• Journal of Sensor Science and Technology
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• v.26 no.4
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• pp.259-265
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• 2017

Magnetometer calibration must be performed before the use of three-axis magnetometers to ensure the accuracy of orientation estimation. Recently, one of the most popular calibration approaches is the ellipsoid fitting technique due to its high performance in calibration. To date, in fact, performances of the existing ellipsoid fitting methods have been evaluated with full range rotation data. However, in case of the calibration of magnetometers attached to vehicles, ships, and planes, it is very difficult to collect the full range rotation data since their allowable ranges in terms of roll and pitch are limited to small. This constraint may result in serious performance degradation of some ellipsoid fitting algorithms. Therefore, to be practical, this paper proposes a weighted least square-based magnetometer calibration method that is robust in roll-pitch limited conditions. Furthermore, the proposed method is a linear approach and thus is free from the well-known initial value issue in nonlinear approaches. Experimental results show the superiority of the proposed method to other ellipsoid-fitting calibration methods.

• The Journal of the Acoustical Society of Korea
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• v.31 no.5
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• pp.273-279
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• 2012

In this thesis, a nonlinear compression fitting method was studied for each frequency channel of a 64 channel digital hearing aid. Unlike conventional fitting formula method done from the result of the hearing loss test, the present fitting method uses the auditory threshold of sound pressure measured near the tympanic membrane while ITE (In-The-Ear) hearing aid is fitted into the user's ear canal. Also, the spectral distribution of the voice sound pressure was used for realizing of output sound pressure compression curves against input sound pressure level. Theoretical research results of FFT-iFFT compression algorithm has been evaluated by experimental gain measurements at each different input sound pressure level 50 dB, 70 dB, 90 dB respectively.

• Smart Structures and Systems
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• v.3 no.4
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• pp.423-437
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• 2007

The empirical mode decomposition (EMD) method is well-known for its ability to decompose a multi-component signal into a set of intrinsic mode functions (IMFs). The method uses a sifting process in which local extrema of a signal are identified and followed by a spline fitting approximation for decomposition. This method provides an effective and robust approach for decomposing nonlinear and non-stationary signals. On the other hand, the IMF components do not automatically guarantee a well-defined physical meaning hence it is necessary to validate the IMF components carefully prior to any further processing and interpretation. In this paper, an attempt to use the EMD method to identify properties of nonlinear elastic multi-degree-of-freedom structures is explored. It is first shown that the IMF components of the displacement and velocity responses of a nonlinear elastic structure are numerically close to the nonlinear normal mode (NNM) responses obtained from two-dimensional invariant manifolds. The IMF components can then be used in the context of the NNM method to estimate the properties of the nonlinear elastic structure. A two-degree-of-freedom shear-beam building model is used as an example to illustrate the proposed technique. Numerical results show that combining the EMD and the NNM method provides a possible means for obtaining nonlinear properties in a structure.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.12 s.354
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• pp.33-39
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• 2006

An empirical nonlinear model with intrinsic nonlinear elements has been newly developed to predict the RF nonlinear characteristics of nano-scale bulk MOSFET accurately over the wide bias range. Using an extraction method suitable for nano-scale MOSFET, the bias-dependent data of intrinsic model parameters have been accurately obtained from measured S-parameters. The intrinsic nonlinear capacitance and drain current equations have been empirically obtained through 3-dimensional curve-fitting to their bias-dependent curves. The modeled S-parameters of 60nm MOSFET have good agreements with measured ones up to 20GHz in the wide bias range, verifying the accuracy of the nano-scale MOSFET model.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.571-575
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• 2003

In this paper, we describe a method for fuzzy polynomial regression analysis for fuzzy input--output data using shape preserving operations for least-squares fitting. Shape preserving operations simplifies the computation of fuzzy arithmetic operations. We derive the solution using mixed nonlinear program.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.759-768
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• 2011

Tree algorithms have been widely developed for regression problems. One of the good features of a regression tree is the flexibility of fitting because it can correctly capture the nonlinearity of data well. Especially, data with sudden structural breaks such as the price of oil and exchange rates could be fitted well with a simple mixture of a few piecewise linear regression models. Now that split points are determined by chi-squared statistics related with residuals from fitting piecewise linear models and the split variable is chosen by an objective criterion, we can get a quite reasonable fitting result which goes in line with the visual interpretation of data. The piecewise linear regression by a regression tree can be used as a good fitting method, and can be applied to a dataset with much fluctuation.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.119-122
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• 1986

An algorithm is proposed to find a stable rational function, which is frequently used in the linear system theory, by curve-fitting a given data. This problem is essentially a nonolinear optimization problem. In order to converge faster to the solution, the following method is used. First, the coefficients of the denominator polynomial are fixed and only the coefficients of the numerator polynomial are adjusted by its linear relationships. Then the coefficients of the numerator are fixed and the coefficients of the denominator polynomial are adjusted by nonlinear programming. This whole process is repeated until a convergent solution is found. The solution obtained by this method converges better than by other algorithms and its versatility is demonstrated by applying it to the design of a feedback control system and a low pass filter.

• Journal of the Korean Institute of Telematics and Electronics
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• v.10 no.6
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• pp.45-55
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• 1973

This paper discusses the nonlinear time-invarient circuit composed of a tunnel diode. Prior to determine the solution of the nonlinear network which has negative resistance elements, the static characteristics of the nonlinear resistance elements need to be represented by function. Polynomial curve fitting is discussed to represent the static characteristies by least squares approximation. In order to solve the nonlinear network, the state equations for the networks are set up and solved by prediction corrector method. Finally, the limit cycle is plotted to discuss the stability of the nonlinear network and the oscillation condition.

• Preventive Nutrition and Food Science
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• v.3 no.2
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• pp.123-127
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• 1998

Fixed-bed adsorption was adapted to separate phenolic acids from diluted phenolic solution. Break-through curve was obtained by nonlinear curve fitting method, and breakpoint, saturation time, and mass transfer coeffi-cient were calculated . Break point and saturation time were reached slower with $\rho$-coumaric acid than ferulic acid .The p-coumaric acid, having small molecular weight, is suposedly traveled longer pathway in characoal than ferulic acid. Fixed-bed adsorption iwht gallic acid having more hydroxyl functional group than other phenolic acids showed break point arrival and the largest saturation time. This fact means that there was bigger electrostatic affinity between gallic acid and charcoal than between other phenolic acids and charcoal.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.816-824
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• 2009

In the face space, face images due to illumination and pose variations have a nonlinear distribution. Active Appearance Models (AAM) based on the linear model have limits to the nonlinear distribution of face images. In this paper, we assume that a few clusters of face images are given; we build local AAMs according to the clusters of face images, and then select a proper AAM model during the fitting phase. To solve the problem of updating fitting parameters among the models due to the model changing, we propose to build in advance relationships among the clusters in the parameter space from the training images. In addition, we suggest a gradual model changing to reduce improper model selections due to serious fitting failures. In our experiment, we apply the proposed model to Yale Face Database B and compare it with the previous method. The proposed method demonstrated successful fitting results with strongly illuminated face images of deep shadows.