• Journal of Korea Society of Industrial Information Systems
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• v.6 no.2
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• pp.16-22
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• 2001

In this paper we mose a new scheme for recognition of deformed letters by extracting feature vectors based on Gibbs distributions which are well suited for representing the spatial continuity. The extracted feature vectors are comprised of 2-D conditional moments which are invariant under translation, rotation, and scale of an image. The Algorithm for pattern recognition of deformed letters contains two parts: the extraction of feature vector and the recognition process. (i) We extract feature vector which consists of an improved 2-D conditional moments on the basis of estimated conditional Gibbs distribution for an image. (ii) In the recognition phase, the minimization of the discrimination cost function for a deformed letters determines the corresponding template pattern. In order to evaluate the performance of the proposed scheme, recognition experiments with a generated document was conducted. on Workstation. Experiment results reveal that the proposed scheme has high recognition rate over 96%.

• KIISE Transactions on Computing Practices
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• v.22 no.4
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• pp.184-188
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• 2016

The cleaning robot is popularly used as a home appliance. The state-of-the-art cleaning robot can clean more efficiently by using information gathered from its sensor, which is difficult for low-price cleaning robots due to limitation in this aspect. In this paper, we suggested a method for the moving pattern of cleaning robot based on grammatical evolution. Optimized program is generated by using moving pattern grammar, which is defined by Backus-Naur form. In addition, conditional probability is used between each of the grammar elements during the program creation process. The proposed method is evaluated by robot simulation in order to verify its performance and further compare it with existing algorithms. The experiment results showed that the proposed method is better than the compared algorithms.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.407-420
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• 2014

In general, the Korean morpheme analysis procedure is divided into two steps. In the first step as an ambiguity generation step, an Eojeol is analyzed into many morpheme sequences as candidates. In the second step, one appropriate candidate is chosen by using contextual information. Hidden Markov Model(HMM) is typically applied in the second step. This paper proposes Sub-word Conditional Probability(SCP) model as an alternate algorithm. SCP uses sub-word information of adjacent eojeol first. If it failed, then SCP use morpheme information restrictively. In the accuracy and speed comparative test, HMM's accuracy is 96.49% and SCP's accuracy is just 0.07% lower. But SCP reduced processing time 53%.

• Journal of KIISE:Software and Applications
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• v.33 no.12
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• pp.1052-1061
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• 2006

This paper presents a method of using virtual examples to improve the classification accuracy for data with nominal attributes. Most of the previous researches on virtual examples focused on data with numeric attributes, and they used domain-specific knowledge to generate useful virtual examples for a particularly targeted learning algorithm. Instead of using domain-specific knowledge, our method samples virtual examples from a naive Bayesian network constructed from the given training set. A sampled example is considered useful if it contributes to the increment of the network's conditional likelihood when added to the training set. A set of useful virtual examples can be collected by repeating this process of sampling followed by evaluation. Experiments have shown that the virtual examples collected this way.can help various learning algorithms to derive classifiers of improved accuracy.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.217-224
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• 2003

The volatility in the financial data is usually measured by conditional variance. Two main streams for gauging conditional variance are stochastic volatility (SV) model and autoregressive type approach (GARCH). This article is conducting comparative study between SV and GARCH through the Korean Stock Prices Index (KOSPI) data. It is seen that SV model is slightly better than GARCH(1,1) in analyzing KOSPI data.

• Journal of the Korean GEO-environmental Society
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• v.17 no.8
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• pp.39-44
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• 2016

Ground deformation around construction site in urban area where typically adjacent structures are located needs to be strictly controlled. Accordingly, it is very important to precisely monitor the ground deformation. Settlement beacon is typically employed to measure the ground deformation, but meanwhile the rapid development in electronic technology enables 3D image scanner to become available for measuring the ground deformation profile in usual construction sites. With respect to the profile measurement, the 3D scanner has an advantage, whereas its accuracy is somewhat limited because it does not measure the displacement directly. In this paper, we developed a conditional merging technique to combine the ground displacement measured from settlement beacon and the profile measured by the 3D scanner. Synthetic ground deformation profile was generated to validate the proposed technique. It is found that the ground deformation measurement error can be reduced significantly via the conditional merging technique.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.287-290
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• 2011

