• 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.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.132-139
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• 2019

We address the problem about forecasting the direction of stock price movement in the Korea market. Recently, the deep neural network is popularly applied in this area of research. In deep neural network systems, proper parameter initialization reduces training time and improves the performance of the model. Therefore, in our study, we propose a novel parameter initialization technique and apply this technique for the stock price movement prediction model. Specifically, we design a framework which consists of two models: a base model and a main prediction model. The base model constructed with LSTM is trained by using the large data which is generated by a large amount of the stock data to achieve optimal parameters. The main prediction model with the same architecture as the base model uses the optimal parameter initialization. Thus, the main prediction model is trained by only using the data of the given stock. Moreover, the stock price movements can be affected by other related information in the stock market. For this reason, we conducted our research with two types of inputs. The first type is the stock features, and the second type is a combination of the stock features and the Korea Composite Stock Price Index (KOSPI) features. Empirical results conducted on the top five stocks in the KOSPI list in terms of market capitalization indicate that our approaches achieve better predictive accuracy and F1-score comparing to other baseline models.

• Proceedings of the KIEE Conference
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• summer
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• pp.680-682
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• 2004

수요관리 프로그램의 개발과 시행을 위해 서는 End-Use별로 수요예측과 잠재량 평가가 필수적이다. 업무용 전력수요는 건물용도에 따라 수요패턴이 상이하다. 여기서는 건물용도별 기준수요 예측을 위한 알고리즘과 절차를 제시하였으며, 이를 토대로 시산분석을 하였다. 용도별 전력수요는 사무실, 호텔, 병원, 도소매, 학교, 창고, 식당, 식료품, 체육시설 둥 건물용도로 구분하였으며, End-Use는 이중 6개 용도에 대해 조명, 동력, 공조, 사무, 기타 등 5개로 나누어 추정하였다.

• Korean System Dynamics Review
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• v.7 no.1
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• pp.213-228
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• 2006

This paper reports the results of a study designed to understand and facilitate disaster mitigation for communities located in low frequency/high magnitude earthquake zones. The study is based on a small town located near the New Madrid Fault Zone and is therefore at significant earthquake risk. A system dynamics model describes the variables and policies governing the distribution of building safety over time. Data from this town is used to establish a 25-year baseline. Simulations are run to demonstrate the consequences of different building policies.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.78-80
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• 2021

In this paper, we proposed an improved method on the picture order of coding (POC) of MPEG-5 Essential video Coding (EVC) encoder to support a short intra period for Video-based Point Cloud Compression (V-PCC). As a codec-agnostically designed standard, V-PCC claimed to be able to work with a lot of codecs. Current EVC test model software shows that the baseline profile could not provide appropriate POC calculation. The proposed method offers a solution to this POC-related problem and provides up to 44.6% coding grains for EVC based V-PCC.

• Tuberculosis and Respiratory Diseases
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• v.48 no.3
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• pp.347-356
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• 2000

Background : Patients with obstructive sleep apnea syndrome are known to have high long-term mortality compared to healthy subjects because of their cardiovascular dysfunction. The observation of hemodynamic changes by obstructive apneas is helpful when attempting to understand the pathophysiological mechanism of the development of cardiovascular dysfunction in those patients. Therefore, we studied the changes in cardiovascular function with an animal model and tried to obtain the basic data for an ideal experimental model (this phrase is unclear), a requirement for a more advanced study. Methods : Sixteen anesthetized dogs with ${\alpha}$-chloralose delete were divided into two groups : 8 dogs of room air breathing group and 8 dogs of oxygen breathing group. We measured $PaO_2$, $PaCO_2$, heart rate, cardiac output, mean femoral artery pressure, and mean pulmonary artery pressure at specified times during the apnea-breathing cycle before endotracheal tube occlusion (baseline), 25 seconds after endotracheal tube occlusion (apneic period), 10 seconds (early phase of postapneic period, EPA) and 25 seconds (late phase of postapneic period, LPA) after spontaneous breathing. Results : In room air breathing group, the heart rate significantly decreased during the apneic period compared to that at baseline (P<0.01) and increased at EPA and LPA compared to that during the apneic period (P<0.01). But, the heart rate showed no significant changes during apneic and postapneic periods in the oxygen breathing group. Cardiac output tended to decrease during apneic period compared to that at baseline, but was statistically significant. Cardiac output significantly decreased at LP A compared to at baseline (P<0.01). Mean femoral artery pressure was significantly decreased at during apneic period compared to that at baseline (P<0.05). Conclusion : Through this experiment, we were partially able to understand the changes of cardiovascular function indirectly, but delete new experimental animal model displaying physiological mechanism close to natural sleep should be established, and the advanced study in the changes of cardiovascular function and their causes should be continued.

