Abstract: The use of artificial neural network (ANN) modeling
for prediction and forecasting variables in water resources
engineering are being increasing rapidly. Infrastructural applications
of ANN in terms of selection of inputs, architecture of networks,
training algorithms, and selection of training parameters in different
types of neural networks used in water resources engineering have
been reported. ANN modeling conducted for water resources
engineering variables (river sediment and discharge) published in
high impact journals since 2002 to 2011 have been examined and
presented in this review. ANN is a vigorous technique to develop
immense relationship between the input and output variables, and
able to extract complex behavior between the water resources
variables such as river sediment and discharge. It can produce robust
prediction results for many of the water resources engineering
problems by appropriate learning from a set of examples. It is
important to have a good understanding of the input and output
variables from a statistical analysis of the data before network
modeling, which can facilitate to design an efficient network. An
appropriate training based ANN model is able to adopt the physical
understanding between the variables and may generate more effective
results than conventional prediction techniques.
Abstract: Soil chemical and physical properties have important
roles in compartment of the environment and agricultural
sustainability and human health. The objectives of this research is
determination of spatial distribution patterns of Cd, Zn, K, pH, TNV,
organic material and electrical conductivity (EC) in agricultural soils
of Natanz region in Esfehan province. In this study geostatistic and
non-geostatistic methods were used for prediction of spatial
distribution of these parameters. 64 composite soils samples were
taken at 0-20 cm depth. The study area is located in south of
NATANZ agricultural lands with area of 21660 hectares. Spatial
distribution of Cd, Zn, K, pH, TNV, organic material and electrical
conductivity (EC) was determined using geostatistic and geographic
information system. Results showed that Cd, pH, TNV and K data
has normal distribution and Zn, OC and EC data had not normal
distribution. Kriging, Inverse Distance Weighting (IDW), Local
Polynomial Interpolation (LPI) and Redial Basis functions (RBF)
methods were used to interpolation. Trend analysis showed that
organic carbon in north-south and east to west did not have trend
while K and TNV had second degree trend. We used some error
measurements include, mean absolute error(MAE), mean squared
error (MSE) and mean biased error(MBE). Ordinary
kriging(exponential model), LPI(Local polynomial interpolation),
RBF(radial basis functions) and IDW methods have been chosen as
the best methods to interpolating of the soil parameters. Prediction
maps by disjunctive kriging was shown that in whole study area was
intensive shortage of organic matter and more than 63.4 percent of
study area had shortage of K amount.
Abstract: The paper proposes a way of parallel processing of
SURF and Optical Flow for moving object recognition and tracking.
The object recognition and tracking is one of the most important task
in computer vision, however disadvantage are many operations cause
processing speed slower so that it can-t do real-time object recognition
and tracking. The proposed method uses a typical way of feature
extraction SURF and moving object Optical Flow for reduce
disadvantage and real-time moving object recognition and tracking,
and parallel processing techniques for speed improvement. First
analyse that an image from DB and acquired through the camera using
SURF for compared to the same object recognition then set ROI
(Region of Interest) for tracking movement of feature points using
Optical Flow. Secondly, using Multi-Thread is for improved
processing speed and recognition by parallel processing. Finally,
performance is evaluated and verified efficiency of algorithm
throughout the experiment.
Abstract: Rapid Prototyping (RP) is a technology that produces models and prototype parts from 3D CAD model data, CT/MRI scan data, and model data created from 3D object digitizing systems. There are several RP process like Stereolithography (SLA), Solid Ground Curing (SGC), Selective Laser Sintering (SLS), Fused Deposition Modeling (FDM), 3D Printing (3DP) among them SLS and FDM RP processes are used to fabricate pattern of custom cranial implant. RP technology is useful in engineering and biomedical application. This is helpful in engineering for product design, tooling and manufacture etc. RP biomedical applications are design and development of medical devices, instruments, prosthetics and implantation; it is also helpful in planning complex surgical operation. The traditional approach limits the full appreciation of various bony structure movements and therefore the custom implants produced are difficult to measure the anatomy of parts and analyze the changes in facial appearances accurately. Cranioplasty surgery is a surgical correction of a defect in cranial bone by implanting a metal or plastic replacement to restore the missing part. This paper aims to do a comparative study on the dimensional error of CAD and SLS RP Models for reconstruction of cranial defect by comparing the virtual CAD with the physical RP model of a cranial defect.
Abstract: Knowledge Discovery in Databases (KDD) has
evolved into an important and active area of research because of
theoretical challenges and practical applications associated with the
problem of discovering (or extracting) interesting and previously
unknown knowledge from very large real-world databases. Rough
Set Theory (RST) is a mathematical formalism for representing
uncertainty that can be considered an extension of the classical set
theory. It has been used in many different research areas, including
those related to inductive machine learning and reduction of
knowledge in knowledge-based systems. One important concept
related to RST is that of a rough relation. In this paper we presented
the current status of research on applying rough set theory to KDD,
which will be helpful for handle the characteristics of real-world
databases. The main aim is to show how rough set and rough set
analysis can be effectively used to extract knowledge from large
databases.
