Abstract: Recently, traffic monitoring has attracted the attention
of computer vision researchers. Many algorithms have been
developed to detect and track moving vehicles. In fact, vehicle
tracking in daytime and in nighttime cannot be approached with the
same techniques, due to the extreme different illumination conditions.
Consequently, traffic-monitoring systems are in need of having a
component to differentiate between daytime and nighttime scenes. In
this paper, a HSV-based day/night detector is proposed for traffic
monitoring scenes. The detector employs the hue-histogram and the
value-histogram on the top half of the image frame. Experimental
results show that the extraction of the brightness features along with
the color features within the top region of the image is effective for
classifying traffic scenes. In addition, the detector achieves high
precision and recall rates along with it is feasible for real time
applications.
Abstract: In order to retrieve images efficiently from a large
database, a unique method integrating color and texture features
using genetic programming has been proposed. Opponent color
histogram which gives shadow, shade, and light intensity invariant
property is employed in the proposed framework for extracting color
features. For texture feature extraction, fast discrete curvelet
transform which captures more orientation information at different
scales is incorporated to represent curved like edges. The recent
scenario in the issues of image retrieval is to reduce the semantic gap
between user’s preference and low level features. To address this
concern, genetic algorithm combined with relevance feedback is
embedded to reduce semantic gap and retrieve user’s preference
images. Extensive and comparative experiments have been conducted
to evaluate proposed framework for content based image retrieval on
two databases, i.e., COIL-100 and Corel-1000. Experimental results
clearly show that the proposed system surpassed other existing
systems in terms of precision and recall. The proposed work achieves
highest performance with average precision of 88.2% on COIL-100
and 76.3% on Corel, the average recall of 69.9% on COIL and 76.3%
on Corel. Thus, the experimental results confirm that the proposed
content based image retrieval system architecture attains better
solution for image retrieval.
Abstract: Background modeling and subtraction in video
analysis has been widely used as an effective method for moving
objects detection in many computer vision applications. Recently, a
large number of approaches have been developed to tackle different
types of challenges in this field. However, the dynamic background
and illumination variations are the most frequently occurred problems
in the practical situation. This paper presents a favorable two-layer
model based on codebook algorithm incorporated with local binary
pattern (LBP) texture measure, targeted for handling dynamic
background and illumination variation problems. More specifically,
the first layer is designed by block-based codebook combining with
LBP histogram and mean value of each RGB color channel. Because
of the invariance of the LBP features with respect to monotonic
gray-scale changes, this layer can produce block wise detection results
with considerable tolerance of illumination variations. The pixel-based
codebook is employed to reinforce the precision from the output of the
first layer which is to eliminate false positives further. As a result, the
proposed approach can greatly promote the accuracy under the
circumstances of dynamic background and illumination changes.
Experimental results on several popular background subtraction
datasets demonstrate very competitive performance compared to
previous models.
Abstract: The modelling of physical phenomena, such as the
earth’s free oscillations, the vibration of strings, the interaction of
atomic particles, or the steady state flow in a bar give rise to Sturm-
Liouville (SL) eigenvalue problems. The boundary applications of
some systems like the convection-diffusion equation, electromagnetic
and heat transfer problems requires the combination of Dirichlet and
Neumann boundary conditions. Hence, the incorporation of Robin
boundary condition in the analyses of Sturm-Liouville problem. This
paper deals with the computation of the eigenvalues and
eigenfunction of generalized Sturm-Liouville problems with Robin
boundary condition using the finite element method. Numerical
solution of classical Sturm–Liouville problem is presented. The
results show an agreement with the exact solution. High results
precision is achieved with higher number of elements.
Abstract: The rapid development technology and widespread
Internet make business environment changing a lot. In order to stand in
the global market and to keep subsistence, “changing” is unspoken
rule for the company’s survival. The purpose of this paper is building
up change model by using SWOT, strategy map, KPI and change
management theory. The research findings indicate that the company
needs to deal with employee’s resistance emotion firstly before
building up change model. The ways of providing performance
appraisal reward, consulting and counseling mechanisms that will
great help to achieve reducing staff negative emotions and motivate
staff’s efficiencies also. To revise strategy map, modify corporate
culture, and improve internal operational processes which is based on
change model. Through the change model, the increasing growth rate
of net income helps company to achieve the goals and be a leading
brand of precision machinery industry.
