• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.45-50
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• 2019

With the gradual advancement of IT, passive work processes are automated and the overall quality of life has greatly improved. This is made possible by the formation of an organic topology between a wide variety of real-life smart devices. To serve these diverse smart devices, businesses or users are using the cloud. The services in the cloud are divided into Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). SaaS runs on PaaS, and PaaS runs on IaaS. Since IaaS is the basis of all services, an algorithm is required to operate virtualization resources efficiently. Among them, desktop resource virtualization is used for resource high availability of unused state time of existing desktop PC. Clustering of hierarchical structures is important for high availability of these resources. In addition, it is very important to select a suitable algorithm because many clustering algorithms are mainly used depending on the distribution ratio and environment of the desktop PC. If various attempts are made to find an algorithm suitable for desktop resource virtualization in an operating environment, a great deal of power, time, and manpower will be incurred. Therefore, this paper proposes a resource clustering simulator for cluster selection of desktop virtualization. This provides a clustering simulation to properly select clustering algorithms and apply elements in different environments of desktop PCs.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.1
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• pp.35-42
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• 2016

The core of the machine vision that is frequently used at the pre/post-production stages is a marker alignment technology. In this paper, a method to detect the angle and position of a product at high speed by use of a unique pattern present in the marker stamped on the product, and calibrate them is proposed. In the proposed method, to determine the angle and position of a marker, the candidates of the marker are extracted by using a variation of the integral histogram, and then clustering is applied to reduce the candidates. The experimental results revealed about 5s 719ms improvement in processing time and better precision in detecting the rotation angle of a product.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.911-921
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• 2004

Accurate minutiae extraction from input fingerprint images is one of the critical modules in robust automatic fingerprint identification system. However, the performance of a minutiae extraction is heavily dependent on the quality of the input fingerprint images. If the preprocessing is performed according to the fingerprint image characteristics in the image enhancement step, the system performance will be more robust. In this paper, we propose a knowledge-based preprocessing method, which extracts S features (the mean and variance of gray values, block directional difference, orientation change level, and ridge-valley thickness ratio) from the fingerprint images and analyzes image quality with Ward's clustering algorithm, and enhances the images with respect to oily/neutral/dry characteristics. Experimental results using NIST DB 4 and Inha University DB show that clustering algorithm distinguishes the image Quality characteristics well. In addition, the performance of the proposed method is assessed using quality index and block directional difference. The results indicate that the proposed method improves both the quality index and block directional difference.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1054-1061
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• 2003

Q-Learning, a representative algorithm of reinforcement learning, experiences repeatedly until estimation values about all state-action pairs of state space converge and achieve optimal policies. When the state space is high dimensional or continuous, complex reinforcement learning tasks involve very large state space and suffer from storing all individual state values in a single table. We introduce Q-Map that is new function approximation method to get classified policies. As an agent learns on-line, Q-Map groups states of similar situations and adapts to new experiences repeatedly. State-action pairs necessary for fine control are treated in the form of rule. As a result of experiment in maze environment and mountain car problem, we can achieve classified knowledge and extract easily rules from Q-Map

• KIPS Transactions on Software and Data Engineering
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• v.6 no.10
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• pp.487-492
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• 2017

Clustering is a technique which is used to measure similarities between data in big data analysis and data mining field. Among various clustering methods, k-Modes algorithm is representatively used for categorical data. To increase the performance of iterative-centric tasks such as k-Modes, a distributed and concurrent framework Spark has been received great attention recently because it overcomes the limitation of Hadoop. Spark provides an environment that can process large amount of data in main memory using the concept of abstract objects called RDD. Spark provides Mllib, a dedicated library for machine learning, but Mllib only includes k-means that can process only continuous data, so there is a limitation that categorical data processing is impossible. In this paper, we design RDD for k-Modes algorithm for categorical data clustering in spark environment and implement an algorithm that can operate effectively. Experiments show that the proposed algorithm increases linearly in the spark environment.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.4
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• pp.219-226
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• 2013

