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( La The Vinh ),( Asad Masood Khattak ),( Trinh Van Loan ),이승룡 ( Sungyoung Lee ),구교호 ( Young-ko Lee ) 한국정보처리학회 2009 한국정보처리학회 학술대회논문집 Vol.16 No.2
So far, many researches have been conducted in the area of audio based context recognition. Nevertheless, most of them are based on existing feature extraction techniques derived from linear signal processing such as Fourier transform, wavelet transform, linear prediction... Meanwhile, environmental audio signal may potentially contains non-linear dynamic properties. Therefore, it is a big potential to utilize non-linear dynamic signal processing techniques in audio based context recognition.
아사드마소드가탁 ( Asad Masood Khattak ),( La The Vinh ),이승룡 ( Sungyoung Lee ),구교호 ( Young-koo Lee ) 한국정보처리학회 2009 한국정보처리학회 학술대회논문집 Vol.16 No.2
To accommodate constantly growing knowledge in scientific discourse that is revised over time by domain experts, we need to also evolve our ontology. The body of knowledge will get structured and refined as we develop a deeper understanding of issues. Keeping trail of new changes in semantically rich and formally sound mechanism has pragmatic advantages for providing the undo and redo facility and ontology recovery to a previous state. In this research, we have proposed a framework that support change logging and then using these logged changes for reverting ontology to a previous consistent state and visualization of change effects on ontology. The system is compared with ChangesTab of Protégé and the results depict better accuracy for our system.