• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.639-652
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• 2023

The fundamental background behind the introduction of the fishing vessel system is to allow petty small fishers to engage in pure fishery business activities with fishing vessels during normal times and engage in fishing vessel business only during specific periods (closed fishing season, etc.) thereby granting a qualification as an auxiliary tool for the economic activities of petty small fishers. In addition, fishing boats are allowed to engage in excursion ship activities using fishing vessels registered under the Fishing Vessels Act, the form of fishing vessels should also have a general and universal structure that is practically easy to engage in fishing activities in the field in accordance with the relevant regulations. However, most fishing vessel proprietors are currently focusing only on increasing income, and rather than building fishing vessels in a reasonable form suitable for the original purpose of general fishing vessels, they prefer an abnormal hull form equivalent to expediency, that is biased hull structure biased toward the fishing vessel business. As a result, it is causing serious problems in safety management as well as conflict [damaging relative equity in government support measures (tax-free oil supply, etc.), and depletion of livelihood-type fish stocks] with fishing vessel forces who consider the fishing vessel business only to be a part of the side job among all fishery business activities. Meanwhile, the most fundamental cause of this problem is that the current Fishing Management and Promotion Act, limits the concept of fishing vessels to fishing vessels registered under the Fishing Vessels Act, and applies survey standards accordingly. Accordingly, in this study, through analysis of the distribution status of fishing vessels, structural characteristics, operation status of fishing vessels, and the government's fishing promotion policies, etc., the relevant laws (regulations) have been reorganized to suit the current reality of the concept of fishing vessels to separate the current fishing vessel from fishing vessels and operate it as a fishing-only vessel.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Global Scholars of Marketing Science
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• v.17 no.2
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• pp.31-54
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• 2007

The exchange between buyers and sellers in the industrial market is changing from short-term to long-term relationships. Long-term relationships are governed mainly by formal contracts or informal agreements, but many scholars are now asserting that controlling relationship by using formal contracts under environmental dynamism is inappropriate. In this case, partners will depend on each other's flexibility or interdependence. The former, flexibility, provides a general frame of reference, order, and standards against which to guide and assess appropriate behavior in dynamic and ambiguous situations, thus motivating the value-oriented performance goals shared between partners. It is based on social sacrifices, which can potentially minimize any opportunistic behaviors. The later, interdependence, means that each firm possesses a high level of dependence in an dynamic channel relationship. When interdependence is high in magnitude and symmetric, each firm enjoys a high level of power and the bonds between the firms should be reasonably strong. Strong shared power is likely to promote commitment because of the common interests, attention, and support found in such channel relationships. This study deals with environmental dynamism in high-tech industry. Firms in the high-tech industry regard it as a key success factor to successfully cope with environmental changes. However, due to the lack of studies dealing with environmental dynamism and supply chain commitment in the high-tech industry, it is very difficult to find effective strategies to cope with them. This paper presents the results of an empirical study on the relationship between environmental dynamism and supply chain commitment in the high-tech industry. We examined the effects of consumer, competitor, and technological dynamism on supply chain commitment. Additionally, we examined the moderating effects of flexibility and dependence of supply chains. This study was confined to the type of high-tech industry which has the characteristics of rapid technology change and short product lifecycle. Flexibility among the firms of this industry, having the characteristic of hard and fast growth, is more important here than among any other industry. Thus, a variety of environmental dynamism can affect a supply chain relationship. The industries targeted industries were electronic parts, metal product, computer, electric machine, automobile, and medical precision manufacturing industries. Data was collected as follows. During the survey, the researchers managed to obtain the list of parts suppliers of 2 companies, N and L, with an international competitiveness in the mobile phone manufacturing industry; and of the suppliers in a business relationship with S company, a semiconductor manufacturing company. They were asked to respond to the survey via telephone and e-mail. During the two month period of February-April 2006, we were able to collect data from 44 companies. The respondents were restricted to direct dealing authorities and subcontractor company (the supplier) staff with at least three months of dealing experience with a manufacture (an industrial material buyer). The measurement validation procedures included scale reliability; discriminant and convergent validity were used to validate measures. Also, the reliability measurements traditionally employed, such as the Cronbach's alpha, were used. All the reliabilities were greater than.70. A series of exploratory factor analyses was conducted. We conducted confirmatory factor analyses to assess the validity of our measurements. A series of chi-square difference tests were conducted so that the discriminant validity could be ensured. For each pair, we estimated two models-an unconstrained model and a constrained model-and compared the two model fits. All these tests supported discriminant validity. Also, all items loaded significantly on their respective constructs, providing support for convergent validity. We then examined composite reliability and average variance extracted (AVE). The composite reliability of each construct was greater than.70. The AVE of each construct was greater than.50. According to the multiple regression analysis, customer dynamism had a negative effect and competitor dynamism had a positive effect on a supplier's commitment. In addition, flexibility and dependence had significant moderating effects on customer and competitor dynamism. On the other hand, all hypotheses about technological dynamism had no significant effects on commitment. In other words, technological dynamism had no direct effect on supplier's commitment and was not moderated by the flexibility and dependence of the supply chain. This study makes its contribution in the point of view that this is a rare study on environmental dynamism and supply chain commitment in the field of high-tech industry. Especially, this study verified the effects of three sectors of environmental dynamism on supplier's commitment. Also, it empirically tested how the effects were moderated by flexibility and dependence. The results showed that flexibility and interdependence had a role to strengthen supplier's commitment under environmental dynamism in high-tech industry. Thus relationship managers in high-tech industry should make supply chain relationship flexible and interdependent. The limitations of the study are as follows; First, about the research setting, the study was conducted with high-tech industry, in which the direction of the change in the power balance of supply chain dyads is usually determined by manufacturers. So we have a difficulty with generalization. We need to control the power structure between partners in a future study. Secondly, about flexibility, we treated it throughout the paper as positive, but it can also be negative, i.e. violating an agreement or moving, but in the wrong direction, etc. Therefore we need to investigate the multi-dimensionality of flexibility in future research.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.39 no.2
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• pp.15-28
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• 2021

