• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.6
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• pp.11-21
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• 1997

The delay models for CMOS invertr presented so far predicted the delay time quite accurately whens input transition-time is very small. But the problem that the accuracy is inclined to decrease becomes apparent as input transition tiem increases. In this paper, a delay model for CMOS inverter is presented, which accuractely predicts the delay time even though input transition-time increases. To inverter must be included in modeling process because the main reason of inaccuracy as input transition tiem is the leakage current through the complementary MOS. For efficient modeling, this paper first models the MOSes with simple I-V charcteristic, with which both the pMOS and the nMOS are considered easily in calculating the inverter delay times. This resulting model needs few parameters and re-models each MOS effectively and simply evaluates output voltage to predict delay time, delay values obtained from this effectively and simply evaluates output voltage to predict delay time, delay values obtained from this model have been found to be within about 5% error rate of the SPICE results. The calculation time to predict the delay time with the model from this paper has the speed of more than 70times as fast as to the SPICE.

• JSTS:Journal of Semiconductor Technology and Science
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• v.10 no.1
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• pp.20-27
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• 2010

Modeling is essential to simulate the operation of integrated circuit (IC) before its fabrication. Seeing a large number of Metal-Oxide-Silicon Field-Effect-Transistor (MOSFET) models available, it has become important to understand them and compare them for their pros and cons. The task becomes equally difficult when the complexity of these models becomes very high. The paper reviews the mainstream models with their physical relevance and their comparisons. Major short-channel and quantum effects in the models are outlined. Emphasis is set upon the latest compact models like BSIM, MOS Models 9/11, EKV, SP etc.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.34D no.12
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• pp.31-38
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• 1997

A new CAD-compatible non-quasi-static (NQS) MOS transient model is presented. A new type of weighted residual method, the collcoatin method, is adopted to obtian an approximate ordinary differntial equation from the continuity eqation. Contrasting to the conventional NQS models, the new model can directly include the variatin of the depletion charge and the derived transient current sare expressed with only physically meaningful variables. The new model predicts transient behaviors reasonably well in the calculation including cutoff regions where the depletion charge rapidly changes.

• The Journal of the Korea Contents Association
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• v.5 no.4
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• pp.254-264
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• 2005

In this paper, an advanced E-Model was proposed in order to overcome disadvantages of conventional method. A new model makes the accurate VoIP call quality assessment possible by applying the burst packet loss and recency effect. In order to assess the performance of this advanced E-Model, we gained the estimated MOS value from NR(Network R) value and UR(User R) value resulted from the burst packet loss values by Gilbert Model. Through simulations and comparisons with conventional models such as MOS, PESQ, and I-Model, we reach a conclusion that advanced E-Model is more accurate and reliable method than conventional models.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.11
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• pp.1682-1688
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• 2012

This paper introduces a GP(Genetic Programming) based robust technique for temperature compensation in short-range prediction. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, because forecast models do not reliably determine weather conditions. Most of MOS use a linear regression to compensate a prediction model, therefore it is hard to manage an irregular nature of prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP is suggested. The purpose of this study is to evaluate the accuracy of the estimation by a GP based nonlinear MOS for 3 days temperatures in Korean regions. This method is then compared to the UM model and has shown superior results. The training period of 2007-2009 summer is used, and the data of 2010 summer is adopted for verification.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• JSTS:Journal of Semiconductor Technology and Science
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• v.13 no.3
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• pp.213-223
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• 2013

A unified channel thermal noise model valid in all operation regions is presented for short channel MOS transistors. It is based on smooth interpolation between weak and strong inversion models and consistent physical model including velocity saturation, channel length modulation, and carrier heating. From testing for noise benchmark and comparing with published noise data, it is shown that the proposed noise model could be useful in simulating the MOSFET channel thermal noise in all operation regions.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.369-378
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• 2006

This Study focuses on the binary forecast of occurrence of heavy snow in Honam area based on the MOS(model output statistic) method. For our study daily amount of snow cover at 17 stations during the cold season (November to March) in 2001 to 2005 and Corresponding 45 RDAPS outputs are used. Logistic regression model and neural networks are applied to predict the probability of occurrence of Heavy snow. Based on the distribution of estimated probabilities, optimal thresholds are determined via true shill score. According to the results of comparison the logistic regression model is recommended.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.795-797
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• 2009

Voice over Internet Protocol(VoIP) service becomes more and more popular nowadays. As such, it is developed over many kinds of network models, especially wireless networks. Mean Opinion Score(MOS) computes the QoS of VoIP service which should be supported by robust network environment. However, MANET is not stable enough to supply high MOS values for VoIP service. In this paper, VoIP service over MANET is simulated using ns-2(Network Simulation 2). In oder to get different MOS values in the results, we differentiate between network environments by adjusting the parameters of MANET.Through comparing the results we can know how to improve the QoS.

• Phonetics and Speech Sciences
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• v.16 no.1
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• pp.67-76
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• 2024

Recent advances in text-to-speech (TTS) technology have significantly improved the quality of synthesized speech, reaching a level where it can closely imitate natural human speech. Especially, TTS models offering various voice characteristics and personalized speech, are widely utilized in fields such as artificial intelligence (AI) tutors, advertising, and video dubbing. Accordingly, in this paper, we propose a one-shot multi-speaker TTS system that can ensure acoustic diversity and synthesize personalized voice by generating speech using unseen target speakers' utterances. The proposed model integrates a speaker encoder into a TTS model consisting of the FastSpeech2 acoustic model and the HiFi-GAN vocoder. The speaker encoder, based on the pre-trained RawNet3, extracts speaker-specific voice features. Furthermore, the proposed approach not only includes an English one-shot multi-speaker TTS but also introduces a Korean one-shot multi-speaker TTS. We evaluate naturalness and speaker similarity of the generated speech using objective and subjective metrics. In the subjective evaluation, the proposed Korean one-shot multi-speaker TTS obtained naturalness mean opinion score (NMOS) of 3.36 and similarity MOS (SMOS) of 3.16. The objective evaluation of the proposed English and Korean one-shot multi-speaker TTS showed a prediction MOS (P-MOS) of 2.54 and 3.74, respectively. These results indicate that the performance of our proposed model is improved over the baseline models in terms of both naturalness and speaker similarity.