• 제목/요약/키워드: ${\beta}$-fuzzy filter

검색결과 6건 처리시간 0.023초

차량 추적 성능 향상을 위한 퍼지 $\alpha-\beta$ 필터 (Fuzzy $\alpha-\beta$ filter for vehicle tracking)

  • 정태진;김인택;한승수
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2000년도 하계종합학술대회 논문집(5)
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    • pp.43-46
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    • 2000
  • In this paper, we present a method for vehicle tracking systems using $\alpha$-$\beta$ filter based on fuzzy logic. The $\alpha$-$\beta$ filter estimates the future target positions using fixed $\alpha$.$\beta$ coefficients. We utilize the fuzzy logic to make $\alpha$ and $\beta$ coefficients very with the position. Comparisons of tracking performance made for three different schemes: the $\alpha$-$\beta$ filter, $\alpha$-$\beta$filter using fuzzy logic, and the kalman filter.

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𝛽-FUZZY FILTERS IN MS-ALGEBRAS

  • Alaba, Berhanu Assaye;Alemayehu, Teferi Getachew
    • Korean Journal of Mathematics
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    • 제27권3호
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    • pp.595-612
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    • 2019
  • In this paper, we introduce the concept of ${\beta}$-fuzzy filters in MS-algebras and ${\beta}$-fuzzy filters are characterized in terms of boosters. It is proved that the lattice of ${\beta}$-fuzzy filters is isomorphic to the fuzzy ideal lattice of boosters.

ON FUZZY ${\beta}-COMPACT^*$ SPACES AND FUZZY $\beta$-FILTERS

  • Uma, M.K.;Roja, E.;Balasubramanian, G.
    • East Asian mathematical journal
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    • 제23권2호
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    • pp.151-158
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    • 2007
  • In this paper we introduce the concept of fuzzy ${\beta}-compact^*$ spaces. Besides giving some interesting properties of fuzzy ${\beta}-compact^*$ spaces we also give a characterization on fuzzy $\beta$-compact spaces by making use of newly introduced concept of fuzzy $\beta$-filters.

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퍼지 논리를 이용한 차랑 추적 (Vehicle Tracking Using Fuzzy Logic)

  • 정태진;김인택
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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    • pp.154-157
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    • 2000
  • 본 논문에서는 퍼지 논리를 이용한 차량 추적 시스템의 설계에 관한 방법을 제안한다. $\alpha$-$\beta$ 필터는 고정된 $\alpha$-$\beta$에 따라 표적의 미래 위치를 예측하는데 우리는 if-then 퍼지논리를 사용하여 각 위치마다 $\alpha$,$\beta$를 바꿔줌으로써 추적을 효율적으로 하였다. 카메라 영상에 의해 들어온 차량 데이터를 표준 $\alpha$-$\beta$필터, 퍼지 논리를 이용한 $\alpha$-$\beta$필터, 칼만 필터로 추적하여 각각 비교 분석한다.

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𝛽-FUZZY FILTERS OF STONE ALMOST DISTRIBUTIVE LATTICES

  • ALEMAYEHU, TEFERI GETACHEW;GUBENA, YESHIWAS MEBRAT
    • Journal of applied mathematics & informatics
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    • 제40권3_4호
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    • pp.445-460
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    • 2022
  • In this paper, we studied on 𝛽-fuzzy filters of Stone almost distributive lattices. An isomorphism between the lattice of 𝛽-fuzzy filters of a Stone ADL A onto the lattice of fuzzy ideals of the set of all boosters of A is established. The fact that any 𝛽-fuzzy filter of A is an e-fuzzy filter of A is proved. We discuss on some properties of prime 𝛽-fuzzy filters and some topological concepts on the collection of prime 𝛽-fuzzy filters of a Stone ADL. Further we show that the collection 𝓣 = {X𝛽(λ) : λ is a fuzzy ideal of A} is a topology on 𝓕Spec𝛽(A) where X𝛽(λ) = {𝜇 ∈ 𝓕Spec𝛽(A) : λ ⊈ 𝜇}.

Discriminative Power Feature Selection Method for Motor Imagery EEG Classification in Brain Computer Interface Systems

  • Yu, XinYang;Park, Seung-Min;Ko, Kwang-Eun;Sim, Kwee-Bo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제13권1호
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    • pp.12-18
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    • 2013
  • Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.