Abstract: The purposes of this research were to study the citizen
participation in preventing illegal drugs in one of a poor and small
community of Bangkok, Thailand and to compare the level of
participation and concern of illegal drugs problem by using
demographic variables. This paper drew upon data collected from a
local citizens survey conducted in Bangkok, Thailand during summer
of 2012. A total of 200 respondents were elicited as data input for,
and one way ANOVA test. The findings revealed that the overall
citizen participation was in the level of medium. The mean score
showed that benefit from the program was ranked as the highest and
the decision to participate was ranked as second while the follow-up
of the program was ranked as the lowest.
In terms of the difference in demographic such as gender, age,
level of education, income, and year of residency, the hypothesis
testing’s result disclosed that there were no difference in their level
of participation. However, difference in occupation showed a
difference in their level of participation and concern which was
significant at the 0.05 confidence level.
Abstract: Traditional principal components analysis (PCA)
techniques for face recognition are based on batch-mode training
using a pre-available image set. Real world applications require that
the training set be dynamic of evolving nature where within the
framework of continuous learning, new training images are
continuously added to the original set; this would trigger a costly
continuous re-computation of the eigen space representation via
repeating an entire batch-based training that includes the old and new
images. Incremental PCA methods allow adding new images and
updating the PCA representation. In this paper, two incremental
PCA approaches, CCIPCA and IPCA, are examined and compared.
Besides, different learning and testing strategies are proposed and
applied to the two algorithms. The results suggest that batch PCA is
inferior to both incremental approaches, and that all CCIPCAs are
practically equivalent.
Abstract: Permeability reduction induced by asphaltene
precipitation during gas injection is one of the serious problems in
the oil industry. This problem can lead to formation damage and
decrease the oil production rate. In this work, Malaysian light oil
sample has been used to investigate the effect CO2 injection and
Water Alternating Gas (WAG) injection on permeability reduction.
In this work, dynamic core flooding experiments were conducted to
study the effect of CO2 and WAG injection on the amount of
asphaltene precipitated. Core properties after displacement were
inspected for any permeability reduction to study the effect of
asphaltene precipitation on rock properties.
The results showed that WAG injection gave less asphaltene
precipitation and formation damage compared to CO2 injection. The
study suggested that WAG injection can be one of the important
factors of managing asphaltene precipitation.
Abstract: Nowadays, ontologies are the only widely accepted paradigm for the management of sharable and reusable knowledge in a way that allows its automatic interpretation. They are collaboratively created across the Web and used to index, search and annotate documents. The vast majority of the ontology based approaches, however, focus on indexing texts at document level. Recently, with the advances in ontological engineering, it became clear that information indexing can largely benefit from the use of general purpose ontologies which aid the indexing of documents at word level. This paper presents a concept indexing algorithm, which adds ontology information to words and phrases and allows full text to be searched, browsed and analyzed at different levels of abstraction. This algorithm uses a general purpose ontology, OntoRo, and an ontologically tagged corpus, OntoCorp, both developed for the purpose of this research. OntoRo and OntoCorp are used in a two-stage supervised machine learning process aimed at generating ontology tagging rules. The first experimental tests show a tagging accuracy of 78.91% which is encouraging in terms of the further improvement of the algorithm.
Abstract: Railway Stations are prone to emergency due to
various reasons and proper monitor of railway stations are of
immense importance from various angles. A Petri-net representation
of a web-service-based Emergency management system has been
proposed in this paper which will help in monitoring situation of
train, track, signal etc. and in case of any emergency, necessary
resources can be dispatched.
