Abstract: The incidence of oral cancer in Taiwan increased year
by year. It replaced the nasopharyngeal as the top incurrence among
head and neck cancers since 1994. Early examination and earlier
identification for earlier treatment is the most effective medical
treatment for these cancers. Although the government fully subsidized
the expenses with tremendous promotion program for oral cancer
screening, the citizen-s participation remained low. Purpose of this
study is to understand the factors affecting the citizens- behavior
intensions of taking an oral cancer screening. Based on the Theory of
Planned Behavior, this study adopted four distinctive variables in
explaining the captioned behavior intentions.700 questionnaires were
dispatched with 500 valid responses or 71.4% returned by the citizens
with an age 30 or above from the eastern counties of Taiwan. Test
results has shown that attitude toward, subjective norms of, and
perceived behavioral control over the oral cancer screening varied
from some demographic factors to another. The study proofed that
attitude toward, subjective norms of, and perceived behavioral control
over the oral cancer screening had positive impacts on the
corresponding behavior intention. The test concluded that the theory
of planned behavior was appropriate as a theoretical framework in
explaining the influencing factors of intentions of taking oral cancer
screening. This study suggested the healthcare professional should
provide high accessibility of screening services other than just
delivering knowledge on oral cancer to promote the citizens-
intentions of taking the captioned screening. This research also
provided a practical implication to the healthcare professionals when
formulating and implementing promotion instruments for lifting the
screening rate of oral cancer.
Abstract: In this study, the effects of biogas fuels on the performance of an annular micro gas turbine (MGT) were assessed experimentally and numerically. In the experiments, the proposed MGT system was operated successfully under each test condition; minimum composition to the fuel with the biogas was roughly 50% CH4 with 50% CO2. The power output was around 170W at 85,000 RPM as 90% CH4 with 10% CO2 was used and 70W at 65,000 RPM as 70% CH4 with 30% CO2 was used. When a critical limit of 60% CH4 was reached, the power output was extremely low. Furthermore, the theoretical Brayton cycle efficiency and electric efficiency of the MGT were calculated as 23% and 10%, respectively. Following the experiments, the measured data helped us identify the parameters of dynamic model in numerical simulation. Additionally, a numerical analysis of re-designed combustion chamber showed that the performance of MGT could be improved by raising the temperature at turbine inlet. This study presents a novel distributed power supply system that can utilize renewable biogas. The completed micro biogas power supply system is small, low cost, easy to maintain and suited to household use.
Abstract: The rapid pace of technological advancement and its
consequential widening digital divide has resulted in the
marginalization of the disabled especially the communication
challenged. The dearth of suitable technologies for the development
of assistive technologies has served to further marginalize the
communications challenged user population and widen this chasm
even further. Given the varying levels of disability there and its
associated requirement for customized solution based. This paper
explains the use of a Software Development Kits (SDK) for the
bridging of this communications divide through the use of industry
poplar communications SDKs towards identification of requirements
for communications challenged users as well as identification of
appropriate frameworks for future development initiatives.
Abstract: Many high-risk pathogens that cause disease in
humans are transmitted through various food items. Food-borne
disease constitutes a major public health problem. Assessment of the
quality and safety of foods is important in human health. Rapid and
easy detection of pathogenic organisms will facilitate precautionary
measures to maintain healthy food. The Polymerase Chain Reaction
(PCR) is a handy tool for rapid detection of low numbers of bacteria.
We have designed gene specific primers for most common food
borne pathogens such as Staphylococci, Salmonella and E.coli.
Bacteria were isolated from food samples of various food outlets and
identified using gene specific PCRs. We identified Staphylococci,
Salmonella and E.coli O157 using gene specific primers by rapid and
direct PCR technique in various food samples. This study helps us in
getting a complete picture of the various pathogens that threaten to
cause and spread food borne diseases and it would also enable
establishment of a routine procedure and methodology for rapid
identification of food borne bacteria using the rapid technique of
direct PCR. This study will also enable us to judge the efficiency of
present food safety steps taken by food manufacturers and exporters.
