• Journal of Korea Game Society
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• v.9 no.1
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• pp.93-103
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• 2009

A Serious Game helps the learners to recognize the problems effectively, grasp and classify important information needed to solve the problems and convey the contents of what they have learned. Owing not only to this game-like fun but also to the educational effect, The Serious Game can be usefully applied to education and training in the areas of scientific technology and industrial technology. This study proposes the Serious Game that users can apply to biotechnology by using intuitive multi-modal interfaces. In this study, a stereoscopic monitor is used to make three dimensional molecular structures, and multi-modal interface is used to efficiently control. Based on a such system, this study easily solved the docking simulation function, which is one of the important experiments, by applying these game factors. For this, we suggested the level-up concept as a game factor that depends on numbers of objects and users. The proposed system was evaluated in performance comparison in result time of a new drug design process on AIDS virus with previous approach.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.955-963
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• 2003

In this paper, in order to help one to write contents for education in such a way to increase effectiveness, we implement SMIL editor, which helps anyone to author contents for education and multimedia text in an easy way based on multimeda language SMIL. Thanks to its interface which utilizes WYSIWYGT, this editor allows teachers or other users who do not know SMIL to write contents for education and multimedia text in an easy way and to check in a real time how a partially completed document work and to revise when it is not satisfactory. It also allows one to write contents with explanations to help learning because usable multimedia objects can be inserted. This editor helps the user to reduce his inconvenience that he has in memorizing SMIL tags and to reduce his time and offers in writing the documents.

• The Journal of Korean Association of Computer Education
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• v.17 no.4
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• pp.1-11
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• 2014

In this study, contents of programming curriculum were designed as instrumental subjects for scientifically gifted students, not for IT gifted ones. Firstly, the programming curriculum consisted of 3 sections; Programming Understanding, Object-Oriented Programming, and Simulation Programming as a result of two semesters. Then, the lectures including project-based and problem-solving tasks were given to scientifically gifted students in a high school during one semester to verify whether they could apply the contents to studies and researches in math or science or not. As a result of this study, the students could improve numerical analysis and simulation program development capabilities in math or science as well as the problem-solving ability based on computational thinking. Moreover, it was proved that the students changed their perception about programing learning. They started to think that programing learning was necessary to studies and researches in math or science. The results of this study propose guideline to design programming curriculum as instrumental subjects for scientifically gifted students.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.49-57
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• 2011

Extraction of regions of interest (ROIs) is the first and important step for the applications in computer vision and affects the rest of the application process. However, ROI extraction can be easily affected by the environment such as illumination, camera, etc. Many applications adopt problem-specific knowledge and/or post-processing to correct the error occurred in ROI extraction. In this paper, proposed is a robust framework that could overcome the environmental change and is independent from the rest of the process. The proposed framework uses a differential image and a color distribution to extract ROIs. The color distribution can be learned on-line, which make the framework to be robust to environmental change. Even more, the components of the framework are independent each other, which makes the framework flexible and extensible. The usefulness of the proposed framework is demonstrated with the application of hand region extraction in an image sequence.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.776-788
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• 2020

In this paper, a vehicle type recognition system using deep learning and a license plate recognition system are proposed. In the existing system, the number plate area extraction through image processing and the character recognition method using DNN were used. These systems have the problem of declining recognition rates as the environment changes. Therefore, the proposed system used the one-stage object detection method YOLO v3, focusing on real-time detection and decreasing accuracy due to environmental changes, enabling real-time vehicle type and license plate character recognition with one RGB camera. Training data consists of actual data for vehicle type recognition and license plate area detection, and synthetic data for license plate character recognition. The accuracy of each module was 96.39% for detection of car model, 99.94% for detection of license plates, and 79.06% for recognition of license plates. In addition, accuracy was measured using YOLO v3 tiny, a lightweight network of YOLO v3.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.43-53
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• 2007

Intrusion detection system(IDS) has recently evolved while combining signature-based detection approach with anomaly detection approach. Although signature-based IDS tools have been commonly used by utilizing machine learning algorithms, they only detect network intrusions with already known patterns, Ideal IDS tools should always keep the signature database of your detection system up-to-date. The system needs to generate the signatures to detect new possible attacks while monitoring and analyzing incoming network data. In this paper, we propose a new outlier cluster detection algorithm with density (or influence) function, Our method assumes that an outlier is a kind of cluster with similar instances instead of a single object in the context of network intrusion, Through extensive experiments using KDD 1999 Cup Intrusion Detection dataset. we show that the proposed method outperform the conventional outlier detection method using Euclidean distance function, specially when attacks occurs frequently.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.289-293
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• 2005

Pattern classification extracts various types of pattern information expressing objects in the real world and decides their class. The top priority of pattern classification technologies is to improve the performance of classification and, for this, many researches have tried various approaches for the last 40 years. Classification methods used in pattern classification include base classifier based on the probabilistic inference of patterns, decision tree, method based on distance function, neural network and clustering but they are not efficient in analyzing a large amount of multi-dimensional data. Thus, there are active researches on multiple classifier systems, which improve the performance of classification by combining problems using a number of mutually compensatory classifiers. The present study identifies problems in previous researches on multiple SVM classifiers, and proposes BORSE, a model that, based on 1:M policy in order to expand SVM to a multiple class classifier, regards each SVM output as a signal with non-linear pattern, trains the neural network for the pattern and combine the final results of classification performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.259-266
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• 2016

Recently, CCTV cameras are emplaced actively to reinforce security and intelligent surveillance systems have been under development for detecting and monitoring of the objects in the video. In this study, we propose a method for detection of upper body in intelligent surveillance system using FCM-based RBFNN classifier realized with the aid of HOG features. Firstly, HOG features that have been originally proposed to detect the pedestrian are adopted to train the unique gradient features about upper body. However, HOG features typically exhibit a very high dimension of which is proportional to the size of the input image, it is necessary to reduce the dimension of inputs of the RBFNN classifier. Thus the well-known PCA algorithm is applied prior to the RBFNN classification step. In the computer simulation experiments, the RBFNN classifier was trained using pre-classified upper body images and non-person images and then the performance of the proposed classifier for upper body detection is evaluated by using test images and video sequences.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.1-8
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• 2009

Detecting entry and exit zones in a view covered by multiple cameras is an essential step to determine the topology of the camera setup, which is critical for achieving and sustaining the accuracy and efficiency of multi-camera surveillance system. In this paper, a graph theoretic clustering method is proposed to detect zones using data points which correspond to entry and exit events of objects in the camera view. The minimum spanning tree (MST) is constructed by associating the data points. Then a set of well-formed clusters is sought by removing inconsistent edges of the MST, based on the concepts of the cluster balance and the cluster density defined in the paper. Experimental results suggest that the proposed method is effective, even for sparsely elongated clusters which could be problematic for expectation-maximization (EM). In addition, comparing to the EM-based approaches, the number of data required to obtain stable outcome is relatively small, hence shorter learning period.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.17-24
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• 2007

Critical practicality problems are cause to search the presentation and contents according to user request and purpose in previous internet system. Recently, there are a lot of researches about dynamic adaptable ontology based system. We designed ontology based educational system which uses discrete probability and user profile. This system provided advanced usability of contents by ontology and dynamic adaptive model based on discrete probability distribution function and user profile in ontology educational systems. This models represents application domain to weighted direction graph of dynamic adaptive objects and modeling user actions using dynamically approach method structured on discrete probability function. Proposed probability analysis can use that presenting potential attribute to user actions that are tracing search actions of user in ontology structure. This approach methods can allocate dynamically appropriate profiles to user.