• Fisheries and Aquatic Sciences
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• v.24 no.4
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• pp.139-152
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• 2021

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.

• Bulletin of the Korean Space Science Society
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• 2007.04a
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• pp.67-67
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• 2007

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.11-14
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• 2003

The dynamics of unobservable short rate are frequently estimated directly by using a proxy. We estimate the biases resulting from this practice ("proxy problem"). To solve this problem, State-Space models have been proposed by many researchers. State-Space models have been used to estimate the unobservable variables from the observable variables in econometrics. However, applications of State-Space models often result in a misleading interpretation of the underlying processes especially when the absorbability of the State-Space model and the assumption of noise processes in the state vector are not properly considered. In this study, we propose the exact State-Space model that properly considers the faults of previous researchers to solve the proxy problem.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.387-388
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• 2006

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.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.203-210
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• 2003

In a standard Metropolis-type Monte Carlo simulation, the proposal distribution cannot be easily adapted to "local dynamics" of the target distribution. To overcome some of these difficulties, Duane et al. (1987) introduced the method of hybrid Monte Carlo(HMC) which combines the basic idea of molecular dynamics and the Metropolis acceptance-rejection rule to produce Monte Carlo samples from a given target distribution. In this paper, using the HMC within Gibbs sampler, an asymptotical estimate of the smoothing mean and a general solution to state space modeling in Bayesian framework is obtaineds obtained.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.12
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• pp.1543-1549
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• 2012

This study focuses on the state space model and the design of a state observer for the slip dynamics between rolls in STS cold cluster mills. First, a mathematical model of the roll slip is given as a nonlinear differential equation. Then, by using a Taylor series expansion, it is linearized as a state space model. Next, by using Gopinath's algorithm, a minimal-order state observer based on the state space model is designed to estimate the angular speed of all idle rolls except for an actuated roll that is measureable. Finally, a computer simulation is used to validate that the proposed state space model very well describes slip dynamics between, and moreover, the state observer very well estimates the angular speed of the idle roll.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.44.2-44.2
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• 2016

The solar wind electrons are made of three or four distinct components, which are core Maxwellian background, isotropic halo, and super-halo (and sometimes, highly field-aligned strahl component which can be considered as a fourth element). We put forth a steady-state model for the solar wind electrons by considering both the steady-state particle and wave kinetic equations. Since the steady-state solar wind electron VDFs and the steady-state wave fluctuation spectrum are related to each other, we also investigate the complete fluctuation spectra in the whistler and Langmuir frequency ranges by considering halo- and superhalo-like model electron VDFs. It is found that the energetic electrons make important contributions to the total emission spectrum. Based on this, we complete the steady-state model by considering both the whistler and Langmuir fluctuations. In particular, the Langmuir fluctuation plays an important role in the formation and maintenance of nonthermal electrons.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1045-1049
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• 1995

This paper proposes the state-space model of the thermal behavior of the spindle system to establish dynamic mathematical model of thermal characteristics in machine tool spindle system. the model is derived form physical law of heat transfer and thermoelasticity and represents the thermal behavior induced by uneven thermal expansions whitin a bearing. The model, which is sucessfully validated for two typical configurations of high speed spindle assembles, provides a tool for understanding the basis mechanics of induced thermal expansion as a function of initial preload, spindle speed and housing cooling conditions.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.53 no.6
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• pp.925-933
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• 2020

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.

• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.67-72
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• 2007

An efficient method for identification of MIMO state space model has been developed by combining partial least squares (PLS) regression, balanced realization, and balanced truncation. In the developed method, a MIMO system is decomposed into multiple MISO systems each of which is represented by a high-order ARX model and the parameters of the ARX models are estimated by PLS. Then, MISO state space models for respective MISO ARX transfer function are found through realization and combined to a MIMO state space model. Finally, a minimal balanced MIMO state space model is obtained through balanced realization and truncation. The proposed method was applied to the design of model predictive control for temperature control of a high pressure $CO_2$ solubility measurement system.