상태공간모형에 의한 공주지점의 T-N농도의 시계열예측

류병로 ( Ryu Byong-ro ),한양수 ( Han Yang-su ) 한국환경기술학회 2000 한국환경기술학회지 Vol.1 No.1

An optimizing method about long and short-term water quality in developed to establish State space model for the accurate hydrologic forecasting using pollution load and discharge data at a site of a river basin Input data of state space model is T-N that is classified with non-point source and point source. The non-point sources of this model are selected by populations, livestock, industries, fish-nursers, visitors and the point source are selected by pollution load according to land use. The major findings of this papers are followings: First, for the P value of VAR(P) model to establish state space theory, it used AIC value By time step and VARMA model were established that it was findings to the constituent Unit of state space model using canonical correction coefficients. Therefore this paper confirms that state space model is very significant related with optimization factors of VARMA model. second, the results of comparison with computed and observed values in conformity with the rule of state space model were showed a good results and the model were exhibited to high forecasting ability of water quality third, from the simulated results by the state space model, the model are suitable to forecasting the water qualities for similar to simulated and observed results.

서해 어획대상 잠재생산량 추정을 위한 자원평가모델의 비교 분석

최민제,김도훈,최지훈 한국수산해양기술학회 2019 수산해양기술연구 Vol.55 No.3

This study is aimed to compare stock assessment models depending on how the models fit to observed data. Process-error model, Observation-error model, and Bayesian state-space model for the Korean Western coast fisheries were applied for comparison. Analytical results show that there is the least error between the estimated CPUE and the observed CPUE with the Bayesian state-space model; consequently, results of the Bayesian state-space model are the most reliable. According to the Bayesian State-space model, potential yield of fishery resources in the West Sea of Korea is estimated to be 231,949 tons per year. However, the results show that the fishery resources of West Sea have been decreasing since 1967. In addition, the amounts of stock in 2013 are assessed to be only 36% of the stock biomass at MSY level. Therefore, policy efforts are needed to recover the fishery resources of West Sea of Korea. 본 연구에서는 서해 어획대상의 잠재생산량 추정에 가장 적합한 자원평가모델을 비교․분석하고, 해당 모델을 바탕으로 서해 어획대상의 자원상황을 파악하고자 하였다. 자원평가모델 선정을 위해서 과정오차모델, 관측오차모델, 그리고 Bayesian State-space 모델의 평가결과를 비교하였다. 분석 결과, Bayesian State-space 모델을 통해 추정된 연도별 단위노력당어획량이 실제 단위노력당어획량 자료를 가장 잘 반영하는 것으로 추정되었다. 따라서 분석 모델 중 Bayesian State-space 모델이 서해 어획대상의 잠재생산량 추정에 가장 적합한 것으로 평가되었다. Bayesian State-space 모델을 이용한 서해 어획대상 잠재생산량 추정 결과, 서해 어획대상의 잠재생산량은 231,949톤으로 나타났다. 그리고 서해 수산자원량의 경우 1967년 이후 지속적으로 감소한 것으로 나타났으며, 특히 2013년의 자원량은 최대지속적생산을 위한 자원량 수준의 약 36%에 불과한 것으로 추정되었다.

Spurious Mean-Reversion of Stock Prices in the State-Space Model

최원혁(Won Hyeok Choi),전덕빈(Duk Bin Jun),김동수(Dong Soo Kim),노재선(Jaesun Noh) 한국경영과학회 2011 한국경영과학회지 Vol.36 No.1

In order to explain the U-shaped pattern of autocorrelations of stock returns i.e., autocorrelations starting around 0 for short-term horizons and becoming negative and then moving toward 0 for long-term horizons, researchers suggested the use of a state-space model consisting of an I(1) permanent component and an AR(1) stationary component, where the two components are assumed to be independent. They concluded that auto-regression coefficients derived from the state-space model follow a U-shape pattern and thus there is mean-reversion in stock prices. In this paper, we show that only negative autocorrelations are feasible under the assumption that the permanent component and the stationary component are independent in the state-space model. When the two components are allowed to be correlated in the state-space model, we show that the sign of the auto-regression coefficients is not restricted as negative. Monthly return data for all NYSE stocks for the period from 1926 to 2007 support the state-space model with correlated noise processes. However, the auto-regression coefficients of the ARIMA process, equivalent to the state-space model with correlated noise processes, do not follow a U-shaped pattern, but are always positive.

