Abstract: In the present study, a procedure was developed to
determine the optimum reaction rate constants in generalized
Arrhenius form and optimized through the Nelder-Mead method. For
this purpose, a comprehensive mathematical model of a fixed bed
reactor for dehydrogenation of heavy paraffins over Pt–Sn/Al2O3
catalyst was developed. Utilizing appropriate kinetic rate expressions
for the main dehydrogenation reaction as well as side reactions and
catalyst deactivation, a detailed model for the radial flow reactor was
obtained. The reactor model composed of a set of partial differential
equations (PDE), ordinary differential equations (ODE) as well as
algebraic equations all of which were solved numerically to
determine variations in components- concentrations in term of mole
percents as a function of time and reactor radius. It was demonstrated
that most significant variations observed at the entrance of the bed
and the initial olefin production obtained was rather high. The
aforementioned method utilized a direct-search optimization
algorithm along with the numerical solution of the governing
differential equations. The usefulness and validity of the method was
demonstrated by comparing the predicted values of the kinetic
constants using the proposed method with a series of experimental
values reported in the literature for different systems.
Abstract: The purpose of this study is to identify the critical success factors (CSFs) for the effective implementation of Six Sigma in non-formal service Sectors.
Based on the survey of literature, the critical success factors (CSFs) for Six Sigma have been identified and are assessed for their importance in Non-formal service sector using Delphi Technique. These selected CSFs were put forth to the panel of expert to cluster them and prepare cognitive map to establish their relationship.
All the critical success factors examined and obtained from the review of literature have been assessed for their importance with respect to their contribution to Six Sigma effectiveness in non formal service sector.
The study is limited to the non-formal service sectors involved in the organization of religious festival only. However, the similar exercise can be conducted for broader sample of other non-formal service sectors like temple/ashram management, religious tours management etc.
The research suggests an approach to identify CSFs of Six Sigma for Non-formal service sector. All the CSFs of the formal service sector will not be applicable to Non-formal services, hence opinion of experts was sought to add or delete the CSFs. In the first round of Delphi, the panel of experts has suggested, two new CSFs-“competitive benchmarking (F19) and resident’s involvement (F28)”, which were added for assessment in the next round of Delphi. One of the CSFs-“fulltime six sigma personnel (F15)” has been omitted in proposed clusters of CSFs for non-formal organization, as it is practically impossible to deploy full time trained Six Sigma recruits.
Abstract: In this paper, the notion of Hyperbolic Klingenberg
plane is introduced via a set of axioms like as Affine Klingenberg
planes and Projective Klingenberg planes. Models of such planes are
constructed by deleting a certain number m of equivalence classes
of lines from a Projective Klingenberg plane. In the finite case, an
upper bound for m is established and some combinatoric properties
are investigated.
Abstract: This paper presents design features of a rescue robot, named CEO Mission II. Its body is designed to be the track wheel type with double front flippers for climbing over the collapse and the rough terrain. With 125 cm. long, 5-joint mechanical arm installed on the robot body, it is deployed not only for surveillance from the top view but also easier and faster access to the victims to get their vital signs. Two cameras and sensors for searching vital signs are set up at the tip of the multi-joint mechanical arm. The third camera is at the back of the robot for driving control. Hardware and software of the system, which controls and monitors the rescue robot, are explained. The control system is used for controlling the robot locomotion, the 5-joint mechanical arm, and for turning on/off devices. The monitoring system gathers all information from 7 distance sensors, IR temperature sensors, 3 CCD cameras, voice sensor, robot wheels encoders, yawn/pitch/roll angle sensors, laser range finder and 8 spare A/D inputs. All sensors and controlling data are communicated with a remote control station via IEEE 802.11b Wi-Fi. The audio and video data are compressed and sent via another IEEE 802.11g Wi-Fi transmitter for getting real-time response. At remote control station site, the robot locomotion and the mechanical arm are controlled by joystick. Moreover, the user-friendly GUI control program is developed based on the clicking and dragging method to easily control the movement of the arm. Robot traveling map is plotted from computing the information of wheel encoders and the yawn/pitch data. 2D Obstacle map is plotted from data of the laser range finder. The concept and design of this robot can be adapted to suit many other applications. As the Best Technique awardee from Thailand Rescue Robot Championship 2006, all testing results are satisfied.
Abstract: Previously, harmonic parameters (HPs) have been
selected as features extracted from EEG signals for automatic sleep
scoring. However, in previous studies, only one HP parameter was
used, which were directly extracted from the whole epoch of EEG
signal.
In this study, two different transformations were applied to extract
HPs from EEG signals: Hilbert-Huang transform (HHT) and wavelet
transform (WT). EEG signals are decomposed by the two
transformations; and features were extracted from different
components. Twelve parameters (four sets of HPs) were extracted.
Some of the parameters are highly diverse among different stages.
