Abstract: Laura Island, which is located about 50 km away from
downtown, is a source of water supply in Majuro atoll, which is the
capital of the Republic of the Marshall Islands. Low and flat Majuro
atoll has neither river nor lake. It is very important for Majuro atoll to
ensure the conservation of its water resources. However, upconing,
which is the process of partial rising of the freshwater-saltwater
boundary near the water-supply well, was caused by the excess
pumping from it during the severe drought in 1998. Upconing will
make the water usage of the freshwater lens difficult. Thus,
appropriate water usage is required to prevent up coning in the
freshwater lens because there is no other water source during drought. Numerical simulation of water usage applying SEAWAT model
was conducted at the central part of Laura Island, including the water
supply well, which was affected by upconing. The freshwater lens was
created as a result of infiltration of consistent average rainfall. The lens
shape was almost the same as the one in 1985. 0 of monthly rainfall
and variable daily pump discharge were used to calculate the
sustainable pump discharge from the water supply well. Consequently,
the total amount of pump discharge was increased as the daily pump
discharge was increased, indicating that it needs more time to recover
from upconing. Thus, a pump standard to reduce the pump intensity is
being proposed, which is based on numerical simulation concerning
the occurrence of the up-coning phenomenon in Laura Island during
the drought.
Abstract: The end panels of a large rectangular industrial duct,
which experience significant internal pressures, also experience
considerable transverse shear due to transfer of gravity loads to the
supports. The current design practice of such thin plate panels for
shear load is based on methods used for the design of plate girder
webs. The structural arrangements, the loadings and the resulting
behavior associated with the industrial duct end panels are, however,
significantly different from those of the web of a plate girder. The
large aspect ratio of the end panels gives rise to multiple bands of
tension fields, whereas the plate girder web design is based on one
tension field. In addition to shear, the industrial end panels are
subjected to internal pressure which in turn produces significant
membrane action. This paper reports a study which was undertaken
to review the current industrial analysis and design methods and to
propose a comprehensive method of designing industrial duct end
panels for shear resistance. In this investigation, a nonlinear finite element model was
developed to simulate the behavior of industrial duct end panel, along
with the associated edge stiffeners, subjected to transverse shear and
internal pressures. The model considered the geometric imperfections
and constitutive relations for steels. Six scale independent
dimensionless parameters that govern the behavior of such end panel
were identified and were then used in a parametric study. It was
concluded that the plate slenderness dominates the shear strength of
stockier end panels, and whereas, both the plate slenderness and the
aspect ratio influence the shear strength of slender end panels. Based
on these studies, this paper proposes design aids for estimating the
shear strength of rectangular duct end panels.
Abstract: Ensuring of continuity of business is basic strategy of
every company. Continuity of organization activities includes
comprehensive procedures that help in solving unexpected situations
of natural and anthropogenic character (for example flood, blaze,
economic situations). Planning of continuity operations is a process
that helps identify critical processes and implement plans for the
security and recovery of key processes. The aim of this article is to
demonstrate application of system approach to managing business
continuity called business continuity management systems in military
issues. This article describes the life cycle of business continuity
management which is based on the established cycle PDCA (Plan-
Do-Check-Act). After this is carried out by activities which are
making by University of Defence during activation of forces and
means of the integrated rescue system in case of emergencies -
accidents at a nuclear power plant in Czech Republic. Activities of
various stages of deployment earmarked forces and resources are
managed and evaluated by using MCMS application (Military
Continuity Management System).
Abstract: Sentiment analysis means to classify a given review
document into positive or negative polar document. Sentiment
analysis research has been increased tremendously in recent times
due to its large number of applications in the industry and academia.
Sentiment analysis models can be used to determine the opinion of
the user towards any entity or product. E-commerce companies can
use sentiment analysis model to improve their products on the basis
of users’ opinion. In this paper, we propose a new One-class Support
Vector Machine (One-class SVM) based sentiment analysis model
for movie review documents. In the proposed approach, we initially
extract features from one class of documents, and further test the
given documents with the one-class SVM model if a given new test
document lies in the model or it is an outlier. Experimental results
show the effectiveness of the proposed sentiment analysis model.
