Abstract: The Aggregate Production Plan (APP) is a schedule of
the organization-s overall operations over a planning horizon to
satisfy demand while minimizing costs. It is the baseline for any
further planning and formulating the master production scheduling,
resources, capacity and raw material planning. This paper presents a
methodology to model the Aggregate Production Planning problem,
which is combinatorial in nature, when optimized with Genetic
Algorithms. This is done considering a multitude of constraints of
contradictory nature and the optimization criterion – overall cost,
made up of costs with production, work force, inventory, and
subcontracting. A case study of substantial size, used to develop the
model, is presented, along with the genetic operators.
Abstract: This article considers with the influence of selected economic indicators for the development of the Zlin region. Development of the region is mainly influenced by business entities which are located in the region, as well as investors who contribute to the development of regions. For the development of the region it is necessary for skilled workers remain in the region and not to leave these skilled workers. The above-mentioned and other factors are affecting the development of each region.
Abstract: The paper gives the pilot results of the project that is
oriented on the use of data mining techniques and knowledge
discoveries from production systems through them. They have been
used in the management of these systems. The simulation models of
manufacturing systems have been developed to obtain the necessary
data about production. The authors have developed the way of
storing data obtained from the simulation models in the data
warehouse. Data mining model has been created by using specific
methods and selected techniques for defined problems of production
system management. The new knowledge has been applied to
production management system. Gained knowledge has been tested
on simulation models of the production system. An important benefit
of the project has been proposal of the new methodology. This
methodology is focused on data mining from the databases that store
operational data about the production process.
Abstract: A framework to estimate the state of dynamically
varying environment where data are generated from heterogeneous
sources possessing partial knowledge about the environment is presented.
This is entirely derived within Dempster-Shafer and Evidence
Filtering frameworks. The belief about the current state is expressed
as belief and plausibility functions. An addition to Single Input
Single Output Evidence Filter, Multiple Input Single Output Evidence
Filtering approach is introduced. Variety of applications such as
situational estimation of an emergency environment can be developed
within the framework successfully. Fire propagation scenario is used
to justify the proposed framework, simulation results are presented.
Abstract: Recent advances in wireless sensor networks have led
to many routing methods designed for energy-efficiency in wireless
sensor networks. Despite that many routing methods have been
proposed in USN, a single routing method cannot be energy-efficient
if the environment of the ubiquitous sensor network varies. We present
the controlling network access to various hosts and the services they
offer, rather than on securing them one by one with a network security
model. When ubiquitous sensor networks are deployed in hostile
environments, an adversary may compromise some sensor nodes and
use them to inject false sensing reports. False reports can lead to not
only false alarms but also the depletion of limited energy resource in
battery powered networks. The interleaved hop-by-hop authentication
scheme detects such false reports through interleaved authentication.
This paper presents a LMDD (Low energy method for data delivery)
algorithm that provides energy-efficiency by dynamically changing
protocols installed at the sensor nodes. The algorithm changes
protocols based on the output of the fuzzy logic which is the fitness
level of the protocols for the environment.
Abstract: Matrix metalloproteinase-3 (MMP3) is key member
of the MMP family, and is known to be present in coronary
atherosclerotic. Several studies have demonstrated that MMP-3
5A/6A polymorphism modify each transcriptional activity in allele
specific manner. We hypothesized that this polymorphism may play
a role as risk factor for development of coronary stenosis. The aim of
our study was to estimate MMP-3 (5A/6A) gene polymorphism on
interindividual variability in risk for coronary stenosis in an Iranian
population.DNA was extracted from white blood cells and genotypes
were obtained from coronary stenosis cases (n=95) and controls
(n=100) by PCR (polymerase chain reaction) and restriction
fragment length polymorphism techniques. Significant differences
between cases and controls were observed for MMP3 genotype
frequencies (X2=199.305, p< 0.001); the 6A allele was less
frequently seen in the control group, compared to the disease group
(85.79 vs. 78%, 6A/6A+5A/6A vs. 5A/5A, P≤0.001). These data
imply the involvement of -1612 5A/6A polymorphism in coronary
stenosis, and suggest that probably the 6A/6A MMP-3 genotype is a
genetic susceptibility factor for coronary stenosis.
