• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.622-626
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• 2006

In this paper, we show the usefulness of an methodology using a neural network that it analyzes a relation between learning personality related variables of the Enneargram and learning personality types. The Enneargram is a tool to classify learning personality types. In other words, we analyzed patterns of learning personality types-actaul-spontaneous type, actual-routine type, conceptual-specific type, conceptual-global type - by using the fuzzy TAM network that are very useful tool for pattern analysis.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.153-161
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• 2013

Recently, there has been a tremendous increase in the availability of educational materials for foreign language learning. As part of this trend, there has been an increase in the amount of electronically mediated materials available. However, conventional educational contents developed using computer technology has provided typically one-way information, which is not the most helpful thing for users. Providing the user's convenience requires additional off-line analysis for diagnosing an individual user's learning. To improve the user's comprehension of texts written in a foreign language, we propose an intelligent learning tool based on the analysis of the user's eyeball movements, which is able to diagnose and improve foreign language reading ability by providing necessary supplementary aid just when it is needed. To determine the user's learning state, we correlate their eye movements with findings from research in cognitive psychology and neurophysiology. Based on this, the learning tool can distinguish whether users know or do not know words when they are reading foreign language sentences. If the learning tool judges a word to be unknown, it immediately provides the student with the meaning of the word by extracting it from an on-line dictionary. The proposed model provides a tool which empowers independent learning and makes access to the meanings of unknown words automatic. In this way, it can enhance a user's reading achievement as well as satisfaction with text comprehension in a foreign language.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.90-96
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• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• Journal of The Korean Association of Information Education
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• v.1 no.2
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• pp.16-34
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• 1997

This paper describes a method of building a intelligent learning system consisting of a authoring tool, in the area of language education, and a Intelligent Tutoring System(ITS) to study English. This tool is different from commerical authoring tools, as a tool that has capabilities as a component of an ITS and this ITS is efficient to retrieve the lesson plan from the plan memory than to generate it whenever an instructinoal goal is selected. the results of this research could be used either by a developer of the other area of ITS, or by a human teacher as a curriculum in the actual class.

• Proceedings of the Korea Association of Information Systems Conference
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• 1997.10a
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• pp.39-52
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• 1997

본 연구는 언어 교육용 프로그램을 개발하는 저작도구9authoring tool)와 학생들이 자율적으로 학습할 수 있는 지능형 컴퓨터 교사시스템(ITS : Intelligent Tutoring System)으 로 구성된 지능형 학습환경(Intelligent Learning Environment)을 설계한다. 특히, 범용시스 템에서 제공되는 불필요한 기능들을 제거하고 언어교육에 필요한 기능만을 가진 간편한 저 작도구의 설계와, 인공지능 기법을 이요하여 학생 개개인의 지식수준에 따라 차별화하여 지 능적으로 교육할 수 있는 지능형 교사시스템의 구성 방법을 제안한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.267-270
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• 2001

In this paper, we propose the self-autonomous algorithm for mobile robot system. The proposed mobile robot system which is teamed by learning with the neural networks can trace the target at the same distances. The mobile robot can evaluate the distance between robot and target with ultrasonic sensors. By teaming the setup distance, current distance and command velocity, the robot can do intelligent self-autonomous drive. We use the neural network and back-propagation algorithm as a tool of learning. As a result, we confirm the ability of tracing the target with proposed mobile robot.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.21-26
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• 2016

Canine cataract is developed with aging and can cause the blindness or surgical treatment if not treated timely. The first observation must be made by pet owners but they do not have proper equipment and knowledge to see the abnormalities. In this paper, we propose an intelligent image processing method to extract canine cataract suspicious object from non-professional equipment such as ordinary digital camera and cellular phone photographs so that even casual owners of pet dog can make a pre-diagnosis of such a surgery-needed disease as soon as possible. The experiment shows that the proposed method is successful in most cases except the dog has similar colored hair to the color of cataract.

• The KIPS Transactions:PartA
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• v.16A no.5
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• pp.403-410
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• 2009

In this paper, we present an on-line quiz authoring software that helps teachers create an intelligent on-line quiz. It is designed to give each student appropriate diagnostic report using Bayesian inference networks that represent the relationships among knowledge-items. Once the authors design and edit quizzes in quiz authoring page, the authoring tool automatically produces a knowledge-model based on Bayesian inference network, on-line quizzes, and student report pages. It turns out that the on-line quizzes generated by this tool help students identify their weak parts of subject, make learning strategies for the next learning steps and carry out supplementary learning for their weak knowledge-items.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.203-209
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• 2019

The performance of an intelligent classifier for detecting malwares added to multimedia contents based on machine learning is highly dependent on the properties of feature set. Especially, in order to determine the malicious code in real time the size of feature set should be as short as possible without reducing the accuracy. In this paper, we introduce an optimal feature selection method to satisfy both high detection rate and the minimum length of feature set against the feature set provided by PEFeatureExtractor well known as a feature extraction tool. For the evaluation of the proposed method, we perform the experiments using Windows Portable Executables 32bits.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.37-53
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• 2023

The AI based intelligent command and control system can automatically analyzes the properties of intricate battlefield information and tactical data. In addition, commanders can receive situation analysis results and battlefield awareness through the system to support decision-making. It is necessary to build a battlefield situation analysis dataset similar to the actual battlefield situation for learning AI in order to provide decision-making support to commanders. In this paper, we explain the next step of the dataset construction method of the existing previous research, 'A Virtual Battlefield Situation Dataset Generation for Battlefield Analysis based on Artificial Intelligence'. We proposed a method to build the dataset required for the final battlefield situation analysis results to support the commander's decision-making and recognize the future battlefield. We developed 'Dataset Generator SW', a software tool to build a learning dataset for battlefield situation analysis, and used the SW tool to perform data labeling. The constructed dataset was input into the Siamese Network model. Then, the output results were inferred to verify the dataset construction method using a post-processing ranking algorithm.