Abstract: The purpose of this paper is to propose a framework for constructing correct parallel processing programs based on Equivalent Transformation Framework (ETF). ETF regards computation as In the framework, a problem-s domain knowledge and a query are described in definite clauses, and computation is regarded as transformation of the definite clauses. Its meaning is defined by a model of the set of definite clauses, and the transformation rules generated must preserve meaning. We have proposed a parallel processing method based on “specialization", a part of operation in the transformations, which resembles substitution in logic programming. The method requires “Memo-tree", a history of specialization to maintain correctness. In this paper we proposes the new method for the specialization-base parallel processing without Memo-tree.
Abstract: This paper maps the structure of the social network of
the 2011 class ofsixty graduate students of the Masters of Science
(Knowledge Management) programme at the Nanyang Technological
University, based on their friending relationships on Facebook. To
ensure anonymity, actual names were not used. Instead, they were
replaced with codes constructed from their gender, nationality, mode
of study, year of enrollment and a unique number. The relationships
between friends within the class, and among the seniors and alumni
of the programme wereplotted. UCINet and Pajek were used to plot
the sociogram, to compute the density, inclusivity, and degree,
global, betweenness, and Bonacich centralities, to partition the
students into two groups, namely, active and peripheral, and to
identify the cut-points. Homophily was investigated, and it was
observed for nationality and study mode. The groups students formed
on Facebook were also studied, and of fifteen groups, eight were
classified as dead, which we defined as those that have been inactive
for over two months.
Abstract: The main aim of this study is to identify the most
influential variables that cause defects on the items produced by a
casting company located in Turkey. To this end, one of the items
produced by the company with high defective percentage rates is
selected. Two approaches-the regression analysis and decision treesare
used to model the relationship between process parameters and
defect types. Although logistic regression models failed, decision tree
model gives meaningful results. Based on these results, it can be
claimed that the decision tree approach is a promising technique for
determining the most important process variables.
Abstract: Hand gesture is one of the typical methods used in
sign language for non-verbal communication. It is most commonly
used by people who have hearing or speech problems to
communicate among themselves or with normal people. Various sign
language systems have been developed by manufacturers around the
globe but they are neither flexible nor cost-effective for the end
users. This paper presents a system prototype that is able to
automatically recognize sign language to help normal people to
communicate more effectively with the hearing or speech impaired
people. The Sign to Voice system prototype, S2V, was developed
using Feed Forward Neural Network for two-sequence signs
detection. Different sets of universal hand gestures were captured
from video camera and utilized to train the neural network for
classification purpose. The experimental results have shown that
neural network has achieved satisfactory result for sign-to-voice
translation.
Abstract: Advances in technology (e.g. the internet,
telecommunication) and political changes (fewer trade barriers and an
enlarged European Union, ASEAN, NAFTA and other organizations)
have led to develop international competition and expand into new
markets. Companies in Thailand, Asia and around the globe are
increasingly being pressured on price and for faster time to enter the
market. At the same time, new markets are appearing and many
companies are looking for changes and shifts in their domestic
markets. These factors have enabled the rapid growth for companies
and globalizing many different business activities during the product
development process from research and development (R&D) to
production.
This research will show and clarify methods how to develop
global product. Also, it will show how important is a global product
impact into Thai Economy development.
Abstract: Sleep stage scoring is the process of classifying the
stage of the sleep in which the subject is in. Sleep is classified into
two states based on the constellation of physiological parameters.
The two states are the non-rapid eye movement (NREM) and the
rapid eye movement (REM). The NREM sleep is also classified into
four stages (1-4). These states and the state wakefulness are
distinguished from each other based on the brain activity. In this
work, a classification method for automated sleep stage scoring
based on a single EEG recording using wavelet packet decomposition
was implemented. Thirty two ploysomnographic recording from the
MIT-BIH database were used for training and validation of the
proposed method. A single EEG recording was extracted and
smoothed using Savitzky-Golay filter. Wavelet packets
decomposition up to the fourth level based on 20th order Daubechies
filter was used to extract features from the EEG signal. A features
vector of 54 features was formed. It was reduced to a size of 25 using
the gain ratio method and fed into a classifier of regression trees. The
regression trees were trained using 67% of the records available. The
records for training were selected based on cross validation of the
records. The remaining of the records was used for testing the
classifier. The overall correct rate of the proposed method was found
to be around 75%, which is acceptable compared to the techniques in
the literature.
Abstract: In power systems, protective relays must filter their
inputs to remove undesirable quantities and retain signal quantities of
interest. This job must be performed accurate and fast. A new
method for filtering the undesirable components such as DC and
harmonic components associated with the fundamental system
signals. The method is s based on a dynamic filtering algorithm. The
filtering algorithm has many advantages over some other classical
methods. It can be used as dynamic on-line filter without the need of
parameters readjusting as in the case of classic filters. The proposed
filter is tested using different signals. Effects of number of samples
and sampling window size are discussed. Results obtained are
presented and discussed to show the algorithm capabilities.
