• Geophysics and Geophysical Exploration
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• v.21 no.4
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• pp.262-272
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• 2018

A system for microseismic monitoring due to underground displacements is being operated in several mining areas in order to analyze ground subsidence. Microseismic monitoring system mainly consist of three components; 3-component geophone, data logger and analysis program. The previous analysis program had found the location of microseismic source by analysing only first arrivals of P-waves, but the upgraded analysis program has improved accuracy of the location by analysing both P- and S-waves. This analysis program also has upgraded the function to calculate the microseismic magnitude by using regional specific coefficient and microseismic amplitude. Also the program has upgraded the function to confirm visual location of microseismic source by superimposing field aerial photographs and the results.

• The Journal of the Convergence on Culture Technology
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• v.5 no.2
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• pp.361-366
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• 2019

The amount of data created in the present intelligence information society is beyond imagination. Big data has a great diversity from every information via SNS and internet to the one created by government and enterprises. This various data is close at hand having infinite value as same as crude oil. Big data analysis and utilization by data mining over every areas in the modern industrial society is getting more important for finding useful correlation and strengthening forecasting power against the future uncertainty. Efficient management and utilization of big data produced by complex modern society will be researched in this paper. Also it addresses strategies and methods for securing overall industrial competitiveness, synergy creation among industries, cost reduction and effective application based on big data in the $4^{th}$ industrial revolution era.

• The Journal of Korea Robotics Society
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• v.2 no.4
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• pp.304-309
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• 2007

This paper describes an efficient path generation method for area coverage. Its applications include robots for de-mining, cleaning, painting, and so on. Our method is basically based on a divide and conquer strategy. We developed a novel cell decomposition algorithm that divides a given area into several cells. Each cell is covered by a robot motion that requires minimum time to cover the cell. Using this method, completeness and time efficiency of coverage are easily achieved. For the completeness of coverage in dynamic environments, we also propose a path following method that makes the robot cover missed areas as a result of the presence of unknown obstacles. The effectiveness of the method is verified using computer simulations.

• Journal of Wetlands Research
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• v.4 no.1
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• pp.51-61
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• 2002

The wetland is a biologically integrated system consisting of water, soil, bacteria, plants, and animals. The wetland helps sustain the ecosystem, control the micro-climate and flood, maintain the ground water level, and provide fishing grounds. From the environmental standpoint, the wetland plays a vital role in reducing water pollution by filtering out sand and other polluted matters, producing oxygen, absorbing chemicals and nutrients. For these reasons, interest in restoring the wetlands has been steadily increasing. Artificial wetland, which is also referred to as created wetland or constructed wetland, is an alternative to natural wetland. Like natural wetland, artificial wetland is environmentally friendly and can effectively lower pollutant levels. The Korea government is actively reviewing the construction of artificial wetlands in mining and water supply areas to decrease nonpoint pollutant sources. This paper attempts to develop a pollutant removal model for the water quality improvement function of artificial wetlands. Artificial wetland can improve the quality of the water; however, depending on the type of water inflow, vegetation and hydrology, its effect can be different.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.535-544
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• 2019

This study demonstrated the ability of a Landsat-8 OLI multispectral data to identify and delineate hydrothermal alteration zones around auriferous prospects within the crystalline basement, North-western Nigeria. Remote sensing techniques have been widely used in lithological, structural discrimination and alteration rock delineation, and in general geological studies. Several artisanal mining activities for gold deposit occur in the surrounding areas within the basement complex and the search for new possible mineralized zones have heightened in recent times. Systematic Landsat-8 OLI data processing methods such as colour composite, band ratio and minimum noise fraction were used in this study. Colour composite of band 4, 3 and 2 was displayed in Red-Green-Blue colour image to distinguish lithologies. Band ratio ${\frac{4}{2}}$ image displayed in red was used to highlight ferric-ion bearing minerals(hematite, goethite, jarosite) associated with hydrothermal alteration, band ratio ${\frac{5}{6}}$ image displayed in green was used to highlight ferrous-ion bearing minerals such as olivine, amphibole and pyroxenes, while ratio ${\frac{6}{7}}$ image displayed in blue was used to highlight clay minerals, micas, talc-carbonates, etc. Band rationing helped to reduce the topographic illumination effect within images. The result of this study showed the distribution of the lithological units and the hydrothermal alteration zone which can be further prospected for mineral reserves.

