• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.344-346
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• 2019

In this paper, we propose a distance estimation method using the convolutional neural network in Ultra-Wideband (UWB) systems. The training data set used to learn the deep learning model using the convolutional neural network is generated by the MATLAB program and utilizes the IEEE 802.15.4a standard. The performance of the proposed distance estimation method is verified by comparing the threshold based distance estimation technique and the performance comparison used in the conventional distance estimation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2A
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• pp.43-50
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• 2005

In this paper, we present an improved channel estimation method for orthogonal frequency division multipexing systems using pilot symbol assisted modulation(PSAM). Conventional linear minimum mean square error(LMMSE) channel estimation method uses only pilot symbols for channel estimation. So, as the fading channel varies rapidly, the system performance is degraded. The basic idea of the proposed scheme is that we firstly estimate channel coefficients at the middle point between two pilot symbols and then compute the channel attenuation by using LMMSE method. Superior performance achieved with the proposed method is illustrated by simulation experiments with the Doppler frequency of 36Hz and 185Hz in comparison with conventional LMMSE channel estimator.

• Journal of Korea Multimedia Society
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• v.9 no.2
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• pp.172-188
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• 2006

Sensor localization is one of the fundamental problems in wireless sensor networks. Previous localization algorithms can be classified into two categories, the GGB (Global Geometry-Based) approaches and the LGB (Local Geometry-Based). In the GGB approaches, there are a fixed set of reference nodes of which the coordinates are pre-determined. Other nodes determine their positions based on the distances from the fixed reference nodes. In the LGB approaches, meanwhile, the reference node set is not fixed, but grows up dynamically. Most GGB algorithms assume that the nodes are deployed in a convex shape area. They fail if either nodes are in a concave shape area or there are obstacles that block the communications between nodes. Meanwhile, the LGB approach is vulnerable to the errors in the distance estimations. In this paper, we propose new localization algorithms to cope with those two limits. The key technique employed in our algorithms is to determine, in a fully distributed fashion, if a node is in the line-of-sight from another. Based on the technique, we present two localization algorithms, one for anchor-based, another for anchor-free localization, and compare them with the previous algorithms.

• Journal of Korean Society of Steel Construction
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• v.14 no.5 s.60
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• pp.641-646
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• 2002

A new damage detection technique using static displacement data was developed, in order to assess the structural integrity of bridge structures. In conventional damage assessment techniques using dynamic response, the variation of natural frequencies is intrinsically insensitive to the damage of the bridge: thus, it is usually difficult to obtain them from the measured data. The proposed detection method enables the estimation of the stiffness reduction of bridges using the static displacement data that are measured periodically, without requiring a specific loading test. Devices such as a laser displacement sensor can be used to measure static displacement data due to the dead load of the bridge structure. In this study, structural damage was represented by the reduction in the elastic modulus of the element. The damage factor of the element was introduced to estimate the stiffness reduction of the bridge under consideration. Likewise, the proposed algorithm was verified using various numerical simulations and compared with other damage detection methods. The effects of noise and number of damaged elements on damage detection were also investigated. Results showed that the proposed algorithm efficiently detects damage on the bridge.

• The Journal of the Acoustical Society of Korea
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• v.25 no.4
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• pp.184-190
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• 2006

Recently, Lee et al. have proposed an algorithm utilizing the signals from different paths by using bottom mounted simple linear array to estimate 3-D location of oceanic target. But this algorithm assumes that sound velocity is constant along depth of sea. Consequently, serious performance loss is appeared in real oceanic environment that sound speed is changed variously. In this paper, we present a 3-D near field localization algorithm for inhomogeneous sound speed. The proposed algorithm adopt localization function that utilize ray propagation model for multipath environment with linear sound speed profile(SSP), after that, the proposed algorithm searches for the instantaneous azimuth angle, range and depth from the localization cost function. Several simulations using linear SSP and non linear SSP similar to that of real oceans are used to demonstrate the performance of the proposed algorithm. The estimation error in range and depth is decreased by 100m and 50m respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.397-397
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• 2021

