Abstract: This work addresses the problem of optimizing
completely batch water-using network with multiple contaminants
where the flow change caused by mass transfer is taken into
consideration for the first time. A mathematical technique for
optimizing water-using network is proposed based on
source-tank-sink superstructure. The task is to obtain the freshwater
usage, recycle assignments among water-using units, wastewater
discharge and a steady water-using network configuration by
following steps. Firstly, operating sequences of water-using units are
determined by time constraints. Next, superstructure is simplified by
eliminating the reuse and recycle from water-using units with
maximum concentration of key contaminants. Then, the non-linear
programming model is solved by GAMS (General Algebra Model
System) for minimum freshwater usage, maximum water recycle and
minimum wastewater discharge. Finally, numbers of operating periods
are calculated to acquire the steady network configuration. A case
study is solved to illustrate the applicability of the proposed approach.
Abstract: Segmentation, filtering out of measurement errors and
identification of breakpoints are integral parts of any analysis of
microarray data for the detection of copy number variation (CNV).
Existing algorithms designed for these tasks have had some successes
in the past, but they tend to be O(N2) in either computation time or
memory requirement, or both, and the rapid advance of microarray
resolution has practically rendered such algorithms useless. Here we
propose an algorithm, SAD, that is much faster and much less thirsty
for memory – O(N) in both computation time and memory requirement
-- and offers higher accuracy. The two key ingredients of SAD are the
fundamental assumption in statistics that measurement errors are
normally distributed and the mathematical relation that the product of
two Gaussians is another Gaussian (function). We have produced a
computer program for analyzing CNV based on SAD. In addition to
being fast and small it offers two important features: quantitative
statistics for predictions and, with only two user-decided parameters,
ease of use. Its speed shows little dependence on genomic profile.
Running on an average modern computer, it completes CNV analyses
for a 262 thousand-probe array in ~1 second and a 1.8 million-probe
array in 9 seconds
Abstract: The visualization of geographic information on mobile devices has become popular as the widespread use of mobile Internet. The mobility of these devices brings about much convenience to people-s life. By the add-on location-based services of the devices, people can have an access to timely information relevant to their tasks. However, visual analysis of geographic data on mobile devices presents several challenges due to the small display and restricted computing resources. These limitations on the screen size and resources may impair the usability aspects of the visualization applications. In this paper, a variable-scale visualization method is proposed to handle the challenge of small mobile display. By merging multiple scales of information into a single image, the viewer is able to focus on the interesting region, while having a good grasp of the surrounding context. This is essentially visualizing the map through a fisheye lens. However, the fisheye lens induces undesirable geometric distortion in the peripheral, which renders the information meaningless. The proposed solution is to apply map generalization that removes excessive information around the peripheral and an automatic smoothing process to correct the distortion while keeping the local topology consistent. The proposed method is applied on both artificial and real geographical data for evaluation.
Abstract: An experiment was conducted to examine the effect of the level of performance stabilization on the human adaptability to perceptual-motor perturbation in a complex coincident timing task. Three levels of performance stabilization were established operationally: pre-stabilization, stabilization, and super-stabilization groups. Each group practiced the task until reached its level of stabilization in a constant sequence of movements and under a constant time constraint before exposure to perturbation. The results clearly showed that performance stabilization is a pre-condition for adaptation. Moreover, variability before reaching stabilization is harmful to adaptation and persistent variability after stabilization is beneficial. Moreover, the behavior of variability is specific to each measure.
Abstract: Performing High Voltage (HV) tasks with a multi craft
work force create a special set of safety circumstances. This paper
aims to present vital information relating to when it is acceptable to
use a single or a two-layer soil structure. Also it discusses the
implication of the high voltage infrastructure on the earth grid and the
safety of this implication under a single or a two-layer soil structure.
A multiple case study is investigated to show the importance of using
the right soil resistivity structure during the earthing system design.
Abstract: The frontal area in the brain is known to be involved in
behavioral judgement. Because a Kanji character can be discriminated
visually and linguistically from other characters, in Kanji character
discrimination, we hypothesized that frontal event-related potential
(ERP) waveforms reflect two discrimination processes in separate
time periods: one based on visual analysis and the other based
on lexcical access. To examine this hypothesis, we recorded ERPs
while performing a Kanji lexical decision task. In this task, either a
known Kanji character, an unknown Kanji character or a symbol was
presented and the subject had to report if the presented character was
a known Kanji character for the subject or not. The same response
was required for unknown Kanji trials and symbol trials. As a preprocessing
of signals, we examined the performance of a method
using independent component analysis for artifact rejection and found
it was effective. Therefore we used it. In the ERP results, there
were two time periods in which the frontal ERP wavefoms were
significantly different betweeen the unknown Kanji trials and the
symbol trials: around 170ms and around 300ms after stimulus onset.
