• 한국수학교육학회지시리즈A:수학교육
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• 제62권1호
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• pp.35-55
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• 2023

본 연구는 무리수 개념의 수학 오류 찾기 활동에서 학생의 인식뿐 아니라, 오류 활용에 관한 학생의 학습 태도와 수학적 담론 수준의 변화를 초래하는 교사의 발문 전략을 살펴보는 데 목적이 있다. 이를 위해 133명의 중학교 학생을 대상으로 오류 찾기 개인별 활동, 모둠 활동과 추가 면담을 수행하여, 학생의 인식과 학생의 학습 태도와 수학적 담론 수준의 변화를 위한 교사의 발문 전략을 분석하였다. 연구 결과, 학생들의 인식은 무리수의 기호 표상과 소수 표상에 집중하며 수직선 위의 무리수의 존재성은 인식하지만 도형을 활용한 수직선 표현에는 어려움을 겪는 경향이 있었다. 또한 학생의 학습 태도와 수학적 담론 수준의 변화를 촉진하기 위해 교사의 유도적-탐구적 발문 전략의 중요성을 관찰할 수 있었다. 본 연구는 수학 교수·학습에서 오류의 활용 방법을 구체화하고, 수학 오류 찾기에서 교사의 발문 전략을 정교화하였다는 점에서 가치가 있다.

• 한국전자파학회논문지
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• 제25권8호
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• pp.864-871
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• 2014

본 연구에서는 Rotman 렌즈의 위상 오차를 최소화하는 빔 곡선을 구하는 최적화 방법을 제안하고 있다. 이 방법은 빔 포트로부터 배열안테나의 중심점과 배열안테나를 따라 대칭 또는 비대칭으로 놓인 두 개의 점으로 구성된 동위상점들을 통과하여 대응되는 동위상면까지의 3개의 경로길이는 동일하다는 개념에 근거를 두고 있다. 이 방법을 따르면, 각각의 빔 방향에 대하여 위상 오차를 최소화하는 배열안테나 상의 동위상점 집합을 찾으면 최적의 빔 포트 위치를 직접 구할 수 있다. 시뮬레이션 결과는 제안된 방법이 저 위상 오차를 갖는 Rotman 렌즈의 빔 곡선을 구하는 가장 최적이며 효과적인 방법임을 보여준다.

• 대한의용생체공학회:의공학회지
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• 제30권1호
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• pp.10-17
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• 2009

Accurate electroencephalography (EEG) forward calculation is of importance for the accurate estimation of neuronal electrical sources. Conventional studies concerning the EEG forward problems have investigated various factors influencing the forward solution accuracy, e.g. tissue conductivity values in head compartments, anisotropic conductivity distribution of a head model, tessellation patterns of boundary element models, the number of elements used for boundary/finite element method (BEM/FEM), and so on. In the present paper, we investigated the influence of modeling errors in the boundary element volume conductor models upon the accuracy of the EEG forward solutions. From our simulation results, we could confirm that accurate construction of boundary element models is one of the key factors in obtaining accurate EEG forward solutions from BEM. Among three boundaries (scalp, outer skull, and inner skull boundary), the solution errors originated from the modeling error in the scalp boundary were most significant. We found that the nonuniform error distribution on the scalp surface is closely related to the electrode configuration and the error distributions on the outer and inner skull boundaries have statistically meaningful similarity to the curvature distributions of the boundary surfaces. Our simulation results also demonstrated that the accumulation of small modeling errors could lead to considerable errors in the EEG source localization. It is expected that our finding can be a useful reference in generating boundary element head models.

• 전자공학회논문지S
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• 제34S권11호
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• pp.43-52
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• 1997

Ultrasonic sensors are widely employed in detecting range to a target by the virtue of their low cost and simplicity. However, the sensor's measurements are corrupted by systematic errors due mainly to the dependency of sound speed upon surrounding conditions and random errors of uncertain origin. In this paper, we present the results of research carried out to reduce these errors for increasing the reliability of an untrasonic sensor system to be used in orbotic or other automated system's range finding. The sensor system designed herein is in a peuliar structure having a reference target and two receivers. Echoes from a small reference target placed at a known distance are used for compensating the variations of sound speed according to the changes of sensing conditions. Unlike existing ones, the technique proposed can compensate the effects of temperature or any other physical parameters without an additional sensor dedicated to the compensation. The measurements by two redundantly employed receivers are fused to reduce random errors in a statistical sense. The correlation of the signals from the receivers sharing a hardware in part is considered in the fusion process. The methodology desicribed in this paepr is conceptually simple, easy to be implemented, and effetive to increase the accuracy of the sensor measurements as experimental results confirm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권11호
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• pp.5305-5321
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• 2016

