• Economic and Environmental Geology
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• v.50 no.6
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• pp.487-495
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• 2017

A granite rock body, called 'Bukbawi', located on a mountaineering trail at Mt. Palgong Provincial Park is popular among the public because it resembles a percussion instrument. If someone hits the specific surface area of this rock body, people can hear drum-like sound. Such phenomenon may be geologically associated with exfoliation process of the granite body or miarolitic cavity developed after gasses escaped during formation of granite. To understand better the inner structure causing drum-like sound, we carried out a non-destructive ground-penetrating radar survey. In this study, as our primary target is very close to the surface, we utilized 1 GHz antennas to produce high-resolution near-surface images. In order to construct 3-D internal images, the measurements were conducted along a pre-defined grid. The processed radargrams revealed that the locations associated with 'drum' sound coincide with strong reflections. In addition, both reflection patterns of fracture and cavity were observed. To further quantify the observed reflections, we simulated GPR scans from a synthetic fracture in a granite body, filled with different materials. The simulated results suggest that both exfoliation process and miarolitic cavity may have contributed to the 'drum' phenomena. Furthermore, the radargrams showed a well-developed cavity signature where two major reflection planes were crossed. Thus, our study is an example of non-destructive geophysical studies that can promote Earth Science in the broader community by examining geological structures attracting the public.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.35 no.4
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• pp.408-416
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• 2002

A 3D hydrodynamic-ecological coupled model was applied to estimate carrying capacity in Geoje-Hansan Bay where is one of the most important oyster culturing grounds in Korea. We considered the carrying capacity as the difference between food supply to the oysters and food demand, considering monthly difference of the actual growth. The food supply to the system was determined from the results of the model simulation (tidal exchange and chlorophyll $\alpha$) over the culturing period from September to May of the following year. The food demand was estimated from the food concentration (chlorophyll $\alpha$) multiple the filtration rate of oysters that is considered monthly different growth rate of oysters and food concentration. The values of carrying capacity for the system varied from 6.1 ton/ha (minimum carrying capacity) in february to 14.91 ton/ha (maximum carrying capacity) in April of marketable size oysters (>4 g wet-tissue weight) depending on temporal variations in the food supply. The oyster production calculated from present facilities was 9 ton/ha in wet-tissue weight in Geoje-Hansan Bay. This value corresponded to $60\%$ of maximum carrying capacity of the system. The optimal carrying capacity without negatively affecting on oyster production was 5.5 ton/ha when calculated from annual statistic data and 6.1 ton/ha when determined by this study. These results suggest that it must be reduced $32\%$~$39\%$ of oyster facilities in the system.

• Journal of Dental Rehabilitation and Applied Science
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• v.27 no.2
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• pp.175-184
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• 2011

With a development of implant restoration technique, there are increasing use of computer-guided system for edentulous patients. It was carried out simulated operation based on CT information about patient's bone quantity, quality and anatomical landmark. However, there are some difference between the programmed implant and post-operative implant about it's position. If the deviation was severe, it could happen a failure of 'passive fit' and not suited for path of implant restoration. The aim of this presentation is to evaluate about a degree of deviations between programmed implant and post-operative implant. Five patients treated by 'NobelGuide' system (Nobel Biocare AB, G$\ddot{o}$teborg, Sweden) in Department of Prosthodontics, Inha University were included in this study. The patients were performed CT radiograph taking and intra-oral impression taking at pre-operation. Based on CT images and study model, surgical stent was produced by NobelBiocareTM. To fabricated a pre-operative study model, after connected lab analog to surgical template, accomplished a pre-operative model using type 4 dental stone. At final impression, a post-operative study model was fabricated in the conventional procedures. Each study model was performed CT radiograph taking. Based on CT images, each implant was simulated in three dimensional position using $Procera^{(R)}$ software (Procera Software Clinical Design Premium, version 1.5; Nobel Biocare AB). In 3D simulated model, length and angulation between each implant of both pre- and post-operative implants were measured and recorded about linear and angular deviation between pre-and post-operative implants. A total of 24 implants were included in this study and 58 inter-implant sites between each implant were measured about linear and angular deviations. In the linear deviation a mean deviation of 0.41 mm (range 0~1.7 mm) was reported. In the angular deviation, a mean deviation was $1.99^{\circ}$ (range $0^{\circ}{\sim}6.7^{\circ}$). It appears that the both linear and angular mean deviation value were well acceptable to application of computer-guided implant system.