Abstract: Sociological models (e.g., social network analysis, small-group dynamic and gang models) have historically been used to predict the behavior of terrorist groups. However, they may not be the most appropriate method for understanding the behavior of terrorist organizations because the models were not initially intended to incorporate violent behavior of its subjects. Rather, models that incorporate life and death competition between subjects, i.e., models utilized by scientists to examine the behavior of wildlife populations, may provide a more accurate analysis. This paper suggests the use of biological models to attain a more robust method for understanding the behavior of terrorist organizations as compared to traditional methods. This study also describes how a biological population model incorporating predator-prey behavior factors can predict terrorist organizational recruitment behavior for the purpose of understanding the factors that govern the growth and decline of terrorist organizations. The Lotka-Volterra, a biological model that is based on a predator-prey relationship, is applied to a highly suggestive case study, that of the Irish Republican Army. This case study illuminates how a biological model can be utilized to understand the actions of a terrorist organization.
Abstract: The present paper was concerned primarily with the
analysis, simulation of the air flow and thermal patterns in a lecture
room. The paper is devoted to numerically investigate the influence
of location and number of ventilation and air conditioning supply and
extracts openings on air flow properties in a lecture room. The work
focuses on air flow patterns, thermal behaviour in lecture room where
large number of students. The effectiveness of an air flow system is
commonly assessed by the successful removal of sensible and latent
loads from occupants with additional of attaining air pollutant at a
prescribed level to attain the human thermal comfort conditions and
to improve the indoor air quality; this is the main target during the
present paper. The study is carried out using computational fluid
dynamics (CFD) simulation techniques as embedded in the
commercially available CFD code (FLUENT 6.2). The CFD
modelling techniques solved the continuity, momentum and energy
conservation equations in addition to standard k – ε model equations
for turbulence closure.
Throughout the investigations, numerical validation is carried out by
way of comparisons of numerical and experimental results. Good
agreement is found among both predictions.
Abstract: Addition of milli or micro sized particles to the heat
transfer fluid is one of the many techniques employed for improving
heat transfer rate. Though this looks simple, this method has
practical problems such as high pressure loss, clogging and erosion
of the material of construction. These problems can be overcome by
using nanofluids, which is a dispersion of nanosized particles in a
base fluid. Nanoparticles increase the thermal conductivity of the
base fluid manifold which in turn increases the heat transfer rate.
Nanoparticles also increase the viscosity of the basefluid resulting in
higher pressure drop for the nanofluid compared to the base fluid. So
it is imperative that the Reynolds number (Re) and the volume
fraction have to be optimum for better thermal hydraulic
effectiveness. In this work, the heat transfer enhancement using
aluminium oxide nanofluid using low and high volume fraction
nanofluids in turbulent pipe flow with constant wall temperature has
been studied by computational fluid dynamic modeling of the
nanofluid flow adopting the single phase approach. Nanofluid, up till
a volume fraction of 1% is found to be an effective heat transfer
enhancement technique. The Nusselt number (Nu) and friction factor
predictions for the low volume fractions (i.e. 0.02%, 0.1 and 0.5%)
agree very well with the experimental values of Sundar and Sharma
(2010). While, predictions for the high volume fraction nanofluids
(i.e. 1%, 4% and 6%) are found to have reasonable agreement with
both experimental and numerical results available in the literature.
So the computationally inexpensive single phase approach can be
used for heat transfer and pressure drop prediction of new nanofluids.
Abstract: This paper describes the design of a programmable
FSK-modulator based on VCO and its implementation in 0.35m
CMOS process. The circuit is used to transmit digital data at
100Kbps rate in the frequency range of 400-600MHz. The design
and operation of the modulator is discussed briefly. Further the
characteristics of PLL, frequency synthesizer, VCO and the whole
design are elaborated. The variation among the proposed and tested
specifications is presented. Finally, the layout of sub-modules, pin
configurations, final chip and test results are presented.
Abstract: In comparison to the original SVM, which involves a
quadratic programming task; LS–SVM simplifies the required
computation, but unfortunately the sparseness of standard SVM is
lost. Another problem is that LS-SVM is only optimal if the training
samples are corrupted by Gaussian noise. In Least Squares SVM
(LS–SVM), the nonlinear solution is obtained, by first mapping the
input vector to a high dimensional kernel space in a nonlinear
fashion, where the solution is calculated from a linear equation set. In
this paper a geometric view of the kernel space is introduced, which
enables us to develop a new formulation to achieve a sparse and
robust estimate.
Abstract: CScheme, a concurrent programming paradigm based
on scheme concept enables concurrency schemes to be constructed
from smaller synchronization units through a GUI based composer
and latter be reused on other concurrency problems of a similar
nature. This paradigm is particularly important in the multi-core
environment prevalent nowadays. In this paper, we demonstrate
techniques to separate concurrency from functional code using the
CScheme paradigm. Then we illustrate how the CScheme
methodology can be used to solve some of the traditional
concurrency problems – critical section problem, and readers-writers
problem - using synchronization schemes such as Single Threaded
Execution Scheme, and Readers Writers Scheme.
