• Journal of the Society of Naval Architects of Korea
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• v.41 no.3
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• pp.49-59
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• 2004

The paper describes the preliminary ship design method using deterministic approach and probabilistic approach. In deterministic approach, there are computational aspects to applying not only the integration concurrently of principal dimension decisions and hull form variations but also hydrostatic coefficients that applied to optimization iterative process. Therefore, this paper developed that actual design concept at the preliminary ship design more than sequential design which separated in principal dimension decisions and hull form variations. Furthermore, a probabilistic approach at the preliminary ship design is applied to efficiently solve design information uncertainty that compared to deterministic approach.

• Structural Engineering and Mechanics
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• v.18 no.2
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• pp.231-246
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• 2004

In this paper, a comparison of various random vibration and deterministic dynamic analyses of cable-stayed bridges subjected to asynchronous ground motion is presented. Different random vibration methods are included to determine the dynamic behaviour of a cable-stayed bridge for various ground motion wave velocities. As a numerical example the Jindo Bridge located in South Korea is chosen and a 413 DOF mathematical model is employed for this bridge. The results obtained from a spectral analysis approach are compared with those of two random vibration based response spectrum methods and a deterministic method. The analyses suggest that the structural responses usually show important amplifications depending on the decreasing ground motion wave velocities.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.46-58
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• 2023

Deterministic safety analysis is a crucial part of safety assessment, particularly when it comes to demonstrating the safety of nuclear power plant designs. The traditional approach to deterministic safety analysis models is to model the nuclear core using point kinetics. However, this simplified approach does not fully reflect the real core behavior with proper moderator and fuel reactivity feedbacks during the transient. The use of Multi-Physics approach allows more precise simulation reflecting the inherent three-dimensionality (3D) of the problem by representing the detailed 3D core, with instantaneous updates of feedback mechanisms due to changes of important reactivity parameters like fuel temperature coefficient (FTC) and moderator temperature coefficient (MTC). This paper addresses a CEA ejection accident at hot full power (HFP), in which the underlying strong and un-symmetric feedback between thermal-hydraulics and reactor kinetics exist. For this purpose, a multi-physics analysis tool has been selected with the nodal kinetics code, 3DKIN, implicitly coupled to the thermal-hydraulic code, RELAP5, for real-time communication and data exchange. This coupled approach enables high fidelity three-dimensional simulation and is therefore especially relevant to reactivity initiated accident (RIA) scenarios and power distribution anomalies with strong feedback mechanisms and/or un-symmetrical characteristics as in the CEA ejection accident. The Systems Engineering approach is employed to provide guidance in developing the work in a systematic and efficient fashion.

• Proceedings of the Korea Concrete Institute Conference
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• 2003.05a
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• pp.181-186
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• 2003

There are many uncertainties in structural failures or structures, so probabilistic failure cause assessment should be performed in order to consider the uncertainties. However, in many cases of forensic engineering, the failure cause assessments are performed by deterministic approach though number of uncertainties are existed in the failures or structures. Thus, deterministic approach may have possibility for leading to unreasonable and unrealistic failure cause assessment due to ignorance of the uncertainties. Therefore, probabilistic approach is needed to complement the shortcoming of deterministic approach and to perform the more reasonable and realistic failure cause assessment. In this study, reliability-based failure cause assessment (reliability based forensic engineering) is performed, which can incorporate uncertainties in failures and structures. For more practical application, the modified ETA technique is proposed, which automatically generates the defected structural model, performs structural analysis and reliability analysis, and calculates the failure probabilities of the failure events and the occurrence probabilities of the failure scenarios. Also, for more precise reliability analysis, uncertainties are estimated more reasonably by using bayesian approach based on the experimental laboratory testing data in forensic report.

• Investigative Magnetic Resonance Imaging
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• v.6 no.2
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• pp.137-146
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• 2002

Purpose : This paper introduces an improved classification approach which adopts a deterministic relaxation method and an agglomerative clustering technique for the classification of MRI using neural network. The proposed approach can solve the problems of convergency to local optima and computational burden caused by a large number of input patterns when a neural network is used for image classification. Materials and methods : Application of Hopfield neural network has been solving various optimization problems. However, major problem of mapping an image classification problem into a neural network is that network is opt to converge to local optima and its convergency toward the global solution with a standard stochastic relaxation spends much time. Therefore, to avoid local solutions and to achieve fast convergency toward a global optimization, we adopt MFA to a Hopfield network during the classification. MFA replaces the stochastic nature of simulated annealing method with a set of deterministic update rules that act on the average value of the variable. By minimizing averages, it is possible to converge to an equilibrium state considerably faster than standard simulated annealing method. Moreover, the proposed agglomerative clustering algorithm which determines the underlying clusters of the image provides initial input values of Hopfield neural network. Results : The proposed approach which uses agglomerative clustering and deterministic relaxation approach resolves the problem of local optimization and achieves fast convergency toward a global optimization when a neural network is used for MRI classification. Conclusion : In this paper, we introduce a new paradigm to classify MRI using clustering analysis and deterministic relaxation for neural network to improve the classification results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.61
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• pp.13-22
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• 2000

