• Investigative Magnetic Resonance Imaging
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• v.9 no.1
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• pp.43-49
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• 2005

A few studies have shown that the function of fusiform face area is selectively involved in the perception of faces including a race difference. We investigated the neural substrates of the face-selective region called fusiform face area in the ventral occipital-temporal cortex and same-race memory superiority in the fusiform face area by the event-related fMRI. In our fMRI study, subjects (Oriental-Korean) performed the implicit distinction of the race while they consciously made familiar-judgments, regardless of whether they considered a face as Oriental-Korean or European-American. For race distinction as an implicit task, the fusiform face areas (FFA) and the right parahippocampal gyrus had a greater response to the presentation of Oriental-Korean faces than for the European-American faces, but in the conscious race distinction between Oriental-Korean and European-American faces, there was no significant difference observed in the FFA. These results suggest that different activation in the fusiform regions and right parahippocampal gyrus resulting from superiority of same-race memory could have implicitly taken place by the physiological processes of face recognition.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.37 no.2
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• pp.178-184
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• 1992

The changes in contents of chlorophyll and free proline in the seedling leaves of ten rice cultivars as affected by salt stress were checked in order to obtain the basic information on the judgement of the degrees of salt injury. The difference in salt injury among the cultivars was clearly observed about 25 days after 6% salt treatment. Chlorophyll content was decreased in both Gayabyeo and Taebaegbyeo for 14 days after different salt treatment as salt concentration was increased and the decreased tendency was much higher in Taebaegbyeo than in Gayabyeo over 0.4% salt concentration. Chlorophyll content in Gayabyeo after 0.6% salt treatment was decreased slowly, while in Taebaegbyeo, deminished very rapidly as time progressed, therefore it decreased by about 16% in Gayabyeo and 67% in Taebaebyeo compared to the control at 20 days, respectively. The relationship between chlorophyll content and the degrees of salt injury in ten rice cultivars showed significant negative correlation at 10 day after 0.6% salt treatment. Free proline content in Gayabyeo was increased gradually for 14 days after different salt treatment as salt became higher, while in Taebaebyeo, it was increased rapidly under 0.6% but rather decreased under 0.8% salt concentration. Particularly, it was much higher Taebaegbyeo than in Gayabyeo under salt concentration from 0.4 to 0.6%. Free proline content in Gayabyeo after 0.6% salt treatment was increased from 15 days, on the other hand in Taebaegbyeo, it was increased from 5 days, but rather decreased from 20 days, and it was 6 times higher in Taebaegbyeo than in Gayabyeo at 10 days. There was significant positive correlation between free proline content and the degrees of salt injury in ten rice cultivars at 10 days after 0.6% salt treatment. From the above results, chlorophyll and free proline content may be used as an indicative character of intensity of salt stress as well as varietal difference in resistance to salt stress in the seedling stage.

• Progress in Medical Physics
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• v.3 no.2
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• pp.1-12
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• 1992

For intraoperative radiation therapy using electron beams, a cone system to deliver a large dose to the tumor during surgical operation and to save the surrounding normal tissue should be developed and dosimetry for the cone system is necessary to find proper X-ray collimator setting as well as to get useful data for clinical use. We developed a docking type of a cone system consisting of two parts made of aluminum: holder and cone. The cones which range from 4cm to 9cm with 1cm step at 100cm SSD of photon beam are 28cm long circular tubular cylinders. The system has two 26cm long holders: one for the cones larger than or equal to 7cm diamter and another for the smaller ones than 7cm. On the side of the holder is an aperture for insertion of a lamp and mirror to observe treatment field. Depth dose curve. dose profile and output factor at dept of dose maximum. and dose distribution in water for each cone size were measured with a p-type silicone detector controlled by a linear scanner for several extra opening of X-ray collimators. For a combination of electron energy and cone size, the opening of the X-ray collimator was caused to the surface dose, depths of dose maximum and 80%, dose profile and output factor. The variation of the output factor was the most remarkable. The output factors of 9MeV electron, as an example, range from 0.637 to 1.549. The opening of X-ray collimators would cause the quantity of scattered electrons coming to the IORT cone system. which in turn would change the dose distribution as well as the output factor. Dosimetry for an IORT cone system is inevitable to minimize uncertainty in the clinical use.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.1-12
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• 2005

