Abstract: Knowledge Discovery in Databases (KDD) has
evolved into an important and active area of research because of
theoretical challenges and practical applications associated with the
problem of discovering (or extracting) interesting and previously
unknown knowledge from very large real-world databases. Rough
Set Theory (RST) is a mathematical formalism for representing
uncertainty that can be considered an extension of the classical set
theory. It has been used in many different research areas, including
those related to inductive machine learning and reduction of
knowledge in knowledge-based systems. One important concept
related to RST is that of a rough relation. In this paper we presented
the current status of research on applying rough set theory to KDD,
which will be helpful for handle the characteristics of real-world
databases. The main aim is to show how rough set and rough set
analysis can be effectively used to extract knowledge from large
databases.
Abstract: Availability of high dimensional biological datasets such as from gene expression, proteomic, and metabolic experiments can be leveraged for the diagnosis and prognosis of diseases. Many classification methods in this area have been studied to predict disease states and separate between predefined classes such as patients with a special disease versus healthy controls. However, most of the existing research only focuses on a specific dataset. There is a lack of generic comparison between classifiers, which might provide a guideline for biologists or bioinformaticians to select the proper algorithm for new datasets. In this study, we compare the performance of popular classifiers, which are Support Vector Machine (SVM), Logistic Regression, k-Nearest Neighbor (k-NN), Naive Bayes, Decision Tree, and Random Forest based on mock datasets. We mimic common biological scenarios simulating various proportions of real discriminating biomarkers and different effect sizes thereof. The result shows that SVM performs quite stable and reaches a higher AUC compared to other methods. This may be explained due to the ability of SVM to minimize the probability of error. Moreover, Decision Tree with its good applicability for diagnosis and prognosis shows good performance in our experimental setup. Logistic Regression and Random Forest, however, strongly depend on the ratio of discriminators and perform better when having a higher number of discriminators.
Abstract: Asiatic Houbara ( Chlamydotis macqueenii ) is a
flagship and vulnerable species. In-situ conservation of this
threatened species demands for knowledge of its habitat selection.
The aim of this study was to determine habitat variables influencing
birds wintering and breeding selection in semi- arid central Iran.
Habitat features of the detected nest and pellet sites were compared
with paired and random plots by quantifying a number of habitat
variables. In wintering habitat use at micro scale houbara selected
sites where vegetation cover was significantly lower compard to
control sites( p< 0.001). Areas with low number of larger plant
species (p=0.03) that were not too close to a vegetation
patch(p
Abstract: A novel typical day prediction model have been built and validated by the measured data of a grid-connected solar photovoltaic (PV) system in Macau. Unlike conventional statistical method used by previous study on PV systems which get results by averaging nearby continuous points, the present typical day statistical method obtain the value at every minute in a typical day by averaging discontinuous points at the same minute in different days. This typical day statistical method based on discontinuous point averaging makes it possible for us to obtain the Gaussian shape dynamical distributions for solar irradiance and output power in a yearly or monthly typical day. Based on the yearly typical day statistical analysis results, the maximum possible accumulated output energy in a year with on site climate conditions and the corresponding optimal PV system running time are obtained. Periodic Gaussian shape prediction models for solar irradiance, output energy and system energy efficiency have been built and their coefficients have been determined based on the yearly, maximum and minimum monthly typical day Gaussian distribution parameters, which are obtained from iterations for minimum Root Mean Squared Deviation (RMSD). With the present model, the dynamical effects due to time difference in a day are kept and the day to day uncertainty due to weather changing are smoothed but still included. The periodic Gaussian shape correlations for solar irradiance, output power and system energy efficiency have been compared favorably with data of the PV system in Macau and proved to be an improvement than previous models.
Abstract: The recent development of humanoid robots has led robot designers to imagine a great variety of anthropomorphic forms for human-like machine. Which form is the best ? We try to answer this question from a double meaning of the anthropomorphism : a positive anthropomorphism corresponing to the realization of an effective anthropomorphic form object and a negative one corresponding to our natural tendency in certain circumstances to give human attributes to non-human beings. We postulate that any humanoid robot is concerned by both these two anthropomorphism kinds. We propose to use gestalt theory and Heider-s balance theory in order to analyze how negative anthropomorphism can influence our perception of human-like robots. From our theoretical approach we conclude that an “even shape" as defined by gestalt theory is not a sufficient condition for a good integration of future humanoid robots into a human community. Aesthetic perception of the robot cannot be splitted from a social perception : a humanoid robot, any how the efforts made for improving its appearance, could be rejected if it is devoted to a task with too high affective implications.
