Abstract: The paper presents an approach for handling uncertain
information in deductive databases using multivalued logics. Uncertainty
means that database facts may be assigned logical values other
than the conventional ones - true and false. The logical values represent
various degrees of truth, which may be combined and propagated
by applying the database rules. A corresponding multivalued database
semantics is defined. We show that it extends successful conventional
semantics as the well-founded semantics, and has a polynomial time
data complexity.
Abstract: Effective knowledge support relies on providing
operation-relevant knowledge to workers promptly and accurately. A
knowledge flow represents an individual-s or a group-s
knowledge-needs and referencing behavior of codified knowledge
during operation performance. The flow has been utilized to facilitate
organizational knowledge support by illustrating workers-
knowledge-needs systematically and precisely. However,
conventional knowledge-flow models cannot work well in cooperative
teams, which team members usually have diverse knowledge-needs in
terms of roles. The reason is that those models only provide one single
view to all participants and do not reflect individual knowledge-needs
in flows. Hence, we propose a role-based knowledge-flow view model
in this work. The model builds knowledge-flow views (or virtual
knowledge flows) by creating appropriate virtual knowledge nodes
and generalizing knowledge concepts to required concept levels. The
customized views could represent individual role-s knowledge-needs
in teamwork context. The novel model indicates knowledge-needs in
condensed representation from a roles perspective and enhances the
efficiency of cooperative knowledge support in organizations.
Abstract: Direct search methods are evolutionary algorithms used to solve optimization problems. (DS) methods do not require any information about the gradient of the objective function at hand while searching for an optimum solution. One of such methods is Pattern Search (PS) algorithm. This paper presents a new approach based on a constrained pattern search algorithm to solve a security constrained power system economic dispatch problem (SCED). Operation of power systems demands a high degree of security to keep the system satisfactorily operating when subjected to disturbances, while and at the same time it is required to pay attention to the economic aspects. Pattern recognition technique is used first to assess dynamic security. Linear classifiers that determine the stability of electric power system are presented and added to other system stability and operational constraints. The problem is formulated as a constrained optimization problem in a way that insures a secure-economic system operation. Pattern search method is then applied to solve the constrained optimization formulation. In particular, the method is tested using one system. Simulation results of the proposed approach are compared with those reported in literature. The outcome is very encouraging and proves that pattern search (PS) is very applicable for solving security constrained power system economic dispatch problem (SCED).
Abstract: In this paper variation of spot price and total profits of
the generating companies- through wholesale electricity trading are
discussed with and without Central Generating Stations (CGS) share
and seasonal variations are also considered. It demonstrates how
proper analysis of generators- efficiencies and capabilities, types of
generators owned, fuel costs, transmission losses and settling price
variation using the solutions of Optimal Power Flow (OPF), can
allow companies to maximize overall revenue. It illustrates how
solutions of OPF can be used to maximize companies- revenue under
different scenarios. And is also extended to computation of Available
Transfer Capability (ATC) is very important to the transmission
system security and market forecasting. From these results it is
observed that how crucial it is for companies to plan their daily
operations and is certainly useful in an online environment of
deregulated power system. In this paper above tasks are demonstrated
on 124 bus real-life Indian utility power system of Andhra Pradesh
State Grid and results have been presented and analyzed.
Abstract: Saudi Arabia in recent years has seen drastic increase
in traffic related crashes. With population of over 29 million, Saudi
Arabia is considered as a fast growing and emerging economy. The
rapid population increase and economic growth has resulted in rapid
expansion of transportation infrastructure, which has led to increase
in road crashes. Saudi Ministry of Interior reported more than 7,000
people killed and 68,000 injured in 2011 ranking Saudi Arabia to be
one of the worst worldwide in traffic safety. The traffic safety issues
in the country also result in distress to road users and cause and
economic loss exceeding 3.7 billion Euros annually. Keeping this in
view, the researchers in Saudi Arabia are investigating ways to
improve traffic safety conditions in the country. This paper presents a
multilevel approach to collect traffic safety related data required to do
traffic safety studies in the region. Two highway corridors including
King Fahd Highway 39 kilometre and Gulf Cooperation Council
Highway 42 kilometre long connecting the cities of Dammam and
Khobar were selected as a study area. Traffic data collected included
traffic counts, crash data, travel time data, and speed data. The
collected data was analysed using geographic information system to
evaluate any correlation. Further research is needed to investigate the
effectiveness of traffic safety related data when collected in a
concerted effort.
Abstract: Recently, in some places, optical-fibre access
networks have been used with GPON technology belonging to
organizations (in most cases public bodies) that act as neutral
operators. These operators simultaneously provide network services
to various telecommunications operators that offer integrated voice,
data and television services. This situation creates new problems
related to quality of service, since the interests of the users are
intermingled with the interests of the operators. In this paper, we
analyse this problem and consider solutions that make it possible to
provide guaranteed quality of service for voice over IP, data services
and interactive digital television.
