• Korean Journal of Remote Sensing
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• v.29 no.4
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• pp.423-441
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• 2013

On 21 September 2010, one of Chuseok holidays in Korea, localized heavy rainfalls occurred over the midwestern region of the Korean peninsula. In this study MTSAT-2 infrared and water vapor channel imagery are examined to find out some features which are obvious in each stage of the life cycle of convective cell for this heavy rain event. Also the kinematic and thermodynamic features probably associated with them are investigated. The first clouds related with the Chuseok heavy rain are detected as low-level multicell cloud (brightness temperature: $-15{\sim}0^{\circ}C$) in the middle of the Yellow sea at 1630~1900 UTC on 20 Sept., which are probably associated with the convergence at 1000 hPa. Convective cells are initiated in the vicinity of Shantung peninsula at 1933 UTC 20, which have developed around the edge of the dark region in water vapor images. At two times of 0033 and 0433 UTC 21 the merging of two convective cells happens near midwestern coast of the peninsula and then they have developed rapidly. From 0430 to 1000 UTC 21, key features of convective cell include repeated formation of secondary cell, slow horizontal cloud motion, persistence of lower brightness temperature ($-75{\sim}-65^{\circ}C$), and relatively small cloud size (${\leq}-50^{\circ}C$) of about $30,000km^2$. Radar analysis showed that this heavy rain is featured by a narrow line-shaped rainband with locally heavy rainrate (${\geq}50$ mm/hr), which is located in the south-western edge of the convective cell. However there are no distinct features in the associated synoptic-scale dynamic forcing. After 1000 UTC 21 the convective cell grows up quickly in cloud size and then is dissipated. These satellite features may be employed for very short range forecast and nowcasting of mesoscale heavy rain system.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.183-192
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• 2006

간세포암은 우리나라에서 전체 암사망자 중 17.2%로 3번째의 흔한 사망원인이며, 간암에 의한 사망률은 인구 10만 명당 약 21명에 이른다. 본 논문에서는 간 내부에서 발생하는 간세포암을 CT 영상에서 자동으로 추출하는 방법을 제안하여 간세포암의 보조진단으로서의 유용성에 대해 알아보고자 한다. 간 내부의 종양을 추출하기 위해 흉부의 윗부분에서 시작하여 2.5mm의 간격으로 약 45-50장 정도를 촬영한 CT 영상들을 대상으로 먼저 간 영역을 추출한다. 간 영역 추출은 먼저 관심이 없는 외부 영역을 갈비뼈를 중심으로 제거한 후 영상의 밝기 정보를 이용하여 각 기관의 영역을 분할 한다. 분할된 영역들은 위 아래로 인접한 영상에서의 분할 영역들과 밝기 값을 비교하여 적절하게 병합하는 3차원적 접근방법을 사용한다. 간 영역은 여러개의 영역들 중에서 간 영역의 구조 및 위치 등의 정보를 활용하여 추출한다. 추출된 간 영역에서 종양 판별과 추출을 위해 종양이 가지는 특징을 분석하여 종양을 추출한다. 전형적인 간세포암은 과혈관성 종양이므로 조영증강 CT 영상에서 주위보다 밝은 색으로 나타나며, 팽창 형성장을 보일 경우에는 구형으로 나타나는 특징이 있다. 이에, 주위 보다 밝은 색을 가지고 둥근형태를 가지는 영역을 종양의 후보영역으로 선정한 후, 그 영상의 위와 아래로 연결되는 영상에서도 같은 위치에서 같은 특징을 보이는 영역이 있으면 간 내부의 종양으로 판별하여 추출한다. 제안된 간 영역 및 간 종양 추출 방법의 정확성을 판별하기 위하여 CT 영상을 대상으로 실험하여 영상의학 전문의가 판단한 결과와 비교하였다. 간 영역 추출은 정확히 모두 추출되었으며, 간 종양 추출 및 판별은 전문의의 보조 진단도구로 활용할 수 있는 가능성이 매우 높다는 것을 확인할 수 있었다.emantic Similarity Measure 등을 단계적으로 수행하여 자동화되고 정확한 규칙식별을 하고자 한다. 이러한 방법들의 조합으로 인하여 규칙구성요소 추출이 되지 않을 후보 단어들의 수를 줄여서 보다 더 정확하고, 지능적인 규칙구성요소 추출 방법론을 제시하고 구현하여 지식관리자의 규칙습득에 대한 부담을 줄여 주고자 한다. 도움을 받을 수 있게 되었다.을 거치도록 되어있다. 교통주제도는 국가의 교통정책결정과 관련분야의 기초자료로서 다양하게 활용되고 있으며, 특히 ITS 노드/링크 기본지도로 활용되는 등 교통 분야의 중요한 지리정보로서 구축되고 있다..20{\pm}0.37L$, 72시간에 $1.33{\pm}0.33L$로 유의한 차이를 보였으므로(F=6.153, P=0.004), 술 후 폐환기능 회복에 효과가 있다. 4) 실험군과 대조군의 수술 후 노력성 폐활량은 수술 후 72시간에서 실험군이 $1.90{\pm}0.61L$, 대조군이 $1.51{\pm}0.38L$로 유의한 차이를 보였다(t=2.620, P=0.013). 5) 실험군과 대조군의 수술 후 일초 노력성 호기량은 수술 후 24시간에서 $1.33{\pm}0.56L,\;1.00{\ge}0.28L$로 유의한 차이를 보였고(t=2.530, P=0.017), 술 후 72시간에서 $1.72{\pm}0.65L,\;1.33{\pm}0.3L$로 유의한 차이를 보였다(t=2.540, P=0.016). 6) 대상자의 술 후 폐환기능에 영향을 미치는 요인은 성별로 나타났다. 이에 따른 폐환기능의 차이를 보면, 실험군의 술 후 노력성 폐활량이 48시간에 남자($1.78{\pm}0.61L$)가 여자(

