• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.95-104
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• 2017

This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function of the problem incorporates noise effects generated in case that fitness evaluation is accomplished by computer based experiments such as Monte Carlo simulation or discrete event simulation. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation based multi-valued optimization problems. In applying these population based meta heuristics to simulation based optimization problem, samples size to estimate the expected fitness value of a solution and population (particle) size in a generation (step) should be carefully determined to obtain reliable solutions. Under realistic environment with restriction on available computation time, there exists trade-off between these values. In this paper, the effects of sample and population sizes are analyzed under well-known multi-modal and multi-dimensional test functions with randomly generated noise effects. From the experimental results, it is shown that the performance of DPSO is superior to that of GA. While appropriate determination of population sizes is more important than sample size in GA, appropriate determination of sample size is more important than particle size in DPSO. Especially in DPSO, the solution quality under increasing sample sizes with steps is inferior to constant or decreasing sample sizes with steps. Furthermore, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step. In applying OCBA in DPSO, smaller value of incremental sample size is preferred to obtain better solutions.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.2
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• pp.159-166
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• 2020

In this paper, we propose a heterogenous mission allocation technique for multi-UAV system based on discrete event modeling. We model a series of heterogenous mission creation, mission allocation, UAV departure, mission completion, and UAV maintenance and repair process as a mathematical discrete event model. Based on the proposed model, we then optimize the number of UAVs required to operate in a given scenario. To validate the optimized number of UAVs, the simulations are executed repeatedly, and their results are analyzed. The proposed mission allocation technique can be used to efficiently utilize limited UAV resources, and allow the human operator to establish an optimal mission plan.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.8
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• pp.921-927
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• 2012

Reliability-based design optimization (RBDO) can be used to determine the reliability of a system by means of probabilistic design criteria, i.e., the possibility of failure considering stochastic features of design variables and input parameters. To assure these criteria, various reliability analysis methods have been developed. Most of these methods assume that distribution functions are continuous. However, in real problems, because real data is often discrete in form, it is important to estimate the distributions for discrete information during reliability analysis. In this study, we employ the Akaike information criterion (AIC) method for reliability analysis to determine the best estimated distribution for discrete information and we suggest an RBDO method using AIC. Mathematical and engineering examples are illustrated to verify the proposed method.

• Journal of Drive and Control
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• v.15 no.1
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• pp.28-37
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• 2018

When the wind speed rises above the rated wind speed, the produced power of the wind turbines exceeds the rated power. Even more, the excessive power results in the undesirable mechanical load and fatigue. A solution to this problem is pitch control of the wind turbines. This paper presents a systematic design method of a collective pitch controller for the wind turbines using a discrete fuzzy Proportional-Integral (PI) controller. Unlike conventional PI controllers, the fuzzy PI controller has variable gains according to its input variables. Generally, tuning the parameters of fuzzy PI controller is complex due to the presence of too many parameters strongly coupled. In this paper, a systematic method for the fuzzy PI controller is presented. First, we show the fact that the fuzzy PI controller is a superset of the PI controller in the discrete-time domain and the initial parameters of the fuzzy PI controller is selected by using this relationship. Second, for simplicity of the design, we use only four rules to construct nonlinear fuzzy control surface. The tuning parameters of the proposed fuzzy PI controller are also obtained by the aforementioned relationship between the PI controller and the fuzzy PI controller. As a result, unlike the PI controller, the proposed fuzzy PI controller has variable gains which allow the pitch control system to operate in broader operating regions. The effectiveness of the proposed controller is verified with computer simulations using FAST, a NREL's primary computer-aided engineering tool for horizontal axis wind turbines.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.2
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• pp.159-178
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• 1995

Supervisory control is an approach based on formal language. it is used to model and control discrete event systems in which each discrete event process is represented as an automation. A supervisor is a generator that switches control patterns in such a way that a given discrete evenet process behaves in obedience to various constraints. A flexible manufacturing cell (FMC) is one of discrete evenet systems. Functions necessary for the operation of an FMC are characterized by operational components and informational compoments. The operational components can be modeled using the finite state machines and the informational components can be modeled using the abstract formalism which describes supporting operations of the cell controller. In this paper, we addressed function required for FMC control specification, software engineering aspects on FMC control based on supervisory control, a concept of event queue for resolving synchronization problem, and complexity reduction. Based on the mathematical model of an FMC. we synthesized the controller by integrating a supervisor for FMC with control specification that specifies event-driven operation of the cell controller. The proposed control scheme is stable mathematically so that the system always behaves on a controlled way even under the existence of uncontrollable events. Furthermore, using an event queue concept, we can solve a synchronization problem caused by the violation of instantaneity assumption of supervisory control theory in real life situation. And also, we can propotype a control software rapidly due to the modularity of the proposed control scheme.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.865-871
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• 2009

