• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.261-274
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• 2019

This paper aims to develop an appropriate methodology for establishing an investment strategy for 'demonstration of artificial rainfall technology using UAV' and that include establishment of a technology classification, set of indicators for technology evaluation, suggestion of final key technology as a whole study area. It is designed to complement the latest research trend analysis results and expert committee opinions using quantitative analysis. The key indicators for technology evaluation consisted of three major items (activity, technology, marketability) and 10 detailed indicators. The AHP questionnaire was conducted to analyze the importance of indicators. As a result, it was analyzed that the attribute of the technology itself is most important, and the order of closeness to the implementation of the core function (centrality), feasibility (feasibility). Among the 16 technology groups, top investment priority groups were analyzed as ground seeding, artificial rainfall verification, spreading and diffusion of seeding material, artificial rainfall numerical modeling, and UAV sensor technology.

• Journal of The Korean Association For Science Education
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• v.28 no.6
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• pp.565-578
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• 2008

The purpose of this study is to search for the factors that influence students' understanding of the nature of science through the experience of the cognitive processes of authentic open inquiries. The freshmen of a science high school practiced authentic open inquiries reflecting epistemological characteristics of authentic science. The case study was conducted with four focus students who were successful or unsuccessful at learning the nature of science during the authentic open inquiry activity. Questions that the focus students asked during the inquiries as well as students' answers to pre- and post-VNOS (C type) were analysed, and then elaborated in the semi-structured interview. The findings suggest that open inquiry activities provide the inquiry contexts that help science high school students to understand the nature of science, and that the characteristics of students' cognition influence the understanding of the nature of science. For instance, designing experiments with their own research questions had an influence on the students' understanding about the scientific methods and the diversity of research types, and drawing conclusions from their own data made students experience scientific reasoning. In addition, the experience of collecting anomalous data helped students to understand the role of inferences in generating scientific knowledge and the creative nature of scientific knowledge. In this inquiry context, the reflective thinking that came from proactive discussion among students, made students think about the validity of the designing experiments and interpreting data, and helped them to understand the uncertain nature of reasoning and the diverse nature of scientific methods. Moreover, divergent thinking linked to analogical thinking helped students to understand the creative nature of science.

• Smart Media Journal
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• v.11 no.11
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• pp.49-62
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• 2022

Recently, with the development of Smart City Solutions such as Big data, AI, IoT, Autonomous driving, and Digital twins around the world, the proliferation of various smart devices and social media, and the record of the deeds that people have left everywhere, the construction of Smart Cities using the "Big Data" environment in which so much information and data is produced that it is impossible to gauge the scale is actively underway. The Purpose of this study is to construct an objective and systematic analysis Model based on Big Data to improve the transportation convenience of citizens and formulate efficient policies in Urban Information and Public Transportation accessibility in sustainable Smart Cities following the 4th Industrial Revolution. It is also to derive the methodology of developing a Big Data-Based public transport accessibility and policy management Platform using a sustainable Urban Public DB and a Private DB. To this end, Detailed Living Areas made a division and the accessibility of basic living amenities of Gwangju Metropolitan City, and the Public Transportation system based on Big Data were analyzed. As a result, it was Proposed to construct a Big Data-based Urban Information and Public Transportation accessibility Platform, such as 1) Using Big Data for public transportation network evaluation, 2) Supporting Transportation means/service decision-making based on Big Data, 3) Providing urban traffic network monitoring services, and 4) Analyzing parking demand sources and providing improvement measures.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.46-51
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• 2015

The explosive growth of data and the rapidly changing technical social evolution new analysis paradigm for predicting and reacting the future the past and present ig data. Prescriptive analysis has a fundamental difference because can support specific behaviors and results according to user's goals with defin researchers establish judgments and activities achiev the goals. However research methods not widely implemented and even the terminology, Prescriptive analysis, is still unfamiliar. This paper thus propose an infrastructure in the prescriptive analysis field with key considerations for enhancing capability of researchers through a case study based on InSciTe Advisory developed with scientific big data. InSciTe Advisory system s developed in 2013, and offers a prescriptive analytics report which contains various As-Is analysis results and To-Be analysis results 5W1H methodology. InSciTe Advisory therefore shows possibility strategy aims to reach a target role model group. Through the availability and reliability of the measurement model the evaluation results obtained relative advantage of 118.8% compared to Elsevier SciVal.

• Journal of Korea Technology Innovation Society
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• v.11 no.1
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• pp.91-117
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• 2008

The relationships between science, technology, and industry are very complicated and vary according to time. Thus, it would be almost impossible to combine the three categories in a single model. However, the linking of science, technology, and industry, which are divided according to their respective classification standards, is a starting point from which to understand how science and technology, technology and industry, and further science, technology, and industry are related to each other. Studies have been carried out to analyze the relationship between science and technology and between technology and industry, whereas no study has been undertaken to get an overall view of science, technology, and industry. Since an appropriate methodology or an analytical model has not been suggested, this paper proposes a model for generally analyzing science, technology, and industry. More specifically, this paper examines the methodology for linking science, technology, and industry. This paper uses citation analysis to analyze knowledge flow such as absorption and utilization of given knowledge, looks at the provision of knowledge to create new knowledge, and examines the use of network analysis to analyze the complicated phenomenon of knowledge flow. This paper proposes an empirical study of trend analysis of technological innovation by looking into a linkage structure of knowledge flow among science, technology, and industry based on the classification linkage and analysis methodology using scientific paper and patents.

