• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.313-315
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• 2003

분할 및 합병 개념에 바탕을 둔 모듈라 신경망이 자동차 번호판 문자 인식에서 단일 신경망 사용 보다 학습 질 측면이나 학습 속도 면에서 좋은 결과를 보였다. 본 논문에서는 번호판 인식을 위한 모듈라 신경망 구성 시, 특징 벡터 클러스터링 방법에 따른 모듈라 신경망의 성능을 평가하였다. K-means Clustering 알고리즘을 이용하여 유사한 특징 벡터를 그룹핑하는 방법과 본 논문에서 제안한 알고리즘을 사용하여 유사하지 않는 특징 벡터들을 그룹핑하는 방법 각각을 구현하여 실험하였다. 실험결과, 유사하지 않는 특징 벡터들로 모듈라 신경망을 구성할 경우가 그렇지 않은 경우보다 좋은 인식 결과를 보였다.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1177-1190
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• 2009

The traffic operating speed is one of important information to measure a road capacity. When we supply the information of the road of high traffic by using navigation, offering the present traffic information and the forecasted future information are the outstanding functions to serve the more accurate expected times and intervals. In this study, we proposed the traffic speed forecasting model using the accumulated traffic speed data of the road and highway and forecasted the average speed for each the road and high interval and each time interval using Fourier transformation and time series regression model with trigonometrical function. We also propose the proper method of missing data imputation and treatment for the outliers to raise an accuracy of the traffic speed forecasting and the speed grouping method for which data have similar traffic speed pattern to increase an efficiency of analysis.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.38-45
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• 2022

Due to the development of fintech technology, financial transactions using smart phones are being activated. The password for user authentication during financial transactions is entered through the virtual keypad displayed on the screen of the smart phone. When the password is entered, the attacker can find out the password by capturing it with a high-resolution camera or spying over the shoulder. A virtual keypad with security applied to prevent such an attack is difficult to input on a small touch-screen, and there is still a vulnerability in peeping attacks. In this paper, the entire keypad is divided into several groups and displayed on a small screen, touching the group to which the character to be input belongs, and then touching the corresponding character within the group. The proposed method selects the group to which the character to be input belongs, and displays the keypad in the group on a small screen with no more than 10 keypads, so that the size of the keypad can be enlarged more than twice compared to the existing method, and the location is randomly placed, hence location of the touch attacks can be blocked.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.4
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• pp.11-23
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• 2011

The objective of this paper is to develop a daily pattern clustering algorithm using historical traffic data that can reliably detect under various traffic flow conditions in urban streets. The developed algorithm in this paper is categorized into two major parts, that is to say a macroscopic and a microscopic points of view. First of all, a macroscopic analysis process deduces a daily peak/non-peak hour and emphasis analysis time zones based on the speed time-series. A microscopic analysis process clusters a daily pattern compared with a similarity between individuals or between individual and group. The name of the developed algorithm in microscopic analysis process is called "Two-step speed clustering (TSC) algorithm". TSC algorithm improves the accuracy of a daily pattern clustering based on the time-series speed variation data. The experiments of the algorithm have been conducted with point detector data, installed at a Ansan city, and verified through comparison with a clustering techniques using SPSS. Our efforts in this study are expected to contribute to developing pattern-based information processing, operations management of daily recurrent congestion, improvement of daily signal optimization based on TOD plans.

• Journal of KIISE
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• v.44 no.11
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• pp.1209-1218
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• 2017

A variety of sensors is widely used these days, and it has become much easier to acquire spatial big data streams from various sources. Since spatial data streams have inherently skewed and dynamically changing distributions, the system must effectively distribute the load among workers. Previous studies to solve this load imbalance problem are not directly applicable to processing spatial data. In this research, we propose Adaptive Spatial Key Grouping (ASKG). The main idea of ASKG is, by utilizing the previous distribution of the data streams, to adaptively suggest a new grouping scheme that evenly distributes the future load among workers. We evaluate the validity of the proposed algorithm in various environments, by conducting an experiment with real datasets while varying the number of workers, input rate, and processing overhead. Compared to two other alternative algorithms, ASKG improves the system performance in terms of load imbalance, throughput, and latency.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.2
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• pp.141-148
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• 2011