본 연구의 목적은 비매개변수적 리샘플링 기법을 이용하여 농업용 저수지 유입 설계홍수량의 구간을 추정하는 기법을 제안하는 데 있다. 본 연구는 설계홍수량을 점 추정하여 안전계수(safety factor)를 적용하는 기존 방법에 대한 대안을 제시하고자 한다. 설계홍수량의 구간 추정을 수행하기 위해 부트스트랩 기법(bootstrap technique)을 사용하였다. 부트스트랩 기법을 이용하여 95% 신뢰수준에 해당하는 신뢰구간을 추정하였다. 본 연구의 공간적인 범위는 남한의 30개 농업용 저수지이며, 시간적인 범위는 과거 기간(2015s: 1986-2015)과 미래기간(2040s: 2011-2040, 2070s: 2041-2070, 2100s: 2071-2100)을 설정하였다. 본 연구에서는 200년 빈도, 24시간 지속기간을 대표적인 결과로 선정하여 분석하였다. 빈도분석은 GEV 분포를 사용하였고, L-moment 방법을 이용하여 매개변수를 추정하였다. 설계홍수량은 HEC-1 모형을 이용하여 산정하였다. 최종적으로 설계홍수량 구간 추정한 결과를 기존의 점 추정한 뒤 안전계수를 적용한 기존 방법과 비교하였다. 97.5th BCa percentile 기준으로 상대적인 변화를 비교해보면, 미래로 갈수록 구간 추정으로 산정한 설계홍수량이 점차 증가하는 것으로 도출되었다. 한강 및 금강 유역에 위치한 농업용 저수지의 설계홍수량이 낙동강 유역에 비해 상대적으로 큰 변화를 보여주었다. 몇몇 농업용 저수지에 대해서 2040s 기간에 다소 감소하기도 하였으나 2070s 기간 이후에 다시 증가하는 결과를 보여주었다. 낙동강 유역의 위치는 농업용 저수지의 설계홍수량은 미래로 갈수록 크게 증가하지 않는 경향을 보여주었다. 본 연구는 설계홍수량을 추정하는 데 있어 결정론적인 방법에서 더 나아가 자료의 통계적인 특성을 고려하여 구간 추정을 수행하는 방법론을 제공할 수 있을 것으로 사료된다.

• Computational Structural Engineering
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• v.11 no.4
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• pp.361-370
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• 1998

본 논문에서는 역전파학습에 의한 신경망기법을 사용하여 구조물의 미지계수를 추정하는 기법을 연구하였다. 대형구조물의 경우 계측 또는 추정하여야 하는 자유도의 수가 많으므로 인하여 구조계수를 추정하는 데에는 많은 어려움이 존재한다. 이러한 어려움을 극복하기 위하여 부구조추정법과 부행렬계수를 사용하여 추정하고자 하는 미지계수의 수를 효율적으로 줄일 수 있도록 하였다. 구조물의 고유주파수 및 모드형상 등의 모드계수를 신경망의 입력자료로 사용하였으며, 추정하고자 하는 부재의 부행렬계수를 신경방의 출력자료로 사용하였다. 입력자료로 사용되는 모드계수에 포함되어 있는 계측오차 및 신호처리오차의 영향을 줄이기 위하여, 신경망의 학습과정에서 노이즈를 첨가하는 기법을 사용하였다. 일반적인 형태의 자켓구조물을 대상으로 수치해석을 수행함으로써 제안기법의 대형구조계에 대한 적용성을 검증하였다.

• Journal of Korea Multimedia Society
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• v.16 no.5
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• pp.539-548
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• 2013

Although Motion estimation (ME) plays an important role in digital video compression, it requires a complicated search procedure to find an optimal motion vector. Multi-view video is obtained by capturing one three-dimensional scene with many cameras at different positions. The computational complexity of motion estimation for Multi-view video coding increases in proportion to the number of cameras. To reduce computational complexity and maintain the image quality, a low complexity motion estimation search method is proposed in this paper. The proposed search method consists of four-grid diamond search patten, two-gird diamond search pattern and TZ 2 Point search pattern. These search patterns exploit the characteristics of the distribution of motion vectors to place the search points. Experiment results show that the speedup improvement of the proposed method over TZ search method (JMVC) can be up to 1.8~4.5 times faster by reducing the computational complexity and the image quality degradation is about to 0.01~0.24 (dB).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.104-113
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• 2022

In this paper, we propose deep learning-based target distance and velocity estimation technique for OFDM radar systems. In the proposed technique, the 2D periodogram is obtained via 2D fast Fourier transform (FFT) from the reflected signal after removing the modulation effect. The periodogram is the input to the conventional and proposed estimators. The peak of the 2D periodogram represents the target, and the constant false alarm rate (CFAR) algorithm is the most popular conventional technique for the target's distance and speed estimation. In contrast, the proposed method is designed using the multiple output convolutional neural network (CNN). Unlike the conventional CFAR, the proposed estimator is easier to use because it does not require any additional information such as noise power. According to the simulation results, the proposed CNN improves the mean square error (MSE) by more than 5 times compared with the conventional CFAR, and the proposed estimator becomes more accurate as the number of transmitted OFDM symbols increases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3A
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• pp.254-259
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• 2010

In this paper, we propose a low-complexity carrier frequency offset (CFO) estimation algorithm for OFDM based wireless LAN, IEEE 802.11a. The complexity of the arctangent operation to calculate the argument of auto-correlation for CFO estimation is reduced by a novel range pointer method. The proposed algorithm estimates fine CFO value first and then based on the fine CFO value, simple criteria is used for the boundary decision of integer CFO estimation. The simulation results show that the performance of the proposed algorithm is slightly better than the conventional method while the computational complexity is reduced by 50%. Furthermore, the proposed method can be easily implemented for the low complex next generation MIMO-OFDM based WLAN systems.