Abstract: Information and communication service providers
(ICSP) that are significant in size and provide Internet-based services
take administrative, technical, and physical protection measures via
the information security check service (ISCS). These protection
measures are the minimum action necessary to secure the stability and
continuity of the information and communication services (ICS) that
they provide. Thus, information assets are essential to providing ICS,
and deciding the relative importance of target assets for protection is a
critical procedure. The risk analysis model designed to decide the
relative importance of information assets, which is described in this
study, evaluates information assets from many angles, in order to
choose which ones should be given priority when it comes to
protection. Many-sided risk analysis (MSRS) grades the importance of
information assets, based on evaluation of major security check items,
evaluation of the dependency on the information and communication
facility (ICF) and influence on potential incidents, and evaluation of
major items according to their service classification, in order to
identify the ISCS target. MSRS could be an efficient risk analysis
model to help ICSPs to identify their core information assets and take
information protection measures first, so that stability of the ICS can
be ensured.
Abstract: Synthetic juice clarification was done through spiral
wound ultrafiltration (UF) membrane module. Synthetic juice was
clarified at two different operating conditions, such as, with and
without permeates recycle at turbulent flow regime. The performance
of spiral wound ultrafiltration membrane was analyzed during
clarification of synthetic juice. Synthetic juice was the mixture of
deionized water, sucrose and pectin molecule. The operating
conditions are: feed flowrate of 10 lpm, pressure drop of 413.7 kPa
and Reynolds no of 5000. Permeate sample was analyzed in terms of
volume reduction factor (VRF), viscosity (Pa.s), ⁰Brix, TDS (mg/l),
electrical conductivity (μS) and turbidity (NTU). It was observe that
the permeate flux declined with operating time for both conditions of
with and without permeate recycle due to increase of concentration
polarization and increase of gel layer on membrane surface. For
without permeate recycle, the membrane fouling rate was faster
compared to with permeate recycle. For without permeate recycle,
the VRF rose up to 5 and for with recycle permeate the VRF is 1.9.
The VRF is higher due to adsorption of solute (pectin) molecule on
membrane surface and resulting permeateflux declined with VRF.
With permeate recycle, quality was within acceptable limit. Fouled
membrane was cleaned by applying different processes (e.g.,
deionized water, SDS and EDTA solution). Membrane cleaning was
analyzed in terms of permeability recovery.
Abstract: Wavelet neural networks (WNNs) have emerged as a vital alternative to the vastly studied multilayer perceptrons (MLPs) since its first implementation. In this paper, we applied various clustering algorithms, namely, K-means (KM), Fuzzy C-means (FCM), symmetry-based K-means (SBKM), symmetry-based Fuzzy C-means (SBFCM) and modified point symmetry-based K-means (MPKM) clustering algorithms in choosing the translation parameter of a WNN. These modified WNNs are further applied to the heterogeneous cancer classification using benchmark microarray data and were compared against the conventional WNN with random initialization method. Experimental results showed that a WNN classifier with the MPKM algorithm is more precise than the conventional WNN as well as the WNNs with other clustering algorithms.
Abstract: Peer review is an activity where students review their
classmates- writing and then evaluate the content, development, unity
and organization. Studies have shown that peer review activities
benefit both the reviewer and the writer in developing their reading
and writing skills. Furthermore, peer review activities may also
enhance students- soft skills. This study was conducted to find out the
benefits of peer review activity in a technical writing class based on
engineering students- perceptions. The study also highlights how
these benefits could improve the students- soft skills. A set of
questionnaire was given to 200 undergraduate students of a technical
writing course. The results of the study indicate that the activity could
help improve their critical thinking skills, written and oral
communication skills, as well as team work. This paper further
discusses how the implications of these benefits could help enhance
students- soft skills.
Abstract: Understanding the cell's large-scale organization is an interesting task in computational biology. Thus, protein-protein interactions can reveal important organization and function of the cell. Here, we investigated the correspondence between protein interactions and function for the yeast. We obtained the correlations among the set of proteins. Then these correlations are clustered using both the hierarchical and biclustering methods. The detailed analyses of proteins in each cluster were carried out by making use of their functional annotations. As a result, we found that some functional classes appear together in almost all biclusters. On the other hand, in hierarchical clustering, the dominancy of one functional class is observed. In the light of the clustering data, we have verified some interactions which were not identified as core interactions in DIP and also, we have characterized some functionally unknown proteins according to the interaction data and functional correlation. In brief, from interaction data to function, some correlated results are noticed about the relationship between interaction and function which might give clues about the organization of the proteins, also to predict new interactions and to characterize functions of unknown proteins.
Abstract: This paper reports a new approach on identifying the
individuality of persons by using parametric classification of multiple
mental thoughts. In the approach, electroencephalogram (EEG)
signals were recorded when the subjects were thinking of one or
more (up to five) mental thoughts. Autoregressive features were
computed from these EEG signals and classified by Linear
Discriminant classifier. The results here indicate that near perfect
identification of 400 test EEG patterns from four subjects was
possible, thereby opening up a new avenue in biometrics.
