• Proceedings of the Korea Multimedia Society Conference
• /
• 2002.11b
• /
• pp.203-206
• /
• 2002

최근 웹을 기반으로 한 문서의 전자화가 이루어지면서 기존의 전통적인 펜기반 교정 시스템 또한 온라인 상의 전자 문서 환경에 맞게 변화하고 있다. 이러한 펜기반 입력 기법을 사용하는 교정 시스템에서는 일반 문서와 달리 웹 문서의 구조정보를 고려한 편집이 지원되어야 하며 또한 교정 부호와 텍스트 간의 정확한 영역 인식이 이루어져야 한다. 본 연구에서는 온라인 교정 시스템 모델링을 통하여 온라인 환경에 적합한 교정 부호를 정의하고, 교정 대상 텍스트 영역을 편집 가능한 단위로 구분하여 효율적인 편집 연산이 이루어 질 수 있도록 하였다. 또한 웹 기반의 구조문서(HTML/XML) 편집 환경을 고려하여 편집으로 인한 문서의 구조 정보 변경을 지원하기 위하여 텍스트를 비구조 및 구조정보 텍스트로 분류하여 정의하였다. 본 연구에서는 이러한 모델에 기반하여 교정 부호의 특성에 따른 가변적인 편집 텍스트 영역 인식 규칙 모델을 정의하여 교정 부호와 편집 텍스트 영역간의 모호성을 최소화 하고, 편집으로 인한 문서의 구조 정보 변경을 지원하는 시스템을 구현하였다. 결과적으로 온라인 웹 문서 환경에서 펜기반의 모호한 교정 부호의 입력을 인지적인 관점에서 해석하여 보다 정확한 교정 작업 수행을 지원하도록 하였다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2002.10d
• /
• pp.178-180
• /
• 2002

최근 종이 문서의 전자화가 이루어지면서 기존의 전통적인 펜기반 교정 시스템 또한 온라인 상의 전자 문서 환경에 맞게 구축되고 있다. 이러한 펜기반 입력 기법을 사용하는 교정 시스템에서는 교정 부호와 텍스트 간의 정확한 영역 인식이 중요하며 이를 위해 교정 부호의 특성과 텍스트 영역의 분석이 필요하다. 본 연구에서는 온라인 교정 시스템 모델링을 통하여 온라인 환경에 적합한 교정 부호를 정의하고, 교정 대상 텍스트 영역을 편집 가능한 단위로 구분하여 효율적인 편집 연산이 이루어 질 수 있도록 하였다. 또한 웹 기반의 구조문서(HTML/XML) 편집 환경을 고려하여 편집으로 인한 문서의 구조 정보 변경을 지원하기 위하여 텍스트를 비구조 및 구조정보 텍스트로 분류하여 정의하였다. 본 연구에서는 이러한 모델에 기반하여 교정 부호의 특성에 따른 가변적인 편집 텍스트 영역 인식 규칙 모델을 정의하여 교정부호와 편집 텍스트 영역간의 모호성을 최소화 하고, 편집으로 인한 문서의 구조 정보 변경을 지원하는 시스템을 구현하였다. 결과적으로 온라인 웹 문서 환경에서 펜기반의 모호한 교정 부호의 입력을 인지적인 관점에서 해석하여 보다 정확한 교정 작업 수행을 지원하도록 하였다.

• Proceedings of the Korea Institute of Convergence Signal Processing
• /
• 2000.08a
• /
• pp.321-324
• /
• 2000

채널만을 고려한 부호화 방법보다 소스의 특성을 이용해 채널 부호화를 수행하는 것이 더 효율적이라는 사실은 일반적으로 잘 알려져 있다. 따라서 소스의 특성을 이용한 채널 부호화 방법은 중요한 연구과제이며 이에 대한 많은 연구가 진행되었다. 이러한 연구 결과로써 IP-SCCC(Interleaved puncturing serially concatenated convolutional codes)는 터보코드와 마찬가지로 반복복호가 가능하고 가변 부호율의 채널부호화를 수행할 수 있는 특징을 갖는다. 본 논문에서는 영상에 대한 웨이블렛 변환의 다른 스케일간의 자기 유사성을 이용한 SPIHT(set partitioning in hierarchical tree) 알고리즘으로 부호화된 정지영상에 대해 IP-SCCC를 사용한 UEP(unequal error protection) 채널부호화 방법을 제시한다. 또한 레일리 페이딩과 부가 백색 가우시안 잡음이 동시에 존재하는 채널을 모델링해서 실험을 수행하고 제안 방법의 성능을 검증한다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.2
• /
• pp.166-172
• /
• 2014

Image classification is an important problem in computer vision. However, it is a very challenging problem due to the variability, ambiguity and scale change that exists in images. In this paper, we propose a method of a hypergraph based modeling can consider the higher-order relationships of semantic attributes of a scene image and apply it to a scene image classification. In order to generate the hypergraph optimized for specific scene category, we propose a novel search method based on a probabilistic subspace method and also propose a method to aggregate the expression values of the member semantic attributes that belongs to the searched subsets based on a linear transformation method via likelihood based estimation. To verify the superiority of the proposed method, we showed that the discrimination power of the feature vector generated by the proposed method is better than existing methods through experiments. And also, in a scene classification experiment, the proposed method shows a competitive classification performance compared with the conventional methods.