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.713-722
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• 2013

This paper proposes a method of using depth information to detect the hand region in real-time based on the cascade method. In order to ensure stable and speedy detection of the hand region even under conditions of lighting changes in the test environment, this study uses only features based on depth information, and proposes a method of detecting the hand region by means of a classifier that uses boosting and cascading methods. First, in order to extract features using only depth information, we calculate the difference between the depth value at the center of the input image and the average of depth value within the segmented block, and to ensure that hand regions of all sizes will be detected, we use the central depth value and the second order linear model to predict the size of the hand region. The cascade method is applied to implement training and recognition by extracting features from the hand region. The classifier proposed in this paper maintains accuracy and enhances speed by composing each stage into a single weak classifier and obtaining the threshold value that satisfies the detection rate while exhibiting the lowest error rate to perform over-fitting training. The trained classifier is used to classify the hand region, and detects the final hand region in the final merger stage. Lastly, to verify performance, we perform quantitative and qualitative comparative analyses with various conventional AdaBoost algorithms to confirm the efficiency of the hand region detection algorithm proposed in this paper.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.143-151
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• 2010

Fingerprint classification is a step to increase the efficiency of an 1:N fingerprint recognition system and plays a role to reduce the matching time of fingerprint and to increase accuracy of recognition. It is difficult to classify fingerprints, because the ridge pattern of each fingerprint class has an overlapping characteristic with more than one class, fingerprint images may include a lot of noise and an input condition is an exceptional case. In this paper, we propose a novel approach to design a stochastic model and to accomplish fingerprint classification using a directional characteristic of fingerprints for an effective classification of various qualities. We compute the directional value by searching a fingerprint ridge pixel by pixel and extract a directional characteristic by merging a computed directional value by fixed pixels unit. The modified Markov model of each fingerprint class is generated using Markov model which is a stochastic information extraction and a recognition method by extracted directional characteristic. The weight list of classification model of each class is decided by analyzing the state transition matrixes of the generated Markov model of each class and the optimized value which improves the performance of fingerprint classification using GA (Genetic Algorithm) is estimated. The performance of the optimized classification model by GA is superior to the model before the optimization by the experiment result of applying the fingerprint database of various qualities to the optimized model by GA. And the proposed method effectively achieved fingerprint classification to exceptional input conditions because this approach is independent of the existence and nonexistence of singular points by the result of analyzing the fingerprint database which is used to the experiments.

• Journal of Korea Water Resources Association
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• v.42 no.1
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• pp.61-73
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• 2009

To efficiently carry out the flood management of a multipurpose dam, two flood forecasting models are developed, each of which has the capabilities of forecasting upstream inflows and flood discharges downstream of a dam, respectively. The models are calibrated, validated, and evaluated by comparison of the observed and the runoff forecasts upstream and downstream of Namgang Dam. The upstream inflow forecasting model is based on the Grey system theory and employs the sixth order differential equation. By comparing the inflows forecasted by the models calibrated using different data sets with the observed in validation, the most appropriate model is determined. To forecast flood discharges downstream of a dam, a Grey model is integrated with a modified Muskingum flow routing model. A comparison of the observed and the forecasted values in validation reveals that the model can provide good forecasts for the dam's flood management. The applications of the two models to forecasting floods in real situations show that they provide reasonable results. In addition, it is revealed that to enhance the prediction accuracy, the models are necessary to be calibrated and applied considering runoff stages; the rising, peak, and falling stages.

• Journal of Technology Innovation
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• v.25 no.4
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• pp.239-261
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• 2017

This paper analyzes how corporate product innovation affects firms' revenue and financial stability, and thereby draws the implications for the corporate strategy for sustainable growth. Corporate product innovation is defined as the development of new products within the firm, including bought-in products. Corporate revenue is measured by per capita sales and its growth rate, while financial stability is measured by debt-to-equity ratio and liquidity ratio. In the empirical analysis, the two-stage estimation method was used to control for the endogeneity of new product development. The data are drawn from the first (2005) to the sixth (2015) wave of the Human Capital Corporate Panel (HCCP) Survey, which are matched to the data from the Korea Investors Service (KIS). The results of the first-stage estimation indicate that product innovation of the firm is promoted by the firm's knowledge capital stock, human resources investment, and market-leading strategy. The second-stage estimation results indicate a positive relationship between the firm's level of activity in product innovation and short-term revenue (per capita sales and its growth), and financial stability (lower debt-to-equity ratio and higher liquidity ratio). These findings confirm that the firm's investment in technology innovation and subsequent product innovation are important strategies to enhance both short-term corporate revenue and long-term financial stability.

