Abstract: Travel demand forecasting including four travel choices, i.e., trip generation, trip distribution, modal split and traffic assignment constructs the core of transportation planning. In its current application, travel demand forecasting has associated with three important issues, i.e., interface inconsistencies among four travel choices, inefficiency of commonly used solution algorithms, and undesirable multiple path solutions. In this paper, each of the three issues is extensively elaborated. An ideal unified framework for the combined model consisting of the four travel choices and variable demand functions is also suggested. Then, a few remarks are provided in the end of the paper
Abstract: This paper discusses the performance modeling and availability analysis of Yarn Dyeing System of a Textile Industry. The Textile Industry is a complex and repairable engineering system. Yarn Dyeing System of Textile Industry consists of five subsystems arranged in series configuration. For performance modeling and analysis of availability, a performance evaluating model has been developed with the help of mathematical formulation based on Markov-Birth-Death Process. The differential equations have been developed on the basis of Probabilistic Approach using a Transition Diagram. These equations have further been solved using normalizing condition in order to develop the steady state availability, a performance measure of the system concerned. The system performance has been further analyzed with the help of decision matrices. These matrices provide various availability levels for different combinations of failure and repair rates for various subsystems. The findings of this paper are therefore, considered to be useful for the analysis of availability and determination of the best possible maintenance strategies which can be implemented in future to enhance the system performance.
Abstract: One of research issues in social network analysis is to
evaluate the position/importance of users in social networks. As the
information diffusion in social network is evolving, it seems difficult
to evaluate the importance of users using traditional approaches. In
this paper, we propose an evaluation approach for user importance
with fractal view in social networks. In this approach, the global importance
(Fractal Importance) and the local importance (Topological
Importance) of nodes are considered. The basic idea is that the bigger
the product of fractal importance and topological importance of a
node is, the more important of the node is. We devise the algorithm
called TFRank corresponding to the proposed approach. Finally, we
evaluate TFRank by experiments. Experimental results demonstrate
our TFRank has the high correlations with PageRank algorithm
and potential ranking algorithm, and it shows the effectiveness and
advantages of our approach.
Abstract: The presented paper is related to the design methods and neutronic characterization of the reactivity control system in the large power unit of Generation IV Gas cooled Fast Reactor – GFR2400. The reactor core is based on carbide pin fuel type with the application of refractory metallic liners used to enhance the fission product retention of the SiCcladding. The heterogeneous design optimization of control rod is presented and the results of rods worth and their interferences in a core are evaluated. In addition, the idea of reflector removal as an additive reactivity management option is investigated and briefly described.
Abstract: The catalytic dehydroxylation of glycerol to propylene
glycol was investigated over Cu-ZnO/Al2O3 prepared by incipient
wetness impregnation (IWI) method with different purity feedstocks -
refined glycerol and technical grade glycerol. The main purpose is to
investigate the effects of feed impurities that cause the catalyst
deactivation. The prepared catalyst were tested for its catalytic
activity and selectivity in a continuous flow fixed bed reactor at 523
K, 500 psig, H2/feed molar ratio of 4 and WHSV of 3 h-1. The results
showed that conversion of refined glycerol and technical grade
glycerol at time on stream 6 hour are 99% and 71% and selectivity to
propylene glycol are 87% and 56% respectively. The ICP-EOS and
TPO results indicated that the cause of catalyst deactivation was the
amount of impurities in the feedstock. The higher amount of
impurities (especially Na and K) the lower catalytic activity.
Abstract: A new current-mode multifunction filter using minimum number of passive elements is proposed. The proposed filter has single-input and four high-impedance outputs. It uses four passive elements (two capacitors and two resistors) and four dual output second generation current conveyors. Each output provides a different filter response, namely, low-pass, high-pass, band-pass and band-reject. The sensitivity analysis is also carried out on both ideal and non-ideal filter configurations. The validity of the proposed filter is verified through PSPICE simulations.
