Abstract: Among the various cooling processes in industrial
applications such as: electronic devices, heat exchangers, gas
turbines, etc. Gas turbine blades cooling is the most challenging one.
One of the most common practices is using ribbed wall because of
the boundary layer excitation and therefore making the ultimate
cooling. Vortex formation between rib and channel wall will result in
a complicated behavior of flow regime. At the other hand, selecting
the most efficient method for capturing the best results comparing to
experimental works would be a fascinating issue. In this paper 4
common methods in turbulence modeling: standard k-e, rationalized
k-e with enhanced wall boundary layer treatment, k-w and RSM
(Reynolds stress model) are employed to a square ribbed channel to
investigate the separation and thermal behavior of the flow in the
channel. Finally all results from different methods which are used in
this paper will be compared with experimental data available in
literature to ensure the numerical method accuracy.
Abstract: While the explosive increase in information published
on the Web, researchers have to filter information when searching for
conference related information. To make it easier for users to search
related information, this paper uses Topic Maps and social information
to implement ontology since ontology can provide the formalisms and
knowledge structuring for comprehensive and transportable machine
understanding that digital information requires. Besides enhancing
information in Topic Maps, this paper proposes a method of
constructing research Topic Maps considering social information.
First, extract conference data from the web. Then extract conference
topics and the relationships between them through the proposed
method. Finally visualize it for users to search and browse. This paper
uses ontology, containing abundant of knowledge hierarchy structure,
to facilitate researchers getting useful search results. However, most
previous ontology construction methods didn-t take “people" into
account. So this paper also analyzes the social information which helps
researchers find the possibilities of cooperation/combination as well as
associations between research topics, and tries to offer better results.
Abstract: This paper presents an analytical framework for an
effective online personal knowledge management (PKM) of
knowledge workers. The development of this framework is prompted
by our qualitative research on the PKM processes and cognitive
enablers of knowledge workers in eight organisations selected from
three main industries in Malaysia. This multiple-case research
identifies the relationships between the effectiveness of four online
PKM processes: get/retrieve, understand/analyse, share, and connect.
It also establishes the importance of cognitive enablers that mediate
this relationship, namely, method, identify, decide and drive.
Qualitative analysis is presented as the findings, supported by the
preceded quantitative analysis on an exploratory questionnaire
survey.
Abstract: Newton-Raphson State Estimation method using bus
admittance matrix remains as an efficient and most popular method to
estimate the state variables. Elements of Jacobian matrix are computed
from standard expressions which lack physical significance. In this
paper, elements of the state estimation Jacobian matrix are obtained
considering the power flow measurements in the network elements.
These elements are processed one-by-one and the Jacobian matrix H is
updated suitably in a simple manner. The constructed Jacobian matrix
H is integrated with Weight Least Square method to estimate the state
variables. The suggested procedure is successfully tested on IEEE
standard systems.
Abstract: Continuous measurements and multivariate methods are applied in researching the effects of energy consumption on indoor air quality (IAQ) in a Finnish one-family house. Measured data used in this study was collected continuously in a house in Kuopio, Eastern Finland, during fourteen months long period. Consumption parameters measured were the consumptions of district heat, electricity and water. Indoor parameters gathered were temperature, relative humidity (RH), the concentrations of carbon dioxide (CO2) and carbon monoxide (CO) and differential air pressure. In this study, self-organizing map (SOM) and Sammon's mapping were applied to resolve the effects of energy consumption on indoor air quality. Namely, the SOM was qualified as a suitable method having a property to summarize the multivariable dependencies into easily observable two-dimensional map. Accompanying that, the Sammon's mapping method was used to cluster pre-processed data to find similarities of the variables, expressing distances and groups in the data. The methods used were able to distinguish 7 different clusters characterizing indoor air quality and energy efficiency in the study house. The results indicate, that the cost implications in euros of heating and electricity energy vary according to the differential pressure, concentration of carbon dioxide, temperature and season.
