Abstract: The mixture formation prior to the ignition process
plays as a key element in the diesel combustion. Parametric studies of
mixture formation and ignition process in various injection parameter
has received considerable attention in potential for reducing
emissions. Purpose of this study is to clarify the effects of injection
pressure on mixture formation and ignition especially during ignition
delay period, which have to be significantly influences throughout the
combustion process and exhaust emissions. This study investigated
the effects of injection pressure on diesel combustion fundamentally
using rapid compression machine. The detail behavior of mixture
formation during ignition delay period was investigated using the
schlieren photography system with a high speed camera. This method
can capture spray evaporation, spray interference, mixture formation
and flame development clearly with real images. Ignition process and
flame development were investigated by direct photography method
using a light sensitive high-speed color digital video camera. The
injection pressure and air motion are important variable that strongly
affect to the fuel evaporation, endothermic and prolysis process
during ignition delay. An increased injection pressure makes spray tip
penetration longer and promotes a greater amount of fuel-air mixing
occurs during ignition delay. A greater quantity of fuel prepared
during ignition delay period thus predominantly promotes more rapid
heat release.
Abstract: The evaluation and measurement of human body
dimensions are achieved by physical anthropometry. This research
was conducted in view of the importance of anthropometric indices
of the face in forensic medicine, surgery, and medical imaging. The
main goal of this research is to optimization of facial feature point by
establishing a mathematical relationship among facial features and
used optimize feature points for age classification. Since selected
facial feature points are located to the area of mouth, nose, eyes and
eyebrow on facial images, all desire facial feature points are extracted
accurately. According this proposes method; sixteen Euclidean
distances are calculated from the eighteen selected facial feature
points vertically as well as horizontally. The mathematical
relationships among horizontal and vertical distances are established.
Moreover, it is also discovered that distances of the facial feature
follows a constant ratio due to age progression. The distances
between the specified features points increase with respect the age
progression of a human from his or her childhood but the ratio of the
distances does not change (d = 1 .618 ) . Finally, according to the
proposed mathematical relationship four independent feature
distances related to eight feature points are selected from sixteen
distances and eighteen feature point-s respectively. These four feature
distances are used for classification of age using Support Vector
Machine (SVM)-Sequential Minimal Optimization (SMO) algorithm
and shown around 96 % accuracy. Experiment result shows the
proposed system is effective and accurate for age classification.
Abstract: Human immunodeficiency virus infection and
acquired immunodeficiency syndrome is a global pandemic with
cases reporting from virtually every country and continues to be a
common infection in developing country like India.
Microalbuminuria is a manifestation of human immunodeficiency
virus associated nephropathy. Therefore, microalbuminuria may be
an early marker of human immunodeficiency virus associated
nephropathy, and screening for its presence may be beneficial. A
strikingly high prevalence of microalbuminuria among human
immunodeficiency virus infected patients has been described in
various studies. Risk factors for clinically significant proteinuria
include African - American race, higher human immunodeficiency
virus ribonucleic acid level and lower CD4 lymphocyte count. The
cardiovascular risk factors of increased systolic blood pressure and
increase fasting blood sugar level are strongly associated with
microalbuminuria in human immunodeficiency virus patient. These
results suggest that microalbuminuria may be a sign of current
endothelial dysfunction and micro-vascular disease and there is
substantial risk of future cardiovascular disease events. Positive
contributing factors include early kidney disease such as human
immunodeficiency virus associated nephropathy, a marker of end
organ damage related to co morbidities of diabetes or hypertension,
or more diffuse endothelial cells dysfunction. Nevertheless after
adjustment for non human immunodeficiency virus factors, human
immunodeficiency virus itself is a major risk factor. The presence of
human immunodeficiency virus infection is independent risk to
develop microalbuminuria in human immunodeficiency virus patient.
Cardiovascular risk factors appeared to be stronger predictors of
microalbuminuria than markers of human immunodeficiency virus
severity person with human immunodeficiency virus infection and
microalbuminuria therefore appear to potentially bear the burden of
two separate damage related to known vascular end organ damage
related to know vascular risk factors, and human immunodeficiency
virus specific processes such as the direct viral infection of kidney
cells.The higher prevalence of microalbuminuria among the human
immunodeficiency virus infected could be harbinger of future
increased risks of both kidney and cardiovascular disease. Further
study defining the prognostic significance of microalbuminuria
among human immunodeficiency virus infected persons will be
essential. Microalbuminuria seems to be a predictor of cardiovascular
disease in diabetic and non diabetic subjects, hence it can also be
used for early detection of micro vascular disease in human
immunodeficiency virus positive patients, thus can help to diagnose
the disease at the earliest.
