• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.6
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• pp.49-55
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• 2004

The increase of computing power makes stochastic optimization algorithms available in structural design. One of the stochastic algorithms, simulated annealing algorithm, has been applied to various structural optimization problems. By applying several cooling schedules such as simulated annealing (SA), Boltzmann annealing (BA), fast annealing (FA) and adaptive simulated annealing (ASA), truss structures are optimized to improve the quality of objective functions and reduce the number of function evaluations. In this paper, many cooling parameters have been applied to the cooling schedule of ASA. The influence of cooling parameters is investigated to find the rules of thumb for using ASA. Tn addition, the cooling schedule combined with BA and ASA is applied to the optimization of ten bar-truss and twenty five bar-truss structure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.87-95
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• 2010

In this paper, we propose an improved TDoA (Time Difference of Arrival) localization scheme using PSO (Particle Swarm Optimization) in UWB (Ultra Wide Band) systems. The proposed scheme is composed of two steps: re-estimation of TDoA parameters and re-localization of a tag position. In both steps, the PSO algorithm is employed to improve the performance. In the first step, the proposed scheme re-estimates the TDoA parameters obtained by traditional TDoA localization to reduce the TDoA estimation error. In the second step, the proposed scheme with the TDoA parameters estimated in the first step, re-localizes the tag to minimize the location error. The simulation results show that the proposed scheme achieves a more superior location performance to the traditional TDoA localization in both LoS (Line-of-Sight) and NLoS (Non-Line-of-Sight) channel environments.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1382-1399
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• 2023

Pressure relief valve (PRV) is one of the important control valves used in nuclear power plants, and its sealing performance is crucial to ensure the safety and function of the entire pressure system. For the sealing performance improving purpose, an explicit function that accounts for all design parameters and can accurately describe the relationship between the multi-design parameters and the seal performance is essential, which is also the challenge of the valve seal design and/or optimization work. On this basis, a surrogate model-based design optimization is carried out in this paper. To obtain the basic data required by the surrogate model, both the Finite Element Model (FEM) and the Computational Fluid Dynamics (CFD) based numerical models were successively established, and thereby both the contact stresses of valve static sealing and dynamic impact (between valve disk and nozzle) could be predicted. With these basic data, the polynomial chaos expansion (PCE) surrogate model which can not only be used for inputs-outputs relationship construction, but also produce the sensitivity of different design parameters were developed. Based on the PCE surrogate model, a new design scheme was obtained after optimization, in which the valve sealing stress is increased by 24.42% while keeping the maximum impact stress lower than 90% of the material allowable stress. The result confirms the ability and feasibility of the method proposed in this paper, and should also be suitable for performance design optimizations of control valves with similar structures.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.915-923
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• 2008

This paper focuses on optimal tuning of nonlinear parameters of a dual-input power system stabilizer(dual-input PSS), which can improve the system damping performance immediately following a large disturbance. Until recently, various PSS models have developed to bring stability and reliability to power systems, and some of these models are used in industry applications. However, due to non-smooth nonlinearities from the interaction between linear parameters(gains and time constants of linear controllers) and nonlinear parameters(saturation output limits), the output limit parameters cannot be determined by the conventional tuning methods based on linear analysis. Only ad hoc tuning procedures('trial and error' approach) have been used. Therefore, the steepest descent method is applied to implement the optimal tuning of the nonlinear parameters of the dual-input PSS. The gradient required in this optimization technique can be computed from trajectory sensitivities in hybrid system modeling with the differential-algebraic-impulsive-switched(DAIS) structure. The optimal output limits of the dual-input PSS are evaluated by time-domain simulation in both a single machine infinite bus(SMIB) system and a multi-machine power system in comparison with those of a single-input PSS.

• Proceedings of the KWS Conference
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• 2006.10a
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• pp.253-255
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• 2006

Grey relational analysis has been carried out to develop a new approach for optimization of Nd:YAG laser and MIG hybrid welding parameters. The quality of welded material depends on welding parameters. The parameters chosen for current study include wire type, shielding gas, laser energy, laser focus, traveling speed, and wire feed rate. The welding experiments were performed on 6K21-T4 aluminum alloy sheet. Functional demands on products may vary widely depending on their use. The ultimate tensile stress, width, and penetration were chosen as the optimization criterion. Practice based on an orthogonal array which is following Taguchi's method has been progressed. Base on the results of grey relational analysis, the optimal process parameters were obtained. This integrated work was judged and it is observed that the results obtained by using the optimal parameters are much improved compared to those obtained through initial setting.

