• The Journal of Korean Society for Radiation Therapy
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• v.8 no.1
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• pp.19-27
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• 1996

I. Project Title A Study of Brachytherapy for intraocular tumor II. Objective and Importance of the project The eye enucleation or external-beam radiation therapy that has been commonly used for the treatment of intraocular tumor have demerits of visual loss and in deficiency of effective tumor dose. Recently, brachytherapy using the plaques containing radioisotope-now treatment method that decrease the demerits of the above mentioned treatment methods and increase the treatment effect-is introduced and performed in the countries, Our purpose of this research is to design suitable shape of plaque for the ophthalmic brachytherapy, and to measure absorbed doses of Ir-192 ophthalmic plaque and thereby calculate the exact radiation dose of tumor and it's adjacent normal tissue. III. Scope and Contents of the project In order to brachytherapy for intraocular tumor, 1. to determine the eye model and selected suitable radioisotope 2. to design the suitable shape of plaque 3. to measure transmission factor and dose distribution for custom made plaques 4. to compare with the these data and results of computer dose calculation models IV. Results and Proposal for Applications The result were as followed. 1. Eye model was determined as a 25mm diameter sphere, Ir-192 was considered the most appropriate as radioisotope for brachytherapy, because of the size, half, energy and availability. 2. Considering the biological response with human tissue and protection of exposed dose, we made the plaques with gold, of which size were 15mm, 17mm and 20mm in diameter, and 1.5mm in thickness. 3. Transmission factor of plaques are all 0.71 with TLD and film dosimetry at the surface of plaques and 0.45, 0.49 at 1.5mm distance of surface, respectively. 4. As compared the measured data for the plaque with Ir-192 seeds to results of computer dose calculation model by Gary Luxton et al. and CAP-PLAN (Radiation Treatment Planning System), absorbed doses are within ${\pm}10\%$ and distance deviations are within 0.4mm Maximum error is $-11.3\%$ and 0.8mm, respectively. As a result of it, we can treat the intraocular tumor more effectively by using custom made gold plaque and Ir-192 seeds.

• Journal of Wetlands Research
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• v.19 no.1
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• pp.30-36
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• 2017

SS(Suspended Solid) concentration by soil erosion into river at normal and flood season should be measured. However, to present the variation of SS due to various development project such as EIA(Environmental Impact Assessment), River Master Plan, and so on, it is necessary to estimate not measure SS, but there are not exist how to estimate SS. In the present study, therefore, we propose the hydrologic method of estimating SS concentration using the results of particular frequency flood discharge and sediment discharge by RUSLE method. SS consists of silty and clay soil and colloid particle etc. However, in the present study, silty and clay soils of sediment discharge except send set up SS standards. The flow discharge to estimate SS concentration are 1~2 years for normal season, 30~100 years for flood season. Meanwhile, analysis software for probable rainfall uses Fard2006, probable rainfalls under 2-year frequency are estimated using rainfall data and frequency factor of Gumbel distribution. The results of estimating SS concentration using runoff volume by sediment and flow discharges of silty and cray soils as above method show that reliable level of SS concentration is considered in predevelopment of natural condition and under development of barren condition. Especially, SS concentration takes notice that the value of sediment discharge makes a huge difference according to channel slope, it was confirmed that the value obtained by dividing the SS concentration by the channel slope is relatively constant even though the topographical factors are different. Therefore, if the present study will be proceeded for various watersheds, it will be developed as estimation method of SS concentration.

• Restorative Dentistry and Endodontics
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• v.32 no.1
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• pp.19-27
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• 2007

The purpose of this study is to develope new dental color-space system. Twelve kinds of dental composites and one kind of dental porcelain were used in this study. Disk samples (15 mm in diameter, 4 mm in thickness) of used materials were made and sample's CIE $L^*a^*b^*$ value was measured by Spectrocolorimeter (MiniScan XE plus, Model 4000S, diffuse/$8^{\circ}$ viewing mode, 14.3 mm Port diameters, Hunter Lab USA) The range of measured color distribution was analyzed. All the data were applied in the form of T### which is expression unit in CNU Cons Dental Color Chart. The value of $L^*$ lies between 80.40 and 52.70. The value of $a^*$ are between 10.60 and 3.60 and $b^*$ are between 28.40 and 2.21. The average value of $L^*$ is 67.40, and median value is 67.30. The value of $a^*$ are 2.89 and 2.91 respectively. And for the $b^*$, 14.30 and 13.90 were obtained. The data were converted to T### that is the unit count system in CNU-Cons Dental Color Chart. The value of $L^*$ is converted in the first digit of the numbering system. Each unit is 2.0 measured values. The second digit is the value of $a^*$ and is converted new number by 1.0 measured value. For the third digit $b^*$ is replaced and it is 2.0 measured unit apart. T555 was set to the value of $L^*$ ranging from 66.0 to 68.0, value of $a^*$ ranging from 3 to 4 and $b^*$ value ranging from 14 to 16.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.101-119
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• 2020

