• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.2941-2959
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• 2022

The fire brigade non-suppression probability model is a major factor that should be considered in evaluating fire-induced risk through fire probabilistic risk assessment (PRA), and also uncertainty is a critical consideration in support of risk-informed performance-based (RIPB) fire protection decision-making. This study developed an optimal integrated probabilistic fire brigade non-suppression model considering uncertainty of parameters based on the Bayesian Markov Chain Monte Carlo (MCMC) approach on electrical fire which is one of the most risk significant contributors. The result shows that the log-normal probability model with a location parameter (µ) of 2.063 and a scale parameter (σ) of 1.879 is best fitting to the actual fire experience data. It gives optimal model adequacy performance with Bayesian information criterion (BIC) of -1601.766, residual sum of squares (RSS) of 2.51E-04, and mean squared error (MSE) of 2.08E-06. This optimal log-normal model shows the better performance of the model adequacy than the exponential probability model suggested in the current fire PRA methodology, with a decrease of 17.3% in BIC, 85.3% in RSS, and 85.3% in MSE. The outcomes of this study are expected to contribute to the improvement and securement of fire PRA realism in the support of decision-making for RIPB fire protection programs.

• International Journal of Korean Welding Society
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• v.1 no.1
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• pp.17-22
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• 2001

Because the quantitative relationships between welding parameters and welding result are not yet blown, optimal values of welding parameters for $CO_2$ robotic arc welding is a difficult task. Using the various artificial data processing methods may solve this difficulty. This research aims to develop an expert system for $CO_2$ robotic arc welding to recommend the optimal values of welding parameters. This system has three main functions. First is the recommendation of reasonable values of welding parameters. For such work, the relationships in between the welding parameters are investigated by the use of regression analysis and fuzzy system. The second is the estimation of bead shape by a neural network system. In this study the welding current voltage, speed, weaving width, and root gap are considered as the main parameters influencing a bead shape. The neural network system uses the 3-layer back-propagation model and a generalized delta rule as teaming algorithm. The last is the optimization of the parameters for the correction of undesirable weld bead. The causalities of undesirable weld bead are represented in the form of rules. The inference engine derives conclusions from these rules. The conclusions give the corrected values of the welding parameters. This expert system was developed as a PC-based system of which can be used for the automatic or semi-automatic $CO_2$ fillet welding with 1.2, 1.4, and 1.6mm diameter the solid wires or flux-cored wires.

• Structural Engineering and Mechanics
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• v.86 no.4
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• pp.503-518
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• 2023

In tall constructions, the outriggers are regarded as a structural part capable of effectively resisting lateral loads. This study analyses the efficacy of hybrid outrigger system in high rise RCC building for various structural parameters identified. For variations in α, which is defined as the ratio of the relative flexural stiffness of the core to the axial rigidity of the column, static and dynamic analyses of hybrid outrigger system having a virtual and a conventional outrigger at two distinct levels were conducted in the present study. An investigation on the optimal outrigger position was performed by taking the results from absolute maximum inter storey drift ratio (ISD_max), roof acceleration (acc_roof), roof displacement (disp_roof), and base bending moment under both wind and seismic loads on analytical models having 40, 60 and 80 storeys. An ideal performance index parameter was introduced and was utilized to obtain the optimal position of the hybrid outrigger system considering the combined response of ISD_max, acc_roof, disp_roof and, criteria required for the structure under wind and seismic loads. According to the behavioural study, increasing the column area and outrigger arm length will maximise the performance of the hybrid outrigger system. The analysis results are summarized in a flowchart which provides the optimal positions obtained for each dependent parameter and based on ideal performance index which can be used to make initial suggestions for installing a hybrid outrigger system.

• Journal of IKEEE
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• v.1 no.1 s.1
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• pp.19-30
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• 1997

In this paper we design the optimal satellite-tracking antenna $H{\infty}$ control system using genetic algorithms. To do this, we give gain and dynamics parameters to the weighting functions and apply genetic algorithms with reference model to the optimal determination of weighting functions and design parameter ${\gamma}$ that are given by Glover-Doyle algorithm which can design $H{\infty}$ controller in the state space. These weighting functions and design parameter ${\gamma}$ are optimized simultaneously in the search domain guaranteeing the robust stability of closed-loop system. The effectiveness of this satellite-tracking antenna $H{\infty}$ control system is verified by computer simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1542-1550
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• 2019

Legacy studies for classifying arrhythmia have been studied to improve the accuracy of classification, Neural Network, Fuzzy, etc. Deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose optimal parameter extraction method based on a deep learning. For this purpose, R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 97.84% in PVC classification.

• Journal of the Korean Statistical Society
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• v.28 no.3
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• pp.325-336
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• 1999

Collinearity among independent variables can have severe effects on the precision of response estimation for some region of interest in the experiments with mixture. A method of finding optimal linear restriction on regression parameter in linear model for mixture experiments in the sense of minimizing integrated mean squared error is studied. We use the formulation of optimal restrictions on regression parameters for estimating responses proposed by Park(1981) by transforming mixture components to mathematically independent variables.

• Magazine of the Korean Society of Agricultural Engineers
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• v.28 no.2
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• pp.42-52
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• 1986

This study involves the estimation of optimal ranges of parameters for a linear watershed model. A well-known TANK model was chosen and a linear combination of four tanks assumed. The model was used to simulate daily streamflow for six watersheds of different sizes and by a trial-and-error approach a set of optimal parameters defined. The parameters were related to watershed sizes and land use conditions. Optimal parameters for ungaged conditions were defined from the relationships; daily streamflow simulated and compared to the observed date. The simulated results were in a general agreement with the data.

• Journal of the Korean Mathematical Society
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• v.39 no.4
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• pp.509-524
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• 2002

We Study the Problems Of identification for the damped sine-Gordon equations with constant parameters. That is, we establish the existence and necessary conditions for the optimal constant parameters based on the fundamental optimal control theory and the transposition method studied in Lions and Magenes [5].

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.436-439
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• 1992

A tubular reactor model represented by partial differential equations was studied as one of nonlinear distributed parameter optimal control problems. An optimal control theory in the form of maximum principles based on nonlinear integral equations was used to develop an algorithm to solve the problem.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1117-1120
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• 1999

In this paper, we design the H$\infty$ optimal controller satisfying robust stability and performance in spite of the plant uncertainty for an electro-mechanical actuator system and analyze the controller in frequency domain. H$\infty$ optimal controller K was designed using iteration algorithm suggested by DOYLE. Using the controller in an electro-mechanical actuator system, the joint with very small coupling rigidity coefficient was used to vary the control parameter. The plant unstructured uncertainty was assumed to be a multiplicative type.