• Journal of Mechanical Science and Technology
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• v.19 no.spc1
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• pp.265-271
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• 2005

Randomness exists in engineering. Tolerance, assemble-error, environment temperature and wear make the parameters of a mechanical system uncertain. So the behavior or response of the mechanical system is uncertain. In this paper, the uncertain parameters are treated as random variables. So if the probability distribution of a random parameter is known, the simulation of mechanical multibody dynamics can be made by Monte-Carlo method. Thus multibody dynamics simulation results can be obtained in statistics. A new concept called functional reliability is put forward in this paper, which can be defined as the probability of the dynamic parameters(such as position, orientation, velocity, acceleration etc.) of the key parts of a mechanical multibody system belong to their tolerance values. A flexible mechanical arm with random parameters is studied in this paper. The length, width, thickness and density of the flexible arm are treated as random variables and Gaussian distribution is used with given mean and variance. Computer code is developed based on the dynamic model and Monte-Carlo method to simulate the dynamic behavior of the flexible arm. At the same time the end effector's locating reliability is calculated with circular tolerance area. The theory and method presented in this paper are applicable on the dynamics modeling of general multibody systems.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.806-811
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• 1994

Two kinds of static and dynamic state estimation methods are newly discussed for the problem of the measurement disturbance of environmental low-frequency noise in the presence of wind-induced noise. First, the probability characteristics of wind-induced noise are discussed in the form of probability distribution conditioned by wind speed, based on the simultaneous observation of the wind-induced noise and wind speed near a microphone. Next, especially form the viewpoint of simplicity for practical use, two kinds of static and dynamic state estimation methods are discussed. The static estimation method using the information on wind speed is fundamentally supported by the conservation principle of energy sum. The dynamic one is the method by using a recursive digital filter with the parameters successively renewed by the information on wind speed. This can be also simplified by using well-know Kalman filter under the assumption of the Gaussian distribution. The effectiveness of proposed two estimation methods are shown through experiments under a breezy condition in the open filed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3055-3073
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• 2019

In this paper, a new user identification method is presented using real environmental human-computer-interaction (HCI) behavior data to improve method usability. User behavior data in this paper are collected continuously without setting experimental scenes such as text length, action number, etc. To illustrate the characteristics of real environmental HCI data, probability density distribution and performance of keyboard and mouse data are analyzed through the random sampling method and Support Vector Machine(SVM) algorithm. Based on the analysis of HCI behavior data in a real environment, the Multiple Kernel Learning (MKL) method is first used for user HCI behavior identification due to the heterogeneity of keyboard and mouse data. All possible kernel methods are compared to determine the MKL algorithm's parameters to ensure the robustness of the algorithm. Data analysis results show that keyboard data have a narrower range of probability density distribution than mouse data. Keyboard data have better performance with a 1-min time window, while that of mouse data is achieved with a 10-min time window. Finally, experiments using the MKL algorithm with three global polynomial kernels and ten local Gaussian kernels achieve a user identification accuracy of 83.03% in a real environmental HCI dataset, which demonstrates that the proposed method achieves an encouraging performance.

• Journal of KIISE:Software and Applications
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• v.28 no.11
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• pp.833-841
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• 2001

Abstract In this work, we analyze the Levy mutation operations based on the Levy probability distribution in the evolutionary programming via the mean square displacement and the distinctness. The Levy probability distribution is characterized by an infinite second moment and has been widely studied in conjunction with the fractals. The Levy mutation operators not only generate small varied offspring, but are more likely to generate large varied offspring than the conventional mutation operators. Based on this fact, we prove mathematically, via the mean square displacement and the distinctness, that the Levy mutation operations can explore and exploit a search space more effectively. As a result, one can get better performance with the Levy mutation than the conventional Gaussian mutation for the multi-valued functional optimization problems.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.8
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• pp.615-623
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• 2018

The probability density function(PDF) for the amplitude of the reflectivity of low-grazing-angle sea clutter has generally been modeled by a compound-Gaussian distribution, rather than by the Rayleigh distribution, owing to the intensity variation of each clutter patch over time. The texture component forming the reflectivity has been simulated by combining Gamma distribution and memory-less nonlinear transformation(MNLT). On the other hand, there is no typical method available that can be used to simulate the speckle component. We first review Watt's method, wherein the speckle is simulated starting from the Doppler spectrum of the received echoes that is modeled as having a Gaussian shape. Then, we introduce a newly proposed method. The proposed method simulates the speckle by manipulating a clutter covariance matrix through the Cholesky decomposition after minimizing the effect of adjacent clutter patches using an equalizer. The feasibility of the proposed method is validated through simulation, wherein the results from two methods are compared in terms of the Doppler spectrum and the correlation function.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.11
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• pp.1295-1303
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• 2010