'Cheonunjeongsa (天雲精舍)', designated as Gyeongsangbukdo Folklore Cultural Property No. 76, is a Siksanjeongsa built in 1700 by Manbu Lee Shiksan. In this study, we investigate the life and perspective of Manbu Lee in relation to Siksanjeongsa, and estimate the feng shui location, territoriality, and original landscape by analyzing 「Nuhangnok」 and 「Nuhando」, the results of his political management. The following results were derived by examining the philosophy that the scholar wanted to include in his space. First, Manbu Lee Shiksan was a representative hermit-type confucian scholar in the late Joseon Dynasty. 'Siksan', the name of the government official and the nickname of Manbu Lee, is derived from the mountain behind the village, and he wanted to rest in the four areas of thought(思), body(躬), speech(言), and friendship(交). During the difficult years of King Sukjong, Lee Manbu of a Namin family expressed his will to seclude through the title 'Siksan'. Second, There is a high possibility of restoration close to the original. Manbu Lee recorded the location of Siksanjeongsa, spatial structure, buildings and landscape facilities, trees, surrounding landscape, and usage behaviors in 「Nuhangnok」, and left a book of 《Nuhangdo》. Third, Manbu Lee refers to the feng shui geography view that Oenogok is closed in two when viewed from the outside, but is cozy and deep and can be seen from a far when entering inside. The whole village of Nogok was called Siksanjeongsa, which means through the name. It can be seen that the area was formed and expanded. Fourth, the spatial composition of Siksanjeongsa can be divided into a banquet space, an education space, a support space, a rest space, a vegetable and an herbal garden. The banquet space composed of Dang, Lu, and Yeonji is a personal space where Manbu Lee, who thinks about the unity of the heavenly people, the virtue of the gentleman, and humanity, is a place for lectures and a place to live. Fifth, Yangjeongjae area is an educational space, and Yangjeongjae is a name taken from the main character Monggwa, and it is a name that prayed for young students to grow brightly and academically. Sixth, the support space composed of Ganjijeong, Gobandae, and Sehandan is a place where the forested areas in the innermost part of Siksanjeongsa are cleared and a small pavilion is built using natural standing stones and pine trees as a folding screen. The virtue and grace of stopping. It contains the meaning of leisure and the wisdom of a gentleman. Seventh, outside the wall of Siksanjeongsa, across the eastern stream, an altar was built in a place with many old trees, called Yeonggwisa, and a place of rest was made by piling up an oddly shaped stone and planting flowers. Eighth, Manbu Lee, who knew the effects of vegetables and medicinal herbs in detail like the scholars of the Joseon Dynasty, cultivated a vegetable garden and an herbal garden in Jeongsa. Ninth, it can be seen that Lee Manbu realized the Neo-Confucian utopia in his political life by giving meaning to each space of Siksanjeongsa by naming buildings and landscaping facilities and planting them according to ancient events.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2015.05a
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• pp.545-570
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• 2015

