• Journal of Broadcast Engineering
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• v.4 no.2
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• pp.176-186
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• 1999

In order to produce an image that lends realism to audience in the virtual studio system. it is important to synchronize precisely between foreground objects and background image provided by computer graphics. In this paper, we propose a method of camera parameter extraction for the synchronization by tracking the pose of TV camera. We derive an equation for extracting camera parameters from inverse perspective equations for tracking the pose of the camera and 3-D transformation between base coordinates and estimated coordinates. We show the validity of the proposed method in terms of the accuracy ratio between the parameters computed from the equation and the real parameters that applied to a TV camera.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.133-140
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• 2018

In electronic warfare(EW), low probability of intercept(LPI) radar signal is a survival technique. Accordingly, identification techniques of the LPI radar waveform have became significant recently. In this paper, classification and extracting parameters techniques for 7 intrapulse modulated radar signals are introduced. We propose a technique of classifying intrapulse modulated radar signals using Convolutional Neural Network(CNN). The time-frequency image(TFI) obtained from Choi-William Distribution(CWD) is used as the input of CNN without extracting the extra feature of each intrapulse modulated radar signals. In addition a method to extract the intrapulse radar modulation parameters using binary image processing is introduced. We demonstrate the performance of the proposed intrapulse radar waveform identification system. Simulation results show that the classification system achieves a overall correct classification success rate of 90 % or better at SNR = -6 dB and the parameter extraction system has an overall error of less than 10 % at SNR of less than -4 dB.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.181-184
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• 2000

The new method of feature extraction is proposed, considering the statistic feature of human voice, unlike the conventional methods of voice extraction. PCA(principal Component Analysis) is applied to this new method. PCA removes the repeating of data after finding the axis direction which has the greatest variance in input dimension. Then the new method is applied to real voice recognition to assess performance. When results of the number recognition in this paper and the conventional Mel-Cepstrum of voice feature parameter are compared, there is 0.5% difference of recognition rate. Better recognition rate is expected than word or sentence recognition in that less convergence time than the conventional method in extracting voice feature. Also, better recognition tate is expected when the optimum vector is used by statistic feature of data.

• Fibers and Polymers
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• v.4 no.1
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• pp.27-31
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• 2003

A major concern in solvent extraction process is the loss of extractant into the aqueous phase due to its slight solubility in the aqueous phase. Similarly, in membrane extraction processes, extractant loss through extractant leakage from the membrane into the aqueous phase is also a concern. Several published membrane extraction studies using Aliquat 336 ai the extractant, have expressed this concern, but none has studied extractant leakage quantitatively. It is the authors' opinion that the extractant leakage should be considered at a technical parameter of a membrane. In our laboratory active progress has been made in using Aliquat 336 ‘entangled’ into the polymer membranes to remove heavy metal ions from wastewater samples. In this work, we studied the loss of Aliquat 336 from the point of view of its solubility in aqueous solutions. The results showed that the solubilities or Aliquat 3,36 in an aqueous phase acidified with 2 M HCI it about 0.1 g/100 m/ of the solution. This figure provides a useful guideline for evaluating the leakage of the Aliquatoat 336 extractant from the membranes.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.7
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• pp.7-12
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• 2004

An improved extraction method considering ac current crowding effect is investigated to determine intrinsic ( $C_{\mu}$) and extrinsic ( $C_{\mu}$) base-collector capacitances of bipolar junction transistors separately. The drawbacks of conventional methods are pointed out, and the improved extraction equations are derived from a cutoff mode equivalent circuit with the ac crowding capacitance. The frequency response curves of modeled current and power gains using the extracted values of $C_{\mu}$ and $C_{\mu}$ have much better agreements with measured ones than those of the conventional methods, verifying the accuracy of the improved technique.

• KSBB Journal
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• v.18 no.3
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• pp.202-206
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• 2003

A study of hen egg lysozyme extraction using reversed micelles and pressurized CO₂ phase was conducted. The relationship between the lysozyme extraction and water content (W/sub 0/) under the pressurized CO₂ conditions was investigated. The water content of the micellar organic phase was a significant parameter affecting the mass transfer of protein and enzymatic activity in reversed micellar process. It was found that the reversed micelles in the organic phase with pressurized CO₂ were larger than the organic phase without CO₂. Therefore, the extractionrate of lysozyme in the interface of the aqueous phase and the organic phase was increased. W/sub 0/ value was increased at the high surfactant concentration and the extraction rate of lysozyme was enhanced.

• Current Optics and Photonics
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• v.7 no.4
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• pp.387-397
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• 2023

Tooth surface shape error is an important parameter in gear accuracy evaluation. When tooth surface shape error is measured by laser interferometry, the gear interferogram is highly distorted and the gray level distribution is not uniform. Therefore, it is important for gear interferometry to extract the foreground region from the gear interference fringe image directly and accurately. This paper presents an approach for foreground extraction in gear interference images by leveraging the sinusoidal variation characteristics shown by the interference fringes. A gray level mask with an adaptive threshold is established to capture the relevant features, while a local variance evaluation function is employed to analyze the fluctuation state of the interference image and derive a repair mask. By combining these masks, the foreground region is directly extracted. Comparative evaluations using qualitative and quantitative assessment methods are performed to compare the proposed algorithm with both reference results and traditional approaches. The experimental findings reveal a remarkable degree of matching between the algorithm and the reference results. As a result, this method shows great potential for widespread application in the foreground extraction of gear interference images.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.7
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• pp.297-300
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• 2015

In medical ultrasonography, transit time and contrast enhancement patterns are considered as important parameters to analyze liver diseases. In many recent researches, time-intensity curves(TIC) have been used for calculating the transit time of the contrast agents. However, the intensity curve may include the variations which are caused by the micro-bubble effect of contrast agents. In this paper, we propose a complementary approach to diagnostic parameter extraction which utilizes a density information as well as the intensity data. The proposed technique improves the accuracy in extraction of the transit time and velocity of contrast agents for detection and characterization of focal liver lesions. Through the experiments using a set of clinical data, we show that the proposed methods can improve the reliability of the parametric image data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1149-1154
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• 2017

To obtain good recognition performance of speech recognition system under background noise, it is very important to select appropriate feature parameters of speech. The feature parameter used in this paper is Mel frequency cepstral coefficient (MFCC) with the human auditory characteristics applied to Wiener filter method. That is, the feature parameter proposed in this paper is a new method to extract the parameter of clean speech signal after removing background noise. The proposed method implements the speaker recognition by inputting the proposed modified MFCC feature parameter into a multi-layer perceptron network. In this experiments, the speaker independent recognition experiments were performed using the MFCC feature parameter of the 14th order. The average recognition rates of the speaker independent in the case of the noisy speech added white noise are 94.48%, which is an effective result. Comparing the proposed method with the existing methods, the performance of the proposed speaker recognition is improved by using the modified MFCC feature parameter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1542-1550
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• 2019

Legacy studies for classifying arrhythmia have been studied to improve the accuracy of classification, Neural Network, Fuzzy, etc. Deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose optimal parameter extraction method based on a deep learning. For this purpose, R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 97.84% in PVC classification.