Abstract: This paper discusses the development of wireless
structure control of an induction motor scalar drives. This was
realised up on the wireless WiFi networks. This strategy of control is
ensured by the use of Wireless ad hoc networks and a virtual network
interface based on VNC which is used to make possible to take the
remote control of a PC connected on a wireless Ethernet network.
Verification of the proposed strategy of control is provided by
experimental realistic tests on scalar controlled induction motor
drives. The experimental results of the implementations with their
analysis are detailed.
Abstract: Locality Sensitive Hashing (LSH) is one of the most
promising techniques for solving nearest neighbour search problem in
high dimensional space. Euclidean LSH is the most popular variation
of LSH that has been successfully applied in many multimedia
applications. However, the Euclidean LSH presents limitations that
affect structure and query performances. The main limitation of the
Euclidean LSH is the large memory consumption. In order to achieve
a good accuracy, a large number of hash tables is required. In this
paper, we propose a new hashing algorithm to overcome the storage
space problem and improve query time, while keeping a good
accuracy as similar to that achieved by the original Euclidean LSH.
The Experimental results on a real large-scale dataset show that the
proposed approach achieves good performances and consumes less
memory than the Euclidean LSH.
Abstract: This study experimentally investigates the heat transfer effects of forced convection and natural convection under different substrate openings design. A computational fluid dynamics (CFD) model was established and implemented to verify and explain the experimental results and heat transfer behavior. It is found that different opening position will destroy the growth of the boundary layer on substrates to alter the cooling ability for both forced under low Reynolds number and natural convection. Nevertheless, having too many opening may reduce heat conduction and affect the overall heat transfer performance. This study provides future researchers with a guideline on designing and electronic package manufacturing.
Abstract: This paper presents the experimental results on
ageing deterioration of silicone rubber outdoor polymer insulator
under salt water dip wheel test based on IEC 62217. In order to comparison effect of chemical contents, silicone rubber outdoor
polymer insulators having same configuration and leakage distant
from two manufactures were tested together continuously 30,000 test cycles. Many discharge activities were observed in during the test.
After 30,000 test cycles, in spite of same configuration, differences in
degree of surface aging were observed. Physical analysis such as
decreasing in hydrophobicity and increasing in hardness
measurement were measured on two-type tested specimen surface in order to confirm degree of surface ageing. Furthermore, chemical
analysis by ATR-FTIR to diagnose the chemical change of tested
specimen surface was conducted to confirm the physical analysis results.
Abstract: The microbial production of ethanol from biodiesel¬derived crude glycerol by Enterobacter aerogenes TISTR1468, under micro-aerobic and anaerobic conditions, was investigated. The experimental results showed that micro-aerobic conditions were more favorable for cellular growth (4.0 g/L DCW), ethanol production (20.7 g/L) as well as the ethanol yield (0.47 g/g glycerol) than anaerobic conditions (1.2 g/L DCW, 6.3 g/L ethanol and 0.72 g/g glycerol, respectively). Crude glycerol (100 g/L) was consumed completely with the rate of 1.80 g/L/h. Two-stage fermentation (combination of micro-aerobic and anaerobic condition) exhibited higher ethanol production (24.5 g/L) than using one-stage fermentation (either micro-aerobic or anaerobic condition. The two- stage configuration, exhibited slightly higher crude glycerol consumption rate (1.81 g/L/h), as well as ethanol yield (0.56 g/g) than the one-stage configuration. Therefore, two-stage process was selected for ethanol production from E. aerogenes TISTR1468 in scale-up studies.
Abstract: Service discovery is a very important component of Service Oriented Architectures (SOA). This paper presents two alternative approaches to customise the query results of private service registry such as Universal Description, Discovery and Integration (UDDI). The customisation is performed based on some pre-defined and/or real-time changing parameters. This work identifies the requirements, designs and additional mechanisms that must be applied to UDDI in order to support this customisation capability. We also detail the implements of the approaches and examine its performance and scalability. Based on our experimental results, we conclude that both approaches can be used to customise registry query results, but by storing personalization parameters in external resource will yield better performance and but less scalable when size of query results increases. We believe these approaches when combined with semantics enabled service registry will enhance the service discovery methods within a private UDDI registry environment.
Abstract: Most of the biclustering/projected clustering algorithms are based either on the Euclidean distance or correlation coefficient which capture only linear relationships. However, in many applications, like gene expression data and word-document data, non linear relationships may exist between the objects. Mutual Information between two variables provides a more general criterion to investigate dependencies amongst variables. In this paper, we improve upon our previous algorithm that uses mutual information for biclustering in terms of computation time and also the type of clusters identified. The algorithm is able to find biclusters with mixed relationships and is faster than the previous one. To the best of our knowledge, none of the other existing algorithms for biclustering have used mutual information as a similarity measure. We present the experimental results on synthetic data as well as on the yeast expression data. Biclusters on the yeast data were found to be biologically and statistically significant using GO Tool Box and FuncAssociate.
