Abstract: the work contains the results of complex investigation
related to the evaluation of condition of working blades of gas turbine
engines during fatigue tests by applying the acoustic emission
method. It demonstrates the possibility of estimating the fatigue
damage of blades in the process of factory tests. The acoustic
emission criteria for detecting and testing the kinetics of fatigue crack
distribution were detected. It also shows the high effectiveness of the
method for non-destructive testing of condition of solid and cooled
working blades for high-temperature gas turbine engines.
Abstract: Car failure detection is a complicated process and
requires high level of expertise. Any attempt of developing an expert
system dealing with car failure detection has to overcome various
difficulties. This paper describes a proposed knowledge-based
system for car failure detection. The paper explains the need for an
expert system and the some issues on developing knowledge-based
systems, the car failure detection process and the difficulties involved
in developing the system. The system structure and its components
and their functions are described. The system has about 150 rules for
different types of failures and causes. It can detect over 100 types of
failures. The system has been tested and gave promising results.
Abstract: The emergence of mobile application services and App
Store has led to the explosive growth of user innovation, which users
voluntarily contribute to. User innovation communities where end
users freely reveal innovative ideas and needs with other community
members are becoming increasingly influential in this area. However,
user-s ideas in user innovation community are not enough to be new
service opportunity, because some of them can already developed as
existing services in App Store. Moreover, the existing services similar
to new service opportunity can be significant references to apply
analogy to develop service concept. In response, this research
proposes Case-Based Reasoning approach to matching the user needs
and existing services, identifying unmet opportunistic user needs, and
retrieving similar services with opportunity. Due to its intuitive and
transparent algorithm, users related to App Store innovation
communities can easily employ Case-Based Reasoning based
approach to their innovation.
Abstract: The hand is one of the essential parts of the body for
carrying out Activities of Daily Living (ADLs). Individuals use their
hands and fingers in everyday activities in the both the workplace
and home. Hand-intensive tasks require diverse and sometimes
extreme levels of exertion, depending on the action, movement or
manipulation involved. The authors have undertaken several studies
looking at grip choice and comfort. It is hoped that in providing
improved understanding of discomfort during ADLs this will aid in
the design of consumer products.
Previous work by the authors outlined a methodology for
calculating pain frequency and pain level for a range of tasks. From
an online survey undertaken by the authors with regards
manipulating objects during everyday tasks, tasks involving
gripping were seen to produce the highest levels of pain and
discomfort. Questioning of the participants showed that cleaning
tasks were seen to be ADL's that produced the highest levels of
discomfort, with women feeling higher levels of discomfort than
men.
This paper looks at the methodology for calculating pain
frequency and pain level with particular regards to gripping
activities. This methodology shows that activities such as mopping,
sweeping and hoovering shows the highest numbers of pain
frequency and pain level at 3112.5 frequency per month while the
pain level per person doing this action was 0.78.The study then uses
thin-film force sensors to analyze the force distribution in the hand
whilst hoovering and compares this for differing grip styles and
genders. Women were seen to have more of their hand under a
higher pressure than men when undertaking hoovering. This
suggests that women may feel greater discomfort than men since
their hand is at a higher pressure more of the time.
Abstract: Subsurface erosion in river banks and its details, in
spite of its occurrence in various parts of the world has rarely been
paid attention by researchers. In this paper, quantitative concept of
the subsurface bank erosion has been investigated for vertical banks.
Vertical banks were simulated experimentally by considering a sandy
erodible layer overlaid by clayey one under uniformly distributed
constant overhead pressure. Results of the experiments are indicated
that rate of sandy layer erosion is decreased by an increase in
overburden; likewise, substituting 20% of coarse (3.5 mm) sand layer
bed material by fine material (1.4 mm) may lead to a decrease in
erosion rate by one-third. This signifies the importance of the bed
material composition effect on sandy layers erosion due to subsurface
erosion in river banks.
Abstract: This paper describes a system-level SoC energy
consumption estimation method based on a dynamic behavior of
embedded software in the early stages of the SoC development. A
major problem of SOC development is development rework caused by
unreliable energy consumption estimation at the early stages. The
energy consumption of an SoC used in embedded systems is strongly
affected by the dynamic behavior of the software. At the early stages
of SoC development, modeling with a high level of abstraction is
required for both the dynamic behavior of the software, and the
behavior of the SoC. We estimate the energy consumption by a UML
model-based simulation. The proposed method is applied for an actual
embedded system in an MFP. The energy consumption estimation of
the SoC is more accurate than conventional methods and this proposed
method is promising to reduce the chance of development rework in
the SoC development. ∈
Abstract: Information on weed distribution within the field is necessary to implement spatially variable herbicide application. Since hand labor is costly, an automated weed control system could be feasible. This paper deals with the development of an algorithm for real time specific weed recognition system based on Histogram Maxima with threshold of an image that is used for the weed classification. This algorithm is specifically developed to classify images into broad and narrow class for real-time selective herbicide application. The developed system has been tested on weeds in the lab, which have shown that the system to be very effectiveness in weed identification. Further the results show a very reliable performance on images of weeds taken under varying field conditions. The analysis of the results shows over 95 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds.
