• 한국경영과학회지
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• 제14권1호
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• pp.31-31
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• 1989

The Generalized Assignment Problem(GAP) determines the minimum assignment of n tasks to m workstations such that each task is assigned to exactly one workstation, subject to the capacity of a workstation. In this paper, we presented a new heuristic search algorithm for GAPs. Then we tested it on 4 different benchmark sample sets of random problems generated according to uniform distribution on a microcomputer.

• 한국작물학회지
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• 제44권2호
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• pp.112-116
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• 1999

Genetic diversity of 31 rice varieties including 25 japonica and 6 indica varieties was evaluated using a combination of 19 microsatellite or simple sequence repeats (SSRs) and 28 random decamer oligonucle-otide primers. All 19 microsatellite primer sets representing 19 loci in the rice genome showed polymorphisms among the 31 varieties and revealed 91 alleles with an average of 4.80 bands per primer. Also all 28 random decamer primers used were informative and generated 114 non-redundant bands with a mean of 4.07 bands. Microsatellite markers detected higher number of alleles than random primers .although the mean difference was not statistically significant. A cluster analysis based on Nei＇s genetic distances calculated from the 205 bands resolved the 31 varieties into two major groups that correspond to indica and japonica subspecies, which is consistent with the genealogical information. As few as six random decamer primers or a combination of one microsatellite and four random decamer primers were sufficient to uniquely differentiate all 31 varieties. These combinations would be potentially useful in rice variety protection and identification considering that 25 out of 31 varieties used in this study are japonica rices with high grain quality and have close make up.

• 한국세라믹학회지
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• 제56권6호
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• pp.596-600
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• 2019

Artificial intelligence with a sufficient amount of realistic big data in certain applications has been demonstrated to play an important role in designing new materials or in manufacturing high-quality products. To reduce cracks in ceramic products using machine learning, it is desirable to utilize big data in recently developed data-driven optimization schemes. However, there is insufficient big data for ceramic processes. Therefore, we developed a numerical algorithm to make "virtual" manufacturing data sets using indirect methods such as computer simulations and image processing. In this study, a numerical algorithm based on the random walk was demonstrated to generate images of cracks by adjusting the conditions of the random walk process such as the number of steps, changes in direction, and the number of cracks.

• Communications for Statistical Applications and Methods
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• 제9권3호
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• pp.835-844
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• 2002

For the one-way random model when the ratio of the variance components is of interest, Bayesian analysis is often appropriate. In this paper, we develop the noninformative priors for the ratio of the variance components under the balanced one-way random effect model. We reveal that the second order matching prior matches alternative coverage probabilities up to the second order (Mukerjee and Reid, 1999) and is a HPD(Highest Posterior Density) matching prior. It turns out that among all of the reference priors, the only one reference prior (one-at-a-time reference prior) satisfies a second order matching criterion. Finally we show that one-at-a-time reference prior produces confidence sets with expected length shorter than the other reference priors and Cox and Reid (1987) adjustment.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.595-598
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• 2003

A new classifier is constructed by using a generalized regression neural network (GRNN) in conjunction with a random generator (RC). The RG played a role of generating a number of sets of random spreads given a range for gaussian functions in the pattern layer, The range experimentally varied from 0.4 to 1.4. The DNA sequences consisted 4 types of promoters. The performance of classifier is examined in terms of total classification sensitivity (TCS), and individual classification sensitivity (ICS). for comparisons, another GRNN classifier was constructed and optimized in conventional way. Compared GRNN, the RG-GRNN demonstrated much improved TCS along with better ICS on average.

• 한국멀티미디어학회논문지
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• 제13권6호
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• pp.875-883
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• 2010

In this paper, we present a secure biometric hashing scheme for face recognition by random fusion of global and local features. The Fourier-Mellin transform and Radon transform are adopted respectively to form specialized representation of global and local features, due to their invariance to geometric operations. The final biometric hash is securely generated by random weighting sum of both feature sets. A fourfold key is involved in our algorithm to ensure the security and privacy of biometric templates. The proposed biometric hash can be revocable and replaced by using a new key. Moreover, the attacker cannot obtain any information about the original biometric template without knowing the secret key. The experimental results confirm that our scheme has a satisfactory accuracy performance in terms of EER.

