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유창호(Chang Ho Yu),고남곤(Nam Kon Ko),최재원(Jae Weon Choi),서영봉(Young Bong Seo) 제어로봇시스템학회 2010 제어·로봇·시스템학회 논문지 Vol.16 No.7
In this paper, an overload detecting algorithm for an excavator is presented. The proposed overload detecting algorithm is based on the time series analysis especially correlation function. The main purpose of this paper is to prevent damage or crack from the fatigue loaded on an excavator in advance. Generally, the larger data, the longer processing time, and the amount of the data used in this paper are also large, especially every sampling period, 1600 data are gathered and calculated. So this paper focuses on minimizing the number of required sensors by using the correlation function. From the cross correlation function, similar pattern sensors are eliminated and dissimilar pattern sensors are considered, and from the auto correlation function, the overload can be detected. To prove the efficiency of the proposed overload detecting algorithm, this paper shows the computer simulation results.
A Study on the Speech Recognition using Pitch Synchronous LPC Cepstrum-VQ and HMM
이광형,고남곤,Lee, Jin Yi,Kim, Hyung Seuk 대한전자공학회 1994 ISPACS:Intelligent Signal Processing and Communica Vol.1 No.1
In this paper we propose a phoneme recognition model of the Korean speech by using the pitch synchronous LPC cepstrum-VQ and FIMM. The LPC cepstrum coefficient have been obtained by synchronizing the analysis frame per a pitch. The pitch synchronization method reduces the analysis trine and the effect of the pitch pulses. The LPC cepstrum of the Input speedy signal is vector quantized by using the reference codebook of the LPC cepstrum coefficient. This codebook has already been designed with the FCM clustering algorithm. The LPC cepstrum codevector's address in codbook is applied to the f IMM algorithm, and then recognized phoneme by phoneme. As the speech signal vary slowly with tune, the HMM technique is, in general, effective in it modeling in time. The performance evaluation of, the proposed method has been done for both inside training data and outside training data. As the simulation results, it can be shown that the proposed recognition method has improved about 3(%) in recognition ratio than the pitch asynchronous method.