• Proceedings of the KIEE Conference
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• 1979.08a
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• pp.164-165
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• 1979

Two dimensional kernels which reconstruct the tomographic image from the blurred one formed by simple back-projection are investigated and their performances are compared. These kernels are derived from tile point spread function of the tomographic system and have the form of a ramp filter modified by several window functions to suppress ringing in the reconstruction. Computer simulation using a computer generated phantom image data with different correction functions(kernels) has been carried out. In this simulation, filtering in frequency domain by 2-D FFT technique or in space domain by 2-D direct convolution is considered. It is found that the-computation time required for real space convolution technique is much larger than that of Fourier 2-D filtering technique in the pratical situation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.149-152
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• 1996

In this paper, the authors propose a new method for linearizing a nonlinear dynamical system by use of Volterra kernel of the nonlinear system. The authors have recently obtained a new method for measuring Volterra kernels of nonlinear control systems by use of a pseudo-random M-sequence and correlation technique. In this method, an M-sequence is applied to the nonlinear system and the crosscorrelation function between the input and the output gives us every crosssection of Volterra kernels up to 3rd order. Once we can get Volterra kernels of nonlinear system, we can construct a linearization method of the nonlinear system. Simulation results show good agreement between the observed results and the theoretical considerations.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.597-600
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• 1997

In this paper, the authors propose a new method for linearizing a nonlinear dynamical system by use of polynomial compensation. In this method, an M-sequence is applied to the nonlinear system and the crosscorrelation function between the input and the output gives us every crosssections of Volterra kernels of the nonlinear system up to 3rd order. We construct a polynomial compensation function from comparison between lst order Volterra kernel and high order kernels. The polynomial compensation function is, in this case, of third order whose coefficients are variable depending on the amplitude of the input signal. Once we can get compensation function of nonlinear system, we can construct a linearization scheme of the nonlinear system. That is. the effect of second and third order Volterra kernels are subtracted from the output, thus we obtain a sort of linearized output. The authors applied this method to a saturation-type nonlinear system by simulation, and the results show good agreement with the theoretical considerations.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1443-1448
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• 2003

Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impounded by linear models due to the lack of a similarly accepted nonlinear modelling or data based technique. The authors have recently developed a new method for obtaining Volterra kernels of up to third order by use of pseudorandom M-sequence. By use of this method, nonparametric NMPC is derived in discrete-time using multi-dimensional convolution between plant data and Volterra kernel measurements. This approach is applied to an industrial polymerisation process using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.246-248
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• 2019

In the ongoing standardization of Versatile Video Coding (VVC), DCT-2, DST-7 and DCT-8 are accounted as the vital transform kernels. While storing all of those transform kernels, ROM memory storage is considered as the major problem. So, to deal with this scenario, a common sparse unified matrix concept is introduced in this paper. From the proposed matrix, any point transform kernels (DCT-2, DST-7, DCT-8, DST-4 and DCT-4) can be achieved after some mathematical computation. DCT-2, DST-7 and DCT-8 are the used major transform kernel in this paper.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1521-1521
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• 2001

The objective of the research being presented was to construct and calibrate a spectrometer for the analysis of single kernels of corn. In light of the difficulties associated with capturing the spatial variability in composition of corn kernels by single-beam spectrometry, a hyperspectral imaging spectrometer was constructed with the intention that it would be used to analyze single kernels of corn for the prediction of moisture and oil content. The spectrometer operated in the range of 750- 1090 nanometers. After evaluating four methods of standardizing the output from the spectrometer, calibrations were made to predict whole-kernel moisture and oil content from the hyperspectral image data. A genetic algorithm was employed to reduce the number of wavelengths imaged and to optimize the calibrations. The final standard errors of prediction during cross-validation (SEPCV) were 1.22% and 1.25% for moisture and oil content, respectively. It was determined, by analysis of variance, that the accuracy and precision of single-kernel corn analysis by hyperspectral imaging is superior to the single kernel reference chemistry method (as tested).

• Journal of Biosystems Engineering
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• v.23 no.4
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• pp.343-350
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• 1998

Spectroscopic analysis of soybean kernels were made in the wavelength range of 400 to 1100 nm to find effective discrimination factors which are required for developing an opitical soybean sorter. Soybean samples used for the test were the sound and five classes of the defective kernels such as the immature, discolored(brown and violet), damaged by insect and diseased. Effective discrimination factors to classify the soybean kernels into the sound and the defective were found to be $R_{640}$, $R_{580}$/ $R_{990}$, $R_{600}$- $R_{820}$ and ( $R_{590}$- $R_{820}$)/ $R_{990}$. with classification error of less than 4%. Mahalanobis distance was used as a criterion to select significant wavelengths involved in the discrimination factors.s.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.26.3-26
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• 2001

Van de Vusse reactor is known as a highly nonlinear chemical process and has been considered by a number of researchers as a benchmark problem for nonlinear chemical process. Various identification methods for nonlinear system are also verified by applying these methods to Van de Vusse reactor. From the point of view of identification, only the Volterra kernel of second order has been obtained until now. In this paper, the authors show that Volterra kernels of nonlinear Van de Vusse reactor of up to 3rd order are obtained by use of M-sequence correlation method. A pseudo-random M-sequence is applied to Van de Vusse reactor as an input and its output is measured. Taking the cross correlation function between the input and the output, we obtain up to 3rd order Volterra kernels, which is ...

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.145-148
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• 1999

It is known that Volterra kernel models can represent a wide variety of nonlinear chemical processes. Also, it is necessary for Volterra model identification to excite the process to be identified with a signal having wide range of frequency spectrum and high enough amplitude of input signals. Kashiwagi[4 ∼ 7] has recently shown a method for measuring Volterra kernels up to third order using pseudorandom M-sequence signals. However, in practice, since it is not always possible to apply such input sequences to the actual chemical plants. Even when we can apply such a pseudorandom signal to the process, it takes much time to obtain higher order Volterra kernels. Considering these problems, the authors propose here a new method for practical identification of Volterra kernels by use of approximate open differential equation (ODE) model and simple plant test. Simulation results are shown for verifying the usefulness of our method of identification of nonlinear chemical processes.

• Journal of Korea Multimedia Society
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• v.16 no.4
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• pp.444-452
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• 2013

Operation time of a function or procedure is a thing that always needs to be optimized. Parallelizing the operation is the general method to reduce the operation time of the function. One of the most powerful parallelizing methods is using GPU. In image processing field, one of the most commonly used operations is morphology operation. Three types of morphology operations kernel, na$\ddot{i}$ve, global and shared, are presented in this paper. All kernels are made using CUDA and work parallel on GPU. Four morphology operations (erosion, dilation, opening, and closing) using square structuring element are tested on MRI images with different size to measure the speedup of the GPU implementation over CPU implementation. The results show that the speedup of dilation is similar for all kernels. However, on erosion, opening, and closing, shared kernel works faster than other kernels.