• KOREAN JOURNAL OF CROP SCIENCE
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• v.38 no.4
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• pp.312-316
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• 1993

This experiment was conducted to increase the utility of paddy field in southern part of Korea. Six cropping patterns were tested 4 times with a cycle of two years from 1985 to 1992. The variation of yield, gross profit and income among years were evaluated. The variation of yield among years in red pepper, garlic and chinese cabbage was higher than that of cucumber, sweet corn and potato in tested crops. The income was higher in chinese cabbage, garlic and red pepper, and the variation of income among years was lower in peanut and chinese cabbage than that of other crops. The income in cucumber-chinese cabbage-green pea-rice pattern and sesamegarlic-rice pattern was higher than the other cropping patterns, but the variance of income among years in the cropping pattern of cucumber-chinese cabbage-green pea-rice was the highest among the tested cropping patterns.

• International Commerce and Information Review
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• v.7 no.4
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• pp.387-407
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• 2005

This paper is to study globalization motives and strategies of Japanese and Korean industries by analyzing the causes and patterns of foreign direct investment (FDI) of the firms of the two countries during the 1980s and 1990s. First we develop a FDI function from the profit maximizing model of firms. Then we use regression analysis to determine internally driving-out factors and externally-inducing factors. Japanese FDI strategy has gone through three different stages; from natural resource-seeking investment in the 1950s and 1960s to market-expansion investment in the 1970s and 1980s and to a combination of cost-reducing (low-cost labor-seeking) investment and market-penetrating investment in the 1990s. On the other hand, Korean FDI behavior has gone through four different stages; from the learning stage with small investments in the 1970s, to natural resource-seeking investment in the early and mid 1980s, to the growth stage in the late 1980s and the early 1990s, to the maturity stage of the mid and late 1990s. The last two stages were characterized by a combination of cost-reducing investment and market-seeking investment. As a late comer, Korea began its FDI two decades later than Japan, but caught up the patterns of Japanese FDI by the mid 1990s and is in a competing position with Japan. Our findings show that both Japanese FDI and Korean FDI in Asia and other developing countries tendto be in labor-intensive sectors where their firms are losing their comparative advantages at home. The main motive for FDI into these regions is low-cost resource seeking. On the other hand, both Japanese FDI and Korean FDI in the U.S. and Europe tend to be knowledge-intensive sectors where Japanese and Korean firms attempt to internalize transaction and information costs by globalizing its production. The main motive for FDI into these regions is market-seeking. Firms in both countries have increased their investments in Mexico and Western and Eastern Europe in order to penetrate large economic blocs such as the EU and NAFTA area. Korean firms are more aggressive in expanding into new and untested markets than are their counterpart in Japan. Evidence of this can be seen in the scarcity of Japanese FDI and abundance of Korean FDI in Eastern Europe and China.

• Korean Chemical Engineering Research
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• v.56 no.5
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• pp.655-662
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• 2018

To respond effectively to climate change following the launch of the new climate system, the government is seeking to expand the use of distributed power resources. Among them, the district heating system centered on Combined Heat and Power (CHP) is accepted as the most realistic alternative. On the other hand, the government recently announced the change of energy paradigm focusing on eco-friendly power generation from the base power generation through $8^{th}$ Basic Plan for Long-term Electricity Supply and Demand(BPE). In this study, we analyzed the quantitative effects of profit and loss on the CHP operating business by changing patterns of the heat production, caused by the change of energy paradigm. To do this, the power market long-term simulation was carried out according to the $7^{th}$ and $8^{th}$ BPE respectively, using the commercialized power market integrated analysis program. In addition, the CHP operating model is organized to calculate the power and heat production level for each CHP operation mode by utilizing the operating performance of 830MW class CHP in Seoul metropolitan area. Based on this, the operation optimization is performed for realizing the maximum operating profit and loss during the life-cycle of CHP through the commercialized integrated energy optimization program. As a result, it can be seen that the change of the energy paradigm of the government increased the level of the ordered power supply by Korean Power Exchange(KPX), decreased the cost of the heat production, and increased the operating contribution margin by 90.9 billion won for the 30 years.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2007.11a
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• pp.109-113
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• 2007

This paper introduces a system, Simulation based Stochastic Markup Estimation System (S2ME), for estimating optimum markup for a project. The system was designed and implemented to better represent the real world system involved in construction bidding. The findings obtained from the analysis of existing assumptions used in the previous quantitative markup estimation methods were incorporated to improve the accuracy and predictability of the S2ME. The existing methods has four categories of assumption as follows; (1) The number of competitors and who is the competitors are known, (2) A typical competitor, who is fictitious, is assumed for easy computation, (3) the ratio of bid price against cost estimate (B/C) is assumed to follow normal distribution, (4) The deterministic output obtained from the probabilistic equation of existing models is assumed to be acceptable. However, these assumptions compromise the accuracy of prediction. In practice, the bidding patterns of the bidders are randomized in competitive bidding. To complement the lack of accuracy contributed by these assumptions, bidding project was randomly selected from the pool of bidding database in the simulation experiment. The probability to win the bid in the competitive bidding was computed using the profile of the competitors appeared in the selected bidding project record. The expected profit and probability to win the bid was calculated by selecting a bidding record randomly in an iteration of the simulation experiment under the assumption that the bidding pattern retained in historical bidding DB manifest revival. The existing computation, which is handled by means of deterministic procedure, were converted into stochastic model using simulation modeling and analysis technique as follows; (1) estimating the probability distribution functions of competitors' B/C which were obtained from historical bidding DB, (2) analyzing the sensitivity against the increment of markup using normal distribution and actual probability distribution estimated by distribution fitting, (3) estimating the maximum expected profit and optimum markup range. In the case study, the best fitted probability distribution function was estimated using the historical bidding DB retaining the competitors' bidding behavior so that the reliability was improved by estimating the output obtained from simulation experiment.

