• 정보과학회 논문지
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• 제41권11호
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• pp.992-1005
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• 2014

상호참조해결은 문서 내에서 선행하는 명사구와 현재 등장한 명사구 간에 같은 개체를 의미하는 지를 결정하는 문제로 정보 추출, 문서분류 및 요약, 질의응답 등에 적용된다. 본 논문은 상호참조해결의 규칙기반 방법 중 가장 성능이 좋은 Stanford의 다 단계 시브(Multi-pass Sieve) 시스템을 한국어에 적용한다. 본 논문에서는 모든 명사구를 멘션(mention)으로 다루고 있으며, Stanford의 다 단계 시브 시스템과는 달리 멘션 추출을 위해 의존 구문 트리를 이용하고, 동적으로 한국어 약어 리스트를 구축한다. 또한 한국어 대명사를 참조하는데 있어 중심화 이론 중 중심의 전이적인 특성을 적용하여 가중치를 부여하는 방법을 제안한다. 실험 결과 F1 값은 MUC 59.0%, B3 59.5%, Ceafe 63.5%, CoNLL(평균) 60.7%의 성능을 보였다.

• 대한수학회보
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• 제55권6호
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• pp.1659-1666
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• 2018

Let $P_r$ denote an almost-prime with at most r prime factors, counted according to multiplicity. In this paper, it is proved that for every sufficiently large even integer N, the equation $$N=x^2+p_1^2+p_2^3+p_3^3+p_4^4+p_5^4$$ is solvable with x being an almost-prime $P_4$ and the other variables primes. This result constitutes an improvement upon that of $L{\ddot{u}}$ [7].

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2000년도 가을 학술발표회 논문집
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• pp.185-192
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• 2000

Recently, the hydraulic fill method is commonly used in many reclamation projects due to lack of fill materials. The method of hydraulic fill in reclamation is executed by transporting the mixture of water-soil particles into a reclaimed land through dredging pipes, then the dredged soil particles settle down in the water or flow over an out flow weir with the water. In the present study, practice each three method in order to suggest method of determining the loss rate of the dredged fills. The first sieve and hydrometer analysis were performed with the soil samples obtained before and after dredging and then apply theory of particle breakage, the second compare with the volume of dredged soil between at the dredging area and the target pond and the last compare with weight of dredged soil between before and after dredging at the dredging area and in the target pond for estimating the amount of soil particles residual at the reclaimed area and the loss of soil particles passed through the weir. In addition to compare with the loss ratio between as using Marsal's modified theory of particle breakage and measured weight and volume in the field.

• Korean Membrane Journal
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• 제5권1호
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• pp.43-53
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• 2003

The selectivity of a gas in the carbon molecular sieve membrane (CMSM) can be expressed as the ratio of the product of the diffusivity and the solubility of two different gases. The diffusivity is also expressed as the product of the entropy and the total energy (kinetic and potential energy) in the nano-sized pore of the membrane. The present study calculates the entropic-energy and selectivity of penetrant gases such as H$_2$, O$_2$, N$_2$, and CO$_2$ from the gas-in-a box theory to physically analyze the diffusivity of penetrant gas in slit-shaped pore of CMSM focusing on the restriction of gas motion based on the size difference between penetrant gas pairs. The contribution of each energy term is converted to entropic term separately. By the conjugated calculation for each entropic-energy, the entropic effects on diffusivity-selectivity for gas pairs such as H$_2$/N$_2$, CO$_2$/N$_2$, and O$_2$/N$_2$ were analyzed within active pore of CMSM. In the activated diffusion domain, the calculated value of entropic-selectivity lies between 9.25 and 111.6 for H$_2$/N$_2$, between 3.36 and 6.0 for CO$_2$/N$_2$, and between 1.25 and 16.94 for O$_2$/N$_2$, respectively. The size decrement of active pore in CMSM had the direct effect on the reduction of translational entropic-energy and the contribution of vibrational entropic-energy for N$_2$, O$_2$, and H$_2$ was almost negligible. However, the vibrational entropic term of CO$_2$ might extravagantly affect on the entropic-selectivity.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2006년도 춘계 학술발표회 논문집
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• pp.1159-1164
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• 2006

In this paper an artificial neural network model is developed to estimate the unconfined compression strength of Deep Cement Mixing(DCM) treated soil. A database which consists of a number of unconfined compression test result compiled from 9 clay sites is used to train and test of the artificial neural network model. Developed neural network model requires water content of soil, unit weight of soil, passing percent of #200 sieve, weight of cement, w-c ratio as input variables. It is found that the developed artificial neural network model can predict more precise and reliable unconfined compression strength than the conventional empirical models.

• 대한수학회지
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• 제56권4호
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• pp.985-1000
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• 2019

Let k be an integer with $k{\geq}3$. Define $h(k)=[{\frac{k+1}{2}}]$, ${\sigma}(k)={\min}\(2^{h(k)-1},\;{\frac{1}{2}}h(k)(h(k)+1)\)$. Suppose that ${\lambda}_1,{\ldots},{\lambda}_5$ are non-zero real numbers, not all of the same sign, satisfying that ${\frac{{\lambda}_1}{{\lambda}_2}}$ is irrational. Then for any given real number ${\eta}$ and ${\varepsilon}>0$, the inequality $${\mid}{\lambda}_1p^2_1+{\lambda}_2p^2_2+{\lambda}_3p^2_3+{\lambda}_4p^2_4+{\lambda}_5p^k_5+{\eta}{\mid}<({\max_{1{\leq}j{\leq}5}}p_j)^{-{\frac{3}{20{\sigma}(k)}}+{\varepsilon}}$$ has infinitely many solutions in prime variables $p_1,{\ldots},p_5$. This gives an improvement of the recent results.