• 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.

• 수산해양기술연구
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• 제55권2호
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• pp.95-104
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• 2019

This study is aimed to take a stock assessment of blackthroat seaperch Doederleinia seaperch regarding the fishing effort of large-powered Danish Seine Fishery and Southwest Sea Danish Seine Fishery. For the assessment, the state-space model was implemented and the standardized catch per unit effort (CPUE) of large powered Danish Seine Fishery and Southwest Sea Danish Seine Fishery which is necessary for the model was estimated with generalized linear model (GLM). The model was adequate for stock assessment because its r-square value was 0.99 and root mean square error (RMSE) value was 0.003. According to the model with 95% confidence interval, maximum sustainable yield (MSY) of Blackthroat seaperch is from 2,634 to 6,765 ton and carrying capacity (K) is between 33,180 and 62,820. Also, the catchability coefficient (q) is between 2.14E-06 and 3.95E-06 and intrinsic growth rate (r) is between 0.31 and 0.72.

• 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.

• 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.

• 한국수산과학회지
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• 제55권2호
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• pp.183-188
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• 2022

It is a difficult task to estimate parameters in even a simple stock assessment model such as a surplus production model, using only data about temporal catch-per-unit-effort (CPUE) (or survey index) and fishery yields. Such difficulty is exacerbated when time-varying parameters are treated as random effects (aka state variables). To overcome the difficulty, previous studies incorporated somewhat subjective assumptions (e.g., B₁=K) or informative priors of parameters. A key is how to build an objective joint prior of parameters, reducing subjectivity. Given the limited data on temporal CPUEs and fishery yields from 1999-2020 for common squid Todarodes pacificus, we built a joint prior of only two parameters, intrinsic growth rate (r) and carrying capacity (K), based on the resilience level of the population (Froese et al., 2017), and used a Bayesian state-space production assessment model. We used template model builder (TMB), a R package for implementing the assessment model, and estimating all parameters in the model. The predicted annual biomass was in the range of 0.76×10⁶ to 4.06×10⁶ MT, the estimated MSY was 0.13×10⁶ MT, the estimated r was 0.24, and the estimated K was 2.10×10⁶ MT.

• 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.

• 한국경영과학회지
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• 제12권1호
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• pp.19-26
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• 1987

In analyzing time series, estimating the level or the current mean of the process plays an important role in understanding its structure and in being able to make forecasts. The studies the class of time series models where the level of the process is assumed to follow a random walk and the deviation from the level follow an ARMA process. The estimation and forecasting problem in a Bayesian framework and uses the Kalman filter to obtain forecasts based on estimates of level. In the analysis of time series, we usually make the assumption that the time series is generated by one model. However, in many situations the time series undergoes a structural change at one point in time. For example there may be a change in the distribution of random variables or in parameter values. Another example occurs when the level of the process changes abruptly at one period. In order to study such problems, the assumption that level follows a random walk process is relaxed to include a major level change at a particular point in time. The major level change is detected by examining the likelihood raio under a null hypothesis of no change and an alternative hypothesis of a major level change. The author proposes a method for estimation the size of the level change by adding one state variable to the state space model of the original Kalman filter. Detailed theoretical and numerical results are obtained for th first order autoregressive process wirth level changes.

• 한국지능시스템학회논문지
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• 제17권7호
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• pp.907-912
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• 2007

현재의 유비쿼터스 컴퓨팅 기술은 공간에서 사용자가 요구하는 직접적인 서비스만을 제공하는 정도의 기술 구현에 그치고 있으며, 앞으로는 좀 더 능동적인 서비스를 제공할 수 있는 지능적인 시스템이 필요해지고 있다. 사용자의 개입 없이 사용자가 처한 상황에 대한 정보를 통해 제공할 서비스를 결정할 수 있는 시스템을 구성하기 위해 본 논문에서는 지능형 홈과 같은 유비쿼터스 컴퓨팅 공간에서의 사용자가 처한 상황에 대한 환경정보를 Context로 정의하고 4W1H의 형태로 정형화하여 수집한다. 추가적으로 사용자의 감정 상태에 대한 정보를 수집하고 이러한 정보들을 확률 추론을 위한 베이지안 네트워크의 노드로 사용하여 사용자가 구체적으로 어떠한 상황에 처해 있는가에 대한 상황인식을 구현하다. 또한 그 상황 인식결과를 통해 사용자에게 제공될 서비스를 다시 한 번 베이지안 네트워크를 통해 추론하는 모델을 제시하고자 한다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제37권5호
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• pp.354-362
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• 2010

본 연구는 동적 베이스 망을 이용하여, 사람의 보행 동작을 보행 방향과 보행 자세로 분리하여 계층적으로 분석하는 방법을 제안한다. DBN의 일종인 FHMM을 기본 바탕으로 하여, 걸음걸이 동작 특성을 고려하여 순환 고리형 상태 공간 구조로 '보행 동작 디코더'(Gait Motion Decoder, GMD)를 설계한다. 기존 연구에는 보행자의 식별에만 치중을 하고 보행 방향의 변화, 관찰 각도에 제한적이거나 보행 동작에 대한 분석이 없었다. 반면에 본 연구에서는 동작과 자세를 적극적으로 표현하여 임의 방향의 보행, 방향의 변화, 보행 자세까지 인식할 수 있도록 하였다. 실험 결과 동작과 자세의 관점에서 걸음걸이 방향을 분석한 결과 96.5%의 방향 인식률을 기록하였다. 본 연구는 보행 동작을 방향과 보행 자세로 계층적으로 분석하는 최초의 방법 및 시도이며 향후 상황별 휴먼 동작 분석에 크게 활용할 수 있을 것이다.

• 대한조선학회논문집
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• 제57권3호
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• pp.175-181
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• 2020

For the success of a structural integrity management, it is essential to acquire structural response data at some critical locations with limited number of sensors. In this study, the structural response of numerical model was estimated by data fusion approach based on the Kalman filter known as stochastic recursive filter. Firstly, transient direct analysis was conducted to calculate the acceleration and strain of the numerical standing beam model, then the noise signals were mixed to generate the numerical measurement signals. The acceleration measurement signal was provided to the Kalman filter as an information on the external load, and the displacement measurement, which was transformed from the strain measurement by using strain-displacement conversion relationship, was provided into the Kalman filter as an observation information. Finally, the Kalman filter estimated the displacement by combining both displacements calculated from each numerically measured signal, then the estimated results were compared with the results of the transient direct analysis.