• 제목/요약/키워드: parameters fitting

검색결과 707건 처리시간 0.039초

Geometric Fitting of Parametric Curves and Surfaces

  • Ahn, Sung-Joon
    • Journal of Information Processing Systems
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    • 제4권4호
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    • pp.153-158
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    • 2008
  • This paper deals with the geometric fitting algorithms for parametric curves and surfaces in 2-D/3-D space, which estimate the curve/surface parameters by minimizing the square sum of the shortest distances between the curve/surface and the given points. We identify three algorithmic approaches for solving the nonlinear problem of geometric fitting. As their general implementation we describe a new algorithm for geometric fitting of parametric curves and surfaces. The curve/surface parameters are estimated in terms of form, position, and rotation parameters. We test and evaluate the performances of the algorithms with fitting examples.

Digital Hearing Aid DSP Chip Parameter Fitting Optimization

  • Jarng, Soon-Suck;Kwon, You-Jung;Lee, Je-Hyung
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2005년도 ICCAS
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    • pp.1820-1825
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    • 2005
  • DSP chip parameters of a digital hearing aid (HA) should be optimally selected or fitted for hearing impaired persons. The more precise parameter fitting guarantees the better compensation of the hearing loss (HL). Digital HAs adopt DSP chips for more precise fitting of various HL threshold curve patterns. A specific DSP chip such as Gennum GB3211 was designed and manufactured in order to match up to about 4.7 billion different possible HL cases with combination of 7 limited parameters. This paper deals with a digital HA fitting program which is developed for optimal fitting of GB3211 DSP chip parameters. The fitting program has completed features from audiogram input to DSP chip interface. The compensation effects of the microphone and the receiver are also included. The paper shows some application examples.

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디지털 보청기 DSP Chip 파라미터 적합 최적화 (Digital Hearing Aid DSP Chip Parameter Fitting Optimization)

  • 장순석
    • 제어로봇시스템학회논문지
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    • 제12권6호
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    • pp.530-538
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    • 2006
  • DSP chip parameters of a digital hearing aid (HA) should be optimally selected or fitted for hearing impaired persons. The more precise parameter fitting guarantees the better compensation of the hearing loss (HL). Digital HAs adopt DSP chips for more precise fitting of various HL threshold curve patterns. A specific DSP chip such as Gennum GB3211 was designed and manufactured in order to match up to about 4.7 billion different possible HL cases with combination of 7 limited parameters. This paper deals with a digital HA fitting program which is developed for optimal fitting of GB3211 DSP chip parameters. The fitting program has completed features from audiogram input to DSP chip interface. The compensation effects of the microphone and the receiver are also included. The paper shows some application examples.

디지털 보청기 적합 검증을 위한 전기음향 시험장치 개발 (Digital Hearing Aid Fitting Program Testing System Development)

  • 장순석;권유정;이제형
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2005년도 추계종합학술대회
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    • pp.415-418
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    • 2005
  • DSP chip parameters of a digital hearing aid (HA) should be optimally selected or fitted for hearing impaired persons. The more precise parameter fitting guarantees the better compensation of the hearing loss (HL). Digital HAs adopt DSP chips for more precise fitting of various HL threshold curve patterns. A specific DSP chip such as Gennum GB3211 was designed and manufactured in order to match up to about 4.7 billion different possible HL cases with combination of 7 limited parameters. This paper deals with a digital HA fitting program which is developed for optimal fitting of GB3211 DSP chip parameters. The fitting program has completed feature from audiogram input to DSP chip interface. The compensation effects of the microphone and the receiver are also included. The paper shows some application examples.

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Influence in Fitting an Equicorrelation Model

  • Kim, Myung Geun;Jung, Kang-Mo
    • Communications for Statistical Applications and Methods
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    • 제8권3호
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    • pp.841-849
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    • 2001
  • The influence in fitting an equicorrelation model is investigated using the influence function. The influence functions for the model parameters are derived and its sample versions are used for investigating the influence of observations on the estimators of the parameters. Some relationships among the sample versions are found. We will derive a measure for identifying observations that have a large influence on the test of fitting the equicorrelation model using the influence function method. An example is given for illustration.