Abstract: One of the robust fault detection filter (RFDF)
designing method is based on sliding-mode theory. The main purpose
of our study is to introduce an innovative simplified reference
residual model generator to formulate the RFDF as a sliding-mode
observer without any manipulation package or transformation matrix,
through which the generated residual signals can be evaluated. So the
proposed design is more explicit and requires less design parameters
in comparison with approaches requiring changing coordinates. To
the best author's knowledge, this is the first time that the sliding
mode technique is applied to detect actuator and sensor faults in a
real boiler. The designing procedure is proposed in a drum boiler in
Synvendska Kraft AB Plant in Malmo, Sweden as a multivariable
and strongly coupled system. It is demonstrated that both sensor and
actuator faults can robustly be detected. Also sensor faults can be
diagnosed and isolated through this method.
Abstract: In this paper, we present a maintenance model of a
two-unit series system with economic dependence. Unit#1 which is
considered to be more expensive and more important, is subject to
condition monitoring (CM) at equidistant, discrete time epochs and
unit#2, which is not subject to CM has a general lifetime distribution.
The multivariate observation vectors obtained through condition
monitoring carry partial information about the hidden state of unit#1,
which can be in a healthy or a warning state while operating. Only the
failure state is assumed to be observable for both units. The objective
is to find an optimal opportunistic maintenance policy minimizing
the long-run expected average cost per unit time. The problem
is formulated and solved in the partially observable semi-Markov
decision process framework. An effective computational algorithm
for finding the optimal policy and the minimum average cost is
developed, illustrated by a numerical example.
Abstract: Mankind has entered into an extremely complex and
controversial stage of its development: the world is simultaneously
organized and chaoticized, globalized and localized, combined and
split. Analysts point out that globalization as a process of
strengthening economic, cultural, financial and other ties of states
cause many problems. In the economic sphere, it creates the danger
of growing gap between the states, in the sphere of politics it leads to
the weakening of political power and influence of nation-states.
Abstract: Cry j 1 is a causative substance of Japanese cedar
pollinosis, and it may deteriorate by Cry j 1 invasion to a lower
respiratory tract. We observed airborne particles containing Cry j 1 by
an immunofluorescence technique using a fluorescence microscope,
and we clarified that Cry j 1 exist as aggregates of airborne fine
particles (< 1.1 μm) in the urban atmosphere. Airborne Cry j 1 may
react with air pollutants and be denature to a substance deteriorated
Japanese cedar pollinosis. Therefore, we applied a sodium dodecyl
sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) to evaluate a
Cry j 1 reacted with various air pollutants by liquid phase reaction,
and calculated kinetics constants of Cry j 1 extracted from pollens
collected in various sites and airborne fine particles containing Cry j
1 by using a surface plasmon resonance (SPR) method. As a result, it
is suggested that Cry j 1 may be denatured by air pollutants during
the transportation to the urban atmosphere.
Abstract: In order to maximize efficiency of an information management platform and to assist in decision making, the collection, storage and analysis of performance-relevant data has become of fundamental importance. This paper addresses the merits and drawbacks provided by the OLAP paradigm for efficiently navigating large volumes of performance measurement data hierarchically. The system managers or database administrators navigate through adequately (re)structured measurement data aiming to detect performance bottlenecks, identify causes for performance problems or assessing the impact of configuration changes on the system and its representative metrics. Of particular importance is finding the root cause of an imminent problem, threatening availability and performance of an information system. Leveraging OLAP techniques, in contrast to traditional static reporting, this is supposed to be accomplished within moderate amount of time and little processing complexity. It is shown how OLAP techniques can help improve understandability and manageability of measurement data and, hence, improve the whole Performance Analysis process.