Abstract: Fourty one strains of ESBL producing P.aeruginosa
which were previously isolated from burn patients in Kerman
University general hospital, Iran were subjected to PCR, RFLP and
sequencing in order to determine the type of extended spectrum β-
lactamases (ESBL), the restriction digestion pattern and possibility of
mutation among detected genes. DNA extraction was carried out by
phenol chloroform method. PCR for detection of bla genes was
performed using specific primer for each gene. Restriction Fragment
Length Polymorphism (RFLP) for ESBL genes was carried out using
EcoRI, NheI, PVUII, EcoRV, DdeI, and PstI restriction enzymes. The
PCR products were subjected to direct sequencing of both the strands
for identification of the ESBL genes.The blaCTX-M, blaVEB-1, blaPER-1,
blaGES-1, blaOXA-1, blaOXA-4 and blaOXA-10 genes were detected in the
(n=1) 2.43%, (n=41)100%, (n=28) 68.3%, (n=10) 24.4%, (n=29)
70.7%, (n=7)17.1% and (n=38) 92.7% of the ESBL producing isolates
respectively. The RFLP analysis showed that each ESBL gene has
identical pattern of digestion among the isolated strains. Sequencing
of the ESBL genes confirmed the genuinety of PCR products and
revealed no mutation in the restriction sites of the above genes. From
results of the present investigation it can be concluded that blaVEB-1
and blaCTX-M were the most and the least frequently isolated ESBL
genes among the P.aeruginosa strains isolated from burn patients. The
RFLP and sequencing analysis revealed that same clone of the bla
genes were indeed existed among the antibiotic resistant strains.
Abstract: A novel typical day prediction model have been built and validated by the measured data of a grid-connected solar photovoltaic (PV) system in Macau. Unlike conventional statistical method used by previous study on PV systems which get results by averaging nearby continuous points, the present typical day statistical method obtain the value at every minute in a typical day by averaging discontinuous points at the same minute in different days. This typical day statistical method based on discontinuous point averaging makes it possible for us to obtain the Gaussian shape dynamical distributions for solar irradiance and output power in a yearly or monthly typical day. Based on the yearly typical day statistical analysis results, the maximum possible accumulated output energy in a year with on site climate conditions and the corresponding optimal PV system running time are obtained. Periodic Gaussian shape prediction models for solar irradiance, output energy and system energy efficiency have been built and their coefficients have been determined based on the yearly, maximum and minimum monthly typical day Gaussian distribution parameters, which are obtained from iterations for minimum Root Mean Squared Deviation (RMSD). With the present model, the dynamical effects due to time difference in a day are kept and the day to day uncertainty due to weather changing are smoothed but still included. The periodic Gaussian shape correlations for solar irradiance, output power and system energy efficiency have been compared favorably with data of the PV system in Macau and proved to be an improvement than previous models.
Abstract: This paper discusses a discrete event simulation model
for the availability analysis of weapon systems. This model
incorporates missions, operational tasks and system reliability
structures to analyze the availability of a weapon system. The
proposed simulation model consists of 5 modules: Simulation Engine,
Maintenance Organizations, System, its Mission Profile and RBD
which are based on missions and operational tasks. Simulation Engine
executes three kinds of discrete events in chronological order. The
events are mission events generated by Mission Profile, failure events
generated by System, and maintenance events executed by
Maintenance Organization. Finally, this paper shows the case study of
a system's availability analysis and mission reliability using the
simulation model.
Abstract: Refractive index control of benzocyclobutene (BCB 4024-40) is achieved by facilitating different conditions during the thermal curing of BCB film. Refractive index (RI) change of 1.49% is obtained with curing of BCB film using an oven, while the RI change is 0.1% when the BCB is cured using a hotplate. The two different curing methods exhibit a temperature dependent refractive index change of the BCB photosensitive polymer. By carefully controlling the curing conditions, multiple layers of BCB with different RI can be fabricated, which can then be applied in the fabrication of optical waveguides.
Abstract: The resident registration number was adopted for the purposes of enhanced services for resident convenience and effective performance of governmental administrative affairs. However, it has been used for identification purposes customarily and irrationally in line with the development and spread of the Internet. In response to the growing concern about the leakage of collected RRNs and possible abuses of stolen RRNs, e.g. identity theft, for crimes, the Korean Communications Commission began to take legal/regulatory actions in 2011 to minimize the online collection and use of resident registration numbers. As the use of the RRN was limited after the revision of the Act on Promotion of Information and Communications Network Utilization and Information Protection, etc., online business providers were required to have alternatives to the RRN for the purpose of identifying the user's identity and age, in compliance with the law, and settling disputes with customers. This paper presents means of verifying the personal identity by taking advantage of the commonly used infrastructure and simply replacing personal information entered and stored, without requiring users to enter their RRNs.
