• East Asian mathematical journal
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• v.31 no.1
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• pp.55-63
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• 2015

Let $\mathbb{B}$ be the unit ball in $\mathbb{C}^n$. For a weight function ${\omega}$, we define the generalized growth space $A^{\omega}(\mathbb{B})$ by the space of holomorphic functions f on $\mathbb{B}$ such that $${\mid}f(z){\mid}{\leq}C{\omega}({\mid}{\rho}(z){\mid},\;z{\in}\mathbb{B}$$. Our main purpose in this note is to get the corona type decomposition in generalized growth spaces on $\mathbb{B}$.

• Bulletin of the Korean Mathematical Society
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• v.51 no.6
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• pp.1805-1827
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• 2014

Let $\mathcal{K}^{\prime}_M$ be the generalized tempered distributions of $e^{M(t)}$-growth, where the function M(t) grows faster than any linear functions as ${\mid}t{\mid}{\rightarrow}{\infty}$, and let $K^{\prime}_M$ be the Fourier transform spaces of $\mathcal{K}^{\prime}_M$. We obtain the relationship between certain classes of analytic functions in tubes, $\mathcal{K}^{\prime}_M$ and $K^{\prime}_M$.

• Journal of the Korea Furniture Society
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• v.29 no.1
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• pp.8-17
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• 2018

Economic crisis in 2008 has changed South Korean market including furniture related field. Owing to Subprime Mortgage Crisis, new economic order, in other words, New Normal was established. Low growth rate, low interest, high unemployment rate, high risks, regulation strengthening, and all that sort of negative things have became generalized. South Korean economy has developed drastically since the Korean War, however recent economic crisis and Internet and smart phone have leading roles in shaping new consumption market. In a way, furniture market has expanded despite economic recession. Total service for housing is suited to South Korean consumers and shortened Product Life Cycle induces consumers to buy more furniture. In addition, Internet and smart phone allow people to show off their private spaces to unspecified masses. As a result, consumer prefers inexpensive and expendable furniture. It is certain that furniture market makes quantitative growth, but qualitative sides are questionable. Even though the study is focused on the existent circumstances, It will help to find out the proper ways of future furniture market in South Korea.

• Journal of applied mathematics & informatics
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• v.32 no.1_2
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• pp.137-152
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• 2014

In this paper, we prove, in the spirit of [3, 12, 20, 22, 23], the existence of infinitely many small solutions to the following quasilinear elliptic equation $-{\Delta}_{p(x)}u+{\mid}u{\mid}^{p(x)-2}u={\mid}u{\mid}^{q(x)-2}u+{\lambda}f(x,u)$ in a smooth bounded domain ${\Omega}$ of ${\mathbb{R}}^N$. We also assume that $\{q(x)=p^*(x)\}{\neq}{\emptyset}$, where $p^*(x)$ = Np(x)/(N - p(x)) is the critical Sobolev exponent for variable exponents. The proof is based on a new version of the symmetric mountainpass lemma due to Kajikiya [22], and property of these solutions are also obtained.

• Journal of Animal Environmental Science
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• v.21 no.3
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• pp.83-92
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• 2015

An attempt to produce more pigs in limited spaces inevitably generalized concentrated feeding operation (CFO). As concentrated pig production practice expanded, concerns on environmental issues grow concurrently. Since odor is the concerned most among those, we attempted to develop means to tackle odor emission from livestock operations. Previously, we excavated few microorganisms from pig manure and, one of them, Bacillus licheniformis was particularly useful to handle odor problem. In this study, we conducted our investigation to further characterize Bacillus licheniformis. Strain identification was conducted using Vitek 2 compact, and the optimal temperature and pH conditions to growth B. licheniformis were searched for by analyzing turbidity on O.D 600 nm. Results of this study can be summarized as these, (1) it was re-verified that the bacterial strain that purified from pig manure was, in fact, Bacillus licheniformis, (2) the bacterial growth was highest when the temperature was kept at $30^{\circ}C$, also (3) growth rate was dependent on media pH as it was high at neutral (6, 7 and 8) but dropped when it was diverged from neutral (4, 5, 9 and 10), and (4) regarding ammonia removal efficiency, B. licheniformis recorded 64% effectiveness after 48 h incubation and reached its highest (80%) at 72 h.

• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.87-102
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• 2009

With the growth of Web 2.0, lots of services allow yours to post their personal information and useful knowledges on networked information spaces such as blogs and online communities etc. As the services are generalized, recent researches related to social network have gained momentum. However, most social network services do not support machine-processable semantic knowledge, so that the information cannot be shared and reused between different domains. Moreover, as explicit definitions of relationships between individual social entities do not be described, it is difficult to analyze social network for inferring unknown semantic relationships. To overcome these limitations, in this paper, we propose a social network analysis system with personal photographic data up-loaded by virtual community users. By using ontology, an informative connectivity between a face entity extracted from photo data and a person entity which already have social relationships was defined clearly and semantic social links were inferred with domain rules. Then the inferred links were provided to yours as a visualized graph. Based on the graph, more efficient social network analysis was achieved in online community.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.