• The Korean Journal of Nuclear Medicine
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• v.31 no.1
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• pp.73-82
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• 1997

Regional myocardial blood flow (rMBF) can be noninvasively quantified using N-13 ammonia and dynamic positron emission tomography (PET). The quantitative accuracy of the rMBF values, however, is affected by the distortion of myocardial PET images caused by finite PET image resolution and cardiac motion. Although different methods have been developed to correct the distortion typically classified as partial volume effect and spillover, the methods are too complex to employ in a routine clinical environment. We have developed a refined method incorporating a geometric model of the volume representation of a region-of-interest (ROI) into the two-compartment N-13 ammonia model. In the refined model, partial volume effect and spillover are conveniently corrected by an additional parameter in the mathematical model. To examine the accuracy of this approach, studies were performed in 9 coronary artery disease patients. Dynamic transaxial images (16 frames) were acquired with a GE $Advance^{TM}$ PET scanner simultaneous with intravenous injection of 20 mCi N-13 ammonia. rMBF was examined at rest and during pharmacologically (dipyridamole) induced coronary hyperemia. Three sectorial myocardium (septum, anterior wall and lateral wall) and blood pool time-activity curves were generated using dynamic images from manually drawn ROIs. The accuracy of rMBF values estimated by the refined method was examined by comparing to the values estimated using the conventional two-compartment model without partial volume effect correction rMBF values obtained by the refined method linearly correlated with rMBF values obtained by the conventional method (108 myocardial segments, correlation coefficient (r)=0.88). Additionally, underestimated rMBF values by the conventional method due to partial volume effect were corrected by theoretically predicted amount in the refined method (slope(m)=1.57). Spillover fraction estimated by the two methods agreed well (r=1.00, m=0.98). In conclusion, accurate rMBF values can be efficiently quantified by the refined method incorporating myocardium geometric information into the two-compartment model using N-13 ammonia and PET.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.4
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• pp.203-214
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• 2017

There are two references of genetic information in Korean soybean cultivar. This study suggested that the new seven genetic information to supplement the uncertainty on prediction of potential yield of two references in soybean, and assessed the availability of two references and seven genetic information for future research. We carried out evaluate the prediction on flowering time and potential yield of the two references of genetic parameters and the new seven genetic parameters (New1~New7); the new seven genetic parameters were calibrated in Jinju, Suwon, Chuncheon during 2003-2006. As a result, in the individual and regional combination genetic parameters, the statistical indicators of the genetic parameters of the each site or the genetic parameters of the participating stations showed improved results, but did not significant. In Daegu, Miryang, and Jeonju, the predictability on flowering time of genetic parameters of New7 was not improved than that of two references. However, the genetic parameters of New7 showed improvement of predictability on potential yield. No predictability on flowering time of genetic parameters of two references as having the coefficient of determination ($R^2$) on flowering time respectively, at 0.00 and 0.01, but the predictability of genetic parameter of New7 was improved as $R^2$ on flowering time of New7 was 0.31 in Miryang. On the other hand, $R^2$ on potential yield of genetic parameters of two references were respectively 0.66 and 0.41, but no predictability on potential yield of genetic parameter of New7 as $R^2$ of New7 showed 0.00 in Jeonju. However, it is expected that the regional combination genetic parameters with the good evaluation can be utilized to predict the flowering timing and potential yields of other regions. Although it is necessary to analyze further whether or not the input data is uncertain.

• Korean Journal of Remote Sensing
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• v.14 no.1
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• pp.53-68
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• 1998

Observation of hydrometeors' behavior in the atmosphere is important to understand weather and climate. By conventional observations, we can get the distribution of water vapor at limited number of points on the earth. In this study, the precipitable water has been estimated from the split window channel data on GMS-5 based upon the technique developed by Chesters et al.(1983). To retrieve the precipitable water, water vapor absorption parameter depending on filter function of sensor has been derived using the regression analysis between the split window channel data and the radiosonde data observed at Osan, Pohang, Kwangiu and Cheju staions for 4 months. The air temperature of 700 hPa from the Global Spectral Model of Korea Meteorological Administration (GSM/KMA) has been used as mean air temperature for single layer radiation model. The retrieved precipitable water for the period from August 1996 through December 1996 are compared to radiosonde data. It is shown that the root mean square differences between radiosonde observations and the GMS-5 retrievals range from 0.65 g/$cm^2$ to 1.09 g/$cm^2$ with correlation coefficient of 0.46 on hourly basis. The monthly distribution of precipitable water from GMS-5 shows almost good representation in large scale. Precipitable water is produced 4 times a day at Korea Meteorological Administration in the form of grid point data with 0.5 degree lat./lon. resolution. The data can be used in the objective analysis for numerical weather prediction and to increase the accuracy of humidity analysis especially under clear sky condition. And also, the data is a useful complement to existing data set for climatological research. But it is necessary to get higher correlation between radiosonde observations and the GMS-5 retrievals for operational applications.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Pharmaceutical Investigation
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• v.39 no.3
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• pp.185-199
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• 2009