Abstract: This paper includes a review of three physics simulation packages that can be used to provide researchers with a virtual ground for modeling, implementing and simulating complex models, as well as testing their control methods with less cost and time of development. The inverted pendulum model was used as a test bed for comparing ODE, DANCE and Webots, while Linear State Feedback was used to control its behavior. The packages were compared with respect to model creation, solving systems of differential equation, data storage, setting system variables, control the experiment and ease of use. The purpose of this paper is to give an overview about our experience with these environments and to demonstrate some of the benefits and drawbacks involved in practice for each package.
Abstract: Selection of maize (Zea mays) hybrids with wide adaptability across diverse farming environments is important, prior to recommending them to achieve a high rate of hybrid adoption. Grain yield of 14 maize hybrids, tested in a randomized completeblock design with four replicates across 22 environments in Iran, was analyzed using site regression (SREG) stability model. The biplot technique facilitates a visual evaluation of superior genotypes, which is useful for cultivar recommendation and mega-environment identification. The objectives of this study were (i) identification of suitable hybrids with both high mean performance and high stability (ii) to determine mega-environments for maize production in Iran. Biplot analysis identifies two mega-environments in this study. The first mega-environments included KRM, KSH, MGN, DZF A, KRJ, DRB, DZF B, SHZ B, and KHM, where G10 hybrid was the best performing hybrid. The second mega-environment included ESF B, ESF A, and SHZ A, where G4 hybrid was the best hybrid. According to the ideal-hybrid biplot, G10 hybrid was better than all other hybrids, followed by the G1 and G3 hybrids. These hybrids were identified as best hybrids that have high grain yield and high yield stability. GGE biplot analysis provided a framework for identifying the target testing locations that discriminates genotypes that are high yielding and stable.
Abstract: This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of pulping problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified odified problem M-1 Ax= M-1b where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.
Abstract: We have measured the pressure drop and convective
heat transfer coefficient of water – based AL(25nm),AL2O3(30nm)
and CuO(50nm) Nanofluids flowing through a uniform heated
circular tube in the fully developed laminar flow regime. The
experimental results show that the data for Nanofluids friction factor
show a good agreement with analytical prediction from the Darcy's
equation for single-phase flow. After reducing the experimental
results to the form of Reynolds, Rayleigh and Nusselt numbers. The
results show the local Nusselt number and temperature have
distribution with the non-dimensional axial distance from the tube
entry. Study decided that thenNanofluid as Newtonian fluids through
the design of the linear relationship between shear stress and the rate
of stress has been the study of three chains of the Nanofluid with
different concentrations and where the AL, AL2O3 and CuO – water
ranging from (0.25 - 2.5 vol %). In addition to measuring the four
properties of the Nanofluid in practice so as to ensure the validity of
equations of properties developed by the researchers in this area and
these properties is viscosity, specific heat, and density and found that
the difference does not exceed 3.5% for the experimental equations
between them and the practical. The study also demonstrated that the
amount of the increase in heat transfer coefficient for three types of
Nano fluid is AL, AL2O3, and CuO – Water and these ratios are
respectively (45%, 32%, 25%) with insulation and without insulation
(36%, 23%, 19%), and the statement of any of the cases the best
increase in heat transfer has been proven that using insulation is
better than not using it. I have been using three types of Nano
particles and one metallic Nanoparticle and two oxide Nanoparticle
and a statement, whichever gives the best increase in heat transfer.
Abstract: In this paper we propose a family of algorithms based
on 3rd and 4th order cumulants for blind single-input single-output
(SISO) Non-Minimum Phase (NMP) Finite Impulse Response (FIR)
channel estimation driven by non-Gaussian signal. The input signal
represents the signal used in 10GBASE-T (or IEEE 802.3an-2006)
as a Tomlinson-Harashima Precoded (THP) version of random
Pulse-Amplitude Modulation with 16 discrete levels (PAM-16). The
proposed algorithms are tested using three non-minimum phase
channel for different Signal-to-Noise Ratios (SNR) and for different
data input length. Numerical simulation results are presented to
illustrate the performance of the proposed algorithms.
Abstract: Based on a non-linear single track model which
describes the dynamics of vehicle, an optimal path planning strategy
is developed. Real time optimization is used to generate reference
control values to allow leading the vehicle alongside a calculated lane
which is optimal for different objectives such as energy consumption,
run time, safety or comfort characteristics. Strict mathematic
formulation of the autonomous driving allows taking decision on
undefined situation such as lane change or obstacle avoidance. Based
on position of the vehicle, lane situation and obstacle position, the
optimization problem is reformulated in real-time to avoid the
obstacle and any car crash.
Abstract: Since 1984 many schemes have been proposed for
digital signature protocol, among them those that based on discrete
log and factorizations. However a new identification scheme based
on iterated function (IFS) systems are proposed and proved to be
more efficient. In this study the proposed identification scheme is
transformed into a digital signature scheme by using a one way hash
function. It is a generalization of the GQ signature schemes. The
attractor of the IFS is used to obtain public key from a private one,
and in the encryption and decryption of a hash function. Our aim is
to provide techniques and tools which may be useful towards
developing cryptographic protocols. Comparisons between the
proposed scheme and fractal digital signature scheme based on RSA
setting, as well as, with the conventional Guillou-Quisquater
signature, and RSA signature schemes is performed to prove that, the
proposed scheme is efficient and with high performance.
