• Restorative Dentistry and Endodontics
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• v.35 no.1
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• pp.13-19
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• 2010

The purpose of this study was to compare the microshear bond strength of a self-etching primer adhesive to dentin prepared with different diamond points, carbide burs and SiC papers, and also to determine which SiC paper yield similar strength to that of dentinal surface prepared with points or burs. Fifty-six human molar were sectioned to expose the occlusal dentinal surfaces of crowns and slabs of 1.2 mm thick were made. Dentinal surfaces were removed with three diamond points, two carbide burs, and three SiC papers. They were divided into one of eight equal groups (n = 7); Group 1: standard diamond point(TF-12), Group 2: fine diamond point (TF-12F), Group 3: extrafine diamond point (TF-12EF), Group 4: plain-cut carbide bur (no. 245), Group 5: cross-cut carbide bur (no. 557), Group 6 : P 120-grade SiC paper, Group 7: P 220-grade SiC paper, Group 8: P 800-grade SiC paper. Clearfil SE Bond was applied on dentinal surface and Clearfil AP-X was placed on dentinal surface using Tygon tubes. After the bonded specimens were subjected to uSBS testing, the mean uSBS (n = 20 for each group) was statistically compared using one-way ANOV A and Tukey HSD test. In conclusion, the use of extrafine diamond point is recommended for improved bonding of Clearfil SE Bond to dentin. Also the use of P 220-grade SiC paper in vitro will be yield the results closer to dentinal surface prepared with fine diamond point or carbide burs in vivo.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.389-397
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• 2017

Recently semantic web document is produced and added in repository in a cloud computing environment and requires an intelligent semantic agent for analytical classification of documents and information retrieval. The traditional methods of information retrieval uses keyword for query and delivers a document list returned by the search. Users carry a heavy workload for examination of contents because a former method of the information retrieval don't provide a lot of semantic similarity information. To solve these problems, we suggest a key word frequency and concept matching based semantic clustering model using hadoop and NoSQL to improve classification accuracy of the similarity. Implementation of our suggested technique in a cloud computing environment offers the ability to classify and discover similar document with improved accuracy of the classification. This suggested model is expected to be use in the semantic web retrieval system construction that can make it more flexible in retrieving proper document.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.610-617
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• 2003

Similarity matching in video database is of growing importance in many new applications such as video clustering and digital video libraries. In order to provide efficient access to relevant data in large databases, there have been many research efforts in video indexing with diverse spatial and temporal features. however, most of the previous works relied on sequential matching methods or memory-based inverted file techniques, thus making them unsuitable for a large volume of video databases. In order to resolve this problem, this paper proposes an effective and scalable indexing technique using a trie, originally proposed for string matching, as an index structure. For building an index, we convert each frame into a symbol sequence using a window order heuristic and build a disk-resident trie from a set of symbol sequences. For query processing, we perform a depth-first search on the trie and execute a temporal segmentation. To verify the superiority of our approach, we perform several experiments with real and synthetic data sets. The results reveal that our approach consistently outperforms the sequential scan method, and the performance gain is maintained even with a large volume of video databases.

• Journal of KIISE:Software and Applications
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• v.26 no.10
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• pp.1237-1246
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• 1999

최근 모든 분야에서 외국과의 교류가 증대됨에 따라서 한국어 문서에는 점점 더 많은 외국어 음차 표기가 사용되는 경향이 있다. 하지만 같은 외국어에 대한 음차 표기에 개인차가 심하여 이들 음차 표기를 포함한 문서들에 대한 검색을 어렵게 만드는 원인이 되고 있다. 한 가지 해결 방법은 색인 시에 같은 외국어에서 온 음차 표기들을 등가부류로 묶어서 색인해 놓았다가 질의 시에 확장하는 방법이다. 본 논문에서는 외국어 음차 표기들의 등가부류를 만드는데 필요한 음차 표기의 음성적 유사도 비교 알고리즘인 Kodex를 제안한다. Kodex 방법은 기존의 스트링 비교 방법인 비음성적 방법에 비해 음차 표기들을 등가부류로 클러스터링하는데 있어 더 나은 성능을 보이면서도, 계산이 간단하여 훨씬 효율적으로 구현될 수 있는 장점이 있다.Abstract With the advent of digital communication technologies, as Koreans communicate with foreigners more frequently, more foreign word transliterations are being used in Korean documents more than ever before. The transliterations of foreign words are very various among individuals. This makes text retrieval tasks about these documents very difficult. In this paper we propose a new method, called Kodex, of measuring the phonetic similarity among foreign word transliterations. Kodex can be used to generate the equivalence classes of the transliterations while indexing and conflate the equivalent transliterations at the querying stage. We show that Kodex gives higher precision at the similar recall level and is more efficient in computation than non-phonetic methods based on string similarity measure.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.5
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• pp.628-638
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• 1999

