Abstract: Many digital signal processing, techniques have been used to automatically distinguish protein coding regions (exons) from non-coding regions (introns) in DNA sequences. In this work, we have characterized these sequences according to their nonlinear dynamical features such as moment invariants, correlation dimension, and largest Lyapunov exponent estimates. We have applied our model to a number of real sequences encoded into a time series using EIIP sequence indicators. In order to discriminate between coding and non coding DNA regions, the phase space trajectory was first reconstructed for coding and non-coding regions. Nonlinear dynamical features are extracted from those regions and used to investigate a difference between them. Our results indicate that the nonlinear dynamical characteristics have yielded significant differences between coding (CR) and non-coding regions (NCR) in DNA sequences. Finally, the classifier is tested on real genes where coding and non-coding regions are well known.
Abstract: The purpose of this paper is to develop models that would enable predicting student success. These models could improve allocation of students among colleges and optimize the newly introduced model of government subsidies for higher education. For the purpose of collecting data, an anonymous survey was carried out in the last year of undergraduate degree student population using random sampling method. Decision trees were created of which two have been chosen that were most successful in predicting student success based on two criteria: Grade Point Average (GPA) and time that a student needs to finish the undergraduate program (time-to-degree). Decision trees have been shown as a good method of classification student success and they could be even more improved by increasing survey sample and developing specialized decision trees for each type of college. These types of methods have a big potential for use in decision support systems.
Abstract: The paper describes the futures trading and aims to
design the speculators trading strategy. The problem is formulated as
the decision making task and such as is solved. The solution of the
task leads to complex mathematical problems and the approximations
of the decision making is demanded. Two kind of approximation are
used in the paper: Monte Carlo for the multi-step prediction and
iteration spread in time for the optimization. The solution is applied to the real-market data and the results of the off-line experiments are
presented.
Abstract: Interaction effects of xanthan gum (XG), carboxymethyl
cellulose (CMC), and locust bean gum (LBG) on the flow properties
of oil-in-water emulsions were investigated by a mixture design
experiment. Blends of XG, CMC and LBG were prepared according
to an augmented simplex-centroid mixture design (10 points) and used
at 0.5% (wt/wt) in the emulsion formulations. An appropriate
mathematical model was fitted to express each response as a function
of the proportions of the blend components that are able to
empirically predict the response to any blend of combination of the
components. The synergistic interaction effect of the ternary
XG:CMC:LBG blends at approximately 33-67% XG levels was
shown to be much stronger than that of the binary XG:LBG blend at
50% XG level (p < 0.05). Nevertheless, an antagonistic interaction
effect became significant as CMC level in blends was more than 33%
(p < 0.05). Yield stress and apparent viscosity (at 10 s-1) responses
were successfully fitted with a special quartic model while flow
behaviour index and consistency coefficient were fitted with a full
quartic model (R2
adjusted ≥ 0.90). This study found that a mixture
design approach could serve as a valuable tool in better elucidating
and predicting the interaction effects beyond the conventional twocomponent
blends.
Abstract: The aim of this paper is to rank the impact of Object
Oriented(OO) metrics in fault prediction modeling using Artificial
Neural Networks(ANNs). Past studies on empirical validation of
object oriented metrics as fault predictors using ANNs have focused
on the predictive quality of neural networks versus standard
statistical techniques. In this empirical study we turn our attention to
the capability of ANNs in ranking the impact of these explanatory
metrics on fault proneness. In ANNs data analysis approach, there is
no clear method of ranking the impact of individual metrics. Five
ANN based techniques are studied which rank object oriented
metrics in predicting fault proneness of classes. These techniques are
i) overall connection weights method ii) Garson-s method iii) The
partial derivatives methods iv) The Input Perturb method v) the
classical stepwise methods. We develop and evaluate different
prediction models based on the ranking of the metrics by the
individual techniques. The models based on overall connection
weights and partial derivatives methods have been found to be most
accurate.
Abstract: Antiseismic property of telecommunication equipment
is very important for the grasp of the damage and the restoration after
earthquake. Telecommunication business operators are regulating
seismic standard for their equipments. These standards are organized
to simulate the real seismic situations and usually define the minimum
value of first natural frequency of the equipments or the allowable
maximum displacement of top of the equipments relative to bottom.
Using the finite element analysis, natural frequency can be obtained
with high accuracy but the relative displacement of top of the
equipments is difficult to predict accurately using the analysis.
Furthermore, in the case of simulating the equipments with access
floor, predicting the relative displacement of top of the equipments
become more difficult.
In this study, using enormous experimental datum, an empirical
formula is suggested to forecast the relative displacement of top of the
equipments. Also it can be known that which physical quantities are
related with the relative displacement.
Abstract: The sensitivity of UAVs to the atmospheric effects are
apparent. All the same the meteorological support for the UAVs
missions is often non-adequate or partly missing.
In our paper we show a new complex meteorological support
system for different types of UAVs pilots, specialists and decision
makers, too. The mentioned system has two important parts with
different forecasts approach such as the statistical and dynamical
ones.
