• Journal of Korean Society of Rural Planning
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• v.19 no.4
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• pp.47-54
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• 2013

Climate change is the most direct threatening factors in sustaining agricultural productivity. It is necessary to reduce the damages from the natural hazards such as flood, drought, typhoons, and snowstorms caused by climate change. Through the vulnerability assessment to adapt the climate change, it is possible to analyze the priority, feasibility, effect of the reduction policy. For the vulnerability assessment, broad amount of weather data for each meterological station are required. Making the database management system for the meteorologic data could troubleshoot of the difficulties lie in handling and processing the weather data. In this study, we generated the meteorologic data retrieval system (MetSystem) for climate change vulnerability assessment. The user interface of MetSystem was implemented in the web-browser so as to access to a database server at any time and place, and it provides different query executions according to the criteria of meteorologic stations, temporal range, meteorologic items, statistics, and range of values, as well as the function of exporting to Excel format (*.xls). The developed system is expected that it will make it easier to try different analyses of vulnerability to natural hazards by the simple access to meteorologic database and the extensive search functions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.68-81
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• 2021

The agricultural ecosystem is one of the major sources of greenhouse gas (GHG) emissions. In order to search for climate change adaptation options which mitigate GHG emissions while maintaining crop yield, it is advantageous to integrate multiple models at a high spatial resolution. The objective of this study was to develop a tool to support integrated assessment of climate change impact b y coupling the DSSAT model and the DNDC model. DNDC Regional Input File Tool(DRIFT) was developed to prepare input data for the regional mode of DNDC model using input data and output data of the DSSAT model. In a case study, GHG emissions under the climate change conditions were simulated using the input data prepared b y the DRIFT. The time to prepare the input data was increased b y increasing the number of grid points. Most of the process took a relatively short time, while it took most of the time to convert the daily flood depth data of the DSSAT model to the flood period of the DNDC model. Still, processing a large amount of data would require a long time, which could be reduced by parallelizing some calculation processes. Expanding the DRIFT to other models would help reduce the time required to prepare input data for the models.

• Journal of Ocean Engineering and Technology
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• v.33 no.6
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• pp.590-596
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• 2019

Numerous deaths and substantial property damage have occurred recently due to frequent disasters of the highest intensity according to the abnormal climate, which is caused by various problems, such as global warming, all over the world. Such large-scale disasters have become an international issue and have made people aware of the disasters so they can implement disaster-prevention measures. Extensive information on disaster prevention actively has been announced publicly to support the natural disaster reduction measures throughout the world. In Japan, diverse developmental studies on disaster prevention systems, which support hazard map development and flood control activity, have been conducted vigorously to estimate external forces according to design frequencies as well as expected maximum frequencies from a variety of areas, such as rivers, coasts, and ports based on broad disaster prevention data obtained from several huge disasters. However, the current reduction measures alone are not sufficiently effective due to the change of the paradigms of the current disasters. Therefore, in order to obtain the synergy effect of reduction measures, a study of the establishment of an integrated system is required to improve the various disaster prevention technologies and the current disaster prevention system. In order to develop a similar typhoon search system and establish a disaster prevention infrastructure, in this study, techniques will be developed that can be used to forecast typhoons before they strike by using artificial intelligence (AI) technology and offer primary disaster prevention information according to the direction of the typhoon. The main function of this model is to predict the most similar typhoon among the existing typhoons by utilizing the major typhoon information, such as course, central pressure, and speed, before the typhoon directly impacts South Korea. This model is equipped with a combination of AI and DNN forecasts of typhoons that change from moment to moment in order to efficiently forecast a current typhoon based on similar typhoons in the past. Thus, the result of a similar typhoon search showed that the quality of prediction was higher with the grid size of one degree rather than two degrees in latitude and longitude.

• Archives of design research
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• v.18 no.2 s.60
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• pp.219-232
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• 2005

Moderns are living within flood of web contents, animation, reflex data etc. as well as sight, product, environment design. There fore, modern consumer has much options. Designer must provide various result for consumer for this reason. And must invent new sensitivity and propose to consumer. As purpose of this MCC sensitivity palette research takes advantage of the most sensitive color, do. Because applying correct sensitivity more than when design with matter already settled, rid private prejudice, and is thing to convey design intention exactly to user. Excellent culture contents must be able to equip international color design sensitivity. MCC sensitivity palette research studies and carries on the head emotion and sensitivity language that is nationality first, and collect End arranged sensitivity adjective through data analysis and picture data analysis that is the next time research leader Munheonjeok. And distributed collected adjective equally, and arrange distributed adjective by field of each sensitivity and collect system. Do 3 colors, 4 colors color scheme in selected sensitivity adjective and completed Simheom version. Result of beta version research to color specialist and designer last digital palette through question and inquiry compose. Through this process, completed more real and correct digital color sensitivity palette. Completed color scheme is operated in www.mcdri.net on web, and also programs to windows base and developed to software. MCC color scheme palette that research result is made includes sensitivity data database. This database can use directly in industry and continuous development is available. Software can search color scheme in language and idea development through classification search that use 3 attributes of color is available there is cough data of each output device different color error.

