• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.485-494
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• 2008

Huge amounts of various web items such as keywords, images, and web pages are being made widely available on the Web. The popularities of such web items continuously change over time, and mining temporal patterns in popularities of web items is an important problem that is useful for several web applications. For example, the temporal patterns in popularities of search keywords help web search enterprises predict future popular keywords, enabling them to make price decisions when marketing search keywords to advertisers. However, presence of millions of web items makes it difficult to scale up previous techniques for this problem. This paper proposes an efficient method for mining temporal patterns in popularities of web items. We treat the popularities of web items as time-series, and propose gapmeasure to quantify the similarity between the popularities of two web items. To reduce the computation overhead for this measure, an efficient method using the Fast Fourier Transform (FFT) is presented. We assume that the popularities of web items are not necessarily following any probabilistic distribution or periodic. For finding clusters of web items with similar popularity trends, we propose to use a density-based clustering algorithm based on the gap measure. Our experiments using the popularity trends of search keywords obtained from the Google Trends web site illustrate the scalability and usefulness of the proposed approach in real-world applications.

• Korean journal of applied entomology
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• v.50 no.4
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• pp.257-265
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• 2011

To investigate host preferencse of Myzus persicae on 15 sweet pepper cultivars grown in Jeonnam Province, EPG (electrical penetration graph) and life table experiments were carried out in the laboratory. Phloem phase times were significantly longer on Ferrari, Jinju, Debla, and Rapido than Orobell and Thialf. Non-penetration times were conversely observed. Life span, reproduction period, total fecundity, and intrinsic rate of increase ($r_m$) of M. persicae, were higher on Ferrari, Debla, Orange glory, and Jinju than on Purple, GreenAce, Orobell, and Thialf. On the bases of these results, we conclude that M. persicae preferred Ferrari, Jinju, Orange glory, and Debla among the 15 tested sweet pepper varieties. However, we could not show the preference of the aphid for Purple, Orobell, and Thialf.

• Journal of the Economic Geographical Society of Korea
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• v.10 no.1
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• pp.44-63
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• 2007

The purpose of this study is to propose mining processes in the large trip-transaction database of the Metropolitan Seoul area and to analyze the spatial characteristics of travel behavior. For the purpose. this study introduces a mining algorithm developed for exploring trip patterns from the large trip-transaction database produced every day by transit users in the Metropolitan Seoul area. The algorithm computes trip chains of transit users by using the bus routes and a graph of the subway stops in the Seoul subway network. We explore the transfer frequency of the transit users in their trip chains in a day transaction database of three different years. We find the number of transit users who transfer to other bus or subway is increasing yearly. From the trip chains of the large trip-transaction database, trip patterns are mined to analyze how transit users travel in the public transportation system. The mining algorithm is a kind of level-wise approaches to find frequent trip patterns. The resulting frequent patterns are illustrated to show top-ranked subway stations and bus stops in their supports. From the outputs, we explore the travel patterns of three different time zones in a day. We obtain sufficient differences in the spatial structures in the travel patterns of origin and destination depending on time zones. In order to examine the changes in the travel patterns along time, we apply the algorithm to one day data per year since 2004. The results are visualized by utilizing GIS, and then the spatial characteristics of travel patterns are analyzed. The spatial distribution of trip origins and destinations shows the sharp distinction among time zones.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.3
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• pp.461-467
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• 2019

Advances in location-aware and IoT (Internet of Things) technology increase the rapid generation of massive movement data. Knowledge discovery from massive movement data helps us to understand the urban flow and traffic management. This paper proposes a method to analyze frequent origin-destination sequence patterns from irregular spatiotemporal taxi pick-up locations. The proposed method starts by conducting cluster analysis and then run a frequent sequence pattern analysis based on identified clusters as a base unit. The experimental data is Seoul taxi trajectory data between 7 a.m. and 9 a.m. during one week. The experimental results present that significant frequent sequence patterns occur within Gangnam. The significant frequent sequence patterns of different regions are identified between Gangnam and Seoul City Hall area. Further, this study uses administrative boundaries as a base unit. The results based on administrative boundaries fails to detect the frequent sequence patterns between different regions. The proposed method can be applied to decrease not only taxis' empty-loaded rate, but also improve urban flow management.

