Abstract: This paper mathematically analyses the varying
magnitude of production loss, which may occur due to idle time (inprocess
waiting time and traveling time) on a linear walking worker
assembly line. Within this flexible and reconfigurable assembly
system, each worker travels down the line carrying out each
assembly task at each station; and each worker accomplishes the
assembly of a unit from start to finish and then travels back to the
first station to start the assembly of a new product. This strategy of
system design attempts to combine the flexibility of the U-shaped
moving worker assembly cell with the efficiency of the conventional
fixed worker assembly line. The paper aims to evaluate the effect of
idle time that may offset the labor efficiency of each walking worker
providing an insight into the mechanism of such a flexible and
reconfigurable assembly system.
Abstract: For decades financial economists have been attempted to determine the optimal investment policy by recognizing the option value embedded in irreversible investment whose project value evolves as a geometric Brownian motion (GBM). This paper aims to examine the effects of the optimal investment trigger and of the misspecification of stochastic processes on investment in real options applications. Specifically, the former explores the consequence of adopting optimal investment rules on the distributions of corporate value under the correct assumption of stochastic process while the latter analyzes the influence on the distributions of corporate value as a result of the misspecification of stochastic processes, i.e., mistaking an alternative process as a GBM. It is found that adopting the correct optimal investment policy may increase corporate value by shifting the value distribution rightward, and the misspecification effect may decrease corporate value by shifting the value distribution leftward. The adoption of the optimal investment trigger has a major impact on investment to such an extent that the downside risk of investment is truncated at the project value of zero, thereby moving the value distributions rightward. The analytical framework is also extended to situations where collection lags are in place, and the result indicates that collection lags reduce the effects of investment trigger and misspecification on investment in an opposite way.
Abstract: Probabilistic techniques in computer programs are becoming
more and more widely used. Therefore, there is a big
interest in the formal specification, verification, and development
of probabilistic programs. In our work-in-progress project, we are
attempting to make a constructive framework for developing probabilistic
programs formally. The main contribution of this paper
is to introduce an intermediate artifact of our work, a Z-based
formalism called PZ, by which one can build set theoretical models of
probabilistic programs. We propose to use a constructive set theory,
called CZ set theory, to interpret the specifications written in PZ.
Since CZ has an interpretation in Martin-L¨of-s theory of types, this
idea enables us to derive probabilistic programs from correctness
proofs of their PZ specifications.
Abstract: The design of a complete expansion that allows for
compact representation of certain relevant classes of signals is a
central problem in signal processing applications. Achieving such a
representation means knowing the signal features for the purpose of
denoising, classification, interpolation and forecasting. Multilayer
Neural Networks are relatively a new class of techniques that are
mathematically proven to approximate any continuous function
arbitrarily well. Radial Basis Function Networks, which make use of
Gaussian activation function, are also shown to be a universal
approximator. In this age of ever-increasing digitization in the
storage, processing, analysis and communication of information,
there are numerous examples of applications where one needs to
construct a continuously defined function or numerical algorithm to
approximate, represent and reconstruct the given discrete data of a
signal. Many a times one wishes to manipulate the data in a way that
requires information not included explicitly in the data, which is
done through interpolation and/or extrapolation.
Tidal data are a very perfect example of time series and many
statistical techniques have been applied for tidal data analysis and
representation. ANN is recent addition to such techniques. In the
present paper we describe the time series representation capabilities
of a special type of ANN- Radial Basis Function networks and
present the results of tidal data representation using RBF. Tidal data
analysis & representation is one of the important requirements in
marine science for forecasting.
Abstract: The purpose of this paper is to describe the process of
setting up a learning community within an elementary school in
Ontario, Canada. The description is provided through reflection and
examination of field notes taken during the yearlong training and
implementation process. Specifically the impact of teachers- capacity
on the creation of a learning community was of interest. This paper is
intended to inform and add to the debate around the tensions that
exist in implementing a bottom-up professional development model
like the learning community in a top-down organizational structure.
