• 제어로봇시스템학회논문지
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• 제10권12호
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• pp.1295-1304
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• 2004

In this paper, the Bayesian recurrent neural network is proposed to predict time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one needs to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, the weights vector is set as a state vector of state space method, and its probability distributions are estimated in accordance with the particle filtering process. This approach makes it possible to obtain more exact estimation of the weights. In the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent neural network with Bayesian inference, what we call Bayesian recurrent neural network (BRNN), is expected to show higher performance than the normal neural network. To verify the proposed method, the time series data are numerically generated and various kinds of neural network predictor are applied on it in order to be compared. As a result, feedback structure and Bayesian learning are better than feedforward structure and backpropagation learning, respectively. Consequently, it is verified that the Bayesian reccurent neural network shows better a prediction result than the common Bayesian neural network.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.1129-1140
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• 2005

A nonparametric Bayesian method for calculating posterior probabilities of the multiple comparison problem on the parameters of several Geometric populations is presented. Bayesian multiple comparisons under two different prior/ likelihood combinations was studied by Gopalan and Berry(1998) using Dirichlet process priors. In this paper, we followed the same approach to calculate posterior probabilities for various hypotheses in a statistical experiment with a partition on the parameter space induced by equality and inequality relationships on the parameters of several geometric populations. This also leads to a simple method for obtaining pairwise comparisons of probability of successes. Gibbs sampling technique was used to evaluate the posterior probabilities of all possible hypotheses that are analytically intractable. A numerical example is given to illustrate the procedure.

• 응용통계연구
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• 제36권4호
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• pp.349-360
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• 2023

동적 넬슨-시겔 모형은 채권과 같은 기간 구조를 갖고 있는 금융상품의 이자율 곡선모형에서 널리 사용되고 있다. 본 연구에서는 동적 넬슨-시겔 모형을 상태 공간 모형의 관점에서 설명하고 해당 모형에 적용할 수 있는 베이지안 접근법에 대해 알아보고자 한다. 그리고 SOFR 기간 데이터를 베이지안 동적 넬슨-시겔 모형에 적용하여 그 성능을 확인하고 바시첵 모형, 빈도주의 접근법을 활용한 동적 넬슨-시겔 모형, 2요인 베이지안 동적 넬슨-시겔 모형과 같은 다른 경쟁 모형들과 성능을 비교해보고자 한다. 우리는 베이지안 동적 넬슨-시겔 모형이 SOFR 기간 데이터에 대해서 다른 모형들보다 우수한 성능을 보여준다는 것을 확인할 수 있었다.

• 자원ㆍ환경경제연구
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• 제28권3호
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• pp.415-435
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• 2019

본 연구의 목적은 중서부태평양(WCPFC) 수역 우리나라 원양연승어업의 눈다랑어 생산함수를 추정하여 규모 수익을 분석하는 것이다. 분석에 있어 투입요소는 선원수, 선박톤수, 투입낚시수, 눈다랑어 자원량 그리고 산출요소는 눈다랑어 생산량으로 하는 Cobb-Douglas 형태의 생산함수를 추정하였다. 함수 추정에 앞서 투입요소 중 눈다랑어 자원량은 Bayesian State-space 모델로 추정하였다. 생산함수 추정 결과, 하우즈만 검정을 통해 고정효과 모델이 선택되었고, 선원수를 제외한 선박톤수, 투입낚시수, 눈다랑어 자원량이 눈다랑어 생산량에 직접적인 영향을 미치는 것으로 나타났다. 추정된 생산함수의 투입요소를 바탕으로 규모 수익 수준을 분석한 결과, WCPFC 수역에서 눈다랑어를 조업하는 원양연승어업은 규모 수익 체증(IRS)의 성격인 것으로 추정되었다.

• 대한원격탐사학회지
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• 제7권1호
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• pp.45-67
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• 1991

An overview is given on stochastic techniques with which remotely sensed data may be used together with ground measurements for purposes of state estimation and prediction. They can explicitly account for spatiotemporal differences in measurement characteristics between ground measurements and remotely sensed data, and are suitable for highly variant space or space-time processes, such as atmosperic processes, which may be viewed as (containing) a random process. For state estimation of static ststems, optimal linear estimation is described. As alternatives, various co-kriging estimation techniques are also described, including simple, ordinary, universal, lognormal, disjunctive, indicator, and Bayesian extersion to simple and lognormal. For illustrative purposes, very simple examples of optimal linear estimation and simple co-kriging are given. For state estimation and prediction of dynamic system, distributed-parameter kalman filter is described. Issues concerning actual implemention are given, and with application potential are described.