The statistical prediction approach is based on a large
climatological data base and the special analog method which is able
to select similar weather situations from the mentioned data base to
apply them during the forecasting procedure.
The applied dynamic approach uses the specific WRF model runs
twice a day and produces 96 hours, high resolution weather forecast
for the UAV users over the Hungary. An easy to use web-based
system can give important weather information over the Carpathian
basin in Central-Europe. The mentioned products can be reached via
internet connection.
Abstract: The flow field around a flat plate of infinite span has
been investigated for several values of the angle of attack. Numerical
predictions have been compared to experimental measurements, in
order to examine the effect of turbulence model and grid resolution
on the resultant aerodynamic forces acting on the plate. Also the
influence of the free-stream turbulence intensity, at the entrance of
the computational domain, has been investigated. A full campaign of
simulations has been conducted for three inclination angles (9°, 15°
and 30°), in order to obtain some practical guidelines to be used for
the simulation of the flow field around inclined plates and discs.
Abstract: A method to predict the column size for displacement based design of reinforced concrete frame buildings with higher target inter storey drift is reported here. The column depth derived from empirical relation as a function of given beam section, target inter-story drift, building plan features and common displacement based design parameters is used. Regarding the high drift requirement, a minimum column-beam moment capacity ratio is maintained during capacity design. The method is used in designing four, eight and twelve story frame buildings with displacement based design for three percent target inter storey drift. Non linear time history analysis of the designed buildings are performed under five artificial ground motions to show that the columns are found elastic enough to avoid column sway mechanism assuring that for the design the column size can be used with or without minor changes.
Abstract: Many systems in the natural world exhibit chaos or non-linear behavior, the complexity of which is so great that they appear to be random. Identification of chaos in experimental data is essential for characterizing the system and for analyzing the predictability of the data under analysis. The Lyapunov exponents provide a quantitative measure of the sensitivity to initial conditions and are the most useful dynamical diagnostic for chaotic systems. However, it is difficult to accurately estimate the Lyapunov exponents of chaotic signals which are corrupted by a random noise. In this work, a method for estimation of Lyapunov exponents from noisy time series using unscented transformation is proposed. The proposed methodology was validated using time series obtained from known chaotic maps. In this paper, the objective of the work, the proposed methodology and validation results are discussed in detail.
Abstract: A new analytical method to predict the torsional
capacity and behavior of R.C multi-cell box girders strengthened with
carbon fiber reinforced polymer (CFRP) sheets is presented.
Modification was done on the Softened Truss Model (STM) in the
proposed method; the concrete torsional problem is solved by
combining the equilibrium conditions, compatibility conditions and
constitutive laws of materials by taking into account the confinement
of concrete with CFRP sheets. A specific algorithm is developed to
predict the torsional behavior of reinforced concrete multi-cell box
girders with or without strengthening by CFRP sheets. Applications
of the developed method as an assessment tool to strengthened multicell
box girders with CFRP and first analytical example that
demonstrate the contribution of the CFRP materials on the torsional
response is also included.
Abstract: An adaptive software reliability prediction model
using evolutionary connectionist approach based on Recurrent Radial
Basis Function architecture is proposed. Based on the currently
available software failure time data, Fuzzy Min-Max algorithm is
used to globally optimize the number of the k Gaussian nodes. The
corresponding optimized neural network architecture is iteratively
and dynamically reconfigured in real-time as new actual failure time
data arrives. The performance of our proposed approach has been
tested using sixteen real-time software failure data. Numerical results
show that our proposed approach is robust across different software
projects, and has a better performance with respect to next-steppredictability
compared to existing neural network model for failure
time prediction.
Abstract: Employing a recently introduced unified adaptive filter
theory, we show how the performance of a large number of important
adaptive filter algorithms can be predicted within a general framework
in nonstationary environment. This approach is based on energy conservation
arguments and does not need to assume a Gaussian or white
distribution for the regressors. This general performance analysis can
be used to evaluate the mean square performance of the Least Mean
Square (LMS) algorithm, its normalized version (NLMS), the family
of Affine Projection Algorithms (APA), the Recursive Least Squares
(RLS), the Data-Reusing LMS (DR-LMS), its normalized version
(NDR-LMS), the Block Least Mean Squares (BLMS), the Block
Normalized LMS (BNLMS), the Transform Domain Adaptive Filters
(TDAF) and the Subband Adaptive Filters (SAF) in nonstationary
environment. Also, we establish the general expressions for the
steady-state excess mean square in this environment for all these
adaptive algorithms. Finally, we demonstrate through simulations that
these results are useful in predicting the adaptive filter performance.
Abstract: This article provides partial evaluation index and its
standard of sports aerobics, including the following 12 indexes: health
vitality, coordination, flexibility, accuracy, pace, endurance, elasticity,
self-confidence, form, control, uniformity and musicality. The
three-layer BP artificial neural network model including input layer,
hidden layer and output layer is established. The result shows that the
model can well reflect the non-linear relationship between the
performance of 12 indexes and the overall performance. The predicted
value of each sample is very close to the true value, with a relative
error fluctuating around of 5%, and the network training is successful.
