• Journal of KIISE:Software and Applications
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• v.32 no.2
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• pp.75-84
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• 2005

Sequential pattern mining is an important data mining problem with broad applications. While the current methods are generating sequential patterns within a single attribute, the proposed method is able to detect them among different attributes. By incorporating these additional attributes, the sequential patterns found are richer and more informative to the user This paper proposes a new method for generating multi-dimensional sequential patterns with the use of Hellinger entropy measure. Unlike the Previously used methods, the proposed method can calculate the significance of each sequential pattern. Two theorems are proposed to reduce the computational complexity of the proposed system. The proposed method is tested on some synthesized purchase transaction databases.

• Annual Conference of KIPS
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• 2014.11a
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• pp.333-335
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• 2014

스마트폰의 보급에 따라 사용자간에 공유되는 정보가 점점 늘어나고 있다. 시간표 관리 시스템의 경우 과거에는 개별 사용자의 시간표를 관리하는 것이 주요 기능이었으나, 사용자간의 시간표를 비교함으로써 공통으로 가능한 비어있는 시간을 찾는 등 공유 기능에 대한 필요성이 증대되고 있다. 본 논문에서는 이화여대 학생들에게 특화된 편의성과 공유기능을 제공하는 시간표 관리 어플리케이션과 서버 프로그램을 설계하고 개발한다. 제공되는 편의성으로는 사용자가 자신의 시간표를 직접 입력하는 것이 아니라 이화 포탈(eportal.ewha.ac.kr)에서 내 시간표 정보를 자동으로 바로 받아 볼 수 있는 기능과 관련 교과목의 담당교수와 강의실 등의 추가적인 정보가 같이 저장되도록 하여 시간표 작성 시간이 획기적으로 줄일 수 있도록 한다. 또한 모든 사용자가 계정을 등록하여 타 사용자와 함께 듣는 수업과 공강 시간을 자동으로 계산해서 보여주는 공유 기능을 제공한다. 사용자 피드백을 통한 본 논문에서 개발된 시스템을 평가한 결과 편의성과 공유 기능에 만족함을 확인하였다.

• Journal of KIISE:Software and Applications
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• v.27 no.8
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• pp.795-803
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• 2000

This paper presents a shared data decomposition model for improving concurrency in multi-user, distributed software developments. In our model, the target software system is decomposed into the independent components based on project roles to be distributed over clients. The distributed components are decomposed into view objects and core objects to replicate only view objects in a distributed collaboration session. The core objects are kept in only one client and the locking is used to prevent inconsistencies. The grain size of a lock is a role instead of a class which is commonly used as the locking granularity in the existing systems. The experimental result shows that our model reduces response time by 12${\sim}$18% and gives good scalability.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.261-279
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• 2023

In this article, the uplink achievable rate is investigated for massive multiple-input multiple-output (MIMO) under correlated Ricean fading channel, where each base station (BS) and user are both deployed multiple antennas. Considering the availability of prior knowledge at BS, two different channel estimation approaches are adopted with and without prior knowledge. Based on these channel estimations, a two-layer decoding scheme is adopted with maximum ratio precoding as the first layer decoder and optimal second layer precoding in the second layer. Based on two aforementioned channel estimations and two-layer decoding scheme, the exact closed form expressions for uplink achievable rates are computed with and without prior knowledge, respectively. These derived expressions enable us to analyze the impacts of line-of-sight (LoS) component, two-layer decoding, data transmit power, pilot contamination, and spatially correlated Ricean fading. Then, numerical results illustrate that the system with spatially correlated Ricean fading channel is superior in terms of uplink achievable rate. Besides, it reveals that compared with the single-layer decoding, the two-layer decoding scheme can significantly improve the uplink achievable rate performance.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.163-171
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• 2023

The intelligent transportation system has made a huge leap in the level of human services, which has had a positive impact on the quality of life of users. On the other hand, these services are becoming a new source of risk due to the use of data collected from vehicles, on which intelligent systems rely to create automatic contextual adaptation. Most of the popular privacy protection methods, such as Dummy and obfuscation, cannot be used with many services because of their impact on the accuracy of the service provided itself, they depend on changing the number of vehicles or their physical locations. This research presents a new approach based on the shuffling Nicknames of vehicles. It fully maintains the quality of the service and prevents tracking users permanently, penetrating their privacy, revealing their whereabouts, or discovering additional details about the nature of their behavior and movements. Our approach is based on creating a central Nicknames Pool in the cloud as well as distributed subpools in fog nodes to avoid intelligent delays and overloading of the central architecture. Finally, we will prove by simulation and discussion by examples the superiority of the proposed approach and its ability to adapt to new services and provide an effective level of protection. In the comparison, we will rely on the wellknown privacy criteria: Entropy, Ubiquity, and Performance.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.133-139
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• 2009

A P2P technology in distributed computing fields is presented various methods to share resources between network connected peers. This is very efficient that a degree of resources to good use as compared with peers by using centralized network by a few servers. However peers to compose P2P system is not always online status, therefore it is difficult to support high reliability to user. In our previous work of this paper, it is contributing to reduce the loading rates to select of new resource support peer but a selection method the peers to share works to download resources is very simple that it is just selected about peer to have lowest job. In this paper, we reduced frequency offline peers by estimate based on a average value of success rates for peers.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.601-604
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• 2021

본 논문에서는 모바일 디바이스의 카메라로부터 얻은 RGB이미지를 분석하여 장애물을 안정적으로 탐지할 수 있는 프레임워크를 제안한다. 본 논문에서는 장애물을 안정적으로 찾기 위해 RANSAC(Random Sample Consensus)기반의 다중 평면 방식을 이용한 위험감지 시스템을 제안한다. 우리의 접근 방식은 RGB영상으로부터 특징점(Feature point)을 추출하고, 특징점을 분석(Feature point analysis)하여 영상내의 평면을 감지한다. 복잡한 지형으로 인해 생성되는 다수의 평면을 RANSAC을 통해 단일 평면으로 정규화하고, 이로부터 특징점을 분류하기 위한 기준점을 계산한다. 모바일 디바이스의 위치와 회전 제약 없이 효과적으로 기준평면(Reference plane)을 탐색할 수 있고, 영상 내 특징점을 실시간으로 계산한다. 다양한 실험을 통해 기준평면과 장애물과의 거리를 파악하여 장애물을 효과적으로 분류하는 결과를 얻었다. 우리의 기법은 실세계에서의 위험요소를 감지하고 모바일 디바이스 사용자의 안전성 확보에 활용할 수 있을 거라 기대한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.530-532
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• 2021

This paper deals with AI education service that enables developers who have completed basic programming education to program in C/C++ language in order to learn big data and artificial intelligence. In addition, a customized development environment configuration system according to the development environment and how the user implements and tests artificial intelligence are explained. And also it has a function to check the effect on artificial intelligence through manipulation of various internal parameters. It is expected that it will be possible to develop artificial intelligence education services without language restrictions through networks in the future.

• Annual Conference of KIPS
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• 2023.11a
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• pp.737-739
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• 2023

몰입형 가상현실 시스템은 더 나은 3차원 시각정보를 제공할 수 있어, 의료계에서 해부학에 대한 이해를 높이는 데 사용되고 있다. 우리는 몰입형 가상현실에서 다중 사용자가 함께 MRI 영상으로부터 생성된 볼륨 렌더링 된 객체를 관찰하고 수술을 계획할 수 있는 시스템을 개발하여 소개하고자 한다.