In this paper, we presents a method of retrieving 24 bpp RGB images based on color-spatial features. For each image, it is subdivided into regions by using similarity of color after converting RGB color space to CIE L*u*v* color space that is perceptually uniform. Our segmentation algorithm constrains the size of region because a small region is discardable and a large region is difficult to extract spatial feature. For each region, averaging color and center of region are extracted to construct color-spatial features. During the image retrieval process, the color and spatial features of query are compared with those of the database images using our similarity measure to determine the set of candidate images to be retrieved. We implement a content-based color image retrieval system using the proposed method. The system is able to retrieve images by user graphic or example image query. Experimental results show that Recall/Precision is 0.80/0.84.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1615-1622
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• 2022

In order to overcome the limitations of the instructor-centered lecture-style teaching method, recently, flipped learning, a learner-centered teaching method, has been widely introduced. However, despite the many advantages of flipped learning, there is a problem that students cannot solve questions that arise during prior learning in real time. Therefore, in order to solve this problem, we developed DBbot, an assistant chatbot for database design course managed in the flipped learning method. The DBBot is composed of a chatbot app for learners and a chatbot management app for instructors. Also, it's implemented so that questions that instructors can anticipate in advance, such as questions related to class operation and every semester repeated questions related to learning content, can be answered using Google's DialogFlow. It's implemented so that questions that the instructor cannot predict in advance, such as questions related to team projects, can be answered using the question/answer DB and the BM25 algorithm, which is a similarity comparison algorithm.

• Journal of KIISE
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• v.42 no.9
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• pp.1109-1116
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• 2015

In this paper, we propose a query processing approach that uses the Spark functional programming and distributed memory system to solve the computational overhead of SPARQL. In the semantic web, RDF ontology data is produced at large scale, and the main challenge for the semantic web is to query and manipulate such a large ontology with a high throughput. The most existing studies on SPARQL have focused on deploying the Hadoop MapReduce framework, and although approaches based on Hadoop MapReduce have shown promising results, they achieve a low level of throughput due to the underlying distributed file processes. Therefore, in order to speed up the query processes, we suggest query- processing methods that are based on memory caching in distributed memory system. Our approach is also integrated with a clause unification method for propagating between the clauses that exploits Spark join, map and filter methods along with caching. In our experiments, we have achieved a high level of performance relative to other approaches. In particular, our performance was nearly similar to that of Sempala, which has been considered to be the fastest query processing system.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.183-187
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• 2011