Image registration is a process to establish the spatial correspondence between images of the same scene, which are acquired at different view points, at different times, or by different sensors. This paper presents a new algorithm for robust registration of the images acquired by multiple sensors having different modalities; the EO (electro-optic) and IR(infrared) ones in the paper. The two feature-based and intensity-based approaches are usually possible for image registration. In the former selection of accurate common features is crucial for high performance, but features in the EO image are often not the same as those in the R image. Hence, this approach is inadequate to register the E0/IR images. In the latter normalized mutual Information (nHr) has been widely used as a similarity measure due to its high accuracy and robustness, and NMI-based image registration methods assume that statistical correlation between two images should be global. Unfortunately, since we find out that EO and IR images don't often satisfy this assumption, registration accuracy is not high enough to apply to some applications. In this paper, we propose a two-stage NMI-based registration method based on the analysis of statistical correlation between E0/1R images. In the first stage, for robust registration, we propose two preprocessing schemes: extraction of statistically correlated regions (ESCR) and enhancement of statistical correlation by filtering (ESCF). For each image, ESCR automatically extracts the regions that are highly correlated to the corresponding regions in the other image. And ESCF adaptively filters out each image to enhance statistical correlation between them. In the second stage, two output images are registered by using NMI-based algorithm. The proposed method provides prospective results for various E0/1R sensor image pairs in terms of accuracy, robustness, and speed.

• Journal of Life Science
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• v.26 no.8
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• pp.904-910
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• 2016

Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The gene network analysis has been performed by diverse types of methods such as gene co-expression, gene regulatory relationships, protein-protein interaction (PPI) and genetic networks. Moreover, the network-based methods were described for predicting gene functions such as graph theoretic method, neighborhood counting based methods and weighted function. However, there are a limited number of researches in livestock. The present study systemically analyzed genes associated with 102 types of economic traits based on the Animal Trait Ontology (ATO) and identified their relationships based on the gene co-expression network and PPI network in cattle. Then, we constructed the two types of gene network databases and network visualization system (http://www.nabc.go.kr/cg). We used a gene co-expression network analysis from the bovine expression value of bovine genes to generate gene co-expression network. PPI network was constructed from Human protein reference database based on the orthologous relationship between human and cattle. Finally, candidate genes and their network relationships were identified in each trait. They were typologically centered with large degree and betweenness centrality (BC) value in the gene network. The ontle program was applied to generate the database and to visualize the gene network results. This information would serve as valuable resources for exploiting genomic functions that influence economically and agriculturally important traits in cattle.

• 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.

• Journal of Korean Elementary Science Education
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• v.41 no.4
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• pp.658-672
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• 2022

The unique teaching and learning difficulties of speed-related units in elementary school science are mainly due to the student's lack of mathematical thinking ability and procedural knowledge on speed measurement, and curriculums and textbooks must be constructed with these in mind. To identify the implications of composing a new science curriculum and relevant textbooks, this study reviewed the structure and contents of the speed-related units of three curriculums from the 2007 revised curriculum to the 2015 revised curriculum and the resulting textbooks and examined their relevance in light of the literature. Results showed that the current content carries the risk of making students calculate only the speed of an object through a mechanical algorithm by memorization rather than grasp the multifaceted relation between traveled distance, duration time, and speed. Findings also highlighted the need to reorganize the curriculum and textbooks to offer students the opportunity to learn the meaning of speed step-by-step by visualizing materials such as double number lines and dealing with simple numbers that are easy to calculate and understand intuitively. In addition, this paper discussed the urgency of improving inquiry performance such as process skills by observing and measuring an actual object's movement, displaying it as a graph, and interpreting it rather than conducting data interpretation through investigation. Lastly, although the current curriculum and textbooks emphasize the connection with daily life in their application aspects, they also deal with dynamics-related content somewhat differently from kinematics, which is the main learning content of the unit. Hence, it is necessary to reorganize the contents focusing on cases related to speed so that students can grasp the concept of speed and use it in their everyday lives. With regard to the new curriculum and textbooks, this study proposes that students be provided the opportunity to systematically and deeply study core topics rather than exclude content that is difficult to learn and challenging to teach so that students realize the value of science and enjoy learning it.

• 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.