• (The) Research of the performance art and culture
• /
• no.42
• /
• pp.47-75
• /
• 2021

This thesis looks into the interpretation of the Korean War and mystery genre in Korea in the 1970s by analyzing the special drama , in which the theme was directly related to the Korean War, airing through MBC in 1979. It begins by finding the change in direction in the 1970s when the world of TV was dictated through the heavy censorship and the memory of the war by the government. It also looks at the intentions of the producer who was taking in the new way and the viewers who also accepted this drama and its reflections. In order to gain some insights into these issues, it compares between the drama "The Last Witness" and the original novel by Seong-jong Kim who holds the same time to see the way in which this is dramatized. The drama, "The Last Witness", was produced with a plan to generate a high-quality special drama which combined both artistry and sense of purpose. Nevertheless, as watching TV became a leisurely past-time during this period, TV dramas become more aggressive and suggestive in order to attract viewers. This ultimately was encored with obstacles due to the regime and the heavy censorship at the time. The genre of special drama that is well known in South Korea, is designed as an art form to satisfy both their unique artistry and its purpose. The conflict is seen between the key elements of the artistic drama crated by the producers and the 'encouraged' elements that often are needed to engage the viewers. Thus, more often than not, special dramas defeat the original intention of national harmony, encouraged by the regime. This is due to the 'novelty' aspect which grows from the effort of bringing enjoyment to viewers whilst also trying to achieve the artistic drama to life. Alongside this, crime element in this drama is designed in a way that visually embodies the process of deduction, becoming a new possibility to secure the reality of the times. However, it was also a paradoxical existence since it was indicated as an example of unrefined culture that lost its original intention. In that way, it is worth to think that detective suspense stories, which were not popular in Korea, influenced viewers as a tv drama series in the 1970s through the various elements that compose the genre. They went through a process of transplantation and acceptance whilst also attempting to satisfy the viewers and their encouraged elements to engage them. As is well known, crime drama in Korea has its own style by mixing anticommunism and detective reasoning. This combination is found in the way in which the genre naturally forms through the elements selected and excluded in the dramatization of "The Last Witness". The point is that the special drama "The Last Witness" can be seen as an intermediate form that shows the tendency of transformation from the detective reasoning form alongside the crime aspects as TV dramas began to include anticommunism messaging and investigation in the 1970s. In conclusion, when the detective reasoning is used as an element in a TV drama, it shows the trust of the public system and it constantly seeks the possibility of circumventing the political interpretation. The memories of the war is seen as a tool that neutralizes the dismal imaginations inscribed on the dark side of society and the system. As a result, "The Last Witness", broadcasted at the end of the Yushin regime in Korea, is a strange result which combines the logic of a special drama and the encouraged characteristics of television dramas. The viewers' desire which is the discussion about the hidden traces from the texts needs to be restored again.

• Journal of Intelligence and Information Systems
• /
• v.22 no.1
• /
• pp.63-82
• /
• 2016

Individuals gather information online to resolve problems in their daily lives and make various decisions about the purchase of products or services. With the revolutionary development of information technology, Web 2.0 has allowed more people to easily generate and use online reviews such that the volume of information is rapidly increasing, and the usefulness and significance of analyzing the unstructured data have also increased. This paper presents an analysis on the lexical features of expert product reviews to determine their influence on consumers' purchasing decisions. The focus was on how unstructured data can be organized and used in diverse contexts through text mining. In addition, diverse lexical features of expert reviews of contents provided by a third-party review site were extracted and defined. Expert reviews are defined as evaluations by people who have expert knowledge about specific products or services in newspapers or magazines; this type of review is also called a critic review. Consumers who purchased products before the widespread use of the Internet were able to access expert reviews through newspapers or magazines; thus, they were not able to access many of them. Recently, however, major media also now provide online services so that people can more easily and affordably access expert reviews compared to the past. The reason why diverse reviews from experts in several fields are important is that there is an information asymmetry where some information is not shared among consumers and sellers. The information asymmetry can be resolved with information provided by third parties with expertise to consumers. Then, consumers can read expert reviews and make purchasing decisions by considering the abundant information on products or services. Therefore, expert reviews play an important role in consumers' purchasing decisions and the performance of companies across diverse industries. If the influence of qualitative data such as reviews or assessment after the purchase of products can be separately identified from the quantitative data resources, such as the actual quality of products or price, it is possible to identify which aspects of product reviews hamper or promote product sales. Previous studies have focused on the characteristics of the experts themselves, such as the expertise and credibility of sources regarding expert reviews; however, these studies did not suggest the influence of the linguistic features of experts' product reviews on consumers' overall evaluation. However, this study focused on experts' recommendations and evaluations to reveal the lexical features of expert reviews and whether such features influence consumers' overall evaluations and purchasing decisions. Real expert product reviews were analyzed based on the suggested methodology, and five lexical features of expert reviews were ultimately determined. Specifically, the "review depth" (i.e., degree of detail of the expert's product analysis), and "lack of assurance" (i.e., degree of confidence that the expert has in the evaluation) have statistically significant effects on consumers' product evaluations. In contrast, the "positive polarity" (i.e., the degree of positivity of an expert's evaluations) has an insignificant effect, while the "negative polarity" (i.e., the degree of negativity of an expert's evaluations) has a significant negative effect on consumers' product evaluations. Finally, the "social orientation" (i.e., the degree of how many social expressions experts include in their reviews) does not have a significant effect on consumers' product evaluations. In summary, the lexical properties of the product reviews were defined according to each relevant factor. Then, the influence of each linguistic factor of expert reviews on the consumers' final evaluations was tested. In addition, a test was performed on whether each linguistic factor influencing consumers' product evaluations differs depending on the lexical features. The results of these analyses should provide guidelines on how individuals process massive volumes of unstructured data depending on lexical features in various contexts and how companies can use this mechanism from their perspective. This paper provides several theoretical and practical contributions, such as the proposal of a new methodology and its application to real data.