• Ocean and Polar Research
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• v.28 no.2
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• pp.129-144
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• 2006

Small-scale effects of sand mining on macrobenthic communities have been reported previously. However, little information is available as to how dredging affects the macrobenthic community structure. The objective of this study was to determine the impacts of large-scale exhibition dredging on the macrobenthic community of Gyeonggi Bay, Korea, where sand mining has continued for 20 years. Prior to dredging, the macrobenthic species composition was similar to that of areas near the dredging site, with several common dominant species found before dredging. After dredging, the number of species, density, and diversity (H') in the experimental area (sites 0 and 1) decreased significantly, but no difference was observed at other sites. Multivariate analysis (multidimensional scaling) revealed significant differences in community structure before and after dredging. The amphipod Urothoe grimaldii japonica, which was the most dominant species at sites 0,1, and 2, decreased rapidly at sites 0 and 1 after dredging, but no difference was observed at site 2. In addition, the index of multivariate dispersion (IMD) and the relative IMD (r. IMD) were large at sites 0 and 1, suggesting that the effects of dredging were direct at site 0 and 1, but indirect at site 2. The macrobenthic communities at sites 3 and 4 were not affected by dredging, but they were affected by physical conditions and biological interactions. We suggest that benthic biotope indices such as the IMD and r.IMD may constitute a valid tool for assessing the effects of dredging on the macrobenthic community; long-term monitoring is required to verify this.

• Journal of Korean Society of Forest Science
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• v.111 no.2
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• pp.234-241
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• 2022

We performed this study to determine the foliar nutrient concentration and the nutrient stocks of restoration areas and adjacent Pinus densiflora S. et. Z. (red pine) stands in opencast kaolinite mines in Sancheong-gun, Gyeongsangnam-do, southern Korea. We chose six sites to determine foliage nutrient concentrations and the nutrient stocks of soils (0-10 cm depth). The dominant vegetation planted in restoration areas comprised Quercus acutissima Carruth., P. koraiensis S. et. Z., Festuca arundinacea Schreb., and Lespedeza cuneata G. Don. Invading vegetation in the restoration areas comprised Alnus incana (L.) Medik., Robinia pseudoacacia L., and Lespedeza spp., among others. The carbon and nitrogen stocks at 10 cm soil depth were significantly higher in the red pine stands than those in the restoration areas, whereas those of phosphorus, potassium, and magnesium were not significantly different between the two areas. However, calcium stocks were significantly higher in the restoration areas than in the red pine stands. Nitrogen concentration in foliage was higher in L. cuneata (20.28 mg N g^-1) than that in F. arundinacea (5.67 mg N g^-1), whereas potassium concentration was twice as high in F. arundinacea (18.8 mg K g^-1) as that in L. cuneata (9.07 mg K g^-1). Foliar nitrogen concentrations in invasive vegetation such as A. incana, R. pseudoacacia, and Lespedeza spp. were twice or four times higher than those of Q. acutissima and P. koraiensis. Our results indicate the development of suitable vegetation and soil amendment treatments to improve poor soil environmental conditions in restoration areas are necessary following opencast kaolinite mining.