Abstract: Combustion of sprays is of technological importance, but its flame behavior is not fully understood. Furthermore, the multiplicity of dependent variables such as pressure, temperature, equivalence ratio, and droplet sizes complicates the study of spray combustion. Fundamental study on the influence of the presence of liquid droplets has revealed that laminar flames within aerosol mixtures more readily become unstable than for gaseous ones and this increases the practical burning rate. However, fundamental studies on turbulent flames of aerosol mixtures are limited particularly those under near mono-dispersed droplet conditions. In the present work, centrally ignited expanding flames at near atmospheric pressures are employed to quantify the burning rates in gaseous and aerosol flames. Iso-octane-air aerosols are generated by expansion of the gaseous pre-mixture to produce a homogeneously distributed suspension of fuel droplets. The effects of the presence of droplets and turbulence velocity in relation to the burning rates of the flame are also investigated.
Abstract: This paper demonstrates how the soft systems
methodology can be used to improve the delivery of a module in data warehousing for fourth year information technology students.
Graduates in information technology needs to have academic skills
but also needs to have good practical skills to meet the skills requirements of the information technology industry. In developing
and improving current data warehousing education modules one has to find a balance in meeting the expectations of various role players such as the students themselves, industry and academia. The soft
systems methodology, developed by Peter Checkland, provides a
methodology for facilitating problem understanding from different world views. In this paper it is demonstrated how the soft systems methodology can be used to plan the improvement of data
warehousing education for fourth year information technology students.
Abstract: The Information and Communication Technologies
(ICTs), and the Wide World Web (WWW) have fundamentally
altered the practice of teaching and learning world wide. Many
universities, organizations, colleges and schools are trying to apply
the benefits of the emerging ICT. In the early nineties the term
learning object was introduced into the instructional technology
vernacular; the idea being that educational resources could be broken
into modular components for later combination by instructors,
learners, and eventually computes into larger structures that would
support learning [1]. However in many developing countries, the use
of ICT is still in its infancy stage and the concept of learning object
is quite new. This paper outlines the learning object design
considerations for developing countries depending on learning
environment.
Abstract: In this article, we aim to discuss the formulation of two explicit group iterative finite difference methods for time-dependent two dimensional Burger-s problem on a variable mesh. For the non-linear problems, the discretization leads to a non-linear system whose Jacobian is a tridiagonal matrix. We discuss the Newton-s explicit group iterative methods for a general Burger-s equation. The proposed explicit group methods are derived from the standard point and rotated point Crank-Nicolson finite difference schemes. Their computational complexity analysis is discussed. Numerical results are given to justify the feasibility of these two proposed iterative methods.
Abstract: Structural Integrity Management (SIM) is
important for the protection of offshore crew, environment, business assets and company and industry reputation. API RP 2A contained guidelines for assessment of existing platforms mostly for the Gulf
of Mexico (GOM). ISO 19902 SIM framework also does not
specifically cater for Malaysia. There are about 200 platforms in
Malaysia with 90 exceeding their design life. The Petronas Carigali
Sdn Bhd (PCSB) uses the Asset Integrity Management System and
the very subjective Risk based Inspection Program for these
platforms. Petronas currently doesn-t have a standalone Petronas
Technical Standard PTS-SIM. This study proposes a recommended
practice for the SIM process for offshore structures in Malaysia,
including studies by API and ISO and local elements such as the
number of platforms, types of facilities, age and risk ranking. Case
study on SMG-A platform in Sabah shows missing or scattered
platform data and a gap in inspection history. It is to undergo a level
3 underwater inspection in year 2015.
Abstract: Using activity theory, organisational theory and
didactics as theoretical foundations, a comprehensive model of the
organisational dimensions relevant for learning and knowledge
transfer will be developed. In a second step, a Learning Assessment
Guideline will be elaborated. This guideline will be designed to
permit a targeted analysis of organisations to identify the status quo
in those areas crucial to the implementation of learning and
knowledge transfer. In addition, this self-analysis tool will enable
learning managers to select adequate didactic models for e- and
blended learning. As part of the European Integrated Project
"Process-oriented Learning and Information Exchange" (PROLIX),
this model of organisational prerequisites for learning and knowledge
transfer will be empirically tested in four profit and non-profit
organisations in Great Britain, Germany and France (to be finalized
in autumn 2006). The findings concern not only the capability of the
model of organisational dimensions, but also the predominant
perceptions of and obstacles to learning in organisations.
