• Journal of Korea Technology Innovation Society
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• v.20 no.4
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• pp.1262-1287
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• 2017

Recently, forecasting for next-generation technologies have influenced the competitiveness of companies. However, in previous studies, only extract factors influencing the adoption of technology have been investigated. Also, there are few researches on the importance of each decision factors or the competition between technologies. In this research, Lotka-Volterra model is used to confirm the technological competition in the new technology choice timing when the competition is intensified due to the emergence of new technologies. For purpose of this study, estimate the LVC model based on the data of the past competition and then derived the factors affecting the technology of competition and substitution from the literature survey. After that, we confirmed the factor value between the past and the present technology competition. The difference between the factor values derived from the previous step is used to revise the model estimated from the past data base. At this stage, regression analysis is used to derive the importance of each factor and use it as the weight. Through the correction model, the competitiveness is identified through 1:1 comparison with competition candidate technology and existing dominant design technology. In this research, we quantitatively propose the possibility that a specific technology can become a dominant design in the next generation, based on the difference in factor values and importance. This results will help the company's R&D strategy and decision making.

• Journal of the Korean Chemical Society
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• v.66 no.1
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• pp.50-60
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• 2022

The purpose of this study is to explore the Nature of Argumentation in Science education (NAS). For this purpose, we collected previous studies conducted on the argumentation in science education, and then collected previous studies were analyzed to extract the overall characteristics of argumentation in science education. Based on the results, an expert review was conducted, then the nature of argumentation in science education was finally derived to a total of seven components: 'evidence based', 'linguistic interaction', 'context dependency', 'public decision-making', 'tentative agreement', 'methodological diversity', and 'enculturation of scientific culture'. Understanding the nature of argumentation in science education can promote the practice of argumentation in science learning. Therefore, further studies will be necessary to conduct research to expand and refine the nature of argumentation in science education in order to effectively practice it in science learning.

• Journal of the Korean Chemical Society
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• v.62 no.2
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• pp.137-147
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• 2018

The purpose of this study was to analysis total number of 123 SSI programs by SSI criteria. The criteria was consisted of subject, school level, starting point, scientific evidence, social content, use of scientific knowledge, level of conflict of interest, and evaluation and reflection. The results of the analysis are as follows. First, elementary school programs were the most and middle school programs were relatively few. Second, starting point was mainly in the actual situation, the fiction and nonfiction situation, and the situation including the controversy and conflict was less than 10%. Third, it was based on scientific evidence but mainly influenced by individual values and perceptions. Fourth, social contents were developed mainly in ethics/morality/value, political/social life/economy, environment contents. Fifth, the use of scientific knowledge mainly consisted of scientific decision making, scientific critical thinking, and information search. However, science inquiry, risk assessment, and cost effectiveness were less than 10%. Scientific inquiry is the essential factor of science education, and one of core competencies of national science curriculum. SSI program should be able to experience various kinds of conflicts, and to evaluate and reflect through reflection.

• Journal of Environmental Impact Assessment
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• v.31 no.1
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• pp.47-62
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• 2022

Wind power has been rapidly growing over last decade in the world as well as in South Korea as a feasible renewable energy source. Providing sustainable energy to all while securing environmental sustainability requires evidence based policy making and innovative solutions. Through analysis of 58 cases of South Korean Environmental Impact Assessment (EIA) Report, this paper seeks to identify answers to the following two questions. What are the key characteristics for inland windfarm? Is there a way of measuring environmental sustainability to compare each location to reduce negative environmental impact? Variables related to environmental sustainability of each windfarm case were collected from EIA report and the factor analysis of environmental variables was conducted to calculate the weight for each variable to build environmental sustainability index (ESI) to provide as evidence-based tools for decision making on the location of inland windfarm. 58 cases were categorized as three types 1) Mountain type 2) Ranch Type and 3) Coastal Type depending on their height and degree of naturalness. For analytical research, first, it was successfully calculated environmental sustainability of each windfarm case ranging from 1.04 (#33, Ranch type) to -1.44 (#55, Mountain type). Second, the analysis results showed that ranch type is most environmentally sustainable (Average ESI = 0.4551), followed by coastal type (Ave ESI = 0.3712) and lastly mountain type (Average ESI = -0.3457). These findings are consistent with the previous researches on inland windfarms and provides substantive policy implication on the renewable energy policies.

• Korean Journal of Culture and Social Issue
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• v.29 no.4
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• pp.537-567
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• 2023

This study compared the nature of disgust caused by the crime scene with that by the stereotype of the sexual-minority defendant, and compared the effect of each type of disgust on evidence evaluation and legal judgment. A total of 600 participants (300 men, average age of 44.40) were randomly assigned to sources of disgust (crime scene, sexual minorities defendant, control condition), the existence of additional evidence of innocence (o/x), and the existence of judicial directives (o/x). As a result of the study, disgust under the condition of a cruel crime scene with strong physical disgust was significantly higher than that of the sexual minority defendant, interpreted the evidence in a more guilty direction, and was more prone to_evaluate that the defendant was guilty. It is noteworthy that evidence evaluation was a significant moderating variable between disgust and probability of guilt under conditions where the source of disgust was a sexual minority, but not under control conditions and crime scene condition. It means that the effect of disgust on legal judgment may not be direct when the defendant is a sexual minority. In addition, the existence of the judicial instruction had a significant inverse effect on the sentence. And simple effect analysis found that presenting judicial instruction lowered probability of guilt only under the control condition. This makes it reasonable to infer that disgust derived from the characteristics of the crime scene and the defendant can be recognized as integral emotions that are difficult to correct with instructions. Finally, pity for the defendant was significantly higher under the conditions of sexual minority which shows that an emotional response of sympathy may occur in addition to disgust for sexual minorities. After examining the nature of disgust (physical & moral), legal judgment according to the source and degree of disgust was reviewed. In addition, the meaning of disgust and sympathy for the sexual minority defendant was discussed.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.161-169
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• 2014

