• 제목/요약/키워드: Sequence-based rule

검색결과 93건 처리시간 0.025초

색도 및 색순에 따른 그라비아 인쇄 공정의 작업 순서 결정 규칙 (Dispatching Rule based on Chromaticity and Color Sequence Priorities for the Gravure Printing Operation)

  • 배재호
    • 산업경영시스템학회지
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    • 제43권3호
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    • pp.10-20
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    • 2020
  • This paper presents a method to measure the similarity of assigned jobs in the gravure printing operation based on the chromaticity and color sequence, and order the jobs accordingly. The proposed dispatching rule can be used to fulfill diverse manufacturing site requirements because the parameters can be adjusted to prioritize chromaticity and color sequence. In general, dispatching rules either ignore the job-changing time or require that the time be clearly defined. However, in the gravure printing operation targeted in this study, it is difficult to apply the general dispatching rule because of the difficulties in quantifying the job-changing time. Therefore, we propose a method for generalizing assignment rules of the job planner, allocating relative similarity among assigned jobs, and determining the sequence of jobs accordingly. Chromaticity priority is determined by the arrangement of the color assignments in the printing operation; color sequence priority is determined by the addition, deletion, or change in a specific color sequence. Finally, the job similarity is determined by the dot product of the chromaticity and color sequence priorities. Implementation of the proposed dispatching rule at an actual manufacturing site showed the planner present the same job order as that obtained using the proposed rule. Therefore, this rule is expected to be useful in industrial sites where clear quantification of the job-changing time is not possible.

Event Sequence Tracking을 이용한 침입 감지 시스템의 설계 (Design of Intrusion Detection System Using Event Sequence Tracking)

  • 최송관;이필중
    • 한국정보보호학회:학술대회논문집
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    • 한국정보보호학회 1995년도 종합학술발표회논문집
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    • pp.115-125
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    • 1995
  • 본 논문에서는 컴퓨터 시스템에서 침입 감지 시스템을 설계함에 있어서 사용될 수 있는 새로운 방법인 Event Sequence Tracking 방법을 제안하였다. Event Sequence Tracking 방법에서는 컴퓨터 시스템의 공격방법을 크게 두가지로 분류한다. 첫번째는 일련의 시스템 명령어를 이용한 공격방법이고 두번째는 침입자 자신이 만들었거나 다른 사람으로부터 얻은 프로그램을 이용하는 방법이다. 첫번째 공격방법에 대한 감지방법은 시스템을 공격할 때 사용한 일련의 시스템 명령어들을 감사 데이타를 분석하여 찾아내고 이 결과를 기존에 알려진 공격 시나리오들과 비교하여 침입자를 찾아내는 방식이다. 두번째 공격방법에 대한 감지 방법은 보안 관리자가 정해놓은, 시스템에서 일반 사용자가 할 수 없는 행위에 관한 보안 정책에 따라 Key-Event 데이타 베이스를 만들고 여기에 해당하는 event의 집합을 감사 데이타에서 찾아내는 방법이다. Event Sequence Tracking 방법은 Rule-based Penetration Identification 방법의 일종으로서 시스템의 공격방법을 분류하여 컴퓨터 시스템에의 침입을 효과적으로 감지할 수 있다는 것과 rule-base의 생성과 갱신을 함에 있어서 보다 간단하게 할 수 있다는 장점을 갖는다.

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Accentual Effects on Lateralization