Feature selection is the one of important issues in the field of machine learning and pattern recognition. It is the technique to find a subset from the source data and can give the best classification performance. Ie, it is the technique to extract the subset closely related to the purpose of the classification. In this paper, we experimented to select the best feature subset for improving classification accuracy when classify success and failure factors in software reuse. And we compared with existing studies. As a result, we found that a feature subset was selected in this study showed the better classification accuracy.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.49-50
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• 2021

최근 얼굴인식 시장의 규모가 증가하고 있으며 모바일 결제, 디바이스 본인인증, 신분확인 및 출입 통제, 치안 등의 다양한 분야에 적용 되고 있다. 온라인 화상회의에서 출석하는데 번거로운 방식들이 사용되고 있으며 대규모 회의일 경우 인원파악에 어려움을 겪고 있다. 본 논문에서는 온라인 회의를 할 때 머신러닝을 이용하여 여러 수강자의 얼굴을 자동으로 인식하여 출석을 확인하는 출석시스템을 제안한다. 이후 사용자는 출석 시스템을 사용해 객관적인 데이터를 확보하게 된다.

• Proceedings of the Korea Multimedia Society Conference
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• 1998.10a
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• pp.168-173
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• 1998

최근의 급속한 정보 통신 기술의 발달로 인하여 음향 효과, 정교한 그래픽 처리 등을 이용하는 실제와 유사한 게임 소프트웨어들이 제작되고 있으며, 게임 개발자들에게 개발 기간 단축과 개발의 편이성을 위한 멀티미디어 게임 제작 도구가 필요하게 되었다. 이러한 멀티미디어 게임 저작 도구의 핵심 구성 요소가 되는 클래스 라이브러리 개발을 위해 멀티미디어 게임 객체와 객체들의 행위들을 정교하게 분류할 수 있는 게임 클래스 분류법이 필요하다. 본 논문에서는 게임 클래스 시공간 개념에 대한 정교한 방법을 제안한다. 이를 위해 시공간 개념 중심으로 Enumerative 분류 방법을 이용하여 게임 클래스들을 분류하는 방법을 제시하였으며 게임 클래스의 유사도에 의한 클래스 클러스터링을 통하여 C++ 언어의 특징인 클래스들 사이의 계층 구조를 표시할 수 있으며 소프트웨어의 클래스 구조를 쉽게 확장하여 클래스 구조를 변경할 수 있도록 제시하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.49-50
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• 2019

데이터를 기반으로 한 기계학습은 데이터의 양, 학습 모델, 그리고 데이터의 특징 등 다양한 환경에 민감한 특징을 지니고 있어, 보다 효율적인 기계학습을 위해 데이터의 전처리 과정을 필요로 한다. 데이터의 전처리 과정이란 특징 선택(Feature selection), 노이즈 데이터의 제거, 차원 감소(Demension reduction), 클러스터링(Clustering) 등 보다 효율적인 기계학습을 위한 방법이다. 따라서 본 논문에서는 다양한 환경에서 보다 효율적인 기계학습을 위한 데이터 전처리 기술의 종류 및 간단한 특징에 대해 서술한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.357-359
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• 2023

This paper proposes a hybrid recommendation system (RS) model that overcomes the limitations of traditional approaches such as data sparsity, cold start, and scalability by combining collaborative filtering and context-aware techniques. The objective of this model is to enhance the accuracy of recommendations and provide personalized suggestions by leveraging the strengths of collaborative filtering and incorporating user context features to capture their preferences and behavior more effectively. The approach utilizes a novel method that combines contextual attributes with the original user-item rating matrix of CF-based algorithms. Furthermore, we integrate k-mean++ clustering to group users with similar preferences and finally recommend items that have highly rated by other users in the same cluster. The process of partitioning is the use of the rating matrix into clusters based on contextual information offers several advantages. First, it bypasses of the computations over the entire data, reducing runtime and improving scalability. Second, the partitioned clusters hold similar ratings, which can produce greater impacts on each other, leading to more accurate recommendations and providing flexibility in the clustering process. keywords: Context-aware Recommendation, Collaborative Filtering, Kmean++ Clustering.