Abstract: We present probabilistic multinomial Dirichlet
classification model for multidimensional data and Gaussian process
priors. Here, we have considered efficient computational method that
can be used to obtain the approximate posteriors for latent variables
and parameters needed to define the multiclass Gaussian process
classification model. We first investigated the process of inducing a
posterior distribution for various parameters and latent function by
using the variational Bayesian approximations and important sampling
method, and next we derived a predictive distribution of latent
function needed to classify new samples. The proposed model is
applied to classify the synthetic multivariate dataset in order to verify
the performance of our model. Experiment result shows that our model
is more accurate than the other approximation methods.
Abstract: This paper introduces an effective method of
segmenting Korean text (place names in Korean) from a Korean road
sign image. A Korean advanced directional road sign is composed of
several types of visual information such as arrows, place names in
Korean and English, and route numbers. Automatic classification of
the visual information and extraction of Korean place names from the
road sign images make it possible to avoid a lot of manual inputs to a
database system for management of road signs nationwide. We
propose a series of problem-specific heuristics that correctly segments
Korean place names, which is the most crucial information, from the
other information by leaving out non-text information effectively. The
experimental results with a dataset of 368 road sign images show 96%
of the detection rate per Korean place name and 84% per road sign
image.
Abstract: Password authentication is one of the widely used
methods to achieve authentication for legal users of computers and
defense against attackers. There are many different ways to
authenticate users of a system and there are many password cracking
methods also developed. This paper proposes how best password
cracking can be performed on a CPU-GPGPU based system. The
main objective of this work is to project how quickly a password can
be cracked with some knowledge about the computer security and
password cracking if sufficient security is not incorporated to the
system.
Abstract: A seizure prediction method is proposed by extracting
global features using phase correlation between adjacent epochs for
detecting relative changes and local features using fluctuation/
deviation within an epoch for determining fine changes of different
EEG signals. A classifier and a regularization technique are applied
for the reduction of false alarms and improvement of the overall
prediction accuracy. The experiments show that the proposed method
outperforms the state-of-the-art methods and provides high prediction
accuracy (i.e., 97.70%) with low false alarm using EEG signals in
different brain locations from a benchmark data set.
Abstract: In this paper, a robust fault detection and isolation
(FDI) scheme is developed to monitor a multivariable nonlinear
chemical process called the Chylla-Haase polymerization reactor,
when it is under the cascade PI control. The scheme employs a radial
basis function neural network (RBFNN) in an independent mode to
model the process dynamics, and using the weighted sum-squared
prediction error as the residual. The Recursive Orthogonal Least
Squares algorithm (ROLS) is employed to train the model to
overcome the training difficulty of the independent mode of the
network. Then, another RBFNN is used as a fault classifier to isolate
faults from different features involved in the residual vector. Several
actuator and sensor faults are simulated in a nonlinear simulation of
the reactor in Simulink. The scheme is used to detect and isolate the
faults on-line. The simulation results show the effectiveness of the
scheme even the process is subjected to disturbances and
uncertainties including significant changes in the monomer feed rate,
fouling factor, impurity factor, ambient temperature, and
measurement noise. The simulation results are presented to illustrate
the effectiveness and robustness of the proposed method.
Abstract: Concurrent planning of project scheduling and
material ordering has been increasingly addressed within last decades
as an approach to improve the project execution costs. Therefore, we
have taken the problem into consideration in this paper, aiming to
maximize schedules quality robustness, in addition to minimize the
relevant costs. In this regard, a bi-objective mathematical model is
developed to formulate the problem. Moreover, it is possible to
utilize the all-unit discount for materials purchasing. The problem is
then solved by the E-constraint method, and the Pareto front is
obtained for a variety of robustness values. The applicability and
efficiency of the proposed model is tested by different numerical
instances, finally.
Abstract: In this study, the time-dependent behavior of damaged
reinforced concrete shear wall structures strengthened with composite
plates having variable fibers spacing was investigated to analyze their
seismic response. In the analytical formulation, the adherent and the
adhesive layers are all modeled as shear walls, using the mixed Finite
Element Method (FEM). The anisotropic damage model is adopted to
describe the damage extent of the Reinforced Concrete shear walls.