• Journal of the Microelectronics and Packaging Society
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• v.29 no.1
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• pp.49-54
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• 2022

The copper plating process used to fabricate the submicron damascene pattern of Cu wiring for Si wafer was applied to the plating of a PCB pattern of several tens of microns in size using the same organic additives and current density conditions. In this case, the non-uniformity of the plating thickness inside the pattern was observed. In order to quantitatively analyze the cause, a numerical calculation considering the solution flow and electric field was carried out. The calculation confirmed that the depletion of Cu²⁺ ions in the solution occurred relatively earlier at the bottom corner than the upper part of the pattern due to the plating of the sidewall and the bottom at the corner of the pattern bottom. The diffusion coefficient of Cu²⁺ ions is 2.65 10^-10 m²/s, which means that Cu²⁺ ions move at 16.3 ㎛ per second on average. In the cases of small damascene patterns, the velocity of Cu²⁺ ions is high enough to supply sufficient ions to the inside of the patterns, while sufficient time is required to replenish the exhausted copper ions in the case of a PCB pattern having a size of several tens of microns. Therefore, it is found that the thickness uniformity can be improved by reducing the current density to supply sufficient copper ions to the target area.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.213-216
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• 2007

본 논문에서는 DFT(Discret Fourier Transform)과 웨이블렛을 이용한 고장진단 알고리즘을 제안한다. 제안된 방법은 주파수 기반의 DFT에 의한 고장패턴의 추출방법과 시간-주파수 기반의 웨이블렛을 이용한 고장패턴의 추출방법을 제안한다. 유도전동기의 진단을 DFT와 웨이블렛에 의해 추출된 특정값들을 효과적으로 융합할 수 있는 융합 알고리즘에 의해 수행된다. 개발된 알고리즘은 다양한 실측 데이터에 적용하여 그 타당성을 보이고자 한다.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1268-1269
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• 2011

본 논문에서는 친환경 전동기기의 에너지 저장장치로 널리 사용되고 있는 Lead-Acid 배터리의 특성을 나타내었고, 급속충전을 하기 위한 충전패턴을 보였다. 기존 정전류-정전압 충전패턴이 적용된 상용 제품과 당사가 개발한 급속충전기의 비교 실험을 통해 충전 시간단축을 확인하였다. 급속충전 시스템의 적용을 통해 친환경 전동기기의 성능이 개선될 수 있음을 확인하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.498-500
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• 2003

인터넷의 급속한 팽창으로 인해 가을 정보의 양이 폭발적으로 증가하고 있다. 웹 사용자에게 이용 가치가 없는 정보 범람(information overflow)안이 발생한다면 효율적인 정보검색이 되지 못하므로 사용자가 원하는 정보만을 얻을 수 있다면 시간과 미숙한 정보의 검색을 방지 할 수 있다. 본 논문에서는 한국어 질의 생성과 관련하여 웹 사용자의 편의성과 효율성을 고려한 한국어 질의 처리 방법론과 개인형 메타 검색 모델을 제안하고자 한다. 한국어 질의를 기본으로 하여 한국어 문장 패턴 및 개인 정보 평가 구성 요소를 이용한 방법론과 모델을 제안하고자 한다.

• Proceedings of the KIEE Conference
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• 2007.10c
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• pp.266-267
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• 2007

본 논문에서는 실제 사무용 빌딩에서 많이 사용하는 부하를 조사하여 종류와 시간대별 부하 패턴을 전기적으로 모델링하였고, 일반 저항 부하모델과 연료전지의 입력전류 패턴을 비교 분석하여 빌딩부하 모델의 타당성을 검증하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.817-819
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• 2003

인터넷 환경의 급속한 발전으로 인하여 전자우편을 통한 메시지 교환은 급속히 증가하고 있다. 하지만 전자우편의 편리성에도 불구하고 개인이나 기업에서는 스팸메일로 인한 시간과 비용의 낭비가 크게 증가하고 있다. 기존의 스팸메일에 대한 연구는 패턴 매칭에 의한 분류나 확률에 의한 분류가 대부분인데 이와 같은 방법들은 변형된 형태의 메일에 대한 탐지에 있어서 비효율적이다. 본 논문에서는 기존의 연구에 대한 문제점을 보완하기 위하여 패턴 분류문제에 있어서 우수한 성능을 보이는 SVM을 이용하여 정상적인 메일과 스팸메일을 구분하는 방안에 대하여 제시한다.