Abstract: The correct design of the regulators structure requires complete prediction of the ultimate dimensions of the scour hole profile formed downstream the solid apron. The study of scour downstream regulator is studied either on solid aprons by means of velocity distribution or on movable bed by studying the topography of the scour hole formed in the downstream. In this paper, a new technique was developed to study the scour hole downstream regulators on movable beds. The study was divided into two categories; the first is to find out the sum of the lengths of rigid apron behind the gates in addition to the length of scour hole formed downstream, while the second is to find the minimum length of rigid apron behind the gates to prevent erosion downstream it. The study covers free and submerged hydraulic jump conditions in both symmetrical and asymmetrical under-gated regulations. From the comparison between the studied categories, we found that the minimum length of rigid apron to prevent scour (Ls) is greater than the sum of the lengths of rigid apron and that of scour hole formed behind it (L+Xs). On the other hand, the scour hole dimensions in case of submerged hydraulic jump is always greater than free one, also the scour hole dimensions in asymmetrical operation is greater than symmetrical one.
Abstract: In this paper we propose a computational model for the representation and processing of morpho-phonological phenomena in a natural language, like Modern Greek. We aim at a unified treatment of inflection, compounding, and word-internal phonological changes, in a model that is used for both analysis and generation. After discussing certain difficulties cuase by well-known finitestate approaches, such as Koskenniemi-s two-level model [7] when applied to a computational treatment of compounding, we argue that a morphology-based model provides a more adequate account of word-internal phenomena. Contrary to the finite state approaches that cannot handle hierarchical word constituency in a satisfactory way, we propose a unification-based word grammar, as the nucleus of our strategy, which takes into consideration word representations that are based on affixation and [stem stem] or [stem word] compounds. In our formalism, feature-passing operations are formulated with the use of the unification device, and phonological rules modeling the correspondence between lexical and surface forms apply at morpheme boundaries. In the paper, examples from Modern Greek illustrate our approach. Morpheme structures, stress, and morphologically conditioned phoneme changes are analyzed and generated in a principled way.
Abstract: In this paper, we discuss the paradigm shift in bank
capital from the “gone concern" to the “going concern" mindset. We
then propose a methodology for pricing a product of this shift called
Contingent Capital Notes (“CoCos"). The Merton Model can
determine a price for credit risk by using the firm-s equity value as a
call option on those assets. Our pricing methodology for CoCos also
uses the credit spread implied by the Merton Model in a subsequent
derivative form created by John Hull et al . Here, a market implied
asset volatility is calculated by using observed market CDS spreads.
This implied asset volatility is then used to estimate the probability of
triggering a predetermined “contingency event" given the distanceto-
trigger (DTT). The paper then investigates the effect of varying
DTTs and recovery assumptions on the CoCo yield. We conclude
with an investment rationale.
Abstract: This paper deals with the application of a well-known neural network technique, multilayer back-propagation (BP) neural network, in financial data mining. A modified neural network forecasting model is presented, and an intelligent mining system is developed. The system can forecast the buying and selling signs according to the prediction of future trends to stock market, and provide decision-making for stock investors. The simulation result of seven years to Shanghai Composite Index shows that the return achieved by this mining system is about three times as large as that achieved by the buy and hold strategy, so it is advantageous to apply neural networks to forecast financial time series, the different investors could benefit from it.
Abstract: In this paper, we have proposed two novel plasmonic demultiplexing structures based on metal-insulator-metal surfaces which, beside their compact size, have a very good transmission spectrum. The impact of the key internal parameters on the transmission spectrum is numerically analyzed by using the twodimensional (2D) finite difference time domain (FDTD) method. The proposed structures could be used to develop ultra-compact photonic wavelength demultiplexing devices for large-scale photonic integration.
Abstract: In this paper, we present a new algorithm for clustering data in large datasets using image processing approaches. First the dataset is mapped into a binary image plane. The synthesized image is then processed utilizing efficient image processing techniques to cluster the data in the dataset. Henceforth, the algorithm avoids exhaustive search to identify clusters. The algorithm considers only a small set of the data that contains critical boundary information sufficient to identify contained clusters. Compared to available data clustering techniques, the proposed algorithm produces similar quality results and outperforms them in execution time and storage requirements.
Abstract: The one-class support vector machine “support vector
data description” (SVDD) is an ideal approach for anomaly or outlier
detection. However, for the applicability of SVDD in real-world
applications, the ease of use is crucial. The results of SVDD are
massively determined by the choice of the regularisation parameter C
and the kernel parameter of the widely used RBF kernel. While for
two-class SVMs the parameters can be tuned using cross-validation
based on the confusion matrix, for a one-class SVM this is not
possible, because only true positives and false negatives can occur
during training. This paper proposes an approach to find the optimal
set of parameters for SVDD solely based on a training set from
one class and without any user parameterisation. Results on artificial
and real data sets are presented, underpinning the usefulness of the
approach.