Abstract: This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of pulping problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified odified problem M-1 Ax= M-1b where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.
Abstract: In this paper, a novel approach for the multidisciplinary design optimization (MDO) of complex mechatronic systems. This approach, which is a part of a global project aiming to include the MDO aspect inside an innovative design process. As a first step, the paper considers the MDO as a redesign approach which is limited to the parametric optimization. After defining and introducing the different keywords, the proposed method which is based on the V-Model which is commonly used in mechatronics.
Abstract: 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) catalyzes the conversion of HMG-CoA to mevalonate using NADPH and the enzyme is involved in rate-controlling step of mevalonate. Inhibition of HMGR is considered as effective way to lower cholesterol levels so it is drug target to treat hypercholesterolemia, major risk factor of cardiovascular disease. To discover novel HMGR inhibitor, we performed structure-based pharmacophore modeling combined with molecular dynamics (MD) simulation. Four HMGR inhibitors were used for MD simulation and representative structure of each simulation were selected by clustering analysis. Four structure-based pharmacophore models were generated using the representative structure. The generated models were validated used in virtual screening to find novel scaffolds for inhibiting HMGR. The screened compounds were filtered by applying drug-like properties and used in molecular docking. Finally, four hit compounds were obtained and these complexes were refined using energy minimization. These compounds might be potential leads to design novel HMGR inhibitor.
Abstract: The problem of robust stability and robust stabilization for a class of discrete-time uncertain systems with time delay is investigated. Based on Tchebychev inequality, by constructing a new augmented Lyapunov function, some improved sufficient conditions ensuring exponential stability and stabilization are established. These conditions are expressed in the forms of linear matrix inequalities (LMIs), whose feasibility can be easily checked by using Matlab LMI Toolbox. Compared with some previous results derived in the literature, the new obtained criteria have less conservatism. Two numerical examples are provided to demonstrate the improvement and effectiveness of the proposed method.
Abstract: This paper explores the effectiveness of machine
learning techniques in detecting firms that issue fraudulent financial
statements (FFS) and deals with the identification of factors
associated to FFS. To this end, a number of experiments have been
conducted using representative learning algorithms, which were
trained using a data set of 164 fraud and non-fraud Greek firms in the
recent period 2001-2002. The decision of which particular method to
choose is a complicated problem. A good alternative to choosing
only one method is to create a hybrid forecasting system
incorporating a number of possible solution methods as components
(an ensemble of classifiers). For this purpose, we have implemented
a hybrid decision support system that combines the representative
algorithms using a stacking variant methodology and achieves better
performance than any examined simple and ensemble method. To
sum up, this study indicates that the investigation of financial
information can be used in the identification of FFS and underline the
importance of financial ratios.
Abstract: In single trial analysis, when using Principal
Component Analysis (PCA) to extract Visual Evoked Potential
(VEP) signals, the selection of principal components (PCs) is an
important issue. We propose a new method here that selects only
the appropriate PCs. We denote the method as selective eigen-rate
(SER). In the method, the VEP is reconstructed based on the rate
of the eigen-values of the PCs. When this technique is applied on
emulated VEP signals added with background
electroencephalogram (EEG), with a focus on extracting the
evoked P3 parameter, it is found to be feasible. The improvement
in signal to noise ratio (SNR) is superior to two other existing
methods of PC selection: Kaiser (KSR) and Residual Power (RP).
Though another PC selection method, Spectral Power Ratio (SPR)
gives a comparable SNR with high noise factors (i.e. EEGs), SER
give more impressive results in such cases. Next, we applied SER
method to real VEP signals to analyse the P3 responses for
matched and non-matched stimuli. The P3 parameters extracted
through our proposed SER method showed higher P3 response for
matched stimulus, which confirms to the existing neuroscience
knowledge. Single trial PCA using KSR and RP methods failed to
indicate any difference for the stimuli.
Abstract: The P-Bigram method is a string comparison methods
base on an internal two characters-based similarity measure. The edit
distance between two strings is the minimal number of elementary
editing operations required to transform one string into the other. The
elementary editing operations include deletion, insertion, substitution
two characters. In this paper, we address the P-Bigram method to
sole the similarity problem in DNA sequence. This method provided
an efficient algorithm that locates all minimum operation in a string.
We have been implemented algorithm and found that our program
calculated that smaller distance than one string. We develop PBigram
edit distance and show that edit distance or the similarity and
implementation using dynamic programming. The performance of
the proposed approach is evaluated using number edit and percentage
similarity measures.
Abstract: The flash memory has many advantages such as low power consumption, strong shock resistance, fast I/O and non-volatility. And it is increasingly used in the mobile storage device. The YAFFS, one of the NAND flash file system, is widely used in the embedded device. However, the existing YAFFS takes long time to mount the file system because it scans whole spare areas in all pages of NAND flash memory. In order to solve this problem, we propose a new content-based flash file system using a mounting time reduction technique. The proposed method only scans partial spare areas of some special pages by using content-based block management. The experimental results show that the proposed method reduces the average mounting time by 87.2% comparing with JFFS2 and 69.9% comparing with YAFFS.