In this paper, a novel feedback linearization is proposed for discrete-time nonlinear systems described by discrete-time T-S fuzzy models. The local linear models of a T-S fuzzy model are transformed to a controllable canonical form respectively, and their T-S fuzzy combination results in a feedback linearizable Tagaki-Sugeno fuzzy model. Based on this model, a nonlinear state feedback linearizing input is determined. Nonlinear state transformation is inferred from the linear state transformations for the controllable canonical forms. The proposed method of this paper is more intuitive and easier to understand mathematically compared to the well-known feedback linearization technique which requires a profound mathematical background. The feedback linearizable condition of this paper is also weakened compared to the conventional feedback linearization. This means that larger class of nonlinear systems is linearizable compared to the case of classical linearization.

• Economic and Environmental Geology
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• v.52 no.1
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• pp.81-90
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• 2019

In this study, the effects of fracture tensor component and first invariant on block hydraulic behaviors are evaluated in the 2-D DFN(discrete fracture network) systems. A series of regression analysis is performed between connected fracture tensor components and block hydraulic conductivities estimated at every $30^{\circ}$ hydraulic gradient directions for a total of 36 DFN systems having various joint density and size distribution. The directional block hydraulic conductivity seems to have strong relation with the fracture tensor component estimated in direction perpendicular to it. It is found that an equivalent continuum approach could be acceptable for the 2-D DFN systems under condition that the first invariant of fracture tensor is more than 2.0~2.5. The first invariant of fracture tensor seems highly correlated with average block hydraulic conductivity and can be used to evaluate hydraulic characteristics of the 2-D DFN systems. Also, a possibility of upscaling using the first invariant of fracture tensor for the DFN system is addressed through this study.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.45-52
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• 2021

Although many studies have been conducted to identify unknown attacks in cyber security intrusion detection systems, studies based on outliers are attracting attention. Accordingly, we identify outliers by defining categories for unknown attacks. The unknown attacks were investigated in two categories: first, there are factors that generate variant attacks, and second, studies that classify them into new types. We have conducted outlier studies that can identify similar data, such as variants, in the category of studies that generate variant attacks. The big problem of identifying anomalies in the intrusion detection system is that normal and aggressive behavior share the same space. For this, we applied a technique that can be divided into clear types for normal and attack by discrete wavelet transformation and detected anomalies. As a result, we confirmed that the outliers can be identified through One-Class SVM in the data reconstructed by discrete wavelet transform.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.6
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• pp.33-43
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• 2021

A60 class bulkhead penetration piece is a fire resistance system installed on a bulkhead compartment to protect lives and to prevent flame diffusion in a fire accident on a ship and offshore plant. This study focuses on the approximate optimization of the fire resistance design of the A60 class bulkhead penetration piece using a multi-island genetic algorithm. Transient heat transfer analysis was performed to evaluate the fire resistance design of the A60 class bulkhead penetration piece. For approximate optimization, the bulkhead penetration piece length, diameter, material type, and insulation density were considered discrete design variables; moreover, temperature, cost, and productivity were considered constraint functions. The approximate optimum design problem based on the meta-model was formulated by determining the discrete design variables by minimizing the weight of the A60 class bulkhead penetration piece subject to the constraint functions. The meta-models used for the approximate optimization were the Kriging model, response surface method, and radial basis function-based neural network. The results from the approximate optimization were compared to the actual results of the analysis to determine approximate accuracy. We conclude that the radial basis function-based neural network among the meta-models used in the approximate optimization generates the most accurate optimum design results for the fire resistance design of the A60 class bulkhead penetration piece.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.4
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• pp.208-215
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• 2024

The Sortie Generation Rate (SGR), which measures the number of sorties that an airbase can produce per unit of time, is crucial for assessing operational capacity. However, the unique spatial and environmental constraints on aircraft carriers complicate the direct application of land-based SGR studies to maritime settings. This study introduces a framework for analyzing the Sortie Generation Process (SGP) on aircraft carriers, using discrete event simulation adapted to these constraints. This approach conceptualizes the SGP similar to a logistics and production system, wherein sorties are systematically generated through the operations of the aircraft. The proposed framework defines and implements the necessary simulation functions with the discrete event simulation method for the purpose of SGP analysis. Through a series of experiments, this study demonstrates the framework's effectiveness and its practical applicability to aircraft carrier operations, potentially enhancing sortie generation capabilities in naval aviation.