• Proceedings of the KIEE Conference
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• 2008.04b
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• pp.171-173
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• 2008

This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.408-413
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• 2005

A new methodology for discrete non-Gaussian probability density estimation is investigated in this paper based on a dynamic Bayesian network (DBN) and kernel functions. The estimator consists of a DBN in which the transition distribution is represented with kernel functions. The estimator parameters are determined through a recursive learning algorithm according to the maximum likelihood (ML) scheme. A discrete-type Poisson distribution is generated in a simulation experiment to evaluate the proposed method. In addition, an unknown probability density generated by nonlinear transformation of a Poisson random variable is simulated. Computer simulations numerically demonstrate that the method successfully estimates the unknown probability distribution function (PDF).

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.63-68
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• 2017

In this paper, we propose a bounding box prediction algorithm using multiple probability maps to improve object detection result of object detector. Although the performance of object detectors has been significantly improved, it is still not perfect due to technical problems and lack of learning data. Therefore, we use the result correction method to obtain more accurate object detection results. In the proposed algorithm, the preprocessed bounding box created as a result of object detection by the object detector is clustered in various form, and a conditional probability is given to each cluster to make multiple probability map. Finally, multiple probability map create new bounding box of object using morphological elements. Experiment results show that the newly predicted bounding box reduces the error in ground truth more than 45% on average compared to the previous bounding box.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1503-1504
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• 2008

This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.

• Journal of KIISE:Software and Applications
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• v.37 no.8
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• pp.591-598
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• 2010

In this study, we propose a new method for generating candidate solutions based on the Cauchy probability distribution in order to complement the shortcoming of the solutions generated by the normal distribution. The Cauchy probability distribution has infinite mean and variance, and it has rather large probability in the tail region relative to the normal distribution. Thus, the Cauchy distribution can yield higher probabilities of generating candidate solutions of large-varied variables, which in turn has an advantage of searching wider area of variable space. In order to compare and analyze the performance of the proposed method against the conventional method, we carried out an experiment using benchmarking problems of real valued function. From the result of the experiment, we found that the proposed method based on the Cauchy distribution outperformed the conventional one for all benchmarking problems, and verified its superiority by the statistical hypothesis test.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.783-796
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• 2017

In the theory of probability, a Bernoulli trial is a random experiment with exactly two possible outcomes, "success" and "failure", in which the probability of success is the same every time the experiment is conducted. In the successive games of scissors paper stone there exists the case of draw in each game. In this paper we are interested in the ultimate success probability of each participant and the expected number of the game till any one of the two has the ultimate victory. Using our results, we can calculate the ultimate winning probability of each player of the two players and the expected number of the game till any one of the two has the ultimate victory in any case whether there is draw or not in each game.

• The Korean Journal of Applied Statistics
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• v.9 no.2
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• pp.83-94
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• 1996

The probability of Yut was calculated by using the physical property in previous study, but this article suggested empirical estimators for probability of Yut. In practice, physics-based probability imposes too strong assumptions, which result in the difference between the calculated probabilies and empirical relative frequencies. Experiment shows the probabilities of Yut depend on the integrated shape of Yut rather than the floor type. Maximum likelihood estimator and empirical Bayes estimators are compared and all turn out to be almost identicla for more than 40 trials. For smaller number of trials, Bayes estimators are recommended for its stability. Regression approach is also adopted as an easy-to-use method without empirical trials.

• Korean Management Science Review
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• v.27 no.1
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• pp.75-90
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• 2010

In this paper, we proposed new solution procedure of the air defense missile allocation problem. In order to find the optimal location of missile, we formulated a simple mathematical model maximizing the kill probability of enemy air threat including aircraft and missile. To find the Kill probability, we developed a new procedure using actual experimental data in the mathematical model. Actual experimental data mean real characteristic factor, which was acquired when the missile had been developed through missile fire experiment. The result of this study can offer practical solution for missile allocation and the methodology in this study can be used to the decision making for the optimal military facility allocation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.10
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• pp.1847-1853
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• 2008

This paper presents stochastic methodology based fault detection algorithm for induction motor systems. We measure current of healthy induction motors by means of hall sensor systems and then establish its probability distribution. We propose online probability density estimation which is effective in real-time implementation due to its simplicity and low computational burden. In addition, we accomplish theoretical analysis to demonstrate convergence property of the proposed estimation by using statistical convergence and system stability theory. We apply our fault diagnosis approach to three-phase induction motors and achieve real-time experiment for evaluating its reliability and practicability in industrial fields.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2435-2438
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• 2003

In this paper, we propose a Korean speech understanding model using dictionary and thesaurus. The proposed model search the dictionary for the same word with in input text. If it is not in the dictionary, the proposed model search the high level words in the high level word dictionary based on the thesaurus. We compare the probability of sentence understanding model with threshold probability, and we'll get the speech understanding rate. We evaluated the performance of the sentence speech understanding system by applying twenty questions game. As the experiment results, we got sentence speech understanding accuracy of 79.8％. In this case probability of high level word is 0.9 and threshold probability is 0.38.