• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.977-989
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• 2011

The half logistic distribution has been used intensively in reliability and survival analysis especially when the data is censored. In this paper, we provide Bayesian estimation of the shape parameter and reliability function in the generalized half logistic distribution based on progressively Type-II censored data under various loss functions. We here consider conjugate prior and noninformative prior and corresponding posterior distributions are obtained. As an illustration, we examine the validity of our estimation using real data and simulated data.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.597-603
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• 2011

The half logistic distribution has been used intensively in reliability and survival analysis especially when the data is censored. In this paper, we provide prole likelihood estimation of the shape parameter and scale parameter in the generalized half logistic distribution based on progressively Type-II censored data. We also introduce approximate maximum prole likelihood estimates for the scale parameter. As an illustration, we examine the validity of our estimation using real data and simulated data.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.269-273
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• 2003

In the rice planted acreage estimation, high precise measurement and laborsaving are required by using satellite data. A method referring accurate agricultural plot vector data was used in this paper which improves the estimation accuracy of rice planted acreage compared with conventional methods. In this method, satellite data are not used for totalization, although they are used to discriminate whether the fields are rice planted or not. This paper described the result of the above method using to ASTER-VNIR data.

• ETRI Journal
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• v.42 no.6
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• pp.815-826
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• 2020

We propose a two-dimensional (2D) scattering-center-extraction (SCE) method using sparse recovery based on the compressive-sensing theory, even with data missing from the received radar cross-section (RCS) dataset. First, using the proposed method, we generate a 2D grid via adaptive discretization that has a considerably smaller size than a fully sampled fine grid. Subsequently, the coarse estimation of 2D scattering centers is performed using both the method of iteratively reweighted least square and a general peak-finding algorithm. Finally, the fine estimation of 2D scattering centers is performed using the orthogonal matching pursuit (OMP) procedure from an adaptively sampled Fourier dictionary. The measured RCS data, as well as simulation data using the point-scatterer model, are used to evaluate the 2D SCE accuracy of the proposed method. The results indicate that the proposed method can achieve higher SCE accuracy for an incomplete RCS dataset with missing data than that achieved by the conventional OMP, basis pursuit, smoothed L0, and existing discrete spectral estimation techniques.

• Journal of the Korean Data and Information Science Society
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• v.7 no.2
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• pp.227-234
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• 1996

The discount survival model is proposed for the application of the Cox model on the analysis of survival data with time-varying effects of covariates. Algorithms for the recursive estimation of the parameter vector and the retrospective estimation of the survival function are suggested. Also the algorithm of forecasting of the survival function of individuals of specific covariates in the next time interval based on the information gathered until the end of a certain time interval is suggested.

• Journal of the Korea Society for Simulation
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• v.2 no.1
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• pp.125-133
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• 1993

A statistical method is described for estimation of the unknown constants in a theory using both of the computer simulation data and the real experimental data, The best linear unbiased predictor based on a spatial linear model is fitted from the computer simulation data alone. Then nonlinear least squares estimation method is applied to the real experimental data using the fitted prediction model as if it were the true simulation model. An application to the computational nuclear fusion devices is presented, where the nonlinear least squares estimates of four transport coefficients of the theoretical nuclear fusion model are obtained.

• International Journal of Reliability and Applications
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• v.3 no.1
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• pp.51-60
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• 2002

This paper presents the methodology for obtaining point and interval estimating of the parameters of a new two-parameter distribution with multiple-censored and singly censored data (Type-I censoring or Type-II censoring) as well as complete data, using the maximum likelihood method. The basis is the likelihood expression for multiple-censored data. Furthermore, this model can be extended to a three-parameter distribution that is added a scale parameter. Then, the parameter estimation can be obtained by the graphical estimation on probability plot.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1291-1298
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• 2011

This paper proposes a new reliability estimation method for one-shot systems using quantal response data, which is based on a parametric estimation method. The proposed method considers the time-variant failure ratio of the quantal response data and it can overcome the problems in parametric estimation methods. Seven reliability estimation methods in the literature were compared with the proposed method in terms of the accuracy of reliability estimation in order to verify the proposed method. To compare the accuracy of reliability estimation, the SSEs (Sum of Squared Error) of the reliability estimation results for the different estimation methods were evaluated according to the various numbers of samples tested. The proposed method provided more accurate reliability estimation results than any of the other methods from the results of the accuracy comparison.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.1
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• pp.113-119
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• 2023

In this paper, we propose an optimization of a pose estimation deep learning model for recognition of abnormal behavior in unmanned stores using radio frequencies. The radio frequency use millimeter wave in the 30 GHz to 300 GHz band. Due to the short wavelength and strong straightness, it is a frequency with less grayness and less interference due to radio absorption on the object. A millimeter wave radar is used to solve the problem of personal information infringement that may occur in conventional CCTV image-based pose estimation. Deep learning-based pose estimation models generally use convolution neural networks. The convolution neural network is a combination of convolution layers and pooling layers of different types, and there are many cases of convolution filter size, number, and convolution operations, and more cases of combining components. Therefore, it is difficult to find the structure and components of the optimal posture estimation model for input data. Compared with conventional millimeter wave-based posture estimation studies, it is possible to explore the structure and components of the optimal posture estimation model for input data using genetic algorithms, and the performance of optimizing the proposed posture estimation model is excellent. Data are collected for actual unmanned stores, and point cloud data and three-dimensional keypoint information of Kinect Azure are collected using millimeter wave radar for collapse and property damage occurring in unmanned stores. As a result of the experiment, it was confirmed that the error was moored compared to the conventional posture estimation model.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.716-722
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• 2016

Coordinate estimation is an essential function for autonomous navigation of a mobile robot. The optical mouse sensor is convenient and cost-effective for the coordinate estimation problem. It is possible to overcome the position estimation error caused by the slip and the model mismatch of robot's motion equation using the optical mouse sensor. One of the simple methods for the position estimation using the optical mouse sensor is integration of the velocity data from the sensor with time. However, the unavoidable noise in the sensor data may deteriorate the position estimation in case of the simple integration method. In general, a mobile robot has ready-to-use motion information from the encoder sensors of driving motors. By combining the velocity data from the optical mouse sensor and the motion information of a mobile robot, it is possible to improve the coordinate estimation performance. In this paper, a coordinate estimation algorithm for an autonomous mobile robot is presented based on the well-known Kalman filter that is useful to combine the different types of sensors. Computer simulation results show the performance of the proposed localization algorithm for several types of trajectories in comparison with the simple integration method.