Abstract: Identity verification of authentic persons by their multiview faces is a real valued problem in machine vision. Multiview faces are having difficulties due to non-linear representation in the feature space. This paper illustrates the usability of the generalization of LDA in the form of canonical covariate for face recognition to multiview faces. In the proposed work, the Gabor filter bank is used to extract facial features that characterized by spatial frequency, spatial locality and orientation. Gabor face representation captures substantial amount of variations of the face instances that often occurs due to illumination, pose and facial expression changes. Convolution of Gabor filter bank to face images of rotated profile views produce Gabor faces with high dimensional features vectors. Canonical covariate is then used to Gabor faces to reduce the high dimensional feature spaces into low dimensional subspaces. Finally, support vector machines are trained with canonical sub-spaces that contain reduced set of features and perform recognition task. The proposed system is evaluated with UMIST face database. The experiment results demonstrate the efficiency and robustness of the proposed system with high recognition rates.
Abstract: Based on an analysis of the current research and application of Road maintenance, geographic information system (WebGIS) and ArcGIS Server, the platform overhead construction for Road maintenance development is studied and the key issues are presented, including the organization and design of spatial data on the basis of the geodatabase technology, middleware technology, tiles cache index technology and dynamic segmentation of WebGIS. Road maintenance geographic information platform is put forward through the researching ideas of analysis of the system design. The design and application of WebGIS system are discussed on the basis of a case study of BaNan district of Chongqing highway maintenance management .The feasibility of the theories and methods are validated through the system.
Abstract: This paper provides a key driver-based conceptual framework that can be used to improve a firm-s success in commercializing technology and in new product innovation resulting from collaboration with other organizations through strategic alliances. Based on a qualitative study using an interview approach, strategic alliances of entrepreneurs in the food processing industry in Thailand are explored. This paper describes factors affecting decisions to collaborate through alliances. It identifies four issues: maintaining the efficiency of the value chain for production capability, adapting to present and future competition, careful assessment of value of outcomes, and management of innovation. We consider five driving factors: resource orientation, assessment of risk, business opportunity, sharing of benefits and confidence in alliance partners. These factors will be of interest to entrepreneurs and policy makers with regard to further understanding of the direction of business strategies.
Abstract: In this research, heat transfer of a poly Ethylene
fluidized bed reactor without reaction were studied experimentally
and computationally at different superficial gas velocities. A multifluid
Eulerian computational model incorporating the kinetic theory
for solid particles was developed and used to simulate the heat
conducting gas–solid flows in a fluidized bed configuration.
Momentum exchange coefficients were evaluated using the Syamlal–
O-Brien drag functions. Temperature distributions of different phases
in the reactor were also computed. Good agreement was found
between the model predictions and the experimentally obtained data
for the bed expansion ratio as well as the qualitative gas–solid flow
patterns. The simulation and experimental results showed that the gas
temperature decreases as it moves upward in the reactor, while the
solid particle temperature increases. Pressure drop and temperature
distribution predicted by the simulations were in good agreement
with the experimental measurements at superficial gas velocities
higher than the minimum fluidization velocity. Also, the predicted
time-average local voidage profiles were in reasonable agreement
with the experimental results. The study showed that the
computational model was capable of predicting the heat transfer and
the hydrodynamic behavior of gas-solid fluidized bed flows with
reasonable accuracy.
Abstract: 53 college students answered questions regarding the circumstances in which they first heard about the news of Wenchuan earthquake or the news of their acceptance to college which took place approximately one year ago, and answered again two years later. The number of details recalled about their circumstances for both events was high and didn-t decline two years later. However, consistency in reported details over two years was low. Participants were more likely to construct central (e.g., Where were you?) than peripheral information (What were you wearing?), and the confidence of the central information was higher than peripheral information, which indicated that they constructed more when they were more confident.
Abstract: The objectives of this research paper were to study the
influencing factors that contributed to the success of electronic
commerce (e-commerce) and to study the approach to enhance the
standard of e-commerce for small and medium enterprises (SME).
The research paper focused the study on only sole proprietorship
SMEs in Bangkok, Thailand. The factors contributed to the success
of SME included business management, learning in the organization,
business collaboration, and the quality of website. A quantitative and
qualitative mixed research methodology was used. In terms of
quantitative method, a questionnaire was used to collect data from
251 sole proprietorships. The System Equation Model (SEM) was
utilized as the tool for data analysis. In terms of qualitative method,
an in-depth interview, a dialogue with experts in the field of ecommerce
for SMEs, and content analysis were used.