• The Korean Journal of Pharmacology
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• v.20 no.2
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• pp.15-21
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• 1984

It was aimed to study the effects of ${\alpha}_2-adrenoceptor$ agonists on the sleeping time in $one{\sim}two-day-old$ chickens. Furthermore, it was also evaluated whether ${\alpha}_1-adrenoceptor$ agonist and antagonist might affect the sleeping in the chickens and discussed in relation with opiate receptor. 1) Guanabenz, clonidine, guanfacine and B-HT 933 decreased the latency of the loss of righting reflex in a dose-dependent manner, but B-HT 920 and oxymetazoline slightly prolonged it. 2) ${\alpha}_2-Adrenoceptor$ agonists produced dose·related increase in sleeping time. The potency was guanabenz>clonidine>oxymetazoline${\geq}$B-HT 933${\geq}$B-HT 920>guanfacine in this order. 3) ${\alpha}_2-Adrenoceptor$ antagonists decreased guanabenz-induced sleeping time in a dose ·dependent manner. The rank order of ${\alpha}_2-adrenoceptor$ antagonists was yohimbine>rauwolscine>piperoxan${\geq}$RX 781094. 4) Sleeping time caused by both ethanol and hexobarbital was not affected by yohimbine in chickens. 5) Methoxamine and phenylephrine showed little significant effect on the guanabenz-induced sleeping time. However, prazosin increased it. Paradoxically, corynanthine rather caused to decrease it. These results suggest that the stimulation of central ${\alpha}_2-adrenoceptor$ mediates sleeping, however it is remained uncertain in the role of central ${\alpha}_1-adrenoceptor$ in chickens. In addition, the one~two-day-old chickens may be considered as a useful, inexpensive and simple experimental model to evaluate the in vivo pharmacological action of the ${\alpha}_2-adrenoceptor$ agonist and antagonist related to sedation.

• Restorative Dentistry and Endodontics
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• v.34 no.3
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• pp.169-176
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• 2009

Diabetes Mellitus (DM) is a syndrome accompanied with the abnormal secretion or function of insulin, a hormone that plays a vital role in controlling the blood glucose level (BGL). Type land 2 DM are most common form and the prevalence of the latter is recently increasing, The aim of this article was to assess whet her Type 2 DM could act as a predisposing risk factor on the pulpo-periapical pathogenesis. Previous literature on the pathologic changes of blood vessels in DM was thoroughly reviewed. Furthermore, a histopathologic analysis of artificially-induced periapical specimens obtained from Type 2 diabetic and DM-resistant rats was compared. Histopathologic results demonstrate that the size of periapical bone destruction w as larger and the degree of pulpal inflammation was more severe in diabetic rats, indicating that Type 2 D M itself can be a predisposing risk factor that makes the host more susceptible to pulpal infection. The possible reasons may be that in diabetic state the lumen of pulpal blood vessels are thickened by atheromatous deposits, and microcirculation is hindered, The function of polymorphonuclear leukocyte is also impair ed and the migration of immune cells is blocked, leading to increased chance of pulpal infection. Also, lack of collateral circulation of pulpal blood vessels makes the pulp more susceptible to infection. These decrease the regeneration capacity of pulpal cells or tissues, delaying the healing process, Therefore, when restorative treatment is needed in Type 2 DM patients, dentists should minimize irritation to the pulpal tissue un der control of BGL.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.23-48
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• 2021

The Korean economy has achieved continuous economic growth for the past several decades thanks to the government's export strategy policy. This increase in exports is playing a leading role in driving Korea's economic growth by improving economic efficiency, creating jobs, and promoting technology development. Traditionally, the main factors affecting Korea's exports can be found from two perspectives: economic factors and industrial structural factors. First, economic factors are related to exchange rates and global economic fluctuations. The impact of the exchange rate on Korea's exports depends on the exchange rate level and exchange rate volatility. Global economic fluctuations affect global import demand, which is an absolute factor influencing Korea's exports. Second, industrial structural factors are unique characteristics that occur depending on industries or products, such as slow international division of labor, increased domestic substitution of certain imported goods by China, and changes in overseas production patterns of major export industries. Looking at the most recent studies related to global exchanges, several literatures show the importance of cultural aspects as well as economic and industrial structural factors. Therefore, this study attempted to develop a forecasting model by considering cultural factors along with economic and industrial structural factors in calculating the import volume of each country from Korea. In particular, this study approaches the influence of cultural factors on imports of Korean products from the perspective of PUSH-PULL framework. The PUSH dimension is a perspective that Korea develops and actively promotes its own brand and can be defined as the degree of interest in each country for Korean brands represented by K-POP, K-FOOD, and K-CULTURE. In addition, the PULL dimension is a perspective centered on the cultural and psychological characteristics of the people of each country. This can be defined as how much they are inclined to accept Korean Flow as each country's cultural code represented by the country's governance system, masculinity, risk avoidance, and short-term/long-term orientation. The unique feature of this study is that the proposed final prediction model can be selected based on Design Principles. The design principles we presented are as follows. 1) A model was developed to reflect interest in Korea and cultural characteristics through newly added data sources. 2) It was designed in a practical and convenient way so that the forecast value can be immediately recalled by inputting changes in economic factors, item code and country code. 3) In order to derive theoretically meaningful results, an algorithm was selected that can interpret the relationship between the input and the target variable. This study can suggest meaningful implications from the technical, economic and policy aspects, and is expected to make a meaningful contribution to the export support strategies of small and medium-sized enterprises by using the import forecasting model.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.387-396
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• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.