• Journal of IKEEE
• /
• v.26 no.4
• /
• pp.602-613
• /
• 2022

This paper proposes an algorithm for correcting indirect situations resulting from the wheelchair moving the stairs with wheel-type caterpillar and seat position variable. For analyzing the Yawing movement model, the change of Yaw value was estimated using Roll, Pitch, and Yaw in the driving environment, and it was used as a control variable and the information of the wheel drive controller. The verification confirmed the correction of about 10° of Yawing movement within about 7 seconds. It was confirmed that the angular velocity was reduced by 47.5% in seat position change.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.7
• /
• pp.817-822
• /
• 2005

Recently tile study that exploits visual information for tile services of robot in indoor environments is active. Conventional image processing approaches are based on the pre-defined geometric models, so their performances are likely to decrease when they are applied to the uncertain and dynamic environments. For this, diverse researches to manage the uncertainty based on the knowledge for improving image recognition performance have been doing. In this paper we propose a Bayesian network modeling method for predicting the existence of target objects when they are occluded by other ones for improving the object detection performance of the service robots. The proposed method makes object relationship, so that it allows to predict the target object through observed ones. For this, we define the design method for small size Bayesian networks (primitive Bayesian netqork), and allow to integrate them following to the situations. The experiments are performed for verifying the performance of constructed model, and they shows $82.8\%$ of accuracy in 5 places.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.6A
• /
• pp.473-479
• /
• 2012

In this paper, blind equalization technique using Gaussian two-cluster model is proposed. The proposed approach, by modeling the received M-QAM signals as Gaussian distributed two-cluster, minimizes the computational complexity and enhances the reliability of the signal estimates. In addition, by using a nonlinear estimator with variable parameters to estimate the transmitted signal, and by selectively applying the reduced constellation and the original constellation when estimating the signals, the reliability of the signal estimation was further improved. As a result, the proposed approach has improved the performance while reducing the complexity of the equalizer. Through computer simulations for blind equalization of higher-order signals of 64-QAM, it was confirmed that the proposed method showed better performance than traditional approaches.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.44 no.4 s.316
• /
• pp.21-26
• /
• 2007

Output voltage waves of a DC/AC inverter system are likely to be distorted if variable loads e.g. motors or rectifiers exist in the output terminal. This paper designs a disturbance observer-based PI-controller for a single-phase inverter system that is robust against load changes. In this Paper, we regard the output voltage changes due to various loads as disturbances of the control system. Then we design a disturbance observer for estimation of the disturbances caused by the load current and any other error sources (such as parameter uncertainties and model mismatches etc.). In order to test the performance of the proposed control law, simulation studies are carried out for a single-phase inverter system using SimPowerSystem of Matlab Simuink. Compared to a simple PI-control, the disturbance observer-based controller shows enhanced performance in transient responses for step load changes.

• Journal of Intelligence and Information Systems
• /
• v.14 no.3
• /
• pp.25-39
• /
• 2008

The objective of this study is to forecast the operational continuous ability using Artificial Neural Networks in battalion defensive operation for the commander decision making support. The forecasting of the combat result is one of the most complex issue in military science. However, it is difficult to formulate a mathematical model to evaluate the combat power of a battalion in defensive operation since there are so many parameters and high temporal and spatial variability among variables. So in this study, we used company combat power level data in Battalion Command in Battle Training as input data and used Feed-Forward Multilayer Perceptrons(MLP) and General Regression Neural Network (GRNN) to evaluate operational continuous ability. The results show 82.62%, 85.48% of forecasting ability in spite of non-linear interactions among variables. We think that GRNN is a suitable technique for real-time commander's decision making and evaluation of the commitment priority of troops in reserve.

• The KIPS Transactions:PartB
• /
• v.14B no.3 s.113
• /
• pp.199-214
• /
• 2007

Recently, there have been a lot of works trying to realize automated composition of semantic web services though application of AI planning techniques. The traditional AI planning techniques, however, have some limitations: it is not easy to represent a web service process with complex control constructs as an action or a plan; it is hardly possible to consider enough the rich information contained in domain ontologies during the planning process; it is impossible to model directly the data flow from the outputs of a web service to the inputs of another web service; it is difficult to predict and deal with uncertainty and dynamics of the environment because the plan generation phase is supposed to be separated from the plan execution phase. In order to overcome some of these limitations, this paper suggests a reactive planning approach to automated composition of semantic web services. Through some experiments using several e-commerce web services, we found that the reactive planning is an effective way to realize automated composition of semantic web services.