• Advances in aircraft and spacecraft science
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• v.7 no.5
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• pp.387-404
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• 2020

The numerical simulation of shock waves in supersonic flows is challenging because of several instabilities which can affect the solution. Among them, the carbuncle phenomenon can introduce nonphysical perturbations in captured shock waves. In the present work, a hybrid numerical flux is proposed for the evaluation of the convective fluxes that avoids carbuncle and keeps high-accuracy on shocks and boundary layers. In particular, the proposed flux is a combination between an upwind approximate Riemann problem solver and the Local Lax-Friedrichs scheme. A simple strategy to mix the two fluxes is proposed and tested in the framework of a discontinuous Galerkin discretisation. The approach is investigated on the subsonic flow in a channel, on the supersonic flow around a cylinder, on the supersonic flow on a flat plate and on the flow in a overexpanded rocket nozzle.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.145-154
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• 1999

An intelligent system embedded with multiple sources of knowledge may provide more robust intelligence with highly ill structured problems than the system with a single source of knowledge. This paper proposes the hybrid knowledge integration mechanism that yields the cooperated knowledge by integrating expert, user, and machine knowledge within the fuzzy logic-driven framework, and then refines it with a genetic algorithm (GA) to enhance the reasoning performance. The proposed knowledge integration mechanism is applied for the prediction of Korea stock price index (KOSPI). Empirical results show that the proposed mechanism can make an intelligent system with the more adaptable and robust intelligence.

• Journal of Electrical Engineering and Technology
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• v.6 no.2
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• pp.202-207
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• 2011

Numerous methodologies have been developed in an effort to reduce cogging torque. However, most of these methodologies have side effects that limit their applications. One approach is the optimization methodology that determines an optimized design variable within confined conditions. The response surface methodology (RSM) and the genetic algorithm (GA) are powerful instruments for such optimizations and are matters of common interest. However, they have some weaknesses. Generally, the RSM cannot accurately describe an object function, whereas the GA is time consuming. The current paper describes a novel GA and RSM hybrid algorithm that overcomes these limitations. The validity of the proposed algorithm was verified by three test functions. Its application was performed on a surface-mounted permanent magnet.

• Structural Engineering and Mechanics
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• v.46 no.3
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• pp.403-416
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• 2013

This paper describes new optimization strategy that offers significant improvements in performance over existing methods for geometry design of frame structures. In this study, an imperialist competitive algorithm (ICA) and ant colony optimization (ACO) are combined to reach to an efficient algorithm, called Imperialist Competitive Ant Colony Optimization (ICACO). The ICACO applies the ICA for global optimization and the ACO for local search. The results of optimal geometry for three benchmark examples of frame structures, demonstrate the effectiveness and robustness of the new method presented in this work. The results indicate that the new technique has a powerful search strategies due to the modifications made in search module of ICACO. Higher rate of convergence is the superiority of the presented algorithm in comparison with the conventional mathematical methods and non hybrid heuristic methods such as ICA and particle swarm optimization (PSO).

• Journal of Information Technology Applications and Management
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• v.15 no.1
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• pp.225-242
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• 2008

PM (Inconsistency Pattern Modeling) is a hybrid supervised learning technique using the inconsistence pattern of input variables in mining data sets. The IPM tries to improve prediction accuracy by combining more than two different supervised learning methods. The previous related studies have shown that the IPM was superior to the single usage of an existing supervised learning methods such as neural networks, decision tree induction, logistic regression and so on, and it was also superior to the existing combined model methods such as Bagging, Boosting, and Stacking. The objectives of this paper is explore the characteristics of the IPM. To understand characteristics of the IPM, three experiments were performed. In these experiments, there are high performance improvements when the prediction inconsistency ratio between two different supervised learning techniques is high and the distance among supervised learning methods on MDS (Multi-Dimensional Scaling) map is long.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.411-413
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• 1992

This paper proposes a simple and high performance hybrid position/force control of robots based on disturbance compensation by using the disturbance observer in task coordinate space. The disturbance observer linealizes system of robot manipulators in task coordinate space and realizes acceleration control. To realize the strict acceleration control, the disturbance observer whose input is a position signal by simple computation, works as if it were a disturbance detector. The inverse kinematics can be simplified, because the disturbance observer in task coordinate space compensates not only the disturbance but also the error due to the simplification of the inverse kinematics. The new strategy is applied to a three-degrees-of freedom direct drive robot. The robust and simple hybrid position/force control is realized experimentally.

• Journal of Information Technology Applications and Management
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• v.23 no.1
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• pp.45-59
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• 2016

An ensemble classifier is a method that combines output of multiple classifiers. It has been widely accepted that ensemble classifiers can improve the prediction accuracy. Recently, ensemble techniques have been successfully applied to the bankruptcy prediction. Bagging and random subspace are the most popular ensemble techniques. Bagging and random subspace have proved to be very effective in improving the generalization ability respectively. However, there are few studies which have focused on the integration of bagging and random subspace. In this study, we proposed a new hybrid ensemble model to integrate bagging and random subspace method using genetic algorithm for improving the performance of the model. The proposed model is applied to the bankruptcy prediction for Korean companies and compared with other models in this study. The experimental results showed that the proposed model performs better than the other models such as the single classifier, the original ensemble model and the simple hybrid model.

• Genomics & Informatics
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• v.6 no.2
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• pp.87-90
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• 2008

During the last four years, the pyrosequencing-based 454 platform has rapidly displaced the traditional Sanger sequencing method due to its high throughput and cost effectiveness. Meanwhile, the Sanger sequencing methodology still provides the longest reads, and paired-end sequencing that is based on that chemistry offers an opportunity to ensure accurate assembly results. In this report, we describe an optimized approach for hybrid de novo genome assembly using pyrosequencing data and varying amounts of Sanger-type reads. 454 platform-derived contigs can be used as single non-breakable virtual reads or converted to simpler contigs that consist of editable, overlapping pseudoreads. These modified contigs maintain their integrity at the first jumpstarting assembly stage and are edited by fragmenting and rejoining. Pre-existing assembly software then can be applied for mixed assembly with 454-derived data and Sanger reads. An effective method for identifying genomic differences between reference and sample sequences in whole-genome resequencing procedures also is suggested.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.10
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• pp.840-847
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• 2007

This Paper focuses on modeling and simulation to analyze the characteristic of hybrid vehicle. The system includes a proton exchange membrane fuel cell(PEMFC), photovoltaic generator(PV), lead-acid battery, motor, vehicle and controller. Main electricity is produced by the PEMFC and battery to meet the requirements of a user load. When vehicle is parked in a sunny place, extra power is generated by the photovotaics and is charged in a battery for next drive. Further we evaluate usefulness of this hybrid vehicle by using ADVISOR - the advanced vehicle simulator written in the Matlab/Simulink environment. According to simulation results, the extra power obtained by photovoltaics which have been explored in nature conditions can help to reduce the electrical load of PEMFC and increase the efficiency (over 21 %).

• International Journal of Automotive Technology
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• v.3 no.3
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• pp.123-128
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• 2002

This paper presents the design of an active roll control system for a ground vehicle and an experimental study using an devised electric-actuating roll control system. Based on a three degree of freedom linear vehicle model, the controller is designed using lateral acceleration and rollrate feedback. In order to investigate the feasibility of an active control system, experimental work is carried out using a hardware-in-the-loop (Hil) setup which has been constructed by the devised electric-actuating system and the full vehicle model including tire characteristics. The performance is evaluated by an experiment using the Hil setup with limited bandwidth. Finally, in order to enhance the control performance in the transient region, a hybrid control strategy is proposed and evaluated.