Abstract: Series of experimental tests were conducted on a
section of a 660 kW wind turbine blade to measure the pressure
distribution of this model oscillating in plunging motion. In order to
minimize the amount of data required to predict aerodynamic loads
of the airfoil, a General Regression Neural Network, GRNN, was
trained using the measured experimental data. The network once
proved to be accurate enough, was used to predict the flow behavior
of the airfoil for the desired conditions.
Results showed that with using a few of the acquired data, the
trained neural network was able to predict accurate results with
minimal errors when compared with the corresponding measured
values. Therefore with employing this trained network the
aerodynamic coefficients of the plunging airfoil, are predicted
accurately at different oscillation frequencies, amplitudes, and angles
of attack; hence reducing the cost of tests while achieving acceptable
accuracy.
Abstract: Workflow Management Systems (WfMS) alloworganizations to streamline and automate business processes and reengineer their structure. One important requirement for this type of system is the management and computation of the Quality of Service(QoS) of processes and workflows. Currently, a range of Web processes and workflow languages exist. Each language can be characterized by the set of patterns they support. Developing andimplementing a suitable and generic algorithm to compute the QoSof processes that have been designed using different languages is a difficult task. This is because some patterns are specific to particular process languages and new patterns may be introduced in future versions of a language. In this paper, we describe an adaptive algorithm implemented to cope with these two problems. The algorithm is called adaptive since it can be dynamically changed as the patterns of a process language also change.
Abstract: Recently, lots of researchers are attracted to retrieving
multimedia database by using some impression words and their values.
Ikezoe-s research is one of the representatives and uses eight pairs of
opposite impression words. We had modified its retrieval interface and
proposed '2D-RIB' in the previous work. The aim of the present paper
is to improve his/her satisfaction level to the retrieval result in the
2D-RIB. Our method is to extend the 2D-RIB. One of our extensions is
to define and introduce the following two measures: 'melody
goodness' and 'general acceptance'. Another extension is three types
of customization menus. The result of evaluation using a pilot system
is as follows. Both of these two measures 'melody goodness'
and -general acceptance- can contribute to the improvement.
Moreover, it is effective if we introduce the customization menu
which enables a retrieval person to reduce the strictness level of
retrieval condition in an impression pair based on his/her need.
Abstract: Several studies have been carried out, using various techniques, including neural networks, to discriminate vigilance states in humans from electroencephalographic (EEG) signals, but we are still far from results satisfactorily useable results. The work presented in this paper aims at improving this status with regards to 2 aspects. Firstly, we introduce an original procedure made of the association of two neural networks, a self organizing map (SOM) and a learning vector quantization (LVQ), that allows to automatically detect artefacted states and to separate the different levels of vigilance which is a major breakthrough in the field of vigilance. Lastly and more importantly, our study has been oriented toward real-worked situation and the resulting model can be easily implemented as a wearable device. It benefits from restricted computational and memory requirements and data access is very limited in time. Furthermore, some ongoing works demonstrate that this work should shortly results in the design and conception of a non invasive electronic wearable device.
Abstract: The focus in this work is to assess which method
allows a better forecasting of malaria cases in Bujumbura ( Burundi)
when taking into account association between climatic factors and
the disease. For the period 1996-2007, real monthly data on both
malaria epidemiology and climate in Bujumbura are described and
analyzed. We propose a hierarchical approach to achieve our
objective. We first fit a Generalized Additive Model to malaria cases
to obtain an accurate predictor, which is then used to predict future
observations. Various well-known forecasting methods are compared
leading to different results. Based on in-sample mean average
percentage error (MAPE), the multiplicative exponential smoothing
state space model with multiplicative error and seasonality performed
better.
Abstract: One of the essential sectors of Myanmar economy is
agriculture which is sensitive to climate variation. The most
important climatic element which impacts on agriculture sector is
rainfall. Thus rainfall prediction becomes an important issue in
agriculture country. Multi variables polynomial regression (MPR)
provides an effective way to describe complex nonlinear input output
relationships so that an outcome variable can be predicted from the
other or others. In this paper, the modeling of monthly rainfall
prediction over Myanmar is described in detail by applying the
polynomial regression equation. The proposed model results are
compared to the results produced by multiple linear regression model
(MLR). Experiments indicate that the prediction model based on
MPR has higher accuracy than using MLR.
Abstract: In the paper an effective context based lossless coding
technique is presented. Three principal and few auxiliary contexts are
defined. The predictor adaptation technique is an improved CoBALP
algorithm, denoted CoBALP+. Cumulated predictor error combining
8 bias estimators is calculated. It is shown experimentally that
indeed, the new technique is time-effective while it outperforms the
well known methods having reasonable time complexity, and is
inferior only to extremely computationally complex ones.
