• Journal of Broadcast Engineering
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• v.13 no.6
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• pp.892-904
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• 2008

As a video encoding in resource constrained environments such as sensor networks has become an important issue, DVC(Distributed Video Coding) has been intensively investigated as a solution for light weighted video encoding problem. Known as one of the representative schemes of DVC, the Wyner-Ziv coding generates side information of current frame only at decoder, using correlation among frames, and reconstructs video through noise elimination on the side information using channel code. Accordingly, the better quality of side information brings less channel noise, thus attains better coding performance of the Wyner-Ziv coder. However, since it is hard for decoder to generate an accurate side information without any information of original frame, a method to successively improve side information using successively decoded original frame, based on decoding reliability, was previously developed. However, to improve side information from decoding results, not only an error rate of the decoding result as a reliability, but also the amount of reliable information from the decoding result is important. Therefore, we propose TDWZ(Transform-domain Wyner-Ziv coding) with successively improving side information based on decoding reliability considering not only an error rate but also the amount of reliable information of the decoding results. Our experiment shows the proposed method gains average PSNR up to 1.7 dB over the previous TDWZ, that is without successive side information improvement.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.5
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• pp.422-433
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• 2017

Unmanned Aerial Vehicle (UAV) has several advantages over conventional remote sensing techniques. They can acquire high-resolution images quickly and repeatedly. And with a comparatively lower flight altitude, they can obtain good quality images even in cloudy weather. In this paper, we developed for estimating garlic growth at field scale model in major cultivation regions. We used the $NDVI_{UAV}$ that reflects the crop conditions, and seven meteorological elements for 3 major cultivation regions from 2015 to 2017. For this study, UAV imagery was taken at Taean, Changnyeong, and Hapcheon regions nine times from early February to late June during the garlic growing season. Four plant growth parameters, plant height (P.H.), leaf number (L.N.), plant diameter (P.D.), and fresh weight (F.W.) were measured for twenty plants per plot for each field campaign. The multiple linear regression models were suggested by using backward elimination and stepwise selection in the extraction of independent variables. As a result, model of cold type explain 82.1%, 65.9%, 64.5%, and 61.7% of the P.H., F.W., L.N., P.D. with a root mean square error (RMSE) of 7.98 cm, 5.91 g, 1.05, and 3.43 cm. Especially, model of warm type explain 92.9%, 88.6%, 62.8%, 54.6% of the P.H., P.D., L.N., F.W. with a root mean square error (RMSE) of 16.41 cm, 9.08 cm, 1.12, 19.51 g. The spatial distribution map of garlic growth was in strong agreement with the field measurements in terms of field variation and relative numerical values when $NDVI_{UAV}$ was applied to multiple linear regression models. These results will also be useful for determining the UAV multi-spectral imagery necessary to estimate growth parameters of garlic.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.75-84
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• 2019

In this study, the random forest approach was used to predict the national mean rice yield of South Korea by using mean climatic factors at a national scale. A random forest model that used monthly climate variable and year as an important predictor in predicting crop yield. Annual yield change would be affected by technical improvement for crop management as well as climate. Year as prediction factor represent technical improvement. Thus, it is likely that the variables of importance identified for the random forest model could result in a large error in prediction of rice yield in practice. It was also found that elimination of the trend of yield data resulted in reasonable accuracy in prediction of yield using the random forest model. For example, yield prediction using the training set (data obtained from 1991 to 2005) had a relatively high degree of agreement statistics. Although the degree of agreement statistics for yield prediction for the test set (2006-2015) was not as good as those for the training set, the value of relative root mean square error (RRMSE) was less than 5%. In the variable importance plot, significant difference was noted in the importance of climate factors between the training and test sets. This difference could be attributed to the shifting of the transplanting date, which might have affected the growing season. This suggested that acceptable yield prediction could be achieved using random forest, when the data set included consistent planting or transplanting dates in the predicted area.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.1-6
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• 2021

