• 정보과학회지
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• 제25권1호
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• pp.55-58
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• 2007

• Journal of the Korean Data and Information Science Society
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• 제28권4호
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• pp.957-957
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• 2017

• 한국정보처리학회논문지
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• 제7권7호
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• pp.2108-2115
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• 2000

The conventional Boolean retrieval systems based on vector spae model can provide the results of retrieval fast, they can't reflect exactly user's retrieval purpose including semantic information. Consequently, the results of retrieval process are very different from those users expected. This fact forces users to waste much time for finding expected documents among retrieved documents. In his paper, we designed a bayesian SOM(Self-Organizing feature Maps) in combination with bayesian statistical method and Kohonen network as a kind of unsupervised learning, then perform classifying documents depending on the semantic similarity to user query in real time. If it is difficult to observe statistical characteristics as there are less than 30 documents for clustering, the number of documents must be increased to at least 50. Also, to give high rank to the documents which is most similar to user query semantically among generalized classifications for generalized clusters, we find the similarity by means of Kohonen centroid of each document classification and adjust the secondary rank depending on the similarity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5631-5652
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• 2019

The existing defense strategy selection methods based on game theory basically select the optimal defense strategy in the form of mixed strategy. However, it is hard for network managers to understand and implement the defense strategy in this way. To address this problem, we constructed the incomplete information stochastic game model for the dynamic analysis to predict multi-stage attack-defense process by combining Bayesian game theory and the Markov decision-making method. In addition, the payoffs are quantified from the impact value of attack-defense actions. Based on previous statements, we designed an optimal defense strategy selection method. The optimal defense strategy is selected, which regards defense effectiveness as the criterion. The proposed method is feasibly verified via a representative experiment. Compared to the classical strategy selection methods based on the game theory, the proposed method can select the optimal strategy of the multi-stage attack-defense process in the form of pure strategy, which has been proved more operable than the compared ones.

• 한국지능시스템학회논문지
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• 제15권3호
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• pp.330-336
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• 2005

이동로봇의 주행을 위한 초음파 센서 만에 의한 기존의 베이지안 지도 작성법은 초음파 센서 빔의 퍼짐 특성 등에 의해 굴곡이 많은 환경의 경우 양질의 지도가 형성되지 못한다. 이러한 문제의 개선을 위해 본 논문에서는 적외선 센서를 설치하여 초음파 센서 빔의 각 영역에서의 장애물에 대한 정보를 획득하고, 이 정보를 이용 퍼지 추론시스템에 의하여 초음파 센서에 의한 정보의 신뢰도를 구하여 베이지안 지도 작성법에 의한 결과에 융합시킴으로써 보다 정확한 환경 지도를 작성하는 방법을 제시하였다. 또한, 퍼지 추론 시스템을 최적화하기 위하여 유전 알고리즘을 사용하였다. 그리고 시뮬레이션 및 실제 실험에 의해 제안된 방법이 굴곡이 많은 환경의 경우 기존의 방법 보다 정확한 지도 작성이 가능함을 검증하였다.

• 한국환경과학회지
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• 제27권7호
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• pp.509-518
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• 2018

Weather is the most influential factor for crop cultivation. Weather information for cultivated areas is necessary for growth and production forecasting of agricultural crops. However, there are limitations in the meteorological observations in cultivated areas because weather equipment is not installed. This study tested methods of predicting the daily mean temperature in onion fields using geostatistical models. Three models were considered: inverse distance weight method, generalized additive model, and Bayesian spatial linear model. Data were collected from the AWS (automatic weather system), ASOS (automated synoptic observing system), and an agricultural weather station between 2013 and 2016. To evaluate the prediction performance, data from AWS and ASOS were used as the modeling data, and data from the agricultural weather station were used as the validation data. It was found that the Bayesian spatial linear regression performed better than other models. Consequently, high-resolution maps of the daily mean temperature of Jeonnam were generated using all observed weather information.

• 한국정보전자통신기술학회논문지
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• 제11권2호
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• pp.169-174
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• 2018

일반적으로 자료의 효과 연속형인 경우 분산분석과 이산형인 경우 분할표 카이제곱 검정을 통계적 분석방법으로 사용한다. 다차원의 자료에서는 계층적 구조의 분석이 요구되어지며 자료간의 인과관계를 나타내기 위해 통계적 선형모형을 채택하여 분석한다. 선형모형의 구조에서는 자료의 정규성이 요구되어지며 일부 자료에서는 비 선형모형을 채택할 수도 있다. 특히, 설문조사 자료 구조는 문항의 특성상 이산형 자료의 형태가 많아 모형의 조건에 만족하지 않는 경우가 종종 발생한다. 자료구조의 차원이 높아질수록 인과관계, 교호작용, 연관성분석 등에 다차원 범주형 자료 분석 방법을 사용한다. 본 논문에서는 확률분포의 계산을 이용한 베이지안 네트워크 모형이 범주형 자료 분석에서 분석절차를 줄이고 교호작용 및 인과관계를 분석할 수 있다는 것을 제시하였다.