This result supported our hypothesis. In addition, the result suggests
that Kanji character lexical access may be fully completed by around
260ms after stimulus onset.
Abstract: The aim of this paper is to investigate the
performance of the developed two point block method designed for
two processors for solving directly non stiff large systems of higher
order ordinary differential equations (ODEs). The method calculates
the numerical solution at two points simultaneously and produces
two new equally spaced solution values within a block and it is
possible to assign the computational tasks at each time step to a
single processor. The algorithm of the method was developed in C
language and the parallel computation was done on a parallel shared
memory environment. Numerical results are given to compare the
efficiency of the developed method to the sequential timing. For
large problems, the parallel implementation produced 1.95 speed-up
and 98% efficiency for the two processors.
Abstract: Heart disease (HD) is a major cause of morbidity and mortality in the modern society. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. All doctors are unfortunately not equally skilled in every sub specialty and they are in many places a scarce resource. A system for automated medical diagnosis would enhance medical care and reduce costs. In this paper, a new approach based on coactive neuro-fuzzy inference system (CANFIS) was presented for prediction of heart disease. The proposed CANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach which is then integrated with genetic algorithm to diagnose the presence of the disease. The performances of the CANFIS model were evaluated in terms of training performances and classification accuracies and the results showed that the proposed CANFIS model has great potential in predicting the heart disease.
Abstract: Many factors affect the success of Machine Learning
(ML) on a given task. The representation and quality of the instance
data is first and foremost. If there is much irrelevant and redundant
information present or noisy and unreliable data, then knowledge
discovery during the training phase is more difficult. It is well known
that data preparation and filtering steps take considerable amount of
processing time in ML problems. Data pre-processing includes data
cleaning, normalization, transformation, feature extraction and
selection, etc. The product of data pre-processing is the final training
set. It would be nice if a single sequence of data pre-processing
algorithms had the best performance for each data set but this is not
happened. Thus, we present the most well know algorithms for each
step of data pre-processing so that one achieves the best performance
for their data set.
Abstract: Web sites are rapidly becoming the preferred media
choice for our daily works such as information search, company
presentation, shopping, and so on. At the same time, we live in a
period where visual appearances play an increasingly important
role in our daily life. In spite of designers- effort to develop a web
site which be both user-friendly and attractive, it would be difficult
to ensure the outcome-s aesthetic quality, since the visual
appearance is a matter of an individual self perception and opinion.
In this study, it is attempted to develop an automatic system for
web pages aesthetic evaluation which are the building blocks of
web sites. Based on the image processing techniques and artificial
neural networks, the proposed method would be able to categorize
the input web page according to its visual appearance and aesthetic
quality. The employed features are multiscale/multidirectional
textural and perceptual color properties of the web pages, fed to
perceptron ANN which has been trained as the evaluator. The
method is tested using university web sites and the results
suggested that it would perform well in the web page aesthetic
evaluation tasks with around 90% correct categorization.
Abstract: Knowledge about the magnetic quantities in a magnetic circuit is always of great interest. On the one hand, this information is needed for the simulation of a transformer. On the other hand, parameter studies are more reliable, if the magnetic quantities are derived from a well established model. One possibility to model the 3-phase transformer is by using a magnetic equivalent circuit (MEC). Though this is a well known system, it is often not an easy task to set up such a model for a large number of lumped elements which additionally includes the nonlinear characteristic of the magnetic material. Here we show the setup of a solver for a MEC and the results of the calculation in comparison to measurements taken. The equations of the MEC are based on a rearranged system of the nodal analysis. Thus it is possible to achieve a minimum number of equations, and a clear and simple structure. Hence, it is uncomplicated in its handling and it supports the iteration process. Additional helpful tasks are implemented within the solver to enhance the performance. The electric circuit is described by an electric equivalent circuit (EEC). Our results for the 3-phase transformer demonstrate the computational efficiency of the solver, and show the benefit of the application of a MEC.
Abstract: Switched-mode converters play now a significant role in
modern society. Their operation are often crucial in various electrical
applications affecting the every day life. Therefore, the quality of
the converters needs to be reliably verified. Recent studies have
shown that the converters can be fully characterized by a set of
frequency responses which can be efficiently used to validate the
proper operation of the converters. Consequently, several methods
have been proposed to measure the frequency responses fast and
accurately. Most often correlation-based techniques have been applied.
The presented measurement methods are highly sensitive to
external errors and system nonlinearities. This fact has been often
forgotten and the necessary uncertainty analysis of the measured
responses has been neglected. This paper presents a simple approach
to analyze the noise and nonlinearities in the frequency-response
measurements of switched-mode converters. Coherence analysis is
applied to form a confidence interval characterizing the noise and
nonlinearities involved in the measurements. The presented method is
verified by practical measurements from a high-frequency switchedmode
converter.