In the field of speech recognition, as the DNN is applied, the use of speech recognition is increasing, but the amount of calculation for parallel training needs to be larger than that of the conventional GMM, and if the amount of data is small, overfitting occurs. To solve this problem, we propose an efficient method for robust voice feature extraction and voice signal noise removal even when the amount of data is small. Speech feature extraction efficiently extracts speech energy by applying the difference in frame energy for speech and the zero-crossing ratio and level-crossing ratio that are affected by the speech signal. In addition, in order to remove noise, the noise of the speech signal is removed by removing the noise of the speech signal with an average predictive improved LMS filter with little loss of speech information while maintaining the intrinsic characteristics of speech in detection of the speech signal. The improved LMS filter uses a method of processing noise on the input speech signal by adjusting the active parameter threshold for the input signal. As a result of comparing the method proposed in this paper with the conventional frame energy method, it was confirmed that the error rate at the start point of speech is 7% and the error rate at the end point is improved by 11%.

• Journal of Life Science
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• v.21 no.1
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• pp.119-126
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• 2011

There is a growing recognition of the significance of $H_2S$ as a biological signaling molecule involved in vascular and nervous system functions. In mammals, two enzymes in the transsulfuration pathway, cystathionine ${\beta}$-synthase (CBS) and cystathionine ${\gamma}$-lyase (CGL), are believed to be chiefly responsible for $H_2S$ biogenesis. Genetic inborn error of CGL leads to human genetic disease, cystathioninuria, by accumulating cystathionine in the body. This disease is secondarily associated with a wide range of diseases including diabetes insipidus and Down's syndrome. Although the human CGL (hCGL) overexpression is essential for the investigation of its function, structure, reaction specificity, substrate specificity, and protein-protein interactions, there is no clear report concerning optimum overexpression conditions. In this study, we report a detailed analysis of the overexpression conditions of the hCGL using a bacterial system. Maximum overexpression was obtained in conditions of low culture temperature after inducer addition, performing low aeration during overexpression, and using a low concentration inducer (0.1 mM, IPTG) for induction. Expressed hCGL was purified by His-tag affinity column chromatography and confirmed by Western blot using hCGL antibody and enzyme activity analysis. We also report that the His tag with TEV site attached protein exhibits 76% activity for ${\alpha}-{\gamma}$ elimination reaction with L-cystathionine and 88% for ${\alpha}-{\beta}$ elimination reaction with L-cysteine compared to those of wild type hCGL, respectively. His tag with TEV site attached protein also exhibits a 420 nm absorption maximum, which is attributed to the binding cofactor, pyridoxal 5'-phosphate (PLP).

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.10
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• pp.1061-1070
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• 2009

In this paper, we have designed TDL(True-time Delay Line) for eliminating beam-squint occurring in active phased array antenna with large electrical size operated in wide bandwidth, and have tested its electrical performance. The proposed TDL device is composed of 4-bit microstrip delay line structure and MMIC amplifier for compensation of the delay-line loss. The measured results of gain and phase versus delay state satisfy the electrical requirements, also P1dB output power and noise figure meet the requirement. To verify the performance of fabricated TDL, we have simulated the beam patterns of wide-band active phased array antenna using the measured results and have certified the beam pattern compensation performance. As a result of simulated beam pattern compensation with respect to the 675.8 mm size antenna which is operated in X-band, 800 MHz bandwidth, we have reduced the beam squint error of ${\pm}1^{\circ}$ with ${\pm}0.1^{\circ}$. So this TDL module is able to be applied to active phase array antenna system.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1249-1256
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• 2019

In this paper, we propose a new algorithm to remove acoustic feedback and noise in hearing aids. Instead of using the conventional FIR structure, this algorithm is a deep learning algorithm using neural network adaptive prediction filter to improve the feedback and noise reduction performance. The feedback canceller first removes the feedback signal from the microphone signal and then removes the noise using the Wiener filter technique. Noise elimination is to estimate the speech from the speech signal containing noise using the linear prediction model according to the periodicity of the speech signal. In order to ensure stable convergence of two adaptive systems in a loop, coefficient updates of the feedback canceller and noise canceller are separated and converged using the residual error signal generated after the cancellation. In order to verify the performance of the feedback and noise canceller proposed in this study, a simulation program was written and simulated. Experimental results show that the proposed deep learning algorithm improves the signal to feedback ratio(: SFR) of about 10 dB in the feedback canceller and the signal to noise ratio enhancement(: SNRE) of about 3 dB in the noise canceller than the conventional FIR structure.