• Journal of the Korea Fashion and Costume Design Association
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• v.14 no.3
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• pp.11-22
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• 2012

As the consuming pattern is changing with the expansion of Internet use and the development of communication technology, Internet shopping market is getting bigger and bigger. By product group, clothing and fashion related products occupy the biggest share. Accordingly, in this study it was tried to identify the effects of Internet utilization capability that enables consumers to search for the information that they need in this information flood, variety pursuit trend and product review accommodation status on shopping value, and to analyze the effects of the shopping value on the purchase behavior in online shopping malls. When factor analysis is nude on Internet use level, it was found that Factor 1 was 'Flow Experience,' Factor 2 'Internet Use Capability,' and Factor 3 'Internet Challenge Desire.' When factor analysis is made on Diversity Pursuit Propensity, it was found that Factor 1 was 'Site Diversity Pursuit Propensity,' Factor 2 'Brand Diversity Pursuit Propensity,' and Factor 3 'Brand Value Pursuit Propensity.' When factor analysis is nude on Product Review Accommodation Propensity, it was found that Factor 1 was 'Product Information Provision Propensity,' and Factor 2 'Product Information Receiving Propensity.' Except Internet Use Capability and Product Information Provision Propensity, all other factors showed high correlation. The factor influencing the entertainment value most was Internet challenge desire, while that influencing the practical value most was flow experience. When the effects of the entertainment value and the practical value on product purchase were analyzed, it was found that both of entertainment value and the practical value influenced product purchase and the practical value influenced the product purchase more than the entertainment value.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.21 no.2
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• pp.101-107
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• 2003

Lately, much researches for information management of web GIS base are gone. Specially, disaster Information Management System development of Web-Based for management and offer of disaster information such as flood or earthquake is necessarily necessary for prevention and countermeasure about disaster, disaster information acquisition of interest area. Spatial DB access for plan/analysis/management business of this system should be possible and must become display of disaster information and disaster mapmaking through internet for disaster prevention but research of web GIS base about this field is childhood yet. This research executed research for disaster information offer using HTML and Javascript and ESRI's ArcIMS that is development tools or web GIS. And this research could search disaster information of interest area through various kinds function and offer in display through user selection. Also, topography of interest area could confirm through third dimension topography model who use VRML. And this research could supply disaster information of Chunchon city using various function thorough system of Web-Based.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3559-3568
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• 1999

The information overload with many information resources is an inevitable problem in modern electronic life. It is more difficult to search some information with user's information needs from an uncontrolled flood of many digital information resources, such as the internet which has been rapidly increased. So, many information retrieval systems have been researched and appeared. In text retrieval systems, they have met with user's information needs. While, in image retrieval systems, they have not properly dealt with user's information needs. In this paper, for resolving this problem, we proposed the intelligent user interface for image retrieval. It is based on HCOS(Human-Computer Symmetry) model which is a layed interaction model between a human and computer. Its' methodology is employed to reduce user's information overhead and semantic gap between user and systems. It is implemented with machine learning algorithms, decision tree and backpropagation neural network, for user adaptation capabilities of intelligent image retrieval system(IIRS).

• Membrane and Water Treatment
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• v.14 no.4
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• pp.155-162
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• 2023

Global climate change and urbanization have various demerits, such as water pollution, flood damage, and deterioration of water circulation. Thus, attention is drawn to Nature-based Solution (NbS) that solve environmental problems in ways that imitate nature. Among the NbS, urban wetlands are facilities that perform functions, such as removing pollutants from a city, improving water circulation, and providing ecological habitats, by strengthening original natural wetland pillars. Frequent monitoring and maintenance are essential for urban wetlands to maintain their performance; therefore, there is a need to apply the Internet of Things (IoT) technology to wetland monitoring. Therefore, in this study, we attempted to develop a real-time wetland monitoring device and interface. Temperature, water temperature, humidity, soil humidity, PM1, PM2.5, and PM10 were measured, and the measurements were taken at 10-minute intervals for three days in both indoor and wetland. Sensors suitable for conditions that needed to be measured and an Arduino MEGA 2560 were connected to enable sensing, and communication modules were connected to transmit data to real-time databases. The transmitted data were displayed on a developed web page. The data measured to verify the monitoring device were compared with data from the Korea meteorological administration and the Korea environment corporation, and the output and upward or downward trend were similar. Moreover, findings from a related patent search indicated that there are a minimal number of instances where information and communication technology (ICT) has been applied in wetland contexts. Hence, it is essential to consider further research, development, and implementation of ICT to address this gap. The results of this study could be the basis for time-series data analysis research using automation, machine learning, or deep learning in urban wetland maintenance.

• 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.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.1-17
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• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.