Abstract: The Shortest Approximate Common Superstring
(SACS) problem is : Given a set of strings f={w1, w2, ... , wn},
where no wi is an approximate substring of wj, i ≠ j, find a shortest
string Sa, such that, every string of f is an approximate substring of
Sa. When the number of the strings n>2, the SACS problem becomes
NP-complete. In this paper, we present a greedy approximation
SACS algorithm. Our algorithm is a 1/2-approximation for the SACS
problem. It is of complexity O(n2*(l2+log(n))) in computing time,
where n is the number of the strings and l is the length of a string.
Our SACS algorithm is based on computation of the Length of the
Approximate Longest Overlap (LALO).
Abstract: When a lightning strike falls near an overhead power
line, the intense electromagnetic field radiated by the current of the
lightning return stroke coupled with power lines and there induced
transient overvoltages, which can cause a back-flashover in electrical
network. The indirect lightning represents a major danger owing to
the fact that it is more frequent than that which results from the direct
strikes.
In this paper we present an analysis of the electromagnetic
coupling between an external electromagnetic field generated by the
lightning and an electrical overhead lines, so we give an important
and original contribution: We are based on our experimental
measurements which we carried in the high voltage laboratories of
EPFL in Switzerland during the last trimester of 2005, on the recent
works of other authors and with our mathematical improvement a
new particular analytical expression of the electromagnetic field
generated by the lightning return stroke was developed and presented
in this paper. The results obtained by this new electromagnetic field
formulation were compared with experimental results and give a
reasonable approach.
Abstract: An IEC technique is described for a multi-objective
search of conceptual solutions. The survivability of solutions is
influenced by both model-based fitness and subjective human
preferences. The concepts- preferences are articulated via a hierarchy
of sub-concepts. The suggested method produces an objectivesubjective
front. Academic example is employed to demonstrate the
proposed approach.
Abstract: In determining the electromagnetic properties of
magnetic materials, hysteresis modeling is of high importance. Many
models are available to investigate those characteristics but they tend
to be complex and difficult to implement. In this paper a new
qualitative hysteresis model for ferromagnetic core is presented,
based on the function approximation capabilities of adaptive neuro
fuzzy inference system (ANFIS). The proposed ANFIS model
combined the neural network adaptive capabilities and the fuzzy
logic qualitative approach can restored the hysteresis curve with a
little RMS error. The model accuracy is good and can be easily
adapted to the requirements of the application by extending or
reducing the network training set and thus the required amount of
measurement data.
Abstract: In this paper, some problem formulations of dynamic object parameters recovery described by non-autonomous system of ordinary differential equations with multipoint unshared edge conditions are investigated. Depending on the number of additional conditions the problem is reduced to an algebraic equations system or to a problem of quadratic programming. With this purpose the paper offers a new scheme of the edge conditions transfer method called by conditions shift. The method permits to get rid from differential links and multipoint unshared initially-edge conditions. The advantage of the proposed approach is concluded by capabilities of reduction of a parametric identification problem to essential simple problems of the solution of an algebraic system or quadratic programming.
Abstract: This paper study about using of nonparametric
models for Gross National Product data in Turkey and Stanford heart
transplant data. It is discussed two nonparametric techniques called
smoothing spline and kernel regression. The main goal is to compare
the techniques used for prediction of the nonparametric regression
models. According to the results of numerical studies, it is concluded
that smoothing spline regression estimators are better than those of
the kernel regression.
Abstract: Instead of traditional (nominal) classification we investigate
the subject of ordinal classification or ranking. An enhanced
method based on an ensemble of Support Vector Machines (SVM-s)
is proposed. Each binary classifier is trained with specific weights
for each object in the training data set. Experiments on benchmark
datasets and synthetic data indicate that the performance of our
approach is comparable to state of the art kernel methods for
ordinal regression. The ensemble method, which is straightforward
to implement, provides a very good sensitivity-specificity trade-off
for the highest and lowest rank.