• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.229-229
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• 2016

현재 국내외에서 제공되고 있는 기후변화 시나리오 자료의 경우 일단위로 제공되고 있다. 그러나 수자원 설계 및 계획 시 중요한 입력자료 중 하나는 시간단위 강우 자료이다. 이러한 시간단위 자료는 강우-유추 분석, 댐 설계 및 위험도 분석에 있어 중요한 입력 변수중 하나이므로 기후변화 시나리오에 따른 영향을 평가하기 위해선 신뢰성 있는 상세화 기법이 필요하다. 국내외에서는 일단위에서 일단위로 상세화 하는 기법, 또는 공간상세화 기법 연구는 상당히 진행된바 있는 반면, 시간단위 상세화 기법 연구는 일단위 연구에 비해 상대적으로 미진한 실정이다. 즉 일단위 상세화 기법의 경우 Weather generator, Weather typing 등 다양한 기법이 존재하고 이를 활용한 연구사례가 많지만, 시간단위 상세화 기법의 Poisson 기법을 활용한 사례가 다수 존재하였다. 이러한 이유로 본 연구에서는 기후변화 시나리오에 따른 영향을 평가하기 위해 Bayesian 기법을 도입하여 신뢰성 있는 시간단위 강우량을 생성할 수 있는 모형을 개발하였으며, 연대별로 산정된 결과는 빈도해석을 통해 미래 확률강우량을 제시하였다. 본 연구에서 제안하고자 하는 Bayesian Copula 모형은 기존 주변확률분포(marginal distribution) 매개변수와 Copula 매개변수 추정시 각각 다른 기법을 활용하여 추정하며, 각각 모형에서 발생하는 불확실성은 추정하지 못하는 반면, Bayesian Copula 모형의 경우 매개변수의 사후분포를 정량적으로 제시할 수 있으며, 추정되는 확률강우량 역시 불확실성을 정량적으로 산정할 수 있는 장점을 확인할 수 있었다.

• Journal of Naturopathy
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• v.11 no.1
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• pp.9-17
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• 2022

Background: No studies have reported on the effects of loess beddings on insomnia patients. Purpose: It studied the change in quality of life and quality of sleep after having 15 insomnia subjects use the bedding that emits far-infrared rays. Methods: After using loess bedding for the test group and general yellow bedding for the control group, the study was conducted in the form of a questionnaire on the WHO quality of life of the subjects. Results: In the overall quality of life evaluation, the pre-and post-changes significantly improved in the test group. Using loess bedding was greatly enhanced the physical change, the actual sleep time, and the quality of sleep of the test group. The period of sleep was significantly longer post-treating, and the habitual sleep efficiency was considerably higher, and sleep disturbance was significantly lower than before in the test group. Sleep drug use and daytime dysfunction after treating in the test group significantly improved the sleep effect. Changes in the Sociality Scale, Environmental Change Scale, and Quality of Life Scale significantly improved in the test group. The quality of life for 14 items in the test group was significantly correlated. Daytime drowsiness, depression, and anxiety scale changes were significantly improved in the test group. According to the predictive survey, the subjects felt warmth in their body and comfort in mind during and after using loess bedding and evaluated that sleep quality was good. Conclusions: The overall quality of life in the test group increased using loess bedding.

• Journal of Korea Multimedia Society
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• v.11 no.3
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• pp.301-314
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• 2008

The existing event recognition is accomplished with the limited systematic foundation, and thus much longer learning time is needed for emergency scenario interpretation due to large scale of probability data. In this paper, we propose a method for nile-based event recognition of an independent object(human) which extract a feature vectors from the object and analyze the behavior pattern of each object and interpretation of emergency scenarios using a probability and object's events. The event rule of an independent object is composed of the Primary-event, Move-event, Interaction-event, and 'FALL DOWN' event and is defined through feature vectors of the object and the segmented motion orientated vector (SMOV) in which the dynamic Bayesian network is applied. The emergency scenario is analyzed using current state of an event and its post probability. In this paper, we define diversified events compared to that of pre-existing method and thus make it easy to expand by increasing independence of each events. Accordingly, semantics information, which is impossible to be gained through an.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.3
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• pp.251-259
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• 2004

The prediction accuracy of prestress plays an important role in the quality of maintenance and the decision on rehabilitation of infrastructure such as prestressed concrete bridges. In this paper, the Bayesian statistical method that uses in-situ measurement data for reducing the uncertainties or updating long-term prediction of prestress is presented. For Bayesian analysis, prior probability distribution is developed to represent the uncertainties of creep and shrinkage of concrete and likelihood function is derived and used with data acquired in site. Posterior probability distribution is then obtained by combining prior distribution and likelihood function. The numerical results of this study indicate that more accurate long-term prediction of prestress forces due to creep and shrink age is possible.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.464-464
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• 2018

