• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.63-74
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• 2020

AI products are used 'AI assistants' as embedded in smart phones, speakers, appliances as agents. Studies on anthropomorphism, such as personality, voice with a weak AI are being conducted. Role and function of AI agents will expand from development of AI technology. Various attributes related to the agent, such as user type, usage environment, appearance of the agent will need to be considered. This study intends to categorize interaction factors related to agents from the user's perspective through analysis of concept videos which agents with strong AI. Framework for analysis was built on the basis of theoretical considerations for agents. Concept videos were collected from YouTube. They are analyzed according to perspectives on environment, user, agent. It was categorized into 8 attributes: viewpoint, space, shape, agent behavior, interlocking device, agent interface, usage status, and user interface. It can be used as reference when developing, predicting agents to be commercialized in the future.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.211-218
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• 2021

A recent spread of personal mobility devices such as electric kickboards has brought about a rapid increase in accident cases. Such vehicles are susceptible to falling accidents due to their low dynamic stability and lack of outer protection chassis. This paper presents the development of an automatic emergency braking system and a safe starting system as driving assistance devices for electric kickboards. The braking system employed artificial intelligence to detect nearby threaening objects. The starting system was developed to disable powder to the motor until when the driver's boarding is confirmed. This study is meaningful in that it proposes the convergence technology of advanced driver assistance systems specialized for personal mobility devices.

• Communications of Mathematical Education
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• v.37 no.3
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• pp.497-516
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• 2023

The purpose of this study is to explore the possibility of enhancing teachers' pedagogical design capacity using ChatGPT. For this purpose, a survey was conducted to investigate preservice teachers' perceptions of ChatGPT, and lesson plans created using ChatGPT were analyzed from the perspectives of design elements, conversations with ChatGPT, and information transforming. The results showed that pre-service teachers have a rather passive attitude toward the use of ChatGPT, and that teacher moderation and ChatGPT characteristics affect pre-service teachers' perceptions of the use of ChatGPT. In addition, pre-service teachers mainly used ChatGPT for motivational activities and play activities, and there were significant differences in the level of utilization of ChatGPT among individuals, i.e., how they interacted with ChatGPT and how they transformed information. Based on these findings, we explored the possibility of using ChatGPT for teacher professional development and teacher education.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.389-398
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• 2007

Case-based reasoning (CBR) is one of the most popular prediction techniques for medical diagnosis because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation - its prediction performance is generally lower than other artificial intelligence techniques like artificial neural networks (ANNs). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GAs). Our model improves the prediction performance in three ways - (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating redundant or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.

• Journal of Intelligence and Information Systems
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• v.14 no.3
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• pp.1-23
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• 2008

Nowadays blogging is emerging as a new way of building human-to-human relationship by offering personal information and sharing opinions with others on their own initiatives. These blogging activities would be also useful when people are in a movement surrounding lots of objects, such as products and shops, which are potentially interesting to them. However, immediate response from the objects is not available using legacy blogging systems. This is mainly due to the segregation of the blogging content owners from the objects. This paper, hence, aims to propose a methodology which allows the users to directly communicate with the objects in a timely and context-aware manner, not the object owners, to get useful information or get served immediately. To do so, the concept of artificial bloggers which work on behalf of the objects and eventually the object owners is proposed. The functionalities of u-Blogging are also introduced with prototype system.

• Convergence Security Journal
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• v.21 no.1
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• pp.115-120
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• 2021

An O2O-based real estate brokerage web sites or apps are increasing explosively. As a result, the environment has been changed from the existing offline-based real estate brokerage environment to the online-based environment, and consumers are getting very good feelings in terms of time, cost, and convenience. However, behind the convenience of online-based real estate brokerage services, users often suffer time and money damage due to false information or malicious false information. Therefore, in this study, in order to reduce the damage to consumers that may occur in the O2O-based real estate brokerage service, we designed a false property information filtering system that can determine the authenticity of registered property information using artificial intelligence technology. Through the proposed research method, it was shown that not only the authenticity of the property information registered in the online real estate service can be determined, but also the temporal and financial damage of consumers can be reduced.

