• The Journal of Engineering Geology
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• v.34 no.2
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• pp.279-294
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• 2024

Large-scale civil engineering structures such as dams require a systematic approach to jointed rock-mass grouting to prevent water leakage into the foundations and to ensure safe operation. In South Korea, rock grouting design often relies on the experience of field engineers that was gained in similar projects, highlighting the need for a more systematic and reliable approach. Rock-mass grouting is affected mainly by hydrogeology and the presence of discontinuities, involving factors such as the rock quality designation (RQD), joint spacing (Js), Lugeon value (Lu), and secondary permeability index (SPI). This study, based on data from field investigations of 14 domestic sites, analyzed the correlation between hydrogeological factors (Lu and SPI), discontinuity characteristics (RQD and Js), and grout take, and systematically established a design method for rock grouting. Analysis of correlation between the variables RQD, Js, Lu, and SPI yielded Pearson correlation (r) values as follows: Lu-SPI, 0.92; RQD-Lu, -0.75; RQD-Js, 0.69; RQD-SPI, -0.65; Js-Lu, -0.47; and SPI-Js, -0.41. The grout take increases with Lu and SPI values, but there is no significant correlation between RQD and Js. The proposed approach for grouting design based on SPI values was verified through analysis and comparison with actual curtain-grouting construction, and is expected to be useful in practical applications and future studies.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1859-1867
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• 2021

This study investigated seasonal characteristics of the particulate matter (PM) in the atmosphere and rainwater quality in Busan, South Korea, and evaluated the seasonal effect of PM₁₀ concentration in the atmosphere on the rainwater quality using multivariate statistical analysis. The concentration of PM in the atmosphere and meteorological observations(daily precipitation amount and rainfall intensity) are obtained from automatic weather systems (AWS) by the Korea Meteorological Administration (KMA) from March 2020 to August 2020. Rainwater samples (n = 216, 13 rain events) were continuously collected from the beginning of the precipitation using the rainwater collecting device at Pukyong National University. The samples were analyzed for pH, EC (electrical conductivity), water-soluble cations(Na⁺, Mg²⁺, K⁺, Ca²⁺, and NH₄⁺), and anions(Cl^-, NO₃^-, and SO₄^2-). The concentration of PM₁₀ in the atmosphere was steadily measured before and after the precipitation with a custom-built PM sensor node. The measured data were analyzed using principal component analysis (PCA) and Pearson correlation analysis to identify relationships between the concentration of PM₁₀ in the atmosphere and rainwater quality. In spring, the daily average concentration of PM₁₀ (34.11 ㎍/m³) and PM_2.5 (19.23 ㎍/m³) in the atmosphere were relatively high, while the value of daily precipitation amount and rainfall intensity were relatively low. In addition, the concentration of PM₁₀ in the atmosphere showed a significant positive correlation with the concentration of water-soluble ions (r = 0.99) and EC (r = 0.95) and a negative correlation with the pH (r = -0.84) of rainwater samples. In summer, the daily average concentration of PM₁₀ (27.79 ㎍/m³) and PM_2.5 (17.41 ㎍/m³) in the atmosphere were relatively low, and the maximum rainfall intensity was 81.6 mm/h, recording a large amount of rain for a long time. The results indicated that there was no statistically significant correlation between the concentration of PM₁₀ in the atmosphere and rainwater quality in summer.

• Korean Journal of Food Science and Technology
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• v.48 no.3
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• pp.247-255
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• 2016

This study was performed to investigate the effect of packaging materials on quality characteristics of washed potatoes such as Hunter's a value, chlorophyll and potato glycoalkaloids (PGA) content during their storage for 15 days. Packaging methods were evaluated into five ways: no packaging (NP, positive control), paper bag (PB, negative control), onion net (ON), transparent oriented to polypropylene without hole (TP), opaque oriented polypropylene with 4 holes (OP). Hunter's a values of washed potatoes showed minus in NP and TP after 12 days storage, whereas all plus values were observed in those of PB, ON, and OP. Total chlorophyll content of washed potatoes was the highest in no packaging at 15 days after storage. The PGA content of washed potatoes showed low levels in flesh part (below $5mg/100g{\cdot}FW$) as well as in peel part ($4.5-9.3mg/100mg{\cdot}FW$) in all packagings up to 15 days after storage.

• 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.