• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2470-2491
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• 2018

Web-scale open information extraction (Open IE) plays an important role in NLP tasks like acquiring common-sense knowledge, learning selectional preferences and automatic text understanding. A large number of Open IE approaches have been proposed in the last decade, and the majority of these approaches are based on supervised learning or dependency parsing. In this paper, we present a novel method for web scale open information extraction, which employs cosine distance based on Google word vector as the confidence score of the extraction. The proposed method is a purely unsupervised learning algorithm without requiring any hand-labeled training data or dependency parse features. We also present the mathematically rigorous proof for the new method with Bayes Inference and Artificial Neural Network theory. It turns out that the proposed algorithm is equivalent to Maximum Likelihood Estimation of the joint probability distribution over the elements of the candidate extraction. The proof itself also theoretically suggests a typical usage of word vector for other NLP tasks. Experiments show that the distance-based method leads to further improvements over the newly presented Open IE systems on three benchmark datasets, in terms of effectiveness and efficiency.

• Journal of Mechanical Science and Technology
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• v.14 no.8
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• pp.879-890
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• 2000

This study investigates the nonpremixed $H_2/CO$-air turbulent flames numerically. The turbulent combustion process is represented by a reaction progress variable model coupled with the presumed joint probability function. In the present study, the turbulent combustion model is applied to analyze the nonadiabatic flames by introducing additional variable in the transport equation of enthalpy and the radiative heat loss is calculated using a local, geometry independent model. Calculations are compared with experimental data in terms of temperature, and mass fraction of major species, radical, and NO. Numerical results indicate that the lower and higher fuel-jet velocity flames have the distinctly different flame structures and NO formation characteristics in the proximity of the outer core vortex zone. The present model correctly predicts the essential features of flame structure and the characteristics of NO formation in the bluff-body stabilized flames. The effects of nonequilibrium chemistry and radiative heat loss on the thermal NO formation are discussed in detail.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.301-312
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• 2008

Mutual information is a measure of association of explanatory variable for predicting target variable. It is used for variable ranking and variable subset selection. This study is about the Sample-spacing approach which can be used for the estimation of mutual information from data consisting of continuous explanation variables and categorical target variable without estimating a joint probability density function. The results of Monte-Carlo simulation and experiments with real-world data show that m = 1 is preferable in using Sample-spacing.

• Management Science and Financial Engineering
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• v.11 no.2
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• pp.1-17
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• 2005

In this article we consider a supply chain consisting of a risk-sensitive manufacturer and a riskneutral retailer. The manufacturer maximizes her individual expected profit by designing a supply chain coordinating returns contract (SCRC) that consists of (i) a channel coordinating returns policy that maximizes the supply chain joint expected profit, and (ii) a profit sharing arrangement that gives the retailer an expected profit only slightly higher than that in the no returns case so that it is just enough to induce the retailer to accept the SCRC. Thus, the manufacturer captures as high a percentage as possible of the jointly maximum supply chain profit. However, this contract can sometimes lead to the manufacturer's resulting realized profit being lower than that in the no returns case when demand is lower than expected. In this context, even though profit is sufficiently attractive on average, will the risk-sensitive manufacturer ever consider applying a SCRC? Our research raises this question and focuses on designing a SCRC that can significantly increase the probability of the manufacturer's resulting realized profit being at least higher than that in the no returns case.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.4
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• pp.44-50
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• 2010

Structural vibration is a significant problem in many multi-part or multi-component assemblies. In aircraft industry, structures are composed of various fasteners, such as bolts, snap, hinge, weld or other fastener or connector (collectively "fasteners"). Due to these, prediction and design involving dynamic characteristics is quite complicated. However, the current state of the art does not provide an analytical tool to effectively predict structure's dynamic characteristics, because consideration of structural uncertainties (i.e. material properties, geometric tolerance, dimensional tolerance, environment and so on) is difficult and very small fasteners in the structure cause a huge amount of analysis time to predict dynamic characteristics using the FEM (finite element method). In this study, to resolve the current state of the art, a new approach is proposed using the FEM and probabilistic analysis. Firstly, equivalent elements are developed using simple element (e.g. bar, beam, mass) to replace fasteners' finite element model. Developed equivalent elements enable to explain static behavior and dynamic behavior of the structure. Secondly, probabilistic analysis is applied to evaluate the PDF (probability density function) of dynamic characteristics due to tolerance, material properties and so on. MCS (Monte-Carlo simulation) is employed for this. Proposed methodology offers efficiency of dynamic analysis and reality of the field as well. Simple plates joined by fasteners are taken as an example to illustrate the proposed method.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.51-69
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• 2014

