• Journal of International Area Studies (JIAS)
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• v.13 no.2
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• pp.315-338
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• 2009

The purpose of this paper is to analyze the situation of bi-polarized consumption in China before and after the global financial crisis, to find the factors causing bi-polarized consumption, and to suggest some policies for Korean enterprises. The findings are as follows. First, before the global financial crisis, in China, there were bi-polarized consumption among classes, and between urban and rural areas, and bi-polarization of individual consumption. The sales of both low price goods and high price goods increased more than that of middle price goods. Second, after the global financial crisis, the trend of bi-polarized consumption in China is stronger than those of other countries. The sales of both low price goods and high price goods in China increase more than in other countries. Third, the factors causing bi-polarized consumption are bi-polarized income, black and grey incomes, "Mianz" culture, the increase of unemployment, and the changing form of family. Especially, the level of formal income is not only high, but the level of black and grey income is also very high. And "Mianz" culture means the conspicuous consumption. The degree of the conspicuous consumption of China is very high in comparison with other countries because "Mianz" culture is strong. Finally, the paper suggests strategies appropriate for bi-polarized consumption with Chinese characteristics.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Korean Journal of Human Ecology
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• v.19 no.4
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• pp.675-685
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• 2010

This study analyzed risk perception and risk reduction behaviors of male and female college students in their twenties who purchased fashion products in internet shopping malls. It also investigated the relationship between risk perception and risk reduction behavior as well as the ways in which groups, categorized by risk perception, differed in their risk reduction behaviors. The results of this study were as follows: first, seven factors of risk perception were identified. These were product quality, shipping, product image, payment, economic feasibility, fear of other people's reactions, and size. Six types of risk reduction behavior were also identified. These were product comparison, word-of-mouth information search, price search, preference for name-brand, service comparison, and referring to experiences. Next, a correlational analysis of the factors of risk perception and those of risk reduction behavior showed several patterns. The highest positive correlation was between economic risk perception and product comparison behavior. In addition, shipping risk perception was positively correlated with service comparison behavior and product quality and product image had a positive correlation with word-of-mouth information search behavior. Third, customers of internet shopping malls could be categorized into three groups: shipping risk perception group, high risk perception group, and product quality risk perception group. The groups were shown by factor analysis to be significantly different to each other. Finally, risk reduction behavior was investigated according to the different groups of risk perception of the internet shopping malls and the results showed significant differences among groups.

• Composites Research
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• v.24 no.4
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• pp.23-28
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• 2011

Recently CFRP laminate joints process by bolts and nets are developed rapidly in aircraft industries. However, there are serious drawback during jointing process. Many hole processes are needed for the manufacturing and structural applications using composite materials. Generally, very durable polycrystalline crystalline diamond (PCD) drill has been used for the CFRP hole process. However, due to the expensive price and slow process speed, chemical vapor deposition (CVD) diamond drill has been used increasingly which are relatively-low durability but easily-adjustable process speed via drill shape change and price is much lower. In this study, the comparison of hole process between PCD and CVD diamond coated drills was done. First of all, CFRP hole processbility was evaluated using the equations of hole processing conditions (feed amount per blade, feed speed). The comparison on thermal damage occurring from the CFRP specimen was also studied during drilling process. Empirical equation was made from the temperature photo profile being taken during hole process by infrared thermal camera. In addition, hole processability was compared by checking hole inside condition upon chip exhausting state for two drills. Generally, although the PCD can exhibit better hole processability, hole processing speed of CVD diamond drill exhibited faster than PCD case.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.109-117
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• 2015

This paper proposes the comparison results of various optimal power flow algorithms (OPF) to calculate the locational marginal prices (LMP) of the unreduced full scale Korean transmission system. Five different types of optimal power flow models are employed: Full AC OPF, Cubic AC OPF, Quadratic AC OPF, Linear AC OPF and DC OPF. As the results, full AC OPF and cubic AC OPF model provides LMP calculation results very similar to each other while the calculation time of cubic AC OPF model is faster than that of the Full AC OPF. Other simplified OPF models, quadratic AC OPF, linear AC OPF and DC OPF offer erroneous results even though the calculation times are much faster than the Full AC OPF and the Cubic AC OPF. Given the condition that the OPF models sometimes fail to find the optimal solution due to the severe complexity of the Korean transmission power system, the Full AC OPF should be used as the primary OPF model while the Cubic AC OPF can be a promising backup OPF model for the LMP calculations and/or real-time operation.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.95-101
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• 1999

