• Title/Summary/Keyword: weighting

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Frequency-Weighting linear predictive analysis of speech (Frequency-Weighting을 이용한 음성의 선형상측)

  • 김상준;윤종관;조동활
    • The Journal of the Acoustical Society of Korea
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    • v.4 no.1
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    • pp.43-54
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    • 1985
  • 이 논문에서는 Frequency weighting을 이용하여 선형예측 부호화기의 명료성을 개선하는 방법 을 연구한다. 잡음이 섞이지 않은 음성에 대해서는 음성을 분석하기전에 frequency weighting을 행한다. 또한 잡음이 섞인 음성인 경우에는 잡음성분을 spectral subtraction 방법에 의해서 제거한 다음에 frequency weighting을 준다. 이 때 frequency weighting을 주기 위해서 귀의 특성과 연관되어 잘 알려 진 C- message weighting 함수, flanagan weighting 함수 및 articulation index를 약간 수정한 weighting 함수를 사용했다. 여러 객관적인 distance measure를 사용하여 frequency weighting 방법의 성능을 측정하고 귀로 들어 본 결과, frequency weighting 방법을 사용하여 선형예측 방법에 의한 합성 음의 명료도를 효율적으로 개선할 수 있었다.

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The Comparison of the Adaptive Equalization Performance in MCMA Algorithm by the Weighting Factor (MCMA알고리즘에서 weighting factor에 의한 적응 등화 성능 비교)

  • Lim, Seung-Gag
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.10 no.4
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    • pp.137-143
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    • 2010
  • This paper deals with the performance comparison of self adaptive equalizer by the weighting factor of MCMA cost function for the compensate the amplitude and phase distortion which occurs in the communication channel. The MCMA is improves the cost function of present CMA at the output of equalizer for the minimize of error function in the amplitude and phase, the value of weighting factor is used at this time. When the comparison of equalizer performance, we classified to initial state and steady state, then it represents the convergence time and convergence speed and steady state operation of equalizer to the predetermined level, it is determined by the weighting factor. We confirm to the different result to this 2 state by weighting factor values using computer simulation. By using the result of this paper, if we appropriately choose the weighting factor values in the environment of communication channel, it is expected that the high quality digital transmission is possible.

Estimating Optimal Potential Surface for Spatial Expansion of Built-up Area by Formulating WSM-AHP Method (WSM-AHP법의 정식화를 통한 주거지 확산 지역의 최적 잠재력 표면의 추정)

  • Kim, Dae-Sik
    • Journal of The Korean Society of Agricultural Engineers
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    • v.50 no.3
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    • pp.91-104
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    • 2008
  • This study developed the WSM (weighted scenario method)-AHP method that can optimize the weighting value for multi-criteria to make GIS grid-based potential surface. The potential surface has been used to simulate urban expansion using distributed cellular automata model and to generate land-use planning as basic data. This study formulated the WSM-AHP method in mathematically and applied to test region, Suwon city, which located on south area from Seoul. WSM-AHP method generates potential map for each pair of weighting value for all criteria, which one criterion is weighted with high weighting value and the others use low weighting value, considering that the summation for all criteria weighting values should be "1". The potential change rate to the step of weighted scenario for weighting value of criteria is standardized like AHP intensity matrix in this study. From the standard potential change rate, WSM-AHP intensity matrix is completed, and then the optimal weighting value is calculated from the maximum eigenvector of the WSM-AHP matrix, according to the new WSM-AHP method developed in this study. The applied results of new method showed that the optimal weighting value from WSM-AHP is more resonable than the general AHP specialists' evaluation for weighting value. The another new finding of this study is to suggest the deterministic approach to optimize the weighting value for the distributed CA model, which is used to find new city area and to generate rational land-use planning.

Normalized Term Frequency Weighting Method in Automatic Text Categorization (자동 문서분류에서의 정규화 용어빈도 가중치방법)

  • 김수진;박혁로
    • Proceedings of the IEEK Conference
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    • 2003.11b
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    • pp.255-258
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    • 2003
  • This paper defines Normalized Term Frequency Weighting method for automatic text categorization by using Box-Cox, and then it applies automatic text categorization. Box-Cox transformation is statistical transformation method which makes normalized data. This paper applies that and suggests new term frequency weighting method. Because Normalized Term Frequency is different from every term compared by existing term frequency weighting method, it is general method more than fixed weighting method such as log or root. Normalized term frequency weighting method's reasonability has been proved though experiments, used 8000 newspapers divided in 4 groups, which resulted high categorization correctness in all cases.

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Integration of Current-mode VSFD with Multi-valued Weighting Function

  • Go, H.M.;Takayama, J.;Ohyama, S.;Kobayashi, A.
    • 제어로봇시스템학회:학술대회논문집
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    • 2003.10a
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    • pp.921-926
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    • 2003
  • This paper describes a new type of the spatial filter detector (SFD) with variable and multi-valued weighting function. This SFD called variable spatial filter detector with multi-valued weighting function (VSFDwMWF) uses current-mode circuits for noise resistance and high-resolution weighting values. Total weighting values consist of 7bit, 6-signal bit and 1-sign bit. We fabricate VSFDwMWF chip using Rohm 0.35${\mu}$m CMOS process. VSFDwMWF chip includes two-dimensional 10${\times}$13 photodiode array and current-mode weighting control circuit. Simulation shows the weighting values are varied and multi-valued by external switching operation. The layout of VSFDwMWF chip is shown.

