• Archives of design research
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• v.16 no.3
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• pp.201-210
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• 2003

The world is preparing another unseen war, that is, the cultural war of digital economy which will dominate the new millenium. As the “contents”, which are composed of various ingredients of media, gain vitality, the developed nations are in preparation of the war with the “cultural industry” weapons. The digital economic experts say that the left out nations will become economic colony in the new millenium age. The most important characteristics of cultural industry is the unity of creativity and culture which is all the more improved on the basis of the culture created upon knowledge. This leads to competition between nations or regions, and to survive one has to develop the industrial structure through cognition of its own cultural value. Furthermore, it is not a short-term development and investment of cultural products but a study on the method to graft the cultural value to the industry itself. The multi-media period does not accept an independent medium, and the contents products are becoming the leading industry since il is proved that they last semi-permanently in the digital world. The victory lies in the quality and quantity of the contents as the high ability and variety of the technology of media advance in accordance to the market principles. Since the culture, science and economy are becoming one complex structure, all nations of the world are trying the evolve a unique design of their on culture on the basis of the global universality. In consequence, we should excavate a uniqueness from our cultural heritage and develop into a korean design which will be recognized in the world market. The value of our cultural property should not only be used as academic and research purposes but should be re-evaluated with modem view, recognized as the core element that decides the quality of life and developed into exclusive designs. The korean designs represent the mould concept of our people which evolves from the mould or shape alphabet of Korea To meet the requirements of the changing world and in preparation of the cultural competitive age, it is never too early to make a data on the korean designs through their analysis and evaluation.

• Korean Journal of Food Science and Technology
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• v.42 no.1
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• pp.69-74
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• 2010

Black garlic has recently received significant attention due to its various health functional properties, and there has been an increase in demand for its use as a functional food. This study was performed to determine the optimum concentration for the fermentation of black garlic yakju. In addition, the antioxidant activity of the fermented black garlic yakju was examined. The alcohol content in the black garlic yakju significantly increased for 6 days and the pH gradually increased as the concentration of black garlic increased. The reducing sugar content at each black garlic concentration was maximal when it was fermented for 24 hours, and then rapidly decreased at longer fermentation periods. The main organic acids were lactic, citric, malic and oxalic acid. Also, the lactic acid content increased as the concentration of the black garlic increased where as the content of other organic acids decreased. The total polyphenol content, ferric ion reducing antioxidant power (FRAP) activity and DPPH (1,1-diphenyl-2-picryl-hydrazyl) free radical scavenging activity of black garlic yakju increased as the concentration of black garlic increased. The sensory characteristics of fermented black garlic yakju were evaluated in terms of color, flavor, taste and overall acceptability, and the highest overall acceptability value was obtained for yakju containing a black garlic concentration of 1-3%. Therefore, the optimum concentration of black garlic was determined to be 1% for the production of high quality black garlic yakju.

• Korean Journal of Social Welfare
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• v.58 no.3
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• pp.135-168
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• 2006

This study reviewed pension reform's overall characteristic and(anticipated) positive negative effect in OECD countries's and then analysed middle-old age's retirement transition and determinants of full/gradual retirement through the $3{\sim}7th$ Korea Labor and Income Panel considering that Korea has been aging society quickly and it is necessary to suggest not only solution of early retirement and working age reduction but also pension reform. As a result of this study, about 1/4 of 50 years and older have been continuing to work through various pathways after retirement and 98% among fully retired older who passed by re-employment step of occupational status including retirement are still searching for jobs. This showed that it is also inappropriate to typical retirement concept itself on the lines of labour market participation in Korea and part-time/temporary work or self-employment have been used by means of alternatives of maintaining works for middle-old ages. However, the duration of changed occupational status of gradual retirees is mostly only $1{\sim}2$ years. Therefore it is necessary to support the gradual retirement to minimize a term of income insecurity and promote the work of the old ages who have will and capacity of work. Most of all, partial pension system which is main program of gradual retirement, should make the rules that beneficiaries are those who age less than pensionable age and benefit levels should be actuarial fairness together with pension system and provide substantial help. But, the introduction of partial pension system is not the only way to solve and needs overall social economic approach. Especially guarantee the increase of quantitative qualitative employment for middle-old ages linking labor market policy and supporting gradual retirement not ought to be abused to force the part time works and early retirement route against their own will.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.11
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• pp.1658-1672
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• 2016

This study aimed to investigate preferences for sous-vide cooked jabchae between Korean and Chinese consumers according to sauce mixing proportion. To commercialize sous-vide cooked jabchae and localize it for overseas circumstances in the Korean and Chinese markets, consumers' subjective preferences for sous-vide cooked jabchae were investigated especially in 119 Korean consumers (55 males and 64 females) and 136 Chinese consumers (70 males and 66 females). For jabchae samples, this study set up three different mixture rates of soy sauce and sugar, 8% (LSS), 13% (MSS), and 18% (HSS), and three different salad oil rates, 0% (LO), 12% (MO), and 24% (HO), to propose nine kinds of samples. As a result of consumer preferences, for Koreans, MSS and HSS regardless of oil content were significantly high in overall, appearance, saltiness, sweetness preferences, and purchase intention (P<0.001). In addition, for oiliness preference, LSS, MSS, LO, and MO were significantly high (P<0.001). For Chinese, HSS, MO, and HO were significantly high in overall, flavor preference, and purchase intention (P<0.001). For saltiness and sweetness preference, regardless of oil content, saltiness preference was significantly high in HSS and sweetness in MSS and HSS (P<0.001). For oiliness preference, regardless of content of soy sauce and sugar mixture, LO and MO were significantly higher, and for appearance preference, there was no significant difference among all samples (P<0.01). In general, both Korean and Chinese tended to prefer MS and HO, irrespective of oil content. Especially for Koreans, LSS was the least favorite sample in almost all preference questionnaires. For Chinese, preference scores for LSS and HSS were higher than for Koreans. On the other hand, oil content did not have much effect on consumer preference as compared with contents of soy sauce and sugar mixture.