교량의 안전진단을 위한 상태공간방정식 기반 하이브리드 디지털 트윈

김충길,이유재,이재훈,방건혁,허광희 대한토목학회 2025 대한토목학회논문집 Vol.45 No.2

본 연구에서는 교량 안전 진단을 위한 새로운 접근법으로 상태공간방정식 기반 하이브리드 디지털 트윈을 개발하였다. 기존 3차원 모델 기반 디지털 트윈이 직면하는 데이터 과부하 문제를 해결하기 위해, 특정 목표 지점의 응답 데이터를 활용한 최적화된 모델을 개발하고자 하였다. 이를 위해 유한 요소(FE) 모델을 기반으로 구조적 특성을 분석하고, Guyan Reduction 기법을 적용하여 질량, 강성, 감쇠 계수를 포함한 상태공간방정식을 구성하였다. 상태공간방정식 모델은 구조물의 동적 거동을 정량적으로 분석할 수 있도록 설계하였으며, 실험 데이터를 이용하여 초기 매개변수를 보정하였다. 또한, 실시간 데이터 피드백을 적용하여 교량의 상태 변화를 지속적으로 반영할 수 있도록 설계하였으며, 다목적 최적화 알고리즘(gamultiobj)을 활용하여 모델의 신뢰도를 향상시켰다. 최적화 과정에서는 실험 데이터와의 오차를 최소화하기 위해 질량, 강성, 감쇠계수를 조정하였으며, 이를 통해 디지털 트윈 모델이 보다 정밀한 구조 응답을 예측할 수 있도록 개선하였다. 개발된 하이브리드 디지털 트윈의 유효성을 평가하기 위해 모형 교량을 대상으로 실험을 수행하였다. 실험에서는 다양한 하중 조건을 적용하여 모델의 예측 결과를 실제 계측된 변위데이터와 비교하였다. 그 결과, 디지털 트윈 모델은 실험 데이터와 높은 일치도를 보였으며, 최대 5 % 미만의 오차율을 기록하였다. 상태공간방정식 기반 하이브리드 디지털 트윈은 실시간 구조 응답을 반영하여 교량 상태 변화에 대한 민감도를 높일 수 있으며, 주기적인 상태 업데이트를 통해 보다 정밀한 유지보수 및 안전 진단이 가능함을 입증하였다. In this study, a state-space equation based hybrid digital twin system was developed as a novel approach for bridge safety assessment. To overcome the data overload issue faced by conventional 3D model-based digital twins, an optimized model was designed using response data from specific target points. For this purpose, structural characteristics were analyzed based on a finite element (FE) model, and the Guyan Reduction method was applied to construct a state-space equation incorporating mass, stiffness, and damping coefficients. The state-space equation model was designed to quantitatively analyze the dynamic behavior of the structure, with initial parameters calibrated using experimental data. Furthermore, real-time data feedback was integrated to continuously reflect changes in the bridge’s condition, and a multi-objective optimization algorithm was employed to enhance the model’s reliability. During the optimizationprocess, mass, stiffness, and damping coefficients were adjusted to minimize discrepancies with experimental data, thereby improving the digital twin model’s accuracy in predicting structural responses. To evaluate the effectiveness of the developed hybrid digital twin, experiments were conducted on a scaled bridge model. Various loading conditions were applied during the experiment, and the model’s predicted results were compared with actual measured displacement data. The results demonstrated a high correlation between the digital twin model and the experimental data, with an error rate of less than 5 %. The state-space equation-based hybrid digital twin system enhances sensitivity to bridge condition changes by reflecting real-time structural responses and enables more precise maintenance and safety diagnostics through periodic state updates.

붉은대게(Chinonoecetes japonicus) 자원평가를 위한 잉여생산량모델의 비교 분석

최지훈,김도훈,오택윤,서영일,강희중 한국수산과학회 2020 한국수산과학회지 Vol.53 No.6

This study is aimed to compare stock assessment models which are effective in assessing red snow crab Chinonoecetes japonicus resources and to select and apply an effective stock assessment model in the future. In order to select an effective stock assessment model, a process-error model, observation-error model, and a Bayesian state-space model were estimated. Analytical results show that the least error is observed between the estimated CPUE (catch per unit effort) and the observed CPUE when using the Bayesian state-space model. For the Bayesian state-space model, the 95% credible interval(CI) ranges for the maximum sustainable yield (MSY), carrying capacity (K), catchability coefficient (q), and intrinsic growth (r) are estimated to be 10,420-47,200 tons, 185,200-444,800 tons, 3.81E-06-9.02E-06, and 0.14-0.66, respectively. The results show that the Bayesian state-space model was most reliable among models.

