• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.1-12
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• 1995

An estimator of coefficients of polynomial measurement error model with vector predictor and first-order interaction terms is derived using Hermite polynomial. Asymptotic normality of estimator is provided and some simulation study is performed to compare the small sample properties of derived estimator with those of OLS estimator.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.79-83
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• 2018

An accurate method to measure shielding effectiveness(SE) of EMI spray coating film is presented. After high frequency simulating, Circular coaxial standard test fixture is fabricated. A sample of EMI spray coating film was fabricated for insertion into a circular coaxial standard test fixture. The measuring instrument used an Agilent 8722ES vector network analyzer for the SE measurement. The exact SE of copper and silver mixed spray coating sample was measured by the composition of the measuring instrument and the measuring sample. The SE of copper sample was measured at 70 dB and the SE of copper and silver mixed spray coating sample was measured at 60 dB. As a result of the measurement, the reliability of the circular coaxial standard test fixture was confirmed.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.11 no.9
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• pp.752-757
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• 1998

Rotational Magnetization-vector measurements have been performed on a polycrystalline $Y_1Ba_2Cu_3O_{7-\delta}$ sample in field-cooled condition at 4.2 K. The experimental results show that vortex flux density(B) consists of 3 groups :(1) a weak pinning part ($B_w$) which stays at a fixed angle relative to the magnetic field f(H) ; (2) a strong pining part($B_s$) which rotates rigidly with the sample and has same magnitude with the sample rotation, and(3) and intermediated pining part ($B_i$) which rotates rigidly with the sample, but whose magnitude changes with the sample rotation Our results have been explained in terms of a distribution in the strength of the vortex pinning torque and a repulsive intervortex torque.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.451-457
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• 2010

This paper considers the problem of nonparametric deconvolution density estimation when sample observa-tions are contaminated by double exponentially distributed errors. Three different deconvolution density estima-tors are introduced: a weighted kernel density estimator, a kernel density estimator based on the support vector regression method in a RKHS, and a classical kernel density estimator. The performance of these deconvolution density estimators is compared by means of a simulation study.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.245-258
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• 2023

This study proposes a modification to the objective function of the support vector machine for the linearly non-separable case of a binary classifier y_i ∈ {-1, 1}. The modification takes into account the position of each data item x_i from its corresponding class centroid. The resulting optimization function involves the centroid mean vector, and the spread of data besides the support vectors, which should be minimized by the choice of hyper-plane β. Theoretical assumptions have been tested to derive an optimal separable hyperplane that yields the minimal misclassification rate. The proposed method has been evaluated using simulation studies and real-life COVID-19 patient outcome hospitalization data. Results show that the proposed method performs better than the classical linear SVM classifier as the sample size increases and is preferred in the presence of correlations among predictors as well as among extreme values.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.223-233
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• 2023

Gauss Sieve is an algorithm for solving SVP and requires exponential time and space complexity. The terminationcondition of the Sieve is determined by the size of the constructed list and the number of collisions related to space complexity. The term 'collision' refers to the state in which the sampled vector is reduced to the vector that is already inthe list. if collisions occur more than a certain number of times, the algorithm terminates. When executing previous algorithms, we noticed that unnecessary operations continued even after the shortest vector was found. This means that the existing termination condition is set larger than necessary. In this paper, after identifying the point where unnecessary operations are repeated, optimization is performed on the number of operations required. The tests are conducted by adjusting the threshold of the collision that becomes the termination condition and the distribution in whichthe sample vector is generated. According to the experiments, the operation that occupies the largest proportion decreased by62.6%. The space and time complexity also decreased by 4.3 and 1.6%, respectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.11-18
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• 2012

In this paper, an entropy constrained vector quantizer for high bit-rates is proposed. The sample-adaptive product quantizer (SAPQ), which is based on the product codebooks, is employed, and a design algorithm for the entropy constrained sample adaptive product quantizer (ECSAPQ) is proposed. The performance of the proposed ECSAPQ is better than the case of the entropy constrained vector quantizer by 0.5dB. It is also shown that the ECSAPQ distortion curve, which is based on the scalar quantizer, is lower than the high-rate theoretical curve of the entropy constrained scalar quantizer, where the theoretical curve have 1.53dB difference from Shannon's lower bound.

• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.75-88
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• 2004

The Support Vector Machine(SVM) algorithm has paid attention on maximizing the shortest distance between sample points and discrimination hyperplane. This paper suggests the total margin algorithm which considers the distance between all data points and the separating hyperplane. The method extends existing support vector machine algorithm. In addition, this newly proposed method improves the generalization error bound. Numerical experiments show that the total margin algorithm provides good performance, comparing with the previous methods.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.17 no.2
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• pp.137-144
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• 2021

The mode superposition time history analysis method is commonly used in a seismic analysis. The maximum response in the time history analysis can be derived by combining the responses of individual modes. The residual mode response is the response of the modes which are not considered in the time history analysis. In this paper, the residual vector method to consider the residual mode response in the time history analysis is introduced and evaluated. Seismic analyses for a sample structure model and a reactor vessel model are performed to evaluate the residual vector method. The analysis results show that residual mode response is well calculated when the residual vector method is used. It is confirmed that the residual vector method is useful and acceptable to consider the residual mode response in a seismic analysis of the nuclear power plant equipment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2343-2352
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• 2015

In this paper, we propose novel motion field estimation method using U-disparity map and forward-backward error removal in vehicles environment. Generally, in an image obtained from a camera attached in a vehicle, a motion vector occurs according to the movement of the vehicle. but this motion vector is less accurate by effect of surrounding environment. In particular, it is difficult to extract an accurate motion vector because of adjacent pixels which are similar each other on the road surface. Therefore, proposed method removes road surface by using U-disparity map and performs optical flow about remaining portion. forward-backward error removal method is used to improve the accuracy of the motion vector. Finally, we predict motion of the vehicle by applying RANSAC(RANdom SAmple Consensus) from acquired motion vector and then generate motion field. Through experimental results, we show that the proposed algorithm performs better than old schemes.