• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.6
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• pp.773-785
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• 2014

For decades, modeling of pilots has been intensively studied due to its advantages in reducing costs for training and enhancing safety of pilots. In particular, research for modeling of pilots' non-kinetic behaviors which refer to the decisions made by pilots is beneficial as the expertise of pilots can be inherent in the models. With the recent growth in the amount of combat logs accumulated, employing statistical learning methods for the modeling becomes possible. However, the combat logs consist of heterogeneous data that are not only continuous or discrete but also sequence independent or dependent, making it difficult to directly applying the learning methods without modifications. Therefore, in this paper, we present a kernel function named hybrid sequence kernel which addresses the problem by using multiple kernel learning methods. Based on the empirical experiments by using combat logs obtained from a simulator, the proposed kernel showed satisfactory results.

• Journal of Internet Computing and Services
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• v.2 no.5
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• pp.59-70
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• 2001

Recently, in various fields XML has been become a standard for information exchange in internet. Therefore, the researches on data modeling for storing and fetching the XML documents have been made actively. However, existing researches have weak points that they can support neither versioning nor fast fetching of documents while changing documents in dynamic environments. In this paper, we propose four kinds of hybrid data modeling schemes that combine fragmentation model and nonfragmentation model. Our data modeling schemes are suitable to dynamic environments. We also present guidelines that our hybrid data modeling schemes can be used for various applications. We shaw through various experiments that our hybrid schemes partially outperforms the existing modeling schemes in terms of insertion time, storage space and retrieval time.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.5
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• pp.163-169
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• 2003

Modeling of engine-generator system and its control responses are investigated using high performance generator controller. The nonlinear engine is modeled using mean torque production model based on experimental engine map. In case of diesel engine. the amount of injected fief is decided by engine controller depending on the APS(Acceleration Position Sensor) value. An electromechanical generator model contains electrical circuits and moment of inertia. The generator controller maximizes the performance of generator using decoupling and linearized current feedback control. The generator control system consists of 3-phase IGBT inverter and controller board based on 32 bit floating point DSP. Field oriented control algorithm with digital current feedback control at 10kHz sampling enabled high performance torque and speed control of induction machine. Not only the steady state but also the transient state responses can be evaluated through a batch test of the engine generator system. Developed engine and generator modeling and control can be utilized in various applications such as Series Hybrid Electric Vehicle(SHEV), engine-generator for emergency, and other hybrid generation systems.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.6
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• pp.122-129
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• 2000

This paper describes a hybrid dynamic system(HDS) modeling method and result for the drivertrain of a parallel hybrid electric vehicle(PHEV) which consists of a gasoline engine, an electric machine, and a continuous variable transmission (CVT) and proposes a drivetrain control system. The control system has an engine controller, a motor controller, a CVT controller and a supervisory controller for the coordination of all system. The controller keep the speed of engine wheel and the output torque within the optimal operation range based on the experimental data. We also developed a MATLAB/SIMULINK program for the performance simulation of PHEV drivetrain model and controllers and compared the simulation result with the experiment result in the recent literatures.

• Computational Structural Engineering
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• v.9 no.2
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• pp.163-171
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• 1996

A hybrid knowledge model which amalgamates an object-oriented modeling approach and logic programming implementation is presented for structural health monitoring and diagnosis of instrumented structures. Domain knowledge in structural monitoring and diagnosis is formalized and represented in a logic-based object-oriented modeling environment. The model and environment have been implemented and illustrated in the context of a laboratory case study of damage detection in a successively damaged steel structure.

• Structural Engineering and Mechanics
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• v.18 no.1
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• pp.113-124
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• 2004

The information on local stress acting in a bridge is required in many occasions such as fatigue assessment. The analysis by beam elements cannot yield this class of information adequately, while the finite element modeling of an entire long-span bridge by shell elements is impractical. In the present study, the hybrid modeling is tried out: only part of a bridge in which the point of interest is located is discretized by shell elements and the remaining part is modeled by beam elements. By solving a simple box girder problem, the effectiveness of this approach is discussed. This technique is then applied to the Rama IX Bridge for local stress evaluation. The numerical results compare very well with the results of a full-scale static loading test. The present research thus offers a practical yet accurate technique for the stress analysis of a long-span cable-stayed bridge.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2033-2044
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• 2018

This study presents states-of-the art overview of the system level electrostatic discharge (ESD) analysis and testing. After brief description of ESD compliance standards and ESD coupling mechanisms, the study provides an in-depth review and comparison of the various techniques for the system level ESD coupling analysis using time and frequency domain techniques, full wave electromagnetic modeling and hybrid modeling. The methods used for improving system level ESD testing using troubleshooting and determining the root causes of soft failures, the optimization of ESD testing and the countermeasures to mitigate ESD problems are also discussed.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2001.10a
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• pp.97-102
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• 2001

This paper describes an investigation how to carry out model tests of deepwater moorings exceeding the basin depth range. A hybrid mooring model, a combination of mooring lines scaled model and a couple of linear springs, is taken into account as an equivalent substitute of a full depth mooring system. Such an idea is applied to the model test of an OTEC mooring system to be installed in 1000m deep ocean. A 1/25 scaled model test of surface vessel and the upper part of mooring system is performed at ocean engineering basin. Possibility and limitation of the hybrid mooring modeling is discussed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.87-94
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• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.6
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• pp.2979-2984
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• 2013

This paper proposes a modeling and controller design method of Flywheel Energy Storage System(FESS) for solving the unstable operation problem in hybrid generation system with wind turbine and diesel generator applied in island area. FESS is considered as a permanent magnetic synchronous machine connected to flywheel because of its efficiency. The controller of FESS is composed of AC/DC/AC back-to-back converter. The AC/DC converter is designed to charge/discharge according to the frequency variation and the DC/AC converter to operate to keep the DC bus voltage constant. The proposed modeling and controller design method of FESS was applied to hybrid generation system with wind turbine and diesel generator. The unstable operation problem owing to wind variations was solved through simulation results.