• Architectural research
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• 제8권2호
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• pp.27-36
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• 2006

Structural uncertainties are generally modeled using probabilistic approaches in order to quantify uncertainties in behaviors of structures. This uncertainty results from the uncertainties of structural parameters. Monte Carlo methods have been usually carried out for analyses of uncertainty problems where no analytical expression is available for the forward relationship between data and model parameters. In such cases any direct mathematical treatment is impossible, however the forward relation materializes itself as an algorithm allowing data to be calculated for any given model. This study addresses a new method which is utilized as a basis for the uncertainty estimates of structural responses. It applies double uniform random numbers (i.e. DURN technique) to conventional Monte Carlo algorithm. In DURN method, the scenarios of uncertainties are sequentially selected and executed in its simulation. Numerical examples demonstrate the beneficial effect that the technique can increase uncertainty degree of structural properties with maintaining structural stability and safety up to the limit point of a breakdown of structural systems.

• Journal of the Korean Data and Information Science Society
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• 제27권1호
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• pp.255-264
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• 2016

Classification is a predictive modeling for a categorical target variable. Various classification ensemble methods, which predict with better accuracy by combining multiple classifiers, became a powerful machine learning and data mining paradigm. Well-known methodologies of classification ensemble are boosting, bagging and random forest. In this article, we assume that decision trees are used as classifiers in the ensemble. Further, we hypothesized that tree size affects classification accuracy. To study how the tree size in uences accuracy, we performed experiments using twenty-eight data sets. Then we compare the performances of ensemble algorithms; bagging, double-bagging, boosting and random forest, with different tree sizes in the experiment.

• 응용통계연구
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• 제31권2호
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• pp.241-251
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• 2018

마이크로어레이 자료의 유전자집합분석은 개별유전자분석에 비해 검정력도 높일 수 있고 결과 해석이 쉬워서 이에 대한 연구가 활발히 진행되어 왔다. 표현형에 따라 유의한 차이를 보이는 유전자집합의 검색은 검정통계량들이 유도된 배경에 따라 결과에 차이를 보이지만 대체적으로 t-통계량의 제곱합을 이용한 순열검정이 제일 무난한 방법으로 여겨진다. 그러나 유전자집합분석에서 다중검정은 필수이고 많은 집합들의 유의성에 변별력을 주기 위해서는 순열검정에서 생성하는 치환표본의 수가 많이 필요하고 시간이 오래 걸린다는 문제점이 있다. 순열검정을 대신할 모수적 방법들을 검토한 결과, 적률을 이용한 근사가 각 집합의 유의확률 계산시간도 훨씬 단축하며 순열검정에서 구한 유의확률과 크기와 순위가 거의 일치함을 확인하였다.

• 한국산업정보학회논문지
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• 제17권7호
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• pp.139-148
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• 2012

분류기의 앙상블 학습은 여러 개의 서로 다른 분류기들의 조합을 통해 만들어진다. 앙상블 학습은 기계학습 분야에서 많은 관심을 끌고 있는 중요한 연구주제이며 대부분의 경우에 있어서 앙상블 모형은 개별 기저 분류기보다 더 좋은 성과를 내는 것으로 알려져 있다. 본 연구는 부도 예측 모형의 성능개선에 관한 연구이다. 이를 위해 본 연구에서는 단일 모형으로 그 우수성을 인정받고 있는 SVM을 기저 분류기로 사용하는 앙상블 모형에 대해 고찰하였다. SVM 모형의 성능 개선을 위해 bagging과 random subspace 모형을 부도 예측 문제에 적용해 보았으며 bagging 모형과 random subspace 모형의 성과 개선을 위해 bagging과 random subspace의 통합 모형을 제안하였다. 제안한 모형의 성과를 검증하기 위해 실제 기업의 부도 예측 데이터를 사용하여 실험하였고, 실험 결과 본 연구에서 제안한 새로운 형태의 통합 모형이 가장 좋은 성과를 보임을 알 수 있었다.