• Advances in Computational Design
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• v.2 no.3
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• pp.241-256
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• 2017

This paper introduces the process for Multi-objective Optimization Framework (MOF) which mediates multiple conflicting design targets. Even though the extensive researches have shown the benefits of optimization in engineering and design disciplines, most optimizations have been limited to the performance-related targets or the single-objective optimization which seek optimum solution within one design parameter. In design practice, however, designers should consider the multiple parameters whose resultant purposes are conflicting. The MOF is a BIM-integrated and simulation-based parametric workflow capable of optimizing the configuration of building components by using performance and non-performance driven measure to satisfy requirements including build programs, climate-based daylighting, occupant's experience, construction cost and etc. The MOF will generate, evaluate all different possible configurations within the predefined each parameter, present the most optimized set of solution, and then feed BIM environment to minimize data loss across software platform. This paper illustrates how Multi-objective optimization methodology can be utilized in design practice by integrating advanced simulation, optimization algorithm and BIM.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.9 s.180
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• pp.2302-2312
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• 2000

An active control of the transmission of noise through an aircraft fuselage is investigated numerically. A cylinder-cavity system was used as a model for this study. The fuselage is modeled as a fi nite, thin shel cylinder with constant thickness. The sound field generated by an exterior monopole source is transmitted into the cavity through the cylinder. Point force actuators on the cylinder are driven by error sensor that is placed in 3D cavity. Modal coupling theory is used to formulate the numerical models and describe the system behavior. Minimization of the acoustic potential energy in the fuselage is carried out as a performance index. Continuous parameter genetic algorithm is used to search the optimal actuator position and both results are compared.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.739-742
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• 1997

This paper proposes the structure and parameter optimization technique of fuzzy neural networks using genetic algorithm. Fuzzy neural network has advantages of both the fuzzy inference system and neural network. The determination of the optimal parameters and structure of the fuzzy neural networks, however, requires special efforts. To solve these problems, we propose a new learning method for optimization of fuzzy neural networks using genetic algorithm. It can optimize the structure and parameters of the entire fuzzy neural network globally. Numerical example is provided to show the advantages of the proposed method.

• Journal of the military operations research society of Korea
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• v.23 no.1
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• pp.88-99
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• 1997

Due to the various requests from consumers and rapidly changing market, the product design is continuously changing and the new products are manufactured in various forms. To satisfy this condition, companies must make the product design robust with better quality. However, considering many parameters and increasing the level of each parameter in product design can lead us to a difficult situation. In order to solve this problem, this paper attempts to develop an algorithm that takes into account more parameters and increased level of parameters using Genetic Algorithms. As a result of this study, we can use this algorithm for designing the parameters of new product since it can search the level of parameters in more detail and wider range.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.5
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• pp.913-921
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• 2009

This paper suggests a deign method of the model-following $H{\infty}$ control system having robust performance. This $H{\infty}$ control system is designed by applying genetic algorithm(GA) with reference model to the optimal determination of weighting functions and design parameter ${\gamma}$ that are given by $H{\infty}$ control theory. These weighting functions and design parameter ${\gamma}$ are optimized simultaneously in the search domain guaranteeing the robust performance of closed-loop system. The effectiveness of this $H_{\infty}$ control system is verified by computer simulation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.629-634
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• 2000

Generally, the accuracy of motion control systems is strongly influenced by both the mechanical characteristics and servo characteristics of feed drive systems. In the fed drive systems of machine tools that consist of mechanical parts and electrical parts, a torsional vibration is often generated because of its elastic elements in torque transmission. Especially, a torsional vibration caused by the elasticity of mechanical elements might deteriorate the quick movement of system and lead to shorten the life time of the mechanical transmission elements. So it is necessary to analyze the electromechanical system mathematically to optimize the dynamic characteristics of the feed drive system. In this paper, based on the simplifies feed drive system model, radius errors due to position gain mismatch and servo response characteristic have been developed and an optimal criterion for tuning the gain of speed controller is discussed. The proportional and integral parameter gain of the feed drive controller are optimal design variables for the gain tuning of PI speed controller. Through the optimization problem formulation, both proportional and integral parameter are optimally tuned so as to compensate the radius errors by using the genetic algorithm. As a result, higher performance on circular profile tests has been achieved than the one with standard parameters.

