• Journal of KIISE:Computing Practices and Letters
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• v.15 no.9
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• pp.695-699
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• 2009

In this paper, we propose an automatic prostate segmentation technique using image intensity and gradient information. Our method is composed of four steps. First, rays at regular intervals are generated. To minimize the effect of noise, the start and end positions of the ray are calculated. Second, the profiles on each ray are sorted based on the gradient. And priorities are applied to the sorted gradient in the profile. Third, boundary points are extracted by using gradient priority and intensity distribution. Finally, to reduce the error, the extracted boundary points are corrected by using B-spline interpolation. For accuracy evaluation, the average distance differences and overlapping region ratio between results of manual and automatic segmentations are calculated. As the experimental results, the average distance difference error and standard deviation were 1.09mm $\pm0.20mm$. And the overlapping region ratio was 92%.

• Journal of Korea Multimedia Society
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• v.16 no.1
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• pp.42-49
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• 2013

The camera motion is accompanied with the translation and/or the rotation of objects in frames of a video sequence. An unnecessary rotation of objects declines the quality of the moving pictures and in addition is a primary cause of the viewers' fatigue. In this paper, a novel method for correcting rotated frames in video sequences is presented, where the modified Mojette transform is applied to the motion-compensated area in each frame. The Mojette transform is one of discrete Radon transforms, and is modified for correcting the rotated frames as follows. First, the bin values in the Mojette transform are determined by using pixels on the projection line and the interpolation of pixels adjacent to the line. Second, the bin values are calculated only at some area determined by the motion estimation between current and reference frames. Finally, only one bin at each projection is computed for reducing the amount of the calculation in the Mojette transform. Through the simulation carried out on various test video sequences, it is shown that the proposed scheme has good performance for correcting the rotation of frames in moving pictures.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.578-578
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• 2015

동아시아 지역의 대부분은 몬순의 영향으로 인해 수자원의 계절적 변동성이 크며 이로 인해 홍수 및 가뭄이 빈번하게 발생하고 있다. 기후변화에 따른 기온과 강수량의 변화는 수자원의 변동성을 더욱 악화시킬 수 있으며, 수재해 피해를 더욱 가중시킬 것으로 전망되고 있다. 본 연구에서는 기후변화에 따른 동아시아 지역의 기온 및 강수량의 변화를 전망하고, 그 특성을 분석하고자 한다. 이를 위해 CMIP5의 핵심실험인 2개 RCP시나리오(RCP4.5, RCP8.5)에 대한 다수의 GCMs 결과를 이용하였다. 구축한 기후시나리오를 이중선형보간법(bilinear interpolation)을 이용하여 공간적으로 상세화하였으며, Delta method를 이용하여 편의보정을 수행하였다. GCM 모의자료의 편의를 산정하기 위해 관측자료는 APHRODITE의 기온 및 강수량 자료를 이용하였다. GCM에 따라 차이가 나지만, 우리나라의 경우 평균적으로 100~300mm 정도 과소모의 되는 것으로 나타났다. 미래 기온 및 강수량 전망을 위해 과거기간은 1976~2005년, 미래기간은 2021~2050년(2040s), 2061~2090년(2070s)으로 구분하였다. 우리나라의 경우 RCP 4.5 하에서 연평균기온은 $1.4{\sim}1.7^{\circ}C$(2040s), $2.2{\sim}3.4^{\circ}C$(2070s) 정도 상승할 것으로 나타났으며, 연평균 강수량은 4.6~5.3% (2040s), 8.4~10.5% (2070s) 정도 증가할 것으로 나타났다. RCP 8.5에서는 연평균 기온은 RCP4.5에 비해 상승폭이 더 컸으며, 강수량은 유사한 결과가 나타났다. 또한, 동아시아 지역에서도 연평균 기온이 상승하고 연평균 강수량은 증가하는 것으로 나타났다. 다만, 지역별로 계절별 기온 및 강수량이 매우 다른 양상으로 나타났다. 이는 동아시아 지역과 같이 계절별 강수량 발생패턴이 다른 지역에서는 홍수 및 가뭄에 매우 중요한 역할을 할 것이다. 따라서 지역적으로 계절별 강수량의 변화를 분석해야 할 것으로 판단되며, 추후 유출량 모의를 기반으로 홍수 및 가뭄의 영향을 직접적으로 분석해야할 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.120-120
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• 2019

국내에서는 예측 불가능한 재난으로 인한 침수 피해 발생사례가 증가하였다. 따라서 침수 피해 예측이 더욱 중요해지고 있는 실정이다. 기존에는 주로 수치모형을 통한 침수예측을 하였고, 정보통신기술도 발달해왔지만 아직까지 수치모의에 많은 시간이 소요되기 때문에 침수 피해의 실시간 예측이 힘든 상황이다. 이에 국립재난안전연구원(2017)에서 침수예측을 위한 보간 모델인 SIND(Scientific Interpolation for Natural Disaster) Model을 개발하였다. 이는 보간을 이용한 모델이기 때문에 그동안 사용해왔던 물리 모형보다 간단하다. 그러나 정확한 값이 아닌 보간을 이용한 모델이기 때문에 정확도를 검토할 필요가 있다. 따라서 본 연구에서는 Mapping분야에서 사용하는 CRITIC(CRiteria Importance Through Intercriteria Correlation) 기법을 활용하여 지도의 정확도 검토를 수행하였다. CRITIC은 형상기준, 위치기준, 면적기준을 이용하여 형상유사도를 산정하는 방법이며, 이 기법을 활용하여 국가가 제공한 침수예상도(국립해양조사원, 2010)와 SIND모델 결과 지도를 비교하였다. 형상기준은 지도의 형상을 나타내는 형상지수를 비교하고, 위치기준은 지도의 무게중심의 일치정도, 면적기준은 형상 면적을 비교하는 것이다. 지도는 총 300여개의 매칭 객체 쌍을 가지고 수행하였고, 위험도 등급은 Grade 1부터 Grade 5 까지 분류하여 나타내었다. 연구 대상지역은 ${{\bigcirc}{\bigcirc}}$시이다. 그 결과, 형상유사도는 약 200여개의 매체쌍이 0.80 이상의 값을 나타냈고, 나머지 매체 쌍은 0.75이하의 값을 나타내었다. 위험도 등급이 낮을수록 형상유사도 값은 크게 나타나고, 위험도 등급이 높을수록 형상유사도 값이 작게 나타나는 경향을 보였다. 이는 위험도 등급이 높은 곳의 경우, 해안선의 복잡한 지형형태 때문으로 판단된다. Mapping 분야에서 형상유사도 적합성 기준이 0.75이므로 결과는 60%이상이 정확하다고 판단할 수 있다. 따라서 본 연구에서 검토를 수행했던 간단한 방정식을 이용한 SIND 모델은 정확하다고 판단할 수 있다. 다만, 복잡한 지형과 현재 고려되고 있는 영향인자 외에 다양한 구조물 등을 고려한다면 형상유사도가 향상될 것이라 기대된다.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.12
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• pp.136-144
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• 2021