분산 동영상 부호화 시스템에서는 복호기에서 움직임 보상 보간 기법을 이용하여 부가정보를 생성한다. 생성된 부가정보와 원 Wyner-Ziv 프레임간의 차이를 채널 부호로 오류 정정하게 되는데 이때 부호기에서는 복호기에서의 오류 정정을 위하여 패리티 비트인 Wyner-Ziv 비트를 복호기로 보내게 되고 복호기에서는 이 Wyner-Ziv 비트를 이용하여 Wyner-Ziv 프레임을 복원하는데 더 많은 Wyner-Ziv 비트가 필요할 경우 피드백 채널을 통해 Wyner-Ziv 비트를 요청하게 된다. 이때 부호기에서 조건부 엔트로피를 구할 수 있다면 이를 이용하여 Wyner-Ziv 비트 전송량을 제어함으로써 피드백 채널을 제거 할 수 있다. 이를 위해 부호기에서도 부가정보를 알아야하는데 복호기에서 사용하는 부가정보 생성 기법은 복잡도가 높기 때문에 사용할 수 없다. 본 논문에서는 부호기에서 간단한 부가정보를 생성하는 방법을 제안하고 분산 동영상 부호화 시스템에 적용하여 피드백 채널을 제거하였을 때의 성능을 분석하였다.

• Journal of Korea Water Resources Association
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• v.49 no.10
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• pp.863-876
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• 2016

Climate change projections for precipitation are in general provided at daily time step. However, sub-daily precipitation data is necessarily required for hydrologic design and management. Thus, a reliable downscaling model is needed to analyze impact of climate change on water resources. While daily downscaling models have been widely developed and applied in hydrologic and climate community, hourly downscaling models have not been properly developed. In this regard, this study aims at developing a hourly downscaling model that can better reproduce sub-daily extreme rainfalls using conditional copula model. The proposed model was applied to generate extreme rainfalls under the RCP 8.5 scenario for weather stations in South Korea, and design rainfalls were then finally provided. We expected that the future design rainfalls can be used for baseline data to evaluate impact of climate change on water resources.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.1
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• pp.41-50
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• 2010

This paper proposes a mutual identification and session key exchange scheme in secure vehicular communication based on the group signature. In VANETs, security requirements such as authentication, conditional privacy, non-repudiation, and confidentiality are required to satisfy various vehicular applications. However, existing VANET security methods based on the group signature do not support a mutual identification and session key exchange for data confidentiality. The proposed scheme allows only one credential to authenticate ephemeral Diffie-Hellman parameters generated every key exchange session. Our scheme provides a robust key exchange and reduces storage and communication overhead. The proposed scheme also satisfies security requirements for various application services in VANETs.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.183-188
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• 2006

본 논문에서는 베이지안 네트워크를 기반으로 생성하고 평가한 가상예제를 활용하여 범주속성 및 수치속성 데이터에 대한 분류 성능을 향상시키는 방안을 제안한다. 가상예제를 활용하는 종래의 연구들은 주로 수치 속성 데이터를 대상으로 한 반면 본 연구에서는 범주속성 데이터에 대해서도 가상예제를 적용하여 효과를 확인하였다. 그리고 대상 도메인에 특화된 지식을 활용하여 특정 학습 알고리즘의 성능을 향상시키는 것을 목표로 한 기존 연구들과는 달리 본 연구에서는 도메인에 특화된 지식을 활용하는 대신 주어진 훈련 집합을 기반으로 만든 베이지안 네트워크로부터 가상예제를 생성하고, 그 예제가 네트워크의 조건부 우도를 증가시키는데 기여할 경우 유용한 것으로 선별한다. 이러한 생성 및 선별과정을 반복하여 적절한 크기의 가상예제 집합을 수집하여 사용한다. 범주 속성 데이터와 수치 속성을 포함한 데이터를 대상으로 한 실험 결과, 여러 가지 학습 모델의 성능이 향상됨을 확인하였다.