• Land and Housing Review
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• v.12 no.3
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• pp.11-26
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• 2021

With the increasing demand for office space, there have been questions on how office rent distribution produces a change in the urban spatial structure in Seoul. The purpose of this paper is to investigate a relative price gradient and to present a time-series model that can quantitatively explain the dynamic changes in the urban spatial structure. The analysis was dealt with office rent above 3,306 m² for the past 10 years from 1Q 2010 to 4Q 2019 within Seoul. A modified repeat sales model was employed. The main findings are briefly summarized as follows. First, according to the estimates of the office price gradient in the three major urban centers of Seoul, the CBD remained at a certain level with little change, while those in the GBD and the YBD continued to increase. This result reveals that the urban form of Seoul has shifted from monocentric to polycentric. This shows that the spatial distribution of companies has gradually accelerated decentralized concentration implying that the business networks have become significant. Second, contrary to small and medium-sized office buildings that have undertaken no change in the gradient, large office buildings have seen an increase in the gradient. The relative price gradients in small and medium-sized buildings were inversely proportional among the CBD, the GBD, and the YBD, implying their heterogeneous submarkets by office rent movements. Presumably, those differences in the submarkets were attributed to investment attraction, industrial competition, and the credit and preference of tenants. The findings are consistent with the hierarchical system identified in the Seoul 2030 Plan as well as the literature about Seoul's urban form. This research claims that the proposed method, based on the modified repeat sales model, is useful in understanding temporal dynamic changes. Moreover, the findings can provide implications for urban growth strategies under rapidly changing market conditions.

• Journal of International Area Studies (JIAS)
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• v.14 no.2
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• pp.287-310
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• 2010

Developed nations are increasingly seeking to secure competitiveness in the international market through the development of human resources of workers in high value-added industries. And what is especially important in this process is the fact that workers, employers, and concerned government agencies are participating together in building and improving workers' skills through partnerships. This is based on the perception that workers training programs conducted according to the interest of one side are difficult to bring desired results. For the past decades, Korea has focused mostly on labor-management-government partnerships and strategies for developing the human resources of workers in developed nations in Europe. Related case studies show labor-management-government partnerships in European countries established through powerful trade unions, and interested parties actively cooperate and participate in employment and training programs that benefit both workers and employers. In contrast, studies on human resource development participated by workers and employers are relatively rare in the US, the reason being the lack of a mechanism for establishing labor-management-government partnership due to the country's strong tradition of decentralization and the emphasis on market principles. However, while it is difficult to find such channels for dialogue between workers, employers, and the government in th US on the federal level, there are many regional-level or industry-level programs that tackle common problems through partnerships between interested parties. This study analyzes how the regional labor-management-government partnerships in the US work and examines the types of programs operated by investigating the One-Stop Center based on the Workforce Investment Act and the Wisconsin Regional Training Partnership. While the One-Stop Center is a regional labor-management-government partnership model that is institutionally executed in each state according to the Workforce Investment Act, the WRTP is a regional labor-management -government partnership model led by the private sector. The two examples are introduced in the OECD as best practice examples of regional partnerships, and are key references to Korea's current human resource development policy.

• (The) Research of the performance art and culture
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• no.22
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• pp.223-252
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• 2011

If we remember that modern world history has built systems of meaning through the concepts "difference," "different," and "other-ness" and has constructed new identity based on opposing hierarchy, music anthropology which tried to build "difference" between the west and the non-west was thoroughly west -centered, in the sense that it has perceived the heterogeneous symbolic systems among nations, as well as the barrier between the two cultures. On the other hand, world music, which has emerged as the most attractive field in culture industry and concert-art-market by crossing over global capitals, markets, and barriers, can be considered the most post-modernist and glocal. However, it is interesting to note that world music, which has been described as post-modern and glocal, has "difference" and "different" in its basis, just like the precepts for modern music anthropology (Meintjes 1990; Guilbault 1993; Taylor 1997; Frith 2000; Feld 1988). Furthermore, one can understand that the "different" and "difference," generally termed as being "non-western," are fundamentally based on ethnic or national imagination. In this sense it is interesting and important to examine such ethnic imagination in the "non-western ethnic musics" in music anthropology and in world music. Notwithstanding the attention paid and research made by music anthropologists, they have failed to elevate the "non-western ethnic musics" to become universally communicative, and these ethnic musics were reborn as "global" and "world music," through the process of "acculturation," "derivation," and "hybridization," with the west as major site for production and consumption. Meanwhile, the audience for world music, which did not exist before the birth of world music as a term, was now born as world music emerged. They are global populace who consume the musical "difference" and "imagined ethnicity," who through their consumption are constructing new social meanings including ethnicity, race, nation, and class identity. This study, by examining current discourse, performance, and process for the world music through media and field studies and scholarly debates, attempts to understand the production and consumption of "imagined ethnicity." This will also shed light on how "ethnicity" is created and consumed, and how this is involved in the process of world music.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.