Abstract: The general idea behind the filter is to average a pixel
using other pixel values from its neighborhood, but simultaneously to
take care of important image structures such as edges. The main
concern of the proposed filter is to distinguish between any variations
of the captured digital image due to noise and due to image structure.
The edges give the image the appearance depth and sharpness. A
loss of edges makes the image appear blurred or unfocused.
However, noise smoothing and edge enhancement are traditionally
conflicting tasks. Since most noise filtering behaves like a low pass
filter, the blurring of edges and loss of detail seems a natural
consequence. Techniques to remedy this inherent conflict often
encompass generation of new noise due to enhancement.
In this work a new fuzzy filter is presented for the noise reduction
of images corrupted with additive noise. The filter consists of three
stages. (1) Define fuzzy sets in the input space to computes a fuzzy
derivative for eight different directions (2) construct a set of IFTHEN
rules by to perform fuzzy smoothing according to
contributions of neighboring pixel values and (3) define fuzzy sets in
the output space to get the filtered and edged image.
Experimental results are obtained to show the feasibility of the
proposed approach with two dimensional objects.
Abstract: The accelerated sonophotocatalytic degradation of
Reactive Red (RR) 120 dye under visible light using dye sensitized
TiO2 activated by ultrasound has been carried out. The effect of
sonolysis, photocatalysis and sonophotocatalysis under visible light
has been examined to study the influence on the degradation rates by
varying the initial substrate concentration, pH and catalyst loading to
ascertain the synergistic effect on the degradation techniques.
Ultrasonic activation contributes degradation through cavitation
leading to the splitting of H2O2 produced by both photocatalysis and
sonolysis. This results in the formation of oxidative species, such as
singlet oxygen (1O2) and superoxide (O2
-●) radicals in the presence of
oxygen. The increase in the amount of reactive radical species which
induce faster oxidation of the substrate and degradation of
intermediates and also the deaggregation of the photocatalyst are
responsible for the synergy observed under sonication. A
comparative study of photocatalysis and sonophotocatalysis using
TiO2, Hombikat UV 100 and ZnO was also carried out.
Abstract: In this paper we present an autoregressive model with
neural networks modeling and standard error backpropagation
algorithm training optimization in order to predict the gross domestic
product (GDP) growth rate of four countries. Specifically we propose
a kind of weighted regression, which can be used for econometric
purposes, where the initial inputs are multiplied by the neural
networks final optimum weights from input-hidden layer after the
training process. The forecasts are compared with those of the
ordinary autoregressive model and we conclude that the proposed
regression-s forecasting results outperform significant those of
autoregressive model in the out-of-sample period. The idea behind
this approach is to propose a parametric regression with weighted
variables in order to test for the statistical significance and the
magnitude of the estimated autoregressive coefficients and
simultaneously to estimate the forecasts.
Abstract: In many cases, there are some time lag between the consumption of inputs and the production of outputs. This time lag effect should be considered in evaluating the performance of organizations. Recently, a couple of DEA models were developed for considering time lag effect in efficiency evaluation of research activities. Multi-periods input(MpI) and Multi-periods output(MpO) models are integrate models to calculate simple efficiency considering time lag effect. However, these models can’t discriminate efficient DMUs because of the nature of basic DEA model in which efficiency scores are limited to ‘1’. That is, efficient DMUs can’t be discriminated because their efficiency scores are same. Thus, this paper suggests a super-efficiency model for efficiency evaluation under the consideration of time lag effect based on the MpO model. A case example using a long term research project is given to compare the suggested model with the MpO model.
Abstract: In this paper we present a new approach to deal with
image segmentation. The fact that a single segmentation result do not
generally allow a higher level process to take into account all the
elements included in the image has motivated the consideration of
image segmentation as a multiobjective optimization problem. The
proposed algorithm adopts a split/merge strategy that uses the result
of the k-means algorithm as input for a quantum evolutionary
algorithm to establish a set of non-dominated solutions. The
evaluation is made simultaneously according to two distinct features:
intra-region homogeneity and inter-region heterogeneity. The
experimentation of the new approach on natural images has proved
its efficiency and usefulness.
