• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.2B no.4
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• pp.168-173
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• 2002

A new identification algorithm for the Preisach model is presented. The algorithm treats the identification procedure of the Preisach model as an inverse problem where the independent variables are parameters of the distribution function and the objective function is constructed using only the initial magnetization curve or only tile major loop of the hysteresis curve as well as the whole reversal curves. To parameterize the distribution function, the Bezier spline and Gaussian function are used for the coercive and interaction fields axes, respectively. The presented algorithm is applied to the ferrite permanent magnets, and the distribution functions are correctly found from the major loop of the hysteresis curve or the initial magnetization curve.

• Journal of the Korean Mathematical Society
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• v.38 no.5
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• pp.1047-1059
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• 2001

The classical Geometric Brownian motion (GBM) model for the price of a risky asset, from which the huge financial derivatives industry has developed, stipulates that the log returns are iid Gaussian. however, typical log returns data show a distribution with much higher peaks and heavier tails than the Gaussian as well as evidence of strong and persistent dependence. In this paper we describe a simple replacement for GBM, a fractal activity time Geometric Brownian motion (FATGBM) model based on fractal activity time which readily explains these observed features in the data. Consequences of the model are explained, and examples are given to illustrate how the self-similar scaling properties of the activity time check out in practice.

• Journal of Auto-vehicle Safety Association
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• v.11 no.2
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• pp.29-34
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• 2019

This paper presents a new method for construction of a static obstacle map. A static obstacle is important since it is utilized to path planning and decision. Several established approaches generate static obstacle map by grid method and counting algorithm. However, these approaches are occasionally ineffective since the density of LiDAR layer is low. Our approach solved this problem by applying probability theory. First, we converted all LiDAR point to Gaussian distribution to considers an uncertainty of LiDAR point. This Gaussian distribution represents likelihood of obstacle. Second, we modeled dynamic transition of a static obstacle map by adopting the Hidden Markov Model. Due to the dynamic characteristics of the vehicle in relation to the conditions of the next stage only, a more accurate map of the obstacles can be obtained using the Hidden Markov Model. Experimental data obtained from test driving demonstrates that our approach is suitable for mapping static obstacles. In addition, this result shows that our algorithm has an advantage in estimating not only static obstacles but also dynamic characteristics of moving target such as driving vehicles.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.437-442
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• 2005

For the purpose of speaker normalization in speaker independent speech recognition systems, experiments are conducted on a method based on Gaussian mixture model(GMM). The method, which is an improvement of the previous study based on vector quantizer, consists of modeling the probability distribution of canonical feature vectors by a GMM with an appropriate number of clusters, and of estimating the warp factor of a test speaker by making use of the obtained probabilistic model. The purpose of this study is twofold: improving the existing ML based methods, and comparing the performance of what is called 'soft decision' method with that of the previous study based on vector quantizer. The effectiveness of the proposed method is investigated by recognition experiments on the TIMIT corpus. The experimental results showed that a little improvement could be obtained tv adjusting the number of clusters in GMM appropriately.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.12A
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• pp.1828-1835
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• 2000

In this paper, we analyzed the other-cell interference characteristics for various non-uniform traffic distributions and their effects on the capacity of multi-cell CDMA system. We consider three different traffic distributions, i.e., linear, exponential and Gaussian traffic distribution with distribution parameters. Changing the distribution parameter, we can obtain the center-focused distributions or uniform distributions for each model. From the results of other-cell interference calculation we can see that the other-cell interference decreases, as the user concentrates on the base station. Also using frequency reuse efficiency indicating the capacity reduction of a multi-cell system when compared to a single cell system, we evaluate the effect of traffic distribution on the reverse link CDMA capacity. For linear case, the capacity of multi-cell system is reduced to 0.637∼0.867 times that of single cell system. On the other hand, for both exponential and Gaussian cases, the capacity under a multi-cell environment is equal to 70∼100% of that under a single cell. Therefore, we conclude that the average capacity of multi-cell CDMA system are increased when users are likely to be at near the cell base station due to reduced total other-cell interference and decreased when users exist at near the cell edge regardless of traffic distribution models.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.257-265
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• 2021