In wireless communication, the multiple-input multiple-output (MIMO) system is a well-known approach to improve the reliability as well as the data rate. In MIMO systems, channel state information (CSI) is typically required at the receiver to detect transmitted signals; however, in practical systems, the CSI is imperfect and contains errors, which affect the overall system performance. In this paper, we propose a novel maximum likelihood (ML) scheme for MIMO systems that is robust to the CSI errors. We apply an optimization method to estimate an instantaneous covariance matrix of the CSI errors in order to improve the detection performance. Furthermore, we propose the employment of the list sphere decoding (LSD) scheme to reduce the computational complexity, which is capable of efficiently finding a reduced set of the candidate symbol vectors for the computation of the covariance matrix of the CSI errors. An iterative detection scheme is also proposed to further improve the detection performance.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2009년도 춘계학술대회 논문집
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• pp.1817-1827
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• 2009

Although human error is recognized as the primary cause of railway accidents and incidents, there have been limitations in finding the root causes of errors and developing effective corrective actions in the Korean railway industry, due to the absence of a systematic method and lack of professional knowledge and skills of investigators. Therefore, there has been a strong need for a systematic methodology for human error analysis. This paper introduces a methodology for analyzing human error m railway operations, called HEAR (Human Error Analysis and Reduction). HEAR is intended to help analysts identify the sequences and various levels of causes of operators' erroneous actions in railway accidents or incidents and make recommendations to eliminate or reduce the future possibility of similar errors and accidents. It was developed based on a thorough investigation of various techniques for human errors analysis and feedback from field investigators.

• 전기전자학회논문지
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• 제20권1호
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• pp.1-8
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• 2016

In this paper, we propose an effective time-of-arrival (TOA)-based localization method with adaptive bias computation in indoor environments. The goal of the localization is to estimate an accurate target's location in wireless localization system. However, in indoor environments, non-line-of-sight (NLOS) errors block the signal propagation between target device and base station. The NLOS errors have significant effects on ranging between two devices for wireless localization. In TOA-based localization, finding the target's location inside the overlapped area in the TOA-circles is difficult. We present an effective localization method using compensated distance with adaptive bias computation. The proposed method is possible for the target's location to estimate an accurate location in the overlapped area using the measured distances with subtracted adaptive bias. Through localization experiments in indoor environments, estimation error is reduced comparing to the conventional localization methods.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.96-99
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• 1987

In the way of perception and depth finding of 3-D scene stereo vision is one of the best ways. Matching is important problem of stereo vision. In this paper we develop an algorithm that is adaptable to errors and computation time. And we demonstrate the way of matching.

• IEIE Transactions on Smart Processing and Computing
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• 제6권1호
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• pp.18-20
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• 2017

In this paper, we propose a new classification method to complement a $na{\ddot{i}}ve$ Bayesian classifier. This classifier assumes data distribution to be Gaussian, finds the discriminant function, and derives the decision curve. However, this method does not investigate finding the decision curve in much detail, and there are some minor problems that arise in finding an accurate discriminant function. Our findings also show that this method could produce errors when finding the decision curve. The aim of this study has therefore been to investigate existing problems and suggest a more reliable classification method. To do this, we utilize the gradient to find the decision curve. We then compare/analyze our algorithm with the $na{\ddot{i}}ve$ Bayesian method. Performance evaluation indicates that the average accuracy of our classification method is about 10% higher than $na{\ddot{i}}ve$ Bayes.

• 대한임베디드공학회논문지
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• 제6권5호
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• pp.327-333
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• 2011

Intelligent robots are widely needed in various areas of industry from extremely dangerous environments to service tasks. For autonomous mobile robots, it is significant to move itself safely to a destination point, recognizing its surroundings. Advances in sensor technology and its applications are achieved in order to develop an intelligent robot. In this paper, a mobile robot with a path-finding algorithm is presented. The path-finding algorithm is the one that does not only find a path to designated destination and also recognizes obstacles on the way, calculating its distance. 10 ultrasonic sensor are mounted on the front and rear of the mobile robot to figure out its position. Specular reflection and wide viewing angle, which are inherent characteristics of ultrasonic waves, cause errors in measuring distance.