Abstract: In this paper, we propose a Connect6 solver which
adopts a hybrid approach based on a tree-search algorithm and image
processing techniques. The solver must deal with the complicated
computation and provide high performance in order to make real-time
decisions. The proposed approach enables the solver to be
implemented on a single Spartan-6 XC6SLX45 FPGA produced by
XILINX without using any external devices. The compact
implementation is achieved through image processing techniques to
optimize a tree-search algorithm of the Connect6 game. The tree
search is widely used in computer games and the optimal search brings
the best move in every turn of a computer game. Thus, many
tree-search algorithms such as Minimax algorithm and artificial
intelligence approaches have been widely proposed in this field.
However, there is one fundamental problem in this area; the
computation time increases rapidly in response to the growth of the
game tree. It means the larger the game tree is, the bigger the circuit
size is because of their highly parallel computation characteristics.
Here, this paper aims to reduce the size of a Connect6 game tree using
image processing techniques and its position symmetric property. The
proposed solver is composed of four computational modules: a
two-dimensional checkmate strategy checker, a template matching
module, a skilful-line predictor, and a next-move selector. These
modules work well together in selecting next moves from some
candidates and the total amount of their circuits is small. The details of
the hardware design for an FPGA implementation are described and
the performance of this design is also shown in this paper.
Abstract: Medical image segmentation based on image smoothing followed by edge detection assumes a great degree of importance in the field of Image Processing. In this regard, this paper proposes a novel algorithm for medical image segmentation based on vigorous smoothening by identifying the type of noise and edge diction ideology which seems to be a boom in medical image diagnosis. The main objective of this algorithm is to consider a particular medical image as input and make the preprocessing to remove the noise content by employing suitable filter after identifying the type of noise and finally carrying out edge detection for image segmentation. The algorithm consists of three parts. First, identifying the type of noise present in the medical image as additive, multiplicative or impulsive by analysis of local histograms and denoising it by employing Median, Gaussian or Frost filter. Second, edge detection of the filtered medical image is carried out using Canny edge detection technique. And third part is about the segmentation of edge detected medical image by the method of Normalized Cut Eigen Vectors. The method is validated through experiments on real images. The proposed algorithm has been simulated on MATLAB platform. The results obtained by the simulation shows that the proposed algorithm is very effective which can deal with low quality or marginal vague images which has high spatial redundancy, low contrast and biggish noise, and has a potential of certain practical use of medical image diagnosis.
Abstract: This paper analyzes the linkage between migration,
economic globalization and terrorism concerns. On a broad level, I
analyze Canadian economic and political considerations, searching
for causal relationships between political and economic actors on the
one hand, and Canadian immigration law on the other. Specifically,
the paper argues that there are contradictory impulses affecting state
sovereignty. These impulses are are currently being played out in the
field of Canadian immigration law through several proposed changes
to Canada-s Immigration and Refugee Protection Act (IRPA). These
changes reflect an ideological conception of sovereignty that is
intrinsically connected with decision-making capacity centered on an
individual. This conception of sovereign decision-making views
Parliamentary debate and bureaucratic inefficiencies as both equally
responsible for delaying essential decisions relating to the protection
of state sovereignty, economic benefits and immigration control This
paper discusses these concepts in relation to Canadian immigration
policy under Canadian governments over the past twenty five years.
Abstract: Mouse L929 fibroblastic cell line, which is widely
used in many experiment aspects, was tested for their differentiation
potency in osteogenic differentiation and adipogenic differentiation.
Human dermal fibroblasts, which their differentiation potency are
still be in confliction, also be taken in the experiment. The
differentiations were conducted by using the inducing medium
ingredients which is generally used to induce differentiation of stem
cells. By the inducing media used, L929 mouse fibroblasts
successfully underwent osteogenic differentiation and adipogenic
differentiation while human dermal fibroblasts underwent only
osteogenic differentiation but not for adipogenic differentiation.
Human dermal fibroblasts are hard to be differentiated in adipogenic
lineage and need specific proper condition for induction.
Abstract: In this study, a fuzzy integrated logical forecasting method (FILF) is extended for multi-variate systems by using a vector autoregressive model. Fuzzy time series forecasting (FTSF) method was recently introduced by Song and Chissom [1]-[2] after that Chen improved the FTSF method. Rather than the existing literature, the proposed model is not only compared with the previous FTS models, but also with the conventional time series methods such as the classical vector autoregressive model. The cluster optimization is based on the C-means clustering method. An empirical study is performed for the prediction of the chartering rates of a group of dry bulk cargo ships. The root mean squared error (RMSE) metric is used for the comparing of results of methods and the proposed method has superiority than both traditional FTS methods and also the classical time series methods.
Abstract: In recent years, the number of the cases of information
leaks is increasing. Companies and Research Institutions make various
actions against information thefts and security accidents. One of the
actions is adoption of the crime prevention system, including the
monitoring system by surveillance cameras. In order to solve
difficulties of multiple cameras monitoring, we develop the automatic
human tracking system using mobile agents through multiple
surveillance cameras to track target persons. In this paper, we develop
the monitor which confirms mobile agents tracing target persons, and
the simulator of video picture analysis to construct the tracking
algorithm.
Abstract: In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods.