Abstract: In this paper, we present C@sa, a multiagent system aiming at modeling, controlling and simulating the behavior of an intelligent house. The developed system aims at providing to architects, designers and psychologists a simulation and control tool for understanding which is the impact of embedded and pervasive technology on people daily life. In this vision, the house is seen as an environment made up of independent and distributed devices, controlled by agents, interacting to support user's goals and tasks.
Abstract: Sparse representation which can represent high dimensional
data effectively has been successfully used in computer vision
and pattern recognition problems. However, it doesn-t consider the
label information of data samples. To overcome this limitation,
we develop a novel dimensionality reduction algorithm namely
dscriminatively regularized sparse subspace learning(DR-SSL) in this
paper. The proposed DR-SSL algorithm can not only make use of
the sparse representation to model the data, but also can effective
employ the label information to guide the procedure of dimensionality
reduction. In addition,the presented algorithm can effectively deal
with the out-of-sample problem.The experiments on gene-expression
data sets show that the proposed algorithm is an effective tool for
dimensionality reduction and gene-expression data classification.
Abstract: Repetitive control and feedback dithering modulation
are applied to a single-phase voltage source inverter, with an aim to
eliminate harmonics and stabilize the inverter under load variations.
The proposed control and modulation scheme comprise multiple loops
of feedback, which helps improve inverter performance and
robustness. Experimental results show that the designed inverter
exhibits very low distortion at its output with THD of about 0.3%
under different load variations.
Abstract: Nowadays there are several grid connected converter
in the grid system. These grid connected converters are generally the
converters of renewable energy sources, industrial four quadrant
drives and other converters with DC link. These converters are
connected to the grid through a three phase bridge. The standards
prescribe the maximal harmonic emission which could be easily
limited with high switching frequency. The increased switching
losses can be reduced to the half with the utilization of the wellknown
Flat-top modulation. The suggested control method is the
expansion of the Flat-top modulation with which the losses could be
also reduced to the half compared to the Flat-top modulation.
Comparing to traditional control these requirements can be
simultaneously satisfied much better with the DLF (DC Link
Floating) method.
Abstract: In this paper, an accurate theoretical analysis for the achievable average channel capacity (in the Shannon sense) per user of a hybrid cellular direct-sequence/fast frequency hopping code-division multiple-access (DS/FFH-CDMA) system operating in a Rayleigh fading environment is presented. The analysis covers the downlink operation and leads to the derivation of an exact mathematical expression between the normalized average channel capacity available to each system-s user, under simultaneous optimal power and rate adaptation and the system-s parameters, as the number of hops per bit, the processing gain applied, the number of users per cell and the received signal-tonoise power ratio over the signal bandwidth. Finally, numerical results are presented to illustrate the proposed mathematical analysis.
Abstract: The success of an e-learning system is highly
dependent on the quality of its educational content and how effective,
complete, and simple the design tool can be for teachers. Educational
modeling languages (EMLs) are proposed as design languages
intended to teachers for modeling diverse teaching-learning
experiences, independently of the pedagogical approach and in
different contexts. However, most existing EMLs are criticized for
being too abstract and too complex to be understood and manipulated
by teachers. In this paper, we present a visual EML that simplifies the
process of designing learning scenarios for teachers with no
programming background. Based on the conceptual framework of the
activity theory, our resulting visual EML focuses on using Domainspecific
modeling techniques to provide a pedagogical level of
abstraction in the design process.
Abstract: This paper presents a new optimization technique based on quantum computing principles to solve a security constrained power system economic dispatch problem (SCED). The proposed technique is a population-based algorithm, which uses some quantum computing elements in coding and evolving groups of potential solutions to reach the optimum following a partially directed random approach. The SCED problem is formulated as a constrained optimization problem in a way that insures a secure-economic system operation. Real Coded Quantum-Inspired Evolution Algorithm (RQIEA) is then applied to solve the constrained optimization formulation. Simulation results of the proposed approach are compared with those reported in literature. The outcome is very encouraging and proves that RQIEA is very applicable for solving security constrained power system economic dispatch problem (SCED).
Abstract: The widely used Total Variation de-noising algorithm can preserve sharp edge, while removing noise. However, since fixed regularization parameter over entire image, small details and textures are often lost in the process. In this paper, we propose a modified Total Variation algorithm to better preserve smaller-scaled features. This is done by allowing an adaptive regularization parameter to control the amount of de-noising in any region of image, according to relative information of local feature scale. Experimental results demonstrate the efficient of the proposed algorithm. Compared with standard Total Variation, our algorithm can better preserve smaller-scaled features and show better performance.