In this paper, a comparison between different methods to combine predictions from neural networks will be given. These methods are bagging, bumping, and balancing. Those are based on the analysis of the ensemble generalization error into an ambiguity term and a term incorporating generalization performances of individual networks. Neural Networks and AI machine learning models are prone to overfitting. A strategy to prevent a neural network from overfitting, is to stop training in early stage of the learning process. The complete data set is spilt up into a training set and a validation set. Training is stopped when the error on the validation set starts increasing. The stability of the networks is highly dependent on the division in training and validation set, and also on the random initial weights and the chosen minimization procedure. This causes early stopped networks to be rather unstable: a small change in the data or different initial conditions can produce large changes in the prediction. Therefore, it is advisable to apply the same procedure several times starting from different initial weights. This technique is often referred to as training ensembles of neural networks. In this paper, we presented a comparison of three statistical methods to prevent overfitting of neural network.

• Proceedings of the KIEE Conference
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• 2007.11c
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• pp.112-116
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• 2007

If any of the set parameters such as the environment temperature, mold temperature are not maintained at a consistent level, the fail rate of injection molding products is increased. The price of the injection molding machine is very high, so in order to maximize the utilization of the machine that is required the production of a number of different products with minimum fail rate using a single machine. To prevent the defect products by an inspection process with perfect quality is very important to minimizing production of defect products in the molding process. Vision inspection systems are widely utilized in various manufacturing industries for quality assurance purposes. The vision inspection system consists of CCD camera and lighting system to capture the image of the subject of inspection, an image comparison algorithm using to determine the pass/fail of the products, and mechanical devices for the operation of the whole system. This research focuses on the development of the vision inspection system to process the inspection of an automobile parts. We developed a mechanical devices for the inspection of the injection molding products and an image comparison algorithm to determine the pass/fail result of the inspection based on the molding image and the accepted product image.

• The Journal of Society for e-Business Studies
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• v.6 no.2
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• pp.87-99
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• 2001

Along with the rapid emergence of the Internet and e-commerce, online auctions are hitting the spotlights. The inconveniences found in off-line auctions, such as time and place restriction and limited number of items, are solved in the online auction. However, not so many auction sites have integrated auction information systems, which monitor the present status of auctions, resulting in greater inconvenience for the online auction users. Moreover, there is no auction site that suggests the appropriate starting or closing price that is useful for users when they make, their bids, What the online auction users need is an auction system that can solve such problems. This study is purported for solving the problems by designing and implementing a real time auction system that applies the comparison search functions and the agent functions. In other words, an integrated database system using a bidder-oriented agent for providing information is built so that the users can search and compare the information on the item they are interested in and make a faster and more accurate purchase. Also the appropriate starting and closing prices are offered to the sellers and bidders through the integrated system for a closer and more accurate comparison and analysis of the prices. For future work, the product recommendation service, which accurately reflects the bidding patterns, and the methods for studying the multi bidding pattern will be applied to the suggested system to realize a real time auction information system that supports CRM(Customer Relationship Management) .

• Journal of Environmental Science International
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• v.18 no.4
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• pp.355-367
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• 2009

Comparison study on the land surface temperatures, which are calculated from four different algorithms for MODIS data, was carried out and the characteristics of each algorithm on land surface temperature estimation were also analysed in this study. Algorithms, which are well used for various satellite data analysis, in the comparisons are proposed by Price, Becker and Li, Ulivieri et al., and Wan. Verification of estimated land surface temperature from each algorithm is also performed using observation based regression data. The coefficient of determination ($R^2$) for daytime land surface temperature estimated from Wan's algorithm is higher than that of another algorithms at all seasons and the value of $R^2$ reach on 0.92 at spring. Although $R^2$ for Ulivieri's algorithm is slightly lower than that for Wan's algorithm, the variation pattern of land surface temperature for two algorithms are similar. However, the difference of estimated values among four algorithms become small at the region of high land surface temperature.

• Applied Chemistry for Engineering
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• v.25 no.6
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• pp.639-644
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• 2014

Domestic vehicle companies have been selling genuine engine oils with higher price than that of the same grade of regular engine oils. In this study, our group investigated the properties of engine oils for 14 kinds of the genuine and equivalent regular engine oil (KS product) species under a fresh as well as used condition recovered after 10,000 km driving. From analytic results, genuine engine oils had similar physical properties to regular engine oils under the fresh condition. But recovered regular engine oils had better properties in lubricity, kinematic viscosity and acid number change than those of recovered genuine engine oils.