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An Information-theoretic Approach for Value-Based Weighting in Naive Bayesian Learning (나이브 베이시안 학습에서 정보이론 기반의 속성값 가중치 계산방법)

  • Lee, Chang-Hwan
    • Journal of KIISE:Databases
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    • v.37 no.6
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    • pp.285-291
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    • 2010
  • In this paper, we propose a new paradigm of weighting methods for naive Bayesian learning. We propose more fine-grained weighting methods, called value weighting method, in the context of naive Bayesian learning. While the current weighting methods assign a weight to an attribute, we assign a weight to an attribute value. We develop new methods, using Kullback-Leibler function, for both value weighting and feature weighting in the context of naive Bayesian. The performance of the proposed methods has been compared with the attribute weighting method and general naive bayesian. The proposed method shows better performance in most of the cases.

노외계측기 반응률 계산을 위한 Weighting Function 민감도 분석

  • 이덕중;김윤호;김용배;이상희;하창주
    • Proceedings of the Korean Nuclear Society Conference
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    • 1997.10a
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    • pp.50-57
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    • 1997
  • 영광 2호기 9주기 노심을 대상으로 다양한 운전조건에서 노외계측기 weighting function을 계산하고 영향 인자들에 대한 민감도 분석을 수행하였다. Weighting function 계산은 2차원 각분할 수송코드인 DORT 2.8.14를 사용하였고 핵단면적 라이브러리는 ENDF/B-VI에 근거한 BUGLE93 라이브러리를 사용하였다. Weighting function은 축방향 weighting function(R-Z 모델)과 집합체별 weighting function(R- 모델)을 계산하였고, 민감도 분석에 사용한 인자는 출력준위, 연소도, 제어봉 삽입, 붕소농도이다. 민감도 분석결과 노외계측기 weighting function은 출력 준위에 민감하고 그외 모든 인자의 영향은 무시할 수 있을 만큼 작았다. 또한 출력분포와 weighting function으로부터 계산되는 단순노외계측기 교정법의 계측기반응상수는 출력준위와 연소도를 고려하여 생산해야함을 확인하였다.

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The Ordered Weighted Averaging (OWA) Operator Weighting Functions with Constant Value of Orness and Application to the Multiple Criteria Decision Making Problems (순위가 있는 가중치 평균 방법에서 일정한 수준의 결합력을 갖는 가중치 함수의 성질 및 다기준의사결정 문제에의 활용)

  • Ahn, Byeong-Seok
    • Asia pacific journal of information systems
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    • v.16 no.1
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    • pp.85-101
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    • 2006
  • Actual type of aggregation performed by an ordered weighted averaging (OWA) operator heavily depends upon the weighting vector. A number of approaches have been suggested for obtaining the associated weights. In this paper, we present analytic forms of OWA operator weighting functions, each of which has such properties as rank-based weights and constant value of orness, irrespective of number of objectives aggregated. Specifically, we propose four analytic forms of OWA weighting functions that can be positioned at 0.25, 0.334, 0.667, and 0.75 on the orness scale. The merits for using these weights over other weighting schemes can be mentioned in a couple of ways. Firstiy, we can efficiently utilize the analytic forms of weighting functions without solving complicated mathematical programs once the degree of orness is specified a priori by decision maker. Secondly, combined with well-known OWA operator weights such as max, min, and average, any weighting vectors, having a desired value of orness and being independent of the number of objectives, can be generated. This can be accomplished by convex combinations of predetermined weighting functions having constant values of orness. Finally, in terms of a measure of dispersion, newly generated weighting vectors show just a few discrepancies with weights generated by maximum entropy OWA.

An Investigation of Automatic Term Weighting Techniques

  • Kim, Hyun-Hee
    • Journal of the Korean Society for information Management
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    • v.1 no.1
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    • pp.43-62
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    • 1984
  • The present study has two main objectives. The first objective is to devise a new term weighting technique which can be used to weight the significance value of each word stem in a test collection of documents on the subject of "enteral hyperalimentation." The next objective is to evaluate retrieval performance of proposed term weighting technique, together with four other term weighting techniques, by conducting a set of experiments. The experimental results have shown that the performance of Sparck Jones's inverse document frequency weighting and the proposed term significance weighting techniques produced better recall and precision ratios than the other three complex weighting techniques.

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An Optimal Weighting Method in Supervised Learning of Linguistic Model for Text Classification

  • Mikawa, Kenta;Ishida, Takashi;Goto, Masayuki
    • Industrial Engineering and Management Systems
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    • v.11 no.1
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    • pp.87-93
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    • 2012
  • This paper discusses a new weighting method for text analyzing from the view point of supervised learning. The term frequency and inverse term frequency measure (tf-idf measure) is famous weighting method for information retrieval, and this method can be used for text analyzing either. However, it is an experimental weighting method for information retrieval whose effectiveness is not clarified from the theoretical viewpoints. Therefore, other effective weighting measure may be obtained for document classification problems. In this study, we propose the optimal weighting method for document classification problems from the view point of supervised learning. The proposed measure is more suitable for the text classification problem as used training data than the tf-idf measure. The effectiveness of our proposal is clarified by simulation experiments for the text classification problems of newspaper article and the customer review which is posted on the web site.