Abstract: In today-s global and competitive market,
manufacturing companies are working hard towards improving their
production system performance. Most companies develop production
systems that can help in cost reduction. Manufacturing systems
consist of different elements including production methods,
machines, processes, control and information systems. Human issues
are an important part of manufacturing systems, yet most companies
do not pay sufficient attention to them. In this paper, a workforce
planning (WP) model is presented. A non-linear programming model
is developed in order to minimize the hiring, firing, training and
overtime costs. The purpose is to determine the number of workers
for each worker type, the number of workers trained, and the number
of overtime hours. Moreover, a decision support system (DSS) based
on the proposed model is introduced using the Excel-Lingo software
interfacing feature. This model will help to improve the interaction
between the workers, managers and the technical systems in
manufacturing.
Abstract: Reciprocating compressors are flexible to handle wide capacity and condition swings, offer a very efficient method of compressing almost any gas mixture in wide range of pressure, can generate high head independent of density, and have numerous applications and wide power ratings. These make them vital component in various units of industrial plants. In this paper optimum reciprocating compressor configuration regarding interstage pressures, low suction pressure, non-lubricated cylinder, speed of machine, capacity control system, compressor valve, lubrication system, piston rod coating, cylinder liner material, barring device, pressure drops, rod load, pin reversal, discharge temperature, cylinder coolant system, performance, flow, coupling, special tools, condition monitoring (including vibration, thermal and rod drop monitoring), commercial points, delivery and acoustic conditions are presented.
Abstract: Since the world printing industry has to confront
globalization with a constant change, the Thai printing industry, as a
small but increasingly significant part of the world printing industry,
cannot inevitably escape but has to encounter with the similar change
and also the need to revamp its production processes, designs and
technology to make them more appealing to both international and
domestic market. The essential question is what is the Thai
competitive edge in the printing industry in changing environment?
This research is aimed to study the Thai level of competitive edge in
terms of marketing, technology, environment friendly, and the level
of satisfaction of the process of using printing machines. To access
the extent to which is the trends in competitiveness of Thai printing
industry, both quantitative and qualitative study were conducted. The
quantitative analysis was restricted to 100 respondents. The
qualitative analysis was restricted to a focus group of 10 individuals
from various backgrounds in the Thai printing industry. The findings
from the quantitative analysis revealed that the overall mean scores
are 4.53, 4.10, and 3.50 for the competitiveness of marketing, the
competitiveness of technology, and the competitiveness of being
environment friendly respectively. However, the level of satisfaction
for the process of using machines has a mean score only 3.20. The
findings from the qualitative analysis have revealed that target
customers have increasingly reordered due to their contentment in
both low prices and the acceptable quality of the products. Moreover,
the Thai printing industry has a tendency to convert to ambient green
technology which is friendly to the environment. The Thai printing
industry is choosing to produce or substitute with products that are
less damaging to the environment. It is also found that the Thai
printing industry has been transformed into a very competitive
industry which bargaining power rests on consumers who have a
variety of choices.
Abstract: Performance of millimeter-wave (mm-wave) multiband
orthogonal frequency division multiplexing (MB-OFDM) ultrawideband
(UWB) signal generation using frequency quadrupling
technique and transmission over fiber is experimentally investigated.
The frequency quadrupling is achived by using only one Mach-
Zehnder modulator (MZM) that is biased at maximum transmission
(MATB) point. At the output, a frequency quadrupling signal is
obtained then sent to a second MZM. This MZM is used for MBOFDM
UWB signal modulation. In this work, we demonstrate 30-
GHz mm-wave wireless that carries three-bands OFDM UWB
signals, and error vector magnitude (EVM) is used to analyze the
transmission quality. It is found that our proposed technique leads to
an improvement of 3.5 dB in EVM at 40% of local oscillator (LO)
modulation with comparison to the technique using two cascaded
MZMs biased at minimum transmission (MITB) point.
Abstract: Extensive use of the Internet coupled with the
marvelous growth in e-commerce and m-commerce has created a
huge demand for information security. The Secure Socket Layer
(SSL) protocol is the most widely used security protocol in the
Internet which meets this demand. It provides protection against
eaves droppings, tampering and forgery. The cryptographic
algorithms RC4 and HMAC have been in use for achieving security
services like confidentiality and authentication in the SSL. But recent
attacks against RC4 and HMAC have raised questions in the
confidence on these algorithms. Hence two novel cryptographic
algorithms MAJE4 and MACJER-320 have been proposed as
substitutes for them. The focus of this work is to demonstrate the
performance of these new algorithms and suggest them as dependable
alternatives to satisfy the need of security services in SSL. The
performance evaluation has been done by using practical
implementation method.
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: 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.
