Elektronika 2009-11.pdf - Instytut Systemów Elektronicznych

Tabl. 4. MD error before and after filtration
Tab. 4. Błąd MD przed i po filtracji
All images were scanned with 600 dpi resolution. Plustek
OpticPro A320 was used as the model scanner and Canon
LiDE100 as the low quality scanner.
Conclusion
MD before filtration
MD after filtration
Images 1 161 54
Images 2 169 57
Images 3 143 43
Images 4 187 67
Images 5 166 57
Analyzing the results presented in tables 3 and 4, it can be
stated that the digital filter of 5 x 5 mask obtained in the course
of learning process of the neural network fulfils its task. After
applying tangle operation, images acquired by low class scanner
have become comparable in quality with model images.
Surely, there is no 100 percent improvement, because the filter
mask is relatively small. However, the advantage of it is
that through realization of the discrete tangle operation, it can
be used in popular programmes for image procession such
as CorelDraw or Photoshop.
Of course, the learning process of the neural network is
long but it occurs only once and filter obtained in such a way
can be applied in signal processors, so that the improvement
of photos can be implemented practically in real time.
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Global multicriteria optimization in weather routing
(Wielokryterialna optymalizacja globalna w nawigacji meteorologicznej)
dr inż. JOANNA SZŁAPCZYŃSKA
Akademia Morska w Gdyni, Wydział Nawigacyjny
A task of finding a global optimum in a complex, non-linear optimization
problem with constraints belongs to NP-hard problem
class. Hence, selecting a global optimization method reliable
and suitable for given problem might be a challenge [1]. The
problem becomes even more complicated when it is defined as
a multicriteria (aka multiobjective) optimization problem (MOP).
Possible approaches include goal function linearization, interactive
optimization techniques and multicriteria searching for
Pareto-optimal solutions. The latter scheme is the most popular
and often performed by metaheuristic methods.
Ship route optimization in weather routing is an example of
a real life MOP class problem. The term “weather routing” refers
to a process of finding the most convenient route for a ship
while taking into account available weather forecasts. The
weather routing optimization problem has already been defined
in terms of MOP [2]. This paper presents the comprehensive,
already implemented solution to finding a global optimum in
weather routing. In addition to that, it includes a discussion on
efficiency of current solution and other possible approaches towards
global optimization. The final conclusions try to appoint
alternative optimization methods being able to improve the efficiency
of current solution.
Problem definition in weather routing
The criteria set in weather routing models the following elements:
passage time (min), fuel consumption (min) and voyage
risk (min). The constraint set include four elements describing
natural obstacles and navigational constraints. A detailed description
of the constrained multicriteria problem definition in
weather routing has been already given in [2].
Multicriteria evolutionary algorithms
and ranking methods in weather routing
The solution [2] to the weather routing optimization problem
comprises two optimization techniques: multicriteria evolutionary
algorithm (SPEA) and multicriteria ranking method
(Fuzzy TOPSIS). The former is responsible for finding a set of
Pareto-optimal solutions, while the latter is utilized to narrow
the vast set of (Pareto-optimal) routes to a single route recommendation.
It is worth noticing that preferences of decisionmaker
towards optimization criteria influence only the ranking
method, leaving the main evolutionary optimization procedure
not affected. The following subsections provide a more de-
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