Digital forensics is the process of proving criminal charges by collecting and analyzing digital evidence which is related to the crime in question. Most digital forensic research is focused on digital forensic techniques themselves or cyber crime. In this paper, we designed a digital forensics-criminal investigation linked model in order to effectively apply digital forensics to various types of criminal investigations. Digital forensic ontology was developed based on this model. For more effective application of digital forensics to criminal investigation we derived specific application fields. The ontology has legality rules and adequacy rules, so it can support investigative decision-making. The ontology can be developed into an intelligent criminal investigation system.

• Journal of Korea Technology Innovation Society
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• v.19 no.3
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• pp.511-544
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• 2016

Standards boost technological innovation by promoting information sharing, compatibility, stability and quality. Identifying groups of companies that particularly benefit from these functions of standards in their technological innovation and commercialization helps to customize planning and implementation of standards-related policies for demand groups. For this purpose, this study engages in profiling of SMEs whose R&D objective is to respond to standards as well as those who need to implement standards system for technological commercialization. Then it suggests a prediction model that can distinguish such companies from others. To this end, decision tree analysis is conducted for profiling of characteristics of subject SMEs through data mining. Subject SMEs include (1) those that engage in R&D to respond to standards (Group1) or (2) those in need of product standard or technological certification policies for commercialization purposes (Group 2). Then the study proposes a prediction model that can distinguish Groups 1 and 2 from others based on several variables by adopting discriminant analysis. The practicality of discriminant formula is statistically verified. The study suggests that Group 1 companies are distinguished in variables such as time spent on R&D planning, KoreanStandardIndustryClassification (KSIC) category, number of employees and novelty of technologies. Profiling result of Group 2 companies suggests that they are differentiated in variables such as KSIC category, major clients of the companies, time spent on R&D and ability to test and verify their technologies. The prediction model proposed herein is designed based on the outcomes of profiling and discriminant analysis. Its purpose is to serve in the planning or implementation processes of standards-related policies through providing objective information on companies in need of relevant support and thereby to enhance overall success rate of standards-related projects.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• Journal of The Korean Association For Science Education
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• v.35 no.3
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• pp.509-521
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• 2015

Argumentation is a social and collaborative dialogic process. A large number of researchers have focused on analyzing the structure of students' argumentation occurring in the scientific inquiry context, using the Toulmin's model of argument. Since SSI dialogic argumentation often presents distinctive features (e.g. interdisciplinary, controversial, value-laden, etc.), Toulmin's model would not fit into the context. Therefore, we attempted to develop an analytical framework for SSI dialogic argumentation by addressing the concepts of 'discourse clusters' and 'discourse schemes.' Discourse clusters indicated a series of utterances created for a similar dialogical purpose in the SSI contexts. Discourse schemes denoted meaningful discourse units that well represented the features of SSI reasoning. In this study, we presented six types of discourse clusters and 19 discourse schemes. We applied the framework to the data of students' group discourse on SSIs (e.g. euthanasia, nuclear energy, etc.) in order to verify its validity and applicability. The results indicate that the framework well explained the overall flow, dynamics, and features of students' discourse on SSI.

• Knowledge Management Research
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• v.22 no.4
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• pp.71-85
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• 2021

The purpose of this study is to find out which artificial intelligence methodology is most suitable for creating a foreign exchange rate prediction model using the indicators of bond market and interest rate market. KTBs and MSBs, which are representative products of the Korea bond market, are sold on a large scale when a risk aversion occurs, and in such cases, the USD/KRW exchange rate often rises. When USD liquidity problems occur in the onshore Korean market, the KRW Cross-Currency Swap price in the interest rate market falls, then it plays as a signal to buy USD/KRW in the foreign exchange market. Considering that the price and movement of products traded in the bond market and interest rate market directly or indirectly affect the foreign exchange market, it may be regarded that there is a close and complementary relationship among the three markets. There have been studies that reveal the relationship and correlation between the bond market, interest rate market, and foreign exchange market, but many exchange rate prediction studies in the past have mainly focused on studies based on macroeconomic indicators such as GDP, current account surplus/deficit, and inflation while active research to predict the exchange rate of the foreign exchange market using artificial intelligence based on the bond market and interest rate market indicators has not been conducted yet. This study uses the bond market and interest rate market indicator, runs artificial neural network suitable for nonlinear data analysis, logistic regression suitable for linear data analysis, and decision tree suitable for nonlinear & linear data analysis, and proves that the artificial neural network is the most suitable methodology for predicting the foreign exchange rates which are nonlinear and times series data. Beyond revealing the simple correlation between the bond market, interest rate market, and foreign exchange market, capturing the trading signals between the three markets to reveal the active correlation and prove the mutual organic movement is not only to provide foreign exchange market traders with a new trading model but also to be expected to contribute to increasing the efficiency and the knowledge management of the entire financial market.