Abstract: In this paper, novel statistical sampling based equalization techniques and CNN based detection are proposed to increase the spectral efficiency of multiuser communication systems over fading channels. Multiuser communication combined with selective fading can result in interferences which severely deteriorate the quality of service in wireless data transmission (e.g. CDMA in mobile communication). The paper introduces new equalization methods to combat interferences by minimizing the Bit Error Rate (BER) as a function of the equalizer coefficients. This provides higher performance than the traditional Minimum Mean Square Error equalization. Since the calculation of BER as a function of the equalizer coefficients is of exponential complexity, statistical sampling methods are proposed to approximate the gradient which yields fast equalization and superior performance to the traditional algorithms. Efficient estimation of the gradient is achieved by using stratified sampling and the Li-Silvester bounds. A simple mechanism is derived to identify the dominant samples in real-time, for the sake of efficient estimation. The equalizer weights are adapted recursively by minimizing the estimated BER. The near-optimal performance of the new algorithms is also demonstrated by extensive simulations. The paper has also developed a (Cellular Neural Network) CNN based approach to detection. In this case fast quadratic optimization has been carried out by t, whereas the task of equalizer is to ensure the required template structure (sparseness) for the CNN. The performance of the method has also been analyzed by simulations.
Abstract: In non destructive testing by radiography, a perfect knowledge of the weld defect shape is an essential step to appreciate the quality of the weld and make decision on its acceptability or rejection. Because of the complex nature of the considered images, and in order that the detected defect region represents the most accurately possible the real defect, the choice of thresholding methods must be done judiciously. In this paper, performance criteria are used to conduct a comparative study of thresholding methods based on gray level histogram, 2-D histogram and locally adaptive approach for weld defect extraction in radiographic images.
Abstract: Protein subchloroplast locations are correlated with its
functions. In contrast to the large amount of available protein
sequences, the information of their locations and functions is less
known. The experiment works for identification of protein locations
and functions are costly and time consuming. The accurate prediction
of protein subchloroplast locations can accelerate the study of
functions of proteins in chloroplast. This study proposes a Random
Forest based method, ChloroRF, to predict protein subchloroplast
locations using interpretable physicochemical properties. In addition
to high prediction accuracy, the ChloroRF is able to select important
physicochemical properties. The important physicochemical
properties are also analyzed to provide insights into the underlying
mechanism.
Abstract: In this paper presents a technique for developing the
computational efficiency in simulating double output induction
generators (DOIG) with two rotor circuits where stator transients are
to be included. Iterative decomposition is used to separate the flux–
Linkage equations into decoupled fast and slow subsystems, after
which the model order of the fast subsystems is reduced by
neglecting the heavily damped fast transients caused by the second
rotor circuit using integral manifolds theory. The two decoupled
subsystems along with the equation for the very slowly changing slip
constitute a three time-scale model for the machine which resulted in
increasing computational speed. Finally, the proposed method of
reduced order in this paper is compared with the other conventional
methods in linear and nonlinear modes and it is shown that this
method is better than the other methods regarding simulation
accuracy and speed.
Abstract: In this work, propagation of uncertainty during calibration
process of TRANUS, an integrated land use and transport model
(ILUTM), has been investigated. It has also been examined, through a
sensitivity analysis, which input parameters affect the variation of the
outputs the most. Moreover, a probabilistic verification methodology
of calibration process, which equates the observed and calculated
production, has been proposed. The model chosen as an application is
the model of the city of Grenoble, France. For sensitivity analysis and
uncertainty propagation, Monte Carlo method was employed, and a
statistical hypothesis test was used for verification. The parameters of
the induced demand function in TRANUS, were assumed as uncertain
in the present case. It was found that, if during calibration, TRANUS
converges, then with a high probability the calibration process is
verified. Moreover, a weak correlation was found between the inputs
and the outputs of the calibration process. The total effect of the
inputs on outputs was investigated, and the output variation was found
to be dictated by only a few input parameters.
