• The KIPS Transactions:PartC
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• v.17C no.3
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• pp.271-280
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• 2010

For the next generation mobile communication system, diverse wireless network techniques such as beyond 3G LTE, WiMAX/WiBro, and next generation WLAN etc. are proceeding to the form integrated into the All-IP core network. According to this development, Beyond 3G integrated into heterogeneous wireless access technologies must support the vertical handover and network to be used of several radio networks. However, unified management of each network is demanded since it is individually serviced. Therefore, in order to solve this problem this study is introducing the theory of Common Radio Resource Management (CRRM) based on Generic Link Layer (GLL). This study designs the structure and functions to support the vertical handover and propose the vertical handover algorithm of which policy-based and MCDM are composed between LTE and WLAN systems using GLL. Finally, simulation results are presented to show the improved performance over the data throughput, handover success rate, the system service cost and handover attempt number.

• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.35-48
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• 2010

For the next generation mobile communication system, diverse wireless network techniques such as beyond 3G LTE, WiMAX/WiBro, and next generation WLAN etc. are proceeding to the form integrated into the All-IP core network. According to this development, Beyond 3G integrated into heterogeneous wireless access technologies must support the vertical handover and network to be used of several radio networks. However, unified management of each network is demanded since it is individually serviced. Therefore, in order to solve this problem this study is introducing the theory of Common Radio Resource Management (CRRM) based on Generic Link Layer (GLL). This study designs the structure and functions to support the vertical handover and propose the vertical handover algorithm of which policy-based and MCDM are composed between LTE and WLAN systems using GLL and CRRM. Finally, simulation results are presented to show the improved performance over the data throughput, handover success rate and the system service cost.

• Korean Journal of Comparative Education
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• v.28 no.1
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• pp.135-151
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• 2018

This study was conducted to help establish the status of comparative education as an academic discipline by investigating its relationship with educational evaluation. Comparative education as a subfield of education covers other areas of study in education, while educational evaluation is a study of methodology. First, international comparative study was investigated, and recent methodologies in educational evaluation were introduced. International comparative study started in 1960's, and is being expanded. The participating countries hope for better education by comparing their educational curricula and practices with others. For international comparative studies, a differential item functioning analysis as a multigroup analysis can provide information on what sociocultural factors other than the construct are affecting the measurement results. The study dataset has a hierarchical structure so that multilevel item response theory is suitable to obtain multidimensional national profiles. Although there have been methodological advances in educational evaluation, the methods are not available in comparative education. In order to reduce the gap, scholars in educational evaluation should try to make the methods easily available, while those in comparative education should try to use the exact and precise methods in their studies.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.129-149
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• 2021

Recently, interest in social robots that can socially interact with humans is increasing. Thanks to the development of ICT technology, social robots have become easier to provide personalized services and emotional connection to individuals, and the role of social robots is drawing attention as a means to solve modern social problems and the resulting decline in the quality of individual lives. Along with the interest in social robots, the spread of social robots is also increasing significantly. Many companies are introducing robot products to the market to target various target markets, but so far there is no clear trend leading the market. Accordingly, there are more and more attempts to differentiate robots through the design of social robots. In particular, anthropomorphism has been studied importantly in social robot design, and many approaches have been attempted to anthropomorphize social robots to produce positive effects. However, there is a lack of research that systematically describes the mechanism by which anthropomorphism for social robots is formed. Most of the existing studies have focused on verifying the positive effects of the anthropomorphism of social robots on consumers. In addition, the formation of anthropomorphism of social robots may vary depending on the individual's motivation or temperament, but there are not many studies examining this. A vague understanding of anthropomorphism makes it difficult to derive design optimal points for shaping the anthropomorphism of social robots. The purpose of this study is to verify the mechanism by which the anthropomorphism of social robots is formed. This study confirmed the effect of the human-likeness of social robots(Within-subjects) and the construal level of consumers(Between-subjects) on the formation of anthropomorphism through an experimental study of 3×2 mixed design. Research hypotheses on the mechanism by which anthropomorphism is formed were presented, and the hypotheses were verified by analyzing data from a sample of 206 people. The first hypothesis in this study is that the higher the human-likeness of the robot, the higher the level of anthropomorphism for the robot. Hypothesis 1 was supported by a one-way repeated measures ANOVA and a post hoc test. The second hypothesis in this study is that depending on the construal level of consumers, the effect of human-likeness on the level of anthropomorphism will be different. First, this study predicts that the difference in the level of anthropomorphism as human-likeness increases will be greater under high construal condition than under low construal condition.Second, If the robot has no human-likeness, there will be no difference in the level of anthropomorphism according to the construal level. Thirdly,If the robot has low human-likeness, the low construal level condition will make the robot more anthropomorphic than the high construal level condition. Finally, If the robot has high human-likeness, the high construal levelcondition will make the robot more anthropomorphic than the low construal level condition. We performed two-way repeated measures ANOVA to test these hypotheses, and confirmed that the interaction effect of human-likeness and construal level was significant. Further analysis to specifically confirm interaction effect has also provided results in support of our hypotheses. The analysis shows that the human-likeness of the robot increases the level of anthropomorphism of social robots, and the effect of human-likeness on anthropomorphism varies depending on the construal level of consumers. This study has implications in that it explains the mechanism by which anthropomorphism is formed by considering the human-likeness, which is the design attribute of social robots, and the construal level of consumers, which is the way of thinking of individuals. We expect to use the findings of this study as the basis for design optimization for the formation of anthropomorphism in social robots.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Archives of design research
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• v.18 no.4 s.62
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• pp.53-64
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• 2005

