• Title/Summary/Keyword: 내포적 질의문 처리

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Intensional Answers in Object-Oriented Database Systems (객체지향 데이터베이스 시스템에서 내포적 답의 처리 기법)

  • Kim, Yang-Hee
    • The KIPS Transactions:PartD
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    • v.9D no.2
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    • pp.227-234
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    • 2002
  • When processing a query in a conventional database systems, a set of facts or tuples are usually returned as an answer. This also applies to object -oriented database where a set of objects is returned. Deductive database systems, however, provide the opportunity to obtain the answer of a query as a set of formulas, thereby reduce the costs to process the query, and represent its "intensional answers" in a more compact way independently of the database state. In this paper, by introducing rules info the object-oriented database systems and integrating the intensional query processing of deductive database systems into talc object-oriented database systems, we make it possible not only to answer incomplete queries which are not able to be answered in conventional object-oriented database systems, but also to express the answer-set abstractly as the names of classes, which provides us better understanding of the answer.

Calibration of Pre-trained Language Model for Korean (사전 학습된 한국어 언어 모델의 보정)

  • Jeong, Soyeong;Yang, Wonsuk;Park, ChaeHun;Park, Jong C.
    • Annual Conference on Human and Language Technology
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    • 2020.10a
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    • pp.243-248
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    • 2020
  • 인공 신경망을 통한 심층 학습 모델의 발전은 컴퓨터 비전, 자연언어 이해 문제들에서 인간을 뛰어넘는 성능을 보이고 있다. 특히 트랜스포머[1] 기반의 사전 학습 모델은 질의응답, 대화문과 같은 자연언어 이해 문제에서 최근 높은 성능을 보이고 있다. 하지만 트랜스포머 기반의 모델과 같은 심층 학습 모델의 급격한 발전 양상에 비해, 이의 동작 방식은 상대적으로 잘 알려져 있지 않다. 인공 신경망을 통한 심층 학습 모델을 해석하는 방법으로 모델의 예측 값과 실제 값이 얼마나 일치하는지를 측정하는 모델의 보정(Calibration)이 있다. 본 연구는 한국어 기반의 심층학습 모델의 해석을 위해 모델의 보정을 수행하였다. 그리고 사전 학습된 한국어 언어 모델이 문장이 내포하는 애매성을 잘 파악하는지의 여부를 확인하고, 완화 기법들을 적용하여 문장의 애매성을 확신 수준을 통해 정량적으로 출력할 수 있도록 하였다. 또한 한국어의 문법적 특징으로 인한 문장의 의미 변화를 모델 보정 관점에서 평가하여 한국어의 문법적 특징을 심층학습 언어 모델이 잘 이해하고 있는지를 정량적으로 확인하였다.

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Data hub system based on XMDR message using Hybrid Agent for distributed data interoperability (분산 데이터 상호 운용을 위한 XMDR 메시지 기반의 하이브리드 에이전트를 이용한 데이터 허브 시스템)

  • Moon, Seok-Jae;Eum, Young-Hyun;Jung, Kye-Dong;Choi, Young-Keun
    • Proceedings of the Korean Information Science Society Conference
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    • 2007.10c
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    • pp.169-174
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    • 2007
  • 분산 데이터를 상호 운용하기 위해서는 공유되는 정보가 효율적으로 처리 및 관리되어야 한다. 특히 레거시 시스템과 같이 이질성을 내포하고 있는 환경에서 협업을 위한 상호 운용성의 확보가 효율적인 관건이다. 따라서 본 논문에서는 레거시 시스템간의 데이터 공유 및 교환에서 발생하는 의미적 상호 운용성의 문제점을 극복하는 XMDR 메시지 기반의 하이브리드 에이전트를 이용한 데이터 허브 시스템을 제안한다. 이 시스템은 데이터 협업 시 실시간으로 변화는 데이터론 일관성 있게 유지하기 위해서 질의 변환 방법인 메시지 사상 기법을 제시하여 이용한다. 이는 레거시 시스템들 간의 협업에 필요한 데이터를 공유 및 교환하는데 실시간으로 변화하는 데이터를 일관성 있게 유지한다. 그리고 통합 검색시 단일 인터페이스를 제공하여 각 시스템의 독립성을 유지하면서 데이터의 투명성과 가용성을 향상 시킬 수 있다.