Abstract: All around the world pulp and paper industries are the
biggest plant production with the environmental pollution as the
biggest challenge facing the pulp manufacturing operations. The
concern among these industries is to produce a high volume of papers
with the high quality standard and of low cost without affecting the
environment. This result obtained from this bleaching study show
that the activation of peroxide was an effective method of reducing
the total applied charge of chlorine dioxide which is harmful to our
environment and also show that softwood and hardwood Kraft pulps
responded linearly to the peroxide treatments. During the bleaching
process the production plant produce chlorines. Under the trial stages
chloride dioxide has been reduced by 3 kg/ton to reduce the
brightness from 65% ISO to 60% ISO of pulp and the dosing point
returned to the E stage charges by pre-treating Kraft pulps with
hydrogen peroxide. The pulp and paper industry has developed
elemental chlorine free (ECF) and totally chlorine free (TCF)
bleaching, in their quest for being environmental friendly, they have
been looking at ways to turn their ECF process into a TCF process
while still being competitive. This prompted the research to
investigate the capability of the hydrogen peroxide as catalyst to
reduce chloride dioxide.
Abstract: The previous study of new metal gasket that contact
width and contact stress an important design parameter for optimizing
metal gasket performance. The optimum design based on an elastic
and plastic contact stress was founded. However, the influence of
flange surface roughness had not been investigated thoroughly. The
flange has many kinds of surface roughness. In this study, we
conducted a gasket model include a flange surface roughness effect. A
finite element method was employed to develop simulation solution. A
uniform quadratic mesh used for meshing the gasket material and a
gradually quadrilateral mesh used for meshing the flange. The gasket
model was simulated by using two simulation stages which is forming
and tightening simulation. A simulation result shows that a smoother
of surface roughness has higher slope for force per unit length. This
mean a squeezed against between flange and gasket will be strong. The
slope of force per unit length for gasket 400-MPa mode was higher
than the gasket 0-MPa mode.
Abstract: In this competitive age, one of the key tools of most successful organizations is knowledge management. Today some organizations measure their current knowledge and use it as an indicator for rating the organization on their reports. Noting that the universities and colleges of medical science have a great role in public health of societies, their access to newest scientific research and the establishment of organizational knowledge management systems is very important. In order to explore the Application of Knowledge Management Factors, a national study was undertaken. The main purpose of this study was to find the rate of the application of knowledge management factors and some ways to establish more application of knowledge management system in Esfahan University-s Medical College (EUMC). Esfahan is the second largest city after Tehran, the capital city of Iran, and the EUMC is the biggest medical college in Esfahan. To rate the application of knowledge management, this study uses a quantitative research methodology based on Probst, Raub and Romhardt model of knowledge management. A group of 267 faculty members and staff of the EUMC were asked via questionnaire. Finding showed that the rate of the application of knowledge management factors in EUMC have been lower than average. As a result, an interview with ten faculty members conducted to find the guidelines to establish more applications of knowledge management system in EUMC.
Abstract: This work considered the thermodynamic feasibility
of scrubbing volatile organic compounds into biodiesel in view of
designing a gas treatment process with this absorbent. A detailed
vapour – liquid equilibrium investigation was performed using the
original UNIFAC group contribution method. The four biodiesels
studied in this work are methyl oleate, methyl palmitate, methyl
linolenate and ethyl stearate. The original UNIFAC procedure was
used to estimate the infinite dilution activity coefficients of 13
selected volatile organic compounds in the biodiesels. The
calculations were done at the VOC mole fraction of 9.213x10-8. Ethyl
stearate gave the most favourable phase equilibrium. A close
agreement was found between the infinite dilution activity coefficient
of toluene found in this work and those reported in literature.
Thermodynamic models can efficiently be used to calculate vast
amount of phase equilibrium behaviour using limited number of
experimental data.
Abstract: Recent years have seen a growing trend towards the
integration of multiple information sources to support large-scale
prediction of protein-protein interaction (PPI) networks in model
organisms. Despite advances in computational approaches, the
combination of multiple “omic" datasets representing the same type
of data, e.g. different gene expression datasets, has not been
rigorously studied. Furthermore, there is a need to further investigate
the inference capability of powerful approaches, such as fullyconnected
Bayesian networks, in the context of the prediction of PPI
networks. This paper addresses these limitations by proposing a
Bayesian approach to integrate multiple datasets, some of which
encode the same type of “omic" data to support the identification of
PPI networks. The case study reported involved the combination of
three gene expression datasets relevant to human heart failure (HF).
In comparison with two traditional methods, Naive Bayesian and
maximum likelihood ratio approaches, the proposed technique can
accurately identify known PPI and can be applied to infer potentially
novel interactions.
