31. Fuzzy urban traffic signal control - an overviewAlenka Malej, Andrej Brodnik, 2007, original scientific article Abstract: Izboljšanje sistemov za upravljanje mestne prometne signalizacije je eden cenovno najučinkovitejših načinov za izboljšanje prometnega pretoka skozi mrežo semaforiziranih križišč. Pri razvoju novih sistemov se zadnja tri leta poleg tradicionalnih metod, ki temeljijo na uporabi modelov prometnega pretoka, čedalje več uporabljajo nove tehnologije, ki vključujejo uporabo umetne inteligence. Raziskave so pokazale učinkovitost metahevrističnih metod. Posebna mehka logika, tudi v kombinaciji z optimizacijskimi metodami, ponuja fleksibilnost, prilagodljivost, upravljanje pri netočnih ingormacijah in v konfliktnih situacijah. Naredili smo pregled raziskav s tega področja
Keywords: prometna signalizacija, mehka logika, hibridni sistemi, porazdeljeno upravljanje, traffic signal control, hybrid systems, fuzzy logic, distributed control
Published in RUP: 10.07.2015; Views: 4106; Downloads: 53
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32. Empowering patients with chronic diseasesMate Beštek, Matic Meglič, Blaž Kurent, Iztok Cukjati, Tatjana Zrimec, Andrej Brodnik, 2012, published scientific conference contribution abstract Keywords: eZdravje, eOskrba, kronične bolezni, astma, diabetes tipa II
Published in RUP: 15.10.2013; Views: 3827; Downloads: 48
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37. Parse tree based machine translation for less-used languagesJernej Vičič, Andrej Brodnik, 2008, original scientific article Abstract: The article describes a method that enhances translation performance of language pairs with a less used source language and a widely used target language. We propose a method that enables the use of parse tree based statistical translation algorithms for language pairs with a less used source language and a widely used target language. Automatic part of speech (POS) tagging algorithms have become accurate to the extent of efficient use in many tasks. Most of these methods are quite easily implementable in most world languages. The method is divided in two parts; the first part constructs alignments between POS tags of source sentences and induced parse trees of target language. The second part searches through trained data and selects the best candidates for target sentences, the translations. The method was not fully implemented due to time constraints; the training part was implemented and incorporated into a functional translation system; the inclusion of a word alignment model into the translation part was not implemented. The empirical evaluation addressing the quality of trained data was carried out on a full implementation of the presented training algorithms and the results confirm the employability of the method.
Keywords: machine translation, parse tree
Published in RUP: 15.10.2013; Views: 5891; Downloads: 99
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