Afterward, HPs from two transformations were used to building a
rough sleep stages scoring model using the classifier SVM. The
performance of this model is about 78% using the features obtained by
our proposed extractions. Our results suggest that these features may
be useful for automatic sleep stages scoring.
Abstract: Three-dimensional simulation of harmonic up
generation in free electron laser amplifier operating simultaneously
with a cold and relativistic electron beam is presented in steady-state
regime where the slippage of the electromagnetic wave with respect
to the electron beam is ignored. By using slowly varying envelope
approximation and applying the source-dependent expansion to wave
equations, electromagnetic fields are represented in terms of the
Hermit Gaussian modes which are well suited for the planar wiggler
configuration. The electron dynamics is described by the fully threedimensional
Lorentz force equation in presence of the realistic planar
magnetostatic wiggler and electromagnetic fields. A set of coupled
nonlinear first-order differential equations is derived and solved
numerically. The fundamental and third harmonic radiation of the
beam is considered. In addition to uniform beam, prebunched
electron beam has also been studied. For this effect of sinusoidal
distribution of entry times for the electron beam on the evolution of
radiation is compared with uniform distribution. It is shown that
prebunching reduces the saturation length substantially. For
efficiency enhancement the wiggler is set to decrease linearly when
the radiation of the third harmonic saturates. The optimum starting
point of tapering and the slope of radiation in the amplitude of
wiggler are found by successive run of the code.
Abstract: The beneficial effects of Si are mainly associated with
its high deposition in plant tissue and enhancing their strength and
rigidity. We investigated the role of Si against cadmium stress in
(Echium C) in house green condition. When the seventh leaves was
be appeared, plants were pretreated with five levels of Si: 0, 0.2, 0.5,
0.7and 1.5 mM Si (as sodium trisilicate, Na2(SiO2)3) and after that
plants were treated with two levels of Cd (30 and 90 mM). The
effects of Silicon and Cd were investigated on some physiological
and biochemical parameters such as: lipid peroxidation
(malondialdehyde (MDA) and other aldehydes, antocyanin and
flavonoid content. Our results showed that Cd significantly increased
MDA, other aldehydes, antocyanin and flavonoids content in
Echium and silicon offset the negative effect and increased tolerance
of Echium against Cd stress. From this results we concluded that Si
increase membrane integrity and antioxidative ability in this plant
against cd stress.
Abstract: Automatic Extraction of Event information from
social text stream (emails, social network sites, blogs etc) is a vital
requirement for many applications like Event Planning and
Management systems and security applications. The key information
components needed from Event related text are Event title, location,
participants, date and time. Emails have very unique distinctions over
other social text streams from the perspective of layout and format
and conversation style and are the most commonly used
communication channel for broadcasting and planning events.
Therefore we have chosen emails as our dataset. In our work, we
have employed two statistical NLP methods, named as Finite State
Machines (FSM) and Hidden Markov Model (HMM) for the
extraction of event related contextual information. An application
has been developed providing a comparison among the two methods
over the event extraction task. It comprises of two modules, one for
each method, and works for both bulk as well as direct user input.
The results are evaluated using Precision, Recall and F-Score.
Experiments show that both methods produce high performance and
accuracy, however HMM was good enough over Title extraction and
FSM proved to be better for Venue, Date, and time.
Abstract: The governing two-dimensional equations of a heterogeneous material composed of a fluid (allowed to flow in the absence of acoustic excitations) and a crystalline piezoelectric cubic solid stacked one-dimensionally (along the z direction) are derived and special emphasis is given to the discussion of acoustic group velocity for the structure as a function of the wavenumber component perpendicular to the stacking direction (being the x axis). Variations in physical parameters with y are neglected assuming infinite material homogeneity along the y direction and the flow velocity is assumed to be directed along the x direction. In the first part of the paper, the governing set of differential equations are derived as well as the imposed boundary conditions. Solutions are provided using Hamilton-s equations for the wavenumber vs. frequency as a function of the number and thickness of solid layers and fluid layers in cases with and without flow (also the case of a position-dependent flow in the fluid layer is considered). In the first part of the paper, emphasis is given to the small-frequency case. Boundary conditions at the bottom and top parts of the full structure are left unspecified in the general solution but examples are provided for the case where these are subject to rigid-wall conditions (Neumann boundary conditions in the acoustic pressure). In the second part of the paper, emphasis is given to the general case of larger frequencies and wavenumber-frequency bandstructure formation. A wavenumber condition for an arbitrary set of consecutive solid and fluid layers, involving four propagating waves in each solid region, is obtained again using the monodromy matrix method. Case examples are finally discussed.