Abstract: This paper presents a several diagnostic methods designed to electrical machinesespecially for permanent magnets (PM) machines. Those machines are commonly used in small wind and water systems and vehicles drives.Thosemethodsare preferred by the author in periodic diagnostic of electrical machines. The special attentionshould be paid to diagnostic method of turn-to-turn insulation and vibrations. Both of those methodswere createdinInstitute of Electrical Drives and MachinesKomel. The vibration diagnostic method is the main thesis of author’s doctoral dissertation. This is method of determination the technical condition of PM electrical machine basing on its own signals is the subject of patent application No P.405669. Specific structural properties of machines excited by permanent magnets are used in this method - electromotive force (EMF) generated due to vibrations. There was analysed number of publications which describe vibration diagnostic methods and tests of electrical machines with permanent magnets and there was no method found to determine the technical condition of such machine basing on their own signals.
Abstract: This paper presents an algorithm which
combining ant colony optimization in the dynamic
programming for solving a dynamic facility layout problem.
The problem is separated into 2 phases, static and dynamic
phase. In static phase, ant colony optimization is used to find
the best ranked of layouts for each period. Then the dynamic
programming (DP) procedure is performed in the dynamic
phase to evaluate the layout set during multi-period planning
horizon. The proposed algorithm is tested over many
problems with size ranging from 9 to 49 departments, 2 and 4
periods. The experimental results show that the proposed
method is an alternative way for the plant layout designer to
determine the layouts during multi-period planning horizon.
Abstract: Mathematical programming has been applied to various
problems. For many actual problems, the assumption that the parameters
involved are deterministic known data is often unjustified. In
such cases, these data contain uncertainty and are thus represented
as random variables, since they represent information about the
future. Decision-making under uncertainty involves potential risk.
Stochastic programming is a commonly used method for optimization
under uncertainty. A stochastic programming problem with recourse
is referred to as a two-stage stochastic problem. In this study, we
consider a stochastic programming problem with simple integer
recourse in which the value of the recourse variable is restricted to a
multiple of a nonnegative integer. The algorithm of a dynamic slope
scaling procedure for solving this problem is developed by using a
property of the expected recourse function. Numerical experiments
demonstrate that the proposed algorithm is quite efficient. The
stochastic programming model defined in this paper is quite useful
for a variety of design and operational problems.
Abstract: In this paper, we discuss the paradigm shift in bank
capital from the “gone concern" to the “going concern" mindset. We
then propose a methodology for pricing a product of this shift called
Contingent Capital Notes (“CoCos"). The Merton Model can
determine a price for credit risk by using the firm-s equity value as a
call option on those assets. Our pricing methodology for CoCos also
uses the credit spread implied by the Merton Model in a subsequent
derivative form created by John Hull et al . Here, a market implied
asset volatility is calculated by using observed market CDS spreads.
This implied asset volatility is then used to estimate the probability of
triggering a predetermined “contingency event" given the distanceto-
trigger (DTT). The paper then investigates the effect of varying
DTTs and recovery assumptions on the CoCo yield. We conclude
with an investment rationale.
Abstract: The application of a high frequency signal injection method as speed and position observer in PMSM drives has been a research focus. At present, the precision of this method is nearly good as that of ten-bit encoder. But there are some questions for estimating position polarity. Based on high frequency signal injection, this paper presents a method to compensate position polarity for permanent magnet synchronous motor (PMSM). Experiments were performed to test the effectiveness of the proposed algorithm and results present the good performance.
Abstract: While many studies have conducted the achievement
gap between groups of students in school districts, few studies have
utilized resilience research to investigate achievement gaps within
classrooms. This paper aims to summarize and discuss some recent
studies Waxman, Padr├│n, and their colleagues conducted, in which
they examined learning environment differences between resilient
and nonresilient students in reading and mathematics classrooms.
The classes consist of predominantly Hispanic elementary school
students from low-income families. These studies all incorporated
learning environment questionnaires and systematic observation
methods. Significant differences were found between resilient and
nonresilient students on their classroom learning environments and
classroom behaviors. The observation results indicate that the amount
and quality of teacher and student academic interaction are two of the
most influential variables that promote student outcomes. This paper
concludes by suggesting the following teacher practices to promote
resiliency in schools: (a) using feedback from classroom observation
and learning environment measures, (b) employing explicit teaching
practices; and (c) understanding students on a social and personal
level.
Abstract: In Knowledge Structure Graph, each course unit
represents a phase of learning activities. Both learning portfolios and
Knowledge Structure Graphs contain learning information of students
and let teachers know which content are difficulties and fails. The
study purposes "Dual Mode On-line Learning Diagnosis System" that
integrates two search methods: learning portfolio and knowledge
structure. Teachers can operate the proposed system and obtain the
information of specific students without any computer science
background. The teachers can find out failed students in advance and
provide remedial learning resources.