• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.240-244
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• 1992

A new method for generation of binary random sequences, called random sampling method, has been proposed by the authors. However, the random sampling method has the defect that binary random sequence can not be rapidly generated. In this paper, two methods based on the random sampling method are proposed for fast generation of binary random sequences. The optimum conditions for obtaining ideal binary random sequences are derived.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.979-983
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• 1989

This paper proposes a new concept, called merit factor Fr, for evaluating the randomness of binary random sequences. The merit factor Fr is obtained from the expected values of the autocorrelation function of the binary random sequence. Using this merit factor Fr, randomness of the binary random sequences generated by the random sampling method is evaluated.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.261-272
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• 2019

In this paper, we propose a procedure to build a prediction interval of the sum of dependent binary random variables over a graph to account for the dependence among binary variables. Our main interest is to find a prediction interval of the weighted sum of dependent binary random variables indexed by a graph. This problem is motivated by the prediction problem of various elections including Korean National Assembly and US presidential election. Traditional and popular approaches to construct the prediction interval of the seats won by major parties are normal approximation by the CLT and Monte Carlo method by generating many independent Bernoulli random variables assuming that those binary random variables are independent and the success probabilities are known constants. However, in practice, the survey results (also the exit polls) on the election are random and hardly independent to each other. They are more often spatially correlated random variables. To take this into account, we suggest a spatial auto-regressive (AR) model for the surveyed success probabilities, and propose a residual based bootstrap procedure to construct the prediction interval of the sum of the binary outcomes. Finally, we apply the procedure to building the prediction intervals of the number of legislative seats won by each party from the exit poll data in the $19^{th}$ and $20^{th}$ Korea National Assembly elections.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.4
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• pp.263-269
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• 2015

Digital watermarking is imperceptible and statistically undetectable information embeds into digital data. Most information in digital audio watermarking schemes have used binary random sequences. The embedded binary random sequence distorts and modifies the original data while it plays a vital role in security. In this paper, a binary random sequence to improve imperceptibility in perceptual region of the human auditory system is proposed. The basic idea of this work is a modification of a binary random sequence according to the frequency analysis of adjacent binary digits that have different signs in the sequence. The canonical signed digit code (CSDC) is also applied to modify a general binary random sequence and the pair-matching function between original and its modified version. In our experiment, frequency characteristics of the proposed binary random sequence was evaluated and analyzed by Bark scale representation of frequency and frequency gains.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10a
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• pp.832-835
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• 1987

This paper proposes a new method of generating binary random sequences using a randomly sampled M-sequence. In this paper two methods of sampling are proposed. Expected values of the autocorrelation function of the sequence generated by these methods are calculated theoretically. From the results of computer simulation, it is shown that using these methods, we can get binary random sequences which have good random properties.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1043-1051
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• 2016

In this paper, double encryption technique of binary image using random phase mask and 2-step phase-shifting digital holography is proposed. After phase modulating of binary image, firstly, random phase mask to be used as key image is generated through the XOR operation with the binary phase image. And the first encrypted image is encrypted again through the fresnel transform and 2-step phase-shifting digital holography. In the decryption, simple arithmetic operation and inverse Fresnel transform are used to get the first decryption image, and second decryption image is generated through XOR operation between first decryption image and key image. Finally, the original binary image is recovered through phase modulation.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.7
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• pp.120-127
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• 2003

A novel three degrees of freedom sensing method utilizing binary grid pattern image and vision camera is presented. The binary grid pattern image is designed by Pseudo-Random Binary Arrays and referenced to encode in-plane position of a moving stage of the contact-free planar actuator. First, the yaw motion of the stage is detected using fast image processing and then the other planar positions, x and y, are decoded with a sequence of images. This method can be applied to the system that needs feedback of in-plane position, with advantages of a good accuracy and high resolution comparable with the encoder, a relatively compact structure, no friction, and a low cost. In this paper, all the procedures of the above sensing mechanism are described in detail, including simulation and experiment results.

• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.421-431
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• 2014

The market basket data in the form of a binary user-item matrix or a binary item-user matrix can be modelled as a binary classification problem. The binary logistic regression approach tackles the binary classification problem, where principal components are predictor variables. If users or items are sparse in the training data, the binary classification problem can be considered as a cold-start problem. The binary logistic regression approach may not function appropriately if the principal components are inefficient for the cold-start problem. Assuming that the market basket data can also be considered as a special regression problem whose response is either 0 or 1, we propose three supervised learning approaches: random forest regression, random forest classification, and elastic net to tackle the cold-start problem, comparing the performance in a variety of experimental settings. The experimental results show that the proposed supervised learning approaches outperform the conventional approaches.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1295-1299
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• 1990

A new method called random sampling method has been proposed for generation of binary random sequences. In this paper, a new concept, called merit factor Fn, is proposed for evaluating the randomness of the binary random sequences generated by the random sampling method. Using this merit factor Fn, some desirable conditions are investigated for uniform random numbers used in the random sampling method.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.1033-1036
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• 1988

This paper proposes a new method for generation of uniform random numbers using binary random sequences. These binary sequences are obtained from a de Bruijn sequence by random sampling method. Several statistical tests are carried out for the random numbers generated by the proposed method, and it is shown that the random numbers have good random properties.