• 유체기계공업학회:학술대회논문집
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• 2003.12a
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• pp.154-160
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• 2003

Multi-path ultrasonic flowrate measuring technology is being received much attentions from a variety of industrial fields to exactly measure the flowmeter. Multi-path ultrasonic flowmeter has much advantage since it has no moving parts and not occurred pressure loss. It offers good accuracy, repeatability, linearity and Tum-down ratio can measure over 1:50. The present study investigates flowrate integration errors using weighting factors. A theoretical flow model uses power law to describe a fully developed velocity profiles and wall roughness changes. The methods of weighting factor simulate three configurations of measuring location of gaussian, chebyshev and tailor method. The obtained results show that many chord arrangements are not affected for wall roughness changes and can measure accurate flowrate.

• 유체기계공업학회:학술대회논문집
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• 2005.12a
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• pp.287-294
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• 2005

Flow rate measurement uncertainties of the ultrasonic flow meter are generally influenced by many different factors, such as Reynolds number, flow distortion, turbulence intensity, wall surface roughness, velocity integration method along the acoustic paths, and transducer installation method, etc. Of these influencing factors, one of the most important uncertainties comes from the velocity integration method. In the present study, a optimization weighting factor method for 5-chord, which is given by a function of the chord locations of acoustic paths, is employed to obtain the mean velocity in the flow through a pipe. The power law profile is assumed to model the axi-symmetric pipe flow and its results are compared with the present weighting factor concept. For an asymmetric pipe flow, the Salami flow model is applied to obtain the velocity profiles. These theoretical methods are also compared with the previous Gaussian, Chebyshev, and Tailor methods. The results obtained show that for the fully developed turbulent pipe flows with surface roughness effects, the present weighting factor method is much less sensitive than Chebyshev and Tailor methods, leading to a better reliability in flow rate measurement using the ultrasonic flow meters.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1024-1026
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• 1998

In this paper we present a Neural network approach to select weighting matrices of Linear-Quadratic-Gaussian (LQG) controller for TCSC control. The selection of weighting matrices is usually carried out by trial and error. A weighting matrices of LQG control selected effectively using Neural network. It is shown that simulation results in application of this method to one machine infinite bus system are satisfactory.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.212-219
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• 1999

In this paper we present a neural network approach to select weighting matrices of Linear-Quadratic-Gaussian(LQG) controller for TCSC control. The selection of weighting matrices is usually carried out by trial and error. A weighting matrices of LQG control are selected effectively using Kohonen network. It is shown that simulation results in application of this method to three machine nine bus system are satisfactory.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.73-76
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• 2018

A Gaussian noise is caused by surrounding environment or channel interference when transmitting image. The noise reduces not only image quality degradation but also high-level image processing performance. The Non-Local Means (NLM) filter finds similarity in the neighboring sets of pixels to remove noise and assigns weights according to similarity. The weighted average is calculated based on the weight. The NLM filter method shows low noise cancellation performance and high complexity in the process of finding the similarity using weight allocation and neighbor set. In order to solve these problems, we propose an algorithm that shows an excellent noise reduction performance by using Summed Square Image (SSI) to reduce the complexity and applying the weighting function based on a cosine Gaussian kernel function. Experimental results demonstrate the effectiveness of the proposed algorithm.

• The Journal of the Acoustical Society of Korea
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• v.25 no.8
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• pp.370-374
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• 2006

Mel-frequency cepstral coefficients are widely used as the feature for speech recognition. In FMCC extraction process. the spectrum. obtained by Fourier transform of input speech signal is divided by met-frequency bands, and each band energy is extracted for the each frequency band. The coefficients are extracted by the discrete cosine transform of the obtained band energy. In this Paper. we calculate the output energy for each bandpass filter by taking the weighting function when applying met-frequency scaled bandpass filter. The weighting function is Gaussian distributed function whose center is at the formant frequency In the experiments, we can see the comparative performance with the standard MFCC in clean condition. and the better Performance in worse condition by the method proposed here.

• Journal of Korea Multimedia Society
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• v.5 no.5
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• pp.512-522
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• 2002

In this paper, we propose a hybrid method which is mixed with frame selection and weighting model rank method, based on GMM(gaussian mixture model), for real-time text-independent speaker recognition system. In the system, maximum likelihood estimation was used for GMM parameter optimization, and maximum likelihood was used for recognition basically Proposed hybrid method has two steps. First, likelihood score was calculated with speaker models and test data at frame level, and the difference is calculated between the biggest likelihood value and second. And then, the frame is selected if the difference is bigger than threshold. The second, instead of calculated likelihood, weighting value is used for calculating total score at each selected frame. Cepstrum coefficient and regressive coefficient were used as feature parameters, and the database for test and training consists of several data which are collected at different time, and data for experience are selected randomly In experiments, we applied each method to baseline system, and tested. In speaker recognition experiments, proposed hybrid method has an average of 4% higher recognition accuracy than frame selection method and 1% higher than W method, implying the effectiveness of it.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.11
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• pp.937-944
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• 1990

For the performance improvement in DSBC speech coder two possibilities are proposed. To reduce computational complexity the conventional dynamic bit allocation algorithm are modified. The subband weighting is also presented to avoid hissing noise effect when Gaussian noises are inserted in the regeneration of empty band. The simulation demonstrates that the discussed techniques may suitable for the performance enhancement at the speech output.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.824-826
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• 1999

In this paper, we design an Fuzzy-Neural Networks(FNN) by means of divisions of fuzzy input space with multi-input variables. Fuzzy input space of Yamakawa's FNN is divided by each separated input variable, but that of the proposed FNN is divided by mutually combined input variables. The membership functions of the proposed FNN use both triangular and gaussian membership types. The parameters such as apexes of membership functions, learning rates, momentum coefficients, weighting value, and slope are adjusted using genetic algorithms. Also, an aggregate objective function(performance index) with weighting value is utilized to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the data of sewage treatment process.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.3
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• pp.49-58
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• 2020

Image interpolation, a technology that converts low resolution images into high resolution images, has been widely used in various image processing fields such as CCTV, web-cam, and medical imaging. This technique is based on the fact that the statistical distributions of the white Gaussian noise and the difference between the interpolated image and the original image is similar to each other. The proposed algorithm is composed of three steps. In first, the interpolated image is derived by random image interpolation. In second, we derive weighting functions that are used to apply non-local mean filtering. In the final step, the prediction error is corrected by performing non-local mean filtering by applying the selected weighting function. It can be considered as a post-processing algorithm to further reduce the prediction error after applying an arbitrary image interpolation algorithm. Simulation results show that the proposed method yields reasonable performance.