• Proceedings of the Korean Society of Crop Science Conference
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• 2001.05a
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• pp.100-101
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• 2001

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.442-445
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• 2023

최근 딥러닝 및 로봇기술의 발전으로 인해 대량의 데이터를 빠르게 수집하고 처리하는 연구 분야들로 확대되었다. 이와 관련된 한 가지 분야로써 다중 로봇을 이용한 분산학습 연구가 있으며, 이는 단일 에이전트를 이용할 때보다 대량의 데이터를 빠르게 수집 및 처리하는데 용이하다. 본 연구에서는 기존 Distributed Neural Network Optimization (DiNNO) 알고리즘에서 제안한 정적 분산 학습방법과 달리 단계적 분산학습 방법을 새롭게 제안하였으며, 모델 성능을 향상시키기 위해 원시 변수를 근사하는 단계수를 상수로 고정하는 기존의 방식에서 통신회차가 늘어남에 따라 점진적으로 근사 횟수를 높이는 방법을 고안하여 새로운 알고리즘을 제안하였다. 기존 알고리즘과 제안된 알고리즘의 정성 및 정량적 성능 평가를 수행하기 MNIST 분류와 2 차원 평면도 지도화 실험을 수행하였으며, 그 결과 제안된 알고리즘이 기존 DiNNO 알고리즘보다 동일한 통신회차에서 높은 정확도를 보임과 함께 전역 최적점으로 빠르게 수렴하는 것을 입증하였다.

• Korean Journal of Optics and Photonics
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• v.33 no.4
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• pp.167-176
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• 2022

Adaptive optics (AO) systems compensate for atmospheric disturbance, especially phase distortion, by introducing counter-wavefront deformation calculated from real-time wavefront sensing or prediction. Because AO system implementations are time-consuming and costly, it is highly desirable to estimate the system's performance during the development of the AO system or its parts. Among several techniques, we mostly apply statistical analysis, computational simulation, and optical-bench tests. Statistical analysis estimates performance based on the sum of performance variances due to all design parameters, but ignores any correlation between them. Computational simulation models every part of an adaptive optics system, including atmospheric disturbance and a closed loop between wavefront sensor and deformable mirror, as close as possible to reality, but there are still some differences between simulation models and reality. The optical-bench test implements an almost identical AO system on an optical bench, to confirm the predictions of the previous methods. We are currently developing an AO system for a 1.6-m ground telescope using a deformable mirror that was recently developed in South Korea. This paper reports the results of the statistical analysis and computer simulation for the system's design and confirmation. For the analysis, we apply the Strehl ratio as the performance criterion, and the median seeing conditions at the Bohyun observatory in Korea. The statistical analysis predicts a Strehl ratio of 0.31. The simulation method similarly reports a slightly larger value of 0.32. During the study, the simulation method exhibits run-to-run variation due to the random nature of atmospheric disturbance, which converges when the simulation time is longer than 0.9 seconds, i.e., approximately 240 times the critical time constant of the applied atmospheric disturbance.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.821-832
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• 1998

This study investigates the prediction of daily water supply, which is a necessary for the efficient management of water distribution system. Fuzzy neuron, namely artificial intelligence, is a neural network into which fuzzy information is inputted and then processed. In this study, daily water supply was predicted through an adaptive learning method by which a membership function and fuzzy rules were adapted for daily water supply prediction. This study was investigated methods for predicting water supply based on data about the amount of water supplied to the city of Kwangju. For variables choice, four analyses of input data were conducted: correlation analysis, autocorrelation analysis, partial autocorrelation analysis, and cross-correlation analysis. Input variables were (a) the amount of water supplied (b) the mean temperature, and (c)the population of the area supplied with water. Variables were combined in an integrated model. Data of the amount of daily water supply only was modelled and its validity was verified in the case that the meteorological office of weather forecast is not always reliable. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 18.35% and the average error was lower than 2.36%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.136-143
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• 2009

Typically, local methods for stereo matching are fast but have relatively low degree of accuracy while global ones, though costly, can achieve a higher degree of accuracy in retrieving disparity information. Recently, some local methods like the ones based on segmentation or adaptive weights are suggested which achieve more accuracy than global ones. These newly suggested local methods that can estimate more accurate disparity information cannot be easily used since they require more computational costs which increase in proportion to the window size they use. In this paper, we propose the method by using distance weights and pixel difference weights similar to those of the bilateral filter. Specifically, we present constant time O(1) algorithm for the case the distance weights are equal. The suggested method requires constant time for computation regardless of the used window size. Furthermore, experiments show that the matching performance of our method is as good as the ones of other recent methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3C
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• pp.157-162
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• 2011