Korea is among the ten countries with the largest R&D budget and the highest R&D investment-to-GDP ratio, yet the subject of security and protection of R&D results remains relatively unexplored in the country. Countries have implemented in their legal systems measures to properly protect cutting-edge industrial technologies that would adversely affect national security and economy if leaked to other countries. While Korea has a generally stable legal framework as provided in the Regulation on the National R&D Program Management (the "Regulation") and the Act on Industrial Technology Protection, many difficulties follow in practice when determining details on security management and obligations and setting standards in carrying out national R&D projects. This paper proposes to modify and improve security level classification standards in the Regulation. The Regulation provides a dual security level decision-making system for R&D projects: the security level can be determined either by researcher or by the central agency in charge of the project. Unification of such a dual system can avoid unnecessary confusions. To prevent a leakage, it is crucial that research projects be carried out in compliance with their assigned security levels and standards and results be effectively managed. The paper examines from a practitioner's perspective relevant legal provisions on leakage of confidential R&D projects, infringement, injunction, punishment, attempt and conspiracy, dual liability, duty of report to the National Intelligence Service (the "NIS") of security management process and other security issues arising from national R&D projects, and manual drafting in case of a breach. The paper recommends to train security and technological experts such as industrial security experts to properly amend laws on security level classification standards and relevant technological contents. A quarterly policy development committee must also be set up by the NIS in cooperation with relevant organizations. The committee shall provide a project management manual that provides step-by-step guidance for organizations that carry out national R&D projects as a preventive measure against possible leakage. In the short term, the NIS National Industrial Security Center's duties should be expanded to incorporate national R&D projects' security. In the long term, a security task force must be set up to protect, support and manage the projects whose responsibilities should include research, policy development, PR and training of security-related issues. Through these means, a social consensus must be reached on the need for protecting national R&D projects. The most efficient way to implement these measures is to facilitate security training programs and meetings that provide opportunities for communication among industrial security experts and researchers. Furthermore, the Regulation's security provisions must be examined and improved.

• KDI Journal of Economic Policy
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• v.12 no.2
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• pp.3-49
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• 1990

This paper analyzes U.S. demand for imports from Asian NIEs and Japan, utilizing the Almost Ideal Demand System (AIDS) developed by Deaton and Muellbauer, with an emphasis on the effect of changes in the exchange rate. The empirical model assumes a two-stage budgeting process in which the first stage represents the allocation of total U.S. demand among three groups: the Asian NIEs and Japan, six Western developed countries, and the U.S. domestic non-tradables and import competing sector. The second stage represents the allocation of total U.S. imports from the Asian NIEs and Japan among them, by country. According to the AIDS model, the share equation for the Asia NIEs and Japan in U.S. nominal GNP is estimated as a single equation for the first stage. The share equations for those five countries in total U.S. imports are estimated as a system with the general demand restrictions of homogeneity, symmetry and adding-up, together with polynomially distributed lag restrictions. The negativity condition is also satisfied for all cases. The overall results of these complicated estimations, using quarterly data from the first quarter of 1972 to the fourth quarter of 1989, are quite promising in terms of the significance of individual estimators and other statistics. The conclusions drawn from the estimation results and the derived demand elasticities can be summarized as follows: First, the exports of each Asian NIE to the U.S. are competitive with (substitutes for) Japan's exports, while complementary to the exports of fellow NIEs, with the exception of the competitive relation between Hong Kong and Singapore. Second, the exports of each Asian NIE and of Japan to the U.S. are competitive with those of Western developed countries' to the U.S, while they are complementary to the U.S.' non-tradables and import-competing sector. Third, as far as both the first and second stages of budgeting are coneidered, the imports from each Asian NIE and Japan are luxuries in total U.S. consumption. However, when only the second budgeting stage is considered, the imports from Japan and Singapore are luxuries in U.S. imports from the NIEs and Japan, while those of Korea, Taiwan and Hong Kong are necessities. Fourth, the above results may be evidenced more concretely in their implied exchange rate effects. It appears that, in general, a change in the yen-dollar exchange rate will have at least as great an impact, on an NIE's share and volume of exports to the U.S. though in the opposite direction, as a change in the exchange rate of the NIE's own currency $vis-{\grave{a}}-vis$ the dollar. Asian NIEs, therefore, should counteract yen-dollar movements in order to stabilize their exports to the U.S.. More specifically, Korea should depreciate the value of the won relative to the dollar by approximately the same proportion as the depreciation rate of the yen $vis-{\grave{a}}-vis$ the dollar, in order to maintain the volume of Korean exports to the U.S.. In the worst case scenario, Korea should devalue the won by three times the maguitude of the yen's depreciation rate, in order to keep market share in the aforementioned five countries' total exports to the U.S.. Finally, this study provides additional information which may support empirical findings on the competitive relations among the Asian NIEs and Japan. The correlation matrices among the strutures of those five countries' exports to the U.S.. during the 1970s and 1980s were estimated, with the export structure constructed as the shares of each of the 29 industrial sectors' exports as defined by the 3 digit KSIC in total exports to the U.S. from each individual country. In general, the correlation between each of the four Asian NIEs and Japan, and that between Hong Kong and Singapore, are all far below .5, while the ones among the Asian NIEs themselves (except for the one between Hong Kong and Singapore) all greatly exceed .5. If there exists a tendency on the part of the U.S. to import goods in each specific sector from different countries in a relatively constant proportion, the export structures of those countries will probably exhibit a high correlation. To take this hypothesis to the extreme, if the U.S. maintained an absolutely fixed ratio between its imports from any two countries for each of the 29 sectors, the correlation between the export structures of these two countries would be perfect. Therefore, since any two goods purchased in a fixed proportion could be classified as close complements, a high correlation between export structures will imply a complementary relationship between them. Conversely, low correlation would imply a competitive relationship. According to this interpretation, the pattern formed by the correlation coefficients among the five countries' export structures to the U.S. are consistent with the empirical findings of the regression analysis.