• Journal of IKEEE
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• v.22 no.4
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• pp.1079-1087
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• 2018

Recently, computer vision field based deep learning artificial intelligence has become a hot topic among various image analysis boundaries. In this study, flames are detected in fire images using the Faster R-CNN algorithm, which is used to detect objects within the image, among various image recognition algorithms based on deep learning. In order to improve fire detection accuracy through a small amount of data sets in the learning process, we use image augmentation techniques, and learn image augmentation by dividing into 6 types and compare accuracy, precision and detection rate. As a result, the detection rate increases as the type of image augmentation increases. However, as with the general accuracy and detection rate of other object detection models, the false detection rate is also increased from 10% to 30%.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.576-581
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• 2021

Compared to a low concentration, a high concentration clearly entails limitations in terms of predictive performance owing to differences in its frequency and environment of occurrence. To resolve this problem, in this study, an artificial intelligence neural network algorithm was used to classify low and high concentrations; furthermore, two prediction models trained using the characteristics of the classified concentration types were used for prediction. To this end, we constructed training datasets using weather and air pollutant data collected over a decade in the Cheonan region. We designed a DNN-based classification model to classify low and high concentrations; further, we designed low- and high-concentration prediction models to reflect characteristics by concentration type based on the low and high concentrations classified through the classification model. According to the results of the performance assessment of the prediction model by concentration type, the low- and high-concentration prediction accuracies were 90.38% and 96.37%, respectively.

• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.49-57
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• 2020

The society we are living in has being changed to the age of the intelligent information society after passing through the knowledge-based information society in the early 21st century. In this study, we have developed the instructional model for software education based on the machine learning which is a field of artificial intelligence(AI) to enhance the core competencies of learners required in the intelligent information society. This model is focusing on enhancing the core competencies through the process of problem-solving as well as reducing the burden of learning about AI itself. The specific stages of the developed model are consisted of seven levels which are 'Problem Recognition and Analysis', 'Data Collection', 'Data Processing and Feature Extraction', 'ML Model Training and Evaluation', 'ML Programming', 'Application and Problem Solving', and 'Share and Feedback'. As a result of applying the developed model in this study, we were able to observe the positive response about learning from the students and parents. We hope that this research could suggest the future direction of not only the instructional design but also operation of software education program based on machine learning.

• Journal of Ocean Engineering and Technology
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• v.33 no.6
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• pp.590-596
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• 2019

Numerous deaths and substantial property damage have occurred recently due to frequent disasters of the highest intensity according to the abnormal climate, which is caused by various problems, such as global warming, all over the world. Such large-scale disasters have become an international issue and have made people aware of the disasters so they can implement disaster-prevention measures. Extensive information on disaster prevention actively has been announced publicly to support the natural disaster reduction measures throughout the world. In Japan, diverse developmental studies on disaster prevention systems, which support hazard map development and flood control activity, have been conducted vigorously to estimate external forces according to design frequencies as well as expected maximum frequencies from a variety of areas, such as rivers, coasts, and ports based on broad disaster prevention data obtained from several huge disasters. However, the current reduction measures alone are not sufficiently effective due to the change of the paradigms of the current disasters. Therefore, in order to obtain the synergy effect of reduction measures, a study of the establishment of an integrated system is required to improve the various disaster prevention technologies and the current disaster prevention system. In order to develop a similar typhoon search system and establish a disaster prevention infrastructure, in this study, techniques will be developed that can be used to forecast typhoons before they strike by using artificial intelligence (AI) technology and offer primary disaster prevention information according to the direction of the typhoon. The main function of this model is to predict the most similar typhoon among the existing typhoons by utilizing the major typhoon information, such as course, central pressure, and speed, before the typhoon directly impacts South Korea. This model is equipped with a combination of AI and DNN forecasts of typhoons that change from moment to moment in order to efficiently forecast a current typhoon based on similar typhoons in the past. Thus, the result of a similar typhoon search showed that the quality of prediction was higher with the grid size of one degree rather than two degrees in latitude and longitude.