• 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.

• 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 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.

• 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.

• Structural Engineering and Mechanics
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• v.29 no.2
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• pp.203-221
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• 2008

In this paper, we present an idea of the geometry-dependent MITC method. The simple concept is exemplified to improve a 2-node iso-beam (isoparametric beam) finite element of varying section. We first study the behavior of a standard 2-node iso-beam finite element of prismatic section, which has been widely used with reduced integration (or the equivalent MITC method) in order to avoid shear locking. Based on analytical studies on cantilever beams of varying section, we propose the axial strain correction (ASC) scheme and the geometry-dependent tying (GDT) scheme for the 2-node iso-beam element. We numerically analyze varying section beam problems and present the improved performance by using both ASC and GDT schemes.

• 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.

• Journal of the korean academy of Pediatric Dentistry
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• v.49 no.1
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• pp.113-120
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• 2022

Oral mucocele is a common exophytic lesion resulting from the accumulation of saliva due to pathological changes in the minor salivary glands. It is typically asymptomatic and painless and characterized by semipermeable, fluctuant nodules. General treatment methods for mucocele include surgical excision, marsupialization, cryosurgery, and steroid injection. This case report presents the treatment of oral mucocele using micro-marsupialization and a tying method as minimally invasive techniques rather than surgical treatment. Based on this case report, it is suggested that micro-marsupialization and the tying method can be used as alternative methods for the treatment of oral mucocele infants and children with behavioral control problems.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.339-344
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• 2012

Established continuous vocabulary recognition system improved recognition rate by using decision tree based tying modeling method. However, since system model cannot support the retrieve of phoneme data, it is hard to secure the accuracy. In order to improve this problem, we remodeled a system that could retrieve probabilistic model from continuous vocabulary clustering model to phoneme unit. Therefore in this paper showed 95.88%of recognition rate in system performance.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1995.03a
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• pp.155-161
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• 1995

Superplastic forming/diffusion bonding(SPF/DB) processes are analyzed using a rigid visco-plastic finite element method. The optimum pressure-time relationship for a target strain rate and thickness distributions were predicted using two-node line element based on membrane approximation for plane strain shapes. Material behavior during SPF/DB of the integral structures with complicated shapes are investigated. The tying condition is employed for the analysis inter-sheet contact problems. A movement of rib structure is successfully prodicted during the forming.

• The Journal of the Acoustical Society of Korea
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• v.18 no.6
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• pp.49-56
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• 1999

In many continuous speech recognition systems based on HMMs, decision tree-based state tying has been used for not only improving the robustness and accuracy of context dependent acoustic modeling but also synthesizing unseen models. To construct the phonetic decision tree, standard method performs one-level pruning using just single Gaussian triphone models. In this paper, two novel approaches, two-level decision tree and multi-mixture decision tree, are proposed to get better performance through more accurate acoustic modeling. Two-level decision tree performs two level pruning for the state tying and the mixture weight tying. Using the second level, the tied states can have different mixture weights based on the similarities in their phonetic contexts. In the second approach, phonetic decision tree continues to be updated with training sequence, mixture splitting and re-estimation. Multi-mixture Gaussian as well as single Gaussian models are used to construct the multi-mixture decision tree. Continuous speech recognition experiment using these approaches on BN-96 and WSJ5k data showed a reduction in word error rate comparing to the standard decision tree based system given similar number of tied states.