• 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.

• (The) Research of the performance art and culture
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• no.41
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• pp.5-38
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• 2020

The current state of performance halls in South Korea is closely related to the performance art and culture of the nation as the culture of putting on and enjoying a performance is deeply rooted in public culture and arts halls representing each area at the local government level. Today, public culture and arts halls have multiple management purposes, and the subjects of their management are in the public domain including the central and local governments or investment and donation foundations in overwhelming cases. Public culture and arts halls thus have close correlations with the institutional aspect of cultural policies as the objects of culture and art policies at the central and local government level. The full-blown era of public culture and arts halls opened up in the 1980s~1990s, during which multi-purpose performance halls of a similar structure became universal around the nation. Public culture and arts halls of the uniform shape were distributed around the nation with no premise of genre characteristics or local environments for arts, and this was attributed to the cultural policies of the military regime. The Park Chung-hee regime proclaimed Yusin that was beyond the Constitution and enacted the Culture and Arts Promotion Act(September, 1972), which was the first culture and arts act in the nation. Based on the act, a five-year plan for the promotion of culture and arts(1973) was made and led to the construction of cultural facilities. "Public culture and arts" halls or "culture" halls were built to serve multiple purposes around the nation because the Culture and Arts Promotion Act, which is called the starting point of the nation's legal system for culture and arts, defined "culture and arts" as "matters regarding literature, art, music, entertainment, and publications." The definition became a ground for the current "multi-purpose" concept. The organization of Ministry of Culture and Public Information set up a culture and administration system to state its supervision of "culture and arts" and distinguish popular culture from the promotion of arts. During the period, former President Park exhibited his perception of "culture=arts=culture and arts" in his speeches. Arts belonged to the category of culture, but it was considered as "culture and arts." There was no department devoted to arts policies when the act was enacted with a broad scope of culture accepted. This ambiguity worked as a mechanism to mobilize arts in ideological utilizations as a policy. Against this backdrop, the Sejong Center for the Performing Arts, a multi-purpose performance hall, was established in 1978 based on the Culture and Arts Promotion Act under the supervision of Ministry of Culture and Public Information. There were, however, conflicts of value over the issue of accepting the popular music among the "culture and arts = multiple purposes" of the system, "culture ≠ arts" of the cultural organization that pushed forward its establishment, and "culture and arts = arts" perceived by the powerful class. The new military regime seized power after Coup d'état of December 12, 1979 and failed at its culture policy of bringing the resistance force within the system. It tried to differentiate itself from the Park regime by converting the perception into "expansion of opportunities for the people to enjoy culture" to gain people's supports both from the side of resistance and that of support. For the Chun Doo-hwan regime, differentiating itself from the previous regime was to secure legitimacy. Expansion of opportunities to enjoy culture was pushed forward at the level of national distribution. This approach thus failed to settle down as a long-term policy of arts development, and the military regime tried to secure its legitimacy through the symbolism of hardware. During the period, the institutional ground for public culture and arts halls was based on the definition of "culture and arts" in the Culture and Arts Promotion Act enacted under the Yusin system of the Park regime. The "multi-purpose" concept, which was the management goal of public performance halls, was born based on this. In this context of the times, proscenium performance halls of a similar structure and public culture and arts halls with a similar management goal were established around the nation, leading to today's performance art and culture in the nation.