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좁은 파장대역폭을 갖는 격자도움형 방향성 결합기 필터의 제작과 특성측정
김덕봉,박찬용,김정수,이승원,오광룡,김흥만,편광의,윤태훈 한국광학회 1997 한국광학회지 Vol.8 No.2
InGaAsP/InP 격자도움형 방향성 결합기(GACC)필터를 제작하고, 필터의 도파로 구조에 대해서 중심파장, 파장대역폭, TE/TM 편광의존성 그리고 파장가변특성 등을 측정하였다. 필터가 좁은 파장대역폭을 갖도록 도파로의 구조를 설계하였다. 균일한 두께와 폭을 갖는 도파로를 제작하기 위해 reactive ion etching 방법을 이용하였다. 제작된 InGaAsP/InP GACC 필터의 출력 스펙트럼은 TM편광에서 1494.0 nm의 중심파장과 1.3 mn의 파장대역폭을 가졌고, TE편광에서는 1530.6 nm의 중심파장과 1.5 nm의 파장대역폭을 보였다. 이 파장대역폭은 지금까지 보고된 1.5mu.m파장대역 부근에서 GACC필터가 갖는 대역폭 중 가장 좁은 것이다. 또한 100 mA의 전류인가에 대한 8 nm정도의 중심파장 이동을 관찰하였다. 그리고 제작된 여러 가지 도파로 구조에 대해 측정한 GACC필터의 동작특성과 계산한 결과가 잘 일치함을 보였다. We demonstrate the operating characteristics(center wavelength, bandwidth, TE/TM polarization, tuning range) of grating-assisted co-directional coupler(GACC) filter fabricated with InGaAsP compound semiconductor. A design of waveguide structure has been focused on the narrow bandwidth characteristics of the filter. Reactive ion etching technique was employed for the uniform waveguide formation. The bandwidths(FWHM) and center wavelengths of the fabricated GACC filter were measured by 1.5 nm and 1530.6 nm for TE polarization and 1.3 nm and 1494.0 nm for TM polarization. This is the one of the narrowest bandwidth at 1530 nm region ever reported. The center wavelength shifted form 1530 nm to 1538 nm when the current of 100 mA was injected at 4.5 mm-long device. Good agreement between the designed and measured operating characteristics for some waveguide structures is demonstrated.
랜드마크 윈도우 기반의 빈발 패턴 마이닝 기법의 분석 및 성능평가
편광범 ( Gwangbum Pyun ),윤은일 ( Unil Yun ) 한국인터넷정보학회 2014 인터넷정보학회논문지 Vol.15 No.3
본 논문에서는 랜드마크 윈도우 기반의 빈발 패턴 마이닝 기법을 분석하고 성능을 평가한다. 본 논문에서는 Lossy counting 알고리즘과 hMiner 알고리즘에 대한 분석을 진행한다. 최신의 랜드마크 알고리즘인 hMiner는 트랜잭션이 발생할 때 마다 빈발 패턴을 마이닝 하는 방법이다. 그래서 hMiner와 같은 랜드마크 기반의 빈발 패턴 마이닝을 온라인 마이닝이라고 한다. 본 논문에서는 랜드마크 윈도우 마이닝의 초기 알고리즘인 Lossy counting와 최신 알고리즘인 hMiner의 성능을 평가하고 분석한다. 우리는 성능평가의 척도로 마이닝 시간과 트랜잭션 당 평균 처리 시간을 평가한다. 그리고 우리는 저장 구조의 효율성을 평가하기 위하여 최대 메모리 사용량을 평가한다. 마지막으로 우리는 알고리즘이 안정적으로 마이닝이 가능한지 평가하기 위해 데이터베이스의 아이템 수를 변화시키면서 평가하는 확장성 평가를 수행한다. 두 알고리즘의 평가 결과로, 랜드마크 윈도우 기반의 빈발 패턴 마이닝은 실시간 시스템에 적합한 마이닝 방식을 가지고 있지만 메모리를 많이 사용했다. With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms` total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash`s buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting`s memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).