Abstract: The inherent flexibilities of XML in both structure
and semantics makes mining from XML data a complex task with
more challenges compared to traditional association rule mining in
relational databases. In this paper, we propose a new model for the
effective extraction of generalized association rules form a XML
document collection. We directly use frequent subtree mining
techniques in the discovery process and do not ignore the tree
structure of data in the final rules. The frequent subtrees based on the
user provided support are split to complement subtrees to form the
rules. We explain our model within multi-steps from data preparation
to rule generation.
Abstract: The purpose of this paper is to analyze the case of the
U.S. Pivot and to suggest an appropriate model including entry
strategies and success factors for QPS of Cable TV. The
telecommunication companies have been operating QPS including
IPTV service, which enables them to cross over broadcasting areas.
Due to this circumstance, the Cable TV operators are now concerned
and are planning to add QPS with the mobile service. Based on the
Porter's five forces model, an analytical framework has been proposed
to MVNO in Cable TV industry in the United States. As a result of this
study, MVNO in Cable TV industry has to have a clear killer
application with their sufficient contents. Subsequently, the direction
of the future Cable TV industry is proposed.
Abstract: Single nucleotide polymorphisms (SNPs) hold much promise as a basis for disease-gene association. However, research is limited by the cost of genotyping the tremendous number of SNPs. Therefore, it is important to identify a small subset of informative SNPs, the so-called tag SNPs. This subset consists of selected SNPs of the genotypes, and accurately represents the rest of the SNPs. Furthermore, an effective evaluation method is needed to evaluate prediction accuracy of a set of tag SNPs. In this paper, a genetic algorithm (GA) is applied to tag SNP problems, and the K-nearest neighbor (K-NN) serves as a prediction method of tag SNP selection. The experimental data used was taken from the HapMap project; it consists of genotype data rather than haplotype data. The proposed method consistently identified tag SNPs with considerably better prediction accuracy than methods from the literature. At the same time, the number of tag SNPs identified was smaller than the number of tag SNPs in the other methods. The run time of the proposed method was much shorter than the run time of the SVM/STSA method when the same accuracy was reached.
Abstract: The electrolyte stirring method of anodization etching
process for manufacturing porous silicon (PS) is reported in this work.
Two experimental setups of nature air stirring (PS-ASM) and
electrolyte stirring (PS-ESM) are employed to clarify the influence of
stirring mechanisms on electrochemical etching process. Compared to
traditional fabrication without any stirring apparatus (PS-TM), a large
plateau region of PS surface structure is obtained from samples with
both stirring methods by the 3D-profiler measurement. Moreover, the
light emission response is also improved by both proposed electrolyte
stirring methods due to the cycling force in electrolyte could
effectively enhance etch-carrier distribution while the electrochemical
etching process is made. According to the analysis of statistical
calculation of photoluminescence (PL) intensity, lower standard
deviations are obtained from PS-samples with studied stirring methods,
i.e. the uniformity of PL-intensity is effectively improved. The
calculated deviations of PL-intensity are 93.2, 74.5 and 64,
respectively, for PS-TM, PS-ASM and PS-ESM.
Abstract: This paper presents an intelligent speed control
system based on fuzzy logic for a voltage source PWM inverter-fed
indirect vector controlled induction motor drive. Traditional indirect
vector control system of induction motor introduces conventional PI
regulator in outer speed loop; it is proved that the low precision of the
speed regulator debases the performance of the whole system. To
overcome this problem, replacement of PI controller by an intelligent
controller based on fuzzy set theory is proposed. The performance of
the intelligent controller has been investigated through digital
simulation using MATLAB-SIMULINK package for different
operating conditions such as sudden change in reference speed and
load torque. The simulation results demonstrate that the performance
of the proposed controller is better than that of the conventional PI
controller.
Abstract: The aim of this study is to discuss the relationship between tourist awareness of environmental issues and their own recreational behaviors in the Taipei Guandu Wetland. A total of 392 questionnaires were gathered for data analysis using descriptive statistics, t-testing, one-way analysis of variance (ANOVA) and least significant difference (LSD) post hoc comparisons. The results showed that most of the visitors there enjoying the beautiful scenery are 21 to 30 years old with a college education. The means and standard deviations indicate that tourists express a positive degree of cognition of environmental issues and recreational behaviors. They suggest that polluting the environment is harmful to the natural ecosystem and that the natural resources of ecotourism are fragile, as well as expressing a high degree of recognition of the need to protect wetlands. Most of respondents are cognizant of the regulations proposed by the Guandu Wetland administration which asks that users exercise self-control and follow recommended guidelines when traveling the wetland. There were significant differences in the degree of cognition related to the variables of age, number of visits and reasons for visiting. We found that most respondents with relatively high levels of education would like to learn more about the wetland and are supportive of its conservation.
Abstract: In this paper, a novel deinterlacing algorithm is
proposed. The proposed algorithm approximates the distribution of the
luminance into a polynomial function. Instead of using one
polynomial function for all pixels, different polynomial functions are
used for the uniform, texture, and directional edge regions. The
function coefficients for each region are computed by matrix
multiplications. Experimental results demonstrate that the proposed
method performs better than the conventional algorithms.