My reflections of the process illustrate that implementation of the
learning community professional development model may be
difficult and yet transformative in the professional lives of the
teachers, students, and administration involved in the change process.
I conclude by suggesting the need for a new model of professional
development that requires a transformative shift in power dynamics
and a shift in the view of what constitutes effective professional
learning.
Abstract: The paper deals with the pulsating flow of an incompressible couple stress fluid between permeable beds. The couple stress fluid is injected into the channel from the lower permeable bed with a certain velocity and is sucked into the upper permeable bed with the same velocity. The flow between the permeable beds is assumed to be governed by couple stress fluid flow equations of V. K. Stokes and that in the permeable regions by Darcy-s law. The equations are solved analytically and the expressions for velocity and volume flux are obtained. The effects of the material parameters are studied numerically and the results are presented through graphs.
Abstract: The amount of the information being churned out by the field of biology has jumped manifold and now requires the extensive use of computer techniques for the management of this information. The predominance of biological information such as protein sequence similarity in the biological information sea is key information for detecting protein evolutionary relationship. Protein sequence similarity typically implies homology, which in turn may imply structural and functional similarities. In this work, we propose, a learning method for detecting remote protein homology. The proposed method uses a transformation that converts protein sequence into fixed-dimensional representative feature vectors. Each feature vector records the sensitivity of a protein sequence to a set of amino acids substrings generated from the protein sequences of interest. These features are then used in conjunction with support vector machines for the detection of the protein remote homology. The proposed method is tested and evaluated on two different benchmark protein datasets and it-s able to deliver improvements over most of the existing homology detection methods.
Abstract: This paper presents a forgetting factor scheme for variable step-size affine projection algorithms (APA). The proposed scheme uses a forgetting processed input matrix as the projection matrix of pseudo-inverse to estimate system deviation. This method introduces temporal weights into the projection matrix, which is typically a better model of the real error's behavior than homogeneous temporal weights. The regularization overcomes the ill-conditioning introduced by both the forgetting process and the increasing size of the input matrix. This algorithm is tested by independent trials with coloured input signals and various parameter combinations. Results show that the proposed algorithm is superior in terms of convergence rate and misadjustment compared to existing algorithms. As a special case, a variable step size NLMS with forgetting factor is also presented in this paper.
Abstract: The theoretical investigation is carried out to describe
the effect of increase of pressure waves amplitude in clean and bubbly liquid. The goal of the work is to capture the regime of multiple magnification of acoustic and shock waves in the liquid,
which enables to get appropriate conditions to enlarge collapses of
micro-bubbles. The influence of boundary conditions and frequency
of the governing acoustic field is studied for the case of the
cylindrical acoustic resonator. It has been observed the formation of
standing waves with large amplitude at resonant frequencies. The
interaction of the compression wave with gas and vapor bubbles is
investigated for the convergent channel. It is shown theoretically that
the chemical reactions, which occur inside gas bubbles, provide additional impulse to the wave, that affect strongly on the collapses
of the vapor bubbles
Abstract: Digital watermarking is a way to provide the facility of secure multimedia data communication besides its copyright protection approach. The Spread Spectrum modulation principle is widely used in digital watermarking to satisfy the robustness of multimedia signals against various signal-processing operations. Several SS watermarking algorithms have been proposed for multimedia signals but very few works have discussed on the issues responsible for secure data communication and its robustness improvement. The current paper has critically analyzed few such factors namely properties of spreading codes, proper signal decomposition suitable for data embedding, security provided by the key, successive bit cancellation method applied at decoder which have greater impact on the detection reliability, secure communication of significant signal under camouflage of insignificant signals etc. Based on the analysis, robust SS watermarking scheme for secure data communication is proposed in wavelet domain and improvement in secure communication and robustness performance is reported through experimental results. The reported result also shows improvement in visual and statistical invisibility of the hidden data.
