• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.7-12
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• 2003

In recent years, many attempts have been made to predict the behavior of bonds, currencies, stock or other economic markets. Most previous experiments used the neural network models for the stock market forecasting. The KOSPI 200 (Korea Composite Stock Price Index 200) is modeled by using different neural networks and fuzzy logic. In this paper, the neural network, the dynamic polynomial neural network (DPNN) and the fuzzy logic employed for the prediction of the KOSPI 200. The prediction results are compared by the root mean squared error (RMSE) and scatter plot, respectively. The results show that the performance of the fuzzy system is little bit worse than that of the DPNN but better than that of the neural network. We can develop the desired fuzzy system by optimization methods.

• Journal of Korean Ophthalmic Optics Society
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• v.15 no.4
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• pp.355-360
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• 2010

Purpose: Analysis performance for spectacle lens which sales in domestic market and optimization design a spectacle lens which is corrected aberration. Methods: Measured center thickness, radius and aspherical surface coefficient for spherical and aspherical lenses which were ${\pm}$5.00D. Refractive index for every lens was 1.6 and they came from 4 different companies. I used 3 types of equipment to measure lenses. ID-F150 (Mitutoyo) : Center Thickness, FOCOVISION (SR-2, Automation Robotics) : Radius, PGI 1240S (Taylor Hobson) : Aspherical surface coefficient. Designed a lens which had 27 mm of distance from lens rear surface to center of eye, 4 mm of pupil diameter and small aberration on center vision $30^{\circ}C$. To shorten axial distance compared with the measured lens rise merits for cosmetic. Lens Design tool was CODE V (Optical Research Associates). Results: -5.00D aspherical lens had somewhat high astigmatism and distortion compared with the spherical lens. But it had a merit for cosmetic because of short axial height and decrease edge thickness. Improved a performance of distortion and ascertain merits for cosmetic due to short axial height and decrease edge thickness same as (-) lens in case of +5.00 aspherical lens. Though an optimization process front surface aspherical lens had a good performance for astigmatism and distortion and the merit for beauty compared with measured spherical lens. Conclusions: Design trend for domestic aspherical lens is decrease axial height and thickness to increase a merit for cosmetic not but increase performance of aberration. From design theory for optimization design front surface aspherical spectacle lens which has improved performance of aberration and merit for cosmetic at the same time compared with the measured lens. Expect an improved performance from design back aspherical lens compared with front aspherical lens.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.9-14
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• 2009

A variety of Certification systems are initiated, as increased demands for housing quality of building & house owner. New building and apartment built in Seoul metropolitan should acquire Building Energy Rating System and Housing Performance Grade indication System, Green Building Certification System. Building Energy Rating System could be acquired through the certification from energy performance index of environment grade in Housing Performance Grade Indication. Between Housing Performance Grade Indication and Green Building Certification System, There are troublesome to acquire certification because a lot of assessment items are overlapped. At a result, the rational and efficient certification system should be initiated not only to avoid overlap among those certification systems and promote Green Building market share but also efficient certification system operation. We will discuss on assessment items between Housing Performance Grade Indication System and Green Building Certification System and release the improvement method those Certification Systems to avoid overlapping to assessment items.

• Journal of Korea Port Economic Association
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• v.35 no.4
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• pp.31-50
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• 2019

The retail industry has been coping with changes in the retail market environment for the past decade or so. Using a total of 14 companies, this study aims to reveal the effect of differences in sales channels and retail business styles on the management performance of retail companies. The financial statements of these companies were used to analyze the five key indicators of their management performance. As research variables, sales channels, retail business style and business period were used as factors affecting their management performance. ANOVA or MANOVA was performed to test differences in management performance between groups according to the number of factors. The effect of three factors on the management performance of retail companies was found to be significant. The multi-comparison test revealed significant differences among retail business styles in terms of the five key indicators. TV home-shopping performed better than others in terms of stability and profitability. Internet and mobile shopping companies performed poorly in terms of profitability compared to others and performed higher than department stores in terms of growth, activity, and productivity.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.105-115
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• 2020

The study empirically examines the effects of loan portfolio diversification on bank risk and return in the nascent banking market of Vietnam. Loan portfolio diversification is captured through the Hirschman-Herfindahl index and the Shannon Entropy with sectoral exposures. We access each bank's financial reports to collect the required data, especially the breakdown of sectoral loan portfolios, thus constituting a unique dataset. To compute bank return, we use the traditional accounting indicators, including return-on-assets, return-on-equity, and net-interest margin. For bank risk, we utilize the loan-loss provisions and non-performing loans relative to gross customer loans. Using a sample of 30 commercial banks over the period from 2008 to 2019 and the system generalized method of moments estimator for the dynamic panel, we indicate the downsides of portfolio diversification. Concretely, we observe that all diversification measures exhibit significantly negative signs in all regressions across different bank return proxies. At the same time, the estimates display the significant and positive impact of diversification on the non-performing loan ratio. Hence, sectoral loan portfolio diversification significantly hampers bank performance in both aspects of lower return and higher credit risk. The results are robust across a rich set of bank performance and portfolio diversification measures.