최근 풍수해 현황 분석(국민안전처, 2016)에서는 2003년 이후 태풍 루사와 매미와 같은 대형태풍이 최근에 발생하지 않아 대부분 하천급류로 인한 인명피해가 대부분이라고 언급하였다. 최근 풍수해로 인한 피해가 발생하지는 않았지만 호우/태풍이 발생할 경우 인명보호와 불편해소를 최우선에 두고 각종 정책들을 선제적으로 추진하고 있어 홍수범람발생 예상지역에 대한 인명피해 분석은 반드시 필요하다고 판단된다. 최근들어 인명피해를 평가하는 기술은 피해자료로부터 비교적 간단히 분석되는 경험적 방법에서 2차원 동적 수리모형과 연계, 그리고 정밀한 인구, 건물 등의 자료를 활용하여 대피율, 사전경보 등 인명피해에 영향을 미치는 다양한 요소를 복합적으로 고려하고 개념적이고 기계적 방법으로 발전하는 추세이다. 우리나라의 경우 인명피해 평가와 관련한 연구사례가 거의 전무한 상태이고, 치수경제성분석에서 제시하는 침수면적에 기반한 간략한 방법만이 실무에서 활용되고 있다. 최근 국외에서 제시한 접근방법은 본 연구에서의 개발하고자 하는 목적과 방향에 부합하지 않다고 판단되며, 국내 실정을 고려할 때 주요 영향인자를 추가하고, 특히 노출인구, 인명 인벤토리의 해상도를 높이는 데 주안점을 두고자 한다. 홍수로 인한 인명피해 발생확률은 사후분석의 일환으로 침수흔적도를 통해 총 2개의 침수구간을 설정한 후 Census data를 활용한 위험인구(Population at Risk, PAR)를 산정한후, NDMS 인명피해 자료를 활용하여 침수구간별 인명피해 발생확률을 제시하였다. 여기서 제시한 침수구간의 경우 데이터의 축적정도에 따라 구간을 세밀화 할 수 있는데, 본 연구에서는 총 2개구간(0-1m, 1m 이상)으로 계략화 하여 제시하였다. 본 연구에서는 4개의 지자체의 인명피해 자료를 통해 인명피해 발생확률을 산정하였으며, 해당내용을 시범유역의 빈도별 침수구역도에 적용하여 인명피해 발생을 분석하였다. 해당 연구결과의 경우 인명피해에 대한 명확한 결과를 유추하는데에는 한계가 있지만, 인명피해에 기반한 해당지역의 장래피해규모를 예측하는 데에는 기초가 될 수 있을 것으로 판단된다.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.733-745
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• 2010

Seasonality of hydrologic extreme variable is a significant element from a water resources managemental point of view. It is closely related with various fields such as dam operation, flood control, irrigation water management, and so on. Hydrological frequency analysis conjunction with partial duration series rather than block maxima, offers benefits that include data expansion, analysis of seasonality and occurrence. In this study, nonstationary frequency analysis based on the Bayesian model has been suggested which effectively linked with advantage of POT (peaks over threshold) analysis that contains seasonality information. A selected threshold that the value of upper 98% among the 24 hours duration rainfall was applied to extract POT series at Seoul station, and goodness-fit-test of selected GEV distribution has been examined through graphical representation. Seasonal variation of location and scale parameter ($\mu$ and $\sigma$) of GEV distribution were represented by Fourier series, and the posterior distributions were estimated by Bayesian Markov Chain Monte Carlo simulation. The design rainfall estimated by GEV quantile function and derived posterior distribution for the Fourier coefficients, were illustrated with a wide range of return periods. The nonstationary frequency analysis considering seasonality can reasonably reproduce underlying extreme distribution and simultaneously provide a full annual cycle of the design rainfall as well.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.77-87
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• 2021

With the development of the Internet and personal computers, various and complex attacks begin to emerge. As the attacks become more complex, signature-based detection become difficult. It leads to the research on behavior-based log anomaly detection. Recent work utilizes deep learning to learn the order and it shows good performance. Despite its good performance, it does not provide any explanation for prediction. The lack of explanation can occur difficulty of finding contamination of data or the vulnerability of the model itself. As a result, the users lose their reliability of the model. To address this problem, this work proposes an explainable log anomaly detection system. In this study, log parsing is the first to proceed. Afterward, sequential rules are extracted by Bayesian posterior probability. As a result, the "If condition then results, post-probability" type rule set is extracted. If the sample is matched to the ruleset, it is normal, otherwise, it is an anomaly. We utilize HDFS datasets for the experiment, resulting in F1score 92.7% in test dataset.

• The Journal of the Korea Contents Association
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• v.10 no.7
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• pp.31-39
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• 2010

In this study, we prepared the real environmental sound data sets arising from people's movement comprising 9 different environment types. The environmental sounds are pre-processed with pre-emphasis and Hamming window, then go into the classification experiments with the extracted features using MFCC (Mel-Frequency Cepstral Coefficients). The GMM (Gaussian Mixture Model) classifier without post processing tends to yield abruptly changing classification results since it does not consider the results of the neighboring frames. Hence we proposed the post processing methods which suppress abruptly changing classification results by taking the probability or the rank of the neighboring frames into account. According to the experimental results, the method using the probability of neighboring frames improve the recognition performance by more than 10% when compared with the method without post processing.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.102-108
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• 2011

Support vector machines are well known for their outstanding performance in pattern recognition fields. One example of their applications is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel scheme that improves the speech/music classification of support vector machines based on the second-order conditional maximum a priori. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. According to experimental results, the proposed algorithm shows its compatibility and potential for improving the performance of support vector machines.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.40-48
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• 2007

This paper presents a novel texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields in multiscale Bayesian framework. Multiscale wavelet coefficients are used as input for the neural networks. The output of the neural network is modeled as a posterior probability. Texture classification at each scale is performed by the posterior probabilities from MLP networks and MAP (maximum a posterior) classification. Then, in order to obtain the more improved segmentation result at the finest scale, our proposed method fuses the multiscale MAP classifications sequentially from coarse to fine scales. This process is done by computing the MAP classification given the classification at one scale and a priori knowledge regarding contextual information which is extracted from the adjacent coarser scale classification. In this fusion process, the MRF (Markov random field) prior distribution and Gibbs sampler are used, where the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. The proposed segmentation method shows better performance than texture segmentation using the HMT (Hidden Markov trees) model and HMTseg.