Recently, as a plan to improve the particulate matter and thermal environment in the city, urban forests acting as wind ventilation corridor(wind ventilation forest) are promoted nationwide. This study analyzed the conditions for the creation of wind ventilation forest(vulnerable areas of the particulate matter and thermal environment, distribution of wind ventilation forest, characteristics of ventilation corridor) of in Pyeongtae-si, one of the target cities of wind ventilation forest project. Based on the results, the direction of developing on the wind ventilation forest in Pyeongtaek-si was suggested. As a result of deriving areas vulnerable to particulate matter and thermal environment, it was most vulnerable in urban areas in the eastern area of Pyeongtaek-si. Especially, emissions were high from industrial complexes and roads such as the Pyeongtaek-si thermal power plant, ports, and the national road no. 1. The wind ventilation forest in Pyeongtaek-si was distributed with small-scale windgenerating forests, wind-spreading forests, and wind-connection forests fragmented and disconnected. The characteristic of the overall wind ventilation corridor in Pyeongtaek-si is that the cold air generated from Mt.Mubong, etc., strongly flowed into Pyeongtaek-si and flowed in the northwest direction. Therefore, it is necessary to preserve and expand the wind-generating forests in Pyeongtaek-si in the long term, and it was important to create wind-spreading forests and wind-connection forests so that cold air could flow into the vulnerable area. In addition, in industrial complexes and roads where particulate matter is generated, planting techniques should be applied to prevent the spread of particulate matte to surrounding areas by creating wind-spreading forests considering the particulate matter blocking. This study can be used not only as the basis data for wind ventilation forest project in Pyeongtaek-si, but also as the basis data for urban forest creation and management.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Journal of Distribution Research
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• v.15 no.3
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• pp.71-110
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• 2010