Abstract: A kinetic model for propane dehydrogenation in an
industrial moving bed reactor is developed based on the reported
reaction scheme. The kinetic parameters and activity constant are
fine tuned with several sets of balanced plant data. Plant data at
different operating conditions is applied to validate the model and
the results show a good agreement between the model
predictions and plant observations in terms of the amount of main
product, propylene produced. The simulation analysis of key
variables such as inlet temperature of each reactor (Tinrx) and
hydrogen to total hydrocarbon ratio (H2/THC) affecting process
performance is performed to identify the operating condition to
maximize the production of propylene. Within the range of operating
conditions applied in the present studies, the operating condition to
maximize the propylene production at the same weighted average
inlet temperature (WAIT) is ΔTinrx1= -2, ΔTinrx2= +1, ΔTinrx3= +1 ,
ΔTinrx4= +2 and ΔH2/THC= -0.02. Under this condition, the surplus
propylene produced is 7.07 tons/day as compared with base case.
Abstract: Protein 3D structure prediction has always been an
important research area in bioinformatics. In particular, the
prediction of secondary structure has been a well-studied research
topic. Despite the recent breakthrough of combining multiple
sequence alignment information and artificial intelligence algorithms
to predict protein secondary structure, the Q3 accuracy of various
computational prediction algorithms rarely has exceeded 75%. In a
previous paper [1], this research team presented a rule-based method
called RT-RICO (Relaxed Threshold Rule Induction from Coverings)
to predict protein secondary structure. The average Q3 accuracy on
the sample datasets using RT-RICO was 80.3%, an improvement
over comparable computational methods. Although this demonstrated
that RT-RICO might be a promising approach for predicting
secondary structure, the algorithm-s computational complexity and
program running time limited its use. Herein a parallelized
implementation of a slightly modified RT-RICO approach is
presented. This new version of the algorithm facilitated the testing of
a much larger dataset of 396 protein domains [2]. Parallelized RTRICO
achieved a Q3 score of 74.6%, which is higher than the
consensus prediction accuracy of 72.9% that was achieved for the
same test dataset by a combination of four secondary structure
prediction methods [2].
Abstract: The use of a Bayesian Hierarchical Model (BHM) to interpret breath measurements obtained during a 13C Octanoic Breath Test (13COBT) is demonstrated. The statistical analysis was implemented using WinBUGS, a commercially available computer package for Bayesian inference. A hierarchical setting was adopted where poorly defined parameters associated with a delayed Gastric Emptying (GE) were able to "borrow" strength from global distributions. This is proved to be a sufficient tool to correct model's failures and data inconsistencies apparent in conventional analyses employing a Non-linear least squares technique (NLS). Direct comparison of two parameters describing gastric emptying ng ( tlag -lag phase, t1/ 2 -half emptying time) revealed a strong correlation between the two methods. Despite our large dataset ( n = 164 ), Bayesian modeling was fast and provided a successful fitting for all subjects. On the contrary, NLS failed to return acceptable estimates in cases where GE was delayed.
Abstract: In this paper, we first give the representation of the general solution of the following least-squares problem (LSP): Given matrices X ∈ Rn×p, B ∈ Rp×p and A0 ∈ Rr×r, find a matrix A ∈ Rn×n such that XT AX − B = min, s. t. A([1, r]) = A0, where A([1, r]) is the r×r leading principal submatrix of the matrix A. We then consider a best approximation problem: given an n × n matrix A˜ with A˜([1, r]) = A0, find Aˆ ∈ SE such that A˜ − Aˆ = minA∈SE A˜ − A, where SE is the solution set of LSP. We show that the best approximation solution Aˆ is unique and derive an explicit formula for it. Keyw
Abstract: Panoramic view generation has always offered
novel and distinct challenges in the field of image processing.
Panoramic view generation is nothing but construction of bigger
view mosaic image from set of partial images of the desired view.
The paper presents a solution to one of the problems of image
seascape formation where some of the partial images are color and
others are grayscale. The simplest solution could be to convert all
image parts into grayscale images and fusing them to get grayscale
image panorama. But in the multihued world, obtaining the colored
seascape will always be preferred. This could be achieved by picking
colors from the color parts and squirting them in grayscale parts of
the seascape. So firstly the grayscale image parts should be colored
with help of color image parts and then these parts should be fused to
construct the seascape image.
The problem of coloring grayscale images has no exact solution.
In the proposed technique of panoramic view generation, the job of
transferring color traits from reference color image to grayscale
image is done by palette based method. In this technique, the color
palette is prepared using pixel windows of some degrees taken from
color image parts. Then the grayscale image part is divided into pixel
windows with same degrees. For every window of grayscale image
part the palette is searched and equivalent color values are found,
which could be used to color grayscale window. For palette
preparation we have used RGB color space and Kekre-s LUV color
space. Kekre-s LUV color space gives better quality of coloring. The
searching time through color palette is improved over the exhaustive
search using Kekre-s fast search technique.
