• Journal of the Korean Society of Safety
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• v.36 no.4
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• pp.1-11
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• 2021

Piping systems comprising pumps and valves are essential in the power plant, oil, and defense industry. Their purpose includes a stable supply of the working fluid or ensuring the target system's safe operation. However, piping system accidents due to leakage of toxic substances, explosions, and natural disasters are prevalent In addition, with the limited maintenance personnel, it becomes difficult to detect, isolate, and reconfigure the damage of the piping system and recover the unaffected area. An autonomous recovery piping system can play a vital role under such circumstances. The autonomous recovery algorithms for the piping system can be divided into low-pressure control algorithms, hydraulic resistance control algorithms, and flow inventory control algorithms. All three methods include autonomous opening/closing logic to isolate damaged areas and recovery the unaffected area of piping systems. However, because each algorithm has its strength and weakness, appropriate application considering the overall design, vital components, and operating conditions is crucial. In this regard, preliminary research on algorithm's working principle, its design procedures, and expected damage scenarios should be accomplished. This study examines the characteristics of algorithms, the design procedure, and working logic. Advantages and disadvantages are also analyzed through simulation results for a simplified piping system.

• Journal of Korean Society of Steel Construction
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• v.24 no.6
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• pp.701-710
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• 2012

We present optimum design for truss and frame structures subject to constraints on stresses, displacement, and natural frequency. The optimum design procedure is used discrete and continuous design variables and Genetic Algorithms. Genetic Algorithms is used the method of Elitism and penalty parameters in order to improved fitness in the reproduction process, and optimum design is used steel(W-section) and pre-made discrete cross-section. Truss and frame structures optimization examples are used for 10-Bar truss, 25-Bar truss, 1-bay 2-story frame, 1-bay 7-story frame, and these examples are employed to demonstrate the availability and serviceability of Genetic Algorithms for solving optimum design of truss and frame.

• Journal of Korea Water Resources Association
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• v.34 no.5
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• pp.567-575
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• 2001

A methodology is developed for designing the minimum-cost water distribution network. The method is based on network simulations and an optimization scheme using genetic algorithms. Being a stochastic optimization scheme, genetic algorithms have advantages over the conventional search algorithms in solving network problems known for their nonlinearities and herculean computational costs. While existing methods focus on the design of either entirely new or parallel augmentation of network systems, the proposed method can be applied to problems having both new branches of tree-type and paralle augmentation in loops. The applicability of the method was shown through a case study for Baekryeon water supply system. The optimized design resulted in the maximum 5.37% savings compared to the conventional design without optimization, while meeting the hydraulic constraints.

• Korean Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.68-85
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• 1998

Solid modeling refers to techniques for unambiguous representations of three-dimensional objects. Feature recognition is a sub-discipline focusing on the design and implementation of algorithms for detecting manufacturing information such as holes, slots, etc. in a solid model. Automated feature recognition has been an active research area in stolid modeling for many years, and is considered to be a critical component for CAD/CAM integration. This paper gives a technical overview of the state of the art in feature recognition research. Rather than giving an exhaustive survey, I focus on the three currently dominant feature recognition technologies: graph-based algorithms, volumetric decomposition techniques, and hint-based geometric reasoning. For each approach, I present a detailed description of the algorithms being employed along with some assessments of the technology. I conclude by outlining important open research and development issues.

• Ocean Systems Engineering
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• v.11 no.4
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• pp.301-312
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• 2021

An optimization framework has been established and applied to a shipshape parametric FPSO hull design. A single point moored (SPM) shipshape floating system suffers a significant level of the roll motion in both the wave frequencies and low wave frequencies, which presents a coupling effect with the horizontal weathervane motion. To guarantee the security of the operating instruments installed onboard, a parametric hull design of an FPSO has been optimized with improved hydrodynamics performance. With the optimized parameters of the various hull stations' longitudinal locations, the optimization through Genetic Algorithms (GAs) has been proven to provide a significantly reduced level of the 1st-order and 2nd-order roll motion. This work presents a meaningful framework as a reference in the process of an SPM shipshape floating system's design.