• Journal of Korean Neurosurgical Society
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• v.29 no.8
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• pp.1126-1135
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• 2000

Objectives : The goal of surgical management of cerebral arteriovenous malformation(AVM) is elimination of the lesion without development of new neurological deficits. To improve the management results of cerebral AVMs in the future, this article discusses about surgical complications of the AVM and their management. Material and Methods : During the past 18 years, 116 patients with cerebral AVMs were managed by surgery. Among these cases, 7 cases died, 7 cases developed new neurological deficits, 11 cases residual AVM and 5 cases intracerebral hematoma(ICH) after surgery. The author analyzes the causes of those complications and investigates the methods to minimized those complications based on the review of the literatures. Results : One stage removal of AVM and ICH in the poor neurological state were performed in 5 of 7 death cases. Subtotal removal of ICH followed by delayed AVM surgery after recovery is regard as one method to improve the outcome of patient with large ICH. Postoperative new neurological deficits developed owing to normal perfusion pressure breakthrough(NPPB) in 3, judgement error in 2, preoperative embolization in 1 and cortical injury in 1 case(s). Proper management of NPPB, accurate anatomical knowledge and physiological monitoring during operation, and well trained skill for embolization are regard as methods to minimize those complications. Residual AVMs after surgery were noticed in 11 cases, in which unintended 6 cases due to inaccurate dissection of peripheral margin of AVM, and intended 3 cases due to massive brain swelling during operation, 1 cases due to diffuse type and 1 case due to multiple type of AVM. Accurate dissection of peripheral margin of AVM and mild hypotension during operation may help to avoid this complication. Postoperative hemorrhage occurred in 3 cases due to rupture of the residual AVM and in 2 cases due to oozing from the AVM bed. Complete resection of AVM, complete control of bleeding points at AVM bed and mild hypotension during early postoperative period are the methods to avoid this complication. Conclusion : A precise but flexible therapeutic strategy and refined skill for endovascular, radiosurgical and microsurgical techniques are required to successful treatment of cerebral AVM. Adequate timing of AVM resection, accurate anatomical knowledge, proper management of NPPB and accurate dissection of peripheral margin of AVM are the key points for avoiding complications of the AVM surgery.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.411-421
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• 2012

The stagnant growth in 3D market due to 3D movie contents shortage is encouraging development of techniques for production cost reduction. Elimination of vertical disparity generated during image acquisition requires heaviest time and effort in the whole stereoscopic film-making process. This matter is directly related to competitiveness in the market and is being dealt with as a very important task. The removal of vertical disparity, i.e. image rectification has been treated for a long time in the photogrammetry field. While computer vision methods are focused on fast processing and automation, photogrammetry methods on accuracy and precision. However, photogrammetric approaches have not been tried for the 3D film-making. In this paper, proposed is a photogrammetry-based rectification algorithm that enable to eliminate the vertical disparity precisely by reconstruction of geometric relationship at the time of shooting. Evaluation of proposed algorithm was carried out by comparing the performance with two existing computer vision algorithms. The epipolar constraint satisfaction, epipolar line accuracy and vertical disparity of result images were tested. As a result, the proposed algorithm showed excellent performance than the other algorithms in term of accuracy and precision, and also revealed robustness about position error of tie-points.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.168-177
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• 2020

Seabed multiple reflections (seabed multiples) are the main cause of misinterpretations of primary reflections in both shot gathers and stack sections. Accordingly, seabed multiples need to be suppressed throughout data processing. Conventional model-driven methods, such as prediction-error deconvolution, Radon filtering, and data-driven methods, such as the surface-related multiple elimination technique, have been used to attenuate multiple reflections. However, the vast majority of processing workflows require time-consuming steps when testing and selecting the processing parameters in addition to computational power and skilled data-processing techniques. To attenuate seabed multiples in seismic reflection data, input gathers with seabed multiples and label gathers without seabed multiples were generated via numerical modeling using the Marmousi2 velocity structure. The training data consisted of normal-moveout-corrected common midpoint gathers fed into a U-Net neural network. The well-trained model was found to effectively attenuate the seabed multiples according to the image similarity between the prediction result and the target data, and demonstrated good applicability to field data.