• The Journal of Bigdata
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• v.8 no.1
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• pp.131-156
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• 2023

The traditional retail industry significantly changed over the past decade due to the mobile and online technologies. This change has been accompanied by a shift in consumer behavior regarding purchasing patterns. Despite the rise of online shopping, there are still specific categories of products, such as "Processed food" in Mongolia, for which traditional shopping remains the preferred purchase method. To prepare for the inevitable future of retail businesses, firms need to closely analyze the performance of their offline stores to plan their further actions in a new multi-channel environment. Retailers must integrate diverse channels into their operations to stay relevant and adjust to the shifting market. In this research, we have analyzed the performance data such as sales, profit, and amount of sales of offline stores by using clustering approach. From the clustering, we have found the several distinct insights by comparing the circumstances and performance of retail stores. For the certain retail stores, we have proposed three different strategies: a fulfillment hub store between online and offline channels, an experience store to elongate customers' time on the premises, and a merge between two non-related channels that could complement each other to increase traffic based on the store characteristics. With the proposed strategies, it may enhance the user experience and profit at the same time.

• The KIPS Transactions:PartD
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• v.17D no.4
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• pp.253-258
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• 2010

Traditional frequent pattern mining considers equal profit/weight value of every item. Weighted Frequent Pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery by considering different weights for different items. Existing algorithms in this area are based on fixed weight. But in our real world scenarios the price/weight/importance of a pattern may vary frequently due to some unavoidable situations. Tracking these dynamic changes is very necessary in different application area such as retail market basket data analysis and web click stream management. In this paper, we propose a novel concept of dynamic weight and an algorithm DWFPM (dynamic weighted frequent pattern mining). Our algorithm can handle the situation where price/weight of a pattern may vary dynamically. It scans the database exactly once and also eligible for real time data processing. To our knowledge, this is the first research work to mine weighted frequent patterns using dynamic weights. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using dynamic weights.

• Journal of Broadcast Engineering
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• v.19 no.6
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• pp.957-966
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• 2014

N-Screen service has experienced rapid business expansion recently, since consumers' watching patterns have been diversified; also business stakeholders including broadcasting companies, internet service providers, and device manufacturers have joined this field. In order to operate N-Screen service in a stable manner, development of advertisement system where it generates continuous advertisement profit is needed. Especially for a streaming service that delivers live TV programs, the new advertising system has to deliver ads in conjunction with the live streaming system that is in use. In this paper, video advertising system that is congenial to N-Screen is proposed. Both real time video/ad converting scheme and direct video ads delivery function were developed and tested. In addition, ad contents management and statistics management functions have been developed for advertisement managers. This system was implemented in a commercial N-Screen service and it is currently being used for delivering video advertisements.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.143-146
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• 2005

Many data mining techniques have been proved useful in revealing important patterns from large data sets. Especially, data mining techniques play an important role in a customer data analysis in a financial industry and an electronic commerce. Also, there are many data mining related research papers in a semiconductor industry and an automotive industry. In addition, data mining techniques are applied to the bioinformatics area. To satisfy customers' various requirements, each industry should develop new processes with more accurate production criteria. Also, they spend more money to guarantee their products' quality. In this manner, we apply data mining techniques to the production-related data such as a test data, a field claim data, and POP (point of production) data in the automotive parts industry. Data collection and transformation techniques should be applied to enhance the analysis results. Also, we classify various types of manufacturing processes and proposed an analysis scheme according to the type of manufacturing process. As a result, we could find inter- or intra-process relationships and critical features to monitor the current status of the each process. Finally, it helps an industry to raise their profit and reduce their failure cost.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.21 no.2
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• pp.39-51
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• 2018

Long-term unexecuted urban parks will be released from urban planning facilities after 2020, this may result in development of those parks. However, little research have been focused on how to develop those parks considering conservation, development, spatial pattern, and so on. Therefore, in this study, we suggested an optimization planning model that minimizes the fragmentation while maximizing the conservation and development profit using ACO (Ant Colony Optimization). Our study area is Suwon Yeongheung Park, which is long-term unexecuted urban parks and have actual plan for private development in 2019. Using our optimization planning model, we obtained four alternatives(A, B, C, D), all of which showed continuous land use patterns and satisfied the objectives related to conservation and development. Each alternative are optimized based on different weight combinations of conservation, development, and fragmentation, and we can also generated other alternatives immediately by adjusting the weights. This is possible because the planning process in our model is very fast and quantitative. Therefore, we expected our optimization planning model can support "spatial decision making" of various issue and sites.

• Journal of Digital Convergence
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• v.14 no.9
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• pp.317-323
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• 2016

Big Data is now seen as a major asset in the company's competitiveness, its influence in the future is expected to grow. Companies that recognize the importance are already actively engaged with Big Data in product development and marketing, which are increasingly applied across sectors of society, including politics, sports. However, lack of knowledge of the system implementation and high costs are still a big obstacles to the introduction of Big Data and systems. It is an objective in this study to build a Big Data system, which is based on open source Hadoop and Hive among Big Data systems, utilizing POS sales data of small and medium-sized offline markets. This approach of convergence is expected to improve existing sales systems that have been simply focusing on profit and loss analysis. It will also be able to use it as the basis for the decisions of the executive to enable prediction of the consumption patterns of customer preference and demand in advance.