The phenomenon of creep and shrinkage of concrete has been
determined by Eurocode 2. Large earthquakes recorded in Algeria
(El-Asnam and Boumerdes) have been tested to demonstrate the
accuracy of the proposed method. Numerical results are obtained for non-uniform distributions of
carbon fibers in epoxy matrices. The effects of damage extent and the
delay mechanism creep and shrinkage of concrete are highlighted.
Prospects are being studied.
Abstract: The purpose of this study is to propose an effective method to improve frictional coefficient between shoe rubber soles with added glass fibers and the surfaces of icy and snowy road in order to prevent slip-and-fall accidents by the users. The additional fibers into the rubber were uniformly tilted to the perpendicular direction of the frictional surface, where tilting angles were -60, -30, +30, +60, 90 degrees and 0 (as normal specimen), respectively. It was found that parallel arraignment was effective to improve the frictional coefficient when glass fibers were embedded in the shoe rubber, while perpendicular to normal direction of the embedded glass fibers on the shoe surface was also effective to do that once after they were exposed from the shoe rubber with its abrasion. These improvements were explained by the increase of stiffness against the shear deformation of the rubber at critical frictional state and adequate scratching of fibers when fibers were protruded in perpendicular to frictional direction, respectively. Most effective angle of tilting of frictional coefficient between rubber specimens and a stone was perpendicular (= 0 degree) to frictional direction. Combinative modified rubber specimen having 2 layers was fabricated where tilting angle of protruded fibers was 0 degree near the contact surface and tilting angle of embedded fibers was 90 degrees near back surface in thickness direction to further improve the frictional coefficient. Current study suggested that effective arraignments in tilting angle of the added fibers should be applied in designing rubber shoe soles to keep the safeties for users in regions of cold climates.
Abstract: Strong anthropogenic impact has uncontrolled
consequences on the nature of the soil. Hence, up-to-date sustainable
methods of soil state improvement are essential. Investigators provide
the evidence that biochar can positively effects physical, chemical,
and biological soil properties and the abundance of mycorrhizal fungi
which are in the focus of this study. The main aim of the present
investigation is to demonstrate the effect of two types of plant growth
promoting bacteria (PGPB) inoculums along with the beech wood
biochar and mineral N additives on mycorrhizal colonization.
Experiment has been set up in laboratory conditions with containers
filled with arable soil from the protection zone of the main water
source “Brezova nad Svitavou”. Lactuca sativa (lettuce) has been
selected as a model plant. Based on the obtained data, it can be
concluded that mycorrhizal colonization increased as the result of
combined influence of biochar and PGPB inoculums amendment. In
addition, correlation analyses showed that the numbers of main
groups of cultivated bacteria were dependent on the degree of
mycorrhizal colonization.
Abstract: With the increasing dependence of countries on the
critical infrastructure, it increases their vulnerability. Big threat is
primarily in the human factor (personnel of the critical infrastructure)
and in terrorist attacks. It emphasizes the development of
methodology for searching of weak points and their subsequent
elimination. This article discusses methods for the analysis of safety
in the objects of critical infrastructure. It also contains proposal for
methodology for training employees of security services in the
objects of the critical infrastructure and developing scenarios of
attacks on selected objects of the critical infrastructure.
Abstract: In this paper, we propose the variational EM inference
algorithm for the multi-class Gaussian process classification model
that can be used in the field of human behavior recognition. This
algorithm can drive simultaneously both a posterior distribution of a
latent function and estimators of hyper-parameters in a Gaussian
process classification model with multiclass. Our algorithm is based
on the Laplace approximation (LA) technique and variational EM
framework. This is performed in two steps: called expectation and
maximization steps. First, in the expectation step, using the Bayesian
formula and LA technique, we derive approximately the posterior
distribution of the latent function indicating the possibility that each
observation belongs to a certain class in the Gaussian process
classification model. Second, in the maximization step, using a derived
posterior distribution of latent function, we compute the maximum
likelihood estimator for hyper-parameters of a covariance matrix
necessary to define prior distribution for latent function. These two
steps iteratively repeat until a convergence condition satisfies.
