• Design Convergence Study
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• v.16 no.6
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• pp.123-135
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• 2017

In recent mobile health care service, health management using number of steps is becoming popular. In addition, a variety of activity trackers have made it possible to measure the number of steps more accurately and easily. Nevertheless, the activity tracker is not popularized, and it is a trend to use the pedometer sensor of the smartphone as an alternative. In this study, we tried to find out how much the number of steps collected by the smartphone versus the actual number of steps in actual situations, and what factors make the difference. We conducted an experiment to collect number of steps data of 21 people using the smartphone and wearable device simultaneously for 7 days. As a result, we found that the average number of steps of the smartphone is 62% compared to the actual number of steps, and that there is a large variation among users. We derived a regression model in which the accuracy of smartphone increases with the degree of awareness of smartphone. We expect that this can be used as a factor to correct the difference from the actual number of steps in the smartphone alone healthcare service.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.2
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• pp.129-139
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• 2013

This paper is targeted on proposing ameliorating alternative to performance assessment method of GSEM through evaluating the current one, which is initiated by SMBA to induce fair competition among 5 GSEM across the country and accommodate the quality improvement of entrepreneurship education since 2005 after beginning the SMBA support, from the perspective of BSC(Balanced Scorecard) tool. Ultimately, it complements the policy defects of SMBA over GSEM, in particular, in the process of performance assessment and management. This paper carries out two studies as follow. First, throughout reviewing the previous studies relating to BSC applications to non-profit organization, it set out the direction of introducing BSC in assessing performance of GSEM in order to enhance its effectiveness. Second, it evaluate the rationality of performance assessing tools apllied to GSEM by SMBA on the basis of BSC application over non-profit organization, especially in education institution. Research results shows the following implications. First, the current evaluation system over GSEM is just merely assessment itself and not much contributions for the post performance management. Second, The annual evaluation just remains to check up whether the policy goals are met or not. Third, the current evaluation puts much emphasis just on financial inputs and hardware infra, not considering human resources and utilization of government policy and institution. Fourth, the policy goals are unilaterally focused on entrepreneurs. Fifth, the current evaluation systems do not contain any indexes relating to learning and growth perspectives for concerning sustainable and independent growing up. However, lack of empirical testing require this paper to need the further study in the future.

• 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.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.3
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• pp.278-285
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• 2008

This research was performed to evaluate the landfilling method of organic sludge from mix of pre-treated organic sludge (OS) and municipal solid waste(MSW). Organic sludges were dried, composted, and solidified as pre-treatment and the OS and MSW were mixed in ratios of 2 to 8 and 4 to 6. Approximately 1,800$\sim$2,500 L of landfill gas(LFG) was generated in the lysimeter with solidified-OS, which was higher than 1,150$\sim$1,650 L of the dried- and composted- ones. Maximum H$_2$S concentration was found in the following order : Composted-20(80 ppmv) > Composted-40(55 ppmv) > Dried-20(30 ppmv) > Dried-40 $\fallingdotseq$ Solidified-20 $\fallingdotseq$ Solidified-40 (20 ppmv). BOD$_5$ at initial leachate generation period was 38,000 mg/L for Composted-40, 28,000 mg/L for Dried-40, 26,000 mg/L for Dried-20, 21,000 mg/L for Composted-20 and Solidified-40, and Solidified-20 for 17,000 mg/L. In the final period of experiment, BOD$_5$ was low as 300$\sim$500 mg/L in the lysimeter with solidified-OS and MSW and showed 2,000$\sim$3,500 mg/L in dried- and composted- ones. As the results, landfilling by mix of solidified-OS and MSW was evaluated as the most appropriate method for biodegradable organics. Direct landfilling of OS is permitted for landfill site with CDM facility. Therefore, mixed landfilling of solidified-OS and MSW should be considered for much more LFG generation as methane.

• The Korean Journal of Air & Space Law and Policy
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• v.30 no.2
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• pp.3-36
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• 2015

As of end of 2014, the population engaging in aviation activities for leisure has reached approximately 13 million, where approximately 356 cases involve a general aircraft, 200 cases involve light aircraft, and 636 cases involve an ULM. The industry for leisure has become a very promising industry in line with rapidly rising living standards which are expected to further increase in the future. The demand for such services is expected to increase over time. The purpose of this paper is to review the development and site selection of airfields in anticipation of these developments in the industry. While the government also has experience in the review of airfield location and candidate sites, it is not the government that carries out the actual construction. As such, the feasibility of the site needs to be verified in terms of actual construction. This study identified factors for Site Selection of factors through a review of related documents and existing research reports. A questionnaire was also used to collect the views of experts in the field, which was then analyzed. The Research model was confirmed in the layered form for an AHP analysis. The factors for Site Selection were identified as the technical / operational factors and economic / political elements for a two-stage configuration. The third step consisted of technical and operational elements. The final step is was constructed a total of 11 elements (weather, surface conditions, obstacle limitation surface, airspace conditions, operating procedures, noise problems, environmental issues, availability of facilities, construction and investment costs, contribution to the local economy, accessibility, demand / the proximity of demand). The surveys are conducted for more than 10 General and light aircraft pilots, professionals, and instructor. The analysis results showed a higher level in the technical / operating elements (73.2%) in the first step, while the next step sawa higher level of the operational elements (30.9%) than the other. The factors for Site Selection were any particular elements did not appear high, the weather conditions (17.5%), noise problems (19.8%), the proximity of demand (6%), accessibility (5.7%), environmental issues (11.1%), availability of facilities (8%), airspace conditions (7.9%), obstacle limitation surface (12%), construction and investment costs (4.2%) and to operating procedures (4.9%), contribution to the local economy (3.8%).