• 천문학회지
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• 제55권4호
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• pp.131-138
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• 2022

We report the discovery of four quasars with M₁₄₅₀ ≳ -25.0 mag at z ~ 5 and supermassive black hole mass measurement for one of the quasars. They were selected as promising high-redshift quasar candidates via deep learning and Bayesian information criterion, which are expected to be effective in discriminating quasars from the late-type stars and high-redshift galaxies. The candidates were observed by the Double Spectrograph on the Palomar 200-inch Hale Telescope. They show clear Lyα breaks at about 7000-8000 Å, indicating they are quasars at 4.7 < z < 5.6. For HSC J233107-001014, we measure the mass of its supermassive black hole (SMBH) using its C _IV λ1549 emission line. The SMBH mass and Eddington ratio of the quasar are found to be ~10⁸ M_⊙ and ~0.6, respectively. This suggests that this quasar possibly harbors a fast growing SMBH near the Eddington limit despite its faintness (L_Bol < 10⁴⁶ erg s^-1). Our 100% quasar identification rate supports high efficiency of our deep learning and Bayesian information criterion selection method, which can be applied to future surveys to increase high-redshift quasar sample.

• Journal of Computing Science and Engineering
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• 제9권4호
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• pp.204-213
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• 2015

Under the conditional independence assumption among local features, the Naive Bayes Nearest Neighbor (NBNN) classifier has been recently proposed and performs classification without any training or quantization phases. While the original NBNN shows high classification accuracy without adopting an explicit training phase, the conditional independence among local features is against the compositionality of objects indicating that different, but related parts of an object appear together. As a result, the assumption of the conditional independence weakens the accuracy of classification techniques based on NBNN. In this work, we look into this issue, and propose a novel Bayesian network for an NBNN based classification to consider the conditional dependence among features. To achieve our goal, we extract a high-level feature and its corresponding, multiple low-level features for each image patch. We then represent them based on a simple, two-level layered Bayesian network, and design its classification function considering our Bayesian network. To achieve low memory requirement and fast query-time performance, we further optimize our representation and classification function, named relation-based Bayesian network, by considering and representing the relationship between a high-level feature and its low-level features into a compact relation vector, whose dimensionality is the same as the number of low-level features, e.g., four elements in our tests. We have demonstrated the benefits of our method over the original NBNN and its recent improvement, and local NBNN in two different benchmarks. Our method shows improved accuracy, up to 27% against the tested methods. This high accuracy is mainly due to consideration of the conditional dependences between high-level and its corresponding low-level features.

• Asian-Australasian Journal of Animal Sciences
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• 제18권8호
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• pp.1088-1097
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• 2005

Main objectives of this study were to investigate accuracy, bias and power of linear and threshold model segregation analysis methods for detection of major genes in categorical traits in farm animals. Maximum Likelihood Linear Model (MLLM), Bayesian Linear Model (BALM) and Bayesian Threshold Model (BATM) were applied to simulated data on normal, categorical and binary scales as well as to disease data in pigs. Simulated data on the underlying normally distributed liability (NDL) were used to create categorical and binary data. MLLM method was applied to data on all scales (Normal, categorical and binary) and BATM method was developed and applied only to binary data. The MLLM analyses underestimated parameters for binary as well as categorical traits compared to normal traits; with the bias being very severe for binary traits. The accuracy of major gene and polygene parameter estimates was also very low for binary data compared with those for categorical data; the later gave results similar to normal data. When disease incidence (on binary scale) is close to 50%, segregation analysis has more accuracy and lesser bias, compared to diseases with rare incidences. NDL data were always better than categorical data. Under the MLLM method, the test statistics for categorical and binary data were consistently unusually very high (while the opposite is expected due to loss of information in categorical data), indicating high false discovery rates of major genes if linear models are applied to categorical traits. With Bayesian segregation analysis, 95% highest probability density regions of major gene variances were checked if they included the value of zero (boundary parameter); by nature of this difference between likelihood and Bayesian approaches, the Bayesian methods are likely to be more reliable for categorical data. The BATM segregation analysis of binary data also showed a significant advantage over MLLM in terms of higher accuracy. Based on the results, threshold models are recommended when the trait distributions are discontinuous. Further, segregation analysis could be used in an initial scan of the data for evidence of major genes before embarking on molecular genome mapping.