• Smart Structures and Systems
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• 제30권6호
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• pp.583-599
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• 2022

In this paper, a multi-objective wireless sensor network configuration optimization method is proposed. The proposed method aims to determine the optimal information and lifespan wireless sensor network for structural health monitoring of large-scale infrastructures. In particular, cluster-based wireless sensor networks with multi-type of sensors are considered. To optimize the lifetime of the wireless sensor network, a cluster-based network optimization algorithm that optimizes the arrangement of cluster heads and base station is developed. On the other hand, based on the Bayesian inference, the uncertainty of the estimated parameters can be quantified. The coefficient of variance of the estimated parameters can be obtained, which is utilized as a holistic measure to evaluate the estimation accuracy of sensor configurations with multi-type of sensors. The proposed method provides the optimal wireless sensor network configuration that satisfies the required estimation accuracy with the longest lifetime. The proposed method is illustrated by designing the optimal wireless sensor network configuration of a cable-stayed bridge and a space truss.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1765-1770
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• 2005

Recently, particle filters have attracted attentions for nonlinear state estimation. In this approaches, a posterior probability distribution of the state variable is evaluated based on observations in simulation using so-called importance sampling. We proposed a new filter, Evolution Strategies based particle (ESP) filter to circumvent degeneracy phenomena in the importance weights, which deteriorates the filter performance, and apply it to simultaneous state and parameter estimation of nonlinear state space models. Results of numerical simulation studies illustrate the applicability of this approach.

• 수산해양기술연구
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• 제56권4호
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• pp.347-360
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• 2020

In this study, the Bayesian state-space model was used for the stock assessment of the Blackfin flounder. In addition, effective measures for the resource management were presentedwith the analysis on the effectiveness of fisheries management plans. According to the result of the analysis using the Bayesian state-space model, the main biometric value of Blackfin flounder was analyzed as 1,985 tons for maximum sustainable yield (MSY), 23,930 tons for carrying capacity (K), 0.000007765 for catchability coefficient (q) and 0.31 for intrinsic growth (r). Also the evaluation on the biological effect of TAC was done. The result showed that the Blackfin flounder biomass will be kept at 14,637 tons 20 years later given the present TAC volume of 1,761 tons. If the Blackfin flounder TAC volume is set to 1,600 tons, the amount of biomass will increase to 16,252 tons in the future. Lastly, the biological effectiveness of the policy to reduce fishing effort was assessed. The result showed that the Blackfin flounder biomass will be maintained at 13,776 tons if the current fishing efforts (currently hp) level is set and maintained. If the fishing effort is reduced by 20%, it will increase to 17,091 tons in the future. The analysis on the economic effect of TAC showed that NPV will be the lowest at 1,486,410 won in 2038, 20 years after the establishment of 2,500 tons of TAC volume. If the TAC volume is set at 2,000 tons, NPV was estimated to be the highest at 2,206,522,000 won. In addition, the analysis on the economic effect of the policy to reduce the amount of fishing effort found that NPV will be 2,235,592,000 won in 2038, 20 years after maintaining the current level of fishing effort. If the fishing effort is increased by 10%, NPV will be the highest at 2,257,575 won even thoughthe amount of biomass will be reduced.

• 자원ㆍ환경경제연구
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• 제31권4호
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• pp.451-480
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• 2022

본 연구는 한국에서 상업적 중요성을 가지며 국민 선호도가 높은 살오징어의 자원상태를 파악해 보고자 하였다. 본 연구에서는 기존 살오징어 자원평가 연구와의 차별성으로 두 가지를 고려하였다. 첫째, 한국에서 살오징어를 어획하는 업종들의 어획 자료를 자원평가 분석에 최대한 활용하였다. 둘째, 살오징어를 공동 어획하는 인접국인 중국과 일본의 어획 자료를 모두 포함하여 자원평가를 실시하였다. 구체적인 분석에 있어서는 어획량 기반 자원평가 모델인 Monte Carlo 방법을 활용한 CMSY(catch-maximum sustainable yield) 모델과 Schaefer 함수를 기반으로 한 Bayesian state-space(BSS) 모델을 이용하여 활용 가능한 자료의 종류와 범위에 따라 '한국' 그리고 '한·중·일'로 해역 범위를 구분하여 분석을 실시하였다. 분석 결과, 살오징어 자원량은 감소하는 추세를 보이고 있으며, 현재 최대지속어획량을 달성할 수 있는 자원량 수준보다 낮은 것으로 추정되었다. 살오징어 자원을 지속적으로 이용하기 위해서는 개별 국가들의 적극적인 자원관리 노력이 필요하며, 특히 한·중·일 공동 자원조사 및 평가 그리고 관리 방안 마련이 필요하다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권2호
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• pp.676-692
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• 2018

Saliency detection in neurobiology is a vehement research during the last few years, several cognitive and interactive systems are designed to simulate saliency model (an attentional mechanism, which focuses on the worthiest part in the image). In this paper, a bottom up saliency detection model is proposed by taking into account the color and luminance frequency features of RGB, CIE $L^*a^*b^*$ color space of the image. We employ low-level features of image and apply band pass filter to estimate and highlight salient region. We compute the likelihood probability by applying Bayesian framework at pixels. Experiments on two publically available datasets (MSRA and SED2) show that our saliency model performs better as compared to the ten state of the art algorithms by achieving higher precision, better recall and F-Measure.