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Studies on Dairy Farming Status, Reproductive Efficiencies and Disorders in New Zealand (I) A Survey on Dairy Farming Status and Milk Yield in Palmerston North Area (뉴질랜드 (Palmerston North) 의 낙농 현황과 번식 및 번식장해에 관한 연구(I) Palmerston North 지역의 낙농 현황과 우유 생산량에 관한 조사 연구)

  • 김중계;맥도날드
    • Korean Journal of Animal Reproduction
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    • v.24 no.1
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    • pp.1-18
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    • 2000
  • Eighty dairy farms in Palmers ton North area in New Zealand were surveyed on 1) general characteristics (10 Questions), 2) milk yield and feed supplementary (7 questions), 3) reproductive efficiencies (12 questions) and 4) reproductive disorders (12 questions) by mail questions from February to July, 1998. Among those 4 items from 38 dairy farms (47.5%), especially in items 1) and 2), overall dairy farming situation, supplementary feeding and milk yields were surveyed and analyzed for Korean dairy farmers (especially in Cheju island) to have better understanding or higher economical gains. The results were as follows. 1. In dairy experience, 21 (45%) among 38 dairy farms surveyed were answered that farming less than 15 years, 15~19 year, 20~25 years and over 26 years experience were 3 (7.9%), 7 (18.4%), 6 (15.8%) and 5 (13.2%) which generally showed longer experience compare to Korean dairy farming situation. In survey of labour input and business goal of dairy farming, self-managing farms, sharemilkers, unpaid family manpowering farms, manager running farms, farms with hired worker, farms with part time helper and other type was 21 (55.3%), 10 (26.3%), 2 (3.5%), 3 (5.3%), 18 (31.6%), 2 (3.5%), and 1 (1.8%), respectively. 2. Analyzing pasture and tillable land, pasture according to feeding scale (200, 300 and 400 heads) were 56, 90 and 165.3 ha, and tillable lands were 51, 78 and 165 ha which showed some differences among feeding scale. In recording methods in 38 farms replied, 36 (95%) dairy handbook and 23 (70%) dual methods taking farms were higher than that of 10 (26.3%) computer and 15(39.5%) well-recorder methods. 3. Dairy waste processing facilities in environmental field were almost perfect except of metropolitan area, and so no problem was developed in its control so far. Hence, 26 farm (68.4%) of pond system was higher rather than those in 8 (21.2%) of using as organic manure after storing feces of dairy cattle, 1(2.6%) bunker system and 3 (7.9%) other type farms. 4. In milking facilities, 33 farms (86.9%) of Harringbone types were higher than those in 3 (7.9%) of Walkthrough types, 1 (2.6%) of Rotary system and other types. Although the construction facilities was not enough, this system show the world-leveled dairy country to attempted to elevate economic gains using the advantage of climatic condition. 5. In milking day and yearly yield per head, average 275 milking days and 87 drying days were longer than that of 228 average milking days in New Zealand. Annual total milk yield per head and milk solid (ms) was 3,990 kg and approximately 319 kg. Dairy milk solid (ms) per head, milk yield, fat percentage was 1.2 kg, 15.5 kg and average 4.83% which was much higher than in other country, and milk protein was average 3.75%. 6. In coclusion, Palmerstone North has been a center of dairy farming in New Zealand for the last 21 years. Their dairy farming history is 6~9 year longer than ours and the average number of milking cows per farm is 355, which is much greater than that (35) of Korea. They do not have dairy barn, but only milking parlors. Cows are taken care of by family 0.5 persons), are on a planned calving schedule in spring (93%) and milked for 240~280 days a year, avoiding winter. Cows are dried according to milk yield and body condition score. This management system is quite different from that of Korean dairy farms. Cows are not fed concentrates, relying entirely on pasture forages and the average milk yield per cow is 3,500 kg, which is about 1/2 milk yield of Korean dairy farms. They were bred to produce high fat milk with an average of 4.5%. Their milk production cost is the lowest in the world and the country's economy relies heavily on milk production. We Korean farmers may try to increase farming size, decreasing labor and management costs.

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