Abstract: Writer identification is one of the areas in pattern
recognition that attract many researchers to work in, particularly in
forensic and biometric application, where the writing style can be
used as biometric features for authenticating an identity. The
challenging task in writer identification is the extraction of unique
features, in which the individualistic of such handwriting styles
can be adopted into bio-inspired generalized global shape for
writer identification. In this paper, the feasibility of generalized
global shape concept of complimentary binding in Artificial
Immune System (AIS) for writer identification is explored. An
experiment based on the proposed framework has been conducted
to proof the validity and feasibility of the proposed approach for
off-line writer identification.
Abstract: Brain Computer Interface (BCI) has been recently
increased in research. Functional Near Infrared Spectroscope (fNIRs)
is one the latest technologies which utilize light in the near-infrared
range to determine brain activities. Because near infrared technology
allows design of safe, portable, wearable, non-invasive and wireless
qualities monitoring systems, fNIRs monitoring of brain
hemodynamics can be value in helping to understand brain tasks. In
this paper, we present results of fNIRs signal analysis indicating that
there exist distinct patterns of hemodynamic responses which
recognize brain tasks toward developing a BCI. We applied two
different mathematics tools separately, Wavelets analysis for
preprocessing as signal filters and feature extractions and Neural
networks for cognition brain tasks as a classification module. We
also discuss and compare with other methods while our proposals
perform better with an average accuracy of 99.9% for classification.
Abstract: In this paper we have suggested a new system for egovernment.
In this method a government can design a precise and
perfect system to control people and organizations by using five
major documents. These documents contain the important
information of each member of a society and help all organizations to
do their informatics tasks through them. This information would be
available by only a national code and a secure program would
support it. The suggested system can give a good awareness to the
society and help it be managed correctly.
Abstract: Mobile adhoc network (MANET) is a collection of
mobile devices which form a communication network with no preexisting
wiring or infrastructure. Multiple routing protocols have
been developed for MANETs. As MANETs gain popularity, their
need to support real time applications is growing as well. Such
applications have stringent quality of service (QoS) requirements
such as throughput, end-to-end delay, and energy. Due to dynamic
topology and bandwidth constraint supporting QoS is a challenging
task. QoS aware routing is an important building block for QoS
support. The primary goal of the QoS aware protocol is to determine
the path from source to destination that satisfies the QoS
requirements. This paper proposes a new energy and delay aware
protocol called energy and delay aware TORA (EDTORA) based on
extension of Temporally Ordered Routing Protocol (TORA).Energy
and delay verifications of query packet have been done in each node.
Simulation results show that the proposed protocol has a higher
performance than TORA in terms of network lifetime, packet
delivery ratio and end-to-end delay.
Abstract: For the past one decade, biclustering has become popular data mining technique not only in the field of biological data analysis but also in other applications like text mining, market data analysis with high-dimensional two-way datasets. Biclustering clusters both rows and columns of a dataset simultaneously, as opposed to traditional clustering which clusters either rows or columns of a dataset. It retrieves subgroups of objects that are similar in one subgroup of variables and different in the remaining variables. Firefly Algorithm (FA) is a recently-proposed metaheuristic inspired by the collective behavior of fireflies. This paper provides a preliminary assessment of discrete version of FA (DFA) while coping with the task of mining coherent and large volume bicluster from web usage dataset. The experiments were conducted on two web usage datasets from public dataset repository whereby the performance of FA was compared with that exhibited by other population-based metaheuristic called binary Particle Swarm Optimization (PSO). The results achieved demonstrate the usefulness of DFA while tackling the biclustering problem.
Abstract: The automatic classification of non stationary signals is an important practical goal in several domains. An essential classification task is to allocate the incoming signal to a group associated with the kind of physical phenomena producing it. In this paper, we present a modular system composed by three blocs: 1) Representation, 2) Dimensionality reduction and 3) Classification. The originality of our work consists in the use of a new wavelet called "Ben wavelet" in the representation stage. For the dimensionality reduction, we propose a new algorithm based on the random projection and the principal component analysis.
Abstract: This work deals with aspects of support vector learning for large-scale data mining tasks. Based on a decomposition algorithm that can be run in serial and parallel mode we introduce a data transformation that allows for the usage of an expensive generalized kernel without additional costs. In order to speed up the decomposition algorithm we analyze the problem of working set selection for large data sets and analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our modifications and settings lead to improvement of support vector learning performance and thus allow using extensive parameter search methods to optimize classification accuracy.
Abstract: System MEMORI automatically detects and recognizes
rotated and/or rescaled versions of the objects of a database within
digital color images with cluttered background. This task is accomplished
by means of a region grouping algorithm guided by heuristic
rules, whose parameters concern some geometrical properties and the
recognition score of the database objects. This paper focuses on the
strategies implemented in MEMORI for the estimation of the heuristic
rule parameters. This estimation, being automatic, makes the system
a self configuring and highly user-friendly tool.