Using a strain-controlled rheometer [Advanced Rheometric Expansion System (ARES)], the steady shear flow properties and the dynamic viscoelastic properties of $Antiphlamine-S^{(R)}$ lotion have been measured at $20^{\circ}C$ (storage temperature) and $37^{\circ}C$ (body temperature). In this article, the temperature dependence of the linear viscoelastic behavior was firstly reported from the experimental data obtained from a temperature-sweep test. The steady shear flow behavior was secondly reported and then the effect of shear rate on this behavior was discussed in detail. In addition, several inelastic-viscoplastic flow models including a yield stress parameter were employed to make a quantitative evaluation of the steady shear flow behavior, and then the applicability of these models was examined by calculating the various material parameters. The angular frequency dependence of the linear viscoelastic behavior was nextly explained and quantitatively predicted using a fractional derivative model. Finally, the strain amplitude dependence of the dynamic viscoelastic behavior was discussed in full to elucidate a nonlinear rheological behavior in large amplitude oscillatory shear flow fields. Main findings obtained from this study can be summarized as follows : (1) The linear viscoelastic behavior is almostly independent of temperature over a temperature range of $15{\sim}40^{circ}C$. (2) The steady shear viscosity is sharply decreased as an increase in shear rate, demonstrating a pronounced Non-Newtonian shear-thinning flow behavior. (3) The shear stress tends to approach a limiting constant value as a decrease in shear rate, exhibiting an existence of a yield stress. (4) The Herschel-Bulkley, Mizrahi-Berk and Heinz-Casson models are all applicable and have an equivalent validity to quantitatively describe the steady shear flow behavior of $Antiphlamine-S^{(R)}$ lotion whereas both the Bingham and Casson models do not give a good applicability. (5) In small amplitude oscillatory shear flow fields, the storage modulus is always greater than the loss modulus over an entire range of angular frequencies tested and both moduli show a slight dependence on angular frequency. This means that the linear viscoelastic behavior of $Antiphlamine-S^{(R)}$ lotion is dominated by an elastic nature rather than a viscous feature and that a gel-like structure is present in this system. (6) In large amplitude oscillatory shear flow fields, the storage modulus shows a nonlinear strain-thinning behavior at strain amplitude range larger than 10 % while the loss modulus exhibits a weak strain-overshoot behavior up to a strain amplitude of 50 % beyond which followed by a decrease in loss modulus with an increase in strain amplitude. (7) At sufficiently large strain amplitude range (${\gamma}_0$>100 %), the loss modulus is found to be greater than the storage modulus, indicating that a viscous property becomes superior to an elastic character in large shear deformations.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.6
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• pp.31-38
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• 2003

Among mitigation techniques for electric and magnetic field (EMF) from an overhead transmission line a passive loop is a way that can be cheap and easily installed on the existing towers and have a satisfactory effect as well. However current induced in the passive loop causes transmission power loss and the phase imbalance increases since geometrical asymmetry of the transmission lines becomes larger. So in order to evaluate the power loss and the phase imbalance due to a passive loop, this paper represent a 345[kV] 1-circuit flat type transmission line as asymmetrical 3-phase distributed parameter line model where the effect of a passive loop is embedded in the line parameters, and then formulates differential equations. By solving these equations voltages and currents of each phase at receiving end become known. We find out that power losses occur differently at each phase and positive sequence component decreases at receiving end while negative sequence component increase. In general phase imbalance due to a passive loop is slight, but it increases in proportional to the induced current and length of section where the passive loop is installed. Thus the phase imbalance should be included in terms of cost for introducing a passive loop.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.25 no.2
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• pp.85-90
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• 2012