• Journal of Global Scholars of Marketing Science
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• v.17 no.3
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• pp.39-59
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• 2007

The international involvement of a firm has been described as a gradual development process "a process in which the enterprise gradually increases its international involvement in many studies. This process evolves in the interplay between the development of knowledge about foreign markets and operations on one hand and increasing commitment of resources to foreign markets on the other." On the basis of Uppsala internationalization model, many studies strengthen strong theoretical and empirical support. According to the predictions of the classic stages theory, the internationalization process of firms have been recognized and characterized gradual evolution to foreign markets, so called stage theory: indirect & direct export, strategic alliance and foreign direct investment. However, termed "international new ventures" (McDougall, Shane, and Oviatt 1994), "born globals" (Knight 1997; Knight and Cavusgil 1996; Madsen and Servais 1997), "instant internationals" (Preece, Miles, and Baetz 1999), or "global startups" (Oviatt and McDougall 1994) have been used and come into spotlight in internationalization study of technology intensity venture companies. Recent researches focused on venture company have suggested the phenomenons of 'born global' firms as a contradiction to the stages theory. Especially the article by Oviatt and McDougall threw the spotlight on international entrepreneurs, on international new ventures, and on their importance in the globalising world economy. Since venture companies have, by definition. lack of economies of scale, lack of resources (financial and knowledge), and aversion to risk taking, they have a difficulty in expanding their market to abroad and pursue internalization gradually and step by step. However many venture companies have pursued 'Born Global Strategy', which is different from process strategy, because corporate's environment has been rapidly changing to globalization. The existing studies investigate that (1) why the ventures enter into overseas market in those early stage, even in infancy, (2) what make the different international strategy among ventures and the born global strategy is better to the infant ventures. However, as for venture's performance(growth and profitability), the existing results do not correspond each other. They also, don't include marketing strategy (differentiation, low price, market breadth and market pioneer) that is important factors in studying of BGV's performance. In this paper I aim to delineate the appearance of international new ventures and the phenomenons of venture companies' internationalization strategy. In order to verify research problems, I develop a resource-based model and marketing strategies for analyzing the effects of the born global venture firms. In this paper, I suggested 3 research problems. First, do the korean venture companies take some advantages in the aspects of corporate's performances (growth, profitability and overall market performances) when they pursue internationalization from inception? Second, do the korean BGV have firm specific assets (foreign experiences, foreign orientation, organizational absorptive capacity)? Third, What are the marketing strategies of korean BGV and is it different from others? Under these problems, I test then (1) whether the BGV that a firm started its internationalization activity almost from inception, has more intangible resources(foreign experience of corporate members, foreign orientation, technological competences and absorptive capacity) than any other venture firms(Non_BGV) and (2) also whether the BGV's marketing strategies-differentiation, low price, market diversification and preemption strategy are different from Non_BGV. Above all, the main purpose of this research is that results achieved by BGV are indeed better than those obtained by Non_BGV firms with respect to firm's growth rate and efficiency. To do this research, I surveyed venture companies located in Seoul and Deajeon in Korea during November to December, 2005. I gather the data from 200 venture companies and then selected 84 samples, which have been founded during 1999${\sim}$2000. To compare BGV's characteristics with those of Non_BGV, I also had to classify BGV by export intensity over 50% among five or six aged venture firms. Many other researches tried to classify BGV and Non_BGV, but there were various criterion as many as researchers studied on this topic. Some of them use time gap, which is time difference of establishment and it's first internationalization experience and others use export intensity, ration of export sales amount divided by total sales amount. Although using a mixed criterion of prior research in my case, I do think this kinds of criterion is subjective and arbitrary rather than objective, so I do mention my research has some critical limitation in the classification of BGV and Non_BGV. The first purpose of research is the test of difference of performance between BGV and Non_BGV. As a result of t-test, the research show that there are statistically efficient difference not only in the growth rate (sales growth rate compared to competitors and 3 years averaged sales growth rate) but also in general market performance of BGV. But in case of profitability performance, the hypothesis that is BGV is more profit (return on investment(ROI) compared to competitors and 3 years averaged ROI) than Non-BGV was not supported. From these results, this paper concludes that BGV grows rapidly and gets a high market performance (in aspect of market share and customer loyalty) but there is no profitability difference between BGV and Non_BGV. The second result is that BGV have more absorptive capacity especially, knowledge competence, and entrepreneur's international experience than Non_BGV. And this paper also found BGV search for product differentiation, exemption strategy and market diversification strategy while Non_BGV search for low price strategy. These results have never been dealt with other existing studies. This research has some limitations. First limitation is concerned about the definition of BGV, as I mentioned above. Conceptually speaking, BGV is defined as company pursue internationalization from inception, but in empirical study, it's very difficult to classify between BGV and Non_BGV. I tried to classify on the basis of time difference and export intensity, this criterions are so subjective and arbitrary that the results are not robust if the criterion were changed. Second limitation is concerned about sample used in this research. I surveyed venture companies just located in Seoul and Daejeon and also use only 84 samples which more or less provoke sample bias problem and generalization of results. I think the more following studies that focus on ventures located in other region, the better to verify the results of this paper.

• 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.