. "[3CFFCEB5282D]" . "SK - Slovensk\u00E1 republika" . "3"^^ . . "3"^^ . . . "1336-7420" . "S, Z(MSM6840770039)" . . "New Classification Attributes for Signal Separation in Acoustic Emission Processing" . "We deal with the classification of acoustic emission random signals by means of fuzzy clustering, model-based statistical clustering and support vector machines. The signals are compared by means of newly introduced attributes obtained directly from the signals and from normed frequency spectra such as phi-divergence distance measure originated from information theory and statistics. We mention the well-known divergences (Kullback, Hellinger, Chi , Power) and we introduce several of its new modifications and extensions as generalized Hellinger divergences. These new families of divergences open new research possibilities in the area of statistical treatment of random signals. We realize experiments to test the proposed classification attributes and also consider industrial data from the real life."@en . "Forum Statisticum Slovacum" . "RIV/68407700:21340/10:00176063" . "Classification attributes; Phi-divergences; Signal separation; Data processing"@en . "5" . "New Classification Attributes for Signal Separation in Acoustic Emission Processing"@en . . "RIV/68407700:21340/10:00176063!RIV11-MSM-21340___" . . . "New Classification Attributes for Signal Separation in Acoustic Emission Processing"@en . "Tl\u00E1skal, Jan" . "Farov\u00E1, Zuzana" . . . . "7" . "7"^^ . "K\u016Fs, V\u00E1clav" . . "21340" . . . "274639" . "New Classification Attributes for Signal Separation in Acoustic Emission Processing" . . . . . . "We deal with the classification of acoustic emission random signals by means of fuzzy clustering, model-based statistical clustering and support vector machines. The signals are compared by means of newly introduced attributes obtained directly from the signals and from normed frequency spectra such as phi-divergence distance measure originated from information theory and statistics. We mention the well-known divergences (Kullback, Hellinger, Chi , Power) and we introduce several of its new modifications and extensions as generalized Hellinger divergences. These new families of divergences open new research possibilities in the area of statistical treatment of random signals. We realize experiments to test the proposed classification attributes and also consider industrial data from the real life." .