http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
유성현(Sung Hyun You),배동성(Dong Sung Pae),최현덕(Hyun Duck Choi) 한국정보기술학회 2023 한국정보기술학회논문지 Vol.21 No.3
The digital phase-locked loop(DPLL) is one of the circuits composed of a digital detector, digital loop filter, voltage controlled oscillator, divider, and etc. as a fundamental circuit, widely used in many fields such as electrical and circuit fields. In order to improve the performance of the DPLL, research has been conducted to improve the performance of the digital loop of the DPLL. An infinite impulse response(IIR) state estimator, which is a mathematical algorithm, is used as one of various methods for improving performance. In this paper, we propose a DPLL based on FIR(Finite Impulse Pulse Response) state estimator. A DPLL using the Frobenius norm-based state estimator, which has more robust performance in inaccurate situations than the minimum variance FIR filter. The Frobenius norm-based FIR filter has robust performance against disturbances by minimizing the norm of the gain, and in this paper, we propose a numerical method to optimize the Frobenius norm gain. The superior performance of the new DPLL is verified through performance comparison with the existing DPLL through simulation.
최적의 측정값 구간의 길이를 갖는 최소 공분산 유한 임펄스 응답 필터 기반 디지털 위상 고정 루프 설계
유성현(Sung-Hyun You),배동성(Dong-Sung Pae),최현덕(Hyun-Duck Choi) 한국전자통신학회 2021 한국전자통신학회 논문지 Vol.16 No.4
디지털 위상 고정 루프는 위상 동기화를 위해 사용되는 회로로 일반적으로 통신, 회로분야 등 다양한 분야에서 사용된다. 디지털 위상 고정 루프를 설계 시 상태추정기를 사용하는 경우 보통 칼만 필터와 같은 무한 임펄스 응답 상태추정기를 활용해왔다. 일반적으로 무한 임펄스 응답 상태추정기 기반 디지털 위상 고정 루프의 성능은 우수하지만, 초기값의 부정확, 모델 오차, 외란 등의 예상하지 못하는 상황에서 급격한 성능저하가 발생할 수 있다. 본 논문에서는 새로운 디지털 위상 고정 루프를 설계 하기 위해 최적의 측정값 구간 길이를 갖는 최소 공분산 유한 임펄스 응답 필터를 제안한다. 제안된 유한 임펄스 응답 필터의 중요 파라미터인 측정값 구간 길이를 구하기 위해 수치적 방법을 소개하며, 필터의 이득을 얻기 위해 비용함수로 오차의 공분산 행렬을 설정하고, 이를 최소화 하기 위하여 선형 행렬 부등식을 사용하였다. 제안된 디지털 위상 동기 루프의 우수성과 강인성을 검증하기 위해 노이즈 정보가 부정확한 상황에서 기존 방법과의 비교 및 분석을 위한 시뮬레이션을 수행하였다. The digital phase-locked loops(DPLL) is a circuit used for phase synchronization and has been generally used in various fields such as communication and circuit fields. State estimators are used to design digital phase-locked loops, and infinite impulse response state estimators such as the well-known Kalman filter have been used. In general, the performance of the infinite impulse response state estimator-based digital phase-locked loop is excellent, but a sudden performance degradation may occur in unexpected situations such as inaccuracy of initial value, model error, and disturbance. In this paper, we propose a minimum variance finite impulse response filter with optimal horizon for designing a new digital phase-locked loop. A numerical method is introduced to obtain the measured value interval length, which is an important parameter of the proposed finite impulse response filter, and to obtain a gain, the covariance matrix of the error is set as a cost function, and a linear matrix inequality is used to minimize it. In order to verify the superiority and robustness of the proposed digital phase-locked loop, a simulation was performed for comparison and analysis with the existing method in a situation where noise information was inaccurate.
바이오센서 기반 특징 추출 기법 및 감정 인식 모델 개발
조예리(Ye Ri Cho),배동성(Dong Sung Pae),이윤규(Yun Kyu Lee),안우진(Ahn Woo Jin),임묘택(Myo Taeg Lim),강태구(Tae Koo Kang) 대한전기학회 2018 전기학회논문지 Vol.67 No.11
The technology of emotion recognition is necessary for human computer interaction communication. There are many cases where one cannot communicate without considering one"s emotion. As such, emotional recognition technology is an essential element in the field of communication. n this regard, it is highly utilized in various fields. Various bio-sensor sensors are used for human emotional recognition and can be used to measure emotions. This paper proposes a system for recognizing human emotions using two physiological sensors. For emotional classification, two-dimensional Russell"s emotional model was used, and a method of classification based on personality was proposed by extracting sensor-specific characteristics. In addition, the emotional model was divided into four emotions using the Support Vector Machine classification algorithm. Finally, the proposed emotional recognition system was evaluated through a practical experiment.