• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.3
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• pp.67-73
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• 2006

In this paper, a reconfigurable model following flight control method is proposed based on direct adaptive scheme using parameter estimation. Adaptive control scheme updates the control gains to make the system output follow the reference output even when fault occurs. By adopting the frequency domain parameter estimation method, system changes by the fault can be estimated. Recursive Fourier transformation is used for system identification. Using recursive Fourier transform, the proposed adaptive control algorithm guarantees the system stability and improves the system characteristics. To evaluate the performance of proposed control method, numerical simulations are performed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.7
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• pp.899-903
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• 2013

A self-bearing is an electric machine that achieves both rotational actuation and magnetic levitation using a single magnetic structure. To be able to stably levitate the rotor in a self-bearing, one needs to have an inverse of the force-current model. However, the force-current model in a self-bearing motor is typically not square. Furthermore, the elements of the matrix vary with respect to the rotational angle, resulting in singularities of the pseudo-inverse at various angles. In this paper, we propose a new force-current model that eliminates the singularities by adding a constraint in coil currents. This constraint eliminates the flux density in the stator core so that the saturation problem in the previous study is avoided. By implementing this force-current model, we are able to implement a levitation controller for a toroidally-wound self-bearing BLDC motor. The model inversion and levitation are validated experimentally.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.29-34
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• 2019

If it takes much time to calculate the fitness of the solution in genetic algorithms, it is essential to create a metamodel. Much research has been completed to improve the performance of metamodels. In this study, we tried to get a better performance of metamotel using discrete Walsh transform in discrete domain. We transforms the basis of the solution and creates a metamodel using the transformed solution. We experimented with NK-landscape, a representative function of the pseudo-boolean function, and provided empirical evidence on the performance of the proposed model. When we performed the genetic algorithm using the proposed model, we confirmed that the genetic algorithm found a better solution. In particular, our metamodel showed better performance than that using the radial basis function network that modified the similarity function for the discrete domain.

• Journal of KIISE
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• v.42 no.9
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• pp.1147-1161
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• 2015

The ISO/IEC 11179 Metadata registry (MDR) is an international standard that was developed to register and share metadata. ISO/IEC 11179 represents an MDR as a metamodel that is an object model. However, it is difficult to develop an MDR based on ISO/IEC 11179 because the standard has no clear criteria to transform the metamodel into a database. In this paper, we suggest the design of an MDR data model that is based on object-relational transformation methodology (ORTM) for the MDR implementation. Hence, we classify the transformation methods of ORTM according to the corresponding relationships. After classification, we propose modeling rules by defining the standard use of the transformation. This paper builds the relational database tables as an implementation result of an MDR data model. Through experiments and evaluation, we verify the proposed modeling rules and evaluate the suitability of the created table structures. As the result, the proposed method shows that the table structures preserve classes and relationships of the standard metamodel well.

• Smart Media Journal
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• v.11 no.3
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• pp.26-30
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• 2022

Spiking neural network is a neural network that applies the working principle of real brain neurons. Due to the biological mechanism of neurons, it consumes less power for training and reasoning than conventional neural networks. Recently, as deep learning models become huge and operating costs increase exponentially, the spiking neural network is attracting attention as a third-generation neural network that connects convolution neural networks and recurrent neural networks, and related research is being actively conducted. However, in order to apply the spiking neural network model to the industry, a lot of research still needs to be done, and the problem of model retraining to apply a new model must also be solved. In this paper, we propose a method to minimize the cost of model retraining by extracting the weights of the existing trained deep learning model and converting them into the weights of the spiking neural network model. In addition, it was found that weight conversion worked correctly by comparing the results of inference using the converted weights with the results of the existing model.