Abstract: We address the problem of joint beamforming and multipath channel parameters estimation in Wideband Code Division Multiple Access (WCDMA) communication systems that employ Multiple-Access Interference (MAI) suppression techniques in the uplink (from mobile to base station). Most of the existing schemes rely on time multiplex a training sequence with the user data. In WCDMA, the channel parameters can also be estimated from a code multiplexed common pilot channel (CPICH) that could be corrupted by strong interference resulting in a bad estimate. In this paper, we present new methods to combine interference suppression together with channel estimation when using multiple receiving antennas by using adaptive signal processing techniques. Computer simulation is used to compare between the proposed methods and the existing conventional estimation techniques.
Abstract: Supplementation of palm vitamin E has been reported
to prevent loss of bone density in ovariectomised female rats. The
mechanism by which palm vitamin E exerts these effects is still
unknown. We hypothesized that palm vitamin E may act by
preventing the protein expression changes. Two dimensional poly
acyrilamide gel electrophoresis (2-D PAGE) and PD Quest software
genomic solutions Investigator (proteomics) was used to analyze the
differential protein expression profile in femoral and humeri bones
harvested from three groups of rats; sham-operated rats (SO),
ovariectomised rats (Ovx) and ovariectomised rats supplemented for
2 months with palm vitamin E. The results showed that there were
over 300 valued spot on each of the groups PVE and OVX as
compared to about 200 in SO. Comparison between the differential
protein expression between OVX and PVE groups showed that ten
spots were down –regulated in OVX but up-regulated in PVE. The
ten differential spots were separately named P1-P10. The
identification and understanding of the pathway of the differential
protein expression among the groups is ongoing and may account for
the molecular mechanism through which palm vitamin E exert its
anti-osteoporotic effect.
Abstract: Serial Analysis of Gene Expression is a powerful
quantification technique for generating cell or tissue gene expression
data. The profile of the gene expression of cell or tissue in several
different states is difficult for biologists to analyze because of the large
number of genes typically involved. However, feature selection in
machine learning can successfully reduce this problem. The method
allows reducing the features (genes) in specific SAGE data, and
determines only relevant genes. In this study, we used a genetic
algorithm to implement feature selection, and evaluate the
classification accuracy of the selected features with the K-nearest
neighbor method. In order to validate the proposed method, we used
two SAGE data sets for testing. The results of this study conclusively
prove that the number of features of the original SAGE data set can be
significantly reduced and higher classification accuracy can be
achieved.
Abstract: This paper proposes a new design of spatial FIR
filter to automatically detect water level from a video signal of
various river surroundings. A new approach in this report applies
"addition" of frames and a "horizontal" edge detector to distinguish
water region and land region. Variance of each line of a filtered
video frame is used as a feature value. The water level is recognized
as a boundary line between the land region and the water region.
Edge detection filter essentially demarcates between two distinctly
different regions. However, the conventional filters are not
automatically adaptive to detect water level in various lighting
conditions of river scenery. An optimized filter is purposed so that
the system becomes robust to changes of lighting condition. More
reliability of the proposed system with the optimized filter is
confirmed by accuracy of water level detection.
Abstract: This paper deals with dynamic load balancing using PVM. In distributed environment Load Balancing and Heterogeneity are very critical issues and needed to drill down in order to achieve the optimal results and efficiency. Various techniques are being used in order to distribute the load dynamically among different nodes and to deal with heterogeneity. These techniques are using different approaches where Process Migration is basic concept with different optimal flavors. But Process Migration is not an easy job, it impose lot of burden and processing effort in order to track each process in nodes. We will propose a dynamic load balancing technique in which application will intelligently balance the load among different nodes, resulting in efficient use of system and have no overheads of process migration. It would also provide a simple solution to problem of load balancing in heterogeneous environment.
Abstract: Group contribution based models are widely used in
industrial applications for its convenience and flexibility. Although a
number of group contribution models have been proposed, there were
certain limitations inherent to those models. Models based on group
contribution excess Gibbs free energy are limited to low pressures and
models based on equation of state (EOS) cannot properly describe
highly nonideal mixtures including acids without introducing
additional modification such as chemical theory. In the present study
new a new approach derived from quantum chemistry have been used
to calculate necessary EOS group interaction parameters. The
COSMO-RS method, based on quantum mechanics, provides a
reliable tool for fluid phase thermodynamics. Benefits of the group
contribution EOS are the consistent extension to hydrogen-bonded
mixtures and the capability to predict polymer-solvent equilibria up to
high pressures. The authors are confident that with a sufficient
parameter matrix the performance of the lattice EOS can be improved
significantly.
Abstract: Wavelet transforms are multiresolution
decompositions that can be used to analyze signals and images.
Image compression is one of major applications of wavelet
transforms in image processing. It is considered as one of the most
powerful methods that provides a high compression ratio. However,
its implementation is very time-consuming. At the other hand,
parallel computing technologies are an efficient method for image
compression using wavelets. In this paper, we propose a parallel
wavelet compression algorithm based on quadtrees. We implement
the algorithm using MatlabMPI (a parallel, message passing version
of Matlab), and compute its isoefficiency function, and show that it is
scalable. Our experimental results confirm the efficiency of the
algorithm also.
