• Journal of Creative Information Culture
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• v.6 no.1
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• pp.41-48
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• 2020

In order to represent and process spatial data, hierarchical data structures such as a quadtree or a bintree are used. Various approaches for linearly representing the bintree have been proposed. S-Tree has the advantage of compressing the storage space by expressing binary region image data as a linear binary bit stream, but the higher the resolution of the image, the longer the length of the binary bit stream, the longer the storage space and the lower the search performance. In this paper, we construct a hierarchical structure of multiple separated bintrees with a full binary tree structure and express each bintree as two linear binary bit streams to reduce the range required for image search. It improves the overall search performance by performing a simple number conversion instead of searching directly the binary bit string path. Through the performance evaluation by the worst-case space-time complexity analysis, it was analyzed that the proposed method has better search performance and space efficiency than the previous one.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.909-917
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• 2003

Although many classification models have been used to classify binary data, none of the classification models dominates all varying circumstances depending on the number of variables and the size of data(Asparoukhov and Krzanowski (2001)). This paper proposes a classification model which uses information on marginal distributions of sub-variables and its maximum entropy distribution. Classification experiments by using simulation are discussed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.6
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• pp.460-469
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• 2015

This study's aim is for reducing big data transfer time of IoT healthcare devices by modulating digital bits into Manchester code including zero-voltage idle as information for secondary compressed binary cluster's compartment after two step compression of compressing binary data into primary and secondary binary compressed clusters for each binary clusters having compression benefit of 1 bit or 2 bits. Also this study proposed that as department information of compressed binary clusters, inserting idle signal into Manchester code will have benefit of reducing transfer time in case of compressing binary cluster into secondary compressed binary cluster by 2 bits, because in spite of cost of 1 clock idle, another 1 bit benefit can play a role of reducing 1 clock transfer time. Idle signal is also never consecutive because the signal is for compartment information between two adjacent secondary compressed binary cluster. Voltage transition on basic rule of Manchester code is remaining while inserting idle signal, so DC balance can be guaranteed. This study's simulation result said that even compressed binary data by another compression algorithms could be transferred faster by as much as about 12.6 percents if using this method.

• The Journal of the Korea institute of electronic communication sciences
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• v.3 no.3
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• pp.177-182
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• 2008

Because output of multi-code CDMA system adapted high speed data transmission becoming multi-level system use linear amplifier in output stage and complex output signal. Therefore, Multi-Code CDMA system has shortcoming of high price, high complexity etc. Binary CDMA technology that allow fetters in existing CDMA technology to supplement this shortcoming proposed. In binary CDMA system When correlator process high speed data, bottle-neck phenomenon is happened on synchronization acquisition process, it is very important parameter. Because existent correlator must there be advantage that power consumption is small but flow addition of several stages to receive correlation's value, the processing speed has disadvantage because the operation amount is much. Therefore in this paper, proposed correlator has characteristic such as data is able to high speed processing, chip area is independent and power consumption is constant in structure in binary CDMA system.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.117-126
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• 2010

Clinical researchers often analyze survival data as binary outcomes using the logistic regression method. This paper examines the information loss resulting from analyzing survival time as binary outcomes. We first demonstrate that, under the proportional hazard assumption, this binary discretization does result in a significant information loss. Second, when fitting a logistic model to survival time data, researchers inadvertently use the maximal statistic. We implement a numerical study to examine the properties of the reference distribution for this statistic, finally, we show that the logistic regression method can still be a useful tool for analyzing survival data in particular when the proportional hazard assumption is questionable.

• Journal of the Optical Society of Korea
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• v.16 no.3
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• pp.263-269
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• 2012

In this paper, we propose a new dual optical encryption method for binary data and secret key based on 2-step phase-shifting digital holography for a cryptographic system. Schematically, the proposed optical setup contains two Mach-Zehnder type interferometers. The inner interferometer is used for encrypting the secret key with the common key, while the outer interferometer is used for encrypting the binary data with the same secret key. 2-step phase-shifting digital holograms, which result in the encrypted data, are acquired by moving the PZT mirror with phase step of 0 or ${\pi}/2$ in the reference beam path of the Mach-Zehnder type interferometer. The digital hologram with the encrypted information is a Fourier transform hologram and is recorded on CCD with 256 gray level quantized intensities. Computer experiments show the results to be encryption and decryption carried out with the proposed method. The decryption of binary secret key image and data image is performed successfully.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.886-893
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• 2022

Outlier detection refers to the task of detecting data that deviate significantly from the normal data distribution. Most outlier detection methods compute an outlier score which indicates the degree to which a data sample deviates from normal. However, setting a threshold for an outlier score to determine if a data sample is outlier or normal is not trivial. In this paper, we propose a binary prediction method for outlier detection based on spectral clustering and one-class SVM ensemble. Given training data consisting of normal data samples, a clustering method is performed to find clusters in the training data, and the ensemble of one-class SVM models trained on each cluster finds the boundaries of the normal data. We show how to obtain a threshold for transforming outlier scores computed from the ensemble of one-class SVM models into binary predictive values. Experimental results with high dimensional text data show that the proposed method can be effectively applied to high dimensional data, especially when the normal training data consists of different shapes and densities of clusters.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.5
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• pp.441-449
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• 2000

Binary volume data has its widespread use in the application of color volume rendering and surgical simulation system where gray-scale volume is inappropriate. For the efficient representation of binary volume, this paper proposes a new data structure - the Slice-based Binary Shell (SBS) - along with its rendering algorithm. Since SBS stores the minimal set of surface voxels in slice order and supports the direct computation of voxel coordinates, it shows high efficiency for rendering multiple objects. The rendering algorithm of SBS running on a PC with no specialized hardware renders more than one hundred binary objects in a second.

• The Korean Journal of Applied Statistics
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• v.8 no.2
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• pp.201-216
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• 1995

A method of determining the factor numbers is explored in this paper, when data and the factors are binary. We applied a bootstrap approach and proposed a criterion for the method. Simulation results suggest that the proposed method in this paper is very useful in determining the factor numbers for binary data and factors.

• Journal of the military operations research society of Korea
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• v.25 no.2
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• pp.158-169
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• 1999

This paper is concerned with the application of generalized linear interactive modelling(GLIM) to the binary categorical data. To analyze the categorical data given by a contingency table, finding a good-fitting loglinear model is commonly adopted. In the case of a contingency table with a response variable, we can fit a logit model to find a good-fitting loglinear model. For a given $2^4$ contingency table with a binary response variable, we show the process of fitting a loglinear model by fitting a logit model using GLIM and SAS and then we estimate parameters to interpret the nature of associations implied by the model.