• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.4
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• pp.41-49
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• 2019

Patents are system to promote the development of industry by disclosing technology. The importance of recent patent is being emphasized. For this reason, companies apply for many patents. And they analyze the patent. Patent analysis helps to protect and foster their technology. Previously this method has been carried out by experts. Expert-based patent analysis, however, has the disadvantage of being time-consuming and expensive. Consequently, we try to solve this problems by developing prediction model. Therefore, this paper proposes a data-based patent analysis method using quantitative indicator and textual information. We confirmed the practical applicability of the proposed method through 1,831 autonomous vehicle patents. As a result, it was possible to confirmed that safety and lane detection related technologies are important.

• Wind and Structures
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• v.22 no.5
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• pp.573-594
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• 2016

Simultaneous pressure measurements on 2D square prisms with various corner modifications were performed in uniform flow with low turbulence level, and the testing Reynolds numbers varied from $1.0{\times}10^5$ to $4.8{\times}10^5$. Experimental models were a square prism, three chamfered-corner square prisms (B/D=5%, 10%, and 15%, where B is the chamfered corner dimension and D is the cross-sectional dimension), and six rounded-corner square prisms (R/D =5%, 10%, 15%, 20%, 30%, and 40%, where R is the corner radius). Experimental results of drag coefficients, wind pressure distributions, power spectra of aerodynamic force coefficients, and Strouhal numbers are presented. Ten models are divided into various categories according to the variations of mean drag coefficients with Reynolds number. The mean drag coefficients of models with $B/D{\leq}15%$ and $R/D{\leq}15%$ are unaffected by the Reynolds number. On the contrary, the mean drag coefficients of models with R/D=20%, 30%, and 40% are obviously dependent on Reynolds number. Wind pressure distributions around each model are analyzed according to the categorized results.The influence mechanisms of corner modifications on the aerodynamic characteristics of the square prism are revealed from the perspective of flow around the model, which can be obtained by analyzing the local pressures acting on the model surface.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1587-1596
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• 2016

This paper presents an interactive locomotion controller using motion capture data and inverted pendulum model. Most of the data-driven character controller using motion capture data have two kinds of limitation. First, it needs many example motion capture data to generate realistic motion. Second, it is difficult to make natural-looking motion when characters navigate dynamic terrain. In this paper, we present a technique that uses dimension reduction technique to motion capture data together with the Gaussian process dynamical model (GPDM), and interpolates the low-dimensional data to make final motion. With the low-dimensional data, we can make realistic walking motion with few example motion capture data. In addition, we apply the inverted pendulum model (IPM) to calculate the root trajectory considering the real-time user input upon the dynamic terrain. Our method can be used in game, virtual training, and many real-time applications.

• Structural Engineering and Mechanics
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• v.76 no.4
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• pp.529-540
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• 2020

Concurrent topology optimization of macrostructure and microstructure has attracted significant interest due to its high structural performance. However, most of the existing works are carried out under deterministic conditions, the obtained design may be vulnerable or even cause catastrophic failure when the load position exists uncertainty. Therefore, it is necessary to take load position uncertainty into consideration in structural design. This paper presents a computational method for robust concurrent topology optimization with consideration of load position uncertainty. The weighted sum of the mean and standard deviation of the structural compliance is defined as the objective function with constraints are imposed to both macro- and micro-scale structure volume fractions. The Bivariate Dimension Reduction method and Gauss-type quadrature (BDRGQ) are used to quantify and propagate load uncertainty to calculate the objective function. The effective properties of microstructure are evaluated by the numerical homogenization method. To release the computation burden, the decoupled sensitivity analysis method is proposed for microscale design variables. The bi-directional evolutionary structural optimization (BESO) method is used to obtain the black-and-white designs. Several 2D and 3D examples are presented to validate the effectiveness of the proposed robust concurrent topology optimization method.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.518-524
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• 2018

The cover song refers to live recordings or reproduced albums. This paper studies two-dimensional Fourier transform as a feature-dimension reduction method to search cover song fast. The two-dimensional Fourier transform is conducive in feature-dimension reduction for cover song search due to musical-key invariance. This paper extends the previous work, which only utilize the magnitude of the Fourier transform, by introducing an invariant from phase based on the assumption that adjacent frames have the same musical-key change. We compare the cover song retrieval accuracy of the Fourier-transform based methods over two datasets. The experimental results show that the addition of the invariant from phase improves the cover song retrieval accuracy over the previous magnitude-only method.