After coloring the grayscale image pieces the next job is fusion of
all these pieces to obtain panoramic view. For similarity estimation
between partial images correlation coefficient is used.
Abstract: Leo Breimans Random Forests (RF) is a recent
development in tree based classifiers and quickly proven to be one of
the most important algorithms in the machine learning literature. It
has shown robust and improved results of classifications on standard
data sets. Ensemble learning algorithms such as AdaBoost and
Bagging have been in active research and shown improvements in
classification results for several benchmarking data sets with mainly
decision trees as their base classifiers. In this paper we experiment to
apply these Meta learning techniques to the random forests. We
experiment the working of the ensembles of random forests on the
standard data sets available in UCI data sets. We compare the
original random forest algorithm with their ensemble counterparts
and discuss the results.
Abstract: Mathematical models of dynamics employing exterior calculus are mathematical representations of the same unifying principle; namely, the description of a dynamic system with a characteristic differential one-form on an odd-dimensional differentiable manifold leads, by analysis with exterior calculus, to a set of differential equations and a characteristic tangent vector (vortex vector) which define transformations of the system. Using this principle, a mathematical model for economic growth is constructed by proposing a characteristic differential one-form for economic growth dynamics (analogous to the action in Hamiltonian dynamics), then generating a pair of characteristic differential equations and solving these equations for the rate of economic growth as a function of labor and capital. By contracting the characteristic differential one-form with the vortex vector, the Lagrangian for economic growth dynamics is obtained.
Abstract: The purpose of this study is to derive parameters
estimating for the Lyman–Kutcher–Burman (LKB) normal tissue
complication probability (NTCP) model using analysis of scintigraphy
assessments and quality of life (QoL) measurement questionnaires for
the parotid gland (xerostomia). In total, 31 patients with
head-and-neck (HN) cancer were enrolled. Salivary excretion factor
(SEF) and EORTC QLQ-H&N35 questionnaires datasets are used for
the NTCP modeling to describe the incidence of grade 4 xerostomia.
Assuming that n= 1, NTCP fitted parameters are given as TD50= 43.6
Gy, m= 0.18 in SEF analysis, and as TD50= 44.1 Gy, m= 0.11 in QoL
measurements, respectively. SEF and QoL datasets can validate the
Quantitative Analyses of Normal Tissue Effects in the Clinic
(QUANTEC) guidelines well, resulting in NPV-s of 100% for the both
datasets and suggests that the QUANTEC 25/20Gy gland-spared
guidelines are suitable for clinical used for the HN cohort to
effectively avoid xerostomia.
Abstract: Mel Frequency Cepstral Coefficient (MFCC) features
are widely used as acoustic features for speech recognition as well
as speaker recognition. In MFCC feature representation, the Mel frequency
scale is used to get a high resolution in low frequency region,
and a low resolution in high frequency region. This kind of processing
is good for obtaining stable phonetic information, but not suitable
for speaker features that are located in high frequency regions. The
speaker individual information, which is non-uniformly distributed
in the high frequencies, is equally important for speaker recognition.
Based on this fact we proposed an admissible wavelet packet based
filter structure for speaker identification. Multiresolution capabilities
of wavelet packet transform are used to derive the new features.
The proposed scheme differs from previous wavelet based works,
mainly in designing the filter structure. Unlike others, the proposed
filter structure does not follow Mel scale. The closed-set speaker
identification experiments performed on the TIMIT database shows
improved identification performance compared to other commonly
used Mel scale based filter structures using wavelets.
Abstract: A genetic algorithm (GA) based feature subset
selection algorithm is proposed in which the correlation structure of
the features is exploited. The subset of features is validated according
to the classification performance. Features derived from the
continuous wavelet transform are potentially strongly correlated.
GA-s that do not take the correlation structure of features into
account are inefficient. The proposed algorithm forms clusters of
correlated features and searches for a good candidate set of clusters.
Secondly a search within the clusters is performed. Different
simulations of the algorithm on a real-case data set with strong
correlations between features show the increased classification
performance. Comparison is performed with a standard GA without
use of the correlation structure.
Abstract: Research into the problem of classification of sonar signals has been taken up as a challenging task for the neural networks. This paper investigates the design of an optimal classifier using a Multi layer Perceptron Neural Network (MLP NN) and Support Vector Machines (SVM). Results obtained using sonar data sets suggest that SVM classifier perform well in comparison with well-known MLP NN classifier. An average classification accuracy of 91.974% is achieved with SVM classifier and 90.3609% with MLP NN classifier, on the test instances. The area under the Receiver Operating Characteristics (ROC) curve for the proposed SVM classifier on test data set is found as 0.981183, which is very close to unity and this clearly confirms the excellent quality of the proposed classifier. The SVM classifier employed in this paper is implemented using kernel Adatron algorithm is seen to be robust and relatively insensitive to the parameter initialization in comparison to MLP NN.