Abstract: Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.
Abstract: Recently, a quality of motors is inspected by human
ears. In this paper, I propose two systems using a method of speech
recognition for automation of the inspection. The first system is based
on a method of linear processing which uses K-means and Nearest
Neighbor method, and the second is based on a method of non-linear
processing which uses neural networks. I used motor sounds in these
systems, and I successfully recognize 86.67% of motor sounds in the
linear processing system and 97.78% in the non-linear processing
system.
Abstract: Business transformation initiatives are required by
any organization to jump from its normal mode of operation to the
one that is suitable for the change in the environment such as
competitive pressures, regulatory requirements, changes in labor
market, etc., or internal such as changes in strategy/vision, changes in
the capability, change in the management, etc. Recent advances in
information technology in automating the business processes have
the potential to transform an organization to provide it with a
sustained competitive advantage. Process constitutes the skeleton of
a business. Thus, for a business to exist and compete well, it is
essential for the skeleton to be robust and agile. This paper details
“transformation" from a business perspective, methodologies to bring
about an effective transformation, process-based transformation, and
the role of services computing in this. Further, it details the benefits
that could be achieved through services computing.
Abstract: In this article, a method has been offered to classify
normal and defective tiles using wavelet transform and artificial
neural networks. The proposed algorithm calculates max and min
medians as well as the standard deviation and average of detail
images obtained from wavelet filters, then comes by feature vectors
and attempts to classify the given tile using a Perceptron neural
network with a single hidden layer. In this study along with the
proposal of using median of optimum points as the basic feature and
its comparison with the rest of the statistical features in the wavelet
field, the relational advantages of Haar wavelet is investigated. This
method has been experimented on a number of various tile designs
and in average, it has been valid for over 90% of the cases. Amongst
the other advantages, high speed and low calculating load are
prominent.
Abstract: The stochastic nature of tool life using conventional discrete-wear data from experimental tests usually exists due to many individual and interacting parameters. It is a common practice in batch production to continually use the same tool to machine different parts, using disparate machining parameters. In such an environment, the optimal points at which tools have to be changed, while achieving minimum production cost and maximum production rate within the surface roughness specifications, have not been adequately studied. In the current study, two relevant aspects are investigated using coated and uncoated inserts in turning operations: (i) the accuracy of using machinability information, from fixed parameters testing procedures, when variable parameters situations are emerged, and (ii) the credibility of tool life machinability data from prior discrete testing procedures in a non-stop machining. A novel technique is proposed and verified to normalize the conventional fixed parameters machinability data to suit the cases when parameters have to be changed for the same tool. Also, an experimental investigation has been established to evaluate the error in the tool life assessment when machinability from discrete testing procedures is employed in uninterrupted practical machining.
Abstract: In this paper, an efficient structural approach for
recognizing on-line handwritten digits is proposed. After reading
the digit from the user, the slope is estimated and normalized for
adjacent nodes. Based on the changing of signs of the slope values,
the primitives are identified and extracted. The names of these
primitives are represented by strings, and then a finite state
machine, which contains the grammars of the digits, is traced to
identify the digit. Finally, if there is any ambiguity, it will be
resolved. Experiments showed that this technique is flexible and
can achieve high recognition accuracy for the shapes of the digits
represented in this work.
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: Droughts are complex, natural hazards that, to a
varying degree, affect some parts of the world every year. The range
of drought impacts is related to drought occurring in different stages
of the hydrological cycle and usually different types of droughts,
such as meteorological, agricultural, hydrological, and socioeconomical
are distinguished. Streamflow drought was analyzed by
the method of truncation level (at 70% level) on daily discharges
measured in 54 hydrometric stations in southwestern Iran. Frequency
analysis was carried out for annual maximum series (AMS) of
drought deficit volume and duration series. Some factors including
physiographic, climatic, geologic, and vegetation cover were studied
as influential factors in the regional analysis. According to the results
of factor analysis, six most effective factors were identified as area,
rainfall from December to February, the percent of area with
Normalized Difference Vegetation Index (NDVI)
Abstract: The assessment of surface waters in Enugu metropolis
for fecal coliform bacteria was undertaken. Enugu urban was divided
into three areas (A1, A2 and A3), and fecal coliform bacteria
analysed in the surface waters found in these areas for four years
(2005-2008). The plate count method was used for the analyses. Data
generated were subjected to statistical tests involving; Normality test,
Homogeneity of variance test, correlation test, and tolerance limit
test. The influence of seasonality and pollution trends were
investigated using time series plots. Results from the tolerance limit
test at 95% coverage with 95% confidence, and with respect to EU
maximum permissible concentration show that the three areas suffer
from fecal coliform pollution. To this end, remediation procedure
involving the use of saw-dust extracts from three woods namely;
Chlorophora-Excelsa (C-Excelsa),Khayan-Senegalensis,(CSenegalensis)
and Erythrophylum-Ivorensis (E-Ivorensis) in
controlling the coliforms was studied. Results show that mixture of
the acetone extracts of the woods show the most effective
antibacterial inhibitory activities (26.00mm zone of inhibition)
against E-coli. Methanol extract mixture of the three woods gave best
inhibitory activity (26.00mm zone of inhibition) against S-areus, and
25.00mm zones of inhibition against E-Aerogenes. The aqueous
extracts mixture gave acceptable zones of inhibitions against the
three bacteria organisms.