Abstract: Groups where the discrete logarithm problem (DLP) is believed to be intractable have proved to be inestimable building blocks for cryptographic applications. They are at the heart of numerous protocols such as key agreements, public-key cryptosystems, digital signatures, identification schemes, publicly verifiable secret sharings, hash functions and bit commitments. The search for new groups with intractable DLP is therefore of great importance.The goal of this article is to study elliptic curves over the ring Fq[], with Fq a finite field of order q and with the relation n = 0, n ≥ 3. The motivation for this work came from the observation that several practical discrete logarithm-based cryptosystems, such as ElGamal, the Elliptic Curve Cryptosystems . In a first time, we describe these curves defined over a ring. Then, we study the algorithmic properties by proposing effective implementations for representing the elements and the group law. In anther article we study their cryptographic properties, an attack of the elliptic discrete logarithm problem, a new cryptosystem over these curves.
Abstract: The term Enterprise 2.0 (E2.0) describes a collection of organizational and IT practices that help organizations establish flexible work models, visible knowledge-sharing practices, and higher levels of community participation. E2.0 parallels and builds on another term commonly being used in the industry – Web 2.0. E2.0 represents also new packaging for strategic collaboration and Knowledge Management (KM). Organizations rely on collaboration and KM initiatives to attain innovation, growth, productivity, and performance goals.
Abstract: One of the determinants of a firm-s prosperity is the
customers- perceived service quality and satisfaction. While service
quality is wide in scope, and consists of various dimensions, there
may be differences in the relative importance of these dimensions in
affecting customers- overall satisfaction of service quality.
Identifying the relative rank of different dimensions of service quality
is very important in that it can help managers to find out which
service dimensions have a greater effect on customers- overall
satisfaction. Such an insight will consequently lead to more effective
resource allocation which will finally end in higher levels of
customer satisfaction. This issue –despite its criticality- has not
received enough attention so far. Therefore, using a sample of 240
bank customers in Iran, an artificial neural network is developed to
address this gap in the literature. As customers- evaluation of service
quality is a subjective process, artificial neural networks –as a brain
metaphor- may appear to have a potentiality to model such a
complicated process. Proposing a neural network which is able to
predict the customers- overall satisfaction of service quality with a
promising level of accuracy is the first contribution of this study. In
addition, prioritizing the service quality dimensions in affecting
customers- overall satisfaction –by using sensitivity analysis of
neural network- is the second important finding of this paper.
Abstract: This study sought to determine whether there were relationships existed among leisure satisfaction, self-esteem, and spiritual wellness. Four hundred survey instruments were distributed, and 334 effective instruments were returned, for an effective rate of 83.5%. The participants were recruited from a purposive sampling that subjects were at least 60 years of age and retired in Tainan City, Taiwan. Three instruments were used in this research: Leisure Satisfaction Scale (LSS), Self-Esteem Scale (SES), and Spirituality Assessment Scale (SAS). The collected data were analyzed statistically. The findings of this research were as follows: 1. There is significantly correlated between leisure satisfaction and spiritual wellness. 2. There is significantly correlated between leisure satisfaction and self-esteem. 3. There is significantly correlated between spiritual wellness and self-esteem.
Abstract: Intravitreal injection (IVI) is the most common treatment for eye posterior segment diseases such as endopthalmitis, retinitis, age-related macular degeneration, diabetic retinopathy, uveitis, and retinal detachment. Most of the drugs used to treat vitreoretinal diseases, have a narrow concentration range in which they are effective, and may be toxic at higher concentrations. Therefore, it is critical to know the drug distribution within the eye following intravitreal injection. Having knowledge of drug distribution, ophthalmologists can decide on drug injection frequency while minimizing damage to tissues. The goal of this study was to develop a computer model to predict intraocular concentrations and pharmacokinetics of intravitreally injected drugs. A finite volume model was created to predict distribution of two drugs with different physiochemical properties in the rabbit eye. The model parameters were obtained from literature review. To validate this numeric model, the in vivo data of spatial concentration profile from the lens to the retina were compared with the numeric data. The difference was less than 5% between the numerical and experimental data. This validation provides strong support for the numerical methodology and associated assumptions of the current study.
Abstract: Plasmodium vivax malaria differs from P. falciparum malaria in that a person suffering from P. vivax infection can suffer relapses of the disease. This is due the parasite being able to remain dormant in the liver of the patients where it is able to re-infect the patient after a passage of time. During this stage, the patient is classified as being in the dormant class. The model to describe the transmission of P. vivax malaria consists of a human population divided into four classes, the susceptible, the infected, the dormant and the recovered. The effect of a time delay on the transmission of this disease is studied. The time delay is the period in which the P. vivax parasite develops inside the mosquito (vector) before the vector becomes infectious (i.e., pass on the infection). We analyze our model by using standard dynamic modeling method. Two stable equilibrium states, a disease free state E0 and an endemic state E1, are found to be possible. It is found that the E0 state is stable when a newly defined basic reproduction number G is less than one. If G is greater than one the endemic state E1 is stable. The conditions for the endemic equilibrium state E1 to be a stable spiral node are established. For realistic values of the parameters in the model, it is found that solutions in phase space are trajectories spiraling into the endemic state. It is shown that the limit cycle and chaotic behaviors can only be achieved with unrealistic parameter values.
