• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.342-346
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• 1991

산업공정현장에서는 독립적으로 운영되고 있는 프로그램식 단위제어기기들에 대한 연계운영을 위하여 실시간 처리 소규모 네트워크가 도입되어 구축운영되고 있다. 특히 제조생산공정에서는 계층화된 분산 구조로서 공정정보처리를 위하여 공장환경에 적절한 네트워크 구축을 MAP등의 표준화된 규격에 의하여 권고되고 구현되어 운영되고 있다. 본 논문에서는 공장 환경에서의 표준규격으로 제안된 Mini MAP을 기준으로 하여 물리 및 데이타 링크계층을 Petrinet 기법을 활용하여 실시간 처리를 위한 개선된 모델을 제안하고 네트워크의 성능측정 및 시뮬레이션을 수행하였다. 또한 이 모델에 의하여 토큰버스 네트워크를 구성하여 전송 서비스시간과 메세지 처리율을 분석하고 Mini MAP 규격과 함께 성능을 해석하였다.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.8
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• pp.432-444
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• 2004

In this paper, we construct the TMO-based object group model on distributed environment, and analyze and evaluate the executability for distributed real-time service of our object group model by developing the distributed real-time application simulator applying the model. The Time-triggered Message-triggered Object(TMO) is a real-time server object having real-time property itself. The TMO-based object group is defined as a set of objects which logically reconfigured the physically distributed one or more TMOs on network by a given distributed application. For supporting group management of the server objects, the TMO-based object group we suggested provides the functions which register and withdraw the solver objects as a group member to an arbitrary object group, and also provides the functions which insert and delete the access rights of server objects from clients. Also, our model was designed and implemented to support the appropriate object selection and dynamic binding service for a single TMO as well as the duplicated TMOs, and to support the real-time scheduling service for the clients which are requesting the service. Finally, we developed the Defence System against Invading Enemy Planes(DSIEP) simulator as a practical example of distributed real-time application by applying our model, and evaluated the adaptability of distributed service strategies for the group components and the executability of real-time services that the TMO-based object group model provides.

• The KIPS Transactions:PartD
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• v.18D no.6
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• pp.443-450
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• 2011

Model-based Component development methodologies consider the whole system as an component and develop physical components through recursive decomposition and refinements of components in a top-down manner. We developed a model translator that can be used to formally verify interaction consistency among components, especially the interaction behavior between before- and after- refinements of components. This translator can be used to identify potential problems in the refinement process so that problems can be addressed from the early stage of development. This paper introduces our translation approach and the organization of the translator. The translator has been applied to two case studies to show its usefulness.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.263-272
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• 2016

Modular product design has contributed to flexible product modification and development, production lead time reduction, and increasing product diversity. Modular product design aims to develop a product architecture that is composed of detachable modules. These modules are constructed by maximizing the similarity of components based on physical and functional interaction analysis among components. Accordingly, a systematic procedure for clustering the components, which is a main activity in modular product design, is proposed in this paper. The first phase in this procedure is to build a component-to-component correlation matrix by analyzing physical and functional interaction relations among the components. In the second phase, network flow modeling is applied to find clusters of components, maximizing their correlations. In the last phase, a network flow model formulated with linear programming is solved to find the clusters and to make them modular. Finally, the proposed procedure in this research and its application are illustrated with an example of modularization for a vacuum cleaner.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.211-228
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• 2023

Ground-penetrating radar (GPR) surveys are commonly used to monitor embankments, which is a nondestructive geophysical method. The results of GPR surveys can be complex, depending on the situation, and data processing and interpretation are subject to expert experiences, potentially resulting in false detection. Additionally, this process is time-intensive. Consequently, various studies have been undertaken to detect cavities in GPR survey data using deep learning methods. Deep-learning-based approaches require abundant data for training, but GPR field survey data are often scarce due to cost and other factors constaining field studies. Therefore, in this study, a deep- learning-based model was developed for embankment GPR survey cavity detection using data augmentation strategies. A dataset was constructed by collecting survey data over several years from the same embankment. A you look only once (YOLO) model, commonly used in computer vision for object detection, was employed for this purpose. By comparing and analyzing various strategies, the optimal data augmentation approach was determined. After initial model development, a stepwise process was employed, including box clustering, transfer learning, self-ensemble, and model ensemble techniques, to enhance the final model performance. The model performance was evaluated, with the results demonstrating its effectiveness in detecting cavities in embankment GPR survey data.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.11a
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• pp.91-94
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• 2009

