• Journal of the Chungcheong Mathematical Society
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• v.24 no.1
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• pp.35-43
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• 2011

In this paper we define the convergence of ${\delta}$-filters and study them. We show that ${\delta}$-filters on a Hausdorff space X converge at most one point in X. We also show that in a P-space X, ${\delta}$-filters on X converge at most one point in X if and only if X is a Hausdorff space.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.3
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• pp.1-14
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• 2024

Gaining an accurate 3D stream geometry has become feasible with Unmanned Aerial Vehicle (UAV), which is crucial for better understanding stream hydrodynamic processes. The objective of this study was to investigate series of filters to remove stream vegetation and propose the best method for generating Digital Terrain Models (DTMs) using UAV-based point clouds. A stream reach approximately 500 m of the Bokha stream in Icheon city was selected as the study area. Point clouds were obtained in August 1st, 2023, using Phantom 4 multispectral and Zenmuse L1 for Structure from Motion (SfM) and Light Detection And Ranging (LiDAR) respectively. Three vegetation filters, two morphological filters, and six composite filters which combined vegetation and morphological filters were applied in this study. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were used to assess each filters comparing with the two cross-sections measured by leveling survey. The vegetation filters performed better in SfM, especially for short vegetation areas, while the morphological filters demonstrated superior performance on LiDAR, particularly for taller vegetation areas. Overall, the composite filters combining advantages of two types of filters performed better than single filter application. The best method was the combination of Progressive TIN (PTIN) and Color Indicies of Vegetation Extraction (CIVE) for SfM, showing the smallest MAE of 0.169 m. The proposed method in this study can be utilized for constructing DTMs of stream and thus contribute to improving the accuracy of stream hydrodynamic simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.781-785
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• 2011

The notions of soft filters, ultra soft filters and bases of a soft filter are introduced and their basic properties are investigated. The adherence and convergence of soft filters in soft topological spaces with related results is also discussed.

• Korean Journal of Mathematics
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• v.10 no.1
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• pp.25-28
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• 2002

We introduce the notion of $s{\gamma}$-sets, and we investigate some properties of $s{\gamma}$-sets. In particular, we characterize the $s{\gamma}$-closure by terms of supra-convergence of filters.

• The Pure and Applied Mathematics
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• v.3 no.1
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• pp.33-40
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• 1996

A convergence structure defined by Kent [4] is a correspondence between the filters on a given set X and the subsets of X which specifies which filters converge to points of X. This concept is defined to include types of convergence which are more general than that defined by specifying a topology on X. Thus, a convergence structure may be regarded as a generalization of a topology. With a given convergence structure q on a set X, Kent [4] introduced associated convergence structures which are called a topological modification and a pretopological modification. (omitted)

• Journal for History of Mathematics
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• v.14 no.2
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• pp.13-20
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• 2001

The topological structure of a topological space is completely determined by the data of convergence of filters on the space. We study the origin of convergence structure in the setting of filters and nets and their ramifications.

• The Pure and Applied Mathematics
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• v.12 no.2 s.28
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• pp.105-124
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• 2005

In this paper convergence of fuzzy filters and graded fuzzy filters have been studied in graded L-fuzzy topological spaces.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2E
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• pp.14-20
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• 1997

The future teleconferencing systems will need an appropriate system which controls properly the acoustic echo for the convenient communication. The conventional acoustic echo cancellation algorithms involve large adaptive filters identifying the impulse response of the echo path. The use of adaptive IIR filters appears to be a reasonable way to reduce computational complexity. Effective cancellation of acoustic echo presented in teleconferencing system requires that adaptive filters have a rapid convergence speed. One of the main problems of acoustic echo cancellation techniques is that the convergence properties degrade for an highly correlated signal input such as speech signals. By the way, the introduction of linear prediction filers onto the structure of the acoustic echo cancellation represents one approach to decorrelate the speech signal. And variable step-size LMS algorithm improves the convergence speed through a little increasing of computational complexity. In this paper, we applied these two methods to the acoustic echo canceller(AEC) and showed that these methods have better performances than the conventional AEC.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.528-537
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• 2021

Face recognition has gained significant notice because of its application in many businesses: security, healthcare, and marketing. In this paper, we will present the recognition method using the combination of correlation filters (CF) and Support Vector Machine (SVM). Firstly, we evaluate the performance and compared four different correlation filters: minimum average correlation energy (MACE), maximum average correlation height (MACH), unconstrained minimum average correlation energy (UMACE), and optimal-tradeoff (OT). Secondly, we propose the machine learning approach by using the OT correlation filter for features extraction and SVM for classification. The numerical results on National Cheng Kung University (NCKU) and Pointing'04 face database show that the proposed method OT-SVM gets higher accuracy in face recognition compared to other machine learning methods. Our approach doesn't require graphics card to train the image. As a result, it could run well on a low hardware system like an embedded system.

• ETRI Journal
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• v.42 no.1
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• pp.26-35
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• 2020

Herein, we consider uplink multiuser massive multiple-input multiple-output systems when multiple users transmit information symbols to a base station (BS) by applying simple space-time block coding (STBC). At the BS receiver, two detection filters for each user are used to detect the STBC information symbols. One of these filters is for odd-indexed symbols and the other for even-indexed symbols. Using constrained output variance metric minimization, we first derive a special relation between the closed-form optimal solutions for the two detection filters. Then, using the derived special relation, we propose a new blind adaptive algorithm for implementing the minimum output variance-based optimal filters. In the proposed adaptive algorithm, filter weight vectors are updated only in the region satisfying the special relation. Through a theoretical analysis of the convergence speed and a computer simulation, we demonstrate that the proposed scheme exhibits faster convergence speed and lower steady-state bit error rate than the conventional scheme.