Abstract: High Performance Liquid Chromatography (HPLC)
method was developed and validated for simultaneous estimation of
6-Gingerol(6G) and 6-Shogaol(6S) in joint pain relief gel containing
ginger extract. The chromatographic separation was achieved by
using C18 column, 150 x 4.6mm i.d., 5μ Luna, mobile phase
containing acetonitrile and water (gradient elution). The flow rate
was 1.0 ml/min and the absorbance was monitored at 282 nm. The
proposed method was validated in terms of the analytical parameters
such as specificity, accuracy, precision, linearity, range, limit of
detection (LOD), limit of quantification (LOQ), and determined
based on the International Conference on Harmonization (ICH)
guidelines. The linearity ranges of 6G and 6S were obtained over 20-
60 and 6-18 μg/ml respectively. Good linearity was observed over the
above-mentioned range with linear regression equation Y= 11016x-
23778 for 6G and Y = 19276x-19604 for 6S (x is concentration of
analytes in μg/ml and Y is peak area). The value of correlation
coefficient was found to be 0.9994 for both markers. The limit of
detection (LOD) and limit of quantification (LOQ) for 6G were
0.8567 and 2.8555 μg/ml and for 6S were 0.3672 and 1.2238 μg/ml
respectively. The recovery range for 6G and 6S were found to be
91.57 to 102.36 % and 84.73 to 92.85 % for all three spiked levels.
The RSD values from repeated extractions for 6G and 6S were 3.43
and 3.09% respectively. The validation of developed method on
precision, accuracy, specificity, linearity, and range were also
performed with well-accepted results.
Abstract: The relationship dependence between RSS and distance
in an enclosed environment is an important consideration because it is
a factor that can influence the reliability of any localization algorithm
founded on RSS. Several algorithms effectively reduce the variance of
RSS to improve localization or accuracy performance. Our proposed
algorithm essentially avoids this pitfall and consequently, its high
adaptability in the face of erratic radio signal. Using 3 anchors in
close proximity of each other, we are able to establish that RSS can be
used as reliable indicator for localization with an acceptable degree of
accuracy. Inherent in this concept, is the ability for each prospective
anchor to validate (guarantee) the position or the proximity of the
other 2 anchors involved in the localization and vice versa. This
procedure ensures that the uncertainties of radio signals due to
multipath effects in enclosed environments are minimized. A major
driver of this idea is the implicit topological relationship among
sensors due to raw radio signal strength. The algorithm is an area
based algorithm; however, it does not trade accuracy for precision
(i.e the size of the returned area).
Abstract: In this article, the radial displacement error correction
capability of a high precision spindle grinding caused by unbalance
force was investigated. The spindle shaft is considered as a flexible
rotor mounted on two sets of angular contact ball bearing. Finite
element methods (FEM) have been adopted for obtaining the
equation of motion of the spindle. In this paper, firstly, natural
frequencies, critical frequencies, and amplitude of the unbalance
response caused by residual unbalance are determined in order to
investigate the spindle behaviors. Furthermore, an optimization
design algorithm is employed to minimize radial displacement of the
spindle which considers dimension of the spindle shaft, the dynamic
characteristics of the bearings, critical frequencies and amplitude of
the unbalance response, and computes optimum spindle diameters
and stiffness and damping of the bearings. Numerical simulation
results show that by optimizing the spindle diameters, and stiffness
and damping in the bearings, radial displacement of the spindle can
be reduced. A spindle about 4 μm radial displacement error can be
compensated with 2 μm accuracy. This certainly can improve the
accuracy of the product of machining.
Abstract: Nowadays, illegal logging has been causing many
effects including flash flood, avalanche, global warming, and etc. The
purpose of this study was to maintain the earth ecosystem by keeping
and regulate Malaysia’s treasurable rainforest by utilizing a new
technology that will assist in real-time alert and give faster response
to the authority to act on these illegal activities. The methodology of
this research consisted of design stages that have been conducted as
well as the system model and system architecture of the prototype in
addition to the proposed hardware and software that have been
mainly used such as microcontroller, sensor with the implementation
of GSM, and GPS integrated system. This prototype was deployed at
Royal Belum forest in December 2014 for phase 1 and April 2015 for
phase 2 at 21 pinpoint locations. The findings of this research were
the capture of data in real-time such as temperature, humidity,
gaseous, fire, and rain detection which indicate the current natural
state and habitat in the forest. Besides, this device location can be
detected via GPS of its current location and then transmitted by SMS
via GSM system. All of its readings were sent in real-time for further
analysis. The data that were compared to meteorological department
showed that the precision of this device was about 95% and these
findings proved that the system is acceptable and suitable to be used
in the field.
Abstract: Predicting earnings management is vital for the capital
market participants, financial analysts and managers. The aim of this
research is attempting to respond to this query: Is there a significant
difference between the regression model and neural networks’
models in predicting earnings management, and which one leads to a
superior prediction of it? In approaching this question, a Linear
Regression (LR) model was compared with two neural networks
including Multi-Layer Perceptron (MLP), and Generalized
Regression Neural Network (GRNN). The population of this study
includes 94 listed companies in Tehran Stock Exchange (TSE)
market from 2003 to 2011. After the results of all models were
acquired, ANOVA was exerted to test the hypotheses. In general, the
summary of statistical results showed that the precision of GRNN did
not exhibit a significant difference in comparison with MLP. In
addition, the mean square error of the MLP and GRNN showed a
significant difference with the multi variable LR model. These
findings support the notion of nonlinear behavior of the earnings
management. Therefore, it is more appropriate for capital market
participants to analyze earnings management based upon neural
networks techniques, and not to adopt linear regression models.
