• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.579-586
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• 2002

The edge descriptor proposed by MPEG-7 standard is a representative approach for the contents-based image retrieval using the edge information. In the edge descriptor, the edge information is the edge histogram derived from a gray-level value image. This paper proposes a new method which extracts color edge information from color images and a new approach for the contents-based image retrieval based on the color edge histogram. The poposed method and technique are applied to image retrieval of the e-catalogue. For the evaluation, the results of image retrieval using the proposed approach are compared with those of image retrieval using the edge descriptor by MPEG-7 and the statistics shows the efficiency of the proposed method. The proposed color edge model is made by combining the R,G,B channel components vectorially and by characterizing the vector norm of the edge map. The color edge histogram using the direction of the color edge model is subsequently used for the contents-based image retrieval.

• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.17-29
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• 2005

The purpose of the study was to explore the potential of the Social Network Analysis as an analytical tool for scientific investigation of learner-learner, or learner-tutor interaction within a Computer Supported Corporative Learning (CSCL) environment. Theoretical and methodological implication of the Social Network Analysis had been discussed. Following theoretical analysis, an exploratory empirical study was conducted to test statistical correlation between traditional performance measures such as achievement and team contribution index, and the centrality measure, one of the many quantitative measures the Social Network Analysis provides. Results indicate the centrality measure was correlated with the higher order teaming performance and the peer-evaluated contribution indices. An interpretation of the results and their implication to instructional design theory and practices were provided along with some suggestions for future research.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.83-92
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• 2009

Complicated business logic makes program complexity more complicated. It's inevitable that the program must undergo reengineering processes all the way of in its lifetime. Hot spot analysis that has diverse purposes is getting an important question more and more. As a rule, reengineering process is done by UML model-based approach to analyze the legacy system. The smallest fragment of targets to be analysed is unit, that is function or class. Today's software development is to deal with huge change of software product and huge class including heavy quantity of LOC(Lines Of Code). However, analysis of unit is not precise approach process for reliable reengineering consequence. In this paper, we propose very precise hot spot analysis approach using Aspect-Oriented Programming languages, such as AspectJ. Typically the consistency between UML and source is needed code to redefine the modified library or framework boundaries. But reengineering approach using AOP doesn't need to analyze UML and source code. This approach makes dynamic event log data that contains detailed program interaction information. This dynamic event log data makes it possible to analyze hot spot.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.19-24
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• 2018

For a long time now, general-purpose processors have provided dedicated hardware / software tracing modules to provide developers with tools to fix bugs. A hardware tracer generates its enormous data into a log that is used for both performance analysis and debugging. Processor Trace (PT) is a new hardware-based tracing feature for Intel CPUs that traces branches executing on the CPU, which allows the reconstruction of the control flow of all executed code with minimal labor. Hardware tracer has been integrated into the operating system, which allows tight integration with its profiling and debugging mechanisms. However, in the Windows environment, existing studies related to PT focused on decoding only one flow in sequence. In this paper, we propose an extended PT decoder structure that provides basic data for real-time trace and malicious code detection using the functions provided by PT in Windows environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.9 no.1
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• pp.57-70
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• 1999

Since a hybrid firewall filters packets at a network layer along with providing gateway functionalities at an application layer, it has a better performance than an If filtering firewall. In addition, it provides both the various kinds of access control mechanisms and transparent services to users. However, the security policies of a network layer are different from those of an application layer. Thus, the user interfaces for managing a hybrid firewalls in a consistent manner are needed. In this paper, we implement a graphical user interface to provide access control mechanisms and management facilities for a hybrid firewall such as log analysis, a real-time monitor for network traffics, and the statisics on traffics. And we also propose a new rule script language for specifying access control rules. By using the script language, users can generate the various forma of access control rules which are adapted by the existing firewalls.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.234-234
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• 2023