• Clinical and Experimental Pediatrics
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• v.50 no.5
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• pp.484-488
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• 2007

Purpose : The purpose of this study was to determine the incidence and potential predictors of weight gain in older children and teens treated with valproate (VPA) for epilepsy. Methods : Sixty-five subjects aged 8 to 17 years of age, who began VPA treatment between January 1, 2001, and December 31, 2004, and who had documented weight and height measurements at medication initiation and at least one follow-up visit were retrospectively identified. Exclusion criteria were follow-up <6 months, discontinuation of VPA within 6 months, and concurrent therapy with medication known to affect weight (such as topiramate, carbamazepin). Body mass index (BMI) was calculated at initiation and either discontinuation of VPA or last follow-up and stratified into four categories: group 1, underweight <5%; group 2, appropriate 5-85%; group 3, potentially overweight 85-95%; group 4, overweight >95%. Results : Twenty-eight subjects (77.8%) remained within their same category and eight (22.2%) moved up at least one category. Weight gain (increase in BMI difference) was observed in 72.2% of the 36 subjects treated with VPA. Three factors, neurocognitive status (P=0.017), seizure type (P=0.001) and duration of VPA treatment (P=0.035) were identified to be significant predictors of BMI difference. Conclusion : VPA induces weight gain in children and teens with epilepsy. These factors which are normal neurocognitive status, primary generalized type and duration of VPA treatment over the 12 months were predictors for an increase of weight gain. Therefore potential weight gain should be discussed with patients before the initiation of therapy and BMI should be monitored closely.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.1
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• pp.19-29
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• 2003

This paper proposes a CMOS readout circuit for uncooled micro-bolometer arrays adopting a four-point step calibration technique. The proposed readout circuit employing an 11b analog-to-digital converter (ADC), a 7b digital-to-analog converter (DAC), and an automatic gain control circuit (AGC) extracts minute infrared (IR) signals from the large output signals of uncooled micro-bolometer arrays including DC bias currents, inter-pixel process variations, and self-heating effects. Die area and Power consumption of the ADC are minimized with merged-capacitor switching (MCS) technique adopted. The current mirror with high linearity is proposed at the output stage of the DAC to calibrate inter-pixel process variations and self-heating effects. The prototype is fabricated on a double-poly double-metal 1.2 um CMOS process and the measured power consumption is 110 ㎽ from a 4.5 V supply. The measured differential nonlinearity (DNL) and integrat nonlinearity (INL) of the 11b ADC show $\pm$0.9 LSB and $\pm$1.8 LSB, while the DNL and INL of the 7b DAC show $\pm$0.1 LSB and $\pm$0.1 LSB.

• Journal of the Korean Geotechnical Society
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• v.39 no.7
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• pp.31-37
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• 2023

Since tunnels are built underground, it is impossible to check visually the location and degree of deterioration of steel ribs. Therefore, in tunnel maintenance, GPR images are generally used to detect steel ribs. While research on GPR image analysis employing artificial neural networks has primarily focused on detecting underground pipes and road damage, there have been limited applications for analyzing tunnel GPR data, specifically for steel rib detection, both internationally and domestically. In this study, a one-step object detection algorithm called YOLO, based on a convolutional neural network, was utilized to automate the localization of steel ribs using GPR data. The performance of the algorithm is then analyzed. Two datasets were employed for the analysis. A dataset comprising 512 original images and another dataset consisting of 2,048 augmented images. The omission rate, which represents the ratio of undetected steel ribs to the total number of steel ribs, was 0.38% for the model using the augmented data, whereas the omission rate for the model using only the original data was 7.18%. Thus, from an automation standpoint, it is more practical to employ an augmented dataset.

• Korean Journal of Clinical Laboratory Science
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• v.55 no.4
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• pp.298-305
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• 2023

Burkitt's lymphoma is a distinct subtype of non-Hodgkin's lymphoma originating from B-cells that is notorious for its aggressive growth and association with immune system impairments, potentially resulting in rapid and fatal outcomes if not addressed promptly. Optimizing the use of Food and Drug Administration-approved medications, such as combining known safe drugs, can lead to time and cost savings. This method holds promise in accelerating the progress of novel treatments, ultimately facilitating swifter access for patients. This study explores the potential of a dual-targeted therapeutic strategy, combining the bruton tyrosine kinase-targeting drug Ibrutinib and the epidermal growth factor receptor/human epidermal growth factor receptor-2-targeting drug Lapatinib. Ramos and Daudi cell lines, well-established models of Burkitt's lymphoma, were used to examine the impact of this combination therapy. The combination of Ibrutinib and Lapatinib inhibited cell proliferation more than using each drug individually. A combination treatment induced apoptosis and caused cell cycle arrest at the S and G2/M phases. This approach is multifaceted in its benefits. It enhances the efficiency of the drug development timeline and maximizes the utility of currently available resources, ensuring a more streamlined and resource-effective research process.