• KIPS Transactions on Computer and Communication Systems
• /
• v.4 no.6
• /
• pp.185-196
• /
• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(Quality of Service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.

• Journal of dental hygiene science
• /
• v.8 no.3
• /
• pp.147-153
• /
• 2008

The purpose of this study was to attempt to lay the foundation for the development of oral health programs geared toward promoting the oral health of low socioeconomic class children. The subjects in this study were 257 school children who used local children's centers. The findings of the study were as follows: 1. The children mean scored 5.74 on oral health knowledge. 2. In terms of oral health awareness, 47.1% viewed the right toothbrushing as the best way to stay away from dental caries. 3. 45% of the subjects reported toothbrushing at least three times daily. 21.4% visited dental institutions three or more times in the past year. 33.1% had never undergone application of fluoride. 30.4% had never received oral health education. 4. The mean level of caries was 4.61 dft index in 1-2th grade, 3.27 DMFT index in 5-6th grade, 1.47 DMFT index in the 3-4th grad and 1.19 DMFT index in the 1-2th grade. 5. The mean level of Patient Hygiene Performance (PHP index) was 3.59, and there was no significant association was pound between PHP index and grade. 6. Oral health behavior wasn't affected by their oral health awareness, and knowledge.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
• /
• v.16 no.2
• /
• pp.219-230
• /
• 2005

Objectives : This study was designed to compare the demographic data, clinical characteristics, developmental delay, and psychological tests between childhood-onset and adolescent-onset schizophrenic in-patients. Methods Medical records of the 17 childhood-onset (very early onset) Schizophrenia and 16 adolescent-onset (early onset) Schizophrenia in-patients were reviewed. Sex, age, psychiatric past history, prodromal symptoms and period, subtype, co-morbid disease, developmental delay, prescribed drug and dosage, treatment response, intelligence quotient (IQ), and Rorschach test were evaluated. Results : The mean admission age of childhood-onset (very early onset) group and adolescent-onset (early onset) group were 12.69$({\pm}2.34)$ and 15.13$({\pm}1.04)$ years. The mean onset age of childhood-onset(very early onset) group and adolescent-onset (early onset) group were 10.79$({\pm}1.95)$ and 14.46$({\pm}0.82)$ years. The mean prodromal period of childhood-onset (very early onset) group and adolescent-onset (early onset) group were 15.94$({\pm}12.33)$ and 8.06$({\pm}6.10)$ month. The time to remission period of childhood-onset (very early onset) group and adolescent-onset (early onset) group were 50.58$({\pm}24.67)$ and 30.06$({\pm}18.04)$ days. Longer time to remission period in childhood-osnet (very early onset) group was associated with earlier age of onset. The mean of total IQ, performance IQ, verbal IQ were at an average level. Discussion : Childhood-onset (very early onset) group and adolescent-onset (early onset) group Schizophrenia had different clinical and psychological features including prodromal period, and IQ subtests.

