• Journal of the Korean Society of Manufacturing Process Engineers
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• v.5 no.1
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• pp.7-12
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• 2006

Recently, the fractal interpolation methods have been widely introduced and used to estimate and analyze various theoretical and experimental data. Because of the chaotic behaviors of dynamic cutting force data, some method for end-milling force analysis must be used. The fractal analysis used in this paper is fractal linear interpolation and fractal dimension. Also, several methods for computing fractal dimensions have been used in which the fractal dimension of the typical dynamic end-milling force was calculated according to number of data points that are generally lower than 200 data points sampled. This fractal analysis shows a possible prediction of end-milling force that has some dynamic chatter property or stationary property in endmilling operation.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.3
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• pp.67-72
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• 2006

There are many modelling methods using theoretical and experimental data. Recently, fractal interpolation methods have been widely used to estimate and analyze various data. Due to the chaotic nature of dynamic roundness profile data in roundness some desirable method must be used for the analysis which is natural to time series data. Fractal analysis used in this paper is within the scope of the fractal interpolation and fractal dimension. Also, two methods for computing the fractal dimension has been introduced which can obtain the dimension of typical dynamic roundness profile data according to the number of data points in which the fixed data are generally lower than 200 data points. This fractal analysis result shows a possible prediction of roundness profile that has some different roundness profile in round shape operation.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.5
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• pp.157-162
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• 2013

To analyze the dynamic acceleration characteristics, it is necessary to identify the acceleration model using some methods that can represent the dynamic properties well. In this sense, fractal methods were used for the verification of characteristics of an acceleration signal. To estimate and analyze the geometry of acceleration signal, a fractal interpolation and its analysis was introduced in this paper. The chaotic nature of acceleration signal was considered in fractal modeling. In this study the fractal signal modeling has brought a focus within the scope of the fractal interpolation and fractal dimension. And a new idea of fractal dimension has been introduced and discussed considering the damping ratio and amplitude for its dynamic properties of the signal. The fractal dimension of acceleration with respect to the scaling factor using fixed data points of 1000 points was calculated and discussed. The acceleration behaviors of this results show some different characteristics. And this fractal analysis can be applied to other signal analysis of several machining such as pendulum type grinding and milling which has many dynamic properties in the signal.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5955-5977
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• 2018

Performance of the interpolation algorithm used in the technique of bi-dimensional empirical mode decomposition directly affects its popularization and application, so that the researchers pay more attention to the algorithm reasonable, accurate and fast. However, it has been a lack of an adaptive interpolation algorithm that is relatively satisfactory for the bi-dimensional empirical mode decomposition (BEMD) and is derived from the image characteristics. In view of this, this paper proposes an image interpolation algorithm based on the particle swarm and fractal. Its procedure includes: to analyze the given image by using the fractal brown function, to pick up the feature quantity from the image, and then to operate the adaptive image interpolation in terms of the obtained feature quantity. All parameters involved in the interpolation process are determined by using the particle swarm optimization algorithm. The presented interpolation algorithm can solve those problems of low efficiency and poor precision in the interpolation operation of bi-dimensional empirical mode decomposition and can also result in accurate and reliable bi-dimensional intrinsic modal functions with higher speed in the decomposition of the image. It lays the foundation for the further popularization and application of the bi-dimensional empirical mode decomposition algorithm.

• Journal of Biomedical Engineering Research
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• v.17 no.1
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• pp.121-128
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• 1996