인공감정에 관련된 기존의 연구는 대부분 감정의 인식과 물리적 표현에 중점 되어 연구되었다. 하지만 감정은 성격에 따라 달리 표출되고, 시간에 따라 변화 양상을 갖는다. 또한 새로운 감정자극을 받기 이 전의 감정상태에 따라서 표출 될 감정은 달라진다. 본 논문은 감정을 성격, 시간, 감정간의 관계에 따라 관리하여 현재 표출될 감정을 시각화 해주는 인공감정을 제안한다. 감정을 시각화하기 위해서 본 논문의 인공감정은 감정그래프와 감정장을 갖는다. 감정그래프는 특정 감정을 성격과 시간에 따라 표현하는 2차원 형태의 그래프 이다. 감정장은 감정그래프에서 표현된 서로 다른 종류의 감정들을 시간과 감정간의 관계에 따라 시각화 해주는 3차원 형태의 모델이다. 제안된 인공감정을 통해 감정을 시각화해 보기 위해, 감정의 인식과 물리적 표현을 텍스트 기반으로 간소화시킨 시뮬레이터에 적용했다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.269-272
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• 2021

본 논문에서는 변수간의 다양한 관계 분석 또는 예측 모델에 많이 적용되는 베이지안 네트워크 모델에 대한 양자 회로를 설계하고, 설계한 양자 회로를 '모여봐요! 동물의 숲' 게임에서 진행되는 무 거래에 대한 무값을 예측하는 시나리오에 적용했다. 제안한 양자 가격 예측 모델은 양자 회로로 표현했으며 IBM 의 Qiskit 을 이용해 구현하였다. 구현한 회로는 시뮬레이션 백엔드 뿐만아니라 IBM 에서 클라우드로 제공하는 실제 양자 컴퓨터 2 종의 백엔드에 실행하였고, 실행 결과와 설계한 회로를 바탕으로 제안한 모델의 성능을 분석하여 제안 모델의 효용성을 보였다.

• Journal of IKEEE
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• v.20 no.1
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• pp.94-102
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• 2016

This paper proposes a new prediction method to reduce times and labor of repetitive multi-physics simulation. To achieve exact results from the whole simulation processes, complex modeling and huge amounts of time are required. Current multi-physics analysis focuses on the simulation method itself and the simulation environment to reduce times and labor. However this paper proposes an alternative way to reduce simulation times and labor by exploiting machine learning algorithm trained with data set from simulation results. Through comparing each machine learning algorithm, Gaussian Process Regression showed the best performance with under 100 training data and how similar results can be achieved through machine-learning without a complex simulation process. Given trained machine learning algorithm, it's possible to predict the result after changing some features of the simulation model just in a few second. This new method will be helpful to effectively reduce simulation times and labor because it can predict the results before more simulation.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2003.05a
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• pp.87-91
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• 2003

It has well known that MUSIC and ESPRIT algorithms estimate angle of arrival(AOA) with high resolution by eigenvalue decomposition of the covariance matrix which were obtained from the array antennas. However, the disadvantage of MUSIC and ESPRIT is that they are computationally ineffective, and then they are difficult to implement in real time. The other problem of MUSIC and ESRPIT is to require calibrated antennas with uniform features, and are sensitive to the manufacturing facult and other physical uncertainties. To overcome these disadvantages, several method using neural model have been study. For multiple signals, those require huge training data prior to AOA estimation. This paper proposes the algorithm for AOA estimation by interconnected hopfield neural model. Computer simulations show the validity of the proposed algorithm. The proposed method does not require huge training procedure and only assigns interconnected coefficients to the neural network prior to AOA estimation.

• Journal of Navigation and Port Research
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• v.27 no.4
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• pp.465-469
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• 2003

It has well known that MUSIC and ESPRIT algorithms estimate angle of arrival(AOA) with high resolution by eigenvalue decomposition of the covariance matrix which were obtained from the array antennas, However, the disadvantage of MUSIC and ESPRIT is that they are computationally ineffective, and then they are difficult to implement in real time. the other problem of MUSIC and ESPRIT is to require calibrated antennas with uniform features, and are sensitive ti the manufacturing fault and other physical uncertainties. To overcome these disadvantages, several method using neural model have been study. For multiple signals, those methods require huge training data prior to AOA estimation. This paper proposes the algorithm for AOA estimation by interconnected Hopfield neural model. Computer simulations show the validity of the proposed algorithm. It follows that the proposed method yields better AOA estimates than MUSIC. Moreover, out method does not require huge training procedure and only assigns interconnected coefficients to the neural network prior to AOA estimation.