Abstract: The current tools for real time management of sewer
systems are based on two software tools: the software of weather
forecast and the software of hydraulic simulation. The use of the first
ones is an important cause of imprecision and uncertainty, the use of
the second requires temporal important steps of decision because of
their need in times of calculation. This way of proceeding fact that
the obtained results are generally different from those waited. The major idea of this project is to change the basic paradigm by
approaching the problem by the "automatic" face rather than by that
"hydrology". The objective is to make possible the realization of a
large number of simulations at very short times (a few seconds)
allowing to take place weather forecasts by using directly the real
time meditative pluviometric data. The aim is to reach a system
where the decision-making is realized from reliable data and where
the correction of the error is permanent. A first model of control laws was realized and tested with different
return-period rainfalls. The gains obtained in rejecting volume vary
from 19 to 100 %. The development of a new algorithm was then
used to optimize calculation time and thus to overcome the
subsequent combinatorial problem in our first approach. Finally, this
new algorithm was tested with 16- year-rainfall series. The obtained
gains are 40 % of total volume rejected to the natural environment
and of 65 % in the number of discharges.
Abstract: Anammox is a novel and promising technology that has changed the traditional concept of biological nitrogen removal. The process facilitates direct oxidation of ammonical nitrogen under anaerobic conditions with nitrite as an electron acceptor without addition of external carbon sources. The present study investigated the feasibility of Anammox Hybrid Reactor (AHR) combining the dual advantages of suspended and attached growth media for biodegradation of ammonical nitrogen in wastewater. Experimental unit consisted of 4 nos. of 5L capacity AHR inoculated with mixed seed culture containing anoxic and activated sludge (1:1). The process was established by feeding the reactors with synthetic wastewater containing NH4-H and NO2-N in the ratio 1:1 at HRT (hydraulic retention time) of 1 day. The reactors were gradually acclimated to higher ammonium concentration till it attained pseudo steady state removal at a total nitrogen concentration of 1200 mg/l. During this period, the performance of the AHR was monitored at twelve different HRTs varying from 0.25-3.0 d with increasing NLR from 0.4 to 4.8 kg N/m3d. AHR demonstrated significantly higher nitrogen removal (95.1%) at optimal HRT of 1 day. Filter media in AHR contributed an additional 27.2% ammonium removal in addition to 72% reduction in the sludge washout rate. This may be attributed to the functional mechanism of filter media which acts as a mechanical sieve and reduces the sludge washout rate many folds. This enhances the biomass retention capacity of the reactor by 25%, which is the key parameter for successful operation of high rate bioreactors. The effluent nitrate concentration, which is one of the bottlenecks of anammox process was also minimised significantly (42.3-52.3 mg/L). Process kinetics was evaluated using first order and Grau-second order models. The first-order substrate removal rate constant was found as 13.0 d-1. Model validation revealed that Grau second order model was more precise and predicted effluent nitrogen concentration with least error (1.84±10%). A new mathematical model based on mass balance was developed to predict N2 gas in AHR. The mass balance model derived from total nitrogen dictated significantly higher correlation (R2=0.986) and predicted N2 gas with least error of precision (0.12±8.49%). SEM study of biomass indicated the presence of heterogeneous population of cocci and rod shaped bacteria of average diameter varying from 1.2-1.5 mm. Owing to enhanced NRE coupled with meagre production of effluent nitrate and its ability to retain high biomass, AHR proved to be the most competitive reactor configuration for dealing with nitrogen laden wastewater.
Abstract: Different terms of the Statistical Process Control (SPC)
has sketch in the fuzzy environment. However, Measurement System
Analysis (MSA), as a main branch of the SPC, is rarely investigated
in fuzzy area. This procedure assesses the suitability of the data to be
used in later stages or decisions of the SPC. Therefore, this research
focuses on some important measures of MSA and through a new
method introduces the measures in fuzzy environment. In this
method, which works based on Buckley approach, imprecision and
vagueness nature of the real world measurement are considered
simultaneously. To do so, fuzzy version of the gauge capability (Cg
and Cgk) are introduced. The method is also explained through
example clearly.
Abstract: This paper presents the local mesh co-occurrence
patterns (LMCoP) using HSV color space for image retrieval system.