In this paper, we compared and analyzed 3D silicon-oxide-nitride-oxide-silicon (SONOS) multi layer flash memory devices fabricated on nitride or oxide layer, respectively. The device fabricated on nitride layer has inferior electrical properties than that fabricated on oxide layer. However, the device on nitride layer has faster program / erase speed (P/E speed) than that on the oxide layer, although having inferior electrical performance. Afterwards, to find out the reason why the device on nitride has faster P/E speed, 1/f noise analysis of both devices is investigated. From gate bias dependance, both devices follow the mobility fluctuation model which results from the lattice scattering and defects in the channel layer. In addition, the device on nitride with better memory characteristics has higher normalized drain current noise power spectral density ($S_{ID}/I^2_D$>), which means that it has more traps and defects in the channel layer. The apparent hooge's noise parameter (${\alpha}_{app}$) to represent the grain boundary trap density and the height of grain boundary potential barrier is considered. The device on nitride has higher ${\alpha}_{app}$ values, which can be explained due to more grain boundary traps. Therefore, the reason why the devices on nitride and oxide have a different P/E speed can be explained due to the trapping/de-trapping of free carriers into more grain boundary trap sites in channel layer.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.285-291
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• 2020

In manufacturing, humans are being replaced with robots, but expert skills remain difficult to convert to data, making them difficult to apply to industrial robots. One method is by visual motion recognition, but physical features may be judged differently depending on the image data. This study aimed to improve the accuracy of vision methods for estimating the posture of humans. Three OpenPose vision models were applied: MPII, COCO, and COCO+foot. To identify the effects of face-covering accessories and image preprocessing on the Convolutional Neural Network (CNN) structure, the presence/non-presence of accessories, image size, and filtering were set as the parameters affecting the identification of a human's posture. For each parameter, image data were applied to the three models, and the errors between the actual and predicted values, as well as the percentage correct keypoints (PCK), were calculated. The COCO+foot model showed the lowest sensitivity to all three parameters. A <50% (from 3024×4032 to 1512×2016 pixels) reduction in image size was considered acceptable. Emboss filtering, in combination with MPII, provided the best results (reduced error of <60 pixels).

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.6
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• pp.1-12
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• 2017

This paper is brought to asses the training effect of KS-QFD boot camp for the companies in the early growth stage. In particular, the focus of research falls on measuring transfer intension of the participants from the early stage companies older than three years old, motivating effect of applying knowledges acquired from KS-QFD training camp into their real business case. KS-QFD program is presented to help company in the early stage companies over three years old of boosting up their sales volume more than 5 times than now for the next 18 months by this training. The training program of KS-QFD is ultimately to design more practical and helpful program to real business and spread out. The research establish model by setting the learner readiness and perceived content validity by doing training design as independent variables, self-efficacy of learner as mediating variable, and transfer intension as dependant variable. Research results shows the following outcomes. First, learner readiness does not have directly effect on transfer intension under keeping statistical significance. But as the parameter of self-efficacy, it has perfect mediating effect. Second, research proves that perceived content validity have directly impact on learning transfer intension of mediating by self-efficacy partially. This research contributes on proving that learning by doing KS-QFD boot camp enable the participants to build up their self-efficacy and lead to enhance transfer intension. In more steps, the research validates that KS-QFD training camp have delivered very practical and helpful on-site knowledge to the participants.

• Journal of Korea Water Resources Association
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• v.50 no.10
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• pp.661-671
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• 2017

The objective of this study was to analyze the flood stage considering the uncertainty caused by the river roughness coefficients and discharge. The methodology of this study involved the GLUE (Generalized Likelihood Uncertainty Estimation) to quantify the uncertainty bounds applying three different storm events. The uncertainty range of the roughness was 0.025~0.040. In case of discharge, the uncertainty stemmed from parameters in stage-discharge rating curve, if h represents stage for discharge Q, which can be written as $Q=A(h-B)^C$. Parameters in rating curve (A, B and C) were estimated by non-linear regression model and assumed by t distribution. The range of parameters in rating curve was 5.138~18.442 for A, -0.524~0.104 for B and 2.427~2.924 for C. By sampling 10,000 parameter sets, Monte Carlo simulations were performed. The simulated stage value was represented by 95% confidence interval. In storm event 1~3, the average bound was 0.39 m, 0.83 m and 0.96 m, respectively. The peak bound was 0.52 m, 1.36 m and 1.75 m, respectively. The recurrence year of each storm event applying the frequency analysis was 1-year, 10-year and 25-year, respectively.