Moreover, we apply the proposed algorithm with human action
classification problem using a public database, namely, the KTH
human action data set. Experimental results reveal that the proposed
algorithm shows good performance on this data set.
Abstract: The Com-Poisson (CMP) model is one of the most
popular discrete generalized linear models (GLMS) that handles
both equi-, over- and under-dispersed data. In longitudinal context,
an integer-valued autoregressive (INAR(1)) process that incorporates
covariate specification has been developed to model longitudinal
CMP counts. However, the joint likelihood CMP function is
difficult to specify and thus restricts the likelihood-based estimating
methodology. The joint generalized quasi-likelihood approach
(GQL-I) was instead considered but is rather computationally
intensive and may not even estimate the regression effects due
to a complex and frequently ill-conditioned covariance structure.
This paper proposes a new GQL approach for estimating the
regression parameters (GQL-III) that is based on a single score vector
representation. The performance of GQL-III is compared with GQL-I
and separate marginal GQLs (GQL-II) through some simulation
experiments and is proved to yield equally efficient estimates as
GQL-I and is far more computationally stable.
Abstract: This paper describes the design and implementation of
a hardware setup for online monitoring of 24 refrigerators inside
blood bank center using the microcontroller and CAN bus for
communications between each node. Due to the security of locations
in the blood bank hall and difficulty of monitoring of each
refrigerator separately, this work proposes a solution to monitor all
the blood bank refrigerators in one location. CAN-bus system is used
because it has many applications and advantages, especially for this
system due to easy in use, low cost, providing a reduction in wiring,
fast to repair and easily expanding the project without a problem.
Abstract: For the last decade, researchers have started to focus
their interest on Multicast Group Key Management Framework. The
central research challenge is secure and efficient group key
distribution. The present paper is based on the Bit model based
Secure Multicast Group key distribution scheme using the most
popular absolute encoder output type code named Gray Code. The
focus is of two folds. The first fold deals with the reduction of
computation complexity which is achieved in our scheme by
performing fewer multiplication operations during the key updating
process. To optimize the number of multiplication operations, an
O(1) time algorithm to multiply two N-bit binary numbers which
could be used in an N x N bit-model of reconfigurable mesh is used
in this proposed work. The second fold aims at reducing the amount
of information stored in the Group Center and group members while
performing the update operation in the key content. Comparative
analysis to illustrate the performance of various key distribution
schemes is shown in this paper and it has been observed that this
proposed algorithm reduces the computation and storage complexity
significantly. Our proposed algorithm is suitable for high
performance computing environment.
Abstract: Multiprocessor task scheduling problem for dependent
and independent tasks is computationally complex problem. Many
methods are proposed to achieve optimal running time. As the
multiprocessor task scheduling is NP hard in nature, therefore, many
heuristics are proposed which have improved the makespan of the
problem. But due to problem specific nature, the heuristic method
which provide best results for one problem, might not provide good
results for another problem. So, Simulated Annealing which is meta
heuristic approach is considered. It can be applied on all types of
problems. However, due to many runs, meta heuristic approach takes
large computation time. Hence, the hybrid approach is proposed by
combining the Duplication Scheduling Heuristic and Simulated
Annealing (SA) and the makespan results of Simple Simulated
Annealing and Hybrid approach are analyzed.
Abstract: Liver segmentation from medical images poses more
challenges than analogous segmentations of other organs. This
contribution introduces a liver segmentation method from a series of
computer tomography images. Overall, we present a novel method for
segmenting liver by coupling density matching with shape priors.
Density matching signifies a tracking method which operates via
maximizing the Bhattacharyya similarity measure between the
photometric distribution from an estimated image region and a model
photometric distribution. Density matching controls the direction of
the evolution process and slows down the evolving contour in regions
with weak edges. The shape prior improves the robustness of density
matching and discourages the evolving contour from exceeding liver’s
boundaries at regions with weak boundaries. The model is
implemented using a modified distance regularized level set (DRLS)
model. The experimental results show that the method achieves a
satisfactory result. By comparing with the original DRLS model, it is
evident that the proposed model herein is more effective in addressing
the over segmentation problem. Finally, we gauge our performance of
our model against matrices comprising of accuracy, sensitivity, and
specificity.