Abstract: The aim of the study was to identify seat belt wearing
factor among road users in Malaysia. Evidence-based approach
through in-depth crash investigation was utilised to determine the
intended objectives. The objective was scoped into crashes
investigated by Malaysian Institute of Road Safety Research
(MIROS) involving passenger vehicles within 2007 and 2010. Crash
information of a total of 99 crash cases involving 240 vehicles and
864 occupants were obtained during the study period. Statistical test
and logistic regression analysis have been performed. Results of the
analysis revealed that gender, seat position and age were associated
with seat belt wearing compliance in Malaysia. Males are 97.6%
more likely to wear seat belt compared to females (95% CI 1.317 to
2.964). By seat position, the finding indicates that frontal occupants
were 82 times more likely to be wearing seat belt (95% CI 30.199 to
225.342) as compared to rear occupants. It is also important to note
that the odds of seat belt wearing increased by about 2.64% (95% CI
1.0176 to 1.0353) for every one year increase in age. This study is
essential in understanding the Malaysian tendency in belting up
while being occupied in a vehicle. The factors highlighted in this
study should be emphasized in road safety education in order to
increase seat belt wearing rate in this country and ultimately in
preventing deaths due to road crashes.
Abstract: An appropriate method for fault identification and classification on extra high voltage transmission line using discrete wavelet transform is proposed in this paper. The sharp variations of the generated short circuit transient signals which are recorded at the sending end of the transmission line are adopted to identify the fault. The threshold values involve fault classification and these are done on the basis of the multiresolution analysis. A comparative study of the performance is also presented for Discrete Fourier Transform (DFT) based Artificial Neural Network (ANN) and Discrete Wavelet Transform (DWT). The results prove that the proposed method is an effective and efficient one in obtaining the accurate result within short duration of time by using Daubechies 4 and 9. Simulation of the power system is done using MATLAB.
Abstract: Environmental aspects plays a central role in environmental management system (EMS) because it is the basis for the identification of an organization-s environmental targets. The
existing methods for the assessment of environmental aspects are grouped into three categories: risk assessment-based (RA-based),
LCA-based and criterion-based methods. To combine the benefits of
these three categories of research, this study proposes an integrated framework, combining RA-, LCA- and criterion-based methods. The
integrated framework incorporates LCA techniques for the identification of the causal linkage for aspect, pathway, receptor and
impact, uses fuzzy logic to assess aspects, considers fuzzy conditions,
in likelihood assessment, and employs a new multi-criteria decision analysis method - multi-criteria and multi-connection comprehensive
assessment (MMCA) - to estimate significant aspects in EMS. The proposed model is verified, using a real case study and the results show
that this method successfully prioritizes the environmental aspects.
Abstract: In this paper, different approaches to solve the
forward kinematics of a three DOF actuator redundant hydraulic
parallel manipulator are presented. On the contrary to series
manipulators, the forward kinematic map of parallel manipulators
involves highly coupled nonlinear equations, which are almost
impossible to solve analytically. The proposed methods are using
neural networks identification with different structures to solve the
problem. The accuracy of the results of each method is analyzed in
detail and the advantages and the disadvantages of them in
computing the forward kinematic map of the given mechanism is
discussed in detail. It is concluded that ANFIS presents the best
performance compared to MLP, RBF and PNN networks in this
particular application.
Abstract: The number of features required to represent an image
can be very huge. Using all available features to recognize objects
can suffer from curse dimensionality. Feature selection and
extraction is the pre-processing step of image mining. Main issues in
analyzing images is the effective identification of features and
another one is extracting them. The mining problem that has been
focused is the grouping of features for different shapes. Experiments
have been conducted by using shape outline as the features. Shape
outline readings are put through normalization and dimensionality
reduction process using an eigenvector based method to produce a
new set of readings. After this pre-processing step data will be
grouped through their shapes. Through statistical analysis, these
readings together with peak measures a robust classification and
recognition process is achieved. Tests showed that the suggested
methods are able to automatically recognize objects through their
shapes. Finally, experiments also demonstrate the system invariance
to rotation, translation, scale, reflection and to a small degree of
distortion.
Abstract: The complex hybrid and nonlinear nature of many processes that are met in practice causes problems with both structure modelling and parameter identification; therefore, obtaining a model that is suitable for MPC is often a difficult task. The basic idea of this paper is to present an identification method for a piecewise affine (PWA) model based on a fuzzy clustering algorithm. First we introduce the PWA model. Next, we tackle the identification method. We treat the fuzzy clustering algorithm, deal with the projections of the fuzzy clusters into the input space of the PWA model and explain the estimation of the parameters of the PWA model by means of a modified least-squares method. Furthermore, we verify the usability of the proposed identification approach on a hybrid nonlinear batch reactor example. The result suggest that the batch reactor can be efficiently identified and thus formulated as a PWA model, which can eventually be used for model predictive control purposes.
