• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1316-1319
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• 2015

본 논문에서는 대규모 분산 병렬 컴퓨팅 환경인 하둡 클러스터 시스템을 이용하여, 공간 객체들 간의 위상 관계를 효율적으로 추론하는 대용량 정성 공간 추론기를 제안한다. 본 논문에서 제안하는 공간 추론기는 추론 작업의 순차성과 반복성을 고려하여, 작업들 간의 디스크 입출력을 최소화할 수 있는 인-메모리 기반의 아파치 스파크 프레임워크를 이용하여 개발하였다. 따라서 본 추론기에서는 추론의 대상이 되는 대용량 공간 지식들을 아파치 스파크의 분산 데이터 집합 형태인 PairRDD와 RDD로 변환하고, 이들에 대한 데이터 오퍼레이션들로 추론 작업들을 구현하였다. 또한, 본 추론기에서는 추론 시간의 많은 부분을 차지하는 이행 관계 추론에 필요한 조합표를 효과적으로 축소함으로써, 공간 추론 작업의 성능을 크게 향상시켰다. 대용량의 공간 지식 베이스를 이용한 성능 분석 실험을 통해, 본 논문에서 제안한 정성 공간 추론기의 높은 성능을 확인할 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.276-279
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• 2010

소프트웨어 제품 라인 개발 패러다임은 관련 제품들 사이의 공통점과 차이점을 이용해 보다 전략적인 재사용을 가능하게 함으로써 소프트웨어 개발 생산성을 높여 주는 개발 방법론이다. 공통점과 차이점을 분석하고 모델링하기 위해 가장 중요한 모델이 특성 모델이다. 특성 모델은 규모가 커짐에 따라 오류를 포함할 가능성이 커지며 이를 검증하기 위한 자동화된 도구가 필요하다. 본 논문에서는 온톨로지를 자바 언어로 구현 가능하게 해주는 Protege API, OWL기반의 시맨틱 웹 규칙 언어인 SWRL, 규칙 추론 엔진인 Pellet Reasoner 등의 기술을 이용한 특성 모델 검증 도구를 제안한다.

• Journal of KIISE
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• v.42 no.3
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• pp.307-319
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• 2015

Current ontology studies use the Hadoop distributed storage framework to perform map-reduce algorithm-based reasoning for scalable ontologies. In this paper, however, we propose a novel approach for scalable Web Ontology Language (OWL) Horst Lite ontology reasoning, based on distributed cluster memories. Rule-based reasoning, which is frequently used for scalable ontologies, iteratively executes triple-format ontology rules, until the inferred data no longer exists. Therefore, when the scalable ontology reasoning is performed on computer hard drives, the ontology reasoner suffers from performance limitations. In order to overcome this drawback, we propose an approach that loads the ontologies into distributed cluster memories, using Spark (a memory-based distributed computing framework), which executes the ontology reasoning. In order to implement an appropriate OWL Horst Lite ontology reasoning system on Spark, our method divides the scalable ontologies into blocks, loads each block into the cluster nodes, and subsequently handles the data in the distributed memories. We used the Lehigh University Benchmark, which is used to evaluate ontology inference and search speed, to experimentally evaluate the methods suggested in this paper, which we applied to LUBM8000 (1.1 billion triples, 155 gigabytes). When compared with WebPIE, a representative mapreduce algorithm-based scalable ontology reasoner, the proposed approach showed a throughput improvement of 320% (62k/s) over WebPIE (19k/s).

• Journal of Microbiology and Biotechnology
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• v.29 no.10
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• pp.1495-1505
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• 2019

The Burkholderia cepacia complex (BCC) is capable of remaining viable in low-nutrient environments and harsh conditions, posing a contamination risk in non-sterile pharmaceutical products as well as a challenge for detection. To develop optimal recovery methods to detect BCC, three oligotrophic media were evaluated and compared with nutrient media for the recovery of BCC from autoclaved distilled water or antiseptic solutions. Serial dilutions ($10^{-1}$ to $10^{-12}CFU/ml$) of 20 BCC strains were inoculated into autoclaved distilled water and stored at $6^{\circ}C$, $23^{\circ}C$ and $42^{\circ}C$ for 42 days. Six suspensions of Burkholderia cenocepacia were used to inoculate aqueous solutions containing $5{\mu}g/ml$ and $50{\mu}g/ml$ chlorhexidine gluconate (CHX) and $10{\mu}g/ml$ benzalkonium chloride (BZK), and stored at $23^{\circ}C$ for a further 199 days. Nutrient media such as Tryptic Soy Agar (TSA) or Tryptic Soy Broth (TSB), oligotrophic media (1/10 strength TSA or TSB, Reasoner's $2^{nd}$ Agar [R2A] or Reasoner's $2^{nd}$ Broth [R2AB], and 1/3 strength R2A or R2AB) were compared by inoculating these media with BCC from autoclaved distilled water and from antiseptic samples. The recovery of BCC in water or antiseptics was higher in culture broth than on solid media. Oligotrophic medium showed a higher recovery efficiency than TSA or TSB for the detection of 20 BCC samples. Results from multiple comparisons allowed us to directly identify significant differences between TSA or TSB and oligotrophic media. An oligotrophic medium pre-enrichment resuscitation step is offered for the United States Pharmacopeia (USP) proposed compendial test method for BCC detection.