• Journal of KIBIM
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• v.9 no.4
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• pp.41-50
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• 2019

Digital models for a cable-stayed bridge are defined considering data-driven engineering from design to construction. Algorithms for digital object generation of each component of the cable-stayed bridge were developed. Using these algorithms, Level-3 BIM practices can be realized from design stages. Based on previous practices, digital object library can be accumulated. Basic digital models are modified according to given design conditions by a designer. Once design models are planned, various applications using the models are linked the models such as estimation, drawings and mechanical properties. Federated bridge models are delivered to construction stages. In construction stage, the models can be efficiently revised according to the changed situations during construction phases. In this paper, measured coordinates are imported to the model generation algorithms and revised models are obtained. Augmented reality devices and their applications are proposed. AR simulations in construction site and in office condition are tested. From this pilot test of digital models, it can be said that Level-3 BIM practices can be realized by using in-house modeling algorithms according to different purposes.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.3
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• pp.1-15
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• 1998

Genetic algorithms(GAs) are suited for solving structural design problems, since they handle the design variables efficiently. This ability of GAs considers then as a good choice for optimization problems. Nevertheless, there are many situations that the conventional genetic algorithms do not perform particularly well, and so various methods of hybridization have been proposed. Thus. this paper develops a hybrid genetic algorithm(HGA) to incorporate a local convergence method and precision search method around optimum in the genetic algorithms. In case study. it is showed that HGA is able consistently to provide efficient, fine quality solutions and provide a significant capability for solving structural design problems.

• Journal of The Korean Association of Information Education
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• v.19 no.4
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• pp.409-418
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• 2015

The purpose of this study is derived implications at software curriculum development utilizing the backward design model. In this study, we developed 'Algorithms and Programming' unit teaching plan based on backward design template. First, we have derived enduring understandings, essential questions, specific knowledge and skill on 'Algorithms and Programming' unit by considering the goal, content, achievement standard of Software education operating instructions. Second, we developed authentic tasks using GRASPS technic and holistic scoring rubrics. Third, we developed 7 lesson 14 WHERETO element for effective teaching in 'Algorithms and Programming' unit. Fourth, we investigated about the effectiveness of the development unit based on backward design. Backward design could be useful of developing curriculum unit and lesson plan at software education.

• Industrial Engineering and Management Systems
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• v.8 no.1
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• pp.22-28
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• 2009

We consider facility layout problems, where mn facility units are assigned into mn cells. These cells are arranged into a rectangular pattern with m rows and n columns. In order to solve this cell type facility layout problem, many approximation algorithms with improved local search methods were studied because it was quite difficult to find exact optimum of such problem in case of large size problem. In this paper, new algorithms based on Simulated Annealing (SA) method with two neighborhood generation methods are proposed. The new neighborhood generation method adopts the exchanging operation of facility units in accordance with adjacent preference. For evaluating the performance of the neighborhood generation method, three algorithms, previous SA algorithm with random 2-opt neighborhood generation method, the SA-based algorithm with the new neighborhood generation method (SA1) and the SA-based algorithm with probabilistic selection of random 2-opt and the new neighborhood generation method (SA2), are developed and compared by experiment of solving same example problem. In case of numeric examples with problem type 1 (the optimum layout is given), SA1 algorithm could find excellent layout than other algorithms. However, in case of problem type 2 (random-prepared and optimum-unknown problem), SA2 was excellent more than other algorithms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.1
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• pp.184-191
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• 2010

Many researchers have been considering genetic algorithms to system optimization problems. Especially, real-coded genetic algorithms are very effective techniques because they are simpler in coding procedures than binary-coded genetic algorithms and can reduce extra works that increase the length of chromosome for wide search space. Thus, this paper presents a fuzzy system design technique to improve the performance of the fuzzy system. The proposed system consists of two procedures. The primary tuning procedure coarsely tunes fuzzy sets of the system using the k-means clustering algorithm of which the structure is very simple, and then the secondary tuning procedure finely tunes the fuzzy sets using enhanced real-coded genetic algorithms based on the primary procedure. In addition, this paper constructs multiple fuzzy systems using a data preprocessing procedure which is contrived for reflecting various characteristics of nonlinear data. Finally, the proposed fuzzy system is applied to the field of time series prediction and the effectiveness of the proposed techniques are verified by simulations of typical time series examples.