Times to multiple events (TMEs) are a major data type in large-scale business and medical data. Despite its importance, the analysis of TME data has not been well studied because of the analysis difficulty from censoring of observation. To address this difficulty, we have developed a Bayesian-based multivariate survival analysis method, which can successfully estimate the joint probability density of survival times. In this work, we extended this method for the analysis of precedence, dependency and causality among multiple events. We applied this method to the electronic health records of 2,111 patients in a children's hospital in the US and the proposed analysis successfully shows the relation between times to two types of hospital visits for different medical issues. The overall result implies the usefulness of the multivariate survival analysis method in large-scale big data in a variety of areas including marketing, human resources, and e-commerce. Lastly, we suggest our future research directions based multivariate survival analysis method.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.3
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• pp.193-202
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• 2014

This paper presents a new method of sampling the climatic data for infrared signature analysis. Historical hourly data from a stationary marine buoy of KMA(Korean Meteorological Administration) are used to select a small number of sample points (N=100) to adequately cover the range of statistics(PDF, CDF) displayed by the original data set (S=56,670). The method uses a coarse bin to subdivide the variable space ($3^5$=243 bins) to make sample points cover the original data range, and a single-point ranking system to select individual points so that uniform coverage (1/N = 0.01) is obtained for each variable. The principal component analysis is used to calculate a joint probability of the coupled climatic variables. The selected sample data show good agreement to the original data set in statistical distribution and they will be used for statistical analysis of infrared signature and susceptibility of naval ships.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.1-19
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• 2015

With the recent popularity of smart terminals, the demand for high-data-rate transmission is growing rapidly, which brings a new challenge for the traditional cellular networks. Both device-to-device (D2D) communication and small cells are effective to improve the transmission efficiency of local communication. In this paper, we apply D2D communication into a small cell network system (SNets) and study about the optimization problem of resource allocation for D2D communication. The optimization problem includes system scheduling and resource allocation, which is exponentially complex and the optimal solution is infeasible to achieve. Therefore, in this paper, the optimization problem is decomposed into several smaller problems and a hierarchical scheme is proposed to obtain the solution. The proposed hierarchical scheme consists of three steps: D2D communication groups formation, the estimation of sub-channels needed by each D2D communication group and specific resource allocation. From numerical simulation results, we find that the proposed resource allocation scheme is effective in improving the spectral efficiency and reducing the outage probability of D2D communication.

• Wind and Structures
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• v.30 no.2
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• pp.141-153
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• 2020

Probabilistic information regarding directional extreme wind speeds is important for the precise estimation of the design wind loads on structures. A joint probability distribution model of directional extreme typhoon wind speeds is established using Monte Carlo simulation and empirical copula function to fully consider the correlations of extreme typhoon wind speeds among the different directions. With this model, a procedure for estimating directional extreme wind speeds for given return periods, which ensures that the overall risk is distributed uniformly by direction, is established. Taking 5 typhoon-prone cities in China as examples, the directional extreme typhoon wind speeds for given return periods estimated by the present method are compared with those estimated by the method proposed by Cook and Miller (1999). Two types of directional factors are obtained based on Cook and Miller (1999) and the UK standard's drafting committee (Standard B, 1997), and the directional risks for the given overall risks are discussed. The influences of the extreme wind speed correlations in the different directions and the simulated typhoon wind speed sample sizes on the estimated extreme wind speeds for a given return period are also discussed.

• Smart Structures and Systems
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• v.21 no.5
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.