• Journal of IKEEE
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• v.22 no.4
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• pp.970-977
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• 2018

The autonomous vehicle is based on an advanced driver assistance system (ADAS) consisting of a sensor that collects information about the surrounding environment and a control module that determines the measured data. As interest in autonomous navigation technology grows recently, an easy development framework for ADAS beginners and learners is needed. However, existing development and verification methods are based on high performance vehicle simulator, which has drawbacks such as complexity of verification method and high cost. Also, most of the schemes do not provide the sensing data required by the ADAS directly from the simulator, which limits verification reliability. In this paper, we present an interactive ADAS development and verification framework using a 3D vehicle simulator that overcomes the problems of existing methods. ADAS with image recognition based artificial intelligence was implemented as a virtual sensor in a 3D car simulator, and autonomous driving verification was performed in real scenarios.

• The Journal of Bigdata
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• v.8 no.2
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• pp.83-95
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• 2023

Big data analytics and AI play a critical role in the development of smart plants. This study presents a big data platform for plant data and an 'AutoML platform' for AI-based plant O&M(Operation and Maintenance). The big data platform collects, processes and stores large volumes of data generated in plants using Hadoop, Spark, and Kafka. The AutoML platform is a machine learning automation system aimed at constructing predictive models for equipment prognostics and process optimization in plants. The developed platforms configures a data pipeline considering compatibility with existing plant OISs(Operation Information Systems) and employs a web-based GUI to enhance both accessibility and convenience for users. Also, it has functions to load user-customizable modules into data processing and learning algorithms, which increases process flexibility. This paper demonstrates the operation of the platforms for a specific process of an oil company in Korea and presents an example of an effective data utilization platform for smart plants.

• Journal of dental hygiene science
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• v.18 no.2
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• pp.113-123
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• 2018

The purpose of this study to review clinicians and educators on required communication education factors of dental hygienists using qualitative research by focus group interview. The participants were dentists, dental hygienists and professors. A questionnaire was developed on communication education to collect data. The collated data derived concepts related to communication education. After transferring the data, were analyzed by open coding and axial coding using computer-aided qualitative data analysis software. Focus group emphasized that higher education on communication should be preceded before they are put into the clinical field. However, the dental hygienist emphasized experiential education in the clinical field, the professor emphasized additional education for continuity of communication education even after graduation. Besides, focus group emphasized role play, and the professor required that the standardization of the dental communication training courses objectives and role play modules and the education environment infrastructure should be established to implement communication education efficiently. The categories of communication education stated in the focus group were time and method for the dental communication training courses, dental communication training courses standardization and educational environment, of evaluation of communication competency, of perception of the dental communication training courses. This study identified the communication education development to conform with the needs of the clinical field strengthen and cultivate communication competency dental hygienists based on factors of communication education emphasized in focus groups.

• Journal of vocational education research
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• v.35 no.4
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• pp.131-153
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• 2016

The purpose of this study was to derive evaluator's role and capability for institution accreditation evaluation of NCS-based vocational competency development training. This study attempted to explore in various ways evaluator's minute roles using Delphi method, and to derive knowledge, skill, attitude and integrity needed to verify the validity. To the end, this study conducted the Delphi research for over three rounds by selecting education training professionals and review evaluation professions as professional panels. From the results, roles of evaluators were defined as the total eight items including operator, moderator-mediator, cooperator, analyzer, verifier, institution evaluator, institution consultant, and learner, and the derived capabilities with respect to each role were 25 items in total. The area of knowledge included four items of capabilities such as HRD knowledge, NCS knowledge, knowledge of vocational competency development training, and knowledge of training institution accreditation evaluation, and the area of skill comprised fourteen items of capabilities such as conflict management ability, interpersonal relation ability, word processing ability, problem-solving ability, analysis ability, pre-preparation ability, time management ability, decision making ability, information comprehension and utilization ability, comprehensive thinking ability, understanding ability of vocational competency development training institutions, communication ability, feedback ability, and core understanding ability. The area of attitude was summarized with the seven items in total including subjectivity and fairness, service mind, sense of calling, ethics, self-development, responsibility, and teamwork. The knowledge, skill and attitude derived from the results of this study may be utilized to design and provide education programs conducive to qualitative and systematic accreditation and assessment to evaluators equipped with essential prerequisites. It is finally expected that this study will be helpful for designing module education programs by ability and for managing evaluator's quality in order to perform pre-service education and in-service education according to evaluator's experience and role.