• Journal of Construction Engineering and Project Management
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• v.5 no.2
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• pp.16-23
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• 2015

In this paper, a survey related to excavation construction activities among national and international construction companies was conducted to evaluate site productivity in construction industry. To analyze the respective productivity levels of each construction company, a benchmarking measures analysis that featured calculations of the performance ratio (PR) and performance management index (PMI) was performed. As a result of these analyses, it has been found that the work performed by local companies was marked by lower productivity and that of international companies was characterized by good productivity. Further analysis of construction workforce resources P% revealed that a construction company's productivity is largely dependent on production capacity and consumption resources, which means that the retention of skilled workers and utilization of high-quality resources yields the highest level of productivity. These results suggest that for a local construction company to be competitive in the construction work market, it must retrain skilled craftspeople, foremen, engineers, and project managers, and strengthen its building capability by leveraging new equipment and technologies.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.525-532
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• 2021

This study examines the association between a firm's business strategy and audit report lags. This study employs 5,072 firm-year observations from 2015 to 2019. Our sample comprises all of the firms listed on the Korea Composite Stock Price Index (KOSPI) market and Korea Securities Dealers Automated Quotation (KOSDAQ). We perform OLS regression analysis to test our hypothesis. The OLS regression analysis was conducted through the SAS and STATA programs. We find that business strategy is positively associated with audit report lags. Especially, we find that defender firms are negatively associated with audit report lags. The findings of this study suggest that prospector-like firms would increase their performance uncertainty as well as audit risk. Therefore, prospector-like firms interfere with the efficient audit procedures of auditors. On the other hand, our findings indicate that defender-like firms would decrease their performance uncertainty as well as an audit risk because they focus on simple product lines and cost-efficiency. For this reason, auditors will be able to carry out the audit procedures much more easily. Our results present that a prospector-like business strategy degrades audit effectiveness as it exacerbates a company's financial risk, willingness to accept uncertainty, and the complexity of organizational structure.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.1
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• pp.23-48
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• 2022

In this paper, we provide predictive models for the market price of fruits, and analyze the performance of each fruit price predictive model. The data used to create the predictive models are fruit price data, weather data, and Korea composite stock price index (KOSPI) data. We collect these data through Open-API for 10 years period from year 2011 to year 2020. Six types of fruit price predictive models are constructed using the LSTM algorithm, a special form of deep learning RNN algorithm, and the performance is measured using the root mean square error. For each model, the data from year 2011 to year 2018 are trained to predict the fruit price in year 2019, and the data from year 2011 to year 2019 are trained to predict the fruit price in year 2020. By comparing the fruit price predictive models of year 2019 and those models of year 2020, the model with excellent efficiency is identified and the best model to provide the service is selected. The model we made will be available in other countries and regions as well.

• Journal of Korea Port Economic Association
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• v.38 no.3
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• pp.105-131
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• 2022

Even after the improvement in 2019, UNCTAD's Liner Shipping Connectivity Index (LSCI), which evaluates the performance of the global container port market, has limited use. In particular, since the liner shipping connectivity index evaluates the performance based only on the distance of the relationship, the performance index combining the port attractiveness of calling would be more efficient. This study used the modified Huff model, the hub-authority algorithm and the eigenvector centrality of social network analysis, and correlation analysis for 2007, 2017, and 2019 data of Ocean-Commerce, Japan. The findings are as follows: Firstly, the port attractiveness of calling and the overall performance of the port did not always match. However, according to the analysis of the attractiveness of a port calling, Busan remained within the top 10. Still, the attractiveness among other Korean ports improved slowly from the low level during the study period. Secondly, Global container ports are generally specialized for long-term specialized inbound and outbound ports by the route and grow while maintaining professionalism throughout the entire period. The Korean ports continue to change roles from analysis period to period. Lastly, the volume of cargo by period and the extended port connectivity index (EPCI) presented in this study showed a correlation from 0.77 to 0.85. Even though the Atlantic data is excluded from the analysis and the ship's operable capacity is used instead of the port throughput volume, it shows a high correlation. The study result would help evaluate and analyze global ports. According to the study, Korean ports need a long-term strategy to improve performance while maintaining professionalism. In order to maintain and develop the port's desirable role, it is necessary to utilize cooperation and partnerships with the complimentary port and attract shipping companies' services calling to the complementary port. Although this study carried out a complex analysis using a lot of data and methodologies for an extended period, it is necessary to conduct a study covering ports around the world, a long-term panel analysis, and a scientific parameter estimation study of the attractiveness analysis.

• 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.