• Journal of Korea Multimedia Society
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• v.10 no.2
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• pp.147-169
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• 2007

Semantic Web Services Discovery matches between users' requirements and the ontological description of Web Services. However, concepts of an ontology can be interpreted differently according to a point of view. Previous works are limited in interpreting concepts. Since they lack a precise scheme of describing the advertisements and requirements of services and users, respectively, and even do not support a sophisticated matching, semantic mismatches may occur. This paper presents a sophisticated method of discovering Web services. The proposed method facilitates specifying semantics precisely and flexibly based on a proposed integrated concept model. Additionally, more sophisticated discovery is supported by computing complex matchings with many-to-many relationships. Experimental results show that the proposed method performs more efficiently for various kinds of user requests, compared with previous works.

• Journal of The Korean Association of Information Education
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• v.26 no.1
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• pp.65-73
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• 2022

In this study, by using Entry text model learning, educational contents for artificial intelligence education of elementary school students are developed and applied to actual classes. Based on the elementary and secondary artificial intelligence content table, the achievement standards of practical software education and artificial intelligence education will be reconstructed.. Among text, images, and sounds capable of machine learning, "production of emotion recognition programs using text model learning" will be selected as the educational content, which can be easily understood while reducing data preparation time for elementary school students. Entry artificial intelligence is selected as an education platform to develop artificial intelligence education contents that create emotion recognition programs using text model learning and apply them to actual elementary school classes. Based on the contents of this study, As a result of class application, students showed positive responses and interest in the entry AI class. it is suggested that quantitative research on the effectiveness of classes for elementary school students is necessary as a follow-up study.

• Smart Media Journal
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• v.11 no.8
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• pp.65-72
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• 2022

Due to COVID-19, the importance of non-face-to-face counseling is increasing as the face-to-face counseling method has progressed to non-face-to-face counseling. The advantage of non-face-to-face counseling is that it can be consulted online anytime, anywhere and is safe from COVID-19. However, it is difficult to understand the client's mind because it is difficult to communicate with non-verbal expressions. Therefore, it is important to recognize emotions by accurately analyzing text and voice in order to understand the client's mind well during non-face-to-face counseling. Therefore, in this paper, text data is vectorized using FastText after separating consonants, and voice data is vectorized by extracting features using Log Mel Spectrogram and MFCC respectively. We propose a multi-emotion recognition model that recognizes five emotions using vectorized data using an LSTM model. Multi-emotion recognition is calculated using RMSE. As a result of the experiment, the RMSE of the proposed model was 0.2174, which was the lowest error compared to the model using text and voice data, respectively.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.51-56
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• 2021

This paper studies VPBL(Various PBL) models, applies them to classes, and analyzes results so that students of various majors can design and implement problems according to the characteristics of their majors in order to improve problem solving in education. VPBL performs the process of designing and implementing problems that reflect the characteristics of the major by applying constraints to the professor's programming language. The professor performs mini_class in the process of solving the designed problem and then shares it throughout. VPBL model apply results, The traditional teaching method was 3.51 points and the application of the VPBL model was 4.52 points, and "interaction, understanding of learning contents, and acquiring knowledge related to curriculum" were improved. In addition, VPBL has the advantage of expanding the learning range in the solving process as it is based on various problem solving, which has the effect of expanding the learning range compared to existing class models. Research on the expanded application of various SW liberal education in the future is required.

• Journal of the Korean Applied Science and Technology
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• v.39 no.3
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• pp.442-453
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• 2022

Various animal models have been used to study the efficacy and action mechanisms of human diseases and medicines. Zebrafish (Danio rerio) is increasingly and successfully used as a model in translational research on human diseases. We obtained necessary information from original peer reviewed articles published in scientific 54 journals, such as Pubmed, Google Scholar, Scopus scince their inception until Dec, 2020 using the following terms: zebrafish animal models, herbal medicine, in vivo screening. In this review, we discuss the recent contributions of the various zebrafish disease models to study of herbal medicines. We focused on cancer, eye diseases, vascular diseases, diabetes and its complications, and cosmetic dermatology. We also highlight the molecular action mechanisms of medicines against these disease, demonstrated using zebrafish embryo. Zebrafish can be pivotal in bridging the gap from lab to clinical bedside. It is used as a model to understand human diseases pathogenies with further scope for drug development. Furthermore, zebrafish can reduce rat and mouse animals in biomedical research.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.509-516
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• 2022