박용 엔진의 유한요소 모드해석을 통한 상태 공간 모델 개발

이원창(W. C. Lee),김성열(S. R. Kim),안병수(B. S. Ahn),최헌오(H. O. Choi),김재실(C. S. Kim) 한국정밀공학회 2006 한국정밀공학회 학술발표대회 논문집 Vol.2006 No.5월

This article provides a dynamic analysis model for huge marine engine that examined analytically variation effects of frequency response by fitting of transverse stays such as hydraulic type. First, vibration analysis using the three dimensional finite element models for the huge marine engine has performed in order to find out the dynamic characteristics. Second, three dimensional finite elements model for the huge marine engine was modifued so that generate forcing nodes in crosshead part and top bracing nodes in cylinder frame part. Third, a system matrix and output matrix was derived for the general siso(single input single out) state space model. Finally, developed state space model for the three dimensional finite elements model for the huge marine engine without the additional modifying process.

State-Space Model Predictive Control Method for Core Power Control in Pressurized Water Reactor Nuclear Power Stations

Guoxu Wang,Jie Wu,Bifan Zeng,Zhibin Xu,Wanqiang Wu,Xiaoqian Ma 한국원자력학회 2017 Nuclear Engineering and Technology Vol.49 No.1

A well-performed core power control to track load changes is crucial in pressurized waterreactor (PWR) nuclear power stations. It is challenging to keep the core power stable at thedesired value within acceptable error bands for the safety demands of the PWR due to thesensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC)method was applied to the control of the core power. The model for core power control wasbased on mathematical models of the reactor core, the MPC model, and quadratic programming(QP). The mathematical models of the reactor core were based on neutron dynamicmodels, thermal hydraulic models, and reactivity models. The MPC model waspresented in state-space model form, and QP was introduced for optimization solutionunder system constraints. Simulations of the proposed state-space MPC control system inPWR were designed for control performance analysis, and the simulation results manifestthe effectiveness and the good performance of the proposed control method for core powercontrol.

베이지안 State-space 모델을 이용한 눈볼대 자원평가 및 관리방안

최지훈,김도훈,최민제,강희중,서영일,이재봉 한국수산해양기술학회(구 한국어업기술학회) 2019 수산해양기술연구 Vol.55 No.2

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.

효과적인 자원평가모델 선정을 위한 잉여생산량모델의 비교 분석: 동해 생태계의 잠재생산량 분석을 대상으로

최민제,김도훈 한국해양과학기술원 2019 Ocean and Polar Research Vol.41 No.3

This study sought to find which model is most appropriate for estimating potential yield in the East Sea, Republic of Korea. For comparison purposes, the Process-error model, ASPIC model, Maximum entropy model, Observation-error model, and Bayesian state-space model were applied using data from catch amounts and total efforts of the whole catchable fishes in the East Sea. Results showed that the Bayesian state-space model was estimated to be the most reliable among the models. Potential yield of catchable species was estimated to be 227,858 tons per year. In addition, it was analyzed that the amount of fishery resources in 2016 was about 63% of the biomass that enables a fish stock to deliver the maximum sustainable yield.

A Bayesian state-space production model for Korean chub mackerel (Scomber japonicus) stock

Jung, Yuri,Seo, Young Il,Hyun, Saang-Yoon The Korean Society of Fisheries and Aquatic Scienc 2021 Fisheries and Aquatic Sciences Vol.24 No.4

The main purpose of this study is to fit catch-per-unit-effort (CPUE) data about Korea chub mackerel (Scomber japonicus) stock with a state-space production (SSP) model, and to provide stock assessment results. We chose a surplus production model for the chub mackerel data, namely annual yield and CPUE. Then we employed a state-space layer for a production model to consider two sources of variability arising from unmodelled factors (process error) and noise in the data (observation error). We implemented the model via script software ADMB-RE because it reduces the computational cost of high-dimensional integration and provides Markov Chain Monte Carlo sampling, which is required for Bayesian approaches. To stabilize the numerical optimization, we considered prior distributions for model parameters. Applying the SSP model to data collected from commercial fisheries from 1999 to 2017, we estimated model parameters and management references, as well as uncertainties for the estimates. We also applied various production models and showed parameter estimates and goodness of fit statistics to compare the model performance. This study presents two significant findings. First, we concluded that the stock has been overexploited in terms of harvest rate from 1999 to 2017. Second, we suggest a SSP model for the smallest goodness of fit statistics among several production models, especially for fitting CPUE data with fluctuations.