The purpose of this study was to develop and verify a precision transmission error measurement system for a gear pair. The transmission error measurement system of the gear pair was developed as a measurement unit, signal processing unit, and signal analysis unit. The angular displacement for calculating the transmission error of the gear pair was measured using an encoder. The signal amplification, interpolation, and transmission error calculation of the measured angular displacement were conducted using a field-programmable gate array (FPGA) and a real-time processor. A high-pass filter (HPF) was applied to the calculated transmission error from the real-time processor. The transmission error measurement test was conducted using a gearbox, including the master gear pair. The same test was repeated three times in the clockwise and counterclockwise directions, respectively, according to the load conditions (0 - 200 N·m). The results of the gear transmission error tests showed similar tendencies, thereby confirming the stability of the system. The measured transmission error was verified by comparing it with the transmission error analyzed using commercial software. The verification showed a slight difference in the transmission error between the methods. In a future study, the measurement and analysis method of the developed precision transmission error measurement system in this study may possibly be used for gear design.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.67-78
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• 2019

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3808-3815
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• 2021

In this paper, the EPICS2017 photoatomic database was used to evaluate the photon mass attenuation coefficients and buildup factors of soils collected at different depths in the Philippine islands. The extraction and interpolation of the library was accomplished at the recommended linear-linear scales to obtain the incoherent and total cross section and mass attenuation coefficient. The buildup factors were evaluated using the G-P fitting method in ANSI/ANS-6.4.3. An agreement was achieved between XCOM, MCNP5, and EPICS2017 for the calculated mass attenuation coefficient values. The buildup factors were reported at several penetration depths within the standard energy grid. The highest values of both buildup factor classifications were found in the energy range between 100 and 400 keV where incoherent scattering interaction probabilities are predominant, and least at the region of predominant photoionization events. The buildup factors were examined as a function of different soil silica contents. The soil samples with larger silica concentrations were found to have higher buildup factor values and hence lower shielding characteristics, while conversely, those with the least silica contents have increased shielding characteristics brought by the increased proportions of the abundant heavier oxides.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.4
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• pp.305-316
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• 2022

Numerous research revealed a strong association between the ionospheric perturbations and various natural hazards. The ionospheric measurements from Global Navigation Satellite System (GNSS) observations provide the state of electron contents in the ionosphere that contributes to investigate the source events. In this study, two geophysical events occurred on 23 January 2018, the 7.9 Mw earthquake in Alaska and Kusatsu-Shiranesan volcanic eruption in Japan, are examined to characterize the fingerprint of each event in the ionosphere. Firstly, we extracted the Total Electron Content (TEC) from GNSS measurements, then isolated disturbed wave signatures from the TEC measurements that is referred to as a traveling ionospheric disturbance (TID). As TIDs are short-term ionospheric variations, the major trend of GNSS TEC measurements should be properly removed. We applied a natural neighbor interpolation method together with a leave-one-out cross validation technique for detrending. After detrending the TEC, the remaining signals are further enhanced by applying a band-pass filter and TIDs are detected from them. Finally, the detected TIDs are verified as the response of the ionosphere to Kusatsu-Shiranesan volcanic eruption and Gulf of Alaska earthquake which propagated through the ionosphere with an average velocity of 530 m/s and 724 m/s, respectively. In addition, a coherence analysis is conducted to discriminate between the signatures from a volcanic explosion and an earthquake. The analysis reveals the TID waveforms from each single event are highly correlated, while a low correlation is found between the TIDs from the earthquake and explosion. This study supports the claim that different geophysical events induce the distinctive characteristics of TIDs that are detectable by the ionospheric measurements of GNSS.

• Korean Journal of Materials Research
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• v.32 no.8
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.407-409
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• 2021

Under the influence of the 4th industrial revolution, various technologies such as artificial intelligence and automation are being grafted into industrial sites, and accordingly, the importance of data processing is increasing. Digital images may generate noise due to various reasons, and may affect various systems such as image recognition and classification and object tracking. To compensate for these shortcomings, we propose an image restoration algorithm based on pattern information of non-noise pixels. According to the distribution of non-noise pixels inside the filtering mask, the proposed algorithm switched the filtering process by dividing the interpolation method into a pattern that can be applied, a pattern based on region division, and a randomly arranged pixel pattern. preserves and restores the image. The proposed algorithm showed superior performance compared to the existing impulse noise removal algorithm.