• Coupled systems mechanics
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• v.7 no.1
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• pp.27-42
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• 2018

Behavior of soil is usually described with continuum type of failure models such as Mohr-Coulomb or Drucker-Prager model. The main advantage of these models is in a relatively simple and efficient way of predicting the main tendencies and overall behavior of soil in failure analysis of interest for engineering practice. However, the main shortcoming of these models is that they are not able to capture post-peak behavior of soil nor the corresponding failure modes under extreme loading. In this paper we will significantly improve on this state-of-the-art. In particular, we propose the use of a discrete beam lattice model to provide a sharp prediction of inelastic response and failure mechanisms in coupled soil-foundation systems. In the discrete beam lattice model used in this paper, soil is meshed with one-dimensional Timoshenko beam finite elements with embedded strong discontinuities in axial and transverse direction capable of representing crack propagation in mode I and mode II. Mode I relates to crack opening, and mode II relates to crack sliding. To take into account material heterogeneities, we determine fracture limits for each Timoshenko beam with Gaussian random distribution. We compare the results obtained using the discrete beam lattice model against those obtained using the modified three-surface elasto-plastic cap model.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.308-315
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• 2016

This paper addresses a formation control problem for a phugoid model-based multi-agent system in discrete time by using a Takagi-Sugeno (T-S) fuzzy model-based controller design technique. The concerned discrete-time model is obtained by Euler's method. A T-S fuzzy model is constructed through a feedback linearization. A fuzzy controller is then designed to stabilize the T-S fuzzy model. Design condition is presented in the linear matrix inequality format.

• Journal of the Korea Society for Simulation
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• v.21 no.4
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• pp.35-46
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• 2012

As complexity of real-time systems is being increased ad hoc approaches to analysis of such systems would have limitations in completeness and coverability for states space search. Formal means using a model-based approach would solve such limitations. This paper proposes a model-based formal method for logical analysis, such as safety and liveness, of real-time systems at a discrete event system level. A discrete event model for real-time systems to be analyzed is specified by DEVS(Discrete Event Systems Specification) formalism, which specifies a discrete event system in hierarchical, modular manner. Analysis of such DEVS models is performed by Communicating DEVS (C-DEVS) formalism of a timed global state transition specification and an associated analysis algorithm. The C-DEVS formalism and an associated analysis algorithm guarantees that all possible states for a given system are visited in an analysis phase. A case study of a safety analysis for a rail road crossing system illustrates the effectiveness of the proposed method of the model-based approach.

• Korean Journal of Computational Design and Engineering
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• v.19 no.1
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• pp.29-40
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• 2014

Conceptual Modeling is the process of abstracting a model from a real or proposed system. It is probably the most important aspect of a simulation study. Relate works show that the elementary developers devoted little time to understanding how the systems actually worked, namely they didn't build appropriate conceptual model. Thus, the result of simulation is inconsistent because it depends on developer's competence. Although many researchers suggested various techniques enabling developer to build conceptual model, there were several limitations. In this study, to overcome the limitations of existing techniques, we proposed COMBINE-DES (COnceptual Model BuildINg framEwork using ontology for Discrete Event Simulation). The COM-BINE-DES supports expediting the conceptual modeling with Solution ontology generated by Domain ontology and Simulation ontology. Moreover, it provides consistent simulation result regardless of repeated modeling.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.4
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• pp.303-310
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• 2020

Estimating the accurate internal state of lithium ion batteries to increase their safety and efficiency is crucial. Various algorithms are used to estimate the internal state of a lithium ion battery, such as the extended Kalman filter and sliding mode observer. A state-space model is essential in using algorithms to estimate the internal state of a battery. Two principal methods are used to express the state-space model, namely, continuous time and discrete time. In this work, the extended Kalman filter is employed to estimate the internal state of a battery. Moreover, this work presents and analyzes the estimation performance of algorithms consisting of a continuous time state-space model and a discrete time state-space model through static and dynamic profiles.

• Journal of the Korea Society for Simulation
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• v.22 no.2
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• pp.11-19
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• 2013

This paper presents the battle space model, which is capable of propagating various types of emissions from platforms in underwater warfare simulation, predicting interesting encounters between pairs of platforms, and managing environmental information. The battle space model has four components: the logger, spatial encounter predictor (SEP), propagator, and geographic information system (GIS) models. The logger model stores brief data on all the platforms in the simulation, and the GIS model stores and updates environmental factors such as temperature and current speed. The SEP model infers an encounter among the platforms in the simulation, and progresses the simulation to the time when this encounter will happen. The propagator model receives various emissions from platforms and propagates these to other "within-range" platforms by considering the propagation losses and delays. The battle space model is based on the discrete event system specification (DEVS) and the discrete time system specification (DTSS) formalisms. To verify the battle space model, simple underwater warfare between a battleship and a submarine was simulated. The simulation results with the model were the same as the simulation results without the model.

• Korean Journal of Computational Design and Engineering
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• v.17 no.2
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• pp.111-122
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• 2012

Capacity planning plays an important role not only for master production plan but also for facility or layout design in shipbuilding. Product work breakdown structure, attributes of production resources, and production method or process data are associated in order to make the discrete event simulation model of shipyard layout plan. The production amount of each process and the process time is assumed to be stochastic. Based on the stochastic discrete event simulation model, the production capacity of each facility in shipyard is estimated. The stochastic model of product arrival time, process time and transferring time is introduced for each process. Also, the production capacity is estimated for the assumed master production schedule.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.11
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• pp.1142-1146
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• 2014

This paper addresses on a $H_{\infty}$ sampled-data stabilization of a Takagi-Sugeno (T-S) fuzzy system. The sampled-data stabilization problem is formulated as a discrete-time stabilization one via a direct discrete-time design approach. It is shown that the sampled-data fuzzy control system is asymptotically stable whenever its exactly discretized model is asymptotically stable. Based on an exact discrete-time model, sufficient design conditions are derived in the format of linear matrix inequalities (LMIs). An example is provided to illustrate the effectiveness of the proposed methodology.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.11
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• pp.53-62
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• 1997

In this study, a controller design method is proposed for controlling the discrete-time chaotic systems efficiently. The proposed control method is based on Generalized Predictive Control and uses NARMAX models as controlled models. In order to evaluate the performance of the proposed method, a proposed controller is applied to discrete-time chaotic systems, and then the control performance and initial sensitivity of the proposed controller are compared with those of the conventional model-based controler through computer simulations. Through simulations results, it is shown that the control performance of the proposed controller is superior to that of the conventional model-based controller and shown that the peorposed controller is less sensitive to initial values of discrete-time chaotic systems in comparison with the conventional model-based controller.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.87-91
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• 1995

Combined models, specified by two or more modeling formalisms, can represent a wide variety of complex systems. This paper describes a methodology for the development of combined models in two model types of discrete events and continuous process. The methodology is based on transformation of continuous state space into discrete one to homomorphically represent dynamics of continuous processes in discrete events. As an example, a combined model of human heart is developed which Incorporates conventional differential equation formalism with Zeigler's DEVS(Discrete Event Specification System) [4]formalism.