• KSCE Journal of Civil and Environmental Engineering Research
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• v.9 no.3
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• pp.57-64
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• 1989

This study presents a reliability-based optimum design of reinforced concrete frames, in which the AFOSM and SOSM methods are applied for the evaluation of the failure probabilities, and the sequential linear programming method is used as a practical approach to the system optimization. One-story two-bay reinforced concrete frame is chosen for the numerical illustration of the proposed reliability-based optimum design. As a result, it is found that the proposed procedure for the reliability-based optimization of RC frames could provide the accurate estimation of the optimal level of safety, and appears applicable to real structures with reasonable complexity. It is shown in the paper that the probability distributions of the basic random variables and the uncertainties of the applied loadings and material strengths may have great effect on the optimum design, but the AFOSM and SOSM methods do not show significant discrepancy in the optimum design results, but the former appears more realistic and time saving than the latter for this specific study.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.5
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• pp.18-24
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• 2003

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative sybcycles. Previous researches predifined the numbers of design processes in groups, but these group sizes should be determined optimally to balance the computing time of each groups. This paper proposes adaptive decomposition method, which determines the group sizes and the order of processes simultaneously to raise design efficiency by expanding the chromosome of the genetic algorithm. Finally, two sample cases are presented to show the effects of optimizing the sequence of processes with the adaptive decomposition method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.8
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• pp.34-41
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• 2005

A parallel computing technique was applied to large scale structure analysis or aerodynamic design and it is a essential element in reducing the huge computation time for large scale design problem. We can use a many computers for reducing the analysis time of multidisciplinary design optimization. But previous MDO frameworks can not support a parallel design process technique so still existing which calls an analysis program continuously. In this paper, We developed a MDO framework(MLR) which supports a parallel design process to solve sequential analysis call. Finally, three sample cases are presented to show the efficiency of design time using the suggested MDO framework.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.12
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• pp.865-873
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• 2019

To improve the survivability of a missile, it needs to be lowered that the detection possibility by radars on the ground. The radar exposure of the target is given as a function of relative distance from the radar to the target and RCS (Radar Cross Section). The RCS of the missile is determined by the incidence angle of the target to electromagnetic radiation emitted from the radar. Under the assumption that the missile equips appropriate attitude control system, the attitude of the missile to minimize radar exposure at a high altitude is investigated in this paper. Two different types of performance cost are considered: the total sum of RCS and the total sum of SNR during the flight. Optimal solutions against multiple ground radars are found by using a SQP (Sequential Quadratic Programming)-based optimization technique.

• Microbiology and Biotechnology Letters
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• v.49 no.2
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• pp.181-191
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• 2021

The present study addresses isolation, optimization, partial purification, and characterization of a haloalkaline serine protease from a newly isolated haloarchaeal strain isolated from Wadi El Natrun in Egypt. We expected that a two-step sequential statistical approach (one variable at a time, followed by response surface methodology) might maximize the production of the haloalkaline serine protease. The enzyme was partially purified using Hiprep 16/60 sephacryl S-100 HR gel filtration column. Molecular identification revealed the newly isolated haloarchaeon to be Natrialba hulunbeirensis strain WNHS14. Among several tested physicochemical determinants, casamino acids, KCl, and NaCl showed the most significant effects on enzyme production as determined from results of the One-Variable-At-A-time (OVAT) study. The BoxBehnken design localized the optimal levels of the three key determinants; casamino acids, KCl, and NaCl to be 0.5% (w/v), 0.02% (w/v), and 15% (w/v), respectively, obtaining 62.9 U/ml as the maximal amount of protease produced after treatment at 40℃, and pH 9 for 9 days with 6-fold enhancement in yield. The enzyme was partially purified after size exclusion chromatography with specific activity, purification fold, and yield of 1282.63 U/mg, 8.9, and 23%, respectively. The enzyme showed its maximal activity at pH, temperature, and NaCl concentration optima of 10, 75℃, and 2 M, respectively. Phenylmethylsulfonyl fluoride (PMSF, 5 mM) completely inhibited enzyme activity.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.31-38
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• 2021

In this paper, in order to obtain the optimization of the RNN model used for sentiment analysis, the correlation of each model was studied by observing the trend of loss and accuracy according to hyperparameter tuning. As a research method, after configuring the hidden layer with LSTM and the embedding layer that are most optimized to process sequential data, the loss and accuracy of each model were measured by tuning the unit, batch-size, and embedding size of the LSTM. As a result of the measurement, the loss was 41.9% and the accuracy was 11.4%, and the trend of the optimization model showed a consistently stable graph, confirming that the tuning of the hyperparameter had a profound effect on the model. In addition, it was confirmed that the decision of the embedding size among the three hyperparameters had the greatest influence on the model. In the future, this research will be continued, and research on an algorithm that allows the model to directly find the optimal hyperparameter will continue.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.3
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• pp.58-63
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• 2005

In order to minimize weight of vehicle seat, an optimum design of aluminum seat is presented while satisfying stress and fatigue life constraints. In this study, the analysis model is validated by comparing it's stress with that of test. Then, two-level orthogonal array is used to estimate the design sensitivity for 7 design variables. Finally, the sequential approximate optimization (SAO) is performed using the constructed RSM models. The approximate RSM models are sequentially updated using the analysis results corresponding to the approximate optimum obtained during the SAO. After 14 analyses, the SAO gives an optimal design that can reduce 16.7$\%$ of weight while increasing 369$\%$ of fatigue life and satisfying stress constraint.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2722-2742
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• 2014

We propose methods of linear transceiver design for two different power constraints, sum relay power constraint and per relay power constraint, which determine signal processing matrices such as base station (BS) transmitter, relay precoders and user receivers to minimize sum mean square error (SMSE) for multi-relay multi-user (MRMU) networks. However, since the formulated problem is non-convex one which is hard to be solved, we suboptimally solve the problems by defining convex subproblems with some fixed variables. We adopt iterative sequential designs of which each iteration stage corresponds to each subproblem. Karush-Kuhn-Tucker (KKT) theorem and SMSE duality are employed as specific methods to solve subproblems. The numerical results verify that the proposed methods provide comparable performance to that of a full relay cooperation bound (FRCB) method while outperforming the simple amplify-and-forward (SAF) and minimum mean square error (MMSE) relaying in terms of not only SMSE, but also the sum rate.

• Journal of Welding and Joining
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• v.27 no.5
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• pp.81-87
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• 2009

Recently, extruded aluminium-alloy panels have been used in the car bodies for the purpose of the light-weight of railway vehicles and FSW(Friction Stir Welding), which is superior to the arc weldings, has been applied in the railway vehicles. This paper presents the optimum design of the FSW process on A6005 extruded alloy for railway vehicles to improve its mechanical properties. Rotational speed, welding speed and tilting angle of the tool tip were chosen as design parameters. Three objective functions were determined; maximizing the tensile strength, minimizing the hardness and maximizing the difference between the normalized tensile strength and hardness. The tensile tests and the hardness tests for fifteen FSW experiments were carried out according to the central composite design table. Recursive model functions on three characteristic values, such as the tensile strength, the hardness difference(${\Delta}Hv$) and the difference of normalized tensile strength and ${\Delta}Hv$, were estimated according to the classical response surface analysis methodology. The reliability of each recursive function was verified by F-test using the analysis of variance table. Sensitivity analysis on each characteristic value was done. Finally, the optimum values of three design parameters were found using Sequential Quadratic Programming algorithm.