• Korean Journal of Environment and Ecology
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• v.31 no.1
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• pp.30-41
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• 2017

Mammals in Korea have been studied in various fields such as animal science, veterinary medicine, laboratory animal science, ecology, and genetics. As the importance of biodiversity has been emphasized recently, conservation and management of mammals have attracted much public attention. However, in spite of such an increase in scientific research and public interest, it is still difficult to find a report or summary to grasp the trend of mammalian research in Korea. The purpose of this study is to provide the basic data for future plans of the detailed research area and the related policies by grasping the research trends of mammals in Korea. Using text-ming and co-word analysis, we analyzed 392 mammalian research papers published in Korean national journals as of 2015. Our results showed that the number of mammalian research papers published in Korea has gradually increased and that the research target species have also become increasingly diverse. The major research areas identified through text-mining and co-word analysis are (1) evolution/phylogenetics/genetics, (2) environmental science/ecology, (3) embryology/reproductive biology/cell biology, (4) veterinary medicine related to parasites, (5) parasitology related to rodents, (6) bacteriology/virology, (7) anatomy/cell biology/laboratory animal science, (8) veterinary science related to morphology and anatomy, (9) animal science, (10) marine mammalogy, and (11) Chiroptera (bat) research. Environmental science/ecology has been the most active field among the 11 research areas in recent times, and the proportion of research has increased sharply compared to the past. Environmental science/ecology is the core of biodiversity conservation, and as the importance of biodiversity has been emphasized in recent years, researchers' interest in mammal ecology appears to have increased. We expect that the results of this study will be useful for future research plan and related policies on mammals in Korea.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.127-138
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• 2011

The core service of most research portal sites is providing relevant research papers to various researchers that match their research interests. This kind of service may only be effective and easy to use when a user can provide correct and concrete information about a paper such as the title, authors, and keywords. However, unfortunately, most users of this service are not acquainted with concrete bibliographic information. It implies that most users inevitably experience repeated trial and error attempts of keyword-based search. Especially, retrieving a relevant research paper is more difficult when a user is novice in the research domain and does not know appropriate keywords. In this case, a user should perform iterative searches as follows : i) perform an initial search with an arbitrary keyword, ii) acquire related keywords from the retrieved papers, and iii) perform another search again with the acquired keywords. This usage pattern implies that the level of service quality and user satisfaction of a portal site are strongly affected by the level of keyword management and searching mechanism. To overcome this kind of inefficiency, some leading research portal sites adopt the association rule mining-based keyword recommendation service that is similar to the product recommendation of online shopping malls. However, keyword recommendation only based on association analysis has limitation that it can show only a simple and direct relationship between two keywords. In other words, the association analysis itself is unable to present the complex relationships among many keywords in some adjacent research areas. To overcome this limitation, we propose the hybrid approach for establishing association network among keywords used in research papers. The keyword association network can be established by the following phases : i) a set of keywords specified in a certain paper are regarded as co-purchased items, ii) perform association analysis for the keywords and extract frequent patterns of keywords that satisfy predefined thresholds of confidence, support, and lift, and iii) schematize the frequent keyword patterns as a network to show the core keywords of each research area and connecting keywords among two or more research areas. To estimate the practical application of our approach, we performed a simple experiment with 600 keywords. The keywords are extracted from 131 research papers published in five prominent Korean journals in 2009. In the experiment, we used the SAS Enterprise Miner for association analysis and the R software for social network analysis. As the final outcome, we presented a network diagram and a cluster dendrogram for the keyword association network. We summarized the results in Section 4 of this paper. The main contribution of our proposed approach can be found in the following aspects : i) the keyword network can provide an initial roadmap of a research area to researchers who are novice in the domain, ii) a researcher can grasp the distribution of many keywords neighboring to a certain keyword, and iii) researchers can get some idea for converging different research areas by observing connecting keywords in the keyword association network. Further studies should include the following. First, the current version of our approach does not implement a standard meta-dictionary. For practical use, homonyms, synonyms, and multilingual problems should be resolved with a standard meta-dictionary. Additionally, more clear guidelines for clustering research areas and defining core and connecting keywords should be provided. Finally, intensive experiments not only on Korean research papers but also on international papers should be performed in further studies.