Abstract: The fine structure of supercavitation in the wake of a
symmetrical cylinder is studied with high-speed video cameras. The
flow is observed in a cavitation tunnel at the speed of 8m/sec when the
sidewall and the wake are partially filled with the massive cavitation
bubbles. The present experiment observed that a two-dimensional
ripple wave with a wave length of 0.3mm is propagated in a
downstream direction, and then abruptly increases to a thicker
three-dimensional layer. IR-photography recorded that the wakes
originated from the horseshoe vortexes alongside the cylinder. The
wake was developed to inside the dead water zone, which absorbed the
bubbly wake propelled from the separated vortices at the center of the
cylinder. A remote sensing classification technique (maximum most
likelihood) determined that the surface porosity was 0.2, and the mean
speed in the mixed wake was 7m/sec. To confirm the existence of
two-dimensional wave motions in the interface, the experiments were
conducted at a very low frequency, and showed similar gravity waves
in both the upper and lower interfaces.
Abstract: A new deployment of the multiple criteria decision
making (MCDM) techniques: the Simple Additive Weighting
(SAW), and the Technique for Order Preference by Similarity to
Ideal Solution (TOPSIS) for portfolio allocation, is demonstrated in
this paper. Rather than exclusive reference to mean and variance as in
the traditional mean-variance method, the criteria used in this
demonstration are the first four moments of the portfolio distribution.
Each asset is evaluated based on its marginal impacts to portfolio
higher moments that are characterized by trapezoidal fuzzy numbers.
Then centroid-based defuzzification is applied to convert fuzzy
numbers to the crisp numbers by which SAW and TOPSIS can be
deployed. Experimental results suggest the similar efficiency of these
MCDM approaches to selecting dominant assets for an optimal
portfolio under higher moments. The proposed approaches allow
investors flexibly adjust their risk preferences regarding higher
moments via different schemes adapting to various (from
conservative to risky) kinds of investors. The other significant
advantage is that, compared to the mean-variance analysis, the
portfolio weights obtained by SAW and TOPSIS are consistently
well-diversified.
Abstract: This paper deals with e-government issues at several
levels. Initially we look at the concept of e-government itself in order
to give it a sound framework. Than we look at the e-government
issues at three levels, first we analyse it at the global level, second we
analyse it at the level of transition economies, and finally we take a
closer look on developments in Croatia. The analysis includes actual
progress being made in selected transition economies given the Euro
area averages, along with e-government potential in future
demanding period.
Abstract: The Spiral development model has been used
successfully in many commercial systems and in a good number of
defense systems. This is due to the fact that cost-effective
incremental commitment of funds, via an analogy of the spiral model
to stud poker and also can be used to develop hardware or integrate
software, hardware, and systems. To support adaptive, semantic
collaboration between domain experts and knowledge engineers, a
new knowledge engineering process, called Spiral_OWL is proposed.
This model is based on the idea of iterative refinement, annotation
and structuring of knowledge base. The Spiral_OWL model is
generated base on spiral model and knowledge engineering
methodology. A central paradigm for Spiral_OWL model is the
concentration on risk-driven determination of knowledge engineering
process. The collaboration aspect comes into play during knowledge
acquisition and knowledge validation phase. Design rationales for the
Spiral_OWL model are to be easy-to-implement, well-organized, and
iterative development cycle as an expanding spiral.
Abstract: The application of a simple microcontroller to deal
with a three variable input and a single output fuzzy logic controller,
with Proportional – Integral – Derivative (PID) response control
built-in has been tested for an automatic voltage regulator. The
fuzzifiers are based on fixed range of the variables of output voltage.