Abstract: In this paper we present an efficient system for
independent speaker speech recognition based on neural network
approach. The proposed architecture comprises two phases: a
preprocessing phase which consists in segmental normalization and
features extraction and a classification phase which uses neural
networks based on nonparametric density estimation namely the
general regression neural network (GRNN). The relative
performances of the proposed model are compared to the similar
recognition systems based on the Multilayer Perceptron (MLP), the
Recurrent Neural Network (RNN) and the well known Discrete
Hidden Markov Model (HMM-VQ) that we have achieved also.
Experimental results obtained with Arabic digits have shown that the
use of nonparametric density estimation with an appropriate
smoothing factor (spread) improves the generalization power of the
neural network. The word error rate (WER) is reduced significantly
over the baseline HMM method. GRNN computation is a successful
alternative to the other neural network and DHMM.
Abstract: The paper makes part from a complex research project
on Romanian Grey Steppe, a unique breed in terms of biological and
cultural-historical importance, on the verge of extinction and which
has been included in a preservation programme of genetic resources
from Romania. The study of genetic polymorphism of protean
fractions, especially kappa-casein, and the genotype relations of
these lactoproteins with some quantitative and qualitative features of
milk yield represents a current theme and a novelty for this breed. In
the estimation of the genetic parameters we used R.E.M.L.
(Restricted Maximum Likelihood) method.
The main lactoprotein from milk, kappa - casein (K-cz),
characterized in the specialized literature as a feature having a high
degree of hereditary transmission, behaves as such in the nucleus under
study, a value also confirmed by the heritability coefficient (h2 = 0.57
%). We must mention the medium values for milk and fat quantity
(h2=0.26, 0.29 %) and the fat and protein percentage from milk
having a high hereditary influence h2 = 0.71 - 0.63 %.
Correlations between kappa-casein and the milk quantity are
negative and strong. Between kappa-casein and other qualitative
features of milk (fat content 0.58-0.67 % and protein content 0.77-
0.87%), there are positive and very strong correlations. At the same
time, between kappa-casein and β casein (β-cz), β lactoglobulin (β-
lg) respectively, correlations are positive having high values (0.37 –
0.45 %), indicating the same causes and determining factors for the
two groups of features.
Abstract: Musculoskeletal problems are common in high
performance dance population. This study attempts to identify lower
extremity muscle flexibility parameters prevailing among
bharatanatyam dancers and analyze if there is any significant
difference exist between normal and injured dancers in flexibility
parameters. Four hundred and one female dancers and 17 male
dancers were participated in this study. Flexibility parameters
(hamstring tightness, hip internal and external rotation and
tendoachilles in supine and sitting posture) were measured using
goniometer. Results of our study it is evident that injured female
bharathnatyam dancers had significantly (p < 0.05) high hamstring
tightness on left side lower extremity compared to normal female
dancers. The range of motion for left tendoachilles was significantly
(p < 0.05) high for the normal female group when compared to
injured dancers during supine lying posture. Majority of the injured
dancers had high hamstring tightness that could be a possible reason
for pain and MSDs.
Abstract: An original DEA model is to evaluate each DMU
optimistically, but the interval DEA Model proposed in this paper
has been formulated to obtain an efficiency interval consisting of
Evaluations from both the optimistic and the pessimistic view points.
DMUs are improved so that their lower bounds become so large as to
attain the maximum Value one. The points obtained by this method
are called ideal points. Ideal PPS is calculated by ideal of efficiency
DMUs. The purpose of this paper is to rank DMUs by this ideal PPS.
Finally we extend the efficiency interval of a DMU under variable
RTS technology.
Abstract: The morphological parameter of a thin film surface
can be characterized by power spectral density (PSD) functions
which provides a better description to the topography than the RMS
roughness and imparts several useful information of the surface
including fractal and superstructure contributions. Through the
present study Nanoparticle copper/carbon composite films were
prepared by co-deposition of RF-Sputtering and RF-PECVD method
from acetylene gas and copper target. Surface morphology of thin
films is characterized by using atomic force microscopy (AFM). The
Carbon content of our films was obtained by Rutherford Back
Scattering (RBS) and it varied from .4% to 78%. The power values of
power spectral density (PSD) for the AFM data were determined by
the fast Fourier transform (FFT) algorithms. We investigate the effect
of carbon on the roughness of thin films surface. Using such
information, roughness contributions of the surface have been
successfully extracted.