• International Journal of High-Rise Buildings
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• v.9 no.4
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• pp.307-314
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• 2020

High-rise buildings with vertical setback are widely used in practice. From the field investigation of the past earthquakes, it was found that such kind of vertically irregular high-rise building structures easily suffer severe damage during strong earthquakes. This paper presents an extensive study on the earthquake responses of moment-resisting frame structures (MFS) popularly applied in high-rise buildings with vertical setback. Four groups of MFS are designed, including three groups of structures with vertical setback and one group of structures with the lateral stiffness varying along the building height but without vertical setback. The numerical models of the structures are established, and the time history analysis of the structures under different levels of earthquakes is conducted. The earthquake responses of the structures are compared. The influence of the ratio between the horizontal setback dimension and the previous plan dimension, the eccentricity of setback, and the position where the setback occurs on the seismic performance of structures is studied. The rationality of the provisions for the structures with vertical setback specified in the current design codes is checked by the findings from this study.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.957-968
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• 2021

Inverse censoring probability weighting (ICPW) is a popular technique in survival data analysis. In applications of the ICPW technique such as the censored regression, it is crucial to accurately estimate the censoring probability. A simulation study is undertaken in this article to see how censoring probability estimate influences model performance in censored regression using the ICPW scheme. We compare three censoring probability estimators, including Kaplan-Meier (KM) estimator, Cox proportional hazard model estimator, and local KM estimator. For the local KM estimator, we propose to reduce the predictor dimension to avoid the curse of dimensionality and consider two popular dimension reduction tools: principal component analysis and sliced inverse regression. Finally, we found that the Cox proportional hazard model estimator shows the best performance as a censoring probability estimator in both mean and median censored regressions.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.1
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• pp.1-8
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• 2004

According to the development of the Internet and the pervasion of Data Base, it is not easy to search for necessary information from the huge amounts of data. In order to do efficient analysis of a large amounts of data, this paper proposes a method for pattern classification based on the effective strategy for dimension reduction for narrowing down the whole data to what users wants to search for. To analyze data effectively, Radial Basis Function Networks based on VC-dimension of Support Vector Machine, a model of statistical teaming, is proposed in this paper. The model of Radial Basis Function Networks currently used performed the preprocessing of Perceptron model whereas the model proposed in this paper, performing independent analysis on VD-dimension, classifies each datum putting precise labels on it. The comparison and estimation of various models by using Machine Learning Data shows that the model proposed in this paper proves to be more efficient than various sorts of algorithm previously used.

• Tunnel and Underground Space
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• v.22 no.2
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• pp.120-129
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• 2012

Recently, mining facilities have being installed in an underground space according to a social demand for environment-friendly mine development. The underground structures for mining facilities usually requires a large volume of space with width greater than height, and thus the stability assessment of the large-scale underground mine structure is an important issue. In this study, we analysed a factor of safety based on strength reduction method, and proposed a numerical design approach to determining the dimension of underground mine structures in combination with a strength reduction method and a multivariate regression analysis. Input design parameters considered in the present study were the stress ratio and shear strength of rock mass, and the width and cover depth of underground mine structures. The stabilities of underground mine structures were assessed in terms of factor of safety under different conditions of the above input parameters. It was calculated by the strength reduction method, and several kinds of fit functions were obtained through various multivariate regression analyses. Using a best-fit regression model, we proposed the charts which provide preliminary design information on the dimension of underground mine structures.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.7
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• pp.615-622
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• 2014

Copper pipes have been widely used as components of System Air-Conditioner due to high thermal conductivity. This system consists of 150 pipes, which are approximately 10m long in total. Dimensional changes occur during pipe processing such as expansion, reduction and bending. This processing induces changes in length of pipes and makes dimensional differences from original pipes. The summation of the differences of pipes components leads to make huge cumulative dimensional differences. The cumulative differences can cause serious problems such as crack, refrigerant leakage. However the differences have not been considered so far. To satisfy target quality of the system, it is essential to predict and calibrate the differences. In this paper, the changes in dimension were predicted using FEM and it was found that cumulative differences could cause indesirable stress during assembly process. As a result, dimensional differences or indesirable stress could be reduced using the proposed method.