This paper presents the ECG data compression method referred the adaptive fractal interpolation algorithm. In the previous piecewise fractal interpolation(PFI) algorithm, the size of range is fixed So, the reconstruction error of the PFI algorithm is nonuniformly distributed in the part of the original ECG signal. In order to improve this problem, the adaptive fractal interpolation(AEI) algorithm uses the variable range. If the predetermined tolerance was not satisfied, the range would be subdivided into two equal size blocks. large ranges are used for encoding the smooth waveform to yield high compression efficiency, and the smaller ranges are U for encoding rapidly varying parts of the signal to preserve the signal quality. The suggested algorithm was evaluated using MIT/BIH arrhythmia database. The AEI algorithm was found to yield a relatively low reconstruction error for a given compression ratio than the PFI algorithm. In applications where a PRD of about 7.13% was acceptable, the ASI algorithm yielded compression ratio as high as 10.51, without any entropy coding of the parameters of the fractal code.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.12
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• pp.45-61
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• 1996

This paper presents the ECG data compression using wavelet transform (WT) and adaptive fractal interpolation (AFI). The WT has the subband coding scheme. The fractal compression method represents any range of ECG signal by fractal interpolation parameters. Specially, the AFI used the adaptive range sizes and got good performance for ECG data cmpression. In this algorithm, the AFI is applied into the low frequency part of WT. The MIT/BIH arhythmia data was used for evaluation. The compression rate using WT and AFI algorithm is better than the compression rate using AFI. The WT and AFI algorithm yields compression ratio as high as 21.0 wihtout any entropy coding.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.221-224
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• 1996

This paper presents the ECG data compression using wavelet transform(WT) and adaptive fractal interpolation(AFI). The WT has the subband coding scheme. The fractal compression method represents any range of ECG signal by fractal interpolation parameters. Specially, the AFI used the adaptive range sizes and got good performance for ECG data compression. In this algorithm, the AFI is applied into the low frequency part of WT. The MIT/BIH arrhythmia data was used for evaluation. The compression rate using WT and AFI algorithm is better than the compression rate using AFI. The WT and AFI algorithm yields compression ratio as high as 21.0 without any entroy coding.

• Journal of Biomedical Engineering Research
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• v.16 no.1
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• pp.17-24
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• 1995

This paper presents a new compression method (or ECG data using iterated contractive transformations. The method represents any range of ECG signal by piecewise self-afrine fractal Interpolation (PSAFI). The piecewise self-afrine rractal model is used where a discrete data set is viewed as being composed of contractive arfine transformation of pieces of itself. This algorithm was evaluated using MIT/BIH arrhythmia database. PSAFI is found to yield a relatively low reconstruction error for a given compression ratio than conventional direct compression methods. The compression ratio achieved was 883.9 bits per second (bps) - an average percent rms difference (AFRD) of 5.39 percent -with the original 12b ECG samples digitized at 400 Hz.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.82-91
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• 1996

In this paper, an image coding technique using fractal interpolation is proposed. Similar to the conventional methods, an image is partitioned into blocks and each block is coded independently. However, an interpolation point is ahsared by its neighboring blocks. This means that each block can use all its interpolation points with minimal increase of new data. For a simple implementation, triangular blocks are used instead of square blocks and new coefficients are difined. Data obtained in the encoding process hav estatistical characteristics suitable sfor entropy coding, an dthus arithmetic coding is perfomred for improving the compression efficiency. The results of the proposed coder in comparison with those of a conventional coder show that the interpolation method reduces block effect caused by a memoryless block coder, especially at low bit rates. This improvement is due to sharing of information between adjacent blocks. Moreover, th enumber of iteration required in ecoding process is reduced since more information is used to decode each block.

• Journal of Korea Water Resources Association
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• v.45 no.9
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• pp.943-957
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• 2012

In this study, a technique based on Fractal Theory with Erosion Model was developed to interpolate the river morphology data at the border area between river bed and river side where both surface and under water surveyings can not be committed easily. Three dimensional river morphology data along the Ara River was generated by the developed technique. The Ara River is an artificially constructed waterway for vessels between the Han River and West Sea of Korea. The result was compared with the survey data by RMSE of 0.384, while the IDW interpolation result has RMSE of 0.802. Consequently, the developed river morphology data interpolation technique using Erosion Model based Fractal Theory is conceived to be superior to the IDW which has been generally used in generating the river morphology data.