Abstract: The present paper proposes high performance nonlinear
force controllers for a servopneumatic real-time fatigue test
machine. A CompactRIO® controller was used, being fully
programmed using LabVIEW language. Fuzzy logic control
algorithms were evaluated to tune the integral and derivative
components in the development of hybrid controllers, namely a FLC
P and a hybrid FLC PID real-time-based controllers. Their
behaviours were described by using state diagrams. The main
contribution is to ensure a smooth transition between control states,
avoiding discrete transitions in controller outputs. Steady-state errors
lower than 1.5 N were reached, without retuning the controllers.
Good results were also obtained for sinusoidal tracking tasks from
1/¤Ç to 8/¤Ç Hz.
Abstract: Actual load, material characteristics and other
quantities often differ from the design values. This can cause worse
function, shorter life or failure of a civil engineering structure, a
machine, vehicle or another appliance. The paper shows main causes
of the uncertainties and deviations and presents a systematic
approach and efficient tools for their elimination or mitigation of
consequences. Emphasis is put on the design stage, which is most
important for reliability ensuring. Principles of robust design and
important tools are explained, including FMEA, sensitivity analysis
and probabilistic simulation methods. The lifetime prediction of
long-life objects can be improved by long-term monitoring of the
load response and damage accumulation in operation. The condition
evaluation of engineering structures, such as bridges, is often based
on visual inspection and verbal description. Here, methods based on
fuzzy logic can reduce the subjective influences.
Abstract: Integration of system process information obtained
through an image processing system with an evolving knowledge
database to improve the accuracy and predictability of wear debris
analysis is the main focus of the paper. The objective is to automate
intelligently the analysis process of wear particle using classification
via self-organizing maps. This is achieved using relationship
measurements among corresponding attributes of various
measurements for wear debris. Finally, visualization technique is
proposed that helps the viewer in understanding and utilizing these
relationships that enable accurate diagnostics.
Abstract: Despite the fact that B2c eCommerce has become
important in numerous economies, its adoption varies from country to
country. This paper aims to identify the factors affecting (enabling or
inhibiting) B2c eCommerce and to determine their quantitative
impact on the diffusion of online sales across countries. A dynamic
panel model analyzing the relationship between 13 factors
(Macroeconomic, Demographic, Socio-Cultural, Infrastructural and
Offer related) stemming from a complete literature analysis and the
B2c eCommerce value in 45 countries over 9 years has been
developed. Having a positive correlation coefficient, GDP, mobile
penetration, Internet user penetration and credit card penetration
resulted as enabling drivers of the B2c eCommerce value across
countries, whereas, having a negative correlation coefficient,equal
distribution of income and the development of traditional retailing
network act as inhibiting factors.
Abstract: In this paper an attempt has been made to correlate the usefulness of electrodes made through powder metallurgy (PM) in comparison with conventional copper electrode during electric discharge machining. Experimental results are presented on electric discharge machining of AISI D2 steel in kerosene with copper tungsten (30% Cu and 70% W) tool electrode made through powder metallurgy (PM) technique and Cu electrode. An L18 (21 37) orthogonal array of Taguchi methodology was used to identify the effect of process input factors (viz. current, duty cycle and flushing pressure) on the output factors {viz. material removal rate (MRR) and surface roughness (SR)}. It was found that CuW electrode (made through PM) gives high surface finish where as the Cu electrode is better for higher material removal rate.
Abstract: The paper makes part from a complex research project
on Romanian Grey Steppe, a unique breed in terms of biological and
cultural-historical importance, on the verge of extinction and which
has been included in a preservation programme of genetic resources
from Romania. The study of genetic polymorphism of protean
fractions, especially kappa-casein, and the genotype relations of
these lactoproteins with some quantitative and qualitative features of
milk yield represents a current theme and a novelty for this breed. In
the estimation of the genetic parameters we used R.E.M.L.
(Restricted Maximum Likelihood) method.
The main lactoprotein from milk, kappa - casein (K-cz),
characterized in the specialized literature as a feature having a high
degree of hereditary transmission, behaves as such in the nucleus under
study, a value also confirmed by the heritability coefficient (h2 = 0.57
%). We must mention the medium values for milk and fat quantity
(h2=0.26, 0.29 %) and the fat and protein percentage from milk
having a high hereditary influence h2 = 0.71 - 0.63 %.
Correlations between kappa-casein and the milk quantity are
negative and strong. Between kappa-casein and other qualitative
features of milk (fat content 0.58-0.67 % and protein content 0.77-
0.87%), there are positive and very strong correlations. At the same
time, between kappa-casein and β casein (β-cz), β lactoglobulin (β-
lg) respectively, correlations are positive having high values (0.37 –
0.45 %), indicating the same causes and determining factors for the
two groups of features.