Abstract: In cognitive radio (CR) systems, the primary user (PU) signal would randomly depart or arrive during the sensing period of a CR user, which is referred to as the high traffic environment. In this paper, we propose a novel spectrum sensing scheme based
on the cyclostationarity of PU signals in high traffic environments. Specifically, we obtain a test statistic by applying an estimate of spectral autocoherence function of the PU signal to the generalized- likelihood ratio. From numerical results, it is confirmed that the proposed scheme provides a better spectrum sensing performance compared with the conventional spectrum sensing scheme based on the energy of the PU signals in high traffic environments.
Abstract: The uniform Roe C*-algebra (also called uniform translation)C^*- algebra provides a link between coarse geometry and C^*- algebra theory. The uniform Roe algebra has a great importance in geometry, topology and analysis. We consider some of the elementary concepts associated with coarse spaces.
Abstract: In the context of business incubation (BI) as strategic
enablers, this paper critically reviews the literature relating to the
strategic benefits of BI in the Middle East. The taxonomy of BI
benefits in the strategic elements on 1) type, 2) financial model, 3)
services, 4) objectives, 5) number of clients, 6) number of graduates,
and 7) jobs creation. Understanding the importance of BI benefits can
be significant in the economic development although most incubators
lead to diversify the economy. Thus, taxonomies of the benefits of BI
are produced from both the academic literature and published case
studies. In this way, a classification of strategic benefits elements as
they relate to incubators has been developed to provide a greater
understanding of the benefits needed to obtain a specific element.
The result of this paper is Business incubators is aimed
entrepreneurship, jobs creation, research commercialization and
profitable enterprises in Middle Eastern countries.
Abstract: Most of fuzzy clustering algorithms have some
discrepancies, e.g. they are not able to detect clusters with convex
shapes, the number of the clusters should be a priori known, they
suffer from numerical problems, like sensitiveness to the
initialization, etc. This paper studies the synergistic combination of
the hierarchical and graph theoretic minimal spanning tree based
clustering algorithm with the partitional Gath-Geva fuzzy clustering
algorithm. The aim of this hybridization is to increase the robustness
and consistency of the clustering results and to decrease the number
of the heuristically defined parameters of these algorithms to
decrease the influence of the user on the clustering results. For the
analysis of the resulted fuzzy clusters a new fuzzy similarity measure
based tool has been presented. The calculated similarities of the
clusters can be used for the hierarchical clustering of the resulted
fuzzy clusters, which information is useful for cluster merging and
for the visualization of the clustering results. As the examples used
for the illustration of the operation of the new algorithm will show,
the proposed algorithm can detect clusters from data with arbitrary
shape and does not suffer from the numerical problems of the
classical Gath-Geva fuzzy clustering algorithm.
Abstract: Evolutionary robotics is concerned with the design of
intelligent systems with life-like properties by means of simulated
evolution. Approaches in evolutionary robotics can be categorized
according to the control structures that represent the behavior and the
parameters of the controller that undergo adaptation. The basic idea
is to automatically synthesize behaviors that enable the robot to
perform useful tasks in complex environments. The evolutionary
algorithm searches through the space of parameterized controllers
that map sensory perceptions to control actions, thus realizing a
specific robotic behavior. Further, the evolutionary algorithm
maintains and improves a population of candidate behaviors by
means of selection, recombination and mutation. A fitness function
evaluates the performance of the resulting behavior according to the
robot-s task or mission. In this paper, the focus is in the use of
genetic algorithms to solve a multi-objective optimization problem
representing robot behaviors; in particular, the A-Compander Law is
employed in selecting the weight of each objective during the
optimization process. Results using an adaptive fitness function show
that this approach can efficiently react to complex tasks under
variable environments.
