• International Journal of Highway Engineering
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• v.12 no.4
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• pp.93-100
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• 2010

Trip generation is the first step in the conventional four-step model and has great effects on overall demand forecasting, so accuracy really matters at this stage. A linear regression model is widely used as a current trip generation model for such plans as urban transportation and SOC facilities, assuming that the relationship between each socio-economic index and trip generation stays linear. But when rapid urban development or an urban planning structure has changed, socio-economic index data for trip estimation may be lacking to bring many errors in estimated trip. Hence, instead of assuming that a socio-economic index widely used for a general purpose, this study aims to develop a new trip generation model by type based on the market separation for the variables to reflect the characteristics of various zones. The study considered the various characteristics (land use, socio-economic) of zones to enhance the forecasting accuracy of a trip generation model, the first-step in forecasting transportation demands. For a market separation methodology to improve forecasting accuracy, data mining (CART) on the basis of trip generation was used along with a regression analysis. Findings of the study indicated as follows : First, the analysis of zone characteristics using the CART analysis showed that trip production was under the influence of socio-economic factors (men-women relative proportion, age group (22 to 29)), while trip attraction was affected by land use factors (the relative proportion of business facilities) and the socio-economic factor (the relative proportion of third industry workers). Second, model development by type showed as a result that trip generation coefficients revealed 0.977 to 0.987 (trip/person) for "production" 0.692 to 3.256 (trip/person) for "attraction", which brought the necessity for type classifications. Third, a measured verification was conducted, where "production" and "attraction" showed a higher suitability than the existing model. The trip generation model by type developed in this study, therefore, turned out to be superior to the existing one.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Economic and Environmental Geology
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• v.23 no.2
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• pp.161-181
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• 1990

The lead-zinc-silver-iron deposits from the Janggun mine are of hydrothermal-metasomatic origin, characterized by the marked hydrothermal alteration of the wallrocks, such as hydrothermal manganese enrichment of carbonate rocks, silicification, chloritization, sericitization, montmorillonitization and argillic alteration. The ore deposits have been emplaced within the Janggun Limestone of Cambro-Ordovician age at the immediate contacts with apophyses injected from the Chunyang Granite plutons of Late Jurrasic age. They have been structurally controlled by fractures in the carbonate rocks and the irregular intrusive contacts of granitic rocks, and are closely associated with hypogene manganese carbonate deposits. In the mine nine seperate orebodies are being mined. On the basis of the petrological study, hydrothermal alteration zone of this mine may be divided into the following four zones from wallrock to orebody. (I) Primary calcite and dolomite zone${\rightarrow}$(II) dolomitic limestone zone${\rightarrow}$(III) dolomitic zone${\rightarrow}$(IV) rhodochrosite zone${\rightarrow}$ orebody. There was not recongnized Mn and Fe elements in the primary calcite and dolomite zone. But, in the dolomitic limestone and dolomite zone, calcite and dolomite were subjected to weak hydrothermal manganese enrichment and the grade of the manganese enrichment increase oreward. By means of electron probe microanalysis, it was found that manganoan dolomite occured between primary dolomite grains, cross the cleavage of the primary dolomite and around the dolomite grains. Above these result supports that the Janggun manganese carbonate deposits are of hydrothermal metasomatic origin.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.57-70
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• 2017

In this study, we investigated the wear measurement methods for slurry pipe applied in the field of mining and oil sand industry and theoretical equations related to the prediction of wear in slurry pipe through literature review. Average daily wear rate and wear rate per excavated distance were determined from slurry discharge pipe thickness measurement data periodically measured at the actual slurry shield TBM site in Singapore. The wear rate of slurry pipe for Bukit Timah Granite was obtained. The wear rates for G (V) grade and mixed zone were 1.5 times higher than that of G (I) to G (IV) grade. Slurry pipe wear rate tends to increase in proportion to the slurry discharge velocity. The optimal slurry pipe replacement or rotation frequency can be estimated through the selection of the pipe wear rate considering geological condition and the reasonable pipe management thickness.

• Fashion & Textile Research Journal
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• v.20 no.5
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• pp.533-547
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• 2018

The objective of this study is to examine the perception of golfwear and related trends based on major keywords and associated words related to golfwear utilizing big data. For this study, the data was collected from blogs, Jisikin and Tips, news articles, and web $caf{\acute{e}}$ from two of the most commonly used search engines (Naver & Daum) containing the keywords, 'Golfwear' and 'Golf clothes'. For data collection, frequency and matrix data were extracted through Textom, from January 1, 2016 to December 31, 2017. From the matrix created by Textom, Degree centrality, Closeness centrality, Betweenness centrality, and Eigenvector centrality were calculated and analyzed by utilizing Netminer 4.0. As a result of analysis, it was found that the keyword 'brand' showed the highest rank in web visibility followed by 'woman', 'size', 'man', 'fashion', 'sports', 'price', 'store', 'discount', 'equipment' in the top 10 frequency rankings. For centrality calculations, only the top 30 keywords were included because the density was extremely high due to high frequency of the co-occurring keywords. The results of centrality calculations showed that the keywords on top of the rankings were similar to the frequency of the raw data. When the frequency was adjusted by subtracting 100 and 500 words, it showed different results as the low-ranking keywords such as J. Lindberg in the frequency analysis ranked high along with changes in the rankings of all centrality calculations. Such findings of this study will provide basis for marketing strategies and ways to increase awareness and web visibility for Golfwear brands.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.639-647
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• 2009

