evaluate the performance of steel structures against earthquake has been caused designing based on performance level or capacity spectrum be used. By using the mentioned methods it is possible to design a structure that its behavior against different earthquakes be predictive. In this article 5- story buildings with different percentage of asymmetric which is because of stiffness changes have been designed. The static and dynamic nonlinear analysis under three acceleration recording has been done. Finally performance level of the structure has been evaluated.
Abstract: An Artificial Neural Network based modeling
technique has been used to study the influence of different
combinations of meteorological parameters on evaporation from a
reservoir. The data set used is taken from an earlier reported study.
Several input combination were tried so as to find out the importance
of different input parameters in predicting the evaporation. The
prediction accuracy of Artificial Neural Network has also been
compared with the accuracy of linear regression for predicting
evaporation. The comparison demonstrated superior performance of
Artificial Neural Network over linear regression approach. The
findings of the study also revealed the requirement of all input
parameters considered together, instead of individual parameters
taken one at a time as reported in earlier studies, in predicting the
evaporation. The highest correlation coefficient (0.960) along with
lowest root mean square error (0.865) was obtained with the input
combination of air temperature, wind speed, sunshine hours and
mean relative humidity. A graph between the actual and predicted
values of evaporation suggests that most of the values lie within a
scatter of ±15% with all input parameters. The findings of this study
suggest the usefulness of ANN technique in predicting the
evaporation losses from reservoirs.
Abstract: This work deals with modeling and simulation of SO2 removal in a ceramic membrane by means of FEM. A mass transfer model was developed to predict the performance of SO2 absorption in a chemical solvent. The model was based on solving conservation equations for gas component in the membrane. Computational fluid dynamics (CFD) of mass and momentum were used to solve the model equations. The simulations aimed to obtain the distribution of gas concentration in the absorption process. The effect of the operating parameters on the efficiency of the ceramic membrane was evaluated. The modeling findings showed that the gas phase velocity has significant effect on the removal of gas whereas the liquid phase does not affect the SO2 removal significantly. It is also indicated that the main mass transfer resistance is placed in the membrane and gas phase because of high tortuosity of the ceramic membrane.