• 한국BIM학회 논문집
• /
• 제5권2호
• /
• pp.46-54
• /
• 2015

Vision-based tracking has been proposed as a means to efficiently track a large number of construction resources operating in a congested site. In order to obtain 3D coordinates of an object, it is necessary to employ stereo-vision theories. Detecting and tracking of multiple objects require an entity matching process that finds corresponding pairs of detected entities across the two camera views. This paper proposes an efficient way of entity matching for tracking of construction workers. The proposed method basically uses epipolar geometry which represents the relationship between the two fixed cameras. Each pixel coordinate in a camera view is projected onto the other camera view as an epipolar line. The proposed method finds the matching pair of a worker entity by comparing the proximity of the all detected entities in the other view to the epipolar line. Experimental results demonstrate its suitability for automated entity matching for 3D vision-based tracking of construction workers.

• International journal of advanced smart convergence
• /
• 제12권2호
• /
• pp.34-46
• /
• 2023

A growing number of large scale knowledge graphs raises several issues how knowledge graph data can be organized, discovered, and integrated efficiently. We present a novel semantic-based mashup platform for contents convergence which consists of acquisition, RDF storage, ontology learning, and mashup subsystems. This platform servers a basis for developing other more sophisticated applications required in the area of knowledge big data. Moreover, this paper proposes an entity matching method using graph convolutional network techniques as a preliminary work for automatic classification and discovery on knowledge big data. Using real DBP15K and SRPRS datasets, the performance of our method is compared with some existing entity matching methods. The experimental results show that the proposed method outperforms existing methods due to its ability to increase accuracy and reduce training time.

• 한국전자통신학회논문지
• /
• 제17권5호
• /
• pp.801-808
• /
• 2022

대규모 링크드 데이터에 어떻게 지식을 임베딩하고, 엔티티 매칭을 위해 어떻게 신경망 모델을 적용할 것인가에 대한 연구는 상대적으로 많이 부족한 상황이다. 이에 대한 가장 근본적인 문제는 서로 다른 레이블이 어휘 이질성을 초래한다는 것이다. 본 논문에서는 이러한 어휘 이질성 문제를 해결하기 위해 재정렬 구조를 결합한 확장된 GCN(Graph Convolutional Network) 모델을 제안한다. 제안된 모델은 기존 임베디드 기반 MTransE 및 BootEA 모델과 비교하여 각각 53% 및 40% 성능이 향상되었으며, GCN 기반 RDGCN 모델과 비교하여 성능이 5.1% 향상되었다.

• 컴퓨터교육학회논문지
• /
• 제14권2호
• /
• pp.83-94
• /
• 2011

생의학분야에서 문헌에 표기된 개체를 인식하기 위해 길이우선매칭기법을 빈번히 사용한다. 길이우선매칭기법은 사전을 이용한 개체인식기법으로 좋은 사전만 구축되어 있다면 빠르고 정확하게 개체를 찾아낼 수 있다는 장점을 가진다. 그러나 개체가 나열되고 중복된 단어가 생략될 경우에는 길이우선매칭기법을 이용할 경우 성능이 현저히 떨어지게 된다. 우리는 이러한 인식성능문제를 해결하기 위해 부분매칭기법을 제안한다. 제안된 부분매칭기법은 생략이 발생될 수 있다는 것을 가정하여 다수의 후보개체를 만들어 내고 그 후에 최적화 알고리즘을 통해 다수의 개체후보 중에서 가장 타당해 보이는 개체를 선택한다. 우리는 생의학분야의 개체 중에서 나열되는 경우가 빈번한 HLA 유전자, HLA 항원, HLA 대립유전자 개체들을 대상으로 길이우선매칭기법과 제안된 부분매칭기법의 개체인식성능을 분석하였다. 3종의 HLA 개체들을 인식하기 위해서 먼저 확장사전과 태그기반사전을 구축하였으며, 그 후 구축된 사전을 이용해 길이우선매칭과 부분매칭을 수행하였다. 실험결과에 따르면 길이우선매칭기법은 HLA 항원 개체에서 좋은 성능을 보였으며 부분매칭기법은 생략된 표현이 빈번한 HLA 유전자 개체, HLA 대립유전자 개체에서 좋은 성능을 보였다. 부분매칭기법은 HLA 대립유전자 개체를 대상으로 95.59%의 높은 F-score를 얻었다.