Abstract: The objective of this research was to study the
influence of marketing mix on customers purchasing behavior. A
total of 397 respondents were collected from customers who were the
patronages of the Chatuchak Plaza market. A questionnaire was
utilized as a tool to collect data. Statistics utilized in this research
included frequency, percentage, mean, standard deviation, and
multiple regression analysis. Data were analyzed by using Statistical
Package for the Social Sciences. The findings revealed that the
majority of respondents were male with the age between 25-34 years
old, hold undergraduate degree, married and stay together. The
average income of respondents was between 10,001-20,000 baht. In
terms of occupation, the majority worked for private companies. The
research analysis disclosed that there were three variables of
marketing mix which included price (X2), place (X3), and product
(X1) which had an influence on the frequency of customer
purchasing. These three variables can predict a purchase about 30
percent of the time by using the equation; Y1 = 6.851 + .921(X2) +
.949(X3) + .591(X1). It also found that in terms of marketing mixed,
there were two variables had an influence on the amount of customer
purchasing which were physical characteristic (X6), and the process
(X7). These two variables are 17 percent predictive of a purchasing
by using the equation: Y2 = 2276.88 + 2980.97(X6) + 2188.09(X7).
Abstract: The aim of this study is to test the “work values"
inventory developed by Tevruz and Turgut and to utilize the concept
in a model, which aims to create a greater understanding of the work
experience. In the study multiple effects of work values, work-value
congruence and work centrality on organizational citizenship
behavior are examined. In this respect, it is hypothesized that work
values and work-value congruence predict organizational citizenship
behavior through work centrality. Work-goal congruence test, Tevruz
and Turgut-s work values inventory are administered along with
Kanungo-s work centrality and Podsakoff et al.-s [47] organizational
citizenship behavior test to employees working in Turkish SME-s.
The study validated that Tevruz and Turgut-s work values inventory
and the work-value congruence test were reliable and could be used
for future research. The study revealed the mediating role of work
centrality only for the relationship of work values and the
responsibility dimension of citizenship behavior. Most important, this
study brought in an important concept, work-value congruence,
which enables a better understanding of work values and their
relation to various attitudinal variables.
Abstract: This research explores on the development of the structure of Carbon Credit Registry System those accords to the need of future events in Thailand. This research also explores the big picture of every connected system by referring to the design of each system, the Data Flow Diagram, and the design in term of the system-s data using DES standard. The purpose of this paper is to show how to design the model of each system. Furthermore, this paper can serve as guideline for designing an appropriate Carbon Credit Registry System.
Abstract: Because of high ductility, aluminum alloys, have been widely used as an important base of metal forming industries. But the main week point of these alloys is their low strength so in forming them with conventional methods like deep drawing, hydro forming, etc have been always faced with problems like fracture during of forming process. Because of this, recently using of explosive forming method for forming of these plates has been recommended. In this paper free explosive forming of A2024 aluminum alloy is numerically simulated and during it, explosion wave propagation process is studied. Consequences of this simulation can be effective in prediction of quality of production. These consequences are compared with an experimental test and show the superiority of this method to similar methods like hydro forming and deep drawing.
Abstract: This study was initiated with a three prong objective.
One, to identify the relationship between Technological
Competencies factors (Technical Capability, Firm Innovativeness
and E-Business Practices and professional service firms- business
performance. To investigate the predictors of professional service
firms business performance and finally to evaluate the predictors of
business performance according to the type of professional service
firms, a survey questionnaire was deployed to collect empirical data.
The questionnaire was distributed to the owners of the professional
small medium size enterprises services in the Accounting, Legal,
Engineering and Architecture sectors. Analysis showed that all three
Technology Competency factors have moderate effect on business
performance. In addition, the regression models indicate that
technical capability is the most highly influential that could
determine business performance, followed by e-business practices
and firm innovativeness. Subsequently, the main predictor of
business performance for all types of firms is Technical capability.
Abstract: Group contribution methods such as the UNIFAC are
very useful to researchers and engineers involved in synthesis,
feasibility studies, design and optimization of separation processes.
They can be applied successfully to predict phase equilibrium and
excess properties in the development of chemical and separation
processes. The main focus of this work was to investigate the
possibility of absorbing selected volatile organic compounds (VOCs)
into polydimethylsiloxane (PDMS) using three selected UNIFAC
group contribution methods. Absorption followed by subsequent
stripping is the predominant available abatement technology of
VOCs from flue gases prior to their release into the atmosphere. The
original, modified and effective UNIFAC models were used in this
work. The thirteen selected VOCs that have been considered in this
research are: pentane, hexane, heptanes, trimethylamine, toluene,
xylene, cyclohexane, butyl acetate, diethyl acetate, chloroform,
acetone, ethyl methyl ketone and isobutyl methyl ketone. The
computation was done for solute VOC concentration of 8.55x10-8
which is well in the infinite dilution region. The results obtained in
this study compare very well with those published in literature
obtained through both measurements and predictions. The phase
equilibrium obtained in this study show that PDMS is a good
absorbent for the removal of VOCs from contaminated air streams
through physical absorption.