Abstract: Today, cancer remains one of the major diseases that
lead to death. The main obstacle in chemotherapy as a main cancer
treatment is the toxicity to normal cells due to Multidrug Resistance
(MDR) after the use of anticancer drugs. Proposed solution to
overcome this problem is the use of MDR efflux inhibitor of cinchona
alkaloids which is delivered together with anticancer drugs
encapsulated in the form of polymeric nanoparticles. The particles
were prepared by the hydration method. The characterization of
nanoparticles was particle size, zeta potential, entrapment efficiency
and in vitro drug release. Combination nanoparticle size ranged 29-45
nm with a neutral surface charge. Entrapment efficiency was above
87% for the use quinine, quinidine or cinchonidine in combination
with etoposide. The release test results exhibited that the cinchona
alkaloids release released faster than that of etoposide. Collectively,
cinchona alkaloids can be packaged along with etoposide in
nanomicelles for better cancer therapy.
Abstract: A high-frequency low-power sinusoidal quadrature
oscillator is presented through the use of two 2nd-order low-pass
current-mirror (CM)-based filters, a 1st-order CM low-pass filter and
a CM bilinear transfer function. The technique is relatively simple
based on (i) inherent time constants of current mirrors, i.e. the
internal capacitances and the transconductance of a diode-connected
NMOS, (ii) a simple negative resistance RN formed by a resistor load
RL of a current mirror. Neither external capacitances nor inductances
are required. As a particular example, a 1.9-GHz, 0.45-mW, 2-V
CMOS low-pass-filter-based all-current-mirror sinusoidal quadrature
oscillator is demonstrated. The oscillation frequency (f0) is 1.9 GHz
and is current-tunable over a range of 370 MHz or 21.6 %. The
power consumption is at approximately 0.45 mW. The amplitude
matching and the quadrature phase matching are better than 0.05 dB
and 0.15°, respectively. Total harmonic distortions (THD) are less
than 0.3 %. At 2 MHz offset from the 1.9 GHz, the carrier to noise
ratio (CNR) is 90.01 dBc/Hz whilst the figure of merit called a
normalized carrier-to-noise ratio (CNRnorm) is 153.03 dBc/Hz. The
ratio of the oscillation frequency (f0) to the unity-gain frequency (fT)
of a transistor is 0.25. Comparisons to other approaches are also
included.
Abstract: Artificial Neural Network (ANN) has been
extensively used for classification of heart sounds for its
discriminative training ability and easy implementation. However, it
suffers from overparameterization if the number of nodes is not
chosen properly. In such cases, when the dataset has redundancy
within it, ANN is trained along with this redundant information that
results in poor validation. Also a larger network means more
computational expense resulting more hardware and time related
cost. Therefore, an optimum design of neural network is needed
towards real-time detection of pathological patterns, if any from heart
sound signal. The aims of this work are to (i) select a set of input
features that are effective for identification of heart sound signals and
(ii) make certain optimum selection of nodes in the hidden layer for a
more effective ANN structure. Here, we present an optimization
technique that involves Singular Value Decomposition (SVD) and
QR factorization with column pivoting (QRcp) methodology to
optimize empirically chosen over-parameterized ANN structure.
Input nodes present in ANN structure is optimized by SVD followed
by QRcp while only SVD is required to prune undesirable hidden
nodes. The result is presented for classifying 12 common
pathological cases and normal heart sound.
Abstract: Although lots of experiments have been done in enhanced oil recovery, the number of experiments which consider the effects of local and global heterogeneity on efficiency of enhanced oil recovery based on the polymer-surfactant flooding is low and rarely done. In this research, we have done numerous experiments of water flooding and polymer-surfactant flooding on a five spot glass micromodel in different conditions such as different positions of layers. In these experiments, five different micromodels with three different pore structures are designed. Three models with different layer orientation, one homogenous model and one heterogeneous model are designed. In order to import the effect of heterogeneity of porous media, three types of pore structures are distributed accidentally and with equal ratio throughout heterogeneous micromodel network according to random normal distribution. The results show that maximum EOR recovery factor will happen in a situation where the layers are orthogonal to the path of mainstream and the minimum EOR recovery factor will happen in a situation where the model is heterogeneous. This experiments show that in polymer-surfactant flooding, with increase of angles of layers the EOR recovery factor will increase and this recovery factor is strongly affected by local heterogeneity around the injection zone.
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: 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.