• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.251-256
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• 2005

Up to now, a number of models have been proposed and discussed to describe a wide range of inelastic behaviors of materials. The fetal problem of using such models is however the existence of model errors, and the problem remains inevitably as far as a material model is written explicitly. In this paper, the authors define the implicit constitutive model and propose an implicit viscoplastic constitutive model using neural networks. In their modeling, inelastic material behaviors are generalized in a state space representation and the state space form is constructed by a neural network using input output data sets. A technique to extract the input-output data from experimental data is also described. The proposed model was first generated from pseudo-experimental data created by one of the widely used constitutive models and was found to replace the model well. Then, having been tested with the actual experimental data, the proposed model resulted in a negligible amount of model errors indicating its superiority to all the existing explicit models in accuracy.

• Ocean and Polar Research
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• v.41 no.3
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• pp.183-191
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• 2019

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.

• Journal of Computing Science and Engineering
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• v.9 no.1
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• pp.9-19
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• 2015

The most important challenge in the region-based abstraction method as an approach to compute the state space of time Petri Nets (TPNs) for model checking is that the method results in a huge number of regions, causing a state explosion problem. Thus, region-based abstraction methods are not appropriate for use in developing practical tools. To address this limitation, this paper applies a modification to the basic region abstraction method to be used specially for computing the state space of TPN models, so that the number of regions becomes smaller than that of the situations in which the current methods are applied. The proposed approach is based on the special features of TPN that helps us to construct suitable and small region graphs that preserve the time properties of TPN. To achieve this, we use TPN-TCTL as a timed extension of CTL for specifying a subset of properties in TPN models. Then, for model checking TPN-TCTL properties on TPN models, CTL model checking is used on TPN models by translating TPN-TCTL to the equivalent CTL. Finally, we compare our proposed method with the current region-based abstraction methods proposed for TPN models in terms of the size of the resulting region graph.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.437-442
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• 2005

The forecasting of flood runoff in the river is essential for flood control. The purpose of this study is to test a development of system for flood runoff forecasting using neural network model. For the flood events the tested rainfall and runoff data were the input to the input layer and the flood runoff data were used in the output layer To choose the forecasting model which would make up of runoff forecasting system properly, real-time runoff in the river when flood periods were forecasted by using the neural network model and the state-space model. A comparison of the results obtained by the two forecasting models indicated the superiority and reliability of the neural network model over the state-space model. The neural network model was modified to work in the Web and developed to be the basic model of the forecasting system for the flood runoff.

• Journal of Environmental Science International
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• v.4 no.1
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• pp.71-80
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• 1995

A discrete state space model for a multiple-reach river system is formulated using the dynamics of biochemical oxygen demand(BOD) and dissolved oxygen(DO). A hierarchical optimization technique, which is applicable to large-scale systems with time-delays in states, is also described to control stream quality in a river as an optimal manner based on the interaction prediction method. The steady state tracking error of the proposed method is determined analytically and a necessary and sufficient condition on which a constant target tracking problem has zero steady-state error is derived. Computer simulations for the river pollution model illustrate the algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.408-411
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• 1995

In this paper, we discuss the force control of flexible manipulators. Since the force control of flexible manipulators with planar one or two links using the distributed-parameter modeling has been the subject of a considerable number of publications until now, real time computations of the force control schemes are possible. But, application of those control schemes to multi-link spatial manipulators is fairly complicated. In this paper, we apply a concise hybrid position/force control scheme for a flexible manipulators. We use a lumped-parameter modeling for the flexible manipulators. The Hamilton's principle is applied to derive the equations of motion for the system and then, state-space model is obtained by the Lagrange's method. Finally, comparison of simulation results with experimental results is given to show the performance of our method.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.525-525
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• 2000

In this paper, we present recursive algorithms for state space model identification using subspace extraction via Schur complement. It is shown that an estimate of the extended observability matrix can be obtained by subspace extraction via Schur complement. A relationship between the least squares residual and the Schur complement matrix obtained from input-output data is shown, and the recursive algorithms for the subspace-based state-space model identification (4SID) methods are developed. We also proposed the above algorithm for an instrumental variable (IV) based 4SID method. Finally, a numerical example of the application of the algorithms is illustrated.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.55 no.2
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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.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.233-236
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• 1996

This paper describes a liner state-space model for a flexible stick balencer. The method employed to generate the model utilizes a separable formulation of assumed modes to represents the transverse displacement due to bending Lagrangian dynamics are applied to determine the kinetic and potential energies for the system. The resultant dynamic equations are then organized into a state space model and linearized using Taylor series expansion method. A minimum order observer is designed to estimate unmeasurable states.

• Smart Structures and Systems
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• v.4 no.1
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• pp.47-66
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• 2008

The present work utilizes system identification technique for health monitoring of shear building, wherein Parametric State Space modeling has been adopted. The method requires input excitation to the structure and also output acceleration responses of both undamaged and damaged structure obtained from numerically simulated model. Modal parameters like eigen frequencies and eigen vectors have been extracted from the State Space model after introducing appropriate transformation. Least square technique has been utilized for the evaluation of the stiffness matrix after having obtained the modal matrix for the entire structure. Highly accurate values of stiffness of the structure could be evaluated corresponding to both the undamaged as well as damaged state of a structure, while considering noise in the simulated output response analogous to real time scenario. The damaged floor could also be located very conveniently and accurately by this adopted strategy. This method of damage detection can be applied in case of output acceleration responses recorded by sensors from the actual structure. Further, in case of even limited availability of sensors along the height of a multi-storeyed building, the methodology could yield very accurate information related to structural stiffness.