Abstract: Currently is characterized production engineering
together with the integration of industrial automation and robotics
such very quick view of to manufacture the products. The production
range is continuously changing, expanding and producers have to be
flexible in this regard. It means that need to offer production
possibilities, which can respond to the quick change. Engineering
product development is focused on supporting CAD software, such
systems are mainly used for product design. That manufacturers are
competitive, it should be kept procured machines made available
capable of responding to output flexibility. In response to that
problem is the development of flexible manufacturing systems,
consisting of various automated systems. The integration of flexible
manufacturing systems and subunits together with product design and
of engineering is a possible solution for this issue. Integration is
possible through the implementation of CIM systems. Such a solution
and finding a hyphen between CAD and procurement system ICIM
3000 from Festo Co. is engaged in the research project and this
contribution. This can be designed the products in CAD systems and
watch the manufacturing process from order to shipping by the
development of methods and processes of integration, This can be
modeled in CAD systems products and watch the manufacturing
process from order to shipping to develop methods and processes of
integration, which will improve support for product design
parameters by monitoring of the production process, by creating of
programs for production using the CAD and therefore accelerates the
a total of process from design to implementation.
Abstract: This paper presents the doping profile measurement
and characterization technique for the pocket implanted nano scale
n-MOSFET. Scanning capacitance microscopy and atomic force
microscopy have been used to image the extent of lateral dopant
diffusion in MOS structures. The data are capacitance vs. voltage
measurements made on a nano scale device. The technique is nondestructive
when imaging uncleaved samples. Experimental data from
the published literature are presented here on actual, cleaved device
structures which clearly indicate the two-dimensional dopant profile
in terms of a spatially varying modulated capacitance signal. Firstorder
deconvolution indicates the technique has much promise for
the quantitative characterization of lateral dopant profiles. The pocket
profile is modeled assuming the linear pocket profiles at the source
and drain edges. From the model, the effective doping concentration
is found to use in modeling and simulation results of the various
parameters of the pocket implanted nano scale n-MOSFET. The
potential of the technique to characterize important device related
phenomena on a local scale is also discussed.
Abstract: In this paper, using (G/G )-expansion method and modified F-expansion method, we give some explicit formulas of exact traveling wave solutions for the (3+1)-dimensional breaking soliton equation. A modified F-expansion method is proposed by taking full advantages of F-expansion method and Riccati equation in seeking exact solutions of the equation.
Abstract: This paper presents a new technique for detection of
human faces within color images. The approach relies on image
segmentation based on skin color, features extracted from the two-dimensional
discrete cosine transform (DCT), and self-organizing
maps (SOM). After candidate skin regions are extracted, feature
vectors are constructed using DCT coefficients computed from those
regions. A supervised SOM training session is used to cluster feature
vectors into groups, and to assign “face" or “non-face" labels to those
clusters. Evaluation was performed using a new image database of
286 images, containing 1027 faces. After training, our detection
technique achieved a detection rate of 77.94% during subsequent
tests, with a false positive rate of 5.14%. To our knowledge, the
proposed technique is the first to combine DCT-based feature
extraction with a SOM for detecting human faces within color
images. It is also one of a few attempts to combine a feature-invariant
approach, such as color-based skin segmentation, together with
appearance-based face detection. The main advantage of the new
technique is its low computational requirements, in terms of both
processing speed and memory utilization.
Abstract: This is a comprehensive large-sample study of Australian earnings management. Using a sample of 4,844 firm-year observations across nine Australia industries from 2000 to 2006, we find substantial corporate earnings management activity across several Australian industries. We document strong evidence of size and return on assets being primary determinants of earnings management in Australia. The effects of size and return on assets are also found to be dominant in both income-increasing and incomedecreasing earnings manipulation. We also document that that periphery sector firms are more likely to involve larger magnitude of earnings management than firms in the core sector.
Abstract: We demonstrate the synthesis of intermediary views
within a sequence of color encoded, materials discriminating, X-ray
images that exhibit animated depth in a visual display. During the
image acquisition process, the requirement for a linear X-ray detector
array is replaced by synthetic image. Scale Invariant Feature
Transform, SIFT, in combination with material segmented morphing
is employed to produce synthetic imagery. A quantitative analysis of
the feature matching performance of the SIFT is presented along with
a comparative study of the synthetic imagery. We show that the total
number of matches produced by SIFT reduces as the angular
separation between the generating views increases. This effect is
accompanied by an increase in the total number of synthetic pixel
errors. The trends observed are obtained from 15 different luggage
items. This programme of research is in collaboration with the UK
Home Office and the US Dept. of Homeland Security.
