• Korean Journal of Ecology and Environment
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• v.40 no.2
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• pp.193-200
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• 2007

The objective of this study was to estimate uncertainty of online TOC analyzer in water quality monitoring system. A procedure for the estimation of measurement uncertainty of total organic compounds (TOC) based on the ISO approach is presented. It is based on a mathematical model that involves 4 input parameters (standardization, sensitivity, solute effect and representativeness). In this study, a major problem in estimating the uncertainty of online TOC analyzer was the solute effect. It was strongly depends on organic materials. So homogeneity of the sample is the most important consideration. Modified concentration and combined standard uncertainty was $4.71{\pm}0.36$ mg $L^{-1}$ by model modified in this study.

• International Journal of Knowledge Content Development & Technology
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• v.5 no.1
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• pp.49-68
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• 2015

Given a query (a health question), retrieval of relevant frequently asked questions (FAQs) is essential as the FAQs provide both reliable and readable information to healthcare consumers. The retrieval requires the estimation of the semantic similarity between the query and each FAQ. The similarity estimation is challenging as semantic structures of Chinese healthcare FAQs are quite different from those of the FAQs in other domains. In this paper, we propose a conceptual model for Chinese healthcare FAQs, and based on the conceptual model, present a technique ECA that estimates conceptual similarities between FAQs. Empirical evaluation shows that ECA can help various kinds of retrievers to rank relevant FAQs significantly higher. We also make ECA online to provide services for FAQ retrievers.

• Proceedings of the KIPE Conference
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• 2017.11a
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• pp.77-78
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• 2017

본 논문에서는 온라인 업데이트 상황에서의 배터리 용량 감소상태를 추정하기 위해 사용되는 전압변동곡선(Differential Voltage; DV)을 실시간으로 추정하는 알고리즘을 개발한다. 동적 전류 특성으로 인한 오차의 최소화를 위해 내부 임피던스 성분에 의한 전압 변동을 고려하는 방법론을 제안하며, 이는 필터링 기법을 통한 파라미터 추정 과정을 포함한다. 본 연구의 타당성은 단전지 전류 프로파일 실험 결과를 기반으로 시뮬레이션을 통하여 검증한다.

• The Journal of Korea Robotics Society
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• v.9 no.4
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• pp.197-205
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• 2014

As computer vision algorithms are developed on a continuous basis, the visual information from vision sensors has been widely used in the context of simultaneous localization and mapping (SLAM), called visual SLAM, which utilizes relative motion information between images. This research addresses a visual SLAM framework for online localization and mapping in an unstructured seabed environment that can be applied to a low-cost unmanned underwater vehicle equipped with a single monocular camera as a major measurement sensor. Typically, an image motion model with a predefined dimensionality can be corrupted by errors due to the violation of the model assumptions, which may lead to performance degradation of the visual SLAM estimation. To deal with the erroneous image motion model, this study employs a local bundle optimization (LBO) scheme when a closed loop is detected. The results of comparison between visual SLAM estimation with LBO and the other case are presented to validate the effectiveness of the proposed methodology.

• ETRI Journal
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• v.31 no.3
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• pp.282-291
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• 2009

An online mean-shift object tracking algorithm, which consists of a learning stage and an estimation stage, is proposed in this work. The learning stage selects the features for tracking, and the estimation stage composes a likelihood image and applies the mean shift algorithm to it to track an object. The tracking performance depends on the quality of the likelihood image. We propose two schemes to generate and integrate likelihood images: one based on the discrete AdaBoost (DAB) and the other based on the real AdaBoost (RAB). The DAB scheme uses tuned feature values, whereas RAB estimates class probabilities, to select the features and generate the likelihood images. Experiment results show that the proposed algorithm provides more accurate and reliable tracking results than the conventional mean shift tracking algorithms.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.779-784
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• 2008

This paper presents Kalman filter based system modeling algorithm for autonomous robot systems. State of the robot system is measured using embedded sensor systems and then carried to a host computer via ubiquitous sensor network (USN). We settle a linear state-space motion equation for unknown robot system dynamics and modify a popular Kalman filter algorithm in deriving suitable parameter estimation mechanism. To represent time-delay nature due to network media in system modeling, we construct an augmented state-space model which is mainly composed of original state and estimated parameter vectors. We conduct real-time experiment to test our proposed estimation algorithm where speed state of the constructed robot is used as system observation.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.3
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• pp.289-297
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• 2014

This paper proposed a novel high performance sensorless control scheme for IPMSM based on an extended nonlinear state observer. The gain-matrix of the observer has been derived by using state linearization method. Steady state errors in estimated rotor position and speed due to parameter inaccuracy have been analyzed, and an equivalent flux error is defined to represent the overall effect of parameter errors contributing to the wrong convergence of the estimated rotor speed as well as rotor position. Then, an online compensation strategy was proposed to limit the estimation errors in rotor position and speed. The effectiveness of the extended nonlinear state observer is validated through simulation and experimental test.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1503-1504
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• 2008

This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.123-124
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• 2010

본 논문은 고주파 유도가열 장치의 실시간 부하 예측 기법을 제안한다. 인버터 출력전압과 콘덴서 양단전압을 센싱하여 adaptive parameter estimation 기법을 이용하여 피가열체인 부하의 등가저항과 인덕턴스를 구한다. 제안된 방법을 이용하여 부하 발열량과 콘덴서 뱅크의 Q factor를 실시간 예측할 수 있다. 콘덴서 뱅크의 Q factor를 통해 부하 부담률을 알 수 있으므로 뱅크의 파손 등의 사고를 미연에 방지 할 수 있게 한다. 본 논문에서 제안한 알고리즘의 타당성을 시뮬레이션을 통해 확인하였고 모의실험장치에 적용하여 실험을 통해 검증하였다.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.742-747
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• 2006

This paper describes online stator-resistance estimation of a linear induction motor(LIM) with cage-type secondary using direct thrust control(DTC), where the resistance value is derived from stator-winding temperature estimation using thermocouple. In this paper, corrected stator resistance has an error in actuality measurement resistance. So compensation coefficient $\kappa$ which is decided through comparison and verifying several times relation of calculated resistance and measured motor line-line resistance. The stator-winding temperature information can also be used for monitoring, protection, and fault-tolerant control of the machine. Also, this paper reports the LIM's responses of the flux measured by the proposed stator resistance compensation algorithm.