• Journal of Venture Innovation
• /
• v.1 no.1
• /
• pp.1-14
• /
• 2018

The 4th Industrial Revolution is making a big change for our company like the tsunami. The CPS system, which is represented by the digital age, is based on the data accumulated in the physical domain and is making business that was not imagined in the past through digital technology. As a result, the business model of the 4th Industrial Revolution era is different from the previous one. In this study, we analyze the trends and the issues of business innovation theory research. Then, the business innovation model of the digital age was compared with the previous period. Based on this, we have searched for a business model suitable for the 4th Industrial Revolution era. The existing business models have many difficulties to explain the model of the digital era. Even though more empirical research should be supported, Michael Porter's diamond model is most suitable for four cases of business models by applying them. Type A sharing outcome with customer is a model that pay differently according to the basis of customer performance. Type B Value Chain Digitalization model provides products and services to customers with faster and lower cost by digitalizing products, services and SCM. Type C Digital Platform is the model that brings the biggest ripple effect. It is a model that can secure profitability by creating new market by creating the sharing economy based on digital platform. Finally, Type D Sharing Resources is a model for building a competitive advantage model by collaborating with partners in related industries. This is the most effective way to complement each other's core competencies and their core competencies. Even though numerous Unicorn companies have differentiated digital competitiveness with many digital technologies in their respective industries in the 4th Industrial Revolution era, there is a limit to the number of pieces to be listed. In future research, it is necessary to identify the business model of the digital age through more specific empirical analysis. In addition, since digital business models may be different in each industry, it is also necessary to conduct comparative analysis between industries

• Journal of Korea Game Society
• /
• v.4 no.4
• /
• pp.11-18
• /
• 2004

The on-line games in the past were played by only two persons exchanging data based on one-to-one connections, whereas recent ones (e.g. MMORPG: Massively Multi-player Online Role-playings Game) enable tens of thousands of people to be connected simultaneously. Specifically, Korea has established an excellent network infrastructure that can't be found anywhere in the world. Almost every household has a high-speed Internet access. What made this possible was, in part, high density of population that has accelerated the formation of good Internet infrastructure. However, this rapid increase in the use of on-line games may lead to surging traffics exceeding the limited Internet communication capacity so that the connection to the games is unstable or the server fails. expanding the servers though this measure is very costly could solve this problem. To deal with this problem, the present study proposes the load distribution technology that connects in the form of local clustering the game servers divided by their contents used in each on-line game reduces the loads of specific servers using the load balancer, and enhances performance of sewer for their efficient operation. In this paper, a cluster system is proposed where each Game server in the system has different contents service and loads are distributed efficiently using the game server resource information such as CPU utilization. Game sewers having different contents are mutually connected and managed with a network file system to maintain information consistency required to support resource information updates, deletions, and additions. Simulation studies show that our method performs better than other traditional methods. In terms of response time, our method shows shorter latency than RR (Round Robin) and LC (Least Connection) by about 12%, 10% respectively.

• Journal of Intelligence and Information Systems
• /
• v.20 no.4
• /
• pp.155-169
• /
• 2014

With the rapid advances in technologies, organizations are more likely to depend on information systems in their decision-making processes. Business Intelligence (BI) systems, in particular, have become a mainstay in dealing with complex problems in an organization, partly because a variety of advanced computational methods from statistics, machine learning, and artificial intelligence can be applied to solve business problems such as demand forecasting. In addition to the ability to analyze past and present trends, these predictive analytics capabilities provide huge value to an organization's ability to respond to change in markets, business risks, and customer trends. While the performance effects of BI system use in organization settings have been studied, it has been little discussed on the use of predictive analytics technologies embedded in BI systems for forecasting tasks. Thus, this study aims to find important factors that can help to take advantage of the benefits of advanced technologies of a BI system. More generally, a BI system can be viewed as an advisor, defined as the one that formulates judgments or recommends alternatives and communicates these to the person in the role of the judge, and the information generated by the BI system as advice that a decision maker (judge) can follow. Thus, we refer to the findings from the advice-giving and advice-taking literature, focusing on the role of explanations of the system in users' advice taking. It has been shown that advice discounting could occur when an advisor's reasoning or evidence justifying the advisor's decision is not available. However, the majority of current BI systems merely provide a number, which may influence decision makers in accepting the advice and inferring the quality of advice. We in this study explore the following key factors that can influence users' advice taking within the setting of a BI system: explanations on how the box-office grosses are predicted, types of advisor, i.e., system (data mining technique) or human-based business advice mechanisms such as prediction markets (aggregated human advice) and human advisors (individual human expert advice), users' evaluations of the provided advice, and individual differences in decision-makers. Each subject performs the following four tasks, by going through a series of display screens on the computer. First, given the information of the given movie such as director and genre, the subjects are asked to predict the opening weekend box office of the movie. Second, in light of the information generated by an advisor, the subjects are asked to adjust their original predictions, if they desire to do so. Third, they are asked to evaluate the value of the given information (e.g., perceived usefulness, trust, satisfaction). Lastly, a short survey is conducted to identify individual differences that may affect advice-taking. The results from the experiment show that subjects are more likely to follow system-generated advice than human advice when the advice is provided with an explanation. When the subjects as system users think the information provided by the system is useful, they are also more likely to take the advice. In addition, individual differences affect advice-taking. The subjects with more expertise on advisors or that tend to agree with others adjust their predictions, following the advice. On the other hand, the subjects with more knowledge on movies are less affected by the advice and their final decisions are close to their original predictions. The advances in predictive analytics of a BI system demonstrate a great potential to support increasingly complex business decisions. This study shows how the designs of a BI system can play a role in influencing users' acceptance of the system-generated advice, and the findings provide valuable insights on how to leverage the advanced predictive analytics of the BI system in an organization's forecasting practices.