Abstract: We propose a fast and robust hierarchical face detection system which finds and localizes face images with a cascade of classifiers. Three modules contribute to the efficiency of our detector. First, heterogeneous feature descriptors are exploited to enrich feature types and feature numbers for face representation. Second, a PSO-Adaboost algorithm is proposed to efficiently select discriminative features from a large pool of available features and reinforce them into the final ensemble classifier. Compared with the standard exhaustive Adaboost for feature selection, the new PSOAdaboost algorithm reduces the training time up to 20 times. Finally, a three-stage hierarchical classifier framework is developed for rapid background removal. In particular, candidate face regions are detected more quickly by using a large size window in the first stage. Nonlinear SVM classifiers are used instead of decision stump functions in the last stage to remove those remaining complex nonface patterns that can not be rejected in the previous two stages. Experimental results show our detector achieves superior performance on the CMU+MIT frontal face dataset.
Abstract: The purpose of this paper is to examine the current
state of corporate social responsibility statements on corporate
websites of Malaysian and Singaporean corporations and analyze
how the CSR statements contribute in building a unique corporate
identity of corporations. Content analysis is employed to examine the
websites of Malaysian and Singaporean consumer corporations. It is
believed that generally most companies tend to publish and
communicate their CSR statements visibly to general stakeholders.
However, there is a significantly different outcome of the articulation
of CSR on practices on websites between Malaysian and Singaporean
consumer corporations. A number of Singaporean organizations were
found less concerned with CSR practices as compared to Malaysian
organizations. The findings indicate a need for corporations in
Malaysia and Singapore to orchestrate their core competence of CSR
activities in order to develop a unique corporate identity in a global
business environment.
Abstract: A bond graph model of a hydroelectric plant is
proposed. In order to analyze the system some structural properties
of a bond graph are used. The structural controllability of
the hydroelctric plant is described. Also, the steady state of the
state variables applying the bond graph in a derivative causality
assignment is obtained. Finally, simulation results of the system
are shown.
Abstract: Public health surveillance system focuses on outbreak detection and data sources used. Variation or aberration in the frequency distribution of health data, compared to historical data is often used to detect outbreaks. It is important that new techniques be developed to improve the detection rate, thereby reducing wastage of resources in public health. Thus, the objective is to developed technique by applying frequent mining and outlier mining techniques in outbreak detection. 14 datasets from the UCI were tested on the proposed technique. The performance of the effectiveness for each technique was measured by t-test. The overall performance shows that DTK can be used to detect outlier within frequent dataset. In conclusion the outbreak detection technique using anomaly-based on frequent-outlier technique can be used to identify the outlier within frequent dataset.
Abstract: The fundamental defect inherent to the thermoforming
technology is wall-thickness variation of the products due to
inadequate thermal processing during production of polymer. A
nonlinear viscoelastic rheological model is implemented for
developing the process model. This model describes deformation
process of a sheet in thermoforming process. Because of relaxation
pause after plug-assist stage and also implementation of two stage
thermoforming process have minor wall-thickness variation and
consequently better mechanical properties of polymeric articles. For
model validation, a comparative analysis of the theoretical and
experimental data is presented.
Abstract: Color constancy algorithms are generally based on the
simplified assumption about the spectral distribution or the reflection
attributes of the scene surface. However, in reality, these assumptions
are too restrictive. The methodology is proposed to extend existing
algorithm to applying color constancy locally to image patches rather
than globally to the entire images.
In this paper, a method based on low-level image features using
superpixels is proposed. Superpixel segmentation partition an image
into regions that are approximately uniform in size and shape. Instead
of using entire pixel set for estimating the illuminant, only superpixels
with the most valuable information are used. Based on large scale
experiments on real-world scenes, it can be derived that the estimation
is more accurate using superpixels than when using the entire image.
Abstract: Bone remodeling occurs by the balanced action of
bone resorbing osteoclasts (OC) and bone-building osteoblasts.
Increased bone resorption by excessive OC activity contributes
to malignant and non-malignant diseases including osteoporosis.
To study OC differentiation and function, OC formed in
in vitro cultures are currently counted manually, a tedious
procedure which is prone to inter-observer differences. Aiming
for an automated OC-quantification system, classification of
OC and precursor cells was done on fluorescence microscope
images based on the distinct appearance of fluorescent nuclei.
Following ellipse fitting to nuclei, a combination of eight
features enabled clustering of OC and precursor cell nuclei.
After evaluating different machine-learning techniques, LOGREG
achieved 74% correctly classified OC and precursor cell
nuclei, outperforming human experts (best expert: 55%). In
combination with the automated detection of total cell areas,
this system allows to measure various cell parameters and most
importantly to quantify proteins involved in osteoclastogenesis.