Abstract: The paper presents the applications of artificial
intelligence technique called adaptive tabu search to design the
controller of a buck converter. The averaging model derived from the
DQ and generalized state-space averaging methods is applied to
simulate the system during a searching process. The simulations
using such averaging model require the faster computational time
compared with that of the full topology model from the software
packages. The reported model is suitable for the work in the paper in
which the repeating calculation is needed for searching the best
solution. The results will show that the proposed design technique
can provide the better output waveforms compared with those
designed from the classical method.
Abstract: Mixed Model Production is the practice of assembling
several distinct and different models of a product on the same
assembly line without changeovers and then sequencing those models
in a way that smoothes the demand for upstream components. In this
paper, we consider an objective function which minimizes total
stoppage time and total idle time and it is presented sequence
dependent set up time. Many studies have been done on the mixed
model assembly lines. But in this paper we specifically focused on
reducing the idle times. This is possible through various help policies.
For improving the solutions, some cases developed and about 40 tests
problem was considered. We use scatter search for optimization and
for showing the efficiency of our algorithm, experimental results
shows behavior of method. Scatter search and help policies can
produce high quality answers, so it has been used in this paper.
Abstract: A multi-block algorithm and its implementation in two-dimensional finite element numerical model CCHE2D are presented. In addition to a conventional Lagrangian Interpolation Method (LIM), a novel interpolation method, called Consistent Interpolation Method (CIM), is proposed for more accurate information transfer across the interfaces. The consistent interpolation solves the governing equations over the auxiliary elements constructed around the interpolation nodes using the same numerical scheme used for the internal computational nodes. With the CIM, the momentum conservation can be maintained as well as the mass conservation. An imbalance correction scheme is used to enforce the conservation laws (mass and momentum) across the interfaces. Comparisons of the LIM and the CIM are made using several flow simulation examples. It is shown that the proposed CIM is physically more accurate and produces satisfactory results efficiently.
Abstract: This paper describes dynamic analysis using proposed
fast finite element method for a shock absorbing structure including a
sponge. The structure is supported by nonlinear concentrated springs.
The restoring force of the spring has cubic nonlinearity and linear
hysteresis damping. To calculate damping properties for the structures
including elastic body and porous body, displacement vectors as
common unknown variable are solved under coupled condition. Under
small amplitude, we apply asymptotic method to complex eigenvalue
problem of this system to obtain modal parameters. And then
expressions of modal loss factor are derived approximately. This
approach was proposed by one of the authors previously. We call this
method as Modal Strain and Kinetic Energy Method (MSKE method).
Further, using the modal loss factors, the discretized equations in
physical coordinate are transformed into the nonlinear ordinary
coupled equations using normal coordinate corresponding to linear
natural modes. This transformation yields computation efficiency. As
a numerical example of a shock absorbing structures, we adopt double
skins with a sponge. The double skins are supported by nonlinear
concentrated springs. We clarify influences of amplitude of the input
force on nonlinear and chaotic responses.
Abstract: Hand gesture is an active area of research in the vision
community, mainly for the purpose of sign language recognition and
Human Computer Interaction. In this paper, we propose a system to
recognize alphabet characters (A-Z) and numbers (0-9) in real-time
from stereo color image sequences using Hidden Markov Models
(HMMs). Our system is based on three main stages; automatic segmentation
and preprocessing of the hand regions, feature extraction
and classification. In automatic segmentation and preprocessing stage,
color and 3D depth map are used to detect hands where the hand
trajectory will take place in further step using Mean-shift algorithm
and Kalman filter. In the feature extraction stage, 3D combined features
of location, orientation and velocity with respected to Cartesian
systems are used. And then, k-means clustering is employed for
HMMs codeword. The final stage so-called classification, Baum-
Welch algorithm is used to do a full train for HMMs parameters.
The gesture of alphabets and numbers is recognized using Left-Right
Banded model in conjunction with Viterbi algorithm. Experimental
results demonstrate that, our system can successfully recognize hand
gestures with 98.33% recognition rate.