When evaluating or ordering alternatives concerning given multiple criteria, decision-makers often use aspiration and reservation levels for criteria, which allows them to define some reference alternatives that build a common framework for the evaluation. In this paper, new multiple criteria approach, called distances to aspiration reference points (DARP), is presented, which can be implemented in a specific evaluation or ranking problem when many different aspiration levels should be taken into consideration. One example of such problem is measuring sustainable development of countries or states within the Union. In DARP, to measure the performance of alternative (state), the notion of distances between alternative and individual or common aspiration reference points is used. To manage the problem of different reference points, a modified max-min normalisation technique is proposed. DARP application for measuring smart growth of the EU countries is conducted to demonstrate the effectiveness of the proposed method.
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A method of direct numerical representation of symbolic data is proposed. The method starts with parsing a sequence into an ordered set (spectrum) of distinct, non-overlapping short strings of symbols (words). Next, the words spectrum is mapped onto a vector of binary components in a high dimensional, linear space. The numerical representation allows for some arithmetical operations on symbolic data. Among them is a meaningful average spectrum of two sequences. As a test, the new numerical representation is used to build centroid vectors for the k-means clustering algorithm. It significantly enhanced the clustering quality. The advantage over the conventional approach is a high score of correct clustering several real character sequences like novel, DNA and protein.
Rozpoznanie otoczenia pojazdów bezzałogowych jest jednym z podstawowych czynników warunkujących prawidłowe ich przemieszczanie się w terenie. W artykule przedstawiono zadania dla systemu rozpoznania oraz zaproponowano zastosowanie dalmierza laserowego 2-D do lokalizowania położenia obiektów w otoczeniu pojazdu. Następnie zaprezentowano rezultaty zastosowania przyjętej metody filtracji wyników pomiarów do przestrzennego zobrazowania otoczenia.
EN
Surroundings recognition of unmanned vehicles is one of the major factors determining its required movement in rugged terrain. This article shows tasks for the recognition system and the usage of a 2D laser telemeter for localizing objects in the vehicles' surrounding. Next, the paper shows results of filtering measurement results using an assumed method for spatial imaging of the environment.
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