The control output is used to control the wiper motor of the auto
transformer to adjust the voltage, using fuzzy logic principles, so that
the voltage is stabilized. In this report, the author will demonstrate
how fuzzy logic might provide elegant and efficient solutions in the
design of multivariable control based on experimental results rather
than on mathematical models.
Abstract: The competitive learning is an adaptive process in
which the neurons in a neural network gradually become sensitive to
different input pattern clusters. The basic idea behind the Kohonen-s
Self-Organizing Feature Maps (SOFM) is competitive learning.
SOFM can generate mappings from high-dimensional signal spaces
to lower dimensional topological structures. The main features of this
kind of mappings are topology preserving, feature mappings and
probability distribution approximation of input patterns. To overcome
some limitations of SOFM, e.g., a fixed number of neural units and a
topology of fixed dimensionality, Growing Self-Organizing Neural
Network (GSONN) can be used. GSONN can change its topological
structure during learning. It grows by learning and shrinks by
forgetting. To speed up the training and convergence, a new variant
of GSONN, twin growing cell structures (TGCS) is presented here.
This paper first gives an introduction to competitive learning, SOFM
and its variants. Then, we discuss some GSONN with fixed
dimensionality, which include growing cell structures, its variants
and the author-s model: TGCS. It is ended with some testing results
comparison and conclusions.
Abstract: The paper deals with the perspectives and possibilities
of "smart solutions" to critical infrastructure protection. It means that
common computer aided technologies are used from the perspective
of new, better protection of selected infrastructure objects. The paper
is focused on the co-product of the Czech Defence Research Project -
ADAPTIV. This project is carrying out by the University of Defence,
Faculty of Economics and Management at the Department of Civil
Protection. The project creates system and technology for adaptive
cybernetic camouflage of armed forces objects, armaments, vehicles
and troops and of mobilization infrastructure. These adaptive
camouflage system and technology will be useful for army tactic
activities protection and for decoys generation also. The fourth
chapter of the paper concerns the possibilities of using the introduced
technology to the protection of selected civil (economically
important), critical infrastructure objects. The aim of this section
is to introduce the scientific capabilities and potential of the
University of Defence research results and solutions for the practice.
Abstract: In this paper we investigated a number of the Internet
congestion control algorithms that has been developed in the last few
years. It was obviously found that many of these algorithms were
designed to deal with the Internet traffic merely as a train of
consequent packets. Other few algorithms were specifically tailored
to handle the Internet congestion caused by running media traffic that
represents audiovisual content. This later set of algorithms is
considered to be aware of the nature of this media content. In this
context we briefly explained a number of congestion control
algorithms and hence categorized them into the two following
categories: i) Media congestion control algorithms. ii) Common
congestion control algorithms. We hereby recommend the usage of
the media congestion control algorithms for the reason of being
media content-aware rather than the other common type of
algorithms that blindly manipulates such traffic. We showed that the
spread of such media content-aware algorithms over Internet will
lead to better congestion control status in the coming years. This is
due to the observed emergence of the era of digital convergence
where the media traffic type will form the majority of the Internet
traffic.
Abstract: This paper presents the findings of an
experimental investigation to study the effect of alkali content
in geopolymer mortar specimens exposed to sulphuric acid.
Geopolymer mortar specimens were manufactured from Class F fly
ash by activation with a mixture of sodium hydroxide and sodium
silicate solution containing 5% to 8% Na2O. Durability of specimens
were assessed by immersing them in 10% sulphuric acid solution and
periodically monitoring surface deterioration and depth of
dealkalization, changes in weight and residual compressive strength
over a period of 24 weeks. Microstructural changes in the specimens
were studied with Scanning electron microscopy (SEM) and EDAX.
Alkali content in the activator solution significantly affects the
durability of fly ash based geopolymer mortars in sulphuric acid.
Specimens manufactured with higher alkali content performed better
than those manufactured with lower alkali content. After 24 weeks in
sulphuric acid, specimen with 8% alkali still recorded a residual
strength as high as 55%.