• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.845-848
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• 2001

본 논문에서는 multipath 환경에서 효과적으로 시지연을 추정하기 위한 알고리즘을 제안하였다. 제안된 알고리즘은 전처리 과정으로 웨이블릿 변환을 적용하였으며, 적응 알고리즘으로는 RLS를 계층적인 구조로 나타낸 HRLS(Hierarchical RLS)를 사용하였다. 시지연은 신호 분해과정 이후 각각의 부밴드에서 primary 신호와 reference 신호 사이의 MSE(Mean of Squared Error)를 최소화 시키는 적응 메카니즘을 사용하여 추정하였다. 아울러 모의실험을 통하여 제안된 알고리즘의 성능을 검증하였다.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06c
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• pp.499-502
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• 1998

본 논문에서는 한 쌍의 수동소나를 이용하여 미지의 잠항물체의 존재 유무를 확인하고 각 센서에 도달하는 시간지연의 차를 평가하는 Detection과 Tracking 알고리즘을 연구한다. 이 과정에서 이동하는 표적의 속력에 의한 도플러효과를 보상하는 2차원 확률분포 함수를 적용함으로 보다 정확한 결과를 도출한다. 관측신호의 Cross-Correlation과 Bayesian Method를 이용하여 계산한 시간지연과 도플러효과 비의 이차원 Likelihood 함수로부터 사후확률 (Posterior Probability)을 구하여 발견 평가와 추적을 수행한다.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.05
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• pp.79-81
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• 1992

This paper describes the design of an adaptive closed circuit anesthesia controller based on a multiplexed mass spectrometer system. The controller deals with measurement deterioration caused by measurement delay and rise time through a tong catheter as well as long sampling times due to the multiplexed measurements. Measurement data is extrapolated between sampling periods to increase the estimation convergence rate. A multiple-step-ahead predictive control algorithm is used to calculate intermediatc control inputs between sampling intervals. Simulations are used to validate the designed controller.

• MALSORI
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• no.67
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• pp.135-147
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• 2008

In this paper, we propose a method for source localization in reverberant environments based on an adaptive eigenvalue decomposition (AED) algorithm which directly estimates channel impulse responses from a speaker to microphones. Unfortunately, the AED algorithm may suffer from whitening effects on channels estimated from temporally correlated natural sounds. The proposed method which applies sparse priors to the estimated channels can avoid the temporal whitening and improve the performance of source localization in reverberant environments. Experimental results show the effectiveness of the proposed method.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06d
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• pp.64-67
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• 1998

원격지간 화상회의 시스템에서 화자의 위치에 따른 카메라 제어를 위해서는 마이크로폰 배렬(Microphone Array)로 수음한 음성신호에 대해 각 마이크로폰간의 빠른 지연시간 추정이 요구된다. 본 연구에서는 음원위치 추정을 위한 지연시간(Time delay) 계산을 위해 AMDF(Average Magnitude Difference Function)를 적용하여 연산시간을 단축시키는데 목적을 두고 있다. 기본의 상호상관함수 (Cross-correlation )알고리즘 과 본 연구에서 적용한 AMDF 알고리즘을 비교하기 위해 SNR 10dB 와 20dB 인 200Hz, 500Hz, 1kHz, 2kHz의 정현파 합성신호와 단음절 음성신호에 대해 시뮬레이션을 행하였다. 시뮬레이션 결과 AMDF 알고리즘의 정확한 지연시간 추정을 확인하였다.

• Annual Conference of KIPS
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• 2024.05a
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• pp.459-460
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• 2024

본 연구에서는 노이즈가 있는 신호에서 코릴레이션과 위상자를 이용해 특정 주파수 성분의 세기와 위상을 검출하고 시간 지연이 있는 두 개의 신호에서 위상차를 통해 시간 지연을 추정하는 알고리즘을 제시하였으며 마이크로폰 어레이와 증폭 회로를 구성하여 단일 주파수 음원의 시간 지연 추정을 구현하였다. 이는 단일 주파수 신호의 시간 지연을 검출하는데 있어 기존의 방식들에 비해 단순하며 보다 자원이 한정적인 임베디드 시스템에서 사용될 수 있을 것으로 예상된다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.23-28
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• 2021

Data analysis research using deep learning has recently been studied in various field. In this paper, we conduct a GNSS (Global Navigation Satellite System)-based meteorological study applying deep learning by estimating the ZWD (Zenith tropospheric Wet Delay) through MLP (Multi-Layer Perceptron) and LSTM (Long Short-Term Memory) models. Deep learning models were trained with meteorological data and ZWD which is estimated using zenith tropospheric total delay and dry delay. We apply meteorological data not used for learning to the learned model to estimate ZWD with centimeter-level RMSE (Root Mean Square Error) in both models. It is necessary to analyze the GNSS data from coastal areas together and increase time resolution in order to estimate ZWD in various situations.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.85-94
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• 2015

In this paper, an enhanced QoS multicast routing scheduling scheme is proposed to adapt to a dynamic mobile traffic condition for wireless mesh networks (WMNs). It handles the network QoS by controlling the delay constraints for multimedia applications. The group size will be controlled according to both the current network state and QoS requirements. The dynamic reconstruction of QoS multicast tree can be obtained from preprocessing with both the partial multicast routing scheme and the traffic estimation. Performance evaluation of the proposed scheme is carried out on randomly generated graph derived from the wireless mesh network, by choosing the optimal value related to the appropriate delay bounds. Simulation results show that the proposed scheme can improve the performance of QoS multicast routing for WMNs.

• 전기의세계
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• v.18 no.1
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• pp.15-23
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• 1969

A lot of recent researches have shown that a Pseudo Random Binary Signal is a quite effective test signal to measure the impulse response of a plant. Generally speaking, however, such a response itself is not satisfactory to determine the appropriate control parameters or control inputs. Here, the author intends to estimate the unknown parameters of the First Order Plant with Dead Time by means of correlation method using M-sequence signal. The time constant T and the dead time L of the plant are eatimated with one tracking loop by automatically adjusting delay time .tau. of M-sequence signal according to variations of T and L. In this paper, a three level M-sequence signal is used as a test signal in order to avoid troublesome operations to calculate partial derivatives of a given performance index with respect to the parameters which are usually required in the Model Method. Several experiments with analogue computer using low pass filters as averaging circuits showed good results as expected.

• Korean Journal of Construction Engineering and Management
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• v.14 no.2
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• pp.96-107
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• 2013

Tower crane hoisting plan is one of the key element for the success of entire High-Rise Building construction. Hoisting time is the basic factor to appropriate hoisting plan which need to the hoisting load estimate and tower crane selection. With this reason, accurate hoisting time is needed to the proper hoisting plan. The current hoisting time estimation for High-Rise Building focus on the hoisting cycle time estimation with historical data. However, this method underestimated the external influences like environmental factor. Thus, this paper aims to develop the hoisting time estimation model with discrete event simulation which include the wind influences with certain height. According to the simulation result, the hoisting time which applied wind influence is increasing with height growth. Because of the high speed wind, the upper area of building has more operation delay time than the mechanical operation time. Seoul, the research area, has the most fastest wind speed on April and the least on October. Due to these differences of wind speed, the hoisting time is estimated with significant differences between April and October. This hosting time estimation model would be used for estimating the influence of wind. Moreover, this could apply to make the realistic hoisting plan.