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sparsity가 변하는 시스템에서 LMS와 ZA-LMS의 선택 방법을 이용한 적응 필터링 알고리즘
구교권(Gyogwon Koo),정재진(Jae Jin Jeong),김승훈(Seung Hun Kim),김상우(Sang Woo Kim) 대한전기학회 2015 정보 및 제어 심포지엄 논문집 Vol.2015 No.4
We proposed an adaptive filtering algorithm with a selection method between the least mean square (LMS) and the zero-attracting LMS (ZA-LMS) for a sparseness-varying system. To select between the LMS and the ZA-LMS, we used l0-norm of the filter coefficients as a non-zero coefficient counter and approximated l0-norm of the filter coefficients because l0-norm cannot be determined practically. In order to update filter coefficients, we choose the LMS or the ZA-LMS algorithm depending on the estimated number of non-zero coefficients. The results of computer simulation show that the proposed algorithm achieved less than half of the computation complexity of the conventional convex combination algorithm of the LMS and the ZA-LMS, and a similar convergence and steady state performance for sparsity-varying system.
이경석 ( Lee Kyeong-seok ),서영득 ( Seo Young-deuk ),최형석 ( Choi Hyoung-suk ) 한국구조물진단유지관리공학회 2021 한국구조물진단유지관리공학회 학술발표대회 논문집 Vol.25 No.1
지진이 발생하면 시설물의 관리자는 구조물의 피해발생 여부를 조사하고 긴급복구를 실시하는 등 대응을 수행할 책임이 있다. 그러나 교량 및 건축물과 같은 대형의 사회기반시설에 대해서 지진발생 후 소수의 관리인원이 제한된 시간 내에 다수구조물의 지진피해를 확인하고 안전성을 평가하는데는 어려움이 있다. 국내에서는 지진재해대응시스템이 개발되어 있으나 확률론적 안전성 평가방법을 적용하고 있어 개별 시설물에 대한 피해 정도를 제공하지는 못하는 실정이다. 이에 본 연구에서는 중, 소형시설물의 지진피해평가·관리시스템을 개발함으로써 지진재해 발생 후 관리자에게 구조물의 피해발생 여부, 긴급점검 등 유지관리활동의 필요유무 정보를 제공하고자 한다.
sparsity가 변하는 시스템에서 LMS와 ZA-LMS의 선택 방법을 이용한 적응 필터링 알고리즘
구교권(Gyogwon Koo),정재진(Jae Jin Jeong),김승훈(Seung Hun Kim),김상우(Sang Woo Kim) 대한전기학회 2015 대한전기학회 학술대회 논문집 Vol.2015 No.4
We proposed an adaptive filtering algorithm with a selection method between the least mean square (LMS) and the zero-attracting LMS (ZA-LMS) for a sparseness-varying system. To select between the LMS and the ZA-LMS, we used l0-norm of the filter coefficients as a non-zero coefficient counter and approximated l0-norm of the filter coefficients because l0-norm cannot be determined practically. In order to update filter coefficients, we choose the LMS or the ZA-LMS algorithm depending on the estimated number of non-zero coefficients. The results of computer simulation show that the proposed algorithm achieved less than half of the computation complexity of the conventional convex combination algorithm of the LMS and the ZA-LMS, and a similar convergence and steady state performance for sparsity-varying system.
Wearable Device-Based System to Monitor a Driver’s Stress, Fatigue, and Drowsiness
Choi, Minho,Koo, Gyogwon,Seo, Minseok,Kim, Sang Woo IEEE 2018 IEEE transactions on instrumentation and measureme Vol.67 No.3
<P>This paper proposes a wearable device-based system to monitor the abnormal conditions of a driver, including stress, fatigue, and drowsiness. The system measures the motional and physiological information of the driver using the developed wearable device on the wrist. Preprocessing is used to distinguish the valid signal parts of the measured signals, because various noises can occur in wearable sensors. Features are extracted from the signal parts, and an optimal feature set is determined by an analysis of variance and a sequential floating forward selection algorithm. To classify the driver’s state, a support vector machine-based classification method is used to obtain high generalization performance considering interdriver variance. Experiments were conducted on an indoor driving simulator, with 28 subjects, to gather data for each state. The classification accuracy was 98.43% for fivefold cross validation on the data. In a subject-independent test, the accuracy was 68.31% for the four states and 84.46% for the three states consisting of normal, stressed, and fatigued or drowsy states. Using the proposed system, the abnormal conditions of the driver can be detected and distinguished. This advantage contributes to safer and more comfortable driving. Furthermore, the utilization of the wearable device makes the system easy to use.</P>
Decision feedback equalizer for holographic data storage
Kim, Kyuhwan,Kim, Seung Hun,Koo, Gyogwon,Seo, Min Seok,Kim, Sang Woo The Optical Society 2018 Applied Optics Vol.57 No.15
<P>Holographic data storage (HDS) has attracted much attention as a next-generation storage medium. Because HDS suffers from two-dimensional (2D) inter-symbol interference (IST), the partial-response maximum likelihood (PRML) method has been studied to reduce 2D IST. However, the PRML method has various drawbacks. To solve the problems, we propose a modified decision feedback equalizer (DFE) for HDS. To prevent the error propagation problem, which is a typical problem in DFEs, we also propose a reliability factor for HDS. Various simulations were executed to analyze the performance of the proposed methods. The proposed methods showed fast processing speed after training, superior bit error rate performance, and consistency. (C) 2018 Optical Society of America</P>
Mean-Square Deviation Analysis of Multiband-Structured Subband Adaptive Filter Algorithm
Jae Jin Jeong,Seung Hun Kim,Gyogwon Koo,Sang Woo Kim Institute of Electrical and Electronics Engineers 2016 IEEE transactions on signal processing Vol.64 No.4
<P>A multiband-structured subband adaptive filter (MSAF) algorithm was introduced to achieve a fast convergence rate for the correlated input signal. The convergence analysis of the adaptive filter algorithm is an important concept because it provides a guideline to design the adaptive filter. However, the convergence analysis of the MSAF algorithm has not been researched as extensively as that of the normalized least-mean-square algorithm. Therefore, it needs to be researched. In this paper, we present a new approach to the mean-square deviation (MSD) analysis of the MSAF algorithm by using the persistently exciting input and the practical assumption that the stopband attenuation of the prototype filter is high. Unlike the previous analysis, the proposed analysis is possible to be applied to the long-length adaptive filter such as the acoustic echo cancellation. The proposed analysis is also applied to a non-stationary model with a random walk of the optimal weight vector. The simulation results match with the theoretical results in both the transient-state and steady-state MSD.</P>