• Journal of Korean Academy of Nursing
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• v.27 no.3
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• pp.503-509
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• 1997

This study was undertaken to identify optimal head elevation and position in the care of the neurosurgical patient with a cerebral aneurysm. The effects of 0°. 15° and 30° head elevation and three positions (supine, side tying position opposite to the operation site, and side tying position on the same side as the operation site) on ICP was studied in fourteen neurosurgical patients with cerebral aneurysms. The results are as follows : 1. The mean intracranial pressure was significantly lower when the patient's head was elevated at 30° as compared to 0° and 15°. 2. The mean intracranial pressure was significantly lower when the patient was positioned in the supine as compared to side tying position opposite to the operation site and side tying position on the same side as the operation site. The data indicate that head elevation to 30° and the supine position reduce ICP in neurosurgical patients with cerebral aneurysm.

• Journal of Biosystems Engineering
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• v.25 no.5
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• pp.341-350
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• 2000

This study was conducted to develop a control unit for a tying device of a variable chamber round baler. The work process of the tying device was thoroughly analyzed and the control sequence was established according to the work process. Based on this control sequence, a control unit using an 8 bit microprocessor AT 89C52 as a CPU was developed. The driving circuit to control the actuator motion was developed and the PWM method was used to regulate the velocity of the actuator. On the front panel of the control unit, indicators were also installed to show the operations being conducted. A prototype of the developed control unit was manufactured and tested. A total of 50 complete cycles of the control sequence was repeated and no failure was observed. It was evaluated that the developed control unit has an excellent performance and can be used practically for variable chamber round balers.

• The Journal of the Acoustical Society of Korea
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• v.13 no.3
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• pp.63-70
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• 1994

In this Paper, we propose a new codeword tying algorithm based on a tree structured classfier. The proposed algorithm which can be viewed as a kind of soft decision using statistical properties between codewords and states has an advantage of fast construction, and guarantees a unique optimal solution. Also, it can easily be applied to any speech recognition system based on discrete hidden Markov model (HMM). Experimental results on speaker-independent isolated word recognition show error reduction of $6\%$ for the codebook of size 256 and $9\%$ for 512 size and also HMM parameter reduction of about $20\%$.

• Journal of Digital Convergence
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• v.13 no.1
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• pp.243-248
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• 2015

Recognition model is not defined when you configure a model, Been added to the model after model building awareness, Model a model of the clustering due to lack of recognition models are generated by modeling is causes the degradation of the recognition rate. In order to improve decision tree state tying modeling using parameter estimation of Bayesian method. The parameter estimation method is proposed Bayesian method to navigate through the model from the results of the decision tree based on the tying state according to the maximum probability method to determine the recognition model. According to our experiments on the simulation data generated by adding noise to clean speech, the proposed clustering method error rate reduction of 1.29% compared with baseline model, which is slightly better performance than the existing approach.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.4
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• pp.722-727
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• 2009

The technology of distinguishing human motion states is required in the areas of measuring and analyzing biosignals changing according to physical activities, diagnosing sleep disorder, screening the effect of treatment, examining chronic patients' kinetic state, prescribing exercise therapy, etc. The present study implemented a fuzzy inference system based on fuzzy rules that distinguish human motion states (tying, sitting, walking, and running) by acquiring and processing data of LAA, TAA, L-MAD, and T-MAD using ADXL202AE of Analog Devices embedded in an armband. The membership degree and fuzzy rules in each area of input (LAA, TAA, L-MAD, and T-MAD) and output (tying, sitting, walking, and running) data used here were determined using numeric data obtained from experiment. In the results of analyzing data for simulation generated in order of tying$\rightarrow$walking$\rightarrow$running$\rightarrow$tying, the sorting rate for motion states tying, sitting, walking, and running was 100% for each motion.

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.197-200
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• 2002

In this paper, we propose a phoneme duration modeling in a speech recognition system based on disicion tree state tying. We assume that phone duration has a Gamma distribution. In a training mode, we model mean and variance of each state duration in context-independent phone model based on decision tree state tying. In a recognition mode, we get mean and variance of each context-dependent phone duration form state duration information obtaind during training mode. We make a comparative study of the proposed meth with conventinal methods. Our method results in good performance compared with conventional methods.

• MALSORI
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• no.45
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• pp.117-126
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• 2003

Decision tree-based state tying has been proposed in recent years as the most popular approach for clustering the states of context-dependent hidden Markov model-based speech recognition. The aims of state tying is to reduce the number of free parameters and predict state probability distributions of unseen models. But, when doing state tying, the size of a decision tree is very important for word independent recognition. In this paper, we try to construct optimized decision tree based on the average of feature vectors in state pool and the number of seen modes. We observed that the proposed optimal decision tree is effective in predicting the state probability distribution of unseen models.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.195-198
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• 2002

We propose an efficient mixture Gaussian synthesis method for decision tree based state tying that produces better context-dependent models in a short period of training time. This method makes it possible to handle mixture Gaussian HMMs in decision tree based state tying algorithm, and provides higher recognition performance compared to the conventional HMM training procedure using decision tree based state tying on single Gaussian GMMs. This method also reduces the steps of HMM training procedure. We applied this method to training of PBS, and we expect to achieve a little point improvement in phoneme accuarcy and reduction in training time.

• Journal of Korea Fair Competition Federation
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• no.31
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• pp.53-57
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• 1998

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.167-176
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• 2005

This paper describes how the state tying model based on the decision tree which is one of Acoustic models used for speech recognition optimizes the model by reducing the number of mixture Gaussians of the output probability distribution. The state tying modeling uses a finite set of questions which is possible to include the phonological knowledge and the likelihood based decision criteria. And the recognition rate can be improved by increasing the number of mixture Gaussians of the output probability distribution. In this paper, we'll reduce the number of mixture Gaussians at the highest point of recognition rate by clustering the Gaussians. Bhattacharyya and Euclidean method will be used for the distance measure needed when clustering. And after calculating the mean and variance between the pair of lowest distance, the new Gaussians are created. The parameters for the new Gaussians are derived from the parameters of the Gaussians from which it is born. Experiments have been performed using the STOCKNAME (1,680) databases. And the test results show that the proposed method using Bhattacharyya distance measure maintains their recognition rate at $97.2\%$ and reduces the ratio of the number of mixture Gaussians by $1.0\%$. And the method using Euclidean distance measure shows that it maintains the recognition rate at $96.9\%$ and reduces the ratio of the number of mixture Gaussians by $1.0\%$. Then the methods can optimize the state tying model.