This study presents the optimal grouping technique of columns which groups together columns of similar shortening trends to improve the efficiency of column shortening compensation. Here, Kohonen's self-organizing feature map which can classify patterns of input data by itself with unsupervised learning was used as the optimal grouping algorithm. The Kohonen network applied in this study is composed of two input neurons and variable output neurons, here the number of output neuron is equal to the column groups to be classified. In input neurons the normalized mean and standard deviation of shortening of each columns are inputted and in the output neurons the classified column groups are presented. The applicability of the proposed algorithm was evaluated by applying it to the two buildings where column shortening analyses had already been performed. The proposed algorithm was able to classify columns with similar shortening trends as one group, and from this we were able to ascertain the field-applicability of the proposed algorithm as the optimal grouping of column shortening.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.150-152
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• 1999

This paper presents a new generator grouping method for speed-up of transient stability evaluation in SIME. The critical generators are grouped by new indices in the early stage of time domain simulation. And the simulation can be early terminated by fast stability evaluation using the time trajectory of equivalent one machine infinite bus. The main advantage of the proposed method is that it can be applied to stable cases as well as unstable ones. The effectiveness of the proposed method is tested on the PSS/E test system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.221-224
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• 2009

본 논문에서는 OpenVG Specification에서 제안한 파이프라인을 기능별, 혹은 연산별로 그룹화 하여 하드웨어 구현에 적합한 새로운 파이프라인을 제안하였다. 래스터라이저에서는 스캔라인 알고리즘과 엣지 플래그 알고리즘의 장점들을 포함하는 스캔라인 엣지 플래그 알고리즘을 구현하여 적용하였으며, Non-Zero 룰을 만족하기 위해 엣지의 방향에 따라 Winding 횟수를 기록하기 위한 추가 버퍼를 이용하였다. 또한, 래스터라이저는 안티 앨리어싱을 위해 슈퍼 샘플링 과정을 수행한다. 액티브 엣지 생성 시 클리핑을 동시에 수행하여 이후 과정에서의 불필요한 연산을 줄였고, 액티브 엣지들의 정렬을 수행하지 않는 방법을 사용하여 처리 속도를 향상 시켰다. 본 연구에서 설계된 OpenVG Rasterizer는 크로노스 그룹에서 제공하는 샘플 이미지들을 사용하여 검증하였다.

• Journal of Digital Contents Society
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• v.13 no.4
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• pp.567-574
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• 2012

In this paper, we propose a buffering scheme to improve the performance by adaptively adjusting the length of the buffering period according to the degree of data duplication among peers that belong to each specific playback period in P2P live streaming systems. To do so, we first assign all the peers into many groups depending on their playback time positions. We then determine the length of their buffering periods according to the number of peers belonging to each group. That is, we increase the length of the buffering periods when much buffered data are duplicated among peers within a group while we decrease it when few buffered data are duplicated. By simulation experiments, we show that our proposed buffering scheme can improve the performance significantly compared to the fixed length buffering scheme that have been employed in the existing mesh-based P2P systems.

• 한국HCI학회:학술대회논문집
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• 2006.02b
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• pp.499-504
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• 2006

본 연구는 사회 트렌드 변화에 따른 적응 속도가 빠르고 소비 또한 타 계층에 비해서 매우 높은 지수로 구성된 20대 여성을 연구 대상으로 선정하여 이들의 다양한 감성 니즈를 파악하고 현 시점에서의 트렌드 분석을 통해 그들의 요구와 취향이 적절하게 반영된 휴대폰 GUI디자인을 대기화면을 중심으로 제시해보고자 한다. 연구방법은 사용자 감성 니즈 분석을 위해 FGI 기법을 사용하였고, 트렌드 분석을 위해서는 Town Watching과 잡지 및 웹사이트 분석을 통하여 감성형용사를 추출하였다. 추출된 형용사간의 상관치와 의미에 따라 상대축을 설정하여 공간을 설정하고 공간 위에 각 형용사와 이미지를 배열하여 감성형용사 공간을 설정하였다. 이러한 감성평가 결과에 FGI를 통해 구분한 사용자의 감성 니즈 분류를 기초로 하여 3가지의 감성테마로 분류를 하였다. 분류 기준은 사용자들이 원하는 휴대폰 이미지와 트랜드 분석을 하여 얻은 형용사를 비교하여 그룹핑하였다. 분류된 그룹에 해당하는 테마명을 지정해주었고 그에 맞는 휴대폰 GUI 디자인을 제안한다. 본 연구는 시장세분화 경향에 따라 특정 사용자 층에 대한 연구가 중요한 시점에서 20대 여성이라는 특정 사용자층에 대한 조사를 바탕으로 디자인을 이끌어내었다는데 의의가 있다.