In this paper, an effective method has been proposed for selection of white Gaussian noise(WGN) band for radio background noise measurement system under broad band noise environment. MMN which comes from industrial devices and equipment mostly happens in the shape of broad band noise mostly like impulsive noise and this is the main reason for increasing level in the present radio noise measurements. The existing method based on singular value decomposition has weak point that it cannot give good performance for the broad band signal because it uses signal's white property. The proposed method overcomes such a weakness of singular value decomposition based method by using signal's Gaussian property based method in parallel. Moreover, this proposed method hires a modelling based method which uses parameter estimation algorithm like maximum likelihood estimation(MLE) and gives more accurate result than the method using amplitude probability distribution(APD) graph. Experiment results under the natural environment has done to verify feasibility of the proposed method.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.6
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• pp.365-373
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• 2013

Representative tidal gauging (TG) stations are selected to cover the tidal characteristics of the Korean peninsula coastal seas, and the statistical parameters of the data are analysed from the perspective of the probability distribution at that TG station. The shape of the distribution in the Incheon and Gunsan TG stations, which are tide-dominated areas, shows two clear modes at HWONT and LWONT in the distributions, and in the Mokpo station, shows an asymmetric double peak distribution. In contrast, the frequency distribution shape shows a smoothed flat peak in the Jeju, Yeosu and Busan TG stations, and a single peak in the Pohang and Sokcho TG stations. The emersion and submersion equations suggested as the 6-parameter Gaussian mixture models in this study are accurate, and well fitted to the observed tidal elevation data. The ${\mu}_1$, ${\mu}_2$ parameters are highly correlated to the LWONT and HWONT, and the ${\sigma}_1$ and ${\sigma}_2$ parameters are also closely correlated to the mean tidal range. The ${\mu}_1$ and ${\mu}_2$ parameters coincide with the modes of the suggested probability distribution of the hourly tidal level data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3342-3360
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• 2014

In this paper, the capacity and BER performance of downlink distributed antenna systems (DAS) with transmit antenna selection and multiple receive antennas are investigated in MIMO composite channel, where path loss, Rayleigh fading and lognormal shadowing are all considered. Based on the performance analysis, using the probability density function (PDF) of the effective SNR and numerical integrations, tightly-approximate closed-form expressions of ergodic capacity and average BER of DAS are derived, respectively. These expressions have more accuracy than the existing expressions, and can match the simulation well. Besides, the outage capacity of DAS is also analyzed, and a tightly-approximate closed-form expression of outage capacity probability is derived. Moreover, a practical iterative algorithm based on Newton's method for finding the outage capacity is proposed. To avoid iterative calculation, another approximate closed-form outage capacity is also derived by utilizing the Gaussian distribution approximation. With these theoretical expressions, the downlink capacity and BER performance of DAS can be effectively evaluated. Simulation results show that the theoretical analysis is valid, and consistent with the corresponding simulation.

• Structural Engineering and Mechanics
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• v.62 no.3
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• pp.365-372
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• 2017

Reliability analysis is a probabilistic approach to determine a safety level of a system. Reliability is defined as a probability of a system (or a structure, in structural engineering) to functionally perform under given conditions. In the 1960s, Basler defined the reliability index as a measure to elucidate the safety level of the system, which until today is a commonly used parameter. However, the reliability index has been formulated based on the pivotal assumption which assumed that the considered limit state function is normally distributed. Nevertheless, it is not guaranteed that the limit state function of systems follow as normal distributions; therefore, there is a need to define a new reliability index for no-normal distributions. The main contribution of this paper is to define a sophisticated reliability index for limit state functions which their distributions are non-normal. To do so, the new definition of reliability index is introduced for non-normal limit state functions according to the probability functions which are calculated based on the convolution theory. Eventually, as the state of the art, this paper introduces a simplified method to calculate the reliability index for non-normal distributions. The simplified method is developed to generate non-normal limit state in terms of normal distributions using series of Gaussian functions.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.6
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• pp.577-588
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• 2016

Due to the uncertainties related to the flaw assessment parameters, such as flaw size, fracture toughness, loading spectrum and so on, the probability concept is preferred over deterministic one in flaw assessment. In this study, efforts have been made to develop the reliability based flaw assessment procedure which combines the flaw assessment procedure of BS7910 and first-and second-order reliability methods (FORM/SORM). Both crack length and depth of semi-elliptical surface crack at weld toe were handled as random variable whose probability distribution was defined as Gaussian with certain means and standard deviations. Then the limit state functions from static rupture and fatigue perspective were estimated using FORM and SORM in joint probability space of crack depth and length. The validity of predicted limit state functions were checked by comparing it with those obtained by Monte Carlo simulation. It was confirmed that the developed methodology worked perfectly in predicting the limit state functions without time-consuming Monte Carlo simulation.