Abstract: This paper proposes a method, combining color and layout features, for identifying documents captured from low-resolution handheld devices. On one hand, the document image color density surface is estimated and represented with an equivalent ellipse and on the other hand, the document shallow layout structure is computed and hierarchically represented. Our identification method first uses the color information in the documents in order to focus the search space on documents having a similar color distribution, and finally selects the document having the most similar layout structure in the remaining of the search space.
Abstract: Design and modeling of nonlinear systems require the
knowledge of all inside acting parameters and effects. An empirical
alternative is to identify the system-s transfer function from input and
output data as a black box model. This paper presents a procedure
using least squares algorithm for the identification of a feed drive
system coefficients in time domain using a reduced model based on
windowed input and output data. The command and response of the
axis are first measured in the first 4 ms, and then least squares are
applied to predict the transfer function coefficients for this
displacement segment. From the identified coefficients, the next
command response segments are estimated. The obtained results
reveal a considerable potential of least squares method to identify the
system-s time-based coefficients and predict accurately the command
response as compared to measurements.
Abstract: The use of renewable energy sources becomes more
necessary and interesting. As wider applications of renewable energy
devices at domestic, commercial and industrial levels has not only
resulted in greater awareness, but also significantly installed
capacities. In addition, biomass principally is in the form of woods,
which is a form of energy by humans for a long time. Gasification is
a process of conversion of solid carbonaceous fuel into combustible
gas by partial combustion. Many gasifier models have various
operating conditions; the parameters kept in each model are different.
This study applied experimental data, which has three inputs, which
are; biomass consumption, temperature at combustion zone and ash
discharge rate. One output is gas flow rate. For this paper, neural
network was used to identify the gasifier system suitable for the
experimental data. In the result,neural networkis usable to attain the
answer.
Abstract: Current image-based individual human recognition
methods, such as fingerprints, face, or iris biometric modalities
generally require a cooperative subject, views from certain aspects,
and physical contact or close proximity. These methods cannot
reliably recognize non-cooperating individuals at a distance in the
real world under changing environmental conditions. Gait, which
concerns recognizing individuals by the way they walk, is a relatively
new biometric without these disadvantages. The inherent gait
characteristic of an individual makes it irreplaceable and useful in
visual surveillance.
In this paper, an efficient gait recognition system for human
identification by extracting two features namely width vector of
the binary silhouette and the MPEG-7-based region-based shape
descriptors is proposed. In the proposed method, foreground objects
i.e., human and other moving objects are extracted by estimating
background information by a Gaussian Mixture Model (GMM) and
subsequently, median filtering operation is performed for removing
noises in the background subtracted image. A moving target classification
algorithm is used to separate human being (i.e., pedestrian)
from other foreground objects (viz., vehicles). Shape and boundary
information is used in the moving target classification algorithm.
Subsequently, width vector of the outer contour of binary silhouette
and the MPEG-7 Angular Radial Transform coefficients are taken as
the feature vector. Next, the Principal Component Analysis (PCA)
is applied to the selected feature vector to reduce its dimensionality.
These extracted feature vectors are used to train an Hidden Markov
Model (HMM) for identification of some individuals. The proposed
system is evaluated using some gait sequences and the experimental
results show the efficacy of the proposed algorithm.
Abstract: Business process model describes process flow of a
business and can be seen as the requirement for developing a
software application. This paper discusses a BPM2CD guideline
which complements the Model Driven Architecture concept by
suggesting how to create a platform-independent software model in
the form of a UML class diagram from a business process model. An
important step is the identification of UML classes from the business
process model. A technique for object-oriented analysis called
domain analysis is borrowed and key concepts in the business
process model will be discovered and proposed as candidate classes
for the class diagram. The paper enhances this step by using ontology
search to help identify important classes for the business domain. As
ontology is a source of knowledge for a particular domain which
itself can link to ontologies of related domains, the search can give a
refined set of candidate classes for the resulting class diagram.
Abstract: Camera calibration is an indispensable step for augmented
reality or image guided applications where quantitative information
should be derived from the images. Usually, a camera
calibration is obtained by taking images of a special calibration object
and extracting the image coordinates of projected calibration marks
enabling the calculation of the projection from the 3d world coordinates
to the 2d image coordinates. Thus such a procedure exhibits
typical steps, including feature point localization in the acquired
images, camera model fitting, correction of distortion introduced by
the optics and finally an optimization of the model-s parameters. In
this paper we propose to extend this list by further step concerning
the identification of the optimal subset of images yielding the smallest
overall calibration error. For this, we present a Monte Carlo based
algorithm along with a deterministic extension that automatically
determines the images yielding an optimal calibration. Finally, we
present results proving that the calibration can be significantly
improved by automated image selection.