• MALSORI
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• v.68
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• pp.33-47
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• 2008

In this paper, we propose a hybrid acoustic and pronunciation model adaptation method based on context dependency for Korean-English speech recognition. The proposed method is performed as follows. First, in order to derive pronunciation variant rules, an n-best phoneme sequence is obtained by phone recognition. Second, we decompose each rule into a context independent (CI) or a context dependent (CD) one. To this end, it is assumed that a different phoneme structure between Korean and English makes CI pronunciation variabilities while coarticulation effects are related to CD pronunciation variabilities. Finally, we perform an acoustic model adaptation and a pronunciation model adaptation for CI and CD pronunciation variabilities, respectively. It is shown from the Korean-English speech recognition experiments that the average word error rate (WER) is decreased by 36.0% when compared to the baseline that does not include any adaptation. In addition, the proposed method has a lower average WER than either the acoustic model adaptation or the pronunciation model adaptation.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2844-2853
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• 2023

A sophisticated Bayesian multilevel model for estimating group bias was developed to improve the utility of the ASTM E900-15 embrittlement trend curve (ETC) to assess the conditions of nuclear power plants (NPPs). For multilevel model development, the Baseline 22 surveillance dataset was basically classified into groups based on the NPP name, product form, and notch orientation. By including the notch direction in the grouping criteria, the developed model could account for TTS differences among NPP groups with different notch orientations, which have not been considered in previous ETCs. The parameters of the multilevel model and biases of the NPP groups were calculated using the Markov Chain Monte Carlo method. As the number of data points within a group increased, the group bias approached the mean residual, resulting in reduced credible intervals of the mean, and vice versa. Even when the number of surveillance test data points was less than three, the multilevel model could estimate appropriate biases without overfitting. The model also allowed for a quantitative estimate of the changes in the bias and prediction interval that occurred as a result of adding more surveillance test data. The biases estimated through the multilevel model significantly improved the performance of E900-15.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1902-1912
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• 2024

This paper presents results from system-level modeling of a water-based reactor cavity cooling system using RELAP5-3D. The computational model is benchmarked with experimental data from a half-scale RCCS test facility at Argonne National Laboratory. The model prediction is first compared with a two-phase oscillatory baseline experimental case where mixed accuracy is obtained. The model shows reasonable prediction of mass flow rate, pressure, and temperature but significant overprediction of void fraction. The model prediction is then compared with a fault case where the inlet of the risers is gradually reduced using a throttling valve. As the valve is closed, the model is able to predict some major flow phenomena observed in the experiment such as the dampening of oscillations, the reintroduction of oscillations, as well as boiling, flashing, and geysering in the risers. However, the timeline of these events are not well captured by the model. The model is also used to investigate the evolution of flow regime in the chimney. This work highlights that the semi-empirical constitutive relations used in RELAP-3D could have a strong influence on the accuracy of the model in two-phase oscillatory flows.

• International Journal of Automotive Technology
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• v.7 no.4
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• pp.459-468
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• 2006

The objective of this paper is the development of the forward-looking dynamic simulation model of a hybrid electric vehicle(HEV) for a fuel economy study. The specification of the vehicle is determined based on two factors, engine peak power to curb weight ratio and specific engine power. The steady state efficiency models of the powertrain components are explained in detail. These include a spark ignition direct injection(SIDI) engine, an integrated starter alternator(ISA), and an infinitely variable transmission(IVT). The paper describes the integration of these models into a forward facing dynamic simulation diagram using the AMESim environment. Appropriate vehicle and driver models have been added and described. The controller was designed in Simulink and was combined with the physical powertrain model by the co-simulation interface. Finally, the simulation results of the HEV are compared with those of a baseline vehicle in order to demonstrate the fuel economy potential. Results for the vehicle speed error and the fuel economy over standard driving cycles are illustrated.