• 한국BIM학회 논문집
• /
• 제7권1호
• /
• pp.45-53
• /
• 2017

Continuous research efforts have been made on acquiring location data on construction sites. As a result, GPS and RFID are increasingly employed on the site to track the location of equipment and materials. However, these systems are based on radio frequency technologies which require attaching tags on every target entity. Implementing the systems incurs time and costs for attaching/detaching/managing the tags or sensors. For this reason, efforts are currently being made to track construction entities using only cameras. Vision-based 3D tracking has been presented in a previous research work in which the location of construction manpower, vehicle, and materials were successfully tracked. However, the proposed system is still in its infancy and yet to be implemented on practical applications for two reasons. First, it does not involve entity matching across two views, and thus cannot be used for tracking multiple entities, simultaneously. Second, the use of a checker board in the camera calibration process entails a focus-related problem when the baseline is long and the target entities are located far from the cameras. This paper proposes a vision-based method to track multiple workers simultaneously. An entity matching procedure is added to acquire the matching pairs of the same entities across two views which is necessary for tracking multiple entities. Also, the proposed method simplified the calibration process by avoiding the use of a checkerboard, making it more adequate to the realistic deployment on construction sites.

• 한국측량학회:학술대회논문집
• /
• 한국측량학회 2004년도 춘계학술발표회논문집
• /
• pp.171-176
• /
• 2004

Image matching is fundamental process in photogrammetry and computer vision to identify and to measure corresponding features on the multiple images. Uniqueness of the matching entities and robustness of the algorithm are the key issues that have influence on quality of the matching result. The optimal solution could be obtained by utilizing appropriate matching entities in the first place. In this study, candidate matching points were extracted by interest operator, and an area-based matching method was applied with characteristics of the gray value distribution as the matching entities. The characteristic information is based on the concept of "intrinsic image" (or parameter image). The information was utilized as additional and/or complementary matching entities. Matching on interest points with the characteristic information resulted in high quality of matching because matching windows were created with surrounding pixels of the interest points that contain distinct and unique features. The experiment shows that matching quality and reliability increase by exploiting interest operator, and the characteristic information has potential to be matching entity.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
• /
• pp.1467-1469
• /
• 2003

Image matching is fundamental process to identify conjugate points on the stereo images. However, standard methods or general solutions for matching problem have not been found yet, in spite of long history. Quality of the matching basically depends on uniqueness of the matching entity and robustness of the algorithm. In this study, conjugate points were extracted by implementing interest operator, then area based matching method was applied to the topographical characteristics of the gray value as the matching entities. The matching entities were utilized based on the concept of the intrinsic image.

• International Journal of Contents
• /
• 제17권4호
• /
• pp.1-15
• /
• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• 정보과학회 논문지
• /
• 제44권8호
• /
• pp.813-821
• /
• 2017

개체 링킹은 입력된 질의에 존재하는 개체를 표현한 개체 표현(entity mention)을 지식베이스에 존재하는 개체와 연결하여 의미를 파악하는 연구이다. 개체 링킹에 관한 연구는 지식 베이스 구축 문제, 다중 표현 문제, 개체 연결 중의성 문제, NIL 개체 인식 문제가 존재한다. 본 연구에서는 지식 베이스 구축 문제와 다중 표현 문제를 해결하기 위해 위키피디아를 기반으로 개체 이름 사전을 구축한다, 또한, 문맥 유사도, 의미적 관련성, 단서 단어 점수, 개체 표현의 개체명 타입 유사도, 개체 이름 매칭 점수, 개체인기도 점수 자질들을 기반으로 SVM(support vector machine)을 학습하여, NIL 개체를 인식하는 문제와 개체 연결 중의성을 해소하는 방법을 제안한다. 구축한 지식 베이스를 기반으로 제안한 두 방법을 순차적으로 적용하였을 때 좋은 개체 링킹 성능을 얻었다. 개체 링킹 시스템의 성능은 NIL 개체 인식 성능이 83.66%, 중의성 해소 성능이 90.81%의 F1 점수를 보였다.

• 한국CDE학회논문집
• /
• 제11권1호
• /
• pp.27-40
• /
• 2006

The exchange of parameterized feature-based CAD models is important for product data sharing among different organizations and automation systems. The role of feature-based modeling is to gonerate the shape of product and capture design intends In a CAD system. A feature is generated by referring to topological entities in a solid. Identifying referenced topological entities of a feature is essential for exchanging feature-based CAD models through a neutral format. If the CAD data contains the modification history in addition to the construction history, a matching mechanism is also required to find the same entity in the new model (post-edit model) corresponding to the entity in the old model (preedit model). This problem is known as the persistent naming problem. There are additional problems arising from the exchange of parameterized feature-based CAD models. Authors have analyzed previous studies with regard to persistent naming and characteristics for the exchange of parameterized feature-based CAD models, and propose a solution to the persistent naming problem. This solution is comprised of two parts: (a) naming of topological entities based on the object spore information (OSI) and secondary name (SN); and (b) name matching under the proposed naming.