• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.12 no.1
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• pp.9-23
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• 2007

To investigate the degree of pollution using the species composition of benthic community and environments, the present study was conducted in Jinhae Bay, May of 1998. In Jinhae Bay, benthic macrofaunal community was investigated on the base of the samples from 67 stations. The main facies of the surface sediment was silty clay and clay. The total species number and the mean density of macrobenthic animals were 255 species and 984 $ind./m^2$, respectively. There were 90 species and 773 $ind./m^2$ of polychaetes as the most major faunal group in Jinhae Bay. At the region between the eastern mouth of Jinhae Bay and Gadeok Is., the species number and density were higher, while lower at the western area of Jinhae Bay. The most dominant benthic macrofauna in Jinhae Bay was the polychaetes, Lumbrineris longifolia(16.9%), and followed by polychaetes Tharyx sp.(6.7%), Clone teres(4.7%), Glycinde sp.(4.2%), bivalves Theora fragilis(4.0%), crustaceans Corophium sp.(4.0%) and so on. The most of the predominant species appeared mainly on the region between the eastern mouth of Jinhae Bay and Gadeok Is. Cluster analysis based on the macrobenthic faunal composition showed that Jinhae Bay could be divided into three station groups: The western Jinhae Bay(Station group A), the mouth of Jinhae Bay(Station groupe B), and offshore area between Gadeok Is. and Geoje Is.(Station group C). The mouth of Jinhae Bay had the highest mean species number and the mean density, and its important species was Lumbrineris longifolia. The offshore area between Gadeok Is. and Geoje Is. had medium mean species number and the mean density. The western Jinhae Bay had the lowest mean species number and the mean density. The distribution of BPI and BC values, used to assess benthic pollution, showed similar patterns. According to the classification proposed by Borja et al.(2000), the stations of the western inner-bay were heavily polluted sites, the stations between mouth of the bay and the offshore area were slightly polluted sites, and the stations of the other area were meanly polluted sites. Benthic community healthiness of the western Jinhae Bay was classified to 'Transitional to pollution' by BC values. The degree of pollution in Jinhae Bay may have extended gradually from the western Jinhae Bay to the mouth of the bay.

• 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.