유해 남조 대발생(Harmful Algal blooms, HABs)이 담수호에 발생하면 마이크로시스틴과 같은 독성물질과 맛·냄새 물질을 생성하여 상수원이용과 친수활동을 방해한다. 그래서 유해 남조 대발생 전 유해남조 세포수를 예측하여 선제적 대응하는 것은 중요하다. 따라서 본 연구는 머신러닝기반 Random Forest(RF)를 활용하여 팔당댐 앞의 유해남조 세포수를 예측하는 모델을 개발하고 성능을 평가하고자 한다. 모델 구축을 위해 2012년 4월부터 2021년 12월까지의 팔당호(삼봉리, 경안천) 및 남북한강(의암댐~이포보)권역의 조류, 수질, 수리/수문, 기상 자료를 수집하여 입력 및 출력 자료로 이용하였다. 수집된 데이터에는 다양한 입력변수들이 있어 남조 세포수 예측 성능 비교를 위한 전체 26개 변수 적용과 통계학적으로 상관관계가 높은 12개 변수 적용을 통해 모델을 구축하였다. 입력, 출력 자료로 이용한 유해남조 세포수는 로그변환된 값으로 사용하였으며 일반적인 조류 시료 채취기간이 7일이므로 7일 후를 예측하기 위한 모델을 구축하였다. 구축한 모델의 성능은 실측데이터와 예측데이터의 R²로 산출하여 평가하였다. 전체 26개 입력변수로 모델 구축 후 학습 및 검증 수행 결과 R²의 학습 0.803, 검증 0.729로 나타났고, 유해남조 세포수와 유의미한 상관관계를 보이는 12개 입력변수로 모델 구축 후 학습 및 검증 수행 R²은 학습 0.784, 검증 0.731로 나타났다. 두 모델의 성능을 살펴본 결과 입력변수 개수의 변화에 따른 성능차이는 크지 않은 것으로 나타났으며, 남조세포수 예측을 위한 모델로서 활용가능함을 알 수 있었다. 향후 연구에서는 Random Forest 외 다른 기계학습 모델들과 딥러닝 모델을 통해 남조세포수 예측 성능이 높은 모델을 구축해볼 필요성이 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.240-243
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• 2021

Recently, there have been research results of applying Big data and AI technologies to the evaluation and individual learning for education. It is information technology innovations that collect dynamic and complex data, including student personal records, physiological data, learning logs and activities, learning outcomes and outcomes from social media, MOOCs, intelligent tutoring systems, LMSs, sensors, and mobile devices. In addition, e-learning was generated a large amount of learning data in the COVID-19 environment. It is expected that learning analysis and AI technology will be applied to extract meaningful patterns and discover knowledge from this data. On the learner's perspective, it is necessary to identify student learning and emotional behavior patterns and profiles, improve evaluation and evaluation methods, predict individual student learning outcomes or dropout, and research on adaptive systems for personalized support. This study aims to contribute to research in the field of education by researching and classifying machine learning technologies used in anomaly detection and recommendation systems for educational data.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.47-53
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• 2022

Recently, with the change of the intelligent security paradigm, study to apply various information generated from various information security systems to AI-based anomaly detection is increasing. Therefore, in this study, in order to convert log-like time series data into a vector, which is a numerical feature, the CBOW and Skip-gram inference methods of deep learning-based Word2Vec model and statistical method based on the coincidence frequency were used to transform the published ADFA system call data. In relation to this, an experiment was carried out through conversion into various embedding vectors considering the dimension of vector, the length of sequence, and the window size. In addition, the performance of the embedding methods used as well as the detection performance were compared and evaluated through GRU-based anomaly detection model using vectors generated by the embedding model as an input. Compared to the statistical model, it was confirmed that the Skip-gram maintains more stable performance without biasing a specific window size or sequence length, and is more effective in making each event of sequence data into an embedding vector.

• Journal of Internet of Things and Convergence
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• v.10 no.3
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• pp.25-33
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• 2024

In tomographic image reconstruction, the focus is on developing CT image reconstruction methods that can maintain high image quality while reducing patient radiation exposure. Typically, statistical image reconstruction methods have the ability to generate high-quality and accurate images while significantly reducing patient radiation exposure. However, in cases like CT image reconstruction, which involve multi-dimensional parameter estimation, the degree of the Hessian matrix of the penalty function is very large, making it impossible to calculate. To solve this problem, the author proposed the PEMG-1 algorithm. However, the PEMG-1 algorithm has issues with the convergence speed, which is typical of statistical image reconstruction methods, and increasing the penalty log-likelihood. In this study, we propose a reconstruction algorithm that ensures fast convergence speed and monotonic increase in likelihood. The basic structure of this algorithm involves sequentially updating groups of pixels instead of updating all parameters simultaneously with each iteration.

• Journal of Korea Spatial Information System Society
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• v.10 no.2
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• pp.1-12
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• 2008

Automatic semiconductor manufacture system generating transaction from 50,000 to 500,000 per a second needs storage management system processing very large data at once. A lot of storage management systems are researched for storing very large data. Existing storage management system is typical DBMS on a disk. It is difficult that the DBMS on a disk processes the 500,000 number of insert transaction per a second. So, the DBMS on main memory appeared to use memory. But it is difficultthat very large data stores into the DBMS on a memory because of limited amount of memory. In this paper we propose storage management system using insert transaction of a block unit that can process insert transaction over 50,000 and store data on low storage cost. A transaction of a block unit can decrease cost for a log and index per each tuple as transforming a transaction of a tuple unit to a block unit. Besides, the proposed system come cost to decompress all block of data because the information of each field be loss. To solve the problems, the proposed system generates the index of each compressed block to prevent reducing speed for searching. The proposed system can store very large data generated in semiconductor system and reduce storage cost.