HSV color space is used in this method to utilize color, intensity and
brightness of images. Local mesh patterns are applied to define the
local information of image and gray level co-occurrence is used to
obtain the co-occurrence of LMeP pixels. Local mesh co-occurrence
pattern extracts the local directional information from local mesh
pattern and converts it into a well-mannered feature vector using gray
level co-occurrence matrix. The proposed method is tested on three
different databases called MIT VisTex, Corel, and STex. Also, this
algorithm is compared with existing methods, and results in terms of
precision and recall are shown in this paper.
Abstract: This contribution presents a friction estimator for
industrial purposes which identifies Coulomb friction in a steering
system. The estimator only needs a few, usually known, steering
system parameters. Friction occurs on almost every mechanical
system and has a negative influence on high-precision position
control. This is demonstrated on a steering angle controller for highly
automated driving. In this steering system the friction induces limit
cycles which cause oscillating vehicle movement when the vehicle
follows a given reference trajectory. When compensating the friction
with the introduced estimator, limit cycles can be suppressed. This
is demonstrated by measurements in a series vehicle.
Abstract: This paper presents an application of Artificial Neural
Network (ANN) algorithm for improving power system voltage
stability. The training data is obtained by solving several normal and
abnormal conditions using the Linear Programming technique. The
selected objective function gives minimum deviation of the reactive
power control variables, which leads to the maximization of
minimum Eigen value of load flow Jacobian. The considered reactive
power control variables are switchable VAR compensators, OLTC
transformers and excitation of generators. The method has been
implemented on a modified IEEE 30-bus test system. The results
obtain from the test clearly show that the trained neural network is
capable of improving the voltage stability in power system with a
high level of precision and speed.
Abstract: In this paper, we propose a new method for threedimensional
object indexing based on D.A.M.C-S.H.C descriptor
(Direct and Analytical Method for Calculating the Spherical
Harmonics Coefficients). For this end, we propose a direct
calculation of the coefficients of spherical harmonics with perfect
precision. The aims of the method are to minimize, the processing
time on the 3D objects database and the searching time of similar
objects to a request object.
Firstly we start by defining the new descriptor using a new
division of 3-D object in a sphere. Then we define a new distance
which will be tested and prove his efficiency in the search for similar
objects in the database in which we have objects with very various
and important size.
Abstract: Electrodeposition is a simple and economic technique
for precision coating of different shaped substrates with pure metal,
alloy or composite films. Dc electrodeposition was used to produce
Cr, Co-Cr and Co-Cr/TiO2 nano-composite coatings from Cr(III)
based electrolytes onto 316L SS substrates. The effects of TiO2 nanoparticles
concentration on co-deposition of these particles along with
Cr content and microhardness of the coatings were investigated.
Morphology of the Cr, Co-Cr and Co-Cr/TiO2 coatings besides their
tribological behavior were studied. The results showed that increment
of TiO2 nanoparticles concentration from 0 to 30 g L-1 in the bath
increased their co-deposition and Cr content of the coatings from 0 to
3.5 wt.% and from 23.7 to 31.2 wt.%, respectively. Microhardness of
Cr coating was about 920 Hv which was higher than Co-Cr and even
Co-Cr/TiO2 films. Microhardness of Co-Cr and Co-Cr/TiO2 coatings
were improved by increasing their Cr and TiO2 content. All the
coatings had nodular morphology and contained microcracks.
Nodules sizes and the number of microcracks in the alloy and
composite coatings were lower than the Cr film. Wear results
revealed that the Co-Cr/TiO2 coating had the lowest wear loss
between all the samples, while the Cr film had the worst wear
resistance.
Abstract: In order to protect data privacy, image with sensitive or
private information needs to be encrypted before being outsourced to
the cloud. However, this causes difficulties in image retrieval and data
management. A secure image retrieval method based on orthogonal
decomposition is proposed in the paper. The image is divided into two
different components, for which encryption and feature extraction are
executed separately. As a result, cloud server can extract features from
an encrypted image directly and compare them with the features of the
queried images, so that the user can thus obtain the image. Different
from other methods, the proposed method has no special requirements
to encryption algorithms. Experimental results prove that the proposed
method can achieve better security and better retrieval precision.
Abstract: Non-water based fixed abrasive polishing was adopted
to manufacture LBO crystal for nano precision surface quality because
of its deliquescent. Ethyl alcohol was selected as the non-water based
slurry solvent and ethanediamine, lactic acid, hydrogen peroxide was
added in the slurry as a chemical additive, respectively. Effect of
different additives with non-water based slurry on material removal
rate, surface topography, microscopic appearances, and surface
roughness were investigated in fixed abrasive polishing of LBO
crystal. The results show the best surface quality of LBO crystal with
surface roughness Sa 8.2 nm and small damages was obtained by
non-water based slurry with lactic acid. Non-water based fixed
abrasive polishing can achieve nano precision surface quality of LBO
crystal with high material removal.