Abstract: Knowledge of an organization does not merely reside
in structured form of information and data; it is also embedded in
unstructured form. The discovery of such knowledge is particularly
difficult as the characteristic is dynamic, scattered, massive and
multiplying at high speed. Conventional methods of managing
unstructured information are considered too resource demanding and
time consuming to cope with the rapid information growth.
In this paper, a Multi-faceted and Automatic Knowledge
Elicitation System (MAKES) is introduced for the purpose of
discovery and capture of organizational knowledge. A trial
implementation has been conducted in a public organization to
achieve the objective of decision capture and navigation from a
number of meeting minutes which are autonomously organized,
classified and presented in a multi-faceted taxonomy map in both
document and content level. Key concepts such as critical decision
made, key knowledge workers, knowledge flow and the relationship
among them are elicited and displayed in predefined knowledge
model and maps. Hence, the structured knowledge can be retained,
shared and reused.
Conducting Knowledge Management with MAKES reduces work
in searching and retrieving the target decision, saves a great deal of
time and manpower, and also enables an organization to keep pace
with the knowledge life cycle. This is particularly important when
the amount of unstructured information and data grows extremely
quickly. This system approach of knowledge management can
accelerate value extraction and creation cycles of organizations.
Abstract: Missing data is a persistent problem in almost all
areas of empirical research. The missing data must be treated very
carefully, as data plays a fundamental role in every analysis.
Improper treatment can distort the analysis or generate biased results.
In this paper, we compare and contrast various imputation techniques
on missing data sets and make an empirical evaluation of these
methods so as to construct quality software models. Our empirical
study is based on NASA-s two public dataset. KC4 and KC1. The
actual data sets of 125 cases and 2107 cases respectively, without
any missing values were considered. The data set is used to create
Missing at Random (MAR) data Listwise Deletion(LD), Mean
Substitution(MS), Interpolation, Regression with an error term and
Expectation-Maximization (EM) approaches were used to compare
the effects of the various techniques.
Abstract: In this work, we present an automatic vehicle detection
system for airborne videos using combined features. We propose a
pixel-wise classification method for vehicle detection using Dynamic
Bayesian Networks. In spite of performing pixel-wise classification,
relations among neighboring pixels in a region are preserved in the
feature extraction process. The main novelty of the detection scheme is
that the extracted combined features comprise not only pixel-level
information but also region-level information. Afterwards, tracking is
performed on the detected vehicles. Tracking is performed using
efficient Kalman filter with dynamic particle sampling. Experiments
were conducted on a wide variety of airborne videos. We do not
assume prior information of camera heights, orientation, and target
object sizes in the proposed framework. The results demonstrate
flexibility and good generalization abilities of the proposed method on
a challenging dataset.
Abstract: The nature, prevalence, cellular composition of
leukocyte infiltrates and immunohistochemical characteristics of
their constituent cells in the liver of patients with chronic viral
hepatitis B and C were investigated. It was found that the area of
distribution and cellular composition of infiltrates depended on the
virus type and process activity. The expediency of
immunohistochemical study using leukocyte infiltrates from liver
biopsies of patients with viral hepatitis aimed at clarifying diagnosis,
making prognosis, and choice of optimal treatment with elements of
immune correction is emphasized.
Abstract: Water quality is a subject of ongoing concern.
Deterioration of water quality has initiated serious management
efforts in many countries. This study endeavors to automatically
classify water quality. The water quality classes are evaluated using 6
factor indices. These factors are pH value (pH), Dissolved Oxygen
(DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen
(NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (TColiform).
The methodology involves applying data mining
techniques using multilayer perceptron (MLP) neural network
models. The data consisted of 11 sites of canals in Dusit district in
Bangkok, Thailand. The data is obtained from the Department of
Drainage and Sewerage Bangkok Metropolitan Administration
during 2007-2011. The results of multilayer perceptron neural
network exhibit a high accuracy multilayer perception rate at 96.52%
in classifying the water quality of Dusit district canal in Bangkok
Subsequently, this encouraging result could be applied with plan and
management source of water quality.
Abstract: The paper presents a modelling methodology for
small scale multi-source renewable energy systems. Using historical
site-specific weather data, the relationships of cost, availability and
energy form are visualised as a function of the sizing of photovoltaic
arrays, wind turbines, and battery capacity. The specific dependency
of each site on its own particular weather patterns show that unique
solutions exist for each site. It is shown that in certain cases the
capital component cost can be halved if the desired theoretical
demand availability is reduced from 100% to 99%.
Abstract: The study on the tree growth for four species groups of commercial timber in Koh Kong province, Cambodia-s tropical rainforest is described. The simulation for these four groups had been successfully developed in the 5-year interval through year-60. Data were obtained from twenty permanent sample plots in the duration of thirteen years. The aim for this study was to develop stand table simulation system of tree growth by the species group. There were five steps involved in the development of the tree growth simulation: aggregate the tree species into meaningful groups by using cluster analysis; allocate the trees in the diameter classes by the species group; observe the diameter movement of the species group. The diameter growth rate, mortality rate and recruitment rate were calculated by using some mathematical formula. Simulation equation had been created by combining those parameters. Result showed the dissimilarity of the diameter growth among species groups.