  • Kim, Soo-Jung
    • 음성과학
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    • 제8권3호
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    • pp.15-30
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    • 2001
  • Lateralization, the change of a coronal nasal into a lateral in an l-n sequence, has been considered to be prosodically unrestricted, e.g. an utterance-span rule, in Korean (Han 1993, Park 1990). However, aerodynamic data of the nasal do not corroborate their claims. In the paper, I look at how lateralization can best be characterized. Specifically, I ask whether its domain is best treated via a syntax-based (Nespor & Vogel 1986, Selkirk 1984) or an intonation-based approach (Pierrehumbert 1980, Jun 1993) to prosodic structure. Based on nasal airflow data as a means of monitoring velum activity coincident with a nasal stop in an l-n sequence, combined with pitch tracks to define an accentual phrase, I argue that lateralization is neither an utterance-span rule nor a syntax-based rule. Sentences recorded with a potential environment for lateralization show that lateralization occurs within an accentural phrase but is blocked between accentual phrase boundaries. When intonation-based and syntax-based models disagree about phrase boundaries, lateralization only occurs where the intonation-based model predicts it will. This indicates that lateralization is best defined as an accentual pheonomenon, being sensitive to the accentual phrase. This finding lends further support to an intonation-based model for Korean prosodic structure (Jun 1993).

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이벤트 파싱 엔진의 구조 설계와 성능 분석 (Architecture Modeling and Performance Analysis of Event Rule Engine)

  • 윤태웅;민덕기
    • 한국시뮬레이션학회:학술대회논문집
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    • 한국시뮬레이션학회 2003년도 추계학술대회 및 정기총회
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    • pp.51-57
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    • 2003
  • In operating distributed systems, proactive management is one of the major concerns for better quality of service and future capacity planning. In order to handle this management problem effectively, it is necessary to analyze performances of the distributed system and events generated by components in the system. This paper provides a rule-based event parsing engine for proactive management. Our event parsing engine uses object hooking-based and event-token approaches. The object hooking-based approach prepares new conditions and actions in Java classes and allows dynamically exchange them as hook objects in run time. The event-token approach allows the event parsing engine consider a proper sequence and relationship among events as an event token to trigger an action. We analyze the performance of our event parsing engine with two different implementations of rule structure; one is table-based and the other is tree-based.

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금형가공 및 측정을 위한 통합 Rule-based CAPP 시스템 개발 (Development of integrated Rule-based CAPP system for Mold Manufacturing and Inspection)

  • 윤길상;최진화;조명우;이홍희;서태일
    • 한국정밀공학회:학술대회논문집
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    • 한국정밀공학회 2003년도 춘계학술대회 논문집
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    • pp.1235-1238
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    • 2003
  • A rule-based CAPP(computer aided process planning) system is proposed in this research to develop integrated manufacturing process of machining and inspection using OMM(On-Machine Measurement) device. Generally workpiece composed of many primitive form features. This features are determined optimum inspection sequence by analyzing the feature information such as features-relationship, probe approach direction and etc. Proposed paper is more efficient method of CAIP(computer aided inspection planning) considered machining process

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Mention Detection Using Pointer Networks for Coreference Resolution

  • Park, Cheoneum;Lee, Changki;Lim, Soojong
    • ETRI Journal
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    • 제39권5호
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    • pp.652-661
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    • 2017
  • A mention has a noun or noun phrase as its head and constructs a chunk that defines any meaning, including a modifier. Mention detection refers to the extraction of mentions from a document. In mentions, coreference resolution refers to determining any mentions that have the same meaning. Pointer networks, which are models based on a recurrent neural network encoder-decoder, outputs a list of elements corresponding to an input sequence. In this paper, we propose mention detection using pointer networks. This approach can solve the problem of overlapped mention detection, which cannot be solved by a sequence labeling approach. The experimental results show that the performance of the proposed mention detection approach is F1 of 80.75%, which is 8% higher than rule-based mention detection, and the performance of the coreference resolution has a CoNLL F1 of 56.67% (mention boundary), which is 7.68% higher than coreference resolution using rule-based mention detection.

