• Transactions on Control, Automation and Systems Engineering
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• v.1 no.2
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• pp.147-152
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• 1999

It is an interesting area in the field of artifical intelligence to find an analytic model of cooperative structure for multiagent system accomplishing a given task. Usually it is difficult to design controllers for multi-agent systems without a comprehensive knowledge about the system. One of the way to overcome this limitation is to implement an evolutionary approach to design the controllers. This paper introduces the use of a genetic algorithm to discover a fuzzy logic controller with rules that govern emergent agents solving a pursuit problem in a continuous world. Simulation results indicate that, given the complexity of the problem, an evolutionary approach to find the fuzzy logic controller seems to be promising.

• KSCI Review
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• v.2 no.2
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• pp.27-40
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• 1996

During the 21st century of informational society, in the learning of various field will utilize the education using multi-media more extensively than ever before. The biggest question is how effective the education using multi-media will be. For effective education, wide-spread supply of not only the hardware and various kinds of CBT or CAI that are being developed in the learning of various fields. It is also felt that the skill for application of more convenient multi-media authoring tool is needed. If the producter of such multi-media authoring tool can store various types of information in a form of data bank, accessing the right information at right time and its application would be possible. It can also provide a lot of information to many out-of town learmers. As seen above, the scope of usage for multi-media authoring tool will be broadened. However, no matter how excellent the Authoring Tool is, the results can be very different depending on the method employed. In order to develop CBT or CAI that can be better used in the learning of various fields, examination and on-site training, more reseach should be done in Authoring Tool using virtual reality and artifical intelligence technology.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.585-590
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• 2000

Recently, It is gradually raised necessity that thickness of thin film is measured accuracy and managed in industrial circles and medical world. Ultrasonic Signal processing method is likely to become a very powerful method for NDE method of detection of microdefects and thickness measurement of thin film below the limit of Ultrasonic distance resolution in the opaque materials, provides useful information that cannot be obtained by a conventional measuring system. In the present research, considering a thin film below the limit of ultrasonic distance resolution sandwiched between three substances as acoustical analysis model, demonstrated the usefulness of ultrasonic Signal processing technique using information of ultrasonic frequency for NDE of measurements of thin film thickness, sound velocity, and step height, regardless of interference phenomenon. Numeral information was deduced and quantified effective information from the image. Also, pattern recognition of a defected input image was performed by neural network algorithm. Input pattern of various numeral was composed combinationally, and then, it was studied by neural network. Furthermore, possibility of pattern recognition was confirmed on artifical defected input data formed by simulation. Finally, application on unknown input pattern was also examined.

• Journal of Environmental Science International
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• v.6 no.3
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• pp.205-211
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• 1997

For the prediction of multi-site rainfall with radar data and ground meteorological data, a rainfall prediction model was proposed, which uses the neural network theory, a kind of artifical Intelligence technique. The Input layer of the prediction model was constructed with current ground meteorological data, their variation, moving vectors of rain- fall field and digital terrain of the measuring site, and the output layer was constructed with the predicted rainfall up to 3 hours. In the application of the prediction model to the Pyungchang river basin, the learning results of neural network prediction model showed more Improved results than the parameter estimation results of an existing physically based model. And the proposed model comparisonally well predicted the time distribution of ralnfall.

• Journal of Information Technology Services
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• v.17 no.3
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• pp.17-36
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• 2018

In this study, we developed a chatbot (Dialogue agent) using small Q & A data and evaluated its performance. The chatbot developed in this study was developed in the form of an FAQ chatbot that responds promptly to customer inquiries. The development of chatbot was conducted in three stages : 1. Analysis and planning, 2. Content creation, 3. API and messenger interworking. During the analysis and planning phase, we gathered and analyzed the question data of the customers and extracted the topics and details of the customers' questions. In the content creation stage, we created scenarios for each topic and sub-items, and then filled out specific answers in consultation with business owners. API and messenger interworking is KakaoTalk. The performance of the chatbot was measured by the quantitative indicators such as the accuracy that the chatbot grasped the inquiry of the customer and correctly answered, and then the questionnaire survey was conducted on the chatbot users. As a result of the survey, it was found that the chatbot not only provided useful information to the users but positively influenced the image of the pension. This study shows that it is possible to develop chatbots by using easily obtainable data and commercial API regardless of the size of business. It also implies that we have verified the validity of the development process by verifying the performance of developed chatbots as well as an explicit process of developing FAQ chatbots.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.1-11
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• 1998