Many studies have been performed to predict a reliable and accurate stress-range distribution and fatigue damage regarding the Gaussian wide-band stress response due to multi-peak waves and multiple dynamic loads. So far, most of the approximation models provide slightly inaccurate results in comparison with the rain-flow counting method as an exact solution. A step-by-step study was carried out to develop new approximate spectral moments that are close to the rain-flow counting moment, which can be used for the development of a fatigue damage model. Using the special parameters and bandwidth parameters, four kinds of parameter-based combinations were constructed and estimated using the R-squared values from regression analysis. Based on the results, four candidate empirical formulas were determined and compared with the rain-flow counting moment, probability density function, and root mean square (RMS) value for relative distance. The new approximate spectral moments were finally decided through comparison studies of eight response spectra. The new spectral moments presented in this study could play an important role in improving the accuracy of fatigue damage model development. The present study shows that the new approximate moment is a very important variable for the enhancement of Gaussian wide-band fatigue damage assessment.

• The Journal of the Acoustical Society of Korea
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• v.27 no.7
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• pp.372-378
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• 2008

Voice activity detectors (VAD) are important in wireless communication and speech signal processing, In the conventional VAD methods, an expression for the likelihood ratio test (LRT) based on statistical models is derived in discrete Fourier transform (DFT) domain, Then, speech or noise is decided by comparing the value of the expression with a threshold, This paper presents a new statistical VAD method based on a signal subspace approach, The probabilistic principal component analysis (PPCA) is employed to obtain a signal subspace model that incorporates probabilistic model of noisy signal to the signal subspace method, The proposed approach provides a novel decision rule based on LRT in the signal subspace domain, Experimental results show that the proposed signal subspace model based VAD method outperforms those based on the widely used Gaussian distribution in DFT domain.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.1
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• pp.123-128
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• 2014

This paper analyzed the change of subthreshold current for channel dimension of double gate(DG) MOSFET. The nano-structured DGMOSFET to reduce the short channel effect had to be preciously analyze. Poisson's equation had been used to analyze the potential distribution in channel, and Gaussian function had been used as carrier distribution. The subthreshold current had been analyzed for device parameters such as channel dimension, and projected range and standard projected deviation of Gaussian function. Since this potential model was verified in the previous papers, we used this model to analyze the subthreshold current. Resultly, we know the subthreshold current was influenced on parameters of Gaussian function and channel dimension for DGMOSFET.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.716-718
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• 2012

This paper have presented the change for subthreshold characteristics for double gate(DG) MOSFET based on scaling theory and the shape of Gaussian function. To obtain the analytical solution of Poisson's equation, Gaussian function been used as carrier distribution and consequently potential distributions have been analyzed closely for experimental results, and the subthreshold characteristics have been analyzed for the shape parameters of Gaussian function such as projected range and standard projected deviation. Since this potential model has been verified in the previous papers, we have used this model to analyze the subthreshold chatacteristics. The scaling theory is to sustain constant outputs for the change of device parameters. As a result to apply the scaling theory for DGMOSFET, we know the subthreshold characteristics have been greatly changed, and the change of threshold voltage is bigger relatively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.753-756
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• 2013

This paper analyzed the change of subthreshold current for channel dimension of double gate(DG) MOSFET. The nano-structured DGMOSFET to reduce the short channel effect had to be preciously analyze. Poisson's equation had been used to analyze the potential distribution in channel, and Gaussian function had been used as carrier distribution. The subthreshold current had been analyzed for device parameters such as channel dimension, and projected range and standard projected deviation of Gaussian function. Since this potential model was verified in the previous papers, we used this model to analyze the subthreshold current. Resultly, we know the subthreshold current was influenced on parameters of Gaussian function and channel dimension for DGMOSFET.