• Earthquakes and Structures
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• v.22 no.1
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• pp.53-64
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• 2022

This study proposes an intelligent semi-active isolation system combining a variable-stiffness control device and ground motion characteristic prediction. To determine the optimal control parameter in real-time, a genetic algorithm (GA)-fuzzy control law was developed in this study. Data on various types of ground motions were collected, and the ground motion characteristics were quantified to derive a near-fault (NF) characteristic ratio by employing an on-site earthquake early warning system. On the basis of the peak ground acceleration (PGA) and the derived NF ratio, a fuzzy inference system (FIS) was developed. The control parameters were optimized using a GA. To support continuity under near-fault and far-field ground motions, the optimal control parameter was linked with the predicted PGA and NF ratio through the FIS. The GA-fuzzy law was then compared with other control laws to verify its effectiveness. The results revealed that the GA-fuzzy control law could reliably predict different ground motion characteristics for real-time control because of the high sensitivity of its control parameter to the ground motion characteristics. Even under near-fault and far-field ground motions, the GA-fuzzy control law outperformed the FPEEA control law in terms of controlling the isolation layer displacement and the superstructure acceleration.

• Journal of Korean Society of Steel Construction
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• v.21 no.4
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• pp.361-373
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• 2009

Using the genetic algorithm, the optimal-design technique of the Nielsen arch bridge was proposed in this paper. The design parameters were the arch-rise ratio and the steel weight ratio of the Nielsen arch bridge, and optimal-design techniques were utilized to analyze the behavior of the bridge. The optimal parameter values were determined for the estimated optimal level. The parameter determination requires the standardization of the safety, utility, and economic concepts as the critical factors of a structure. For this, a genetic algorithm was used, whose global-optimal-solution search ability is superior to the optimization technique, and whose object function in the optimal design is the total weight of the structure. The constraints for the optimization were displacement, internal stress, and time and space. The structural analysis was a combination of the small displacement theory and the genetic algorithm, and the runtime was reduced for parallel processing. The optimal-design technique that was developed in this study was employed and deduced using the optimal arch-rise ratio, steel weight ratio, and optimal-design domain. The optimal-design technique was presented so it could be applied in the industry.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1551-1558
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• 2021

Beacon signals used in indoor positioning system are reflected and distorted, resulting in noise signals. KF(Kalman Filter) has been widely used to remove this noise. In order to apply the KF, optimization process considering the signal type, signal strength, and environmental elements of each product is required. In this paper, we propose a solution to the optimization problem of KF Parameters using GA(Genetic Algorithm) in BLE(Bluetooth Low Energy) Beacon-based indoor positioning system. After optimizing KF Parameters by applying the proposed technique with a certain distance between Beacon and receiver, we compared the estimated distance passed through KF with the unfiltered distance. The proposed technique is expected to reduce the time required and improve accuracy of KF Parameters optimization in an indoor positioning system based on RSSI (Received Signal Strength Indication).

• Journal of Navigation and Port Research
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• v.32 no.5
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• pp.379-385
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• 2008

In this paper, we presents a scheme for the parameter estimation and optimal control scheme for apparatus of container crane system. For parameter estimation, first, we construct the open loop of the container crane system and estimate its parameters based on input-output data, a real-coded genetic algorithm(RCGA) and the model adjustment technique. The RCGA plays an important role in parameter estimation as an adaptive mechanism. For controller design, state feedback gain matrix is searched by another RCGA and the estimated model. The performance of the proposed methods are demonstrated through a set of simulation and experiments of the experimental apparatus.