Abstract: Embedding and extraction of a secret information as
well as the restoration of the original un-watermarked image is
highly desirable in sensitive applications like military, medical, and
law enforcement imaging. This paper presents a novel reversible
data-hiding method for digital images using integer to integer
wavelet transform and companding technique which can embed and
recover the secret information as well as can restore the image to its
pristine state. The novel method takes advantage of block based
watermarking and iterative optimization of threshold for companding
which avoids histogram pre and post-processing. Consequently, it
reduces the associated overhead usually required in most of the
reversible watermarking techniques. As a result, it keeps the
distortion small between the marked and the original images.
Experimental results show that the proposed method outperforms the
existing reversible data hiding schemes reported in the literature.
Abstract: Evolution of one-dimensional electron system under
high-energy-density (HED) conditions is investigated, using the
principle of least-action and variational method. In a single-mode
modulation model, the amplitude and spatial wavelength of the
modulation are chosen to be general coordinates. Equations of motion
are derived by considering energy conservation and force balance.
Numerical results show that under HED conditions, electron density
modulation could exist. Time dependences of amplitude and
wavelength are both positively related to the rate of energy input.
Besides, initial loading speed has a significant effect on modulation
amplitude, while wavelength relies more on loading duration.
Abstract: Clearance in the joints of multibody mechanical
systems such as linkage mechanisms and robots is a main source of
vibration, and noise of the whole system, and wear of the joints
themselves. This clearance is an inevitable matter and cannot be
eliminated, since it allows the relative motion between joint
components and make them assemblage. This paper presents an
experimental verification of the obtained simulation results of a slider
– crank mechanism of one clearance revolute joint. The simulation
results are obtained with the aid of CAD and dynamic simulation
softwares, which is an effective method of simulation multibody
systems with clearance joints and have many advantages. The
comparison between both simulation and experimental results shows
that the simulation results are so close to the experimental ones which
proves the accuracy and efficiency of this method of modeling and
simulation of mechanical systems with clearance joints.
Abstract: The groundwater is one of the main sources for
sustainability in the United Arab Emirates (UAE). Intensive
developments in Al-Ain area lead to increase water demand, which
consequently reduced the overall groundwater quantity in major
aquifers. However, in certain residential areas within Al-Ain, it has
been noticed that the groundwater level is rising, for example in
Sha-ab Al Askher area. The reasons for the groundwater rising
phenomenon are yet to be investigated. In this work, twenty four
seismic refraction profiles have been carried out along the study
pilot area; as well as field measurement of the groundwater level in
a number of available water wells in the area. The processed
seismic data indicated the deepest and shallowest groundwater
levels are 15m and 2.3 meters respectively. This result is greatly
consistent with the proper field measurement of the groundwater
level. The minimum detected value may be referred to perched
subsurface water which may be associated to the infiltration from
the surrounding water bodies such as lakes, and elevated farms. The
maximum values indicate the accurate groundwater level within the
study area. The findings of this work may be considered as a
preliminary help to the decision makers.
Abstract: Least Development Countries (LDC) like
Bangladesh, whose 25% revenue earning is achieved from Textile
export, requires producing less defective textile for minimizing
production cost and time. Inspection processes done on these
industries are mostly manual and time consuming. To reduce error
on identifying fabric defects requires more automotive and
accurate inspection process. Considering this lacking, this research
implements a Textile Defect Recognizer which uses computer
vision methodology with the combination of multi-layer neural
networks to identify four classifications of textile defects. The
recognizer, suitable for LDC countries, identifies the fabric defects
within economical cost and produces less error prone inspection
system in real time. In order to generate input set for the neural
network, primarily the recognizer captures digital fabric images by
image acquisition device and converts the RGB images into binary
images by restoration process and local threshold techniques.