Abstract: In this report we present a rule-based approach to
detect anomalous telephone calls. The method described here uses
subscriber usage CDR (call detail record) data sampled over two
observation periods: study period and test period. The study period
contains call records of customers- non-anomalous behaviour.
Customers are first grouped according to their similar usage
behaviour (like, average number of local calls per week, etc). For
customers in each group, we develop a probabilistic model to describe
their usage. Next, we use maximum likelihood estimation (MLE) to
estimate the parameters of the calling behaviour. Then we determine
thresholds by calculating acceptable change within a group. MLE is
used on the data in the test period to estimate the parameters of the
calling behaviour. These parameters are compared against thresholds.
Any deviation beyond the threshold is used to raise an alarm. This
method has the advantage of identifying local anomalies as compared
to techniques which identify global anomalies. The method is tested
for 90 days of study data and 10 days of test data of telecom
customers. For medium to large deviations in the data in test window,
the method is able to identify 90% of anomalous usage with less than
1% false alarm rate.
Abstract: This paper deals with the application of artificial
neural network (ANN) and fuzzy based Adaptive Neuro Fuzzy
Inference System(ANFIS) approach to Load Frequency Control
(LFC) of multi unequal area hydro-thermal interconnected power
system. The proposed ANFIS controller combines the advantages of
fuzzy controller as well as quick response and adaptability nature of
ANN. Area-1 and area-2 consists of thermal reheat power plant
whereas area-3 and area-4 consists of hydro power plant with electric
governor. Performance evaluation is carried out by using intelligent
controller like ANFIS, ANN and Fuzzy controllers and conventional
PI and PID control approaches. To enhance the performance of
intelligent and conventional controller sliding surface is included.
The performances of the controllers are simulated using
MATLAB/SIMULINK package. A comparison of ANFIS, ANN,
Fuzzy, PI and PID based approaches shows the superiority of
proposed ANFIS over ANN & fuzzy, PI and PID controller for 1%
step load variation.
Abstract: This paper presents an integrated model that
automatically measures the change of rivers, damage area of bridge
surroundings, and change of vegetation. The proposed model is on the
basis of a neurofuzzy mechanism enhanced by SOM optimization
algorithm, and also includes three functions to deal with river imagery.
High resolution imagery from FORMOSAT-2 satellite taken before
and after the invasion period is adopted. By randomly selecting a
bridge out of 129 destroyed bridges, the recognition results show that
the average width has increased 66%. The ruined segment of the
bridge is located exactly at the most scour region. The vegetation
coverage has also reduced to nearly 90% of the original. The results
yielded from the proposed model demonstrate a pinpoint accuracy rate
at 99.94%. This study brings up a successful tool not only for
large-scale damage assessment but for precise measurement to
disasters.
Abstract: Atherosclerosis was identified as a chronic inflammatory process resulting from interactions between plasma lipoproteins, cellular components (monocyte, macrophages, T lymphocytes, endothelial cells and smooth muscle cells) and the extracellular matrix of the arterial wall. Several types of genes were known to express during formation of atherosclerosis. This study is carried out to identify unknown differentially expressed gene (DEG) in atherogenesis. Rabbit’s aorta tissues were stained by H&E for histomorphology. GeneFishing™ PCR analysis was performed from total RNA extracted from the aorta tissues. The DNA fragment from DEG was cloned, sequenced and validated by Real-time PCR. Histomorphology showed intimal thickening in the aorta. DEG detected from ACP-41 was identified as cathepsin B gene and showed upregulation at week-8 and week-12 of atherogenesis. Therefore, ACP-based GeneFishing™ PCR facilitated identification of cathepsin B gene which was differentially expressed during development of atherosclerosis.