Abstract: Quality control charts indicate out of control
conditions if any nonrandom pattern of the points is observed or any
point is plotted beyond the control limits. Nonrandom patterns of
Shewhart control charts are tested with sensitizing rules. When the
processes are defined with fuzzy set theory, traditional sensitizing
rules are insufficient for defining all out of control conditions. This is
due to the fact that fuzzy numbers increase the number of out of
control conditions. The purpose of the study is to develop a set of
fuzzy sensitizing rules, which increase the flexibility and sensitivity
of fuzzy control charts. Fuzzy sensitizing rules simplify the
identification of out of control situations that results in a decrease in
the calculation time and number of evaluations in fuzzy control chart
approach.
Abstract: Biometric techniques are gaining importance for
personal authentication and identification as compared to the
traditional authentication methods. Biometric templates are
vulnerable to variety of attacks due to their inherent nature. When a
person-s biometric is compromised his identity is lost. In contrast to
password, biometric is not revocable. Therefore, providing security
to the stored biometric template is very crucial. Crypto biometric
systems are authentication systems, which blends the idea of
cryptography and biometrics. Fuzzy vault is a proven crypto
biometric construct which is used to secure the biometric templates.
However fuzzy vault suffer from certain limitations like nonrevocability,
cross matching. Security of the fuzzy vault is affected
by the non-uniform nature of the biometric data. Fuzzy vault when
hardened with password overcomes these limitations. Password
provides an additional layer of security and enhances user privacy.
Retina has certain advantages over other biometric traits. Retinal
scans are used in high-end security applications like access control to
areas or rooms in military installations, power plants, and other high
risk security areas. This work applies the idea of fuzzy vault for
retinal biometric template. Multimodal biometric system
performance is well compared to single modal biometric systems.
The proposed multi modal biometric fuzzy vault includes combined
feature points from retina and fingerprint. The combined vault is
hardened with user password for achieving high level of security.
The security of the combined vault is measured using min-entropy.
The proposed password hardened multi biometric fuzzy vault is
robust towards stored biometric template attacks.
Abstract: This paper evaluates performances of an adaptive noise
cancelling (ANC) based target detection algorithm on a set of real test
data supported by the Defense Evaluation Research Agency (DERA
UK) for multi-target wideband active sonar echolocation system. The
hybrid algorithm proposed is a combination of an adaptive ANC
neuro-fuzzy scheme in the first instance and followed by an iterative
optimum target motion estimation (TME) scheme. The neuro-fuzzy
scheme is based on the adaptive noise cancelling concept with the
core processor of ANFIS (adaptive neuro-fuzzy inference system) to
provide an effective fine tuned signal. The resultant output is then
sent as an input to the optimum TME scheme composed of twogauge
trimmed-mean (TM) levelization, discrete wavelet denoising
(WDeN), and optimal continuous wavelet transform (CWT) for
further denosing and targets identification. Its aim is to recover the
contact signals in an effective and efficient manner and then determine
the Doppler motion (radial range, velocity and acceleration) at very
low signal-to-noise ratio (SNR). Quantitative results have shown that
the hybrid algorithm have excellent performance in predicting targets-
Doppler motion within various target strength with the maximum
false detection of 1.5%.
Abstract: Web usage mining has become a popular research
area, as a huge amount of data is available online. These data can be
used for several purposes, such as web personalization, web structure
enhancement, web navigation prediction etc. However, the raw log
files are not directly usable; they have to be preprocessed in order to
transform them into a suitable format for different data mining tasks.
One of the key issues in the preprocessing phase is to identify web
users. Identifying users based on web log files is not a
straightforward problem, thus various methods have been developed.
There are several difficulties that have to be overcome, such as client
side caching, changing and shared IP addresses and so on. This paper
presents three different methods for identifying web users. Two of
them are the most commonly used methods in web log mining
systems, whereas the third on is our novel approach that uses a
complex cookie-based method to identify web users. Furthermore we
also take steps towards identifying the individuals behind the
impersonal web users. To demonstrate the efficiency of the new
method we developed an implementation called Web Activity
Tracking (WAT) system that aims at a more precise distinction of
web users based on log data. We present some statistical analysis
created by the WAT on real data about the behavior of the Hungarian
web users and a comprehensive analysis and comparison of the three
methods