• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.414-417
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• 1997

We propose a decentralized $H_{\infty}$ state estimation method in the multisensor state estimation problem. The proposed method bounds the maximum energy gain from unknown external disturbances to the estimation errors in the suboptimal case. And we formulate the decentralized state estimation method in the general case of different global and local models using alternative gain equation of the $H_{\infty}$ state estimator which can calculate global state estimates from the the linear combination of local state estimates. In addition, the proposed update equation between global and local Riccati solutions can reduce unnecessary calculation burden efficiently.

• 한국정밀공학회지
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• 제13권8호
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• pp.60-71
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• 1996

Mostof existing CAD systems do not provide the advanced function for systematic checking of design and drafting errors in mechanical drawings. We have reported a computer aided drawing check system to single plane projection drawings made by a CAD system. This paper describes a checking method of dimensioning errors in mechanical drawings. The checking items are deficiency and redundancy of dimensions, input-errors in dimension figures and symbols, etc. Checking for deficiency and redundancy of global dimensions has been performed applying Graph Theory. This system has been applied to several examples and we have confirmed the feasibility of this checking method.

• 대한전기학회논문지
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• 제37권3호
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• pp.141-145
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• 1988

This paper reports a new and simple posteriori error estimate method for adaptive finite element mesh genration especially for the magnetic field problems. To estimate local errors, we consider the interelement boundary conditions. Elements which violate much the conditions are considered to have great errors. Magnetic flux density errors are considered as a basis for refinement. This estimator is tested on two dimensional proplems with singular points. The estimated errors are always under estimated but in same order as exact errors, and this algorithm is much simpler and more convenient than other methods. The adaptive mesh gives much better rate of convergence in global errors than the uniform mesh.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(4)
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• pp.101-104
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• 2001

This paper proposes a unique shot boundary detection algorithm for the video indexing and/or browsing. Conventional methods based on the frame differences and the histogram differences are improved. Instead of using absolute frame differences, block by block based relative frame differences are employed. Frame adaptive thresholding values are also employed for the better detection. for the cases that the frame differences are not enough to detect the shot boundary, histogram differences are selectively applied. Experimental results show that the proposed algorithm reduces both the “false positive” errors and the “false negative” errors especially for the videos of dynamic local and/or global motions

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권3호
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• pp.209-215
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• 2014

Sequence tagging is the task of predicting frame-wise labels for a given input sequence and has important applications to diverse domains. Conventional methods such as maximum likelihood (ML) learning matches global features in empirical and model distributions, rather than local features, which directly translates into frame-wise prediction errors. Recent probabilistic sequence models such as conditional random fields (CRFs) have achieved great success in a variety of situations. In this paper, we introduce a novel discriminative CRF learning algorithm to minimize local feature mismatches. Unlike overall data fitting originating from global feature matching in ML learning, our approach reduces the total error over all frames in a sequence. We also provide an efficient gradient-based learning method via gradient forward-backward recursion, which requires the same computational complexity as ML learning. For several real-world sequence tagging problems, we empirically demonstrate that the proposed learning algorithm achieves significantly more accurate prediction performance than standard estimators.

• Journal of Theoretical and Applied Mechanics
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• 제3권1호
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• pp.16-33
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• 2002

A concept of hierarchical modeling, the newest modeling technology. has been introduced early In 1990. This nu technology has a goat potential to advance the capabilities of current computational mechanics. A first step to Implement this concept is to construct hierarchical models, a family of mathematical models which are sequentially connected by a key parameter of the problem under consideration and have different levels in modeling accuracy, and to investigate characteristics In their numerical simulation aspects. Among representative model problems to explore this concept are elastic structures such as beam-, arch-. plate- and shell-like structures because the mechanical behavior through the thickness can be approximated with sequential accuracy by varying the order of thickness polynomials in the displacement or stress fields. But, in the numerical analysis of hierarchical models, two kinds of errors prevail: the modeling error and the numerical approximation errors. To ensure numerical simulation quality, an accurate estimation of these two errors Is definitely essential. Here, a local a posteriori error estimator for elastic structures with thin domain such as plate- and shell-like structures Is derived using element residuals and flux balancing technique. This method guarantees upper bounds for the global error, and also provides accurate local error Indicators for two types of errors, in the energy norm. Comparing to the classical error estimators using flux averaging technique, this shows considerably reliable and accurate effectivity indices. To illustrate the theoretical results and to verify the validity of the proposed error estimator, representative numerical examples are provided.

• 제어로봇시스템학회논문지
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• 제17권6호
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• pp.610-618
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• 2011

For improved localization around the indoor/outdoor transit area of buildings, this paper proposes an efficient algorithm combining the measurements from the WLAN (Wireless Local Area Network) and the GPS (Global Positioning System) for. The proposed hybrid localization algorithm considers both multipath errors and NLOS (Non-Line-of-Sight) errors, which occur in most wireless localization systems. To detect and isolate multipath errors occurring in GPS measurements, the propose algorithm utilizes conventional multipath test statistics. To convert WLAN signal strength measurements to range estimates in the presence of NLOS errors, a simple and effective calibration algorithm is designed to compute conversion parameters. By selecting and combining the reliable GPS and WLAN measurements, the proposed hybrid localization algorithm provides more accurate location estimates. An experiment result demonstrates the performance of the proposed algorithm.

• 정보관리연구
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• 제38권3호
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• pp.59-80
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• 2007

본 연구에서는 국내 11개 국외 5개의 법학도서관 웹사이트 내비게이션 시스템을 전역, 지역, 문맥, 지원 내비게이션으로 나누어 분석하였다. 그 결과 전역 내비게이션은 일관된 내비게이션을 제공해야 하며, 핵심지역과 기능에 바로 접근할 수 있어야 하는 것으로 나타났다. 지역 내비게이션의 Quick Link는 스크롤바의 이동이 가능하도록 제공할 필요가 있었다. 문맥 내비게이션의 메뉴의 수와 링크 유형은 가급적 적은 수를 제공해야 하는 것으로 나타났다. 마지막으로 지원 내비게이션의 사이트맵과 사이트색인은 웹사이트의 콘텐트의 양에 따라 알맞게 제공해야 하며, 누락된 링크가 없어야 하며 제공내용이 충실해야 하는 것으로 나타났다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.395-398
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• 2005

The acquisition of a panoramic image has been popular in recent years due to its large number of applications. In this paper, we deal with generation of a full panoramic mosaic. When it mosaicing consecutive frames, a simple alignment method may lead to local and global alignment errors. These errors can make a full-view panoramic mosaic to be cracked. In order to solve this problem, we propose the bidirectional alignment with multi-anchor (BAMA) algorithm with a basic local alignment formula. Experimental results show that we can create full panoramic mosaics stably through the proposed algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.281-285
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• 2005

Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors. The global search of evolutionary computing in solution space can settle the local optima problems of learning models. In this research, combining co-evolving algorithm into statistical learning theory, we propose an co-evolutionary computing for statistical learning theory for overcoming local optima problems of statistical learning theory. We apply proposed model to classification and prediction problems of the learning. In the experimental results, we verify the improved performance of our model using the data sets from UCI machine learning repository and KDD Cup 2000.