Later, the output of the processed image, the area of the faulty
portion, the number of objects of the image and the sharp factor of
the image, are feed backed as an input layer to the neural network
which uses back propagation algorithm to compute the weighted
factors and generates the desired classifications of defects as an
output.
Abstract: This paper presents a new methodology to study power and energy consumption in mechatronic systems early in the development process. This new approach makes use of two modeling languages to represent and simulate embedded control software and electromechanical subsystems in the discrete event and continuous time domain respectively within a single co-model. This co-model enables an accurate representation of power and energy consumption and facilitates the analysis and development of both software and electro-mechanical subsystems in parallel. This makes the engineers aware of energy-wise implications of different design alternatives and enables early trade-off analysis from the beginning of the analysis and design activities.
Abstract: In this paper performance of Puma 560
manipulator is being compared for hybrid gradient descent
and least square method learning based ANFIS controller with
hybrid Genetic Algorithm and Generalized Pattern Search
tuned radial basis function based Neuro-Fuzzy controller.
ANFIS which is based on Takagi Sugeno type Fuzzy
controller needs prior knowledge of rule base while in radial
basis function based Neuro-Fuzzy rule base knowledge is not
required. Hybrid Genetic Algorithm with generalized Pattern
Search is used for tuning weights of radial basis function
based Neuro- fuzzy controller. All the controllers are checked
for butterfly trajectory tracking and results in the form of
Cartesian and joint space errors are being compared. ANFIS
based controller is showing better performance compared to
Radial Basis Function based Neuro-Fuzzy Controller but rule
base independency of RBF based Neuro-Fuzzy gives it an
edge over ANFIS
Abstract: Machining is an important manufacturing process used to produce a wide variety of metallic parts. Among various machining processes, turning is one of the most important one which is employed to shape cylindrical parts. In turning, the quality of finished product is measured in terms of surface roughness. In turn, surface quality is determined by machining parameters and tool geometry specifications. The main objective of this study is to simultaneously model and optimize machining parameters and tool geometry in order to improve the surface roughness for AISI1045 steel. Several levels of machining parameters and tool geometry specifications are considered as input parameters. The surface roughness is selected as process output measure of performance. A Taguchi approach is employed to gather experimental data. Then, based on signal-to-noise (S/N) ratio, the best sets of cutting parameters and tool geometry specifications have been determined. Using these parameters values, the surface roughness of AISI1045 steel parts may be minimized. Experimental results are provided to illustrate the effectiveness of the proposed approach.
Abstract: The resource-based view of the firm regards
knowledge as one of the most important organizational assets and a
key strategic resource that contributes unique value to organizations.
The acquisition, absorption and internalization of external
knowledge are central to an organization-s innovative capabilities.
This ability to evaluate, acquire and integrate new knowledge from
its environment is referred to as a firm-s absorptive capacity (AC).
This research in progress paper explores the link between interorganizational
Social Networks (SNs) and a firm-s Absorptive
Capacity (AC). Based on an in-depth literature survey of both
concepts, four propositions are proposed that explain the link
between AC and SNs. These propositions suggest that SNs are key
to a firm-s AC. A qualitative research method is proposed to test the
set of propositions in the next stage of this research.
Abstract: Home Automation is a field that, among other
subjects, is concerned with the comfort, security and energy
requirements of private homes. The configuration of automatic
functions in this type of houses is not always simple to its inhabitants
requiring the initial setup and regular adjustments. In this work, the
ubiquitous computing system vision is used, where the users- action
patterns are captured, recorded and used to create the contextawareness
that allows the self-configuration of the home automation
system. The system will try to free the users from setup adjustments
as the home tries to adapt to its inhabitants- real habits. In this paper
it is described a completely automated process to determine the light
state and act on them, taking in account the users- daily habits.
Artificial Neural Network (ANN) is used as a pattern recognition
method, classifying for each moment the light state. The work
presented uses data from a real house where a family is actually
living.