본 연구에서는 3차원 수리해석과 함께 유사의 이송, 침식, 퇴적 현상을 연동하여 모의할 수 있는 유한차분 수치모형인 EFDC(Environmental Fluid Dynamics Code)를 이용하여 주운하천 구간으로 유입되는 다입경 혼합유사의 입경별 시 공간적 퇴적분포 특성을 고찰하고, 하상변동 예측에 있어서 유사의 밀도와 모델의 유한차분 격자 구조에 의한 불확실성 해석을 수행하였다. 유입 유사의 입경별 공간적 퇴적특성은 하천 하류부와 단면 확대부에서 발생하는 3차원적 수리현상과 매우 밀접한 상관성을 보였으며, 굴포천과 합류하는 주운수로 유입부에서는 대부분 입경이 큰 비점착성 유사($63{\mu}m$ 이상)인 사질(sand)입자들이 주로 퇴적되는 것으로 나타났으며, 주운하천 합류부로부터 하류구간까지는 $4\sim63{\mu}m$ 입자의 실트질(silt) 유사가 대부분 이송되어 퇴적되는 것으로 분석되었다. 점착성 유사인 $4{\mu}m$ 이하의 점토(clay)는 단면이 확대되어 유속이 매우 느린 구간이나 사수역을 중심으로 퇴적되는 것으로 나타났다. 단면 횡방향 분포특성은 굴포천과 주운하천이 합류하는 합류부 구간의 주흐름 방향 남쪽에서 흐름의 정체구간이 발생되어 퇴적이 발생하고, 단면 급확대부 양안에서 사수역이 형성되므로 퇴적이 지배적으로 발생되었다. 하상변동 예측의 불확실성 해석을 위해 유사 밀도값에 대한 민감도 분석결과, 하상변동량은 유사밀도($1.3ton/m^3\sim2.65ton/m^3$)가 감소됨에 따라 약 2배까지 증가하는 것으로 분석되어 민감도가 매우 크게 나타났다. 또한 수치격자 구조의 민감도 분석결과, 수층을 3개 층으로 분석한 결과가 단일층 분석결과보다 최대 6배의 하상변동량이 많게 산정되었다. 이는 수심방향의 유속과 부유사 농도의 불균등 분포특성이 실제 자연현상에 더 가깝게 모의되기 때문으로 판단되었다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1133-1141
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• 2004

In order to process video data effectively, it is required that the content information of video data is loaded in database and semantic-based retrieval method can be available for various query of users. In this paper, we propose semantic-based video retrieval system which support semantic retrieval of various users by feature-based retrieval and annotation-based retrieval of massive video data. By user's fundamental query and selection of image for key frame that extracted from query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user become query image and searches the most similar key frame through feature based retrieval method that propose. From experiment, the designed and implemented system showed high precision ratio in performance assessment more than 90 percents.

• Annual Conference on Human and Language Technology
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• 2003.10d
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• pp.163-169
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• 2003

현재의 질의응답 시스템은 TREC(Text Retrieval Conference) 질의집합에 대해 최대 80% 정도의 응답 성공률을 보이고 있다. 하지만 질의 유형에 다라 성능의 많은 차이가 있으며, 인과관계에 대한 질의에 대해서는 매우 낮은 응답 성공률을 보이고 있다. 본 연구는 인접한 두 문장 혹은 두 문장 혹은 두 명사구 사이에 존재하는 인과관계를 추출하고자 한다. 기존의 명사구 간 인과관계 추출 연구에서는 인과관계 단서구문과 두 명사구의 의미를 주요한 정보로 사용하였으나, 사전 미등록어가 사용되었을 때 올바른 선택을 하기 어려웠다. 또한, 학습 코퍼스에 대한 인과관계 부착과정이 선행되어야 하며, 다량의 학습자료를 사용하기가 어려웠다. 본 연구에서는 인과관계 명사구 쌍에서 추출된 어휘 쌍을 기존의 단서구문과 같이 사용하는 방법을 제안한다. 인과관계 분류를 위해 나이브 베이즈 분류기를 사용하였으며, 비지도식 학습과정을 사용하였다. 제안된 분류 모델은 기존의 분류 모델과 달리 사전 미등록어에 의한 성능 저하가 없으며, 학습 코퍼스의 인과관계 분류 작업이 선행될 필요 없다. 문장 내 명사구간의 인과관계 추출 실험 결과 79.07%의 정확도를 얻었다. 이러한 결과는 단서구문과 명사구 의미를 이용한 방법에 비해 6.32% 향상된 결과이며, 지도식 학습방식을 통해 얻은 방법과 유사한 결과이다. 또한 제안된 학습 및 분류 모델은 문장간의 인과관계 추출에도 적용가능하며, 한국어에서 인접한 두 문장간의 인과관계 추출 실험에서 74.68%의 정확도를 보였다.