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3차원 측정기를 이용한 Airfoil Edge 형상의 Fitting 방법에 관한 연구 (A Study on Fitting the Edge Profile of Airfoil with Coordinate Measuring Machines)

  • 강진우;변재현
    • 산업공학
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    • 제13권4호
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    • pp.703-708
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    • 2000
  • In manufacturing processes, manufacturing features always deviate somewhat from their nominal design specifications due to several types of errors. This study suggests a fitting algorithm of the geometric profile parameters of leading and trailing edges for turbine compressor airfoils. In reality, industry personnels inspect the airfoil profile by trial-and-error method to determine the geometric feature parameters. In this study we propose an exploration approach based on factorial design with center point to minimize the effect of measurement errors caused by probe slip. By adopting the fitting method developed in this paper, one can enhance the precision and efficiency of fitting the airfoil edge profile.

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Effects of Edge Detection on Least-squares Model-image Fitting Algorithm

  • Wang, Sendo;Tseng, Yi-Hsing;Liou, Yan-Shiou
    • 대한원격탐사학회:학술대회논문집
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    • 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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    • pp.159-161
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    • 2003
  • Fitting the projected wire-frame model to the detected edge pixels on images by using least-squares approach, called Least-squares Model-image Fitting (LSMIF), is the key of the Model-based Building Extraction (MBBE). It is implemented by iteratively adjusting the model parameters to minimize the squares sum of distances from the extracted edge pixels to the projected wire-frame. This paper describes a series of experiments and studies on various factors affect the fitting results, including the edge detectors, the weighting rules, the initial value of parameters, and the number of overlapped images. The experimental result is not only helpful to clarify the influences of each factor, but is also able to enhance the robustness of the LSMIF algorithm.

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PDA를 이용한 이동형 보청기 보정 시스템 (PDA-Based Hearing Aids fitting System)

  • 윤태호;김경섭;신승원;이상민
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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    • pp.241-243
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    • 2006
  • In this research, we tried to implement a PDA (Personal Digital Assistant)-based hearing aids fitting system to manipulate hearing aids-fitting parameters in the user's local environment. Due to the inherent portability of PDA system, we can consequently perform the hearing aids fitting operation without visiting a special site.

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디지털 보청기의 자동 보정 파라미터 추출 (Auto fitting Parameter Extraction for Digital Hearing Aids)

  • 석수영;정호열;정현열
    • 한국멀티미디어학회논문지
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    • 제3권5호
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    • pp.495-505
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    • 2000
  • 본 논문에서는 난청환자의 청각손실로부터 디지털 보청기의 보정 파라미터를 자동적으로 추출하는 방법을 제안하고 이를 이용한 자동보정시스템을 구현한다. 디지털 보청기의 보정 파라미터는 난청환자의 청각 특성 오디오그램으로부터 자동으로 추출하였으며, 추출된 보정 파라미터는 실제 GM3036 디지털 보청기를 이용하여 보정 시스템으로 구현되었으며, 각 파라미터는 이론적인 2cc 출력에 근접하도록 테이블화 하였다. 제안한 자동 보정 시스템을 적용하여 검사기로부터 출력을 검증하였고, 50명에게 사용하게 한후 만족돌르 조사한 결과 자동 보정 시스템의 유효성을 확인할 수 있었다.

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Learning Less Random to Learn Better in Deep Reinforcement Learning with Noisy Parameters

  • Kim, Chayoung
    • 한국정보기술학회 영문논문지
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    • 제9권1호
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    • pp.127-134
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    • 2019
  • In terms of deep Reinforcement Learning (RL), exploration can be worked stochastically in the action of a state space. On the other hands, exploitation can be done the proportion of well generalization behaviors. The balance of exploration and exploitation is extremely important for better results. The randomly selected action with ε-greedy for exploration has been regarded as a de facto method. There is an alternative method to add noise parameters into a neural network for richer exploration. However, it is not easy to predict or detect over-fitting with the stochastically exploration in the perturbed neural network. Moreover, the well-trained agents in RL do not necessarily prevent or detect over-fitting in the neural network. Therefore, we suggest a novel design of a deep RL by the balance of the exploration with drop-out to reduce over-fitting in the perturbed neural networks.