Accurate pose prediction of objects in 3D space is an important visual recognition technique widely used in many applications such as scene understanding in both indoor and outdoor environments, robotic object manipulation, autonomous driving, and augmented reality. Most previous works for object pose estimation have the limitation that they require an exact 3D CAD model for each object. Unlike such previous works, this paper proposes a novel neural network model that can predict the poses of unknown objects based on only their RGB color images without the corresponding 3D CAD models. The proposed model can obtain depth maps required for unknown object pose prediction by using an adaptive depth estimator, AdaBins,. In this paper, we evaluate the usefulness and the performance of the proposed model through experiments using benchmark datasets.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.9-14
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• 2023

The purpose of information system audit is to proactively identify and efficiently manage all risk factors that may arise during the process of constructing an information system, in order to assist in achieving the objectives of information system development. However, there is currently significant dissatisfaction with the quality and effectiveness of the auditing process, leading to ongoing research aimed at finding effective solutions. In this paper, we propose a multi-level evaluation model to enhance mutual understanding between auditors and evaluators and present a model that undergoes a maturity process, improving its levels and stages. We introduce a maturity-based evaluation model platform, enabling efficient communication between auditors and evaluators, allowing for real-time feedback, and supplementing it through continuous search. By presenting this multi-level model aimed at maturing the entire system, we aim to efficiently manage the system development process.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.287-308
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• 2023

After launching its service in November 2022, ChatGPT has rapidly increased the number of users and is having a significant impact on all aspects of society, bringing a major turning point in the history of artificial intelligence. In particular, the inference ability of large language models such as ChatGPT is improving at a rapid pace through prompt engineering techniques. This reasoning ability can be considered as an important factor for companies that want to adopt artificial intelligence into their workflows or for individuals looking to utilize it. In this paper, we begin with an understanding of in-context learning that enables inference in large language models, explain the concept of prompt engineering, inference with in-context learning, and benchmark data. Moreover, we investigate the prompt engineering techniques that have rapidly improved the inference performance of large language models, and the relationship between the techniques.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.6
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• pp.134-142
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• 2012

This paper presents a study of system identification on the tilt response of the L-type retaining wall located at Tanhyun 11th ACE Apartment, Ilsan in order to understand mechanism how the structure behaves in operational conditions and to provide a reference tilt values for assessing structural abnormality. The retaining wall was extraordinarily tall (14m) in urban area so the long-term monitoring system had been installed with 3 tilts-meters and 9 temperature sensors operational from Oct 2004 upto Nov 2007. By using 5-months continuous data in which all the 12 channels were up and running, the two prediction models, 1) the linear model, and 2) the state-space equation (SSE) model, have been identified by finding the best fitness model among all possible 511 combinations of input temperatures out of the 9 temperatures. The linear model which was simple in the model structure achieved the validation fittness of 68% due to the fact that the static model wasn't able to represent thermal dynamics. The SSE model achieved the validation fitness of 90% which was quite accurate considering various unexpected noises happening in field measurements.

• Journal of the Korean Chemical Society
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• v.62 no.4
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• pp.279-287
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• 2018

In this study, we analyzed the explanations and examples of Brønsted-Lowry model in Chemistry I and Chemistry II textbooks of the 2009 revised curriculum. In particular, the definition of the Brønsted-Lowry model, the examples, and the content of experiments were analyzed by the process perspective of chemical equilibrium, emergent process. The analyzed textbooks were 4 kinds of Chemistry I textbooks and 4 kinds of Chemistry II textbooks in 2009 revision curriculum. As a result, Chemical I textbooks did not adequately show the chemical equilibrium viewpoint when explaining the Brønsted-Lowry model. In the Chemistry II textbooks, the examples of Brønsted-Lowry model were not present emergent process viewpoint, and those were described as sequential viewpoint of Arrhenius model. In addition, examples of experiments to demonstrate the Brønsted-Lowry model of Chemistry II textbooks were insufficient. The experimental examples related to the definition of acid bases were at the level of classification by the color change of indicators. The experimental examples for explaining the strength of acid and base were to compare current intensity or amount of hydrogen gas generated from the reaction with metal. In addition, all textbooks presented the state of aqueous solution when describing the Brønsted-Lowry model, causing problems with differentiation from the Arrhenius model. Therefore, it is necessary to develop examples of experiments to help students understand Brønsted-Lowry model by presenting acid and base reaction in the non-aqueous solution state.