By using the adjusted causal relationship structure model, it was
revealed that the factors affecting the success of e-commerce for
SMEs were found to be congruent with the empirical data. The
hypothesis testing indicated that business management influenced the
learning in the organization, the learning in the organization
influenced business collaboration and the quality of the website, and
these factors, in turn, influenced the success of SMEs. Moreover, the
approach to enhance the standard of SMEs revealed that the majority
of respondents wanted to enhance the standard of SMEs to a high
level in the category of safety of e-commerce system, basic structure
of e-commerce, development of staff potentials, assistance of budget
and tax reduction, and law improvement regarding the e-commerce
respectively.
Abstract: This research paper deals with the implementation of face recognition using neural network (recognition classifier) on low-resolution images. The proposed system contains two parts, preprocessing and face classification. The preprocessing part converts original images into blurry image using average filter and equalizes the histogram of those image (lighting normalization). The bi-cubic interpolation function is applied onto equalized image to get resized image. The resized image is actually low-resolution image providing faster processing for training and testing. The preprocessed image becomes the input to neural network classifier, which uses back-propagation algorithm to recognize the familiar faces. The crux of proposed algorithm is its beauty to use single neural network as classifier, which produces straightforward approach towards face recognition. The single neural network consists of three layers with Log sigmoid, Hyperbolic tangent sigmoid and Linear transfer function respectively. The training function, which is incorporated in our work, is Gradient descent with momentum (adaptive learning rate) back propagation. The proposed algorithm was trained on ORL (Olivetti Research Laboratory) database with 5 training images. The empirical results provide the accuracy of 94.50%, 93.00% and 90.25% for 20, 30 and 40 subjects respectively, with time delay of 0.0934 sec per image.
Abstract: X-ray mammography is the most effective method for
the early detection of breast diseases. However, the typical diagnostic
signs such as microcalcifications and masses are difficult to detect
because mammograms are of low-contrast and noisy. In this paper, a
new algorithm for image denoising and enhancement in Orthogonal
Polynomials Transformation (OPT) is proposed for radiologists to
screen mammograms. In this method, a set of OPT edge coefficients
are scaled to a new set by a scale factor called OPT scale factor. The
new set of coefficients is then inverse transformed resulting in
contrast improved image. Applications of the proposed method to
mammograms with subtle lesions are shown. To validate the
effectiveness of the proposed method, we compare the results to
those obtained by the Histogram Equalization (HE) and the Unsharp
Masking (UM) methods. Our preliminary results strongly suggest
that the proposed method offers considerably improved enhancement
capability over the HE and UM methods.
Abstract: With the explosive growth of information sources available on the World Wide Web, it has become increasingly difficult to identify the relevant pieces of information, since web pages are often cluttered with irrelevant content like advertisements, navigation-panels, copyright notices etc., surrounding the main content of the web page. Hence, tools for the mining of data regions, data records and data items need to be developed in order to provide value-added services. Currently available automatic techniques to mine data regions from web pages are still unsatisfactory because of their poor performance and tag-dependence. In this paper a novel method to extract data items from the web pages automatically is proposed. It comprises of two steps: (1) Identification and Extraction of the data regions based on visual clues information. (2) Identification of data records and extraction of data items from a data region. For step1, a novel and more effective method is proposed based on visual clues, which finds the data regions formed by all types of tags using visual clues. For step2 a more effective method namely, Extraction of Data Items from web Pages (EDIP), is adopted to mine data items. The EDIP technique is a list-based approach in which the list is a linear data structure. The proposed technique is able to mine the non-contiguous data records and can correctly identify data regions, irrespective of the type of tag in which it is bound. Our experimental results show that the proposed technique performs better than the existing techniques.
Abstract: Over the years, many implementations have been
proposed for solving IA networks. These implementations are
concerned with finding a solution efficiently. The primary goal of
our implementation is simplicity and ease of use.
We present an IA network implementation based on finite domain
non-binary CSPs, and constraint logic programming. The
implementation has a GUI which permits the drawing of arbitrary IA
networks. We then show how the implementation can be extended to
find all the solutions to an IA network. One application of finding all
the solutions, is solving probabilistic IA networks.
Abstract: Interactions among proteins are the basis of various
life events. So, it is important to recognize and research protein
interaction sites. A control set that contains 149 protein molecules
were used here. Then 10 features were extracted and 4 sample sets
that contained 9 sliding windows were made according to features.
These 4 sample sets were calculated by Radial Basis Functional neutral
networks which were optimized by Particle Swarm Optimization
respectively. Then 4 groups of results were obtained. Finally, these 4
groups of results were integrated by decision fusion (DF) and Genetic
Algorithm based Selected Ensemble (GASEN). A better accuracy was
got by DF and GASEN. So, the integrated methods were proved to
be effective.