In this work, we propose a modeling method based on an artifical intelligence technique for a stem generator in a nuclear power plant. Modeling the steam generator is known to be difficult due to several facts; especially, the dynamics of the steam generator is nonminimum phase which is mainly caused by the swell and shrink phenomena from thermal effects. In order to overcome this difficulty, we adopt so-called logic processor whose structure itself has a logical meaning to be easily established and also efficiently learned. Such a manner, we could derive an useful model simulating the dynamics of the steam generator in a nuclear power plant.

• The Journal of Information Systems
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• v.30 no.4
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• pp.203-226
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• 2021

Purpose The purpose of this study is to enhance the accuracy score of personal credit scoring using the convolutional neural networks and secure the transparency of the deep learning model using eXplainalbe Artifical Inteligence(XAI) technique. Design/methodology/approach This study built a classification model by using the convolutional neural networks(CNN) and applied a methodology that is transformation of numerical data to imaged data to apply CNN on personal credit data. Then layer-wise relevance propagation(LRP) was applied to model we constructed to find what variables are more influenced to the output value. Findings According to the empirical analysis result, this study confirmed that accuracy score by model using CNN is highest among other models using logistic regression, neural networks, and support vector machines. In addition, With the LRP that is one of the technique of XAI, variables that have a great influence on calculating the output value for each observation could be found.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1729-1738
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• 2021

Machine learning has a close relationship with cognitive psychology and brain science and is developing together. This paper analyzes the OASIS-3 dataset using machine learning techniques and proposes a model for predicting dementia. Dimensional reduction through PCA (Principal Component Analysis) is performed on the data quantifying the volume of each area among OASIS-3 data, and only important elements (features) are extracted and then various machine learning including gradient boosting and stacking Apply the models and compare the performance of each. Unlike previous studies, the proposed technique has a great differentiation because it uses not only the brain biometric data, but also basic information data such as the participant's gender and medical information data of the participant. In addition, it was shown that the proposed technique through various performance evaluations is a model that can better predict dementia by finding features that are more related to dementia among various numerical data.

• Smart Media Journal
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• v.13 no.2
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• pp.145-155
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• 2024

As the importance of software and artificial education is emphasized on the digital transformation era, various educational materials are being developed and distributed. To achieve the purpose of software education, various software education programs suitable for school settings need to be provided. In this paper, we developed a software education program using self-driving that can be applied to secondary school software education and applied it to secondary school students. The developed software education program is a physical computing program consisting of various motion control programs such as object detection, line tracing using various sensors, focusing on experience and practice. As a result of the survey, students' attitudes and career orientation toward software and artificial intelligence, and satisfaction with software education were over 90%, and satisfaction with the proposed program was over 95%.

• Journal of Service Research and Studies
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• v.7 no.3
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• pp.57-70
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• 2017

It is anticipated that the employment structure of the whole industry will change drastically as the Fourth Industrial Revolution era arrives. Particularly, there are numerous reseraches that the development of artifical intelligence will promote automation causing jobs in manufacturing industry to decrease; thus, the economy will be reorganized with service-centered jobs, which heavily depend on human ability. This study was conducted to verify the trend-forecasting model based on the theoretical analysis. We analyzed the change in employment structure over the past decades in each country and period to gain insights from the changes in the employment structure caused by the Fourth Industrial Revoltion. The results of this study are as follows: First, we investigaed whether the current economy is moving along the U-shaped model suggested by an existing researcher. As a result of the analysis, the data substantiated that the change of the employment structure is moving along the U-shaped model. It is also suggested that this U-shaped trend is expected to accelerate in the era of the Fourth Industrial Revolution. In the future, more accurate data analyses are needed to verify the model, and additional researches on the change in the employment structed is also needed.