• Journal of The Korean Association For Science Education
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• v.27 no.9
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• pp.854-869
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• 2007

In this study, we investigated secondary science teachers' internalization of constructivistic science teaching who participated in a collaborative program between teachers and researchers designed by researchers according to constructivist views. The program consisted of lecture, workshop, and small group activities. New trends in science education and framework for science teaching were introduced during lectures, and understanding about the framework were deepened by analyzing school science classes recorded during workshops. In small group activities, participating teachers and researchers cooperated to design science lesson plans using science teaching frameworks. Five secondary science teachers participated in collaborative workshops. Collaborative programs were video-taped. Semi-structured interviews were conducted before and after workshops. All data recorded were transcribed and analyzed. In the process of internalization, participating teachers attended on different parts. Various and discernable factors such as there own background, beliefs, values, and school context produced tensions with or facilitated internalization of constructivistic science teaching. Teaching experiences and student understanding affected teachers' lesson planning activities. Teachers also showed different understandings on inquiry, application, and model from the framework, and they interpret those concepts in the framework based on their prior understanding. They perceived that too much content should be dealt within relatively limited time. Therefore, they tended to separate science class into two parts when developing science lessons: explaining science content by lecture and science laboratory as a constructivistic activity. The results of the study provide meaningful implications to the constructivist teacher education and professional development.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.4
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• pp.117-129
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• 2017

The Purpose of this Study is to Identify the Learning Modules of the National Competency Standard (NCS) Related to the Information Systems Development Curriculum of 4-year Colleges, to Explore the Possibility of Applying NCS to the Curriculum, and to Examine Implications. The Importance of 26 Competency Units in the Applied SW Engineering of NCS was Compared by Area Experts Composed of 14 Information Systems Developers and Professors of 7 Universities who Teach Software Engineering Subjects Through the Hierarchical Analysis Process (AHP). The Results of the Study as Follows. First, the Relative Importance of the Competency Elements was in order of 'Confirming the Requirements', 'Checking the UI Requirements', 'Designing the Physical Data Repository', 'Testing Developer Integration', and 'Fixing Developer Defects'. Second, while the Entry and Intermediate Level Developer Group Determined that the Competency Elements Related to the IS Development Implementation Stage is more Important, the Advanced IS Developer Groups Consider the Competency Elements Related to the Theory-oriented Design Stage as Important Competency. In Addition, the Group of College Professors was Found to Choose all the Practical and Theoretical Competency Elements that Two Developers Groups Considered Important. Implications of the Study Suggested that the Job Performance Competence and Quality of Education Related to IS Development can be Enhanced.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.199-206
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• 2023

The current software becomes the huge size of source codes. Therefore it is increasing the importance and necessity of static analysis for high-quality product. With static analysis of the code, it needs to identify the defect and complexity of the code. Through visualizing these problems, we make it guild for developers and stakeholders to understand these problems in the source codes. Our previous visualization research focused only on the process of storing information of the results of static analysis into the Database tables, querying the calculations for quality indicators (CK Metrics, Coupling, Number of function calls, Bad-smell), and then finally visualizing the extracted information. This approach has some limitations in that it takes a lot of time and space to analyze a code using information extracted from it through static analysis. That is since the tables are not normalized, it may occur to spend space and time when the tables(classes, functions, attributes, Etc.) are joined to extract information inside the code. To solve these problems, we propose a regularized design of the database tables, an extraction mechanism for quality metric indicators inside the code, and then a visualization with the extracted quality indicators on the code. Through this mechanism, we expect that the code visualization process will be optimized and that developers will be able to guide the modules that need refactoring. In the future, we will conduct learning of some parts of this process.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.967-971
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• 2023

Recently, a variety of AI-based platform services are available, and one of them is ChatGPT that processes a large quantity of data in the natural language and generates an answer after self-learning. ChatGPT can perform various tasks including software programming in the IT sector. Particularly, it may help generate a simple program and correct errors using C Language, which is a major programming language. Accordingly, it is expected that ChatGPT is capable of effectively using Verilog HDL, which is a hardware language created in C Language. Verilog HDL synthesis, however, is to generate imperative sentences in a logical circuit form and thus it needs to be verified whether the products are executed properly. In this paper, we aim to select small-scale logical circuits for ease of experimentation and to verify the results of circuits generated by ChatGPT and human-designed circuits. As to experimental environments, Xilinx ISE 14.7 was used for module modeling, and the xc3s1000 FPGA chip was used for module embodiment. Comparative analysis was performed on the use area and processing time of FPGA to compare the performance of ChatGPT products and Verilog HDL products.

• Journal of The Korean Association For Science Education
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• v.38 no.2
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• pp.219-234
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• 2018

In this study, we explored the possibility of automating the process of analyzing elements of scientific argument in the context of a Korean classroom. To gather training data, we collected 990 sentences from science education journals that illustrate the results of coding elements of argumentation according to Toulmin's argumentation structure framework. We extracted 483 sentences as a test data set from the transcription of students' discourse in scientific argumentation activities. The words and morphemes of each argument were analyzed using the Python 'KoNLPy' package and the 'Kkma' module for Korean Natural Language Processing. After constructing the 'argument-morpheme:class' matrix for 1,473 sentences, five machine learning techniques were applied to generate predictive models relating each sentences to the element of argument with which it corresponded. The accuracy of the predictive models was investigated by comparing them with the results of pre-coding by researchers and confirming the degree of agreement. The predictive model generated by the k-nearest neighbor algorithm (KNN) demonstrated the highest degree of agreement [54.04% (${\kappa}=0.22$)] when machine learning was performed with the consideration of morpheme of each sentence. The predictive model generated by the KNN exhibited higher agreement [55.07% (${\kappa}=0.24$)] when the coding results of the previous sentence were added to the prediction process. In addition, the results indicated importance of considering context of discourse by reflecting the codes of previous sentences to the analysis. The results have significance in that, it showed the possibility of automating the analysis of students' argumentation activities in Korean language by applying machine learning.