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"3중모드 기판집적 도파관(SIW) 구조를 이용한 주파수 가변 마이크로스트립 필터 설계"
나경민,김동우,오순수 한국전기전자학회 2024 전기전자학회논문지 Vol.28 No.1
"본 논문에서는 최근 요구되는 이동통신 서비스의 다양한 주파수 대역 요구를 충족시키기 위해 3중 모드 주파수 가변 필터를 제안한다. 이 필터는 가변 커패시터를 활용하여 공진 주파수를 조절할 수 있는 튜닝 가능한 구조를 가지고 있다. 품질 계수를 향상하기위해 SIW (Substrate Integrated Waveguide) 구조를 도입하였고, 중앙에 위치한 원형 홀을 통해 세 개의 공진 모드를 유발하는구조를 구현하였다. 가변 커패시터에 의해 변화에 따른 전계분포와 공진 주파수의 변화를 EM 전파해석툴인 HFSS를 사용하여 시뮬레이션하였으며, 3중 모드의 전계분포와 공진 주파수의 변화를 확인하였다" "In this paper, a triple-mode frequency-tunable filter is proposed to meet the recent demands of various frequency bands of mobile communication services. This filter has a tunable structure that can adjust the resonance frequency using a variable capacitor. To improve the quality factor, a SIW(Substrate Integrated Waveguide) structure was introduced and a structure that induces three resonance modes was implemented through a circular hole located in the center. The change in electric field distribution and resonance frequency by the variable capacitor was simulated using HFSS, and the change in electric field distribution and resonance frequency of Triple Mode mode was confirmed."
패턴분류기를 위한 최소오차율 학습알고리즘과 예측신경회로망모델에의 적용
나경민,임재열,안수길 대한전자공학회 1994 전자공학회논문지-B Vol.b31 No.12
Most pattern classifiers have been designed based on the ML (Maximum Likelihood) training algorithm which is simple and relatively powerful. The ML training is an efficient algorithm to individually estimate the model parameters of each class under the assumption that all class models in a classifier are statistically independent. That assumption, however, is not valid in many real situations, which degrades the performance of the classifier. In this paper, we propose a minimum-error-rate training algorithm based on the MAP (Maximum a Posteriori) approach. The algorithm regards the normalized outputs of the classifier as estimates of the a posteriori probability, and tries to maximize those estimates. According to Bayes decision theory, the proposed algorithm satisfies the condition of minimum-error-rate classificatin. We apply this algorithm to NPM (Neural Prediction Model) for speech recognition, and derive new disrminative training algorithms. Experimental results on ten Korean digits recognition have shown the reduction of 37.5% of the number of recognition errors.