The emergence of new communication media, digital, is something of a wonder, as well as a cultural tension. The industrial technologies that dramatically expand human abilities are being developed much faster than the speed of adaptation by humans. Without an exception, it creates new contents and form of the culture by shaking the very foundation of the notion about human beings. Korean broadcasting environment has stepped into the era of multi-media, multi-channel as the digital technology separated the media into network, cable, satellite and internet. In this digital culture, broadcasting, as a medium of information delivering and communication, has bigger influence than ever. Such changes in broadcasting environment turned the TV viewers into new consumers who participate and play the main role in active communication by choosing and using the media. This study is trying to systemize the question about the core identity of broadcasting through brand as the consumers stand in the center of broadcasting with the power to select channel. The story schema theory can be applied as a cognitive psychological tool to approach the active consumers in order to explain the cognitive processes that are related to information processing. It is a design with stories, which comes up as a case of a brand's story telling. The range of this study covers the current issue and educational programs in network TV during the period of May and August of year 2005. The cases of Korean and foreign programs were compared by the station each program is broadcasted. This study concludes that it is important to take the channel identity into the consideration in the brand strategy of each program. Especially, the leading programs of a station must not be treated as a separate program that has nothing to do with the station's identity. They must be treated to include the contents and form that builds the identity of the channel. Also, this study reconfirmed that building a brand of the anchor person can play as an important factor in the identity of the program's brand.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.237-258
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• 2022

As the global market environment changes, the domestic ICT industry has a growing influence on the world economy. This industry is regarded as an important driving force in the national economy from a technological and social point of view. In particular, small and medium-sized enterprises (SMEs) in the ICT industry are regarded as essential actors of domestic economic development in terms of company diversity, technology development and job creation. However, since it is small compared to large-sized enterprises, it is difficult for SMEs to survive with a differentiated strategy in an incomplete and rapidly changing environment. Therefore, SMEs must make a lot of efforts to improve their own capabilities, and the government needs to provide the desirable help suitable for corporate internal resources so that they can continue to be competitive. This study classifies the types of ICT SMEs participating in government support programs, and analyzes the relationship between resources and performance creation of each type. The data from the "ICT Small and Medium Enterprises Survey" conducted annually by the Ministry of Science and ICT was used. In the first stage, ICT SMEs were clustered based on common factors according to their experiences with government support programs. Three clusters were meaningfully classified, and each cluster was named "active participation type," "initial support type," and "soloist type." As a second step, this study compared the characteristics of each cluster through profiling analysis for each cluster. The third step carried out in this study was to find out the mechanism of R&D performance creation for each cluster through regression analysis. Different factors affected performance creation for each cluster, and the magnitude of the influence was also different. Specifically, for "active participation type", "current manpower", "technology competitiveness", and "R&D investment in the previous year" were found to be important factors in creating R&D performance. "Initial support type" was identified as "whether or not a dedicated R&D organization exists", "R&D investment amount in the previous year", "Ratio of sales to large companies", and "Ratio of vendors supplied to large companies" contributed to the performance. Lastly, in the case of "soloist type", "current workforce" and "future recruitment plan", "technological competitiveness", "R&D investment", "large company sales ratio", and "overseas sales ratio" showed a significant relationship with the performance. This study has practical implications of showing what strategy should be established when supporting SMEs in the future according to the government's participation in the startup program and providing a guide on what kind of support should be provided.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• 한국공간정보시스템학회:학술대회논문집
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• 2000.06a
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• pp.189-201
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• 2000