It shows that BP network has high prediction accuracy and good
generalization capacity if being applied in sports aerobics performance
evaluation after effective training.
Abstract: The purpose of this research is to develop and apply the
RSCMAC to enhance the dynamic accuracy of Global Positioning
System (GPS). GPS devices provide services of accurate positioning,
speed detection and highly precise time standard for over 98% area on
the earth. The overall operation of Global Positioning System includes
24 GPS satellites in space; signal transmission that includes 2
frequency carrier waves (Link 1 and Link 2) and 2 sets random
telegraphic codes (C/A code and P code), on-earth monitoring stations
or client GPS receivers. Only 4 satellites utilization, the client position
and its elevation can be detected rapidly. The more receivable
satellites, the more accurate position can be decoded. Currently, the
standard positioning accuracy of the simplified GPS receiver is greatly
increased, but due to affected by the error of satellite clock, the
troposphere delay and the ionosphere delay, current measurement
accuracy is in the level of 5~15m. In increasing the dynamic GPS
positioning accuracy, most researchers mainly use inertial navigation
system (INS) and installation of other sensors or maps for the
assistance. This research utilizes the RSCMAC advantages of fast
learning, learning convergence assurance, solving capability of
time-related dynamic system problems with the static positioning
calibration structure to improve and increase the GPS dynamic
accuracy. The increasing of GPS dynamic positioning accuracy can be
achieved by using RSCMAC system with GPS receivers collecting
dynamic error data for the error prediction and follows by using the
predicted error to correct the GPS dynamic positioning data. The
ultimate purpose of this research is to improve the dynamic positioning
error of cheap GPS receivers and the economic benefits will be
enhanced while the accuracy is increased.
Abstract: Understanding the cell's large-scale organization is an interesting task in computational biology. Thus, protein-protein interactions can reveal important organization and function of the cell. Here, we investigated the correspondence between protein interactions and function for the yeast. We obtained the correlations among the set of proteins. Then these correlations are clustered using both the hierarchical and biclustering methods. The detailed analyses of proteins in each cluster were carried out by making use of their functional annotations. As a result, we found that some functional classes appear together in almost all biclusters. On the other hand, in hierarchical clustering, the dominancy of one functional class is observed. In the light of the clustering data, we have verified some interactions which were not identified as core interactions in DIP and also, we have characterized some functionally unknown proteins according to the interaction data and functional correlation. In brief, from interaction data to function, some correlated results are noticed about the relationship between interaction and function which might give clues about the organization of the proteins, also to predict new interactions and to characterize functions of unknown proteins.
Abstract: The main purpose of this study is to analyze the
feelings of tourists for the service quality of the bikeway. In addition,
this study also analyzed the causal relationship between service
quality and satisfaction to visitor-s lane loyalty. In this study, the Ya
Tam San bikeway visitor-s subjects, using the designated convenience
sampling carried out the survey, a total of 651 questionnaires were
validly. Valid questionnaires after statistical analysis, the following
findings: 1. Visitor-s lane highest quality of service project: the routes
through the region weather pleasant. Lane "with health and sports," the
highest satisfaction various factors of service quality and satisfaction,
loyal between correlations exist. 4. Guided tours of bikeways, the
quality of the environment, and modeling imagery can effectively
predict visitor satisfaction. 5. Quality of bikeway, public facilities,
guided tours, and modeling imagery can effectively predict visitor
loyalty. According to the above results, the study not only makes
recommendations to the government units and the bicycle industry,
also asked the research direction for future researchers.
Abstract: Missing data is a persistent problem in almost all
areas of empirical research. The missing data must be treated very
carefully, as data plays a fundamental role in every analysis.
Improper treatment can distort the analysis or generate biased results.
In this paper, we compare and contrast various imputation techniques
on missing data sets and make an empirical evaluation of these
methods so as to construct quality software models. Our empirical
study is based on NASA-s two public dataset. KC4 and KC1. The
actual data sets of 125 cases and 2107 cases respectively, without
any missing values were considered. The data set is used to create
Missing at Random (MAR) data Listwise Deletion(LD), Mean
Substitution(MS), Interpolation, Regression with an error term and
Expectation-Maximization (EM) approaches were used to compare
the effects of the various techniques.
Abstract: A method is presented for using thermo-mechanical fatigue analysis as a tool in the design of automotive heat exchangers. Use of infra-red thermography to measure the real thermal history in the heat exchanger reduces the time necessary for calculating design parameters and improves prediction accuracy. Thermal shocks are the primary cause of heat exchanger damage. Thermo-mechanical simulation is based on the mean behavior of the aluminum tubes used in the heat exchanger. An energetic fatigue criterion is used to detect critical zones.
Abstract: A new analytical model is developed which provides
close-formed solutions for both transient indoor and envelope
temperature changes in buildings. Time-dependent boundary
temperature is presented as Fourier series which can approximate real
weather conditions. The final close-formed solutions are simple,
concise, and comprehensive. The model was compared with
numerical results and good accuracy was obtained. The model can
be used as design and control guidelines in engineering applications
for analysing mechanical heat transfer properties for buildings.