1. Introduction Today Internet is recognized as an important way for the transaction of products and services. According to the data surveyed by the National Statistical Office, the on-line transaction in 2007 for a year, 15.7656 trillion, shows a 17.1%(2.3060 trillion won) increase over last year, of these, the amount of B2C has been increased 12.0%(10.2258 trillion won). Like this, because the entry barrier of on-line market of Korea is low, many retailers could easily enter into the market. So the bigger its scale is, but on the other hand, the tougher its competition is. Particularly due to the Internet and innovation of IT, the existing market has been changed into the perfect competitive market(Srinivasan, Rolph & Kishore, 2002). In the early years of on-line business, they think that the main reason for success is a moderate price, they are awakened to its importance of on-line service quality with tough competition. If it's not sure whether customers can be provided with what they want, they can use the Web sites, perhaps they can trust their products that had been already bought or not, they have a doubt its viability(Parasuraman, Zeithaml & Malhotra, 2005). Customers can directly reserve and issue their air tickets irrespective of place and time at the Web sites of travel agencies or airlines, but its empirical studies about these Web sites for reserving and issuing air tickets are insufficient. Therefore this study goes on for following specific objects. First object is to measure service quality and service recovery of Web sites for reserving and issuing air tickets. Second is to look into whether above on-line service quality and on-line service recovery have an impact on overall service quality. Third is to seek for the relation with overall service quality and customer satisfaction, then this customer satisfaction and loyalty intention. 2. Theoretical Background 2.1 On-line Service Quality Barnes & Vidgen(2000; 2001a; 2001b; 2002) had invented the tool to measure Web sites' quality four times(called WebQual). The WebQual 1.0, Step one invented a measuring item for information quality based on QFD, and this had been verified by students of UK business school. The Web Qual 2.0, Step two invented for interaction quality, and had been judged by customers of on-line bookshop. The WebQual 3.0, Step three invented by consolidating the WebQual 1.0 for information quality and the WebQual2.0 for interactionquality. It includes 3-quality-dimension, information quality, interaction quality, site design, and had been assessed and confirmed by auction sites(e-bay, Amazon, QXL). Furtheron, through the former empirical studies, the authors changed sites quality into usability by judging that usability is a concept how customers interact with or perceive Web sites and It is used widely for accessing Web sites. By this process, WebQual 4.0 was invented, and is consist of 3-quality-dimension; information quality, interaction quality, usability, 22 items. However, because WebQual 4.0 is focusing on technical part, it's usable at the Website's design part, on the other hand, it's not usable at the Web site's pleasant experience part. Parasuraman, Zeithaml & Malhorta(2002; 2005) had invented the measure for measuring on-line service quality in 2002 and 2005. The study in 2002 divided on-line service quality into 5 dimensions. But these were not well-organized, so there needed to be studied again totally. So Parasuraman, Zeithaml & Malhorta(2005) re-worked out the study about on-line service quality measure base on 2002's study and invented E-S-QUAL. After they invented preliminary measure for on-line service quality, they made up a question for customers who had purchased at amazon.com and walmart.com and reassessed this measure. And they perfected an invention of E-S-QUAL consists of 4 dimensions, 22 items of efficiency, system availability, fulfillment, privacy. Efficiency measures assess to sites and usability and others, system availability measures accurate technical function of sites and others, fulfillment measures promptness of delivering products and sufficient goods and others and privacy measures the degree of protection of data about their customers and so on. 2.2 Service Recovery Service industries tend to minimize the losses by coping with service failure promptly. This responses of service providers to service failure mean service recovery(Kelly & Davis, 1994). Bitner(1990) went on his study from customers' view about service providers' behavior for customers to recognize their satisfaction/dissatisfaction at service point. According to them, to manage service failure successfully, exact recognition of service problem, an apology, sufficient description about service failure and some tangible compensation are important. Parasuraman, Zeithaml & Malhorta(2005) approached the service recovery from how to measure, rather than how to manage, and moved to on-line market not to off-line, then invented E-RecS-QUAL which is a measuring tool about on-line service recovery. 2.3 Customer Satisfaction The definition of customer satisfaction can be divided into two points of view. First, they approached customer satisfaction from outcome of comsumer. Howard & Sheth(1969) defined satisfaction as 'a cognitive condition feeling being rewarded properly or improperly for their sacrifice.' and Westbrook & Reilly(1983) also defined customer satisfaction/dissatisfaction as 'a psychological reaction to the behavior pattern of shopping and purchasing, the display condition of retail store, outcome of purchased goods and service as well as whole market.' Second, they approached customer satisfaction from process. Engel & Blackwell(1982) defined satisfaction as 'an assessment of a consistency in chosen alternative proposal and their belief they had with them.' Tse & Wilton(1988) defined customer satisfaction as 'a customers' reaction to discordance between advance expectation and ex post facto outcome.' That is, this point of view that customer satisfaction is process is the important factor that comparing and assessing process what they expect and outcome of consumer. Unlike outcome-oriented approach, process-oriented approach has many advantages. As process-oriented approach deals with customers' whole expenditure experience, it checks up main process by measuring one by one each factor which is essential role at each step. And this approach enables us to check perceptual/psychological process formed customer satisfaction. Because of these advantages, now many studies are adopting this process-oriented approach(Yi, 1995). 2.4 Loyalty Intention Loyalty has been studied by dividing into behavioral approaches, attitudinal approaches and complex approaches(Dekimpe et al., 1997). In the early years of study, they defined loyalty focusing on behavioral concept, behavioral approaches regard customer loyalty as "a tendency to purchase periodically within a certain period of time at specific retail store." But the loyalty of behavioral approaches focuses on only outcome of customer behavior, so there are someone to point the limits that customers' decision-making situation or process were neglected(Enis & Paul, 1970; Raj, 1982; Lee, 2002). So the attitudinal approaches were suggested. The attitudinal approaches consider loyalty contains all the cognitive, emotional, voluntary factors(Oliver, 1997), define the customer loyalty as "friendly behaviors for specific retail stores." However these attitudinal approaches can explain that how the customer loyalty form and change, but cannot say positively whether it is moved to real purchasing in the future or not. This is a kind of shortcoming(Oh, 1995). 3. Research Design 3.1 Research Model Based on the objects of this study, the research model derived is