• Strategy21
• /
• s.32
• /
• pp.123-159
• /
• 2013

The purpose of this research paper is to re-valuate the factors that affected the Royal Navy's rearmament and preparation for war by conducting analysis on the discussion held in the Britain on the strategic priorities and Navy's coping measures adopted during the interwar period. After the end of the WWI, each of the military arms of the Britain faced significant difficulty in securing budget and increasing their military power all throughout the interwar period, and the Navy was not an exception. The WWII that got started on September 1939 was the turning point in which this difficulty led to full-fledged crisis. Immensely many criticisms followed after the war and problems were identified when it comes to the Royal Navy's performance during the war. This type of effort to identify problem led to the attempt to analyze whether Royal Navy's preparation for war and rearmament policy during interwar period were adequate, and to identify the root causes of failure. Existing studies sought to find the root cause of failed rearmament from external factors such as the deterioration of the Britain itself or pressure from the Treasury Department to cut the budget for national defense, or sought to detect problems from the development of wrong strategies by the Navy. However, Royal Navy's failed preparation for the war during interwar period is not the result of one or two separate factors. Instead, it resulted due to the diverse factors and situations that the Britain was facing at the time, and due to intricate and complex interaction of these factors. Meanwhile, this research paper focused on the context characterized by 'strategic selection and setting up of priorities' among the various factors to conduct analysis on the Navy's rearmament by linking it with the discussion held at the time on setting up strategic priorities, and sought to demonstrate that the Navy Department's inadequate counter-measures developed during this process waned Royal Navy's position. After the end of WWI, each of the military arms continued to compete for the limited resources and budget all throughout the interwar period, and this type of competition amidst the situation in which the economic situation of Britain was still unstable, made prioritization when it comes to the allocation of resources and setting up of the priorities when it comes to the military power build-up, inevitable. Amidst this situation, the RAF was able to secure resources first and foremost, encouraged by the conviction of some politicians who were affected by the 'theory of aerial threat' and who believed that curtailing potential attack with the Air Force would be means to secure national security at comparatively lower cost. In response, Navy successfully defended the need for the existence of Navy despite the advancement of the aerial power, by emphasizing that the Britain's livelihood depends on trade and on the maintenance of maritime traffic. Despite this counter-measuring logic, however, Navy's role was still limited to the defense of overseas territory and to the fleet run-off instead of sea traffic route production when it comes to the specific power build-up plan, and did not understand the situation in which financial and economic factors gained greater importance when it comes to the setting up of strategic priorities. As a result, Navy's plan to build its powers was met with continual resistance of the Treasury Department, and lost the opportunity to re-gain the status of 'senior service' that it had enjoyed in the past during the competition for strategic prioritization. Given that the strategic and economic situation that Korea faces today is not very different from that of the Britain during the interwar period, our Navy too should leverage the lessons learned from the Royal Navy to make the effort to secure viable position when it comes to the setting of priorities in case of national defense strategy by presenting the basis on why maritime coping should be prioritized among the numerous other threats, and by developing the measures for securing the powers needed effectively amidst the limited resources.

• The KIPS Transactions:PartA
• /
• v.11A no.4
• /
• pp.243-250
• /
• 2004

Monitoring is used to see if a real-time system provides a service on time. Generally, monitoring for real-time focuses on investigating the current status of a real-time system. To support a stable performance of a real-time system, it should have not only a function to see the current status of real-time process but also a function to predict executions of real-time processes, however. The legacy prediction model has some limitation to apply it to a real-time monitoring. First, it performs a static prediction after a real-time process finished. Second, it needs a statistical pre-analysis before a prediction. Third, transition probability and data about clustering is not based on the current data. We propose the execution prediction model based on learning algorithm to solve these problems and apply it to real-time monitoring. This model gets rid of unnecessary pre-processing and supports a precise prediction based on current data. In addition, this supports multi-level prediction by a trend analysis of past execution data. Most of all, We designed the model to support dynamic prediction which is performed within a real-time process' execution. The results from some experiments show that the judgment accuracy is greater than 80% if the size of a training set is set to over 10, and, in the case of the multi-level prediction, that the prediction difference of the multi-level prediction is minimized if the number of execution is bigger than the size of a training set. The execution prediction model proposed in this model has some limitation that the model used the most simplest learning algorithm and that it didn't consider the multi-regional space model managing CPU, memory and I/O data. The execution prediction model based on a learning algorithm proposed in this paper is used in some areas related to real-time monitoring and control.