• 한국컴퓨터정보학회논문지
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• 제21권8호
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• pp.77-84
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• 2016

In general statistical analysis, we need to make a normal assumption. If this assumption is not satisfied, we cannot expect a good result of statistical data analysis. Most of statistical methods processing the outlier and noise also need to the assumption. But the assumption is not satisfied in big data because of its large volume and heterogeneity. So we propose a methodology based on box-plot and data smoothing for controling outlier and noise in big data analysis. The proposed methodology is not dependent upon the normal assumption. In addition, we select patent documents as target domain of big data because patent big data analysis is a important issue in management of technology. We analyze patent documents using big data learning methods for technology analysis. The collected patent data from patent databases on the world are preprocessed and analyzed by text mining and statistics. But the most researches about patent big data analysis did not consider the outlier and noise problem. This problem decreases the accuracy of prediction and increases the variance of parameter estimation. In this paper, we check the existence of the outlier and noise in patent big data. To know whether the outlier is or not in the patent big data, we use box-plot and smoothing visualization. We use the patent documents related to three dimensional printing technology to illustrate how the proposed methodology can be used for finding the existence of noise in the searched patent big data.

• 대한건축학회논문집:구조계
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• 제35권9호
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• pp.159-169
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• 2019

This work presents the three-dimensional extended strain smoothing approach in the framework of finite element method, so-called smoothed finite element method (S-FEM) for quasi-incompressible hyperelastic materials undergoing the large deformations. The proposed method is known that the incompressible limits, such as over-estimation of stiffness and distorted mesh sensitivity, can be overcome in two dimensions. Therefore, in this paper, the idea of Cell-based, Edge-based and Node-based strain smoothing approaches is extended to three-dimensions. The construction of subcells and smoothing domains for each methods are explained. The smoothed strain-displacement matrix and the stiffness matrix are obtained on each smoothing domain in the same manner with two-dimensional S-FEM. Various numerical tests are studied to demonstrate the validity and accuracy of 3D-S-FEM. The obtained results are compared with analytical solutions to express the efficacy of the methods.

• Journal of applied mathematics & informatics
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• 제32권5_6호
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• pp.609-619
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• 2014

Nonsmooth equations with finitely many maximum functions is often used in the study of complementarity problems, variational inequalities and many problems in engineering and mechanics. In this paper, we consider the global convergence methods for nonsmooth equations with finitely many maximum functions. The steepest decent method and the smoothing gradient method are used to solve the nonsmooth equations with finitely many maximum functions. In addition, the convergence analysis and the applications are also given. The numerical results for the smoothing gradient method indicate that the method works quite well in practice.

• 제어로봇시스템학회논문지
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• 제22권3호
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• pp.217-225
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• 2016

In this paper, we propose an improved path planning method and obstacle avoidance algorithm for two-wheel mobile robots, which can be effectively applied in an environment where obstacles can be represented by circles. Firstly, we briefly introduce the rapidly exploring random tree (RRT) and single polar polynomial (SPP) algorithm. Secondly, we present additional two methods for applying our proposed method. Thirdly, we propose a global path planning, smoothing and obstacle avoidance method that combines the RRT and SPP algorithms. Finally, we present a simulation using our proposed method and check the feasibility. This shows that proposed method is better than existing methods in terms of the optimality of the trajectory and the satisfaction of the kinematic constraints.

• 산업공학
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• 제18권3호
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• pp.343-349
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• 2005

This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.

• International Journal of Advanced Culture Technology
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• 제10권1호
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• pp.236-241
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• 2022

We consider methods of estimating a binary regression function using a nonparametric kernel estimation when there is only one covariate. For this, the Nadaraya-Watson estimation method using single and double bandwidths are used. For choosing a proper smoothing amount, the cross-validation and plug-in methods are compared. In the real data analysis for case study, German credit data and heart disease data are used. We examine whether the nonparametric estimation for binary regression function is successful with the smoothing parameter using the above two approaches, and the performance is compared.

• Archives of Pharmacal Research
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• 제10권1호
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• pp.1-8
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• 1987

One of the problems of derivatie spectrophotometry, the decrease of signal-to-noise ratio by derivative operations, was solved by three concepts of digital filtering, ensemble averaging, least squares polynomial smoothing and Fourier smoothing. The suthors made several compouter programs written in APPLE SOFT BASIC language for the actual applications of the concepts of these digital filters on UV spectrophotometer system. As a result, ensemble averaging could not be used as a routine operation for the spectrophotometer used. The maximum S/N ratio enhancement factors achieved by least squares polynomial smoothing were 6.17 and 7.47 for the spectra of Gaussian and Lorentzian distribution models, and by Fourier smoothing 16.42 and 11.78 for the spectra of two models, respectively.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권2호
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• pp.84-90
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• 2014

This paper presents the results of three-dimensional face point cloud smoothing based on a modified anisotropic diffusion method. The focus of this research was to obtain a 3D face point cloud with a smooth texture and number of vertices equal to the number of vertices input during the smoothing process. Different from other methods, such as using a template D face model, modified anisotropic diffusion only uses basic concepts of convolution and filtering which do not require a complex process. In this research, we used 6D point cloud face data where the first 3D point cloud contained data pertaining to noisy x-, y-, and z-coordinate information, and the other 3D point cloud contained data regarding the red, green, and blue pixel layers as an input system. We used vertex selection to modify the original anisotropic diffusion. The results show that our method has improved performance relative to the original anisotropic diffusion method.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2006년도 추계학술대회
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• pp.165-168
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• 2006

Exponential smoothing methods do not adapt well to unexpected changes in underlying process. Over the past few decades a number of adaptive smoothing models have been proposed which allow for the continuous adjustment of the smoothing constant value in order to provide a much earlier detection of unexpected changes. However, most of previous studies presented ad hoc procedure of adaptive forecasting without any theoretical background. In this paper, we propose a detection-adaptation procedure applied to simple and Holt's linear method. We derive level and slope change detection statistics based on Bayesian statistical theory and present distribution of the statistics by simulation method. The proposed procedure is compared with previous adaptive forecasting models using simulated data and economic time series data.

• Communications for Statistical Applications and Methods
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• 제15권2호
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• pp.283-292
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• 2008

커널밀도함수의 추정을 이용한 분류 문제에서 평활모수(smoothing parameter, bandwidth)의 선택은 핵심적으로 중요한 역할을 한다. 본 논문에서는 분류에서 베이즈 리스크를 최적화하기 위한 평활모수의 선택이 각 개별 확률밀도함수를 추정하기 위한 최적의 평활모수와 어떤 관계가 있는지 살펴보았다. 실제 상황에서 사용할 수 있는 평활모수의 선택 방법으로 붓스트랩(bootstrap)과 교차확인법(cross-validation)을 이용하는 것을 비교한 결과, 붓스트랩 방법은 Hall과 Kang (2005)에서 밝혀진 이론적인 성질에 부합하는 반면 교차확인법은 그렇지 못함을 확인하였다. 또한, 각 방법으로 정한 평활모수를 사용하여 오분류율을 조사해 본 결과에서도 붓스트랩 방법이 우월함을 알 수 있었다.