• KIISE Transactions on Computing Practices
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• v.22 no.4
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• pp.184-188
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• 2016

The cleaning robot is popularly used as a home appliance. The state-of-the-art cleaning robot can clean more efficiently by using information gathered from its sensor, which is difficult for low-price cleaning robots due to limitation in this aspect. In this paper, we suggested a method for the moving pattern of cleaning robot based on grammatical evolution. Optimized program is generated by using moving pattern grammar, which is defined by Backus-Naur form. In addition, conditional probability is used between each of the grammar elements during the program creation process. The proposed method is evaluated by robot simulation in order to verify its performance and further compare it with existing algorithms. The experiment results showed that the proposed method is better than the compared algorithms.

• Animal Systematics, Evolution and Diversity
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• v.37 no.3
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• pp.255-272
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• 2021

Etymologies are explanations of what our words meant and how they sounded 600 to 2,000 years ago. When Linnaeus in the mid-1700s began naming animals with a binomial nomenclature, he based names on the Latin Grammatical Form. Since many names have Latin or Greek roots, the name for an animal is the same throughout the world because Latin is no longer a spoken language and meanings of names will not evolve or change. In his use of Latin or a Latinized word for the genus and species, Linnaeus used descriptive words that will always be the same. Notwithstanding the importance of etymologies for scientific names, no study has addressed the etymology of scientific names for Korean mammals. Here, we list etymologies for scientific names of 127 mammal species, 84 genera, 32 families, and 8 orders from Korea. The origins of etymologies are mostly based on morphology, color of pelage, behavior, distribution, locality, country name, or a person's name. This paper will be useful for new students and trained scholars studying Korean mammals.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.43-51
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• 2022

In this paper, we suggest the method to improve the existing method leaning context-free grammar(CFG) using tabular representation(TBL) as a chromosome of genetic algorithm in grammatical inference and show the more efficient experimental result. We have two improvements. The first is to improve the formula to reflect the learning evaluation of positive and negative examples at the same time for the fitness function. The second is to classify partitions corresponding to TBLs generated from positive learning examples according to the size of the learning string, proceed with the evolution process by class, and adjust the composition ratio according to the success rate to apply the learning method linked to survival in the next generation. These improvements provide better efficiency than the existing method by solving the complexity and difficulty in the crossover and generalization steps between several individuals according to the size of the learning examples. We experiment with the languages proposed in the existing method, and the results show a rather fast generation rate that takes fewer generations to complete learning with the same success rate than the existing method. In the future, this method can be tried for extended CYK, and furthermore, it suggests the possibility of being applied to more complex parsing tables.