To apply the queueing network theory in evaluating performances of flexible manufacturing systems, it is generally assumed that the processing times are distributed exponentially. However, in FMS, processing times are usually deterministic. In this study the performance measures of FMS are approximated under the assumption that processing times are usually deterministic. Multi-classes of parts and single server and multi-server stations are considered in the model. This study also that, in the numerical example, this approach yields better solutions than those obtained by the pure Linearizer algorithm, when the processing times are deterministic.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.988-992
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• 2011

The buckling characteristics of the continuous welded rail track(CWR) is uncertainly varied by many influence factors, such as rail temperature, operating conditions of a train and maintenance of the track etc. Therefore, applying the probabilistic approach method is essential to rationally consider uncertainty and randomness of the various parameters that affect the track buckling. In this study, the probabilistic approach analysis was carried out and the results were compared with the deterministic approach using the buckling probability evaluation system of CWR tracks developed by our research team. From the comparison, it was identified that a probabilistic approach can quantitatively assess the reliability of the CWR tracks based on failure probability and can be used as a tool for decision making in track design, maintenance and operating etc.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.58-67
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• 2022

In this paper, we propose an approach resolving inaccuracy of the low-cost redundant manipulator workspace with low encoder and low stiffness. When the manipulators are manufactured with low-cost encoders and low-cost links, the robots can run into workspace inaccuracy issues. Furthermore, trajectory generation based on conventional forward/inverse kinematics without taking into account inaccuracy issues will introduce the risk of end-effector fluctuations. Hence, we propose an optimization for the trajectory generation method based on the DDPG (Deep Deterministic Policy Gradient) algorithm for the low-cost redundant manipulators reaching the target position in Euclidean space. We designed the DDPG algorithm minimizing the distance along with the jacobian condition number. The training environment is selected with an error rate of randomly generated joint spaces in a simulator that implemented real-world physics, the test environment is a real robotic experiment and demonstrated our approach.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.13 no.2
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• pp.60-66
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• 2017

The standard master curve (MC) approach has the major limitation because it is only applicable to homogeneous datasets. In nature, materials are macroscopically inhomogeneous and involve scatter of fracture toughness data due to various deterministic material inhomogeneity and random inhomogeneity. RPV(reactor pressure vessel) steel has different fracture toughness with varying distance from the inner surface of the wall due to cooling rate in manufacturing process; deterministic inhomogeneity. On the other hand, reference temperature, $T_0$, used in the evaluation of fracture toughness is acting as a random parameter in the evaluation of welding region; random inhomogeneity. In the present paper, four regions, the surface, 1/8T, 1/4T and 1/2T, were considered for fracture toughness specimens of KSNP (Korean Standard Nuclear Plant) SA508 Gr. 3 steel to investigate deterministic material inhomogeneity and random inhomogeneity. Fracture toughness tests were carried out for four regions and three test temperatures in the transition region. Fracture toughness evaluation was performed using the bimodal master curve (BMC) approach which is applicable to the inhomogeneous material. The results of the bimodal master curve analyses were compared with that of conventional master curve analyses. As a result, the bimodal master approach considering inhomogeneous materials provides better description of scatter in fracture toughness data than conventional master curve analysis. However, the difference in the $T_0$ determined by two master curve approaches was insignificant.

• Geomechanics and Engineering
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• v.10 no.6
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• pp.709-726
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• 2016

Corrugated-core sandwich panels are prevalent for many applications in industries. The researches performed with the aim of optimization of such structures in the literature have considered a deterministic approach. However, it is believed that deterministic optimum points may lead to high-risk designs instead of optimum ones. In this paper, an effort has been made to provide a reliable and robust design of corrugated-core sandwich structures through stochastic and probabilistic multi-objective optimization approach. The optimization is performed using a coupling between genetic algorithm (GA), Monte Carlo simulation (MCS) and finite element method (FEM). To this aim, Prob. Design module in ANSYS is employed and using a coupling between optimization codes in MATLAB and ANSYS, a connection has been made between numerical results and optimization process. Results in both cases of deterministic and probabilistic multi-objective optimizations are illustrated and compared together to gain a better understanding of the best sandwich panel design by taking into account reliability and robustness. Comparison of results with a similar deterministic optimization study demonstrated better reliability and robustness of optimum point of this study.