For blind equalization of communication channels, concurrent equalization is useful to improve convergence characteristics. However, the concurrent equalization will result in limited performance enhancement by continuing concurrent adaptation with two algorithms after the equalizer converges to steady-state. In this paper, to improve the convergence characteristics and steady-state performance of the concurrent equalization, proposed is a new concurrent equalization technique with variable step-size parameter and weight-based tap coefficients update. The proposed concurrent vsCMA+DD equalization calculates weight factors using error signals of the variable step-size CMA (vsCMA) and DD (decision-directed) algorithm, and then updates the two equalizers based on the weights respectively. The proposed method, first, improves the error performance of the CMA by the vsCMA, and enhances the steady-state performance as well as the convergence speed further by the weight-based tap coefficients update. The performance improvement by the proposed scheme is verified through simulations.

• Journal of Broadcast Engineering
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• v.10 no.1 s.26
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• pp.4-13
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• 2005

An equalization algorithm is proposed to guarantee a stable performance in fast fading channels for digital television (DTV) systems from the advanced television system committee (ATSC) standard. In channels with high Doppler shifts, the conventional equalization algorithm shows severe performance degradation. Although the conventional equalizer compensates poor channel conditions to some degree, long filter taps required to overcome long delay profiles are not suitable for fast fading channels. The Proposed sparse equalization algorithm is robust to the multipaths with long delay Profiles as well as fast fading by utilizing channel estimation and equalizer initialization. It can compensate fast fading channels with high Doppler shifts using a filter tap selection technique as well as variable step-sizes. Under the ATSC test channels, the proposed algorithm is analyzed and compared with the conventional equalizer. Although the proposed algorithm uses small number of filter taps compared to the conventional equalizer, it is stable and has the advantages of fast convergence and channel tracking.

• Journal of Biomedical Engineering Research
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• v.23 no.5
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• pp.333-340
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• 2002

Powerline interference in bioelectric recordings is a common source of noise. IEF(Incremental Estimation Filter) has been used to eliminate powerline interferences in biosignals, especially in ECG(Electrocadiogram) signals. The constant incremental factor in the IEF filter, which affects the performance of noise rejection, is usually determined empirically or experimentally based on the input signals. This paper presents the design of neural network based IEF filter for time-varying control of the incremental factor. The proposed IEF filter is evaluated by applying to artificial signals as well as ECG signals of MIT-BIH database. For the relative comparison of noise-rejection performance, it is compared with adaptive noise canceler and conventional IEF filter. Simulation results show that the neural network based IEF filter outperforms these adaptive filters with respect to convergence speed and noise rejection is specific frequencies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1851-1856
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• 2013

An XML is widely used for web services, such as SOAP(Simple Object Access Protocol) and REST (Representational State Transfer), and also de facto standard for representing data. Since the XML parser using DOM(Document Object Model) requires a preprocessing task creating a DOM-tree, and then storing it into memory, embedded systems with limited resources typically employ a streaming XML parser without preprocessing. In this paper, we propose a new architecture for the streaming XML parser using an APSearch(Adaptive Parallel Search) on FPGA(Field Programmable Gate Array). Compared to other approaches, the proposed APSearch parser dramatically reduces overhead on the software side and achieves about 2.55 and 2.96 times improvement in the time needed for an XML parsing. Therefore, our APSearch parser is suitable for systems to speed up XML parsing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.714-720
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• 2021

The ITL (information theoretic learning) based on the kernel density estimation method that has successfully been applied to machine learning and signal processing applications has a drawback of severe sensitiveness in choosing proper kernel sizes. For the maximization of correntropy criterion (MCC) as one of the ITL-type criteria, several methods of adapting the remaining kernel size ( ) after removing the term have been studied. In this paper, it is shown that the main cause of sensitivity in choosing the kernel size derives from the term and that the adaptive adjustment of in the remaining terms leads to approach the absolute value of error, which prevents the weight adjustment from continuing. Thus, it is proposed that choosing an appropriate constant as the kernel size for the remaining terms is more effective. In addition, the experiment results when compared to the conventional algorithm show that the proposed method enhances learning performance by about 2dB of steady state MSE with the same convergence rate. In an experiment for channel models, the proposed method enhances performance by 4 dB so that the proposed method is more suitable for more complex or inferior conditions.