접근성이란 특정 목적을 가진 통행자가 한 지점에서 다른 지점으로 얼마나 쉽게 갈 수 있는가 를 나타내는 척도를 말한다. 따라서 접근성은 주로 공간적인 거리나 시간이 가장 중요한 요인으로 작용하며 최근에는 공간적 거리보다는 접근시간이 더 중요한 접근성 척도로 사용되고 있다. 본 연구는 GIS공간분석기법과 접근성이론을 접목하여 도시시설의 적정후보지 평가방법을 정립한 후 현재 검토되고 있는 수도권 도심공항터미널 입지 후보지 중 어떤 후보지가 이용들의 총통행시간을 최소화하는 후보 지인가에 대해 평가해보았다. 평가 결과 다음과 같은 사실을 밝힐 수 있었다. 첫째, 수도권 전체로 볼 때 평균접근시간이 가장 양호한 후보지는 쌍문터미널(현 간이 도심공항터미널)이고, 2위는 현 삼성동 도심공항터미널과 강남고속터미널, 3위는 동서울터미널과 남서울터미널 순으로 나타났다. 둘째, 수도권전체 이용자들의 총통행시간을 최소로 하는 도심공항터미널 후보지는 접근성이 가장 양호한 쌍문동터미널이 아니라 현 서울역이 1위이고, 2위는 강남고속 터미널, 3위는 용산 역으로 나타났다. 셋째, 터미널 후보지간의 가중평균접근 시간은 도심인 서울시내에 입지한 후보지와 수도권 외곽에 입지 한 후보지간에 큰 차이가 없으나 총 이용자 접근시간은 서울시에 입지한 후보지보다 외곽의 후보지가 훨씬 높게 나타나 뚜렷한 차별성을 보이고 있다. 넷째, 최적후보지 1,2,3순위 모두 서울도심과 강남도심에 입지한 지역들로 나타나 교통의 접근성 보다는 아직 인구밀집도가 주요 도시시설의 입지결정에 주요한 결정요인 되는 것으로 나타났다.해석 시스템을 구축할 예정이다. 추후에는 하수도 관망해석 컴포넌트와 하수도 업무 컴포넌트와의 통합부분에 대한 연구가 진행되어야 할 것이다.7.0로 하고 표준(標準) EDTA 용액(溶液)을 소량(少量)넣고 8N-KOH로 pH $12{\sim}13$으로 하고 N-N 희석분말(稀釋粉末)을 지시약(指示藥) 으로써 표준(標準) EDTA 용액(溶液)으로 적정(滴定)하여 Ca 치(値)를 얻었다. Ca와 Mg의 합계결정치(合計決定値)와 Ca 적정치(滴定値) 차(差)로 Mg 치(値)를 얻었다. 음(陰) ion 구분(區分)으로부터 상법(常法)에 의하여 $MgNH_4PO_4$의 침전(沈澱)을 만들어서 HCl에 녹키고 일정량(一定量)의 표준(標準) EDTA 용액(溶液)을 넣어 pH 7.0로 한다음 완충액(緩衝液)으로 pH 10으로 하고 BT 지시약(指示藥)을 써서 표준(標準) Mg $SO_4$용액(溶液)으로 적정(滴定)하여 P 치(値)를 얻었다. 본법(本法)으로 Na-phytate를 분석(分析)한 결과(結果) Na-phytate의 분자식(分子式)을 $C_6H_6O_{24}P_6Mg_4CaNa_2{\cdot}5H_2O$라고 하였을 때의 이론치(理論値)에 비(比)하여 P가 98.9% Cark 97.1%, Mg가 99.1%이고 통계처리(統計處理)한 결과분석치(結果分析値)와 이론치(理論値)는 잘 일치(一致)된다. 그러나 종래법(從來法)에 의(依)한 분석치(分析値)는 이론치(理論値)에 비(比)하여 P가 92.40%, Cark 86.80

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.1
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• pp.17-28
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• 2019

In the financial investment management strategy, the distributed investment selecting and combining various financial assets is called portfolio management theory. In recent years, the blockchain based financial assets, such as cryptocurrencies, have been traded on several well-known exchanges, and an efficient portfolio management approach is required in order for investors to steadily raise their return on investment in cryptocurrencies. On the other hand, deep learning has shown remarkable results in various fields, and research on application of deep reinforcement learning algorithm to portfolio management has begun. In this paper, we propose an efficient financial portfolio investment management method based on Asynchronous Advantage Actor-Critic (A3C), which is a representative asynchronous reinforcement learning algorithm. In addition, since the conventional cross-entropy function can not be applied to portfolio management, we propose a proper method where the existing cross-entropy is modified to fit the portfolio investment method. Finally, we compare the proposed A3C model with the existing reinforcement learning based cryptography portfolio investment algorithm, and prove that the performance of the proposed A3C model is better than the existing one.