From development of computer science, genetic algorithm has been applied to many fields for search like non-linear problem based on various variables and optimization process. Among others, in the data mining field, there are methods to select the best input variables for model accuracy and various predict models which were merged by using the genetic algorithm. In the meantime, to improve and preserve quality of the Hanwoo (Korean cattle) which is represented the agricultural industry in our country, we need to find out outstanding economical traits of Hanwoo in having specific genotype of single nucleotide polymorphism (SNP) which is inherited to next generation. According to, This research proposed the selecting method to find genotype of SNPs marker which affects economical traits of the Hanwoo by using the genetic algorithm. And we selected the best genotypes of the principal SNPs marker by applying to real data on Hanwoo genetic.

• Journal of the Korea Convergence Society
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• v.9 no.4
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• pp.31-45
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• 2018

Recent dramatical development of computer technology related to internet technology intensifies the dispute over software of computer or smart device. Research on software has been flourished with political issuing of fierce competition among nations for software development. Particularly industrial growth in ethernet based big data and IoT (Internet of Things) has promoted to build and develop open source programs based on java, xcode and C. On these circumstances, issue on software piracy has been confronted despite the basic security policy protecting intellectual property rights of software and thus it is of substantial importance to protect the rights of originality of source program license. However, the other issue on source technology protection of developer is the possibility of hindrance to advancement in industry and culture by developing programs. This study discuss the way of enhancing legal stability of IoT application program development and reinforcing precision in inspection of program plagiarism by analyzing the source programs with newly introducing text mining technique, thus suggests an alternative protective way of infringement of personal information due to duplicating program.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2005.10a
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• pp.59-61
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• 2005

In Korea, hundreds of abandoned and closed coal and metallic mines are present in the steep mountain valleys due to the depression of the mining industry since the late 1980s. From these mines, enormous amounts of coal waste were dumped on the slopes, which causes sedimentation and acid mine drainage (AMD) to be discharged directly into streams causing detrimental effects on soil and water environments. A limestone slurry by-product (lime cake) is produced from the Solvay process in manufacturing soda ash. It has very fine particles, low hydraulic conductivities ($10^{-8}{\sim}10^{-9}cm/sec$), high pH, high EC due to the presence of CaO, MgO and $CaCl_2$ as major components, and traces of heavy metals. Due to these properties, it has potential to be used as a neutralizer for acid-producing materials. A field plot experiment was used to test the application of lime cake for reclaiming coal wastes. Each plot was 20 x 5 m (L x W) in size on a 56% slope. Treatments included a control (waste only), calcite ($CaCO_3$), and lime cake. The lime requirement (LR) for the coal waste to pH 7.0 was determined and treatments consisted of adding 100%, 50%, and 25% of the LR. The lime cake and calcite were also applied in either a layer between the coal waste and topsoil or mixed into the topsoil and coal waste. Each plot was hydroseeded with grasses and planted with trees. In each plot, surface runoff and subsurface water were collected. The lime cake treatments increased the pH of coal waste from 3.5 to 6, and neutralized the pH of the runoff and leachate of the coal waste from 4.3 to 6.7.

• Explosives and Blasting
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• v.33 no.1
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• pp.1-12
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• 2015

In this study, the domestic bench blasting sites were researched to set the blast coefficient C according to the type of rock and type of industry. With the use of the experimental data on the representative industrial explosives and the data of the manufacturers'data on explosives, powder coefficient e was set up. The blast coefficient C was 0.21~0.30 when the average value for 5 representative kinds of rocks including granite was searched. The blast coefficient C for quarrying, mining and construction sites were 0.22, 0.13 and 0.26 respectively. On the other hand, powder coefficient e was obtained in four elements such as reactive energy, ballistic mortar test, VOD, Langefors'strength per unit weight. e value for emulsion which is one of the representative explosives was found to be 1 while those of high performance emulsion and ANFO were found to be 0.9 and 1, respectively.

• Environmental and Resource Economics Review
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• v.13 no.1
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• pp.33-47
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• 2004

Even though the price of extracted but unprocessed coal has been available in Korea, the use of it as scarcity index would be inappropriate because of price subsidy. Following Halvorsen and Smith(1984), Kim and Lee(2002) derived estimates of the shadow price of unextracted coal by estimating the restricted cost function and differentiating with respect to the quantity of coal extracted. In Korea, however, due to the limited data the capital prices have been computed inconsistently case by case without relying on the robust formula like the Christensen-Jorgenson methodology used in US, which could result in biased estimators of the restricted cost function. In the paper the shadow prices of the resources in situ are obtained by measuring an input distance function defined by Shephard (1970), which requires only the data on the quantities of inputs and output. Empirical results for the Korean coal mining industry show that these shadow prices as a coal scarcity have increased fast by approximately three times in comparisons with those obtained by Kim and Lee.