• Research in Plant Disease
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• v.24 no.2
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• pp.138-144
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• 2018

In December 2016, stem rot symptoms were observed on Persian buttercup (Ranunculus asiaticus) plants in Chilgok, Gyeongbuk, Korea. In the early stage of the disease, several black spots appeared on the stem of infected plants. As the disease progressed, the infected stem cleaved and wilted. The causal agent was isolated from a lesion and incubated on Reasoner's 2A (R2A) agar at $25^{\circ}C$. Total genomic DNA was extracted for phylogenetic analysis. Based on the 16S rRNA gene analysis, the isolated strain was found to belong to the genus Pseudomonas. To identify the isolated bacterial strain at the species level, the nucleotide sequences of the gyrase B (gyrB) and RNA polymerase D (rpoD) genes were obtained and compared with the sequences in the GenBank database. As the result, the causal agent of the stem rot disease was identified as Pseudomonas marginalis. To determine the pathogenicity of the isolated bacterial strain, it was inoculated into the stem of healthy R. asiaticus plant, the inoculated plant showed a lesion with the same characteristics as the naturally infected plant. Based on these results, this is the first report of bacterial stem rot on R. asiaticus caused by P. marginalis in Korea.

• Journal of KIISE
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• v.42 no.5
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• pp.601-608
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• 2015

In order to answer questions successfully on behalf of the human contestant in DeepQA environments such as 'Jeopardy!', the American quiz show, the computer needs to have the capability of fast temporal and spatial reasoning on a large-scale commonsense knowledge base. In this paper, we present a hybrid spatial reasoning algorithm, among various efficient spatial reasoning methods, for handling directional and topological relations. Our algorithm not only improves the query processing time while reducing unnecessary reasoning calculation, but also effectively deals with the change of spatial knowledge base, as it takes a hybrid method that combines forward and backward reasoning. Through experiments performed on the sample spatial knowledge base with the hybrid spatial reasoner of our algorithm, we demonstrated the high performance of our hybrid spatial reasoning algorithm.

• Journal of Species Research
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• v.12 no.2
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• pp.158-164
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• 2023

In March 2021, marine sediment from East Sea samples were suspended in a 2% NaCl solution, and serial dilution was performed in fresh marine and Reasoner's 2A agar. Isolated bacterial strains were identified based on 16S rRNA gene sequences, and showed at least 98.7% sequence similarity with previously reported bacterial species. Finally, seven bacterial strains which were validly published but not reported in Korea, were obtained. These isolates were allocated to the orders Bacillales and Flavobacteriales. The three Flavobacteriales strains are classified into the family Flavobacteriaceae. The other four Bacillales belong to the families Bacillaceae and Paenibacillaceae. The seven unrecorded bacterial strains in this study are classified into seven different genera, which are assigned to Mesobacillus, Paenibacillus, Gramella, Gillisia, Arenibacter, Fictibacillus, and Brevibacillus. During the investigation, the possibility of excavation of various unrecorded species in domestic marine sediment was confirmed. Gram-staining, cell morphology, physiological and basic biochemical characteristics, and phylogenetic analysis were performed in this study and provided in the description of each strain.

• Journal of Species Research
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• v.12 no.spc2
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• pp.54-59
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• 2023

To obtain unrecorded bacterial species from Korean islands, various samples were collected from the islands in 2022. After plating the samples on marine agar or Reasoner's 2A, and incubating aerobically, approximately 1,200 bacterial strains were isolated and identified using 16S rRNA gene sequences. A total of 10 strains showed ≥98.7% 16S rRNA gene sequence similarity with the bacterial species that were validly published but not reported in Korea. The unrecorded bacterial strains belong to three phyla, five classes, 10 orders, 10 families, and 10 genera, which are assigned to Sphingomonas, Falsirhodobacter and Asticcacaulis of the class Alphaproteobacteria; Colwellia and Halomonas of the class Gammaproteobacteria; Chitinophaga of the class Chitinophagia; Chryseobacterium of the class Flavobacteriia; Microlunatus, Zhihengliuella, and Streptomyces of the class Actinomycetia. The details of the unreported species including Gram reaction, colony and cell morphology, biochemical characteristics, and phylogenetic position are also provided in the description of the strains.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.651-654
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• 2008

상황인지(Context-awareness)란 사용자의 상황을 인지하여 이를 통해 사용자에게 적합한 최적의 정보를 제공하는 것으로 현재 많은 연구가 진행되고 있다. 이미 상황인지를 위한 프레임워크들이 존재하지만, 작은 메모리가 요구되는 제한적인 하드웨어 상황에서 적용하기에 적합하게 설계된 프레임워크는 흔하지 않다. 이에 대한 문제점을 인식하여 하드웨어 리소스가 부족한 상황에서 빠르게 동작할 수 있는 프레임워크를 구상하여, 기존과는 다른 제한적인 환경 안에서 상황인지를 통해 사용자에게 맞는 최적의 서비스를 판단, 제공하는 모바일 오브젝트를 설계하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.409-414
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• 2013

With the increase of multimedia information such as images, researches have been realized on how to extract the high-level semantic information from low-level visual information, and a variety of techniques have been proposed to generate this information automatically. However, most of these technologies extract the semantic information between single images, it's difficult to extract semantic information when a combination of multiple objects within the image. In this paper, we extract the visual features of objects within the image and training images stored in the DB and the features of each object are defined by measuring the similarity. Using ontology reasoner, each object feature within images infers the semantic information by positional relation and associative relation. With this, it's possible to infer semantic information between objects within images, we proposed a method for inferring more complicated and a variety of high-level semantic information.