Abstract: Acute disseminated encephalomyelitis (ADEM) has
been reported to develop after a hymenoptera sting, but its
pathogenesis is not known in detail. Myelin basic protein (MBP)-
specific T cells have been detected in the blood of patients with
ADEM, and a proportion of these patients develop multiple sclerosis
(MS). In an attempt to understand the mechanisms underlying
ADEM, molecular mimicry between hymenoptera venom peptides
and the human immunodominant MBP peptide was scrutinized,
based on the sequence and structural similarities, whether it was the
root of the disease. The results suggest that the three wasp venom
peptides have low sequence homology with the human
immunodominant MBP residues 85-99. Structural similarity analysis
among the three venom peptides and the MS-related HLA-DR2b
(DRA, DRB1*1501)-associated immunodominant MHC
binding/TCR contact residues 88-93, VVHFFK showed that
hyaluronidase residues 7-12, phospholipase A1 residues 98-103, and
antigen 5 residues 109-114 showed a high degree of similarity
83.3%, 100%, and 83.3% respectively. In conclusion, some wasp
venom peptides, particularly phospholipase A1, may potentially act
as the molecular motifs of the human 3HLA-DR2b-associated
immunodominant MBP88-93, and possibly present a mechanism for
induction of wasp sting-associated ADEM.
Abstract: This research work proposes a model of network security systems aiming to prevent production system in a data center from being attacked by intrusions. Conceptually, we introduce a decoy system as a part of the security system for luring intrusions, and apply network intrusion detection (NIDS), coupled with the decoy system to perform intrusion prevention. When NIDS detects an activity of intrusions, it will signal a redirection module to redirect all malicious traffics to attack the decoy system instead, and hence the production system is protected and safe. However, in a normal situation, traffic will be simply forwarded to the production system as usual. Furthermore, we assess the performance of the model with various bandwidths, packet sizes and inter-attack intervals (attacking frequencies).
Abstract: Service identification is one of the main activities in
the modeling of a service-oriented solution, and therefore errors
made during identification can flow down through detailed design
and implementation activities that may necessitate multiple
iterations, especially in building composite applications. Different
strategies exist for how to identify candidate services that each of
them has its own benefits and trade offs. The approach presented in
this paper proposes a selective identification of services approach,
based on in depth business process analysis coupled with use cases
and existing assets analysis and goal service modeling. This article
clearly emphasizes the key activities need for the analysis and
service identification to build a optimized service oriented
architecture. In contrast to other approaches this article mentions
some best practices and steps, wherever appropriate, to point out the
vagueness involved in service identification.
Abstract: This paper proposes an auto-classification algorithm
of Web pages using Data mining techniques. We consider the
problem of discovering association rules between terms in a set of
Web pages belonging to a category in a search engine database, and
present an auto-classification algorithm for solving this problem that
are fundamentally based on Apriori algorithm. The proposed
technique has two phases. The first phase is a training phase where
human experts determines the categories of different Web pages, and
the supervised Data mining algorithm will combine these categories
with appropriate weighted index terms according to the highest
supported rules among the most frequent words. The second phase is
the categorization phase where a web crawler will crawl through the
World Wide Web to build a database categorized according to the
result of the data mining approach. This database contains URLs and
their categories.
Abstract: Presented herein is an assessment of current nonlinear
static procedures (NSPs) for seismic evaluation of bucklingrestrained
braced frames (BRBFs) which have become a favorable
lateral-force resisting system for earthquake resistant buildings. The
bias and accuracy of modal, improved modal pushover analysis
(MPA, IMPA) and mass proportional pushover (MPP) procedures
are comparatively investigated when they are applied to BRBF
buildings subjected to two sets of strong ground motions. The
assessment is based on a comparison of seismic displacement
demands such as target roof displacements, peak floor/roof
displacements and inter-story drifts. The NSP estimates are compared
to 'exact' results from nonlinear response history analysis (NLRHA).
The response statistics presented show that the MPP
procedure tends to significantly overestimate seismic demands of
lower stories of tall buildings considered in this study while MPA
and IMPA procedures provide reasonably accurate results in
estimating maximum inter-story drift over all stories of studied BRBF
systems.
Abstract: A topologically oriented neural network is very
efficient for real-time path planning for a mobile robot in changing
environments. When using a recurrent neural network for this
purpose and with the combination of the partial differential equation
of heat transfer and the distributed potential concept of the network,
the problem of obstacle avoidance of trajectory planning for a
moving robot can be efficiently solved. The related dimensional
network represents the state variables and the topology of the robot's
working space. In this paper two approaches to problem solution are
proposed. The first approach relies on the potential distribution of
attraction distributed around the moving target, acting as a unique
local extreme in the net, with the gradient of the state variables
directing the current flow toward the source of the potential heat. The
second approach considers two attractive and repulsive potential
sources to decrease the time of potential distribution. Computer
simulations have been carried out to interrogate the performance of
the proposed approaches.