• KIISE Transactions on Computing Practices
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• v.24 no.3
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• pp.151-156
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• 2018

In this paper, a method to classify objects in outdoor CCTV images using Convolutional Neural Network(CNN) and background subtraction is proposed. Object candidates are extracted using background subtraction and they are classified with CNN to detect objects in the image. At the end, computation complexity is highly reduced in comparison to other object detection algorithms. A database is constructed by filming alleys and playgrounds, places where crime occurs mainly. In experiments, different image sizes and experimental settings are tested to construct a best classifier detecting person. And the final classification accuracy became 80% for same camera data and 67.5% for a different camera.

• Journal of The Korean Association For Science Education
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• v.40 no.6
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• pp.657-670
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• 2020

The knowledge-information-processing competency is the most essential competency in a knowledge-information-based society and is the most fundamental competency in the new problem-solving ability. Data-driven science inquiry, which emphasizes how to find and solve problems using vast amounts of data and information, is a way to cultivate the problem-solving ability in a knowledge-information-based society. Therefore, this study aims to develop a teaching-learning model and strategy for data-driven science inquiry and to verify the validity of the model in terms of knowledge information processing competency. This study is developmental research. Based on literature, the initial model and strategy were developed, and the final model and teaching strategy were completed by securing external validity through on-site application and internal validity through expert advice. The development principle of the inquiry model is the literature study on science inquiry, data science, and a statistical problem-solving model based on resource-based learning theory, which is known to be effective for the knowledge-information-processing competency and critical thinking. This model is titled "Exploratory Scientific Data Analysis" The model consisted of selecting tools, collecting and analyzing data, finding problems and exploring problems. The teaching strategy is composed of seven principles necessary for each stage of the model, and is divided into instructional strategies and guidelines for environment composition. The development of the ESDA inquiry model and teaching strategy is not easy to generalize to the whole school level because the sample was not large, and research was qualitative. While this study has a limitation that a quantitative study over large number of students could not be carried out, it has significance that practical model and strategy was developed by approaching the knowledge-information-processing competency with respect of science inquiry.

• Journal of KIISE
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• v.42 no.11
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• pp.1459-1466
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• 2015

Lockscreen is one of the most frequently encountered interfaces by smartphone users. Although users perform unlocking actions every day, there are no benefits in using lockscreens apart from security and authentication purposes. In this paper, we replace the traditional lockscreen with an application that analyzes facial expressions in order to collect facial expression data and provide real-time feedback to users. To evaluate this concept, we have implemented Quantified Lockscreen application, supporting the following contributions of this paper: 1) an unobtrusive interface for collecting facial expression data and evaluating emotional patterns, 2) an improvement in accuracy of facial expression detection through a personalized machine learning process, and 3) an enhancement of the validity of emotion data through bidirectional, multi-channel and multi-input methodology.

• The Journal of Bigdata
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• v.5 no.2
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• pp.53-67
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• 2020

This study employees a supervised learning prediction model to detect nonconformity in advance of processed food manufacturing and processing businesses. The study was conducted according to the standard procedure of machine learning, such as definition of objective function, data preprocessing and feature engineering and model selection and evaluation. The dependent variable was set as the number of supervised inspection detections over the past five years from 2014 to 2018, and the objective function was to maximize the probability of detecting the nonconforming companies. The data was preprocessed by reflecting not only basic attributes such as revenues, operating duration, number of employees, but also the inspections track records and extraneous climate data. After applying the feature variable extraction method, the machine learning algorithm was applied to the data by deriving the company's risk, item risk, environmental risk, and past violation history as feature variables that affect the determination of nonconformity. The f1-score of the decision tree, one of ensemble models, was much higher than those of other models. Based on the results of this study, it is expected that the official food control for food safety management will be enhanced and geared into the data-evidence based management as well as scientific administrative system.

• Journal of the Korea Convergence Society
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• v.6 no.1
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• pp.77-83
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• 2015

Though the SNA (social network analysis ; SNA) has been used for various fields, esp. social science field, ig. politics, journalism, and science of public administration as well as natural science field, there are few studies about the introduction of analysis tools. In order to perform the SNA, collecting data which are fit for the purpose, statistical values deduction and visualized results made by analysis tool are necessary, but the studies, which explain them systematically, are not sufficient yet. So, in this study, we are intended to introduce the analytic process, from the data input to the interpretation, with proven data. using the R program, which is free, in order to help researchers who have any plan to study using the SNA. The proven data in this study are quoted ones in the domestic scientific journals of food, which are those supplied citation index DB of Korean scientific journals. As a study methodology, the SNA is a new paradigm to substitute existing research methods as well as a complement of statistical analysis. Therefore, this study would contribute to vitalization of the SNA.