A Semantics of Sequence of Tense without a Sequence-of-tense Rule

  • Song, Mean-Young
    • 한국언어정보학회지:언어와정보
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    • 제4권2호
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    • pp.93-105
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    • 2000
  • I argue in this paper that the sequence of tense (SOT) phenomenon can be accounted for without positing a SOT rule, focusing on the contrast between the past under-past sentences which lead to ambiguity and those sentences which do not. The different interpreta- tion of past under past sentences depends on whether the stative or then non-stative predicates occur in the complement clauses in the propositional attitude verbs. Based on this, I also argue that the embedded past tense does not contribute to the seman- tics past tense in the complement clause. Instead, it is due to the occurrence of the stative or non-stative predicates in the complement clauses. The stative predicates are associated with the temporal precedence or the overlap relation, whereas the non-stative predicates the precedence relation only. This fact triggers the contrast in past- under- past sentences.(Korea University)

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시간 속성을 갖는 이벤트 집합에서 인터벌 연관 규칙 마이닝 기법 (A Method for Mining Interval Event Association Rules from a Set of Events Having Time Property)

  • 한대영;김대인;김재인;나철수;황부현
    • 정보처리학회논문지D
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    • 제16D권2호
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    • pp.185-190
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    • 2009
  • 시간 속성을 갖는 이벤트 집합에서 동일한 이벤트 타입에 대한 이벤트 시퀀스는 하나의 이벤트로 요약될 수 있다. 그러나 정의된 시간 간격이 경과된 후 발생된 이벤트 타입은 하나 이상의 독립된 서브 이벤트 시퀀스로 요약하는 것이 바람직하다. 본 논문은 Allen의 시간 관계 대수에 기반하여 인터벌 이벤트를 요약하고, 요약된 인터벌 이벤트들로부터 인터벌 연관 규칙을 찾아내는 새로운 시간 데이터 마이닝 기법을 제안한다. 제안하는 기법은 독립적인 서브 시퀀스 개념을 도입하고 인터벌 이벤트 사이의 연관 규칙을 탐사함으로써 질적으로 우수한 정보를 제공한다.

Exploiting Multichannel Diversity in Spectrum Sharing Systems Using Optimal Stopping Rule

  • Xu, Yuhua;Wu, Qihui;Wang, Jinlong;Anpalagan, Alagan;Xu, Yitao
    • ETRI Journal
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    • 제34권2호
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    • pp.272-275
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    • 2012
  • This letter studies the problem of exploiting multichannel diversity in a spectrum sharing system, where the secondary user (SU) sequentially explores channel state information on the licensed channels with time consumption. To maximize the expected achievable throughput for the SU, we formulate this problem as an optimal stopping problem, whose objective is to choose the right channel to stop exploration based on the observed signal-to-noise ratio sequence. Moreover, we propose a myopic but optimal rule, called one-stage look-ahead rule, to solve the stopping problem.

LSTM 기반의 sequence-to-sequence 모델을 이용한 한글 자동 띄어쓰기 (LSTM based sequence-to-sequence Model for Korean Automatic Word-spacing)

  • 이태석;강승식
    • 스마트미디어저널
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    • 제7권4호
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    • pp.17-23
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    • 2018
  • 자동 띄어쓰기 특성을 효과적으로 처리할 수 있는 LSTM(Long Short-Term Memory Neural Networks) 기반의 RNN 모델을 제시하고 적용한 결과를 분석하였다. 문장이 길거나 일부 노이즈가 포함된 경우에 신경망 학습이 쉽지 않은 문제를 해결하기 위하여 입력 데이터 형식과 디코딩 데이터 형식을 정의하고, 신경망 학습에서 드롭아웃, 양방향 다층 LSTM 셀, 계층 정규화 기법, 주목 기법(attention mechanism)을 적용하여 성능을 향상시키는 방법을 제안하였다. 학습 데이터로는 세종 말뭉치 자료를 사용하였으며, 학습 데이터가 부분적으로 불완전한 띄어쓰기가 포함되어 있었음에도 불구하고, 대량의 학습 데이터를 통해 한글 띄어쓰기에 대한 패턴이 의미 있게 학습되었다. 이것은 신경망에서 드롭아웃 기법을 통해 학습 모델의 오버피팅이 되지 않도록 함으로써 노이즈에 강한 모델을 만들었기 때문이다. 실험결과로 LSTM sequence-to-sequence 모델이 재현율과 정확도를 함께 고려한 평가 점수인 F1 값이 0.94로 규칙 기반 방식과 딥러닝 GRU-CRF보다 더 높은 성능을 보였다.