• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.141-157
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• 2002

extensible Markup Language (XML) is a new markup language for data exchange on the Internet. In this paper, we propose a language extensible Rule Markup Language (XRML) which is an extension of XML. The implicit rules embedded in the Web pages should be identifiable, interchangeable with structured rule format, and finally accessible by various applications. It is possible to realize by using XRML. In this light, Web based Knowledge Management Systems (KMS) can be integrated with rule-based expert systems. To meet this end, we propose the six design criteria: Expressional Completeness, Relevance Linkability, Polymorphous Consistency, Applicative Universality, Knowledge Integrability and Interoperability. Furthermore, we propose three components such as RIML (Rule Identification Markup Language), RSML (Rule Structure Markup Language) and RTML (Rule Triggering Markup Language), and the Document Type Definition DTD). We have designed the XRML version 0.5 as illustrated above, and developed its prototype named Form/XRML which is an automated form processing for disbursement of the research fund in the Korea Advanced Institute of Science and Technology (KAISI). Since XRML allows both human and software agent to use the rules, there is huge application potential. We expect that XRML can contribute to the progress of Semantic Web platforms making knowledge management and e-commerce more intelligent. Since there are many emerging research groups and vendors who investigate this issue, it will not take long to see XRML commercial products. Matured XRML applications may change the way of designing information and knowledge systems in the near future.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.639-653
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• 2019

The purpose of this study is to design an instructional framework and cognitive learning environment for AI education based on computational thinking in order to ground the theoretical rationale for AI education. Based on the literature review, the learning model is proposed to select the algorithms and problem-solving models through the abstraction process at the stage of data collection and discovery. Meanwhile, the instructional model of AI education through computational thinking is suggested to enhance the problem-solving ability using the AI by performing the processes of problem-solving and prediction based on the stages of automating and evaluating the selected algorithms. By analyzing the research related to the cognitive learning environment for AI education, the instructional framework was composed mainly of abstraction which is the core thinking process of computational thinking through the transition from the stage of the agency to modeling. The instructional framework of AI education and the process of constructing the cognitive learning environment presented in this study are characterized in that they are based on computational thinking, and those are expected to be the basis of further research for the instructional design of AI education.

• Korean Journal of Materials Research
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• v.33 no.5
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• pp.175-188
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• 2023

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.600-602
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• 2021

With the development of the mobile industry and growing interest in artificial intelligence (AI) technology, a lot of research for AI processors which applicable to embedded systems is under study. When implementing AI to embedded systems, the design should be considered the restriction of resource and power consumption. Moreover, it is efficient to include a dedicated hardware accelerator in order to complement the low computational performance of the embedded system. In this paper, we propose an stand-alone embedded AI processor. The proposed AI processor includes a hardware accelerator that is dedicated to the distance-based AI algorithm and a general-purpose MCU that supports flexible programmability for application to various embedded systems. The AI processor was designed with Verilog HDL and verified by implementing on Field Programmable Gate Array (FPGA).

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.1-16
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• 2024

The purpose of this study is to develop a machine learning-based AI convergence class model and class design principles that can foster data literacy in high school students, and to develop detailed guidelines accordingly. We developed a machine learning-based teaching model, design principles, and detailed guidelines through research on prior literature, and applied them to 15 students at a specialized high school in Seoul. As a result of the study, students' data literacy improved statistically significantly (p<.001), so we confirmed that the model of this study has a positive effect on improving learners' data literacy, and it is expected that it will lead to related research in the future.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2010.10a
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• pp.144-145
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• 2010

VTM is regarded as a core element in e-Navigation under developing by IMO. IMO's e-Navigation aims for VTM which could provide more navigational information for ships and provide more active VTS services for safe and efficient marine traffic environment. To be successful for VTM under e-Navigation, VTS operator's experience and ability for carrying out VTM tasks are essential. In this study, Decision-making support system for VTS operators are designed conceptually which support VTS operators' VTM tasks by providing informations on traffic risk, and predicted behaviour of ships. This paper describes a designed system with design concept, main functions, main elements and necessary technologies to be developed.

• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.572-580
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• 2020

Recently released AI speakers show a pattern of interacting with the user by mainly with voice and simultaneously displaying simple and formal visual feedback through status LED light. This is due to the limitations of the product characteristics of the speaker, which makes it difficult to interact variously, and even such visual feedback is not standardized for each product, and thus does not give a consistent user experience. By maximizing the visual elements that can be expressed through color and abstract movement to assist voice feedback, the product can provide the user with an extended experience that includes not only functional satisfaction but also emotional satisfaction. In this study, after analyzing the interaction methods of the existing AI speakers, we examined the theory of color communication in order to expand the visual feedback effect, and examined the meaning and expression technique of Color Field Painting, an art genre that maximizes the emotional experience by using only color. Through this, the AI speaker's visual communication function was expanded by designing a way to feedback communication status using LED light.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.674-681
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• 2021

Cyber attacks targeting critical infrastructure are on the rise. Critical infrastructure is defined as core infrastructures within a country with a high degree of interdependence between the different structures; therefore, it is difficult to sufficiently protect it using outdated cybersecurity techniques. In particular, the distinction between the physical and logical risks of critical infrastructure is becoming ambiguous; therefore, risk management from a comprehensive perspective must be implemented. Accordingly, as a means of further actively protecting critical infrastructure, major countries have begun to apply their security and cybersecurity systems by design, as a more expanded concept is now being considered. This proactive security approach (CSbD, Cybersecurity by Design) includes not only securing the stability of software (SW) safety design and management, but also physical politics and device (HW) safety, precautionary and blocking measures, and overall resilience. It involves a comprehensive security system. Therefore, this study compares and analyzes security by design measures towards critical infrastructure that are leading the way in the US, Europe, and Singapore. It reflects the results of an analysis of optimal cybersecurity solutions for critical infrastructure. I would like to present a plan for applying by Design.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2061-2066
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• 2017

The voice recognition technology is to recognize the voice of an user and execute the command. Recently, the voice recognition is evolving to the artificial intelligence voice recognition by adding the scheme of the natural language processing. The AI voice recognition is exploited to control the IoT devices or provide the information, such as the news or the wether. The University Information which is one of fields serviced by the information provider is mainly presented on the web. However, since too much information are presented on the web, it is difficult for an user to find efficiently the specific information which the user want to know. In this paper, we design and implement the university guidance system to recognize the user voice searching the information and provide the result using the voice. To do this, we classify the university data and design the lambda function to provide the data.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.7
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• pp.81-91
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• 2006

Recently, G.A studies have studied and demonstrated that artificial intelligence like G.A networks, G.A PI controller. The design techniques of PI controller using G.A with the newly proposed teaming algorithm was presented, and the designed controller with AC servo motor system. The goal of this paper is to design the AC servo motor using genetic algorithm and to control drive robot. And in this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for genetic algorithm PI controller. Our experimental results show that this approach increases overall classification accuracy rate significantly. Finally, we executed for the implementation of high performance speed control system. It is used a 16-bit DSP, IMS320LF2407, which is capable of the high speed and floating point calculation.