• ETRI Journal
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• v.22 no.1
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• pp.38-46
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• 2000

The extraction of model parameters for embedded passive components is crucial for designing and characterizing the performance of multichip module (MCM) substrates. In this paper, a method for optimizing the extraction of these parameters using genetic algorithms is presented. The results of this method are compared with optimization using the Levenberg-Marquardt (LM) algorithm used in the HSPICE circuit modeling tool. A set of integrated resistor structures are fabricated, and their scattering parameters are measured for a range of frequencies from 45 MHz to 5 GHz. Optimal equivalent circuit models for these structures are derived from the s-parameter measurements using each algorithm. Predicted s-parameters for the optimized equivalent circuit are then obtained from HSPICE. The difference between the measured and predicted s-parameters in the frequency range of interest is used as a measure of the accuracy of the two optimization algorithms. It is determined that the LM method is extremely dependent upon the initial starting point of the parameter search and is thus prone to become trapped in local minima. This drawback is alleviated and the accuracy of the parameter values obtained is improved using genetic algorithms.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1006-1011
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• 2003

In this study, a novel systematic design procedure by Genetic Algorithm of a two stage relief valve is proposed. First of all, a mathematical model describing the dynamics of a balanced piston type relief valve has been derived. Governing equations such as dynamic equations for the main spool and the pilot spool and flow equations for each orifice are established. The mathematical model is verified by comparing the results of simulation with that of experiments. Furthermore, influences of the parameters on the dynamic characteristics of a relief valve have been investigated by simulation of the proposed model. Major design parameters on the valve response are determined, which affect the system response significantly. And then, using the determined parameters, the optimization of the two stage relief valve by Genetic Algorithm, which is a random search algorithm can find the global optimum without converging local optimum, is performed. The optimal design process of a two stage relief valve is presented to determine the major design parameters. Fitness function reflects the changing pressure according to parameters. It is shown that the genetic algorithms satisfactorily optimized the major design parameters of the two stage relief valve.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.286-292
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• 2014

This paper presents the parameters optimization technology for generating the first short stroke lightning current waveform($10/350{\mu}s$) which is necessary for the performance tests of components of lightning protection systems, as required under IEC 62305 and the newly amended IEC 62561. The circuit using the crowbar device specified in IEC 62305 was applied to generate the lightning current waveform. To find the proper parameters of the circuit is not easy because the circuit consists of two parts; circuit I, which relates to the front of current waveform, and circuit II, which relates to the tail. A simulation in PSpise was carried out to find main factors related to the front and tail of $10/350{\mu}s$. The lightning current generator was developed by utilizing the circuit parameters found in the simulation. In the result of experiments, new parameters of the circuits need to be changed because of the difference between the simulation and the experiment results. Using the iterative method, the optimized parameters of the circuits was determined. Also a multistage-type external coil and a damping resistor were proposed to make the efficiency of generation to enhance. According to the result in this paper, an optimized first short stroke lightning current waveform was obtained.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.501-506
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• 2002

In this study, a novel systematic design procedure by Genetic Algorithm of a two stage relief valve is proposed. First of all. a mathematical model describing the dynamics of a balanced piston type relief valve has been derived. Governing equations such as dynamic equations for the main spool and the pilot spool and flow equations for each orifice are established. The mathematical model is verified by comparing the results of simulation with that of experiments. Furthermore, influences of the parameters on the dynamic characteristics of a relief valve have been investigated by simulation of the proposed model. Major design parameters on the valve response are determined, which affect the system response significantly. And then, using the determined parameters, the optimization of the two stage relief valve by Genetic Algorithm, which is a random search algorithm can find the global optimum without converging local optimum, is performed. The optimal design process of a two stage relief valve is presented to determine the major design parameters. Fitness function reflects the changing pressure according to parameters. It is shown that the genetic algorithms satisfactorily optimized the major design parameters of the two stage relief valve.

• Advances in nano research
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• v.12 no.4
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• pp.375-386
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• 2022

Synthesis of acrylate-based dispersion resins involves many parameters including temperature, ingredients concentrations, and rate of adding ingredients. Proper controlling of these parameters results in a uniform nano-size chain of polymer on one side and elimination of hazardous residual monomer on the other side. In this study, we aim to screen the process parameters via Internet of Things (IoT) to ensure that, first, the nano-size polymeric chains are in an acceptable range to acquire high adhesion property and second, the remaining hazardous substance concentration is under the minimum value for safety of public and personnel health. In this regard, a set of experiments is conducted to observe the influences of the process parameters on the size and dispersity of polymer chain and residual monomer concentration. The obtained dataset is further used to train an Adaptive Neural network Fuzzy Inference System (ANFIS) to achieve a model that predicts these two output parameters based on the input parameters. Finally, the ANFIS will return values to the automation system for further decisions on parameter adjustment or halting the process to preserve the health of the personnel and final product consumers as well.