• Journal of Advanced Navigation Technology
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• v.22 no.4
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• pp.271-278
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• 2018

For ionospheric correction of low earth orbiter (LEO) satellites using single frequency global navigation satellite system (GNSS) receiver, ionospheric scale factor should be applied to the ground-based ionosphere model. The ionospheric scale factor can be calculated by using a NeQuick model, which provides a three-dimensional ionospheric distribution. In this study, the ionospheric scale factor is calculated by using NeQuick G model during 2015, and it is compared with the scale factor computed from the combination of LEO satellite measurements and international GNSS service (IGS) global ionosphere map (GIM). The accuracy of the ionospheric delay calculated by the NeQuick G model and IGS GIM with NeQuick G scale factor is analyzed. In addition, ionospheric delay errors calculated by the NeQuick G model and IGS GIM with the NeQuick G scale factor are compared. The ionospheric delay error variations along to latitude and solar activity are also analyzed. The mean ionospheric scale factor from the NeQuick G model is 0.269 in 2015. The ionospheric delay error of IGS GIM with NeQuick G scale factor is 23.7% less than that of NeQuick G model.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.484-486
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• 1999

객체지향 방법으로 시스템을 개발할 때 분석단계의 객체모델을 객체지향 스키마로 변환을 용이하도록 하기 위하여 분석단계의 객체모델을 정제할 필요가 있다. 따라서 본 논문에서는 분석단계의 객체모델을 정제하여 다단계 데이터베이스 어플리케이션으로 설계하는 방법을 제시한다. 또한 자료의 잘못된 유출이나 수정을 예방하고 모호성을 제거하기 위하여 보안을 적용한다. 보안을 적용한 분석단계의 객체모델을 다단계 데이터베이스 어플리케이션으로 설계할 때 이 보안이 다단계 데이터베이스 어플리케이션의 설계에서 적용되는 방법을 제시한다.

• Journal of KIISE:Databases
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• v.33 no.5
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• pp.526-537
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• 2006

The most representative approach for efficient storing of XML data is to store XML data in relational databases. The merit of this approach is that it can easily accept the realistic status that most data are still stored in relational databases. This approach needs to convert XML data into relational data or relational data into XML data. The most important issue in the translation is to reflect structural and semantic relations of RDB to XML schema model exactly. Many studies have been done to resolve the issue, but those methods have several problems: Not cover structural semantics or just support explicit referential integrity relations. In this paper, we propose an algorithm for extracting implicit referential integrities automatically. We also design and implement the suggested algorithm, and execute comparative evaluations using translated XML documents. The proposed algorithm provides several good points such as improving semantic information extraction and conversion, securing sufficient referential integrity of the target databases, and so on. By using the suggested algorithm, we can guarantee not only explicit referential integrities but also implicit referential integrities of the initial relational schema model completely. That is, we can create more exact XML schema model through the suggested algorithm.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.467-471
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• 2008

With active research on Web ontology, various storages and query languages have been developed to store Web Ontology. As SPARQL usage increases and most of storages are based on relational data base, the necessity of SPARQL-to-SQL translation algorithm development becomes issued. Even though several translation algorithms have been proposed, there still remain the following problems. They do not support fully SPARQL clauses and they are dependent on a specific storage model. This paper proposes a new model to use a specific translation algorithm independently on storages.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.81-84
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• 2000

본 연구에서는 1000Hz 미만의 저주파 대역에서 음향방사 특성을 향상시키기 위한 새로운 형태의 음향 변환기를 연구하였다. 이 음향 변환기는 세 개의 혼을 직렬의 조합으로 연결한 형태를 띄고 있고 설계상에 필요한 계산을 위해 혼 방정식에 근거해 간단한 모델을 개발했다. 음향 발생을 위한 변환기로써 원형판에 얇은 압전체를 붙인 작동기를 사용하였다. 제안된 음향 변환기는 개발된 모델을 통해 설계되었고 실험을 위해 제작되었다. 제안된 음향 변환기의 성능은 실험과 수치해석을 통해 검증하였다 음향 해석 프로그램인 SYSNOISE를 이용한 수치해석과 실험에 근거한 결과는 제안된 음향변환기가 600Hz 미만의 주파수 대역에서 5-30dB의 음압을 발생시키는 것을 보여준다.