Abstract: In this paper, the application of sliding-mode control to a permanent-magnet synchronous motor (PMSM) is presented. The control design is based on a generic mathematical model of the motor. Some dynamics of the motor and of the power amplification stage remain unmodelled. This model uncertainty is estimated in realtime. The estimation is based on the differentiation of measured signals using the ideas of robust exact differentiator (RED). The control law is implemented on an industrial servo drive. Simulations and experimental results are presented and compared to the same control strategy without uncertainty estimation. It turns out that the proposed concept is superior to the same control strategy without uncertainty estimation especially in the case of non-smooth reference signals.
Abstract: This study investigates the in-situ regeneration of deactivated Pt-Pd catalyst in a laboratory-scale catalysis reactor. Different regeneration conditions are tested and the activity and characteristics of regenerated catalysts are analyzed. Experimental results show that the conversion efficiencies of C3H6 by different regenerated Pt-Pd catalysts were significantly improved from 77%, 55% and 41% to 86%, 98% and 99%, respectively. The best regeneration conditions was 52ppm ozone, 500oC, and 10min. Regeneration temperature has more influences than ozone concentration and regeneration time. With the comparisons of characteristics of deactivated catalyst and regenerated catalyst, the major poison species (carbon, metals, chloride, and sulfate) on the spent catalysts can be effectively removed by ozone regeneration.
Abstract: Ethanol has become more attractive in fuel industry
either as fuel itself or an additive that helps enhancing the octane
number and combustibility of gasoline. This research studied a
pressure swing adsorption using cassava-based adsorbent prepared
from mixture of cassava starch and cassava pulp for dehydration of
ethanol vapor. The apparatus used in the experiments consisted of
double adsorption columns, an evaporator, and a vacuum pump. The
feed solution contained 90-92 %wt of ethanol. Three process
variables: adsorption temperatures (110, 120 and 130°C), adsorption
pressures (1 and 2 bar gauge) and feed vapor flow rate (25, 50 and 75
% valve opening of the evaporator) were investigated. According to
the experimental results, the optimal operating condition for this
system was found to be at 2 bar gauge for adsorption pressure, 120°C
for adsorption temperature and 25% valve opening of the evaporator.
Production of 1.48 grams of ethanol with concentration higher than
99.5 wt% per gram of adsorbent was obtained. PSA with cassavabased
adsorbent reported in this study could be an alternative method
for production of nearly anhydrous ethanol. Dehydration of ethanol
vapor achieved in this study is due to an interaction between free
hydroxyl group on the glucose units of the starch and the water
molecules.
Abstract: Assessment for image quality traditionally needs its
original image as a reference. The conventional method for assessment
like Mean Square Error (MSE) or Peak Signal to Noise Ratio (PSNR)
is invalid when there is no reference. In this paper, we present a new
No-Reference (NR) assessment of image quality using blur and noise.
The recent camera applications provide high quality images by help of
digital Image Signal Processor (ISP). Since the images taken by the
high performance of digital camera have few blocking and ringing
artifacts, we only focus on the blur and noise for predicting the
objective image quality. The experimental results show that the
proposed assessment method gives high correlation with subjective
Difference Mean Opinion Score (DMOS). Furthermore, the proposed
method provides very low computational load in spatial domain and
similar extraction of characteristics to human perceptional assessment.
Abstract: In this paper, a thermal model of cast- resin dry type
transformer is proposed. The proposed thermal model is solved by
finite element technique to get the temperature at any location of the
transformer. The basic modes of heat transfer such as conduction;
convection and radiation are used to get the steady state temperature
distribution of the transformer. The predicted temperatures are
compared with experimental results reported in this paper and it is
found a good agreement between them. The effects of various
parameters such as width of air duct, ambient temperature and
emissivity of the outer surface were also studied.
Abstract: New regulations and standards for noise emission increasingly compel the automotive firms to make some improvements about decreasing the engine noise. Nowadays, the perforated reactive mufflers which have an effective damping capability are specifically used for this purpose. New designs should be analyzed with respect to both acoustics and back pressure. In this study, a reactive perforated muffler is investigated numerically and experimentally. For an acoustical analysis, the transmission loss which is independent of sound source of the present cross flow, the perforated muffler was analyzed by COMSOL. To be able to validate the numerical results, transmission loss was measured experimentally. Back pressure was obtained based on the flow field analysis and was also compared with experimental results. Numerical results have an approximate error of 20% compared to experimental results.
Abstract: Conceptualization strengthens intelligent systems in generalization skill, effective knowledge representation, real-time inference, and managing uncertain and indefinite situations in addition to facilitating knowledge communication for learning agents situated in real world. Concept learning introduces a way of abstraction by which the continuous state is formed as entities called concepts which are connected to the action space and thus, they illustrate somehow the complex action space. Of computational concept learning approaches, action-based conceptualization is favored because of its simplicity and mirror neuron foundations in neuroscience. In this paper, a new biologically inspired concept learning approach based on the probabilistic framework is proposed. This approach exploits and extends the mirror neuron-s role in conceptualization for a reinforcement learning agent in nondeterministic environments. In the proposed method, instead of building a huge numerical knowledge, the concepts are learnt gradually from rewards through interaction with the environment. Moreover the probabilistic formation of the concepts is employed to deal with uncertain and dynamic nature of real problems in addition to the ability of generalization. These characteristics as a whole distinguish the proposed learning algorithm from both a pure classification algorithm and typical reinforcement learning. Simulation results show advantages of the proposed framework in terms of convergence speed as well as generalization and asymptotic behavior because of utilizing both success and failures attempts through received rewards. Experimental results, on the other hand, show the applicability and effectiveness of the proposed method in continuous and noisy environments for a real robotic task such as maze as well as the benefits of implementing an incremental learning scenario in artificial agents.
Abstract: In this paper, we present a novel approach to accurately
detect text regions including shop name in signboard images with
complex background for mobile system applications. The proposed
method is based on the combination of text detection using edge
profile and region segmentation using fuzzy c-means method. In the
first step, we perform an elaborate canny edge operator to extract all
possible object edges. Then, edge profile analysis with vertical and
horizontal direction is performed on these edge pixels to detect
potential text region existing shop name in a signboard. The edge
profile and geometrical characteristics of each object contour are
carefully examined to construct candidate text regions and classify the
main text region from background. Finally, the fuzzy c-means
algorithm is performed to segment and detected binarize text region.
Experimental results show that our proposed method is robust in text
detection with respect to different character size and color and can
provide reliable text binarization result.
Abstract: To assess the effects of functional protein on osteoblast, Large quantity of high-purity osteoblasts had been cultivated successfully by adopting sequential enzyme digestion. The growth curve of osteoblasts was protracted by cell counting. Proliferation of osteoblasts was assessed by MTT colorimetry. The experimental results show the functional protein can enhance proliferation, the properties of adhesion and discuss the effect of osteopontin on osteoblast.
Abstract: In large datasets, identifying exceptional or rare cases
with respect to a group of similar cases is considered very significant
problem. The traditional problem (Outlier Mining) is to find
exception or rare cases in a dataset irrespective of the class label of
these cases, they are considered rare events with respect to the whole
dataset. In this research, we pose the problem that is Class Outliers
Mining and a method to find out those outliers. The general
definition of this problem is “given a set of observations with class
labels, find those that arouse suspicions, taking into account the
class labels". We introduce a novel definition of Outlier that is Class
Outlier, and propose the Class Outlier Factor (COF) which measures
the degree of being a Class Outlier for a data object. Our work
includes a proposal of a new algorithm towards mining of the Class
Outliers, presenting experimental results applied on various domains
of real world datasets and finally a comparison study with other
related methods is performed.
Abstract: Color image segmentation plays an important role in
computer vision and image processing areas. In this paper, the
features of Volterra filter are utilized for color image segmentation.
The discrete Volterra filter exhibits both linear and nonlinear
characteristics. The linear part smoothes the image features in
uniform gray zones and is used for getting a gross representation of
objects of interest. The nonlinear term compensates for the blurring
due to the linear term and preserves the edges which are mainly used
to distinguish the various objects. The truncated quadratic Volterra
filters are mainly used for edge preserving along with Gaussian noise
cancellation. In our approach, the segmentation is based on K-means
clustering algorithm in HSI space. Both the hue and the intensity
components are fully utilized. For hue clustering, the special cyclic
property of the hue component is taken into consideration. The
experimental results show that the proposed technique segments the
color image while preserving significant features and removing noise
effects.
Abstract: In this note, a theoretical model for analyzing of
normal penetration of the ogive – nose projectile into metallic targets
is presented .The failure is assumed to be asymmetry petalling and
the analysis is performed by using the energy balance and work done
.The work done consist of the work required for plastic deformation
Wp, the work for transferring the matter to new position Wd and the
work for bending of the petals Wb. In several studies, it has been
shown that we can neglect the loss of energy by temperature.
In this present study, in first, by assuming the crater formation
after perforation, the value of work done is calculated during the
normal penetration of conical projectiles into thin metallic targets.
Then the value of residual velocity and ballistic limit of the projectile
is predicated by using the energy balance. In final, theoretical and
experimental results is compared.
Abstract: In this paper, a simple heuristic genetic algorithm is
used for Multistage Multiuser detection in fast fading environments.
Multipath channels, multiple access interference (MAI) and near far
effect cause the performance of the conventional detector to degrade.
Heuristic Genetic algorithms, a rapidly growing area of artificial
intelligence, uses evolutionary programming for initial search, which
not only helps to converge the solution towards near optimal
performance efficiently but also at a very low complexity as